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European Journal of Radiology 67(2008)218–229ReviewThe principles of quantiﬁcation applied toin vivo proton MR spectroscopyGunther Helms ∗MR-Research in Neurology and Psychiatry,Faculty of Medicine,University of G¨o ttingen,D-37075G¨o ttingen,GermanyReceived 27February 2008;accepted 28February 2008AbstractFollowing the identiﬁcation of metabolite signals in the in vivo MR spectrum,quantiﬁcation is the procedure to estimate numerical values oftheir concentrations.The two essential steps are discussed in detail:analysis by ﬁtting a model of prior knowledge,that is,the decomposition of the spectrum into the signals of singular metabolites;then,normalization of these signals to yield concentration estimates.Special attention is given to using the in vivo water signal as internal reference.©2008Elsevier Ireland Ltd.All rights reserved.Keywords:MRS;Brain;Quantiﬁcation;QAContents1.Introduction ............................................................................................................2192.Spectral analysis/decomposition..........................................................................................2192.1.Principles........................................................................................................2192.2.Statistical and systematic ﬁtting errors ..............................................................................2212.3.Examples of analysis software......................................................................................2212.3.1.LCModel ................................................................................................2212.3.2.jMRUI...................................................................................................2213.Signal normalization ....................................................................................................2233.1.Principles........................................................................................................2233.2.Internal referencing and metabolite ratios............................................................................2233.3.External referencing...............................................................................................2233.4.Global transmitter reference........................................................................................2233.5.Local ﬂip angle...................................................................................................2243.6.Coil impedance effects ............................................................................................2243.7.External phantom and local reference ...............................................................................2253.8.Receive only-coils ................................................................................................2253.9.Internal water reference............................................................................................2253.10.Partial volume correction.........................................................................................2264.Calibration .............................................................................................................2275.Discussion..............................................................................................................2286.Experimental ...........................................................................................................2287.Recommendations.......................................................................................................228Acknowledgements .....................................................................................................229References .............................................................................................................229∗Tel.:+495513913132;fax:+495513913243.E-mail address:ghelms@gwdg.de .0720-048X/$–see front matter ©2008Elsevier Ireland Ltd.All rights reserved.doi:10.1016/j.ejrad.2008.02.034G.Helms/European Journal of Radiology67(2008)218–2292191.IntroductionIn vivo MRS is a quantitative technique.This statement is often mentioned in the introduction to clinical MRS studies. However,the quantiﬁcation of signal produced by the MR imag-ing system is a complex and rather technical issue.Inconsistent terminology and scores of different approaches make the prob-lem appear even more complicated,especially for beginners. This article is intended to give a structured introduction to the principles of quantiﬁcation.The associated problems and pos-sible systematic errors(“bias”)are explained to encourage a critical appraisal of published results.Quantiﬁcation is essential for clinical research,less so for adding diagnostic information for which visual inspection often may sufﬁce.Subsequent to the identiﬁcation of metabolites,its foremost rationale is to provide numbers for comparison of spec-tra from different subjects and brain regions;and–ideally–different scanners and sequences.These numbers are then used for evaluation;e.g.statistical comparison of cohorts or correla-tion with clinical parameters.The problem is that the interaction of the radio-frequency(RF)hardware and the dielectric load of the subject’s body may lead to rather large signal variations(up to30%)that may blur systematic relationships to cohorts or clinical parameters.One of the purposes of quantiﬁcation is to reduce such hardware related variation in the numbers.Thus, quantiﬁcation is closely related to quality assurance(QA).In summary,quantiﬁcation is a procedure of data processing. The post-processing scheme may require additional data acqui-sitions or extraction of adjustment parameters from the scanner. The natural order of steps in the procedure is1.acquisition and pre-processing of raw data,reconstruction ofthe spectrum(e.g.averaging and FFT),2.analysis:estimation of the relative signal for each identiﬁedmetabolite(here,proton numbers and linewidth should be taken into account),3.normalization of RF-induced signal variations,4.calibration of signals by performing the quantiﬁcationscheme on a standard of known concentration.In turn,these steps yield the metabolite signals1.for visual inspection of the displayed spectrum on the ppmscale,2.in arbitrary units,from which metabolite ratios can be cal-culated,3.in institutional units(for your individual MR scanner andquantiﬁcation scheme;these numbers are proportional to the concentration),4.in absolute units of concentration(commonly inmM=mmol/l);estimated by comparison to a standard of known concentration.The term quantiﬁcation(or sometimes“quantitation”)is occasionally used to denote singular steps of this process.In this review,it will refer to the whole procedure,and further differ-entiation is made for the sake of clarity.In practice,some these steps may be performed together.Already at this stage it should be made clear that the numbers obtained by“absolute quantiﬁca-tion”are by no means“absolute”but depend on the accuracy and precision of steps1–4.Measurement and reconstruction(step1) must be performed in a consistent way lest additional errors have to be accounted for in individual experiments.Only in theory it should be possible to correct all possible sources of variation;in clinical practice it is generally is too time consum-ing.Yet the more sources of variation are cancelled(starting with the biggest effects)the smaller effects one will be able to detect.Emphasis will be put on the analysis(the models and the automated tools available),the signal normalization(and basic quality assurance issues),and the use of the localized water signal as internal reference.2.Spectral analysis/decomposition2.1.PrinciplesThe in vivo spectrum becomes more complicated with decreasing echo time(TE):next to the singlet resonances and weakly coupled multiplets,signals from strongly coupled metabolites and baseline humps from motion-restricted macro-molecules appear.Contrary to long-TE spectra short-TE spectra should not be evaluated step-by-step and line-by-line.For exam-ple,the left line of the lactate doublet is superposed onto the macromolecular signal at1.4ppm.The total signal at this fre-quency is not of interest but rather the separate contributions of lactate and macromolecules/lipids.Differences between the two whole resonance patterns can be used to separate the metabolites;e.g.the doublet of lactate versus the broad linewidth.In visual inspection,one intuitively uses such‘prior knowledge’about the expected metabolites to discern partly overlying metabolites in a qualitative way.This approach is also used to simplify the problem to automaticallyﬁnd the metabolite resonances to order to evaluate the whole spectrum“in one go”.Comparing the resonance pattern of MR spectra in vivo at highﬁeld and short TE with those of tissue extracts and sin-gle metabolites in vitro at matchedﬁeld strengths hasﬁrmly established our‘prior’knowledge about which metabolites con-tribute to the in vivo MR spectra[1].Next to TE,theﬁeld strength exerts the second biggest inﬂuence on the appearance of in vivo MR spectra.Overlap and degeneration of binomial multiplets due to strong coupling increase at the lowerﬁeld strengths of clinical MR systems(commonly3,2,or1.5T). These effects can be either measured on solutions of single metabolites[2]or simulated fromﬁrst quantum-mechanical principles,once the chemical shifts and coupling constants(J in Hz)of a certain metabolite have been determined at sufﬁ-ciently highﬁeld[3].Motion-restricted‘macromolecules’are subject to rapid relaxation that blurs the coupling pattern(if the linewidth1/πT∗2>J)and hampers the identiﬁcation of speciﬁc compounds.These usually appear as broad‘humps’that form the unresolved baseline of short-TE spectra(Fig.1).These vanish at longer TE(>135ms).The baseline underlying the metabo-220G.Helms /European Journal of Radiology 67(2008)218–229Fig.1.Including lipids/macromolecules into the basis set.Without inclusion of lipids/macromolecules in the basis set (A)the broad “humps”at 1.3and 0.9ppm are ﬁtted by the baseline.Inclusion of lipids/macromolecules (B)resulted in a better ﬁt and a lower baseline between 2.2and 0.6ppm.The SNR improved from 26to 30.The signals at 2.0ppm partly replaced the co-resonating tNAA.The 6%reduction in tNAA was larger than the ﬁtting error (3%).This may illustrate that the ﬁtting error does not account for the bias in the model.LCModel (exp.details:6.1-0;12.5ml VOI in parietal GM,3T,STEAM,TE/TM/TR/avg =20/10/6000/64).lite signals is constituted from all rapidly relaxing signals that have not decayed to zero at the chosen TE (macromolecules and lipids),the “feet”of the residual water signal,plus possible arte-facts (e.g.echo signals from moving spins that were not fully suppressed by gradient selection).The ‘prior knowledge’about which metabolites to detect and how the baseline will look like is used to construct a math-ematical model to describe the spectrum.Selecting the input signals reduces the complexity of the analysis problem.In con-trast to integrating or ﬁtting singlet lines the whole spectrum is evaluated together (“in one go”)by ﬁtting a superposition of metabolite signals and baseline signals.Thus,the in vivo spec-trum is decomposed into the constituents of the model.Without specifying the resonances this is often too complicated to be per-formed successfully,in the sense that an unaccountable number of ‘best’combinations exist.G.Helms/European Journal of Radiology67(2008)218–229221Prior knowledge may be implemented in the metabolite basis set adapting experimental data(like in LCModel[2]),theoretical patterns simulated fromﬁrst principles(QUEST[4]),or purely phenomenological functions like a superposition of Gaussians of different width to model strongly coupled signals and baseline humps alike(AMARES[5]).The least squaresﬁt may be per-formed in either time domain[6]or frequency domain or both [7].For an in-depth discussion of technical details,the reader is referred to a special issue of NMR in Biomedicine(NMR Biomed14[4];2001)dedicated to“quantitation”(in the sense of spectrum analysis)by mathematical methods.2.2.Statistical and systematicﬁtting errorsModelﬁtting yields the contribution of each input signal. Usually Cr´a mer–Rao lower bounds(CLRB)are provided as an estimate for theﬁtting error or the statistical uncertainty of the concentration estimate.These are calculated from the residual error and the Fisher matrix of the partial derivatives of the con-centrations.In the same way,correlations between the input data can be estimated.Overlapping input signals(e.g.from glutamate (Glu)and glutamine(Gln))are inversely correlated.In this case, the sum has a smaller error than the single metabolites.The uncertainties are fairly well proportional to the noise level(both must be given in the same units).The models are always an approximate,but never a com-plete description of the in vivo MR spectrum.Every model thus involves some kind of systematic error or“bias”,in the sense of deviation from the unknown“true”concentration.Contrary to the statistical uncertainty,the bias cannot be assessed within the same model.In particular,the CRLB does not account for the bias.Changes in the model(e.g.,by leaving out a minor metabo-lite)may result in systematic differences that soon become signiﬁcant(by a paired t-test).These are caused by the pro-cess of minimizing the squared residual difference whenﬁtting the same data by two different models.Spurious artefacts or“nuisance signals”that are not included in the model will results in errors that are neither statistical nor systematic.It is also useful to know,that for every non-linear function(as used in MRS)there is a critical signal-to-noise (SNR)threshold for convergence onto meaningful values.2.3.Examples of analysis softwareA number of models and algorithms have been published dur-ing the past15years.A few are available to the public and shared by a considerable number of users.These program packages are generally combined with some automated or interactive pre-processing features,such as correction of frequency offset,zero andﬁrst order,as well as eddy-current induced phase errors.We shall in brief describe the most common programs for analysis of in vivo1H MRS data.2.3.1.LCModelThe Linear Combination Model(LCModel)[2]comes as stand-alone commercial software(/ lcmodel).It comprises automated pre-processing to achieve a high degree of user-independence.An advanced regularization ensures convergence for the vast majority of in vivo spectra.It was theﬁrst program designed toﬁt a basis set(or library)of experimental single metabolite spectra to incorporate maximum information and uniqueness.This means that partly overlap-ping spectra(again such as,Glu and Gln)are discerned by their unique features,but show some residual correlation as mentioned above.Proton numbers are accounted for,even“frac-tional proton numbers”in“pseudo-singlets”(e.g.,the main resonance of mIns).Thus,the ratios provided by LCModel refer to the concentrations rather than proton numbers.The basis set of experimental spectra comprises the prior information on neurochemistry(metabolites)as well as technique(TE,ﬁeld strength,localization technique).The non-analytic line shape is constrained to unit area and capable toﬁt even distorted lines (due to motion or residual eddy currents).The number of knots of the baseline spline increases with the noise level.Thus,the LCModel is a mixture of experimental and phenomenological features.Although the basis spectra are provided in time domain, the evaluation is performed across a speciﬁed ppm interval.LCModel comes with a graphical user interface for routine application.Optionally the water signal may be used as quan-tiﬁcation reference.Recently,lipids and macromolecular signals have been included to allow evaluation of tumour and muscle spectra.An example is shown in Fig.1.LCModel comprises basic signal normalization(see below) according to the global transmitter reference[8]to achieve a consistent scaling of the basis spectra.An in-house acquired basis set can thus be used to estimate absolute concentrations. Imported basis sets are available for a wide range of scanners and measurement protocols,but require a calibration to match the individual sensitivity(signal level)of the MR system[9]. Owing to LCModel’sﬂexibility,the basis set may contain also simulated spectra or an experimentally determined baseline to account for macromolecular signals.Such advanced applica-tions require good theoretical understanding and some practical experience.Care must be taken to maintain consistent scaling when adding new metabolite spectra to an existing basis.This is easiest done by cross-evaluation,that is evaluating a reference peak(e.g.,formate)in spectrum to be included by the singlet of the original basis and correcting to the known value.Caveat:The fact that LCModel converges does not ensure reliability of the estimates;least in absolute units(see Sections 3and4).Systematic difference in SNR may translate into bias via the baseline spline(see Fig.2).The same may be due an inconsistent choice of the boundaries of the ppm interval,partic-ularly next to the water resonance.In particular,with decreasing SNR(lower than4)one may observe more often systematically low or high concentrations.This is likely due to the errors in the feet of the non-analytical line shape,as narrow lines lead to underestimation and broad lines to overestimation.The metabo-lite ratios are still valid,as all model spectra are convoluted by the same lineshape.2.3.2.jMRUIThe java-based MR user interface for the processing of in vivo MR-spectra(jMRUI)is provided without charge222G.Helms /European Journal of Radiology 67(2008)218–229Fig.2.Systematic baseline differences between low and high SNR.Single spectrum from an 1.7ml VOI in white matter of the splenium (A)and the averaged spectra of seven healthy subjects (B).Note how the straight baseline leads to a severe underestimation of all metabolites except mIns.Differences were most prominent for Glu +Gln:3.6mM (43%)in a single subject vs.6.7mM (7%)in the averaged spectrum.(http://www.mrui.uab.es/mrui/mrui Overview.shtml ).It comes with a wide range of pre-processing features and interac-tive graphical software applications,including linear prediction and a powerful water removal by Hankel–Laclosz single value decomposition (HLSVD).In contrast to LCModel,it is designed to support user interaction.Several models for analy-sis/evaluation have been implemented in jMRUI,in particular AMARES [5]and QUEST [4].These focus on time-domain analysis,including line shape conversion,time-domain ﬁlter-ing and eddy-current deconvolution.Note that in the context of jMRUI ‘quantitation’refers to spectrum analysis.The pre-processing steps may exert a systematic inﬂuence on the results of model ﬁtting.jMRUI can handle large data sets as from time-resolved MRS,two-dimensional MRS,and spatially resolved MRS,so-called MR spectroscopic imaging (MRSI)or chemical-shift imaging (CSI).G.Helms/European Journal of Radiology67(2008)218–2292233.Signal normalization3.1.PrinciplesThe signal is provided in arbitrary units of signed integer numbers,similar to MRI,and then converted toﬂoating complex numbers.In addition to scaling along the scanner’s receiver line, the proportionality between signal strength and number of spins per volume is strongly inﬂuenced by interaction of the RF hard-ware and its dielectric and conductive load,the human body.It is the correction of this interaction that forms the non-trivial part of signal normalization.Signal normalization is mainly applied to single-volume MRS,since spatially resolved MRSI poses addi-tional technical problems that are not part of this review.For sake of simplicity we assume homogeneous conditions across the whole volume-of-interest(VOI).Normalization consists of multiplications and divisions that render the signal,S,proportional to the concentration(of spins), C.Regardless whether in time domain(amplitude)or frequency domain(area),the signal is proportional to the size V of the VOI and the receiver gain R.S∼CVR or(1a) S/V/R∼C(1b) Logarithmic(decibel)units of the receiver gain must be con-verted to obtain a linear scaling factor,R.If R can be manually changed,it is advisable to check whether the characteristic of S(R)follows the assumed dependence.If a consistent(often the highest possible)gain used by default for single voxel MRS, one does not have to account for R.Correction of V for partial volume effects is discussed below.The proportionality constant will vary under the inﬂuence of the speciﬁc sample“loading”the RF coil.The properties of a loaded transmit–receive(T/R)coil are traditionally assessed by measuring the amplitude(or width)of a speciﬁc RF pulse,e.g., a180◦rectangular pulse.This strategy may also be pursued in vivo.The signal theory for T/R coils is given in concise form in [10]without use of complex numbers.Here,we develop it by presenting a chronology of strategies of increasing complexity that have been used for in vivo quantiﬁcation.3.2.Internal referencing and metabolite ratiosBy assuming a concentration C int for the signal(S int)of ref-erence substance acquired in the same VOI,one has not to care about the inﬂuence of RF or scanner parameters:SS intC int=C(2)When using the total creatine(tCr)signal,internal referencing is equivalent to converting creatine ratios to absolute units.In early quantiﬁcation work,the resonance of tCr has been assigned to 10mM determined by biochemical methods[11].However,it turned out that the MRS estimates of tCr are about25%lower and show some spatial dependence.In addition,tCr may increase in the presence of gliosis.3.3.External referencingThe most straightforward way is to acquire a reference sig-nal from an external phantom during the subject examination, with C ext being the concentration of the phantom substance [12,13].The reference signal S ext accounts for any changes in the proportionality constant.It may be normalized like the in vivo signal:S(VR)C extS ext/(V ext R ext)=C(3)If,however,the phantom is placed in the fringeﬁeld of the RF receive coil,the associated reduction in S ext will result in an overestimation of C.Care has to be taken to mount the external phantom reproducibly into the RF coil if this bias cannot be corrected otherwise.3.4.Global transmitter referenceAlready in high-ﬁeld MR spectrometers it has been noticed that by coil load the sample inﬂuences both the transmit pulse and the signal:a high load requires a longer RF pulse for a 90◦excitation,which then yields reciprocally less signal from the same number of spins.This is the principle-of-reciprocity (PoR)for transmit/receive(T/R)coils in its most rudimentary form.It has been applied to account for the coil load effect, that is,large heads giving smaller signals than small heads [8].On MRI systems,RF pulses are applied with constant duration and shape.A high load thus requires a higher volt-age U tra(or transmitter gain),as determined during pre-scan calibration.S/V/R∼Ctraor(4a) S U tra/V/R∼C(4b)Of course,U tra must always refer to a pulse of speciﬁc shape, duration andﬂip angle,as used forﬂip angle calibration.On Siemens scanners,the amplitude of a non-selective rectangu-lar pulse(rect)is used.The logarithmic transmitter gain of GE scanners is independent of the RF pulse,but has to be converted from decibel to linear units[9].Normalization by the PoR requires QA at regular intervals,as the proportionality constant in Eqs.((4a)and(4b))may change in time.This may happen gradually while the performance of the RF power ampliﬁer wears down,or suddenly after parts of the RF hardware have been replaced.For this purpose,the MRS protocol is run on a stable QA phantom of high concentration and the concentration estimate C QA(t i)obtained at time point, t i,is used to refer any concentration C back to time point zero byC→C C QA(t0)C QA(t i)(5)An example of serial QA monitoring is given in Fig.3.224G.Helms /European Journal of Radiology 67(2008)218–229Fig.3.QA measurement of temporal variation.Weekly QA performed on stable phantom of 100mM lactate and 100mM acetate from January 1996to June 1996.The standard single-volume protocol and quantiﬁcation procedure (LCModel and global reference)were applied.(A)The mean estimated concentration is shown without additional calibration.The A indicates the state after installation,B a gradual breakdown of the system;the sudden jumps were due to replacement of the pre-ampliﬁer (C and D)or head-coil (E),and retuning of the system (F).Results were used to correct proportionality to obtain longitudinally consistency.(B)The percentage deviation from the preceding measurement in Shewhart’s R-diagram indicates the weeks when quantiﬁcation may not be reliable (data courtesy of Dr.M.Dezortov´a ,IKEM,Prague,Czech Republic).3.5.Local ﬂip angleDanielsen and Hendriksen [10]noted that the PoR is a local relationship,so they used the amplitude of the water suppression pulse,U tra (x ),that had been locally adjusted on the VOI signal.S (x )U tra (x )/V/R ∼C(6)The local transmitter amplitude may also be found be ﬁtting the ﬂip angle dependence of the local signal [14].The example in Fig.4illustrates the consistency of Eq.(6)at the centre (high signal,low voltage)and outside (low signal,high voltage)the volume headcoil.Fig.4.Local veriﬁcation of the principle of reciprocity.Flip angle dependence of the STEAM signal measured at two positions along the axis of a GE birdcage head-coil by varying the transmitter gain (TG).TG was converted from logarith-mic decibel to linear units (linearized TG,corresponding to U tra ).At coil centre (×)and 5cm outside the coil (+)the received signal,S (x ),was proportional to the transmitted RF,here given by 1/lin TG(x )at the signal maximum or 90◦ﬂip angle.Like in large phantoms,there are considerable ﬂip angle devi-ations across the human head as demonstrated at 3T in Fig.5a [15].The local ﬂip angle,α(x ),may be related to the nominal value,αnom ,by α(x )=f (x )αnom(7)The spatially dependent factor is reciprocal to U tra (x ):f (x )∼1/U tra (x ).The ﬂip angle will also alter the local signal.If a local transmitter reference is used,S (x )needs to be corrected for excitation effects.For the ideal 90◦–90◦–90◦STEAM local-ization and 90◦–180◦–180◦PRESS localization in a T/R coil,the signals areS (x )STEAM ∼M tr (x )∼C2f (x )sin 3(f (x )90◦)(8a)S (x )PRESS ∼M tr (x )∼C f (x )sin 5(f (x )90◦)(8b)The dependence of S (x )was simulated for a parabolic RF proﬁle.A constant plateau is observed as the effects of transmission and reception cancel out for higher ﬂip angles in the centre of the head where the VOI is placed.This is the reason why the global ﬂip angle method works even in the presence of ﬂip angle inhomogeneities.Note that the signal drops rapidly for smaller ﬂip angles,i.e.close to the skull.3.6.Coil impedance effectsOlder quantiﬁcation studies were performed on MR systems where the coil impedance Z was matched to 50 [8,10].Since the early 1990s,most volume head coils are of the high Q design and approximately tuned and matched by the RF load of the head and the stray capacitance of the shoulders.The residual variation of the impedance Z will affect the signal by S (x )U tra (x )/V/R ∼CZ(9)G.Helms/European Journal of Radiology67(2008)218–229225Fig.5.Flip angle inhomogeneities across the human brain.(Panel A)T1-w sagittal view showing variation in the RFﬁeld.Flip angles are higher in the centre of the brain.The contours correspond to80–120◦localﬂip angle for a nominal value of90◦.(Panel B)The spatial signal dependence of STEAM and PRESS was simulated for a parabolicﬂip angle distribution with a maximum of115%relative to the global transmitter reference.This resulted in a constant signal obtained from the central regions of the brain,and a rapid decline at the edges.Reﬂection losses due to coil mismatch are symmetric in trans-mission and reception and are thus accounted for by U tra.These are likely to occur with exceptionally large or small persons (infants)or with phantoms of insufﬁcient load.3.7.External phantom and local referenceWhen the impedance is not individually matched to50 , the associated change in proportionality must be monitored by a reference signal.In aqueous phantoms,the water signal can be used as internal reference.For in vivo applications,one may resort to an extra measurement in an external phantom[14].An additionalﬂip angle calibration in the phantom will account for local differences in the RFﬁeld,especially if the phantom is placed in the fringe RFﬁeld:SU tra(x)/(VR)S ext U tra(x ext)/(V ext R ext)C ext=C(10)This is the most comprehensive signal normalization.The com-bination of external reference and localﬂip angle method corrects for all effects in T/R coils.The reference signal accounts for changes in the proportionality,while the localﬂip angle cor-rects for RF inhomogeneity.Note also that systematic errors in S,U tra and V cancel out by division.Calibration of each individual VOI may be sped up by rapid RF mapping in three dimensions.3.8.Receive only-coilsThe SNR of the MRS signal can be increased by using sur-face coils or phased arrays of surface coils.The inhomogeneous receive characteristic cannot be mapped directly.The normaliza-tions discussed above(except Section3.2)cannot be performed directly on the received signal,as the coils are not used for trans-mission.Instead,the localized water signal may be acquired with both the receive coil and the body coil to scale the low SNR metabolite signal to obey the receive characteristics of the T/R body coil[16,17]:S rec met S bodywaterS rec water=S bodymet(11)For use with phased array coils it is essential that the metabolite and water signals are combined using consistent weights,since the low SNR of the water suppressed acquisition is most likely inﬂuenced by noise.3.9.Internal water referenceThe tissue water appears to be the internal reference of choice, due to its high concentration and well established values for water content of tissues(βper volume[18]):SS waterβ55mol/litre=C(12)It should be kept in mind that in vivo water exhibits a wide range of relaxation times,with the main component relaxing consider-able faster than the main metabolites.T2-times range from much shorter(myelin-associated water in white mater T2of15ms)to much longer(CSF,2400ms in bulk down to700ms in sulci with large surface-to-volume ratio).This implies an inﬂuence of TE on the concentration estimates.In addition,relaxation time and water content are subject to change in pathologies.Since the water signal is increasing in most pathologies(by content and relaxation),water referencing tends to give lower concentration estimates in pathologies.Ideally,the water signal should be determined by a multi-componentﬁt of the T2-decay curve[12].An easy but time-consuming way is to increase TE in consecutive fully relaxed single scans.A reliable way to determine the water sig-nal is toﬁt a2nd order polynomial through theﬁrst50ms of the magnitude signal(Fig.6).Thus,determining the amplitude cancels out initial receiver instabilities and avoids lineﬁtting at an ill deﬁned phase.If care is taken to avoid partial saturation by RF leakage from the water suppression pulses,this is consistent with multi-echo measurements using a CPMG MRI sequence [18](Fig.7).。

高三现代科技前沿探索英语阅读理解20题

高三现代科技前沿探索英语阅读理解20题1<背景文章>Artificial intelligence (AI) is rapidly transforming the field of healthcare. In recent years, AI has made significant progress in various aspects of medical care, bringing new opportunities and challenges.One of the major applications of AI in healthcare is in disease diagnosis. AI-powered systems can analyze large amounts of medical data, such as medical images and patient records, to detect diseases at an early stage. For example, deep learning algorithms can accurately identify tumors in medical images, helping doctors make more accurate diagnoses.Another area where AI is making a big impact is in drug discovery. By analyzing vast amounts of biological data, AI can help researchers identify potential drug targets and design new drugs more efficiently. This can significantly shorten the time and cost of drug development.AI also has the potential to improve patient care by providing personalized treatment plans. Based on a patient's genetic information, medical history, and other factors, AI can recommend the most appropriate treatment options.However, the application of AI in healthcare also faces some challenges. One of the main concerns is data privacy and security. Medicaldata is highly sensitive, and ensuring its protection is crucial. Another challenge is the lack of transparency in AI algorithms. Doctors and patients need to understand how AI makes decisions in order to trust its recommendations.In conclusion, while AI holds great promise for improving healthcare, it also poses significant challenges that need to be addressed.1. What is one of the major applications of AI in healthcare?A. Disease prevention.B. Disease diagnosis.C. Health maintenance.D. Medical education.答案：B。

Literature Search Strategies

Literature Search StrategiesLiterature search strategies are essential for conducting thorough research and finding relevant information in the vast sea of academic publications. As a researcher, I understand the importance of employing effective search strategies to locate the most pertinent literature for my work. There are various perspectives to consider when discussing literature search strategies, including the use of different databases, search techniques, and the challenges that researchers may face in this process.One perspective to consider is the importance of selecting the right databases for literature searches. Different databases cater to specific disciplines, and it is crucial to identify the most relevant ones for the research topic at hand. For example, PubMed is widely used in the field of medicine and life sciences, while PsycINFO is valuable for psychology-related research. As a researcher, I often utilize multiple databases to ensure comprehensive coverage of the literature. This approach helps me access a wide range of sources and gather diverse perspectives on the topic.In addition to selecting appropriate databases, employing effective search techniques is another crucial aspect of literature search strategies. Boolean operators, truncation, and phrase searching are some of the techniques that can enhance the precision and relevance of search results. By using these techniques, researchers can narrow down their searches and retrieve literature that is closely aligned with their research questions. Personally, I have found that combining different search techniques, such as using Boolean operators to connect key terms and employing truncation to capture various word endings, significantly improves the efficiency and effectiveness of my literature searches.Despite the benefits of employing literature search strategies, researchers often encounter challenges during the process. One common challenge is the overwhelming volume of search results, which can make it difficult to sift through the literature and identify the most relevant sources. This issue is particularly prevalent in fields with a large volume of publications, such as biomedicine and social sciences. As a researcher, I have experienced the frustration of sorting through numerous search results, and I have learned to refine my search strategies to mitigate this challenge. Utilizing advanced search featuresand carefully selecting search terms are some of the approaches that have helped me manage the abundance of search results.Another challenge in literature search strategies is the potential for bias in the selection and interpretation of literature. Researchers may unintentionally overlook relevant sources or prioritize certain perspectives over others, leading to a skewed representation of the literature. To address this challenge, it is important for researchers to approach literature searches with an open mind and critically evaluate the sources they encounter. Personally, I make a conscious effort to consider diverse viewpoints and seek out literature from a variety of sources to ensure a comprehensive and balanced representation of the topic.In conclusion, literature search strategies are essential for conducting rigorous and comprehensive research. By considering perspectives such as database selection, search techniques, and the challenges of literature searches, researchers can refine their approach to finding and evaluating relevant literature. As a researcher, I have learned the value of employing diverse search strategies and critically evaluating the literature to ensure the thoroughness and integrity of my research. Despite the challenges that may arise, I remain committed to refining my literature search strategies and embracing the wealth of knowledge that can be found in academic publications.。

meta分析的SCI写作模板—search strategy

呕血整理，meta分析的SCI写作模板—search strategy我们在写Meta时，尤其是第一次，可能不知道去如何进行写作，其实一般的Meta 分析类文章写作还是相对比较简单的，有一定结构化的东西，只要按一定套路就能把复杂的问题简单化。

为此，小编特意从一些高分的SCI原文中整理了一些比较好的句子，大家赶紧过来看看吧，目的是让大家能更快的写出高质量的SCI 文章。

①We did our best to include all×××studies published until date, regarding the association between×××and×××.Eligible studies were found by searching the×××database for relevant reports published between×××and×××.②A literature search was performed in×××without restriction to regions,publication types,or languages.The primary sources were the electronic databases of×××.③Trials were excluded if any of the following factors were identified:(1)insufficientinformation concerning evaluation rates;(2)animal trials,×××××××××.④The methods of this meta-analysis were performed in accordance with the Cochrane Collaboration criterion.×××were searched for relevant electronic studies of randomized controlled trials(RCTs)published before×××.Hand searching techniques also were used to identify appropriate studies(Manual searches of reference lists were also performed.We did not apply any language restrictions).⑤To identify eligible studies,the main search was conducted in the electronic databases×××from inception through×××,using various combinations of Medical Subject Headings(MeSH)and non-MeSH terms.The procedure was concluded by:(i)the perusal of the reference sections of all relevant studies,(ii)a manual search of key journal sand abstracts from the major annual meetings in the field of×××and(iii) contact with experts.The main search was completed independently by investigators.Any discrepancy was solved by consultation of an investigator,not involved in the initialprocedure.我们目的是让大家能更快的写出高质量的SCI文章，希望对大家有所帮助。

Meta分析的基本思想及顺序

M e t a分析的思想及步骤Meta分析的前身源于Fisher1920年“合并P值”的思想,1955年由Beecher首次提出初步的概念,1976年心理学家Glass进一步按照其思想发展为“合并统计量”,称之为Meta分析;1979年英国临床流行病学家ArchieCochrane提出系统评价systematicreview,SR的概念,并发表了激素治疗早产孕妇降低新生儿死亡率随机对照试验的系统评价,对循证医学的发展起了举足轻重的作用;Meta分析国内翻译为“荟萃分析”,定义是“Thestatisticalanalysisoflargecollectionofanalysisresultsfromindividual studiesforthepurposeofintegratingthefindings.”亦即“对具备特定条件的、同课题的诸多研究结果进行综合的一类统计方法;”Meta从字源来说据考证有“Metalogic：abranchofanalyticphilosophythatdealswiththecriticalexaminationofthebasic conceptsoflogic”；“Metamathematics：thephilosophyofmathematics,especially,thelogicalsyntaxofmathematics.”其中最简洁并且一语中的的是Metascience:：atheoryorscienceofscience,atheoryconcernedwiththeinvestigationanalysisor descriptionoftheoryitself.”意为一种科学中的科学或理论,一种对原理本身进行调查、分析和描述的原理;Meta分析有广义和狭义两种概念：前者指的是一个科学的临床研究活动,指全面收集所有相关研究并逐个进行严格评价和分析,再用定量合成的方法对资料进行统计学处理得出综合结论的整个过程；后者仅仅是一种单纯的定量合成的统计学方法;目前国内外文献中以广义的概念应用更为普遍,系统评价常和Meta分析交叉使用,当系统评价采用了定量合成的方法对资料进行统计学处理时即称为Meta-分;因此,系统评价可以采用Meta-分析quantitativesystematicreview 定量系统评价,也可以不采用Meta-分析non-quantitativesystematicreview,定性系统评价;参照Cochrane协作网系统评价工作手册CochraneReviewers’Handbook制定的统一标准; Meta分析的基本步骤如下：1明确简洁地提出需要解决的问题;2制定检索策略,全面广泛地收集随机对照试验;3确定纳入和排除标准,剔除不符合要求的文献;4资料选择和提取;5各试验的质量评估和特征描述;6统计学处理;a．异质性检验齐性检验;b．统计合并效应量加权合并,计算效应尺度及95％的置信区间并进行统计推断; c．图示单个试验的结果和合并后的结果;d．敏感性分析;e．通过“失安全数”的计算或采用“倒漏斗图”了解潜在的发表偏倚;7结果解释、作出结论及评价;8维护和更新资料;临床医生只需要知道Meta分析的基本思想,具体的统计学方法让统计学家研究,让统计学软件帮我们完成;ReviewManagerRevMan是Cochrane协作网提供给评价者准备和维护更新Cochrane系统评价而设计的软件,也可以说是专门为临床医生度身订做,用于完成Meta分析的软件,它不仅可以协助我们完成Meta分析的计算过程,还可以帮助我们了解Meta分析的架构并学习系统评价的分析方法,最后把完成的系统评价制作成易于通过电子转换的文件以标准统一的格式发送到Cochrane系统评价资料库TheCochraneDatabaseofSystematicReviews,CDSR,便于电子出版和日后更新;充分利用RevMan软件对初次从事系统评价的人员获得方法学上的指导有很大的裨益;系统评价有多种类型,如病因研究、诊断性试验的评价、预后及流行病学研究等;Cochrane系统评价目前主要限于随机对照试验;非随机对照试验的系统评价方法学还处于不太完善的阶段,需要进行更多的相关研究;诊断试验的Meta分析方法与一般的随机对照试验Meta分析不同,需要同时考虑敏感性与特异性,采用综合接受者工作特征summaryreceiveroperatingcharacteristiccurve,SROC的分析,但RevMan4.2未提供Meta分析的完整步骤,根据个人的体会,结合战友的经验总结而成,meta的精髓就是对文献的二次加工和定量合成,所以这个总结也算是对战友经验的meta分析吧;一、选题和立题一形成需要解决的临床问题：系统评价可以解决下列临床问题:1.病因学和危险因素研究;2.治疗手段的有效性研究;3.诊断方法评价;4.预后估计;5.病人费用和效益分析等;进行系统评价的最初阶段就应对要解决的问题进行精确描述,包括人群类型疾病确切分型、分期、治疗手段或暴露因素的种类、预期结果等,合理选择进行评价的指标;二指标的选择直接影响文献检索的准确性和敏感性,关系到制定检索策略;三制定纳入排除标准;二、文献检索一检索策略的制定这是关键,要求查全和查准;推荐Mesh联合freeword检索;二文献检索,获取摘要和全文国内的有维普全文VIP,CNKI,万方数据库,外文的有medline,SD,OVID等;三文献管理强烈推荐使用endnote,procite,noteexpress等文献管理软件进行检索和管理文献;查找文献全文的途径：在这里,讲一下找文献的过程,以请后来的战友们参考不包括网上有电子全文的：1.查找免费全文：1在pubmedcenter中看有无免费全文;有的时候虽然没有显示freefulltext,但是点击进去看全文链接也有提供免费全文的;我就碰到几次;2在google中搜一下;少数情况下,NCBI没有提供全文的,google有可能会找到,使用“学术搜索”;本人虽然没能在google中找到一篇所需的文献,但发现了一篇非常重要的综述,里面包含了所有我需要的文献当然不是数据,但起码提供了一个信息,所需要的文献也就这么多了,因为老外的综述也只包含了这么多的内容;这样,到底找多少文献,找什么文献,心里就更有底了;3免费医学全文杂志网站;;提供很过超过收费期的免费全文;2.图书馆查馆藏目录：包括到本校的,当然方便,使用pubmed的linkout看文献收录的数据库,就知道本校的是否有全文;其它国内高校象复旦、北大、清华等医学院的全文数据库都很全,基本上都有权限;上海的就有华东地区联目、查国内各医学院校的图书馆联目;这里给出几个：1中国高等院校医药图书馆协会的地址：,进入左侧的“现刊联目”,可以看到有“现刊联目查询”和“过刊联目查询”,当然,查询结果不可全信,里面有许多错误;本人最难找的两篇文章全部给出了错误的信息后来电话联系证实的;2再给出两个比较好的图书馆索要文献的email地址有偿服务,但可以先提供文献,后汇钱,当然做为我们,一定要讲信誉吆;一是解放军医学图书馆信息部：,电话：；3二是复旦大学医科图书馆原上医：i,联系人,周月琴,王蔚之,郑荣,电话,,需下载文献传递申请表;其他的图书馆要么要求先交开户费,比如协和500元,要么嫌麻烦,虽然网上讲过可提供有偿服务,在这里我就不一一列出了;3.请DXY战友帮忙,在馆藏文献互助站中发帖,注意格式正确,最好提供linkout的多个数据库的全文链接,此时为帮助的人着想,就是帮助自己;自己也同时帮助别人查文献,一来互相帮助,我为人人,人人为我;二则通过帮助别人可以积分,同时学会如何发帖和下载全文,我就感觉通过帮助别人收获很大,自己积分越高,获助的速度和机会也就相应增加;现在不少免费的网络空间我常用爱存,比发邮件简便很多;所以如果你求助以后,要及时去“我的论坛”中查看帖子,有的很快就把下载链接发过来了,不要一味只看邮箱;4.实在不行,给作者发email;这里给出一个查作者email的方法,先在NCBI中查出原文献作者的所有文章,注意不要只限于第一作者,display,abstract,并尽可能显示多的篇数,100,200,500;然后在网页内查找“”,一般在前的字母会与人名有些地方相似;再根据地址来确定是否是同一作者;5.查找杂志的网址,给主编发信求取全文;这里我就不讲查找的方法了,DXY中有许多帖子;我的一篇全文就是这样得到的;6.向国外大学里的朋友求助;国外大学的图书馆一般会通过馆际互借来查找非馆藏文献,且获得率非常高;我的三篇文献是通过这一途径得到的;如果还是找不到,那就……我也没辙了,还有朋友如有其他的方法,不妨来这里交流;难度不小吧,比起做实验来如何三、对文献的质量评价和数据收集一研究的质量评价对某一试验研究的质量评价主要是评价试验结果是否有效,结果是什么该结果是否适用于当地人群;下面一系列问题可以帮助研究者进行系统的质量评价:①该研究的试验设计是否明确,包括研究人群、治疗手段和结果判定方法;②试验对象是否随机分组;③病人的随访率是否理想及每组病人是否经过统计分析;④受试对象、研究人员及其它研究参与者是否在研究过程中实行“盲法”;⑤各组病人的年龄、性别、职业等是否相似;⑥除进行研究的治疗手段不同外,其它的治疗是否一致;⑦治疗作用大小;⑧治疗效果的评价是否准确;⑨试验结果是否适用于当地的人群,种族差异是否影响试验结果;⑩是否描述了所有重要的治疗结果;治疗取得的效益是否超过了治疗的危险性和费用;系统评价者应根据上述标准进行判断,不满足标准的文献应剔除或区别对待数据合并方法不同,以保证系统评价的有效性;二、数据收集研究者应设计一个适合本研究的数据收集表格;许多电子表格制作软件如Excel、Access,和数据库系统软件如FoxPro等,可以用于表格的制作;表格中应包括分组情况、每组样本数和研究效应的测量指标;根据研究目的不同,测量指标可以是率差、比数odds、相对危险度relativerisk,包括RR和OR;各研究间作用测量指标不一致,需转化为统一指标;常用的统一指标是作用大小EffectSize,ES,ES是两比较组间作用差值除以对照组或合并组的标准差;ES无单位是其优点;三、数据分析系统评价过程中,对上述数据进行定量统计合并的流行病学方法称为Meta分析Metaanalysis;Meta意思是morecomprehensive,即更加全面综合;通过Meta分析可以达到以下目的:1.提高统计检验效能;2.评价结果一致性,解决单个研究间的矛盾;3.改进对作用效应的估计;4.解决以往单个研究未明确的新问题;统计分析的指标一、异质性检验1.检验原理：meta分析的原理首先是假定各个不同研究都是来自非同一个总体H0:各个不同样本来自不同总体,存在异质性,备择假设H1,如果p>0.1,拒绝H0,接受H1,,即来自同一总体这样就要求不同研究间的统计量应该接近总体参数真实值,所以各个不同文献研究结果是比较接近,就是要符合同质性,这时候将所有文献的效应值合并可以采用固定效应模型的有些算法,如倒方差法,mantelhaenszel法,peto法等.2.分类：异质性检验,包括三个方面：临床异质性,统计学异质性和方法学异质性,作meta分析首先应当保证临床同质性,比如研究的设计类型、实验目的、干预措施等相同,否则就要进入亚组分析,或者取消合并,在满足临床同质性的前提下非常重要,不能一味追求统计学同质性,首先考虑专业和临床同质性,我们进一步观测统计学同质性;临床异质性较大时不能行meta分析,随机效应模型也不行.只能行描述性系统综述systemicreviews,SR或分成亚组消除临床异质性.解决临床异质后再考虑统计学异质性的问题.如果各个文献研究间结果不存在异质性p>0.1,选用固定效应模型fixedmodel,这时其实选用随即效应模型的结果与固定效应模型相同；如果不符合同质性要求,即异质性检验有显着性意义p<0.1,这时候固定效应模型的算法来合并效应值就是有偏倚,合并效应值会偏离真实值.所以,异质性存在时候要求采用随机模型,主要是矫正合并效应值的算法,使得结果更加接近无偏估计,即结果更为准确.此外,这里要说明的是,采用的模型不同,和合并效应值的方法不同,都会导致异质性检验P值存在变动,这个可以从算法原理上证明,不过P值变动不会很大,一般在小数点后第三位的改变.异质性检验的Q值在固定模型中采用倒方差法和Mantel-haenszel法中也会不同;随机效应模型是不需要假定各个研究来自同一个总体为前提,本来就是对总体参数的近似无偏估计,这个与固定模型不一样必须要同质为基础,所以随机模型来作异质性检验简直是“画蛇添足”,无奈之举因此,随机模型异质性检验是否有统计学意义都是可以用,而固定模型必须要求无异质性;可以证明和实践,如果无异质性存在的时候,随机模型退化为固定,即固定模型的结果于随机模型的合并效应值是相等的具体见下图：目前,国内外对meta分析存在异质性,尤其是异质性检验P值很小的时候具体范围我不清楚,是0.05～0.1吗请版主补充,学术界有着不同的争论,很多人认为这个时候做meta分析是没有意义,相当于合并了一些来自不同总体的统计结果,也有人认为,这些异质性的存在可能是由于文献发表的时间,研究的分组,研究对象的特征等因素引起,只要采用亚组分析或meta回归分析可以将异质性进行控制或解释,还是可以进行meta分析,至少运用随机效应模型可以相对无偏的估计总体.这里要强调的是,异质性检验P值较小时候,最好能对异质性来源进行分析和说明;合理进行解释,同时进行亚组分析,相当于分层分析,消除混杂因素造成的偏倚bias;3.衡量异质性的指标一个有用的定量衡量异质性的指标是I2,I2=Q–df/Qx100%,此处的Q是卡方检验的统计值,df是其自由度Higgins2003,Higgins2002;这个I2值代表了由于异质性而不是抽样误差机会导致的效应占总效应估计值的百分率;I2值大于50％时,可以认为有明显的异质性;参考二、敏感性分析：1.敏感性分析的含义：改变纳入标准特别是尚有争议的研究、排除低质量的研究、采用不同统计方法/模型分析同一资料等,观察合并指标如OR,RR的变化,如果排除某篇文献对合并RR有明显影响,即认为该文献对合并RR敏感,反之则不敏感,如果文献之间来自同一总体,即不存在异质性,那么文献的敏感性就低,因而敏感性是衡量文献质量纳入和排除文献的证据和异质性的重要指标;敏感性分析主要针对研究特征或类型如方法学质量,通过排除某些低质量的研究、或非盲法研究探讨对总效应的影响;王吉耀第二版P76中“排除某些低质量的研究,再评价,然后前后对比,探讨剔除的试验与该类研究特征或类型对总效应的影响”;王家良第一版八年制P66、154敏感性分析是从文献的质量上来归类,亚组分析主要从文献里分组病例特征分类;敏感性分析是排除低质量研究后的meta分析,或者纳入排除研究后的meta分析;亚组分析是根据纳入研究的病人特点适当的进行分层,过多的分层和过少的分层都是不好的;例如在排除某个低质量研究后,重新估计合并效应量,并与未排除前的Meta分析结果进行比较,探讨该研究对合并效应量影响程度及结果稳健性;若排除后结果未发生大的变化,说明敏感性低,结果较为稳健可信；相反,若排除后得到差别较大甚至截然相反结论,说明敏感性较高,结果的稳健性较低,在解释结果和下结论的时候应非常慎重,提示存在与干预措施效果相关的、重要的、潜在的偏倚因素,需进一步明确争议的来源;2.衡量方法和措施其实常用的就是选择不同的统计模型或进行亚组分析,并探讨可能的偏倚来源,慎重下结论;亚组分析通常是指针对研究对象的某一特征如性别、年龄或疾病的亚型等进行的分析,以探讨这些因素对总效应的影响及影响程度;而敏感性分析主要针对研究特征或类型如方法学质量,通过排除某些低质量的研究、或非盲法的研究以探讨对总效应的影响;建议可以看参考王吉耀主编,科学出版社出版的循证医学与临床实践;敏感性分析只有纳入可能低质量文献时才作,请先保证纳入文献的质量纳入文献的质量评价方法,如果是RCT,可选用JADAD评分;如果病因学研究,我认为使用敏感性分析是评价文献质量前提是符合纳入标准的较为可行的方法;敏感性分析是分析异质性的一种间接方法;有些系统评价在进行异质性检验时发现没有异质性,这时还需不需要作敏感性分析我的看法是需要,因为我觉得异质性也是可以互相抵消的,有时候作出来没有异质性,但经过敏感性分析之后,结果就会有变化;三对入选文献进行偏倚估计发表偏倚publicationbias评估包括作漏斗图,和对漏斗图的对称性作检验;可以用stata软件进行egger检验;人是活的,软件是死的,临床是相对的,统计学是绝对的;四、总结：一结果的解释Meta-分析结果除要考虑是否有统计学意义外,还应结合专业知识判断结果有无临床意义;若结果仅有统计学意义,但合并效应量小于最小的有临床意义的差值时,结果不可取；若合并效应量有临床意义,但无统计学意义时,不能定论,需进一步收集资料;不能推荐没有Meta-分析证据支持的建议;在无肯定性结论时,应注意区别两种情况,是证据不充分而不能定论,还是有证据表明确实无效;二结果的推论Meta-分析的结果的外部真实性如何在推广应用时,应结合该Meta-分析的文献纳入/排除标准,考虑其样本的代表性如何,特别应注意研究对象特征及生物学或文化变异、研究场所、干预措施及研究对象的依从性、有无辅助治疗等方面是否与自己的具体条件一致;理想的Meta-分析应纳入当前所有相关的、高质量的同质研究,无发表性偏倚,并采用合适的模型和正确统计方法;三系统评价的完善与应用系统评价完成后,还需要在实际工作中不断完善,包括:①接受临床实践的检验和临床医师的评价;②接受成本效益评价;③关注新出现的临床研究,要及时对系统评价进行重新评价;临床医师只有掌握了系统评价的方法,才能为本专业的各种临床问题提供证据,循证医学才能够顺利发展;。

FortiWeb和ImmuniWeb AI的集成解决方案：Web应用程序安全测试和可编程虚拟补丁说明

FORTIWEB AND IMMUNIWEB AIWeb Application Security Testing and Agile Virtual Patching Virtual patching is a great method to protect webapplications until they can be permanently ﬁxed by developers. High-Tech Bridge and Fortinet now offer an integrated solution that audits web applications and web services (REST/SOAP) for vulnerabilities with High-Tech Bridge ImmuniWeb AI and then reliably protects themwith FortiWeb virtual patching. Once a vulnerability is discovered, it is protected by FortiWeb instead of issuing disruptive emergency patches, or worse, waiting weeks or months for developers to deploy a new release while the application sits unprotected.FortiWeb virtual patching uses a combination of sophisticated tools such as URLs, parameters, signatures, and HTTP methodsto create a granular rule that addresses each speciﬁc vulnerability discovered by ImmuniWeb AI. A zero false-positives SLA is provided by ImmuniWeb AI to every customer, guaranteeing safe and reliable virtual patching that will not impact web application ﬁrewall (WAF) performance or website availability.While virtual patching will not replace the traditional application development process, it can create a secure bridge between the time a vulnerability is discovered and the time a software releaseis issued to address it. In cases where it may not be possible or practical to change the application code, such as with legacy, inherited, and third-party applications, FortiWeb virtual patching can provide a permanent security solution for vulnerabilities. ImmuniWeb AI uses its award-winning machine learning and AI technology for intelligent automation and acceleration of application security testing. The technology is enhanced with scalable and cost-effective manual testing when required, reliably detecting even the most intricate vulnerabilities and ﬂaws in business logic. FortiWeb complements ImmuniWeb AI with granular application protection rules that take the imported vulnerability results and provide immediate mitigation with the same level of accuracy. This granular virtual patching is able to maintain application security until development teams are able to fully deploy permanent ﬁxes in the application code. It can also extend the windows between security patches to minimize disruptions to the organization and its users.BENEFITSUsing FortiWeb with High-Tech Bridge ImmuniWeb AI gives organizations:n An enhanced solution that exceeds PCI DSS6.5/6.6/11.3 and GDPR Art. 25/Art. 35.n Absolute visibility across sophisticated web application vulnerabilities, weaknesses, and privacy issues.n Prevention of data breaches and targeted attacks via corporate web applications.n Minimized risk of exposure to threats between the time a threat is discovered until it is ﬁxed by developers.n Less disruptions due to emergency ﬁxes and test cycles by virtually patching vulnerabilities until they can be permanently ﬁxed.n Protection for legacy, inherited, and third-party applications where development ﬁxes are not an option or are impractical.n More stability in application security patches as developers have more time to properly ﬁx code vs. issuing emergency patches that have not had timeto be fully tested.n More accurate FortiWeb reporting and identiﬁcation of attempts to exploit vulnerabilities discoveredby ImmuniWeb AIn Additional ﬂexibility and granular management of FortiWeb WAF policies based on ImmuniWeb AIaudit results.SOLUTION BRIEFSOLUTION BRIEF: FORTIWEB AND IMMUNIWEB AICopyright © 2019 Fortinet, Inc. All rights reserved. Fortinet , FortiGate , FortiCare and FortiGuard , and certain other marks are registered trademarks of Fortinet, Inc., and other Fortinet names herein may also be registered and/or common law trademarks of Fortinet. All other product or company names may be trademarks of their respective owners. Performance and other metrics contained herein were attained in internal lab tests under ideal conditions, and actual performance and other results may vary. Network variables, different network environments and other conditions may affect performance results. Nothing herein represents any binding commitment by Fortinet, and Fortinet disclaims all warranties, whether express or implied, except to the extent Fortinet enters a binding written contract, signed by Fortinet’s General Counsel, with a purchaser that expressly warrants that the identified product will perform according to certain expressly-identified performance metrics and, in such event, only the specific performance metrics expressly identified in such binding written contract shall be binding on Fortinet. For absolute clarity, any such warranty will be limited to performance in the same ideal conditions as in Fortinet’s internal lab tests. Fortinet disclaims in full any covenants, representations, and guarantees pursuant hereto, whether express or implied. Fortinet reserves the right to change, modify, transfer, or otherwise revise this publication without notice, and the most current version of the publication shall be applicable. Fortinet disclaims in full any covenants, representations, and guarantees pursuant hereto, whether express or implied. Fortinet reserves the right to change, modify, transfer, or otherwise revise thispublication without notice, and the most current version of the publication shall be applicable.GLOBAL HEADQUARTERS Fortinet Inc.899 Kifer RoadSunnyvale, CA 94086United StatesTel: +/salesEMEA SALES OFFICE 905 rue Albert Einstein 06560 Valbonne FranceTel: +33.4.8987.0500APAC SALES OFFICE8 Temasek Boulevard #12-01Suntec Tower Three Singapore 038988Tel: +65-6395-7899Fax: +65-6295-0015LATIN AMERICA HEADQUARTERS Sawgrass Lakes Center13450 W. Sunrise Blvd., Suite 430Sunrise, FL 33323Tel: +1.954.368.9990February , 26 2019 9:35 PM D:\Fortinet\Work\February 2019\121918\sb-fortiweb-and-htb329416-A -0-ENFIGURE 1: ONCE IMMUNIWEB AI AUDIT RESULTS ARE IMPORTED TO FORTIWEB, THEN FORTIWEB VIRTUAL PATCHINGAUTOMATICALLY CREATES NEW WAF RULESETS TO PROTECT AGAINST NEWLY DISCOVERED VULNERABILITIES AND WEAKNESSES.About FortinetFortinet (NASDAQ: FTNT) protects the most valuable assets of some of the largest enterprise, service provider and government organizations across the globe. The company’s fast, secure and global cybersecurity solutions provide broad, high-performance protection against dynamic security threats while simplifying the IT infrastructure. They are strengthened by the industry’s highest level of threat research, intelligence and analytics. Unlike pure-play network security providers, Fortinet can solve organizations’ most important security challenges, whether in networked, application or mobile environments—be it virtualized/cloud or physical. More than 210,000 customers worldwide, including some of the largest and most complex organizations, trust Fortinet to protect their brands. Learn more at , the Fortinet Blog or FortiGuard Labs .About High-T ech BridgeHigh-Tech Bridge is a global provider of web and mobile application security testing services. Named “Gartner Cool Vendor” and the winner in “Best Usage of Machine Learning/AI” by SC Awards Europe 2019, High-Tech Bridge pioneers the application security testing market with scalable and cost-effective application security testing products for web and mobile applications. ImmuniWeb AI Platform leverages machine learning and AI technology for intelligent automation and acceleration of application security testing. Complemented by scalable and cost-effective manual testing, it detects the most sophisticated vulnerabilities and comes with a zero false-positives SLA for every customer. Learn more at .。

网络英语试题及答案

网络英语试题及答案一、选择题（每题1分，共10分）1. What does the abbreviation "WWW" stand for?A. World Wild WebB. World Wide WebC. World War WebD. World Wonder Web2. Which of the following is the most common way to access the internet?A. RadioB. TelevisionC. Telephone lineD. Satellite3. The term "URL" refers to:A. Uniform Resource LocatorB. Unique Resource LocatorC. User Resource LocatorD. Universal Resource Locator4. What is the primary function of a search engine?A. To play musicB. To send emailsC. To find information on the internetD. To make online purchases5. Which of the following is not a social media platform?A. FacebookB. TwitterC. LinkedInD. Photoshop6. What is the full form of "HTTP"?A. HyperText Transfer ProtocolB. HyperText Transport ProtocolC. HighText Transfer ProtocolD. HighText Transport Protocol7. What is the purpose of cookies on the internet?A. To store user preferences and track browsing historyB. To send messages to friendsC. To play videosD. To make online reservations8. Which of the following is a type of malware?A. VirusB. EmailC. FirewallD. Antivirus9. What does "VPN" stand for?A. Virtual Private NetworkB. Very Personal NetworkC. Video Personal NetworkD. Virtual Programming Network10. Which protocol is used for sending emails?A. FTPB. SMTPC. TCPD. UDP二、填空题（每空1分，共10分）11. The internet is a global system of interconnected computer networks that use the __________ protocol suite to link devices worldwide.12. When you want to download a file from the internet, you might use a __________ client.13. A __________ is a program that displays and runs documents written in HTML.14. The process of making a website accessible to users with disabilities is known as __________.15. The term "cyberbullying" refers to bullying or harassment that takes place __________.三、简答题（每题5分，共20分）16. Explain the difference between an intranet and an extranet.17. What are the benefits of using a cloud service for data storage?18. Describe the steps to create a new email account.19. What are some common security measures to protect personal information online?四、论述题（每题15分，共30分）20. Discuss the impact of social media on modern communication.21. Analyze the role of the internet in e-commerce and its implications for traditional businesses.五、翻译题（每题5分，共10分）22. 将以下句子翻译成英文：“网络改变了我们的生活和工作方式。

Meta分析讨论写作要点

Meta分析讨论写作要点关于一般SCI论文的讨论部分如何写作有许多大道理，但听了许多大道理，却仍写不好分析讨论。

meta分析的讨论与原始研究文章一样，常规需要解释结果、提出优缺点及临床意义，但又与这些文章不同，在分析讨论部分有些自己的特点。

作为文章的精髓，讨论部分的好坏在文章发表中占据了非常重要的作用。

下面以一篇发表在the Lancet 上的文章[1]的讨论部分为例，讲解Meta分析讨论部分的写作要点。

明确提出本研究的主要发现：在引言部分，许多文章会利用picos原则提出本研究做了哪些工作。

讨论部分应该与这句话相呼应，明确提出本研究的主要发现，分析主要结果的临床意义，本研究结果的适用人群及地区范围。

另外，异质性分析、亚组分析的结果也应讨论，几个结果可分为几个小段，它们之前是平行或递进的关系。

举例该文章提及的引言主要有以下几个问题：whether blood pressure lowering treatment reduces the risk of cardiovascular disease in all patient populations remains unclear.人群中降低血压是否能减少心血管事件的风险。

结论的第一句话就呼应引言，给出答案：In this meta-analysis, blood pressure lowering treatment significantly reduced the risk of cardiovascular disease and death in various populations of patients.是的，可以降低心血管事件及死亡的风险。

引证总结证据，分析与本研究结果的异同：引言部分一般会引用已有的系统评价、meta分析、指南及专家共识、纳入文献指出本研究问题的富有争议的主本人系天天论文网就职11年的资深论文编辑；工作中与各大医学期刊杂志社进行学术交流过程中建立了稳定的编辑朋友圈，系多家医学杂志社的特约编辑，常年为医学期刊杂志供稿，负责天天论文网医学论文·分检·编校·推送·指导等工作！工作企鹅1:1550116010工作企鹅2：766085044要观点，而在讨论部分，与前言类似，但更详细，也不可以重复，不需再与纳入文献的结果作对比。

PowerUp

Flexible configuration
Supports multiple configuration options, such as output voltage, current limit, etc., which can be adjusted according to actual needs.
Key players in the market include major energy companies, technology providers, equipment manufacturers, and service providers
Competitive strategies adopted by these players include mergers and acquisitions, collaborations and partnerships, new product launches, and expansions into new markets
The market size is primarily driven by the increasing demand for energy efficient and sustainable power solutions, as well as the growing option of advanced technologies and innovations in the energy sector
future planning
01
Introduction
Purpose and background
To provide a comprehensive overview of the PowerUp project, its goals, and the current state of development

高三英语学术研究方法创新不断探索单选题30题

高三英语学术研究方法创新不断探索单选题30题1.In an academic research discussion, what is the most important aspect of a research method?A.AccuracyB.SpeedC.CreativityD.Popularity答案：A。

解析：在学术研究中，准确性是至关重要的，它确保研究结果的可靠性。

速度在某些情况下可能重要，但不是最主要的。

创造力也很重要，但不是最重要的方面。

而受欢迎程度与研究方法的重要性关系不大。

2.What does a good research method ensure?A.Lots of dataB.Accurate resultsC.Fast completionD.High popularity答案：B。

解析：一个好的研究方法能确保得到准确的结果。

大量的数据不一定能保证结果准确。

快速完成也不是主要目的。

高人气与研究方法的主要作用无关。

3.In academic research, the definition of a research method mainly includes?A.Question asking and data collectionB.Guessing and intuitionC.Opinion sharing and discussionD.Random selection and chance答案：A。

解析：学术研究方法主要包括提出问题和收集数据。

猜测和直觉不是科学的研究方法。

意见分享和讨论是研究的一部分但不是研究方法的定义。

随机选择和偶然也不是研究方法的主要内容。

4.Which of the following is not a characteristic of an effective research method?A.Biased data collectionB.Systematic approachC.ReliabilityD.Validity答案：A。

英文版计算机试题及答案

英文版计算机试题及答案一、选择题（每题2分，共20分）1. Which of the following is not a function of an operating system?A. Process managementB. Memory managementC. Data storageD. File management2. In a computer network, what does the term "bandwidth" refer to?A. The width of the network cableB. The maximum rate of data transferC. The number of users connectedD. The speed of the network processor3. What is the primary purpose of a firewall?A. To prevent unauthorized access to a networkB. To encrypt dataC. To manage network trafficD. To store user passwords4. Which of the following is a type of software used for creating and editing documents?A. Spreadsheet softwareB. Database softwareC. Word processing softwareD. Graphics software5. What is the term used to describe the process of converting data from one format to another?A. Data migrationB. Data transformationC. Data conversionD. Data translation6. What does the acronym "CPU" stand for in computing?A. Central Processing UnitB. Central Processing UnitC. Computer Processing UnitD. Computing Processing Unit7. What is the function of a router in a network?A. To connect multiple networksB. To store dataC. To provide power to devicesD. To print documents8. What is the process of finding and fixing errors in software called?A. DebuggingB. PatchingC. UpdatingD. Patching9. Which of the following is a type of computer virus that replicates itself by attaching to other programs?A. TrojanB. WormC. RansomwareD. Spyware10. What is the term for the graphical representation of data on a computer screen?A. Data visualizationB. Data representationC. Data graphingD. Data mapping二、填空题（每题2分，共20分）1. The _________ is the primary memory used by a computer to store data and instructions that are currently being processed.2. A _________ is a type of software that allows users to create and edit images.3. The process of converting analog signals to digital signals is known as _________.4. A _________ is a collection of data stored in a structured format.5. The _________ is a hardware component that connects a computer to a network.6. In computer programming, a _________ is a sequence of statements that perform a specific task.7. The _________ is a type of malware that hides its presence and waits for a trigger to activate.8. A _________ is a type of software that is designed to protect a computer from unauthorized access.9. The _________ is the process of organizing and managing data in a database.10. A _________ is a type of software that allows users tocreate and edit spreadsheets.三、简答题（每题10分，共30分）1. Describe the role of a server in a computer network.2. Explain the difference between a compiler and an interpreter in programming.3. Discuss the importance of data backup and recovery in a computing environment.四、编程题（每题15分，共30分）1. Write a simple program in Python that calculates the factorial of a given number.2. Create a function in Java that takes an array of integers and returns the largest number in the array.答案：一、选择题1. C2. B3. A4. C5. C6. A7. A8. A9. B10. A二、填空题1. RAM (Random Access Memory)2. Graphics software3. Analog-to-digital conversion4. Database5. Network interface card (NIC)6. Function or procedure7. Trojan8. Antivirus software9. Database management10. Spreadsheet software三、简答题1. A server in a computer network is a powerful computer or system that manages network resources, including hardware and software, and provides services to other computers on the network, such as file storage, web hosting, and print services.2. A compiler is a program that translates source codewritten in a programming language into machine code that a computer can execute. An interpreter, on the other hand, reads and executes the source code line by line without the need for a separate compilation step.3. Data backup and recovery are crucial in a computing environment to prevent data loss due to hardware failure, software bugs, or malicious attacks. Regular backups ensure that data can be restored to a previous state in case of corruption or deletion.四、编程题1. Python Program for Factorial Calculation:```pythondef factorial(n):if n == 0:return 1 else:。

软考英文题目汇总

软考英文题目汇总以下是部分软考英文题目：1. What is the full form of IP?IP stands for "Internet Protocol".2. What is the difference between a client and a customer?A client is a person or organization that hires a professional service, while a customer is a person who purchases a product or service.3. What is the meaning of "open source"?Open source means that the source code of a program is freely available and can be modified and redistributed by anyone.4. What is the meaning of "bug" in computer science?A bug is an error or glitch in a computer program that causes it to malfunction or behave unexpectedly.5. What is the meaning of "algorithm"?An algorithm is a step-by-step set of instructions for solving a problem or performing a task in a finite number of steps.6. What is the meaning of "database"?A database is a collection of related data that can be检索to answer various queries and perform specific operations.7. What is the meaning of "cybersecurity"?Cybersecurity refers to the practices and technologies used to protect computers, networks, and data from unauthorized access, disclosure, alteration, or destruction.8. What is the meaning of "cryptography"?Cryptography is the practice of transforming information into a code for secure communication and storing data in a secure manner.9. What is the meaning of "GUI"?GUI stands for "Graphical User Interface", which refers to the visual interface that allows users to interact with computers and other devices through graphical elements such as windows, icons, and menus.10. What is the meaning of "TCP/IP"?TCP/IP stands for Transmission Control Protocol/Internet Protocol, which is a set of communication protocols used on the internet and other computer networks to transmit data packets between devices.以上题目主要考察了计算机科学和软考相关的专业术语和概念。

WHOClassificationOfTumoursOfTheLung,Pleura,…

WHO Classification Of Tumours Of The Lung, Pleura, Thymus And Heart (IARC WHO Classification Of Tumours) By International Agency For Research On CancerIf you are searched for the ebook WHO Classification of Tumours of the Lung, Pleura, Thymus and Heart (IARC WHO Classification of Tumours) in pdf form, in that case you come on tothe correct website. We furnish utter version of this book in txt, PDF, DjVu, doc, ePub forms. You can readWHO Classification of Tumours of the Lung, Pleura, Thymus and Heart (IARC WHO Classification of Tumours) online or download. In addition, on our website you can read guides andanother art books online, or downloading their as well. We will to attract consideration what our website does notstore the book itself, but we grant link to website wherever you may downloading or reading online. 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A META-SEARCH WEB SERVICE-BASED ARCHITECTURE FOR P

专利名称：A META-SEARCH WEB SERVICE-BASED ARCHITECTURE FOR PEER-TO-PEERCOLLABORATION AND VOICE-OVER-IP 发明人：LEAUTE, YVES,GOOSE, STUART申请号：US2004007996申请日：20040316公开号：WO2004084030A3公开日：20050106专利内容由知识产权出版社提供摘要：A system and method are presented for discovering mobile collaborators on a peer-to-peer (P2P) network. Each collaborator is identified on the P2P network by a unique ID. Each collaborator may also be identified by an identity file posted on the P2P network. Collaboration applications may include any of a variety of applications involving the exchange of information between 2 or more collaborators, such as voice over IP (VoIP). Dynamic file querying can be performed to filter the identity files most relevant to a particular peer partner. Web services are used end-to-end between P2P mobile devices and also between devices and third party service providers. Search engines use self-provisioning concepts for searches and WEB services querying. End-user devices register their personal information on P2P networks using identity self-provisioning and WEB services templates for a variety of uses, such as personal, gaming or business 申请人：SIEMENS CORPORATE RESEARCH INC.,LEAUTE, YVES,GOOSE, STUART更多信息请下载全文后查看。

Scholastic Inc. 系统44快速开始网络培训参与者指南说明书

System 44®QuickStartWebinar Participant GuideWebinar ChecklistYou need a computer with Internet access and a phone to join the teleconference.Before the Webinar❑ Gather materials:• Printed copy of this Participant Guide• Pen or pencil• Paper (optional)❑ Log in to the training at least5 minutes early.• Click the link in your registration email.• Wait 1–2 minutes for the training window to pop up.❑ Dial in to the teleconference using the informationprovided in your registration email:• Teleconference number: ____________________________• Participant code: __________________________________Tech TipAfter logging in, you will see a Session in Progress window before you aretaken to the training session. If you do not see this, you may need to installActiveX Control or adjust your Internet settings to allow pop-ups.During the Webinar❑ Mute your phone if you have background noise.❑ Record notes in your Participant Guide.❑ Use the coffee mug icon if you need to step away from your computer.❑ Participate!After the Webinar❑ Complete the Webinar Training Evaluation.Webinar BestPractices✓Save your registrationemail!✓Sit in a quiet area.✓Remove distractions.✓Have a glass of waternearby.✓Close all computerprograms except yourWeb browser.®Webinar OverviewThis two-hour interactive online training will provide you an overview for getting started with using System 44 in your classroom.Learning Outcomes:Today’s training will help you:• Understand how the System 44 research foundation meets the needs of older struggling readers.• Identify materials and procedures to teach and manage each part of the System 44 Instructional Model.• Administer assessments to place students in the software and monitor their reading performance.• Use the Scholastic Achievement Manager (SAM) to enroll students, adjust settings, and run reports.• Plan next steps for a successful start and identify ongoing professional development.Agenda:10 minutes Welcome and Introductions 10 minutes Understanding the Research 40 minutes Teaching With System 44 10 minutes Break20 minutes Screening Students and Monitoring Progress 15 minutes Getting Started With SAM10 minutes Planning for a Successful Start 5 minutesQuestions and EvaluationWebinar MapYou may be new to interactive online trainings. Use this map to help you navigate the training session window.Whiteboard Tools (Pointer, Text Tool,WhiteboardSystem 44 Instructional ModelSystem 44 follows a 55- to 60-minute daily Instructional Model to target the needs of your struggling readers. Use the diagram below to take notes for reference.READ 180®/System 44 Instructional ModelUse System 44 in your READ 180 classroom to support your most challenged readers. System 44 integrates seamlessly into the 90-minute Instructional Model.Whole-Group Introduction 5–10 minutes Small-Group Rotations 50 minutesWhole-Group IntroductionBegin class with 5–10 minutes of Whole-Group Introduction. Use the ideas below to build community, reinforce key decoding skills, and motivate students.Software OrganizationRefer to the information below to see how the System 44 software is organized into Series, Topics, strands, and zones., or defined sets of System 44 25 related Topics. Word Strategies (orange) and practice in 4 zones:Self-Monitoring Chart: Scavenger HuntGuide students to use the Self-Monitoring Chart to track which practice materials to use independently to reinforce skills they are learning on the software. During training, practice using the Self-Monitoring Chart to fill in the empty boxes below.Photocopy the Self-Monitoring Chart on pages 117–120 of the System 44 Teacher Implementation Guide or download it from SAM (Keyword: 44 Student Chart). Software Topic Skill 44BookDecodable Digest System 44 Library Book1.4Consonantsp, cpp. 14–15Sight Wordsp. 223.4Book 3Planning for Small GroupUse this suggested weekly schedule to plan for the Small-Group Instruction/ Modeled and Independent Reading rotation. See the Planning and Pacing Guide for additional planning and grouping support.Teach a S.M.A.R.T. lesson to all students in the group.Students workindependently inthe 44Book.* and audiobooks.** Direct students to use the Self-Monitoring Chart to select relevant practice materials.Welcome Back!: Six Syllable TypesIn System 44, your students will learn the six syllable types so they can break down words. Refer to the definitions below for the Welcome Back! training activity. Syllable Type Description ExamplesClosed Ends in one or more consonants. Thevowel sound is usually short. map, plant, truck, hab-it, pic-nic, nap-kin, kit-tenOpen Ends in a vowel, which usually has along sound. she, si-lo, ze-ro, ba-sic, si-lent, ro-bot, hu-manVowel–Consonant–e Ends with a pattern of vowel–consonant–e. The vowel sound is usually long.kite, hope, cube, shine,flake, es-cape, dis-puteConsonant + -le, -el, or -al Ends with a consonant and -le, -el, or -al.It usually represents the sound “ul.”tur-tle, grum-ble, la-bel,fin-al, an-kle, tun-nelVowel Team Includes two vowels that stand for onesound (e.g., ai, ay, ea, ee, ie, oa, ow).train, team, oat, de-lay, greet-ing, ea-gle, shieldr-Controlled Includes r after a vowel. cart, germ, skirt, par-ty,Identifying Students for System 44Use formal assessments—like standardized tests—and informal measures—like teacher observations—to identify students for System 44. Consider the following criteria when selecting students:☑ Low SRI scores: Administer the Scholastic Reading Inventory (SRI) to all students to identify who is reading below grade level. Then give the Scholastic Phonics Inventory (SPI) to those who score below the 25th percentile (BR–400L for elementary, BR–600L for secondary) to identify System 44 candidates . ☑ Below-grade-level reading ☑ Difficulty with content-area texts☑ Frustration and limited participation in classwork ☑ Reliance on memory and sight words ☑ Learning English as a second language ☑ Placement in special educationUnderstanding SPI Decoding StatusRun the SPI Screening and Placement Report after the first SPI test to see students’ Decoding Status. Use the data to select System 44 students, form initial groups, and begin targeting instruction. See below to learn more about each Decoding Status.Decoding Status DescriptionRecommended Placement Areas for TargetedInstruction Pre-DecoderLittle or no knowledge of letter names or letter-sound correspondencesSystem 44 Series 1• Phonemic awareness • Letter names • Letter-sound correspondencesBeginning Decoder Can identify letter names but cannot decode fluently System 44 Series 1• Basic phonics, starting with consonants and short vowels • Related phonemic awareness Developing Decoder Can fluently decode wordswith consonants and shortvowels but cannot fluentlydecode longer or morecomplex words System 44 Series 4• Targeted phonics remediation to address skill gaps• Instruction focused on more advanced skills, such asblends, digraphs, long-vowels, and syllablesAdvancing Decoder Can decode with fluencyand struggles with readingfor a different reasonREAD 180• Vocabulary• Comprehension• Fluency with connected textGrouping Students for RotationsRun the SPI Screening and Placement Report after administering the first SPI test. Use the data to form small groups based on students’ Decoding Status. During training, work with your partner to practice grouping using the steps below.1. Identify which students will benefit from System 44 instruction.2. Place a “1” or “2” next to each student’s name to assign him or her to the lower-level Group 1 or the higher-level Group 2.3. Check the Recommended Instruction and Placement chart at the bottom of thereport to identify skills to teach the students in each group. Prepare to share out.Navigating SAMUse the Scholastic Achievement Manager (SAM) to manage student enrollment , adjust program settings, run data-rich reports, and access resources.Adding a Student to SAMAdd your students to SAM and assign them to classes.Directions:1. Log in to SAM with your username and password. Double-click a class name on the gray SmartBar . The class Profile screen will appear.2. Click Add a Student under Manage Roster .3. Enter information in the Profile tab of the Add a Student window. Check the appropriate boxes in the Add toClasses & Groups window to assign students to a class or group. Items marked with an asterisk (*) are required.4. Click Save to save the studentinformation and return to the class Profile screen. The student’s name will appear in the SmartBar. Repeat the steps to add other students to SAM.Navigating SAM (continued)Targeting SRI Reading LevelsEstimate students’ reading levels before they take the first SRI test.Directions:1. Log in to SAM with your username and password. Double-click a student or class name on the gray SmartBar. The Profile screen will appear.2. Click the Settings link next to the SRI icon in the Programs menu at the bottom of the screen.3. Use the pull-down menu next toEstimated Reading Level under Test Settings. Choose Far below grade level, Below grade level, On grade level, Above grade level, or Far above grade level. Confirm your choice by clicking Okay.4. Click Save & Return to save changes and return to the Profile screen, or click Save to save changes and remain in SRI Settings.Directions:1. Log in to SAM with your username and password. Double-click a student name on the gray SmartBar. The student’s Profile screen will appear.2. Click the Settings link next to the SPI or System 44 icons in the Programs menu at the bottom of the screen.3. For SPI, check the box next to Enable Spanish Audio Instructions to set audio instructions to play in Spanish. Check the box next to EnableAccuracy-Only Scoring to disable fluency scoring for students who cannot easily manipulate a mouse.4. For System 44, click the button next toNavigating SAM (continued)Using the Reports Index Run reports to analyze data for students, groups, and classes.Directions:1. Log in to SAM with your username and password. Click the Reports tab, either at the top or at the middle of the screen.2. Double-click a class or teacher name in the gray SmartBar to access theReports Index for that class or teacher. 3. Choose from the pull-down menu to sort the reports by multi-classroom,classroom, or student. Set a time range for the report data using the buttons under Time Period. 4. Click the button next to the report you want to run. Then click Run Report to view an onscreen version of the report. 5. Click Save a Copy (PDF) or PrintPreview (PDF) in the upper right corner to save or print the report.Your First Three WeeksUse these checklists to complete key next steps for a successful start with System 44. Also see the Planning and Pacing Guide for daily lessons for the first three weeks.❑ Install the program software. • Work with your school’s tech coordinator to install the software on student computers. • Make sure you have your SAM username and password. Week 1 1 ❑ Administer SRI and SPI. • Schedule 20–25 minutes for students to take SRI.• Check the SRI Intervention Grouping Report for students reading far below grade level. Week 2 1Your First Three Weeks (continued)During training, reflect on these key next steps for the first three weeks . . .• Check (✓) next steps you’ve already completed.• Star (★) next steps you want to focus on when you return to your classroom. • Place an exclamation mark (!) next to steps you’re worried about. • Write a question mark () next to steps that confuse you.❑ Form small groups using SPI report data. • Group students with similar SPI scores and Decoding Status.• Consider management issues when forming initial groups. Week 3 1Continuing Your LearningUse the following professional development resources for additional support with implementing System 44 in your classroom.System 44Teaching GuideLook for these professional development sections in the Teaching Guide:•English Language Development provides background on phonics interventionfor English language learners and best instructional practices.•Special Education presents research, best practices, and implementationsupport for System 44 educators working with special education students.•Research Foundations includes articles on the research principles that underlieSystem 44 instruction.•Instructional Routines explains step-by-step direct instruction routines toincorporate into your Teaching Guide lessons.System 44 Implementation TrainingTalk to your principal to schedule this full-day, in-person training. It provides anin-depth, hands-on look at using the program components to accelerate studentreading success. During training, you’ll receive a copy of the System 44 TeacherImplementation Guide and Planning and Pacing Guide.Digital Training Zone®Access to the Digital Training Zone (DTZ) is included with the PremiumMaintenance and Support Plan. Log in at /dtz to findresources to support your System 44 implementation, including:• Just-in-time downloadables for you and your students.• Follow-up System 44 webinars that explore key topics like using report data,teaching phonics, and managing your classroom.• Videos of model lessons in real classrooms.• How-to tutorials and Digital Overviews that provide step-by-step support for keySystem 44 tasks.• Graduate-level online courses that support System 44 implementation.Scholastic U™Ask your principal about subscribing to this online professional developmentdestination. It offers a comprehensive collection of professional developmentresources, including downloadables, training webinars, model lessons, How-toand Digital Overview tutorials, access to more than 20 graduate-level onlinecourses, and a community area to connect with other System 44 educators. Visit。

软件司法鉴定申请书范文

软件司法鉴定申请书范文英文回答：Software Forensic Examination Request Form. Case Information.Case Number:Case Name:Date of Incident:Reporting Party:Contact Information:Software Subject.Name of Software:Version:Serial Number/Activation Key:Installation Location:Forensic Examination Request.Please conduct a forensic examination of the software subject to determine:Evidence of unauthorized access or modification.Malicious or suspicious activity.Potential data breaches or data loss.Compliance with applicable laws and regulations.Specific Questions.Has the software been tampered with or modified in any way?Are there any signs of malware, viruses, or other malicious software?Has the software been used to access or extract sensitive data?Does the software comply with the organization's security policies and procedures?Timeline.Requested Examination Date:Deadline for Completion:Additional Information.Please provide any relevant documentation, logs, or other information that may assist in the examination.The examiner is authorized to access and extract data from the software subject for the purposes of the examination.Contact Information.Primary Contact:Phone Number:Email Address:中文回答：软件司法鉴定申请书。

系统评价概述选题与立题

立题
• 构成系统评价问题的四要素：－研究的受试对象－主要的干预措施－研究的重要结果－研究的设计方案
立题
例：阿司匹林（干预措施）是否能降低
急性脑梗死患者（研究对象）的短期死亡率（研究结果）？---- 随机对照试验（设计方案）的系统评价
研究的受试对象
• 确定疾病种类或亚型
- 疾病的诊断标准
实践循证医学的基本步骤： • 提出问题 • 收集证据 (困难，系统评价可帮助) • 评价证据 (困难，系统评价可帮助) • 使用证据
系统评价：循证医学的最佳证据
寻找证据应首先去找系统评价的报告，因为它比单个试验偏倚较少而更为可靠。
——— David Sackett, 2000
系统评价的选题和立题
原始研究文献来源明确，常为多渠道
检索方法
有明确的检索策略
原始文献的选择有明确的选择标准
原始文献的评价有严格的评价方法
结果的合成
多采用定量方法
结论的推断
多遵循研究证据
更新
定期根据新试验进行更新
涉及的范围常较广泛常未说明，不全面
常未说明，有潜在偏倚常未说明
评价方法不统一多采用定性方法有时遵循研究证据未定期更新
• 避免在进行系统评价的过程中对研究问题作大的改动
• 研究问题改动后，应对文献检索、选择、评价作相应的调整
Cochrane协作网及 Cochrane系统评价
Cochrane Collaboration
1、宗旨 Cochrane Collaboration是一个非盈利的国际性学术组织，旨在通过制作、保存和传播系统评价，提高医疗保健干预措施的效率，帮助人们制定遵循证据的医疗决策

一位读者发现一些错误，这篇SCI就被撤稿了

今天分享一篇撤稿的文章，文章题目就是“Retraction Note: Efficacy of vitamin C for the prevention and treatment of upper respiratory tract infection. A meta-analysis in ch ildren”作者是老外，研究的是meta分析。

这篇文章被一位读者发现了一些错误，然后就被期刊主编撤稿。

撤稿原因如下：After publication, a reader has identified some mistakes in the data reported, these include the following:(i)Inclusion of studies which did not met strict inclusion criteria: one with pseudo randomization allocation [2] and another one with the intervention group receiving vitamin C and herbal preparation containing Echinacea and propolis [3] which is relatively heavily weighted and has one of the strongest effects in the analysis of primary and secondary outcomes.(ii)Some data, numbers of upper respiratory tract infection (URTI) episodes and standard-deviations of URTI durations, extracted from studies were erroneous.(iii)Some statistical approaches performed in our meta-analysis are not appropriate and have not been described accurately. These errors are likely to invalidate the results. The authors acknowledge and apologize for these involuntary errors. For these reasons, the article has been retracted.参考文献：1.Vorilhon P, Arpajou B, Vaillant Roussel H, Merlin , Pereira B, Cabaillot A (2019) Efficacy of vitamin C for the prevention and treatment of upper respiratory tract infection. A meta-analysis in children. Eur J Clin Pharmacol 75(3):303–3112.Coulehan JL, Reisinger KS, Rogers KD, Bradley DW (1974) Vitamin C prophylaxis in a boarding school. N Engl J Med 290(1):6–103.Cohen HA, Varsano I, Kahan E, Sarrell EM, Uziel Y (2004) Effectiveness of an herbal preparation containing echinacea, propolis, and vitamin C in preventingrespiratory tract infections in children: a randomized, double-blind, placebo-controlled, multicenter study. Arch Pediatr Adolesc Med 158(3):217–221。

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A Meta-Search Procedure for the YottaWeb Topology Nodal Arrangement Problem
Jules R.D´e gila,
Brunilde Sans`o
GERAD and Department of Electrical Engineering,
´Ecole Polytechnique de Montr´e al,
C.P.6079,succursale Centre-ville,Montr´e al(Qu´e bec)Canada,H3C3A7
jules.degila@gerad.ca
Keywords:Internet,PetaWeb,YottaWeb,Agile Optical Core,Metasearch,Lagrangian Relaxation
Abstract
This paper deals with the design of a multidimensional network called the YottaWeb[1].The YottaWeb offers information delivery at rates thousand of times those of today’s Internet.It can be seen as a network
of so-called PetaWebs[2][3].A PetaWeb provides fully meshed connectivity with direct optical paths be-
tween edge nodes.It is composed of a set of optical agile core nodes that gives rise to the“agile core”,and
thousands of edge nodes that are electronically controlled.A fundamental question is how the agile cores
must be arranged into a suitable and efﬁcient YottaWeb that could gracefully expand as demand increases.
One proposal to allow for an appropriate control and easy expansion is to create a multidimensional lat-
tice structure of agile cores[1].The main performance measure being considered is the mean hop value
weighted by the demand.The problem of designing such a structure is highly combinatorial,given that the
number of edge nodes is expected to be in the tens of millions.
In this paper we review previous algorithms proposed for the d-lattice structure.Next,we propose a
MetaSearch procedure,based on Tabu and Variable Neighborhood Search to be able to solve the prob-
lem.Finally,in order to assess a tight lower bound for this problem,we propose a general formulation of
the nodal arrangement problem that does not speciﬁcally requires a lattice structure.A Lagrangian relax-
ation is then applied to this formulation.To assess the performance of the proposed algorithms,we have
used a set of randomly generated networks with chaotic trafﬁparative results will be discussed. References
[1]Beshai,M.,F.Blouin,and R.Krishnan,“PetaWeb-Building Block for a Yottabit-per-second Network,”
report to DARPA Next Generation Internet,Technology Investment Agreement TIA F30602-98-2-
0194(2001).
[2]Blouin,F.J.,S.Yazid,and B.Bou-Diab,“Emulation of a vast Adaptative Network,”in Proceedings of
Networks2000“Toward Natural Networks”International Telecommunication Network Planning
Symposium,(Toronto,2000).
[3]Vickers,R.,and M.Beshai,“PetaWeb Architecture,”in Proceedings of Networks2000“Toward Natural
Networks”International Telecommunication Network Planning Symposium,(Toronto,2000).。