Supplemental Digital Content is available in the text APOE) gene polymorphism on the resting-state brain function, structure, and blood flow in healthy adults younger than 35 years, using multimodality magnetic resonance (MR) imaging.To investigate the effect of apolipoprotein E  were included. For resting-state functional MRI data, default mode network (DMN) and amplitude of low-frequency fluctuation maps were extracted and analyzed. Voxel-based morphometry, diffusion tensor imaging from structural imaging, and cerebral blood flow based on arterial spin labeling MR imaging were also analyzed. Correlation analysis was performed between the above mentioned brain parameters and neuropsychological tests.Seventy-six healthy adults  in the left medial prefrontal cortex and bilateral posterior cingulate cortices/precuneus compared with the ε3/ε3 group, and increased FCs in the left medial prefrontal cortex and right temporal lobe compared with the ε2/ε3 group . No differences of DMN FCs were found between the ε2/ε3 and ε3/ε3 groups. FCs in the right temporal lobe positively correlated with the performances of vocabulary learning, delayed recall, and graph recall in all participants (P < 0.05).There were no differences in neuropsychological performances, amplitude of low-frequency fluctuation, gray/white matter volumes, fractional anisotropy, mean diffusivity, or whole brain cerebral blood flow among the 3 groups. As for DMN, the APOE ε4 carriers exhibited significantly increased DMN FCs when compared with ε3 and ε2 carriers. The ε4 affects DMN FCs before brain structure and blood flow in cognitively intact young patients, suggesting DMN FC may serve as a potential biomarker for the detection of early manifestations of genetic effect. Apolipoprotein E (APOE) gene  is highly associated with AD.1APOE ε4 allele is a well established genetic risk for decline of memory, ε3 allele plays a neutral role,2 whereas ε2 allele enhances neuroprotection against AD.4 Recent neuroimaging techniques, including functional magnetic resonance imaging (fMRI), allow for the investigation of genetic influence on the brain. fMRI studies showed decreased6 or increased7 default mode network (DMN) functional connectivities (FCs) and decreased hippocampal volumes8 in healthy middle-aged or older ε4 carriers compared with noncarriers. Healthy older ε2 carriers exhibited greater cortical thickness.4 All these findings support the fact that the effect of ε4 and ε2 allele on brain function and structure begins early in life.Alzheimer disease (AD) is a progressive neurodegenerative disease. APOE polymorphism on brain function and structure in young cognitively intact individuals may help identify the early alterations before ageing leaves an imprint on middle-aged or older individuals,10 and better understand the pathophysiology of APOE on AD. Of limited fMRI studies in young ε4 carriers, the structural and functional alterations varied widely: ε4 carriers exhibited either no difference11 or alterations of hippocampal volume,12 and elevated14 or reduced activity15 during task-related fMRI compared with noncarriers. Majority of these studies recruited only ε4 and ε4 noncarriers14 and depicted no clear profiles of APOE polymorphism on brain cognition, structure, and function. Thus, we used multimodality MRI in cognitively intact ε2/ε3, ε4/ε3, and ε4/ε3 carriers younger than 35 years to investigate the effect of APOE polymorphism on the resting-state brain function, structure, and blood flow.Exploration of The local institutional review board of the Jinling Hospital, Nanjing University, approved this study. Written informed consent was obtained from all participants. A total of 215 cognitively intact adults were recruited from the local community between April and December 2013. Inclusion criteria were as follows: age 18 to 35 years; right-handedness; without any clinical diseases; and 12 years or more educational level. Exclusion criteria were as follows: any history of psychiatric or neurological illness; head trauma; drug or alcohol abuse; chronic corticosteroid therapy; diabetes; hypertension; magnetic resonance (MR) contraindications; and excessive head movement (more than 1.0 mm in translation or 1.0° in rotation).14 including measurements of cognitive states ; attention/executive function (number connection test type A/B [NCT-A/B] and digit symbol test [DST]), intelligence (vocabulary learning and word fluency), Wechsler Adult Memory Scale , and Wechsler Adult Intelligence Scale (similarities and digit span); and reaction capability (Chinese version of Stroop test) and depression . The MoCA tests are associated with the domains of cognitive dysfunctions including executive function, naming, memory, language, attention, ability of abstraction, and orientation and delayed recall. Of MoCA tests, language, attention, ability of abstraction, and orientation were similar among the 3 groups; thus only executive functioning, naming, memory, and delayed recall were involved in further analysis. All the neuropsychological assessments were carried out by a trained doctor (XL) with 4 years of experience in neuropsychological tests, according to standard measurements as previously described.14All participants completed a battery of neuropsychological tests,APOE genotypes was performed by an independent laboratory . Participants with homozygous genotype of ε4/ε4  were extremely rare and were excluded from the study. No participant with ε2/ε2 genotype was found in the study population. Since ε4 and ε2 have been reported having different roles in AD processing,16 participants with genotype of ε4/ε2 were excluded from this study. Of the remaining participants, 14 ε2/ε3 carriers in ε2 group, 31 ε3/ε3 carriers in ε3 group, and 31 ε4/ε3 carriers in ε4 group, matched for age, sex, and education level, were included for further analysis.The analysis of 2; repetition time/echo time (TR/TE) 9000 ms/93 ms; flip angle 130°; inversion time 2500 ms; and bandwidth 287 Hz/pixel). Functional data were collected by using a gradient-echo echo-planar imaging sequence . Each fMRI sequence contained 250 volumes, and each volume included 30 axial slices placed approximately along the anterior commissure-posterior commissure line. High-resolution T1-weighted images were obtained with 3-dimensional magnetization prepared rapid gradient echo (3D MPRAGE) sequence . Diffusion tensor imaging (DTI) was performed using single shot spin echo-based echo planar imaging sequence in contiguous axial planes. The original DTI images of each participant included 20 volumes with diffusion gradients applied along 20 noncollinear directions (b = 1000 s/mm2), and one volume without diffusion weighting (b = 0 s/mm2). Each volume consisted of 30 contiguous axial slices covering the whole brain . Arterial spin-labeled (ASL) MR perfusion images were acquired using pulsed arterial spin-labeling sequence17 .Images were acquired on a 3-Tesla MR instrument . A standard 12-channel phased-array head coil was used fitted with foam padding to reduce head motion. T2-weighted fluid-attenuated inversion-recovery sequence was used to detect clinically silent lesions  implemented in Matlab, Version 7.9 . The first 10 volumes of the functional images were discarded to increase stability of the initial MRI signal and the adaptation of the participants to the environment. Images were corrected for acquisition delays (slice timing) before motion correction. The functional images of the remaining participants were realigned to the standard Montreal Neurological Institute (MNI) template (3 × 3 × 3 mm3) and smoothed by convolution with an isotropic Gaussian kernel of 8 mm Full Width at Half Maximum (FWHM) to decrease spatial noise.Resting state fMRI data were preprocessed by using SPM8 . For DMN analysis, the group spatially independent component analysis (ICA) was performed to extract DMN on the smoothed fMRI data by using GIFT software . ICA separated linear mixed data into spatially independent components. To determine the number of independent components, dimension estimation of the smoothed data of the 3 groups was conducted by using the minimum description the length criterion, and the result was an average of 20 independent components. Then, fMRI data from all participants in each group were concatenated, and the temporal dimension of the aggregated data set was reduced by means of principal component analysis, followed by an independent component  estimation by using the infomax algorithm. To each ICA, the time courses corresponding to the waveform of a specific pattern of coherent brain activity and the intensity of this pattern of brain activity across the voxels were expressed by the associated spatial map. Then, using the GIFT software , the DMN component of each participant was selected based on the largest spatial correlation with DMN templates provided by Dr Wei Liao .19For amplitude of low-frequency fluctuation (ALFF) analysis, the smoothed data were detrended and temporally filtered to extract frequencies of 0.01 Hz to 0.08 Hz, then the ALFF map of each participant was extracted according to a previous fMRI report,http://dbm.neuro.uni-jena.de/vbm). First, high-resolution structural data were bias-corrected, tissue classified, and normalized to the MNI. Second, the standardized images were segmented as gray matter (GM), white matter (WM), and cerebrospinal fluid. Subsequently, analysis was performed on GM and WM segments separately, which was multiplied by the nonlinear components derived from the normalization matrix to preserve actual GM and WM values locally (modulated GM volume). Finally, all GM and WM images were spatially smoothed with an 8-mm FWHM Gaussian kernel.Voxel-based morphometry (VBM) data processing was performed in VBM8 toolbox  as implemented in FMRIB Software Library . Fractional anisotropy (FA) and mean diffusivity (MD) maps were then computed by using Diffusion Toolkit software based on our previous study.20 The b0 maps were standardized into a standard MNI space by using the echo-planar imaging template supplied with SPM8, which generated the deformation parameters that were applied to the normalization for FA and MD images. The normalized FA maps and MD maps were smoothed with an 8-mm FWHM Gaussian kernel.For DTI images, the 20 diffusion-weighted images were registered to bhttp://www.cfn.upenn.edu) based on SPM8 software according to our previous study.17 Ninety ASL images were acquired from each participant, and the first one was taken as the equilibrium magnetization of brain (M0) image for CBF calculation. The first 2 pairs of ASL images were discarded during establishment of equilibrium of spin dynamics. After realigning, motion correction, and spatial normalization to MNI space, the 43 remaining of control/tag CBF image pairs were converted to mL/100 g/min using a single-compartment model. To eliminate the individual differences, the CBF images were normalized by using the equation CBFcorrected = CBFuncorrected/(GM + 0.4WM). Since the hippocampus is a key area associated with AD pathology, CBF values in bilateral hippocampus were extracted. Apart from the whole brain maps and 90 regional CBF maps, the bilateral hippocampus CBF maps, and the maps of the brain regions showing ALFF, DMN, FA, or MD differences among groups were extracted. The corrected CBF images were then spatially normalized.The cerebral blood flow (CBF) values in whole brain and regional brain were calculated within the ASL Toolkit  was used for statistical analysis. Among demographic characteristics, neuropsychological scales, and the extracted MR imaging statistics, the qualitative data were described by relative ratio or percentage and tested by chi-square test. The normality of the remaining quantitative data, including age, education, and all the neurological assessments, were tested by Kolmogorov–Smirnov analysis. The data variance homogeneity of the 3 groups was analyzed by analysis of variance (ANOVA). In case of significant differences in the variance analysis, post-hoc analysis was used for intergroup comparison. In case of non-Gaussian distribution, the median and interquartile range [M (QU-QL)] represented, and k-independent samples nonparametric test was performed among the 3 groups. t test and corrected by false discovery rate (FDR) criterion with a threshold of P <0.05. ANOVA with Alphasim correction (http://afni.nih.gov/afni/docpdf/AlphaSim.pdf) was conducted among the 3 groups at the threshold of P <0.05, using SPM8 software. ANOVA was performed on normalized ALFF maps and DMN maps among the 3 groups by using SPM8 software in process Alphasim correction (http://afni.nih.gov/afni/docpdf/AlphaSim.pdf) at the threshold of P <0.05 with number of clusters of 79 and 121, respectively. If statistical difference was present, post hoc analysis was performed to detect the intergroup differences.Amplitude of low-frequency fluctuation and DMN maps were analyzed for each group by 1-sample P <0.05. If the difference among the 3 groups were statistically significant, post hoc analysis was performed for intergroup differences. Bivariate analysis was conducted to analyze the correlation between brain regions showing functional or structural differences and neuropsychological scales. The data with normal distribution were analyzed by Pearson correlation analysis, whereas data with non-normal distribution was evaluated by Spearman correlation analysis with the threshold of P <0.05.The ANOVA was performed on normalized GM maps and WM maps, FA and MD maps, and CBF maps  among the 3 groups, corrected by FDR criterion with a threshold of APOE ε4, ε3, and ε2 . None of the neuropsychological scales showed any difference .Table P < 0.05) , anterior cingulate cortex (ACC), medial prefrontal cortices (MPFC), inferior parietal lobules (IPL), temporal lobes and bilateral hippocampus, and parahippocampus  Figure .ε4 showed significantly increased FCs in the left MPFC and bilateral PCC/PCu compared with the group ε3, and increased FCs in the left MPFC and right STG and right MTG compared with group ε2  and middle temporal gyrus (MTG). Post hoc analysis demonstrated that group t test showed that ALFF maps were mainly distributed in the bilateral frontal lobes, temporal lobes, occipital cortices, cingulate cortices, PCu, inferior parietal lobules, thalamus, and midbrain among the groups. ALFF maps showed no significant differences among the 3 groups.Results of 1-sample The volumes of GM or WM masks were no different among the 3 groups (Supplementary Table 1).No differences in FA and MD values were found among the 3 groups.ε4 < ε3 < ε2 , delayed recall , and graph recall   on brain structure, function, and blood flow in cognitively intact young adults.Our study showed that ε4 carriers exhibited significantly increased DMN FCs when compared with ε3 and ε2 carriers, which was consistent with prior studies14: ε4 influences functional connectivity within the DMN before the onset of clinical disease in the youth. However, older cognitively intact ε4 carriers manifested decreased DMN FCs.22 It is unclear whether these differentiations embodied in age-dependent alterations or potential pathological progress. The involved brain regions such as bilateral MPFC, PCC, and temporal gyrus were consistent with the preferential areas in AD.23 MPFC had been found to be linked with other components of the limbic system anatomically and functionally, and carries out self-referential mentations, executive function, and working memory.25 Both PCC/PCu and STG play leading roles in episodic memory.27 Positive correlation between the right STG FCs and vocabulary learning, delayed recall, and graph recall, which represent processes of productive and receptive memory, and consolidation28 was also reported. Our findings regarding the increased DMN FCs may be a compensatory for slight alteration of cognitive function in a very young age. ε4 has a potential to disrupt the dynamic balance of excitatory and inhibitory neurotransmitters, which is crucial for learning, memory, and for DMN alteration.30 The increased FCs in DMN observed in this study might be the consequence of high glutamatergic concentration and/or low GABA compensation in the cognitively intact young ε4 carriers. This assumption is supported by a recent study in which high regional inhibitory neurotransmitter levels were associated with enhanced deactivation, whereas high excitatory glutamate concentration was associated with reduced deactivation induced by the task.31 Thus, DMN might be an indirect biomarker for alterations of neurotransmitters in neurons and/or interneurons. In addition, the pathology of amyloid β disposition (ε4 > ε3 > ε2) and clearance (ε2 > ε3 > ε4), including synaptic deficits, mitochondrial dysfunction, and neuroinflammation in the brain11 in ε4 carriers, might contribute to DMN alterations.In our study, 32 Both of these brain functional measurements showed abnormalities in MCI and AD.35 In this study, the DMN but not the ALFF showed abnormalities among the groups. The inconsistence between DMN and ALFF has been previously described in MCI and AD patients.36 One possible explanation was that alterations of spatial connectivity might precede alterations of neuron connectivity, that is, APOE-induced interneuron alteration precedes neuron alteration.The DMN describes the spatial linkages between neuronal activities in different brain regions, whereas the ALFF measures the amplitude of the regional spontaneous neuronal activity.11 Our study did not find any VBM and DTI differences among the 3 groups, consistent with a previously published study.11 Otherwise, 1 study found lower gray matter density in ε4 carriers relative to ε4 noncarriers via VBM in a wide range aged 19 to 80 years.37 However, it is unclear whether ageing or APOE ε4 led to decreased gray matter density. ASL is able to noninvasively quantify CBF. In our study, ASL images showed no abnormalities in cognitively intact young ε4 carriers inconsistent with previously reported results,9 in which young ε4 carriers (23.6 ± 3.1 y) had increased CBF value in ACC. In this study, we observed the alteration of DMN before the alteration of brain macrostructure and microstructure, and CBF. The pathological mechanism of AD might help to explain the reason behind that functional alteration precedes structural alteration. White matter integrity has been reported to be positively associated with cerebrospinal fluid markers of amyloid-beta (amyloid β [42] and amyloid β [42]/p-Tau [181]) in AD.38 In a recent study, Tau protein has been reported to induce pathological changes in the brain including gray matter atrophy, increased white matter radial diffusivity, decreased amide proton transfer, and hyperperfusion in the rTg4510 mouse model of tauopathy.39 Whereas no differences in the concentration of plasma amyloid β peptides have been reported in young (28 ± 7.6 y) ε4, ε3, and ε2 carriers without any memory deficits,40 suggesting that the alterations of brain macrostructure and microstructure, and CBF may not occur at an early age.Voxel-based morphometry reflects brain macrostructure changes of gray matter and white matter. DTI reflects microstructure alterations of the integrity of white matter.ε4 and ε2 alleles may have different contributions to AD, ε2 carriers manifested no significant alterations on brain function, structure, and CBF in this study. In recent studies, ε2 carriers showed similar fMRI activity patterns with ε4 carriers in patients aged 32 to 55 years41 and 60 to 80 years.16 The clear role that ε2 plays needs to be further evaluated.Even though APOE gene , especially that of ε2 allele, is extremely rare. Additional studies with expanded sample size are needed to further investigate the genetic influence on alterations in brain macrostructure and microstructure, as well as CBF. Secondly, whereas the influence of ε4, ε3, and ε2 alleles on brain function, structure, and blood flow was evaluated in this cross-sectional study, further follow-up studies are necessary to investigate whether ε4 and ε2 alleles have dynamic pathophysiologic changes along aging trajectories. Third, this research was focused only on sporadic ε4 carriers, and patients with AD-related family history need to be included in the future studies. Fourth, apart from DMN, other spatially distributed networks including executive-control network and salience network are needed to deepen the understanding of dynamic integration of early pathological manifestations and alterations of brain networks.This study has some limitations. First, the frequency of APOE alleles on brain function precedes alterations in brain structure and blood flow in healthy young adults. DMN abnormalities may serve as potential biomarkers for detecting brain alterations in healthy young adults with APOE alleles.In conclusion, this study shows that the effect of


Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization.  Phellinus is a kind of fungus having great medicinal value, since it is known as one of the elemental components in drugs with functions of avoiding cancers [ Phellinus flavonoids are one of the most popular parasitifers of Phellinus in nature [ Phellinus focuses on polysaccharides, proteoglycans medicinal mechanism, composition, and so forth, which are mostly extracted from the fruiting bodies of Phellinus flavonoids [ Phellinus rarely exists in the wild environment [ Phellinus in the laboratory [ cancers , 2. Pheln nature , and theavonoids . Phellinironment , and it boratory . In receboratory , 8) have Phellinus, it is believed that PH value, temperature, and fermentation time have effect on the productions. Also, in general biochemical experiments, we need to consider the inoculum size, initial liquid volume, seed age, and rotation speed. In the laboratory, plenty of experiments have been designed and operated for maximizing the Phellinus production. The methods can be separated into two major groups. Phellinus in [ Phellinus in [ Phellinus and polysaccharide metabolism regulation are designed in [With biological technologies: it used optimum media on mycelial growth oflinus in  and liqulinus in . Active igned in . Phellinus by differential equations [With mathematical models: some researches focus on building mathematical models for the progress of producingquations , metabolquations , and comquations .From the understanding of the wild conditions of Pleurotus mutilus in [ Phellinus production. And, to the best of our knowledge, few work focuses on the optimization of culture conditions to maximize the production of Phellinus in the laboratory.Artificial algorithms and models have been used in the bioprocess, particularly for the optimization of culture conditions. In , artifictilus in . Recentltilus in  the methtilus in , 19 are  Phellinus from Phellinus flavonoids with different culture conditions, involving parameters PH value, temperature and fermentation time, inoculum size, initial liquid volume, seed age, and rotation speed. With the data collected during the experiments, we use regression analysis method to create a mathematical model, which can forecast the flavonoid yield and the most important element to the production of Phellinus. After that, a gene-set based genetic algorithm (GA) is proposed to optimize the culture condition, where the obtained mathematic model is used as fitness function for the evolution of individuals. Data experimental results show that predicted optimal values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization.In this work, we start from operating 45 experiments for producingIn this section, biological experiments are performed for finding optimal value of certain single factor.In  Phellinus. Different culture conditions have a fundamental influence on the production of Phellinus, but the optimized culture conditions remain unknown.In  Phellinus. In general, we convert the data collected in We consider here using regression analysis and gene-set based genetic algorithm to find the optimized culture conditions for maximizing the production ofIn statistical modeling, regression analysis is a statistical process for estimating the relationships among variables . RegressRegression analysis is widely used in data mining, particularly for biological data analysis in recent years, with the purpose of finding a feasible statistical law by the large amount of data of experiments. The general process is given as follows.Determine the variables.Establish the prediction model.Relate analysis.Calculate the prediction error.Determine the predicted value. Phellinus. These data are taken as extreme data are removed from regression analysis. Extreme data refers to the data which were measured in extreme experimental environment. Also duplicate data were cancelled. Only the following data are selected in regression analysis.Inoculum size 0.5%~1.2%.PH 5~7.Initial liquid volume 60~100 mL.Temperature 25~30°C.Seed age 4~9 days.Fermentation time 6~12 days.Rotation speed 140~200 r/m.From the data collected in Y = A1∗X2(1)+⋯+A7∗X2(7) + B1∗X(1)∗X(2) + B2∗X(1)∗X(3) + B3∗X(1) ∗  X(4) + B4∗X(1)∗X(5) + B5∗X(1)∗X(6) + B7∗X(1)∗X(7) + B8∗X(2)∗X(3) + B9∗X(2)∗X(4) +  B10∗X(2)∗X(5) + B11∗X(2)∗X(6) + B12∗X(2)∗X(7) + B13∗X(3)∗X(4) + B14∗X(3)∗X(5) +  B15∗X(3)∗X(6) + B16∗X(3)∗X(7) + B17∗X(4)∗X(5) + B18∗X(4)∗X(6) + B19∗X(4)∗X(7) +  B20∗X(5)∗X(6) + B21∗X(5)∗X(7) + B22∗X(6)∗X(7) + C, where Y is a dependent variable of the flavonoid yield, X(1), X(2),…, X(7) are the seven independent variables associated with inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed, respectively, and A, B, and C are real numbers.After data filtering, a statistical model is made to represent these data. It is known that there is a correlation between these data relationships, so we applied linear regression analysis to fit them. At this stage, a lot of models were tested one by one with IBM SPSS software and response surface methodology. The statistical model is Although the relationship between the data may not be linear, we can put squared term for a type of data into these data. If this term is useful it will be retained after linear regression analysis; otherwise, the data will be deleted.R-squared and the significance of correlation coefficients for regulating the model. We use the regression analysis tools in the IBM SPSS, setting regression coefficients as estimated (E) and selecting the display model fit (M). Set the stepping method criteria as use of probability F, entry (E) as 0.5, and removal (M) as 0.10. After regression analysis, we can get the results as shown in In the regression analysis, it needs to focus on the values of R-squared value is 0.88, which means that the model is valid for fitting the 88% data. We get the statistical model: Y = 3662.278∗x(1) − 4263.361∗(x(1)2) + 11737.986∗x(2) − 999.556∗x(2)2 − 0.238∗x(3)2 + 3353.461 ∗ x(4) − 59.662∗x(4)2 − 420.854∗x(5) + 42.495∗x(5)2 + 966.796∗x(6) − 53.489∗x(6)2 + 27.213∗x(7)  − 0.234∗x(7)2 + 0.434∗x(3)∗x(7) − 86781.046.It is obtained that significance = 0.006 < 0.05; that is, the regression results are obvious. Genetic algorithm (GA) was first proposed by J. Holland in 1975 , 26, whoIn gene-set based GA, a chromosome is treated as a set of gene-sets, instead of a set of genes as in classical GAs. It starts with gene-sets of the largest size equal to half the chromosome length. It is most appropriate to genetics model because each gene-set represents a set of adjacent parameters of certain factor of the culture conditions.It is noted that, in the selection, only the winning individuals from the population can be selected. Select operators are also known as reclaimed operator (reproduction operator), whose purpose is to optimize the selection of individuals (or solutions) to the next generation. Population can be updated by fitness ratio method and random sampling method to traverse, local selection. Cross operator refers to the part of the structure of the two parent individuals to generate new recombinant replacing individual operation. Variation is to make GA have local random search capability. When the GA crossover neighborhood is close to the optimal solution, the use of such a mutation operator of local random search capability can accelerate the convergence to the optimal solution. Phellinus. The data simulation is achieved by gatool in MATLAB. In the data experiments, we use a binary string composed of 7 segments to represent an individual in GA population, where each segment is associated with the value of one of the 7 parameters for the culture condition. Initial population size is 50, and cross rate is set to 0.8. Mutation rate is set to be 0.01, and selection method is roulette wheel selection. If the time is long enough then the GA process will halt by meeting the stopping conditions, such as generations limit or fitness limit.The statistical model obtained by regression analysis is used as the fitness function here, and gene-set based GA is used to optimize the culture condition for maximizing the production ofAfter 156 iterations the gene-set based GA process returns the best individual and shuts down the process in After the regression analysis and GA process, an optimized culture condition is obtained, shown in  Phellinus growth environmental studies. Specifically, the suitable environment is neutral acidic environment, about PH value 6. The appropriate temperature range is from 22°C to 28°C [The results obtained by our method have accordance with experimental experience in literature of to 28°C . Seed ag to 28°C , 28, 29. Phellinus from Phellinus flavonoids. We use regression analysis method to create a mathematical model with the collected data, and then a gene-set based GA is proposed to optimize the culture condition, where the obtained mathematic model is used as fitness function for the evolution of individuals. In the comparison results, it is believed that PH value is credible and the temperature is also within the appropriate temperature range. Taking into account environmental factors in the laboratory, the temperature value we predicted is also reliable. The seed age and fermentation time predicted are 9, close to the original data 8. Data experimental results show that predicted optimal values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization.In this work, 45 experiments are firstly operated for collecting data related to the production of Phellinus production. Our work would also guide for the “Precision Medicine” with personal SNP data [Neural-like computing models, such as artificial neural networks , spikingSNP data  and otheSNP data , 22.


An in situ transmission electron microscope (TEM) nanoindenter was used to carry out compression tests and investigate the deformation behavior arising at nanopillars of the Zr-based metallic glass. The formation and interaction of shear band during the plastic deformation were investigated. Moreover, it was clearly apparent that the mechanical strength and ductility could be enhanced by impeding the penetration of shear bands with reinforced particles.In this study, the high strain rate deformation behavior and the microstructure evolution of Zr-Cu-Al-Ni metallic glasses under various strain rates were investigated. The influence of strain and strain rate on the mechanical properties and fracture behavior, as well as microstructural properties was also investigated. Before mechanical testing, the structure and thermal stability of the Zr-Cu-Al-Ni metallic glasses were studied with X-ray diffraction (XRD) and differential scanning calorimeter. The mechanical property experiments and microstructural observations of Zr-Cu-Al-Ni metallic glasses under different strain rates ranging from 10 Bulk metallic glass (BMG) and its composites (BMGC) are of interest due to their unique mechanical, physical, and chemical properties, such as amorphous microstructure, ultra-high strength, large limit of elastic deformation, and excellent corrosion resistance ,2,3,4,5.Since Zr-based metallic glass is used in structural materials, it is important to examine the related mechanical properties under different strain rate conditions, although there are few studies that have discussed this issue. Many studies have investigated the basic amorphous forming ability of metallic glass and the tension fracture mechanical behavior ,18,19, bIn situ transmission electron microscope (TEM) compression/tension experiments were recently used to determine the mechanical responses of BMGs at the nano-scale [i.e., nanopillars diameter) on the deformation mode. Furthermore, this study also used an in situ transmission electron microscope (TEM) nanoindenter to carry out compression tests and investigate the related deformation behavior (ex. shear banding and buckling deformations) arising at nanopillars of the Zr-based metallic glass.no-scale ,21. A nuno-scale ,30,31,3253Cu30Ni9Al8) was selected as the base alloy for the preparation of BMG specimens. High purity Zr, Cu, Ni, and Al were melted together using an arc melting system in an argon (Ar) atmosphere. The alloy ingot was fabricated by injecting the material into a water-cooled copper mold to obtain amorphous alloy rods. The cylindrical rods were fabricated with a diameter of 4 mm and length of 4 mm. X-ray diffraction and electron backscatter diffraction (EBSD) were then used to measure the crystalline structure of the rods. Hydrostatic tests were carried out using a compressive MTS tester at strain rates of 10−1 s−1, 10−2 s−1 and 10−3 s−1, and dynamic impact tests were carried out using a compressive split-Hopkinson bar (SHPB) at strain rates of 2.0 × 103 s−1, 3.0 × 103 s−1 and 5 × 103 s−1. The fracture surface observations were examined using scanning electron miocroscopy (SEM).A Zr-based metallic glass ingot  embedded in a JEOL 2010 TEM. The indenter used in the compression experiment was a diamond flat punch, two μm in diameter. The compression tests of the nanopillars were performed using displacement control. In displacement control mode, the maximum punch displacement was 500 nm and the loading/unloading time was 10 seconds. A dual-beam focused ion beam  with an in situ pickup system (Omni-Probe) was then used to fabricate the Zr-based metallic glass nanopillars, as shown in −3 s−1, 10−2 s−1 and 10−1 s−1. From 3 s−1, 3.2 × 103 s−1 and 5.1 × 103 s−1. It can also be observed that the yield stress increases along with the strain rate. Comparing the quasi-static and dynamic condition, the yield strength increases from 10−3 to 5.1 × 103 s−1. The stress-strain relations observed in σ2 and σ1 relate to impact tests conducted at average strain rates of i.e., 10−3 to 5.1 × 103).3 s−1 and temperature of 25 °C. It can be seen that the specimen failed along a plane orientated at 45° to the loading direction, and a few shear bands can be observed on the fracture surface. −3 s−1 to 10−1 s−1. It can be observed that the fracture surface has a vein-like structure [3 s−1 to 5.1 × 103 s−1, respectively. It is also observed that the density of the vein-like structure on the fracture surface decreases as the strain rate increases. For example, comparing −3 s−1 to 5.1 × 103 s−1.tructure , and thitructure ,36. Furt3 s−1. It also can be seen that even though the Zr-based metallic glass deformed at a high strain rate, it still kept its amorphous phase.−3 s−1 to 5.1 × 103 s−1 using an MTS tester and split-Hopkinson bar. The experimental results show that the yield stresses all for all the tested specimens increase along with the strain rate. Moreover, when the strain rate increases the strain rate sensitivity also rises. The SEM observations show that the fracture surfaces are characterized by a vein-like structure, which indicates that the Zr-based metallic glass specimens have good plasticity. Furthermore, the results of the in situ TEM experiment show that the Zr-based metallic glass maintains an amorphous phase during the deformation process.This study conducted an experimental investigation into the effects of strain rate on the mechanical properties and microstructural evolution of Zr-based metallic glass at room temperature and at strain rates ranging from 10


Without any adjustable parameters, our simulations then quantitatively captured experimental observations of latency-reversal when the LRAs were used in pairs. Performing simulations representing a wide range of drug concentrations, we estimated the maximum synergy achievable with these LRA pairs. Importantly, we found with all the LRA pairs we considered that concentrations yielding the maximum synergy did not yield the maximum latency-reversal. Increasing concentrations to increase latency-reversal compromised synergy, unravelling a trade-off between synergy and efficacy in LRA combinations. The maximum synergy realizable with LRA pairs would thus be restricted by the desired level of latency-reversal, a constrained optimum we elucidated with our simulations. We expect this trade-off to be important in defining optimal LRA combinations that would maximize synergy while ensuring adequate latency-reversal.Eradicating HIV-1 infection is difficult because of the reservoir of latently infected cells that gets established soon after infection, remains hidden from antiretroviral drugs and host immune responses, and retains the capacity to reignite infection following the cessation of treatment. Drugs called latency-reversing agents (LRAs) are being developed to reactivate latently infected cells and render them susceptible to viral cytopathicity or immune killing. Whereas individual LRAs have failed to induce adequate reactivation, pairs of LRAs have been identified recently that act synergistically and hugely increase reactivation levels compared to individual LRAs. The maximum synergy achievable with LRA pairs is of clinical importance, as it would allow latency-reversal with minimal drug exposure. Here, we employed stochastic simulations of HIV-1 transcription and translation in latently infected cells to estimate this maximum synergy. We incorporated the predominant mechanisms of action of the two most promising classes of LRAs, namely, protein kinase C agonists and histone deacetylase inhibitors, and quantified the activity of individual LRAs in the two classes by mapping our simulations to corresponding  HIV-1 infection typically requires lifelong treatment because a class of infected cells called latently infected cells remains hidden from drugs and host immune responses and can reignite infection when treatment is stopped. Massive efforts are ongoing to devise ways to eliminate latently infected cells. The most advanced of the strategies developed for this purpose involves using drugs called latency-reversing agents (LRAs), which reactivate latently infected cells, effectively bringing them out of their hiding. Multiple mechanisms are involved in the establishment of latency. Pairs of LRAs targeting distinct mechanisms have been found to synergize and induce significantly higher latency-reversal than individual LRAs. If this synergy can be maximized, latency-reversal can be achieved with minimal drug exposure. Using stochastic simulations of HIV-1 latency, we unraveled a trade-off between synergy and efficacy in LRA pairs. Drug concentrations that maximized synergy did not also maximize latency-reversal. Drug concentrations that yielded higher latency-reversal compromised synergy and vice versa. This trade-off would constrain the synergy realizable between LRAs and guide the identification of optimal LRA combinations that would maximize synergy while ensuring adequate latency-reversal. Combination antiretroviral therapy (cART) for HIV-1 infection can suppress the viral load in infected individuals to below the detection limit but is unable to eradicate the virus  and can The most advanced of the strategies to eliminate the latent reservoir, called the “shock and kill” strategy, advocates the use of drugs called latency-reversing agents (LRAs) which stimulate HIV-1 transcription in latently infected cells, rendering them susceptible to viral cytopathicity or immune recognition and killing –13. Sevein vitro and ex vivo and some have reached clinical trials [ex vivo to be grossly inadequate at reversing latency when compared to the maximal reversal achieved with agents inducing global T cell activation [in vitro [in vivo. Recent studies have therefore examined latency-reversal with combinations of LRAs acting via distinct mechanisms and identified, promisingly, combinations that can reactivate cells ex vivo to extents comparable to that achieved with global T cell activation [in vitro and ex vivo studies have identified LRA combinations that act synergistically [Several LRAs have been tested extensively l trials –24. LRAsl trials . Individtivation , 26. Furin vitro , indicattivation , 29. Witstically –39. An ii.e., the so-called intracellular HIV-1 latency circuit, to quantify the activities of individual LRAs and estimate the maximum synergy realizable between pairs of LRAs. Stochastic simulations of the HIV-1 latency circuit have been employed extensively in previous studies and have provided key insights into the mechanisms underlying the establishment of latency and its reversal [viz., productive infection (or activation) and latency, realized by cells following their infection by HIV-1 [viz., HDACi’s, which have entered clinical trials [in vitro and ex vivo [in vitro. Our simulations then captured quantitatively, without any adjustable parameters, the synergy observed when the LRAs were employed together, giving us confidence in our formalism. We then applied our simulations to predict the maximum synergy realizable with the combinations and the corresponding drug concentrations. We found, interestingly, that a trade-off exists between the synergy and the efficacy of LRA combinations that could introduce new limits on their usage. The maximum synergy realizable would have to be constrained by this trade-off in achieving the desired extent of latency-reversal, a consideration that may be important in defining the optimal usage of LRA combinations.In the present study, we employed stochastic simulations of HIV-1 transcription and translation in infected cells, reversal , 40–43. by HIV-1 , 40–42. by HIV-1 , 43, ideby HIV-1 . Previouby HIV-1 , 44–48. l trials , 20, 23, ex vivo , 28, 31.d. Several other factors, including PTEF-b, are essential for transcriptional elongation [d as PTEF-bd [a. The latter complex triggers HIV-1 transcription at an enhanced rate [We considered the following set of events involved in HIV-1 transcription and translation in an HIV-1-infected cell harboring an integrated, replication-competent HIV-1 genome . The hosongation , which wongation , 40.  Ignoring toxicities, these fits thus allowed rank-ordering of the HDACi’s in terms of the maximum activation levels they can achieve individually. Accordingly, TSA>NaBut>VPA.To mimic HDACi’s, we performed simulations with h ϕHDACi . For ins = 0.082 . We now o ϕHDACi . The HilϕPKCa and ϕHDACi corresponding to each of these concentrations. We employed these latter values in our simulations and predicted fon. We found that our predictions were in good agreement with experiments  Further, because increasing either ϕPKCa or ϕHDACi can increase fon, a locus of points on a ϕPKCa versus ϕHDACi plot could be identified that corresponded to a desired fon. For instance, the combination ϕPKCa ≈ 8 and ϕHDACi = 1 yielded fon = 0.9 as did the combination ϕPKCa ≈ 2 and ϕHDACi = 10 and a number of other combinations in between, yielding the locus for fon = 0.9  in Using the simulations above, we calculated wing Eqs –21). To. Toβ for = 0.853 . The mectivation , S3 Fig.β ranged from 1 to ~1.9 as we spanned the range of values of ϕPKCa and ϕHDACi from 1–10  or very high (~10). We understood these results as follows. When both ϕPKCa and ϕHDACi were low, the effect of either drug was negligible. The chance that the drugs acted on the same cell, given the strong stochastic effects, was small, precluding any synergy. When at least one of ϕPKCa and ϕHDACi was high, the corresponding drug(s) achieved nearly maximal activation, leaving little room for the other drug to exert any influence. Synergy therefore was again not possible.We found that rom 1–10 . Note thϕPKCa and ϕHDACi were both increased moderately above 1. Here, the PKC agonist increased NF-κB synthesis and its LTR binding, leaving the integrated HIV-1 genome poised for transcription. The HDACi could then accelerate transcription, triggering the Tat-mediated positive feedback and inducing activation. Indeed, we found that β was maximum, at ~1.9, when ϕPKCa and ϕHDACi were both ~2.2 . Our stin vitro studies, on which our simulations were based, have been widely used to test drug effects and have led to the identification of LRAs and their synergistic combinations , they d[in vivo . For ins in vivo , 26. Our ex vivo , 29. TheThe events constituting the HIV-1 latency circuit  are listkNFκB, and the basal transcription rate of HIV-1, kBasal, remained uncertain. We fixed them to mimic experiments  and fon (positive control) as the activation level without drugs and with maximal stimulation. The normalized fraction of cells activated by drug ‘x’ was then given byWe performed simulations with rmalisms , 33, 61 The expected activation level if the two drugs were to act independently was “predicted” by the Bliss independence model:fax was obtained by setting ϕPKCa > 1 and fay by setting ϕHDACi > 1, respectively. To mimic the use of the drugs simultaneously, we set both ϕPKCa > 1 and ϕHDACi > 1 and obtained the “observed” activation level, faxy,O. To quantify the interactions between the drugs, we computedHere, β > 1, the drugs act synergistically, whereas if β < 1, they act antagonistically. β = 1 would imply Bliss independence, i.e., the absence of any interaction between the drugs.If in vitro data on the activation of latently infected cells exposed to the PKC agonist bryostatin-1 and the HDACi’s VPA, NaBut, and TSA [ϕ (either ϕPKCa or ϕHDACi) that yielded fon in agreement with the corresponding experimental observation. For bryostatin-1, we thus obtained values of ϕPKCa for the various drug levels employed. Similarly, for each of the HDACi’s we obtained values of ϕHDACi corresponding to the various drug levels employed. We then used each pair of these ϕPKCa and ϕHDACi values in our simulations to predict fon when the drugs were used together.We considered the previously reported  and TSA . To compϕPKCa and ϕHDACi, each spanning the range from 1 to 10, and estimated β Click here for additional data file.S1 FigmRNA(0) = 1 and P(0) = 0. The mean (line) and standard deviation (shaded region) of the resulting time-evolution of P from 104 realizations (or cells) is shown, establishing a lower bound on the threshold P for reactivation of latently infected cells.Protein copy numbers in cells lacking Tat and NF-κB obtained by simulating the reduced latency circuit, (TIF)Click here for additional data file.S2 Figfon = 0.039 ± 0.003, we had to set kNFκB = 9 × 10−5 molecules s-1 and kBasal = 2.81 × 10−3 s-1. With these parameter combinations, we calculated fon as a function of (A) ϕPKCa and (B) ϕHDACi. Following the procedure in Figs To test the implications of alternative parameter combinations, we set the threshold protein copy number for activation to 300 copies. We found that to capture the basal activation level in experiments, (TIF)Click here for additional data file.S3 FigkNFκB = 5 × 10−2 molecules s-1 and kBasal = 3 × 10−2 s-1, the time-evolution of the protein copy number predicted by our simulations (blue line) was indistinguishable from that predicted by a deterministic model (red line) of the HIV-1 latency circuit Click here for additional data file.S4 Figfon on ϕHDACi for 3 different values of ϕPKCa obtained using our simulations with 2000 cells and 10 realizations for each parameter combination (squares) compared with the same simulations using 4000 cells (inverted triangles) or 20 realizations (triangles) for each parameter combination. The other parameter values are in Dependence of (TIF)Click here for additional data file.


To test the effects of vowel inventory size vs. acoustic properties on non-native vowel perception, we compared XAB discrimination and categorization of five Dutch vowel contrasts between monolinguals whose L1 contains more  or fewer (Peruvian Spanish) vowels than Dutch. No effect of language background was found, suggesting that L1 inventory size alone did not account for performance. Instead, participants in both language groups were more accurate in discriminating contrasts that were predicted to be perceptually easy based on L1–L2 acoustic relationships, and were less accurate for contrasts likewise predicted to be difficult. Further, cross-language discriminant analyses predicted listeners' categorization patterns which in turn predicted listeners' discrimination difficulty. Our results show that listeners with larger vowel inventories appear to activate multiple native categories as reflected in lower accuracy scores for some Dutch vowels, while listeners with a smaller vowel inventory seem to have higher accuracy scores for those same vowels. In line with the L2LP model, these findings demonstrate that L1–L2 acoustic relationships better predict non-native and L2 perceptual performance and that inventory size alone is not a good predictor for cross-language perceptual difficulties.Research suggests that the size of the second language (L2) vowel inventory relative to the native (L1) inventory may affect the discrimination and acquisition of L2 vowels. Models of non-native and L2 vowel perception stipulate that naïve listeners' non-native and L2 perceptual patterns may be predicted by the relationship in vowel inventory size between the L1 and the L2. Specifically, having a smaller L1 vowel inventory than the L2 impedes L2 vowel perception, while having a larger one often facilitates it. However, the Second Language Linguistic Perception (L2LP) model specifies that it is the L1–L2  In adulthood, perception of sound categories in a second language (L2) is broadly thought to occur through the lens of the native language (L1). That is, L2 sound categories are mapped to categories of the L1 Best, . L2 percThe relationship between the size of the L1 and L2 vowel inventory may predict non-native and L2 vowel perception  were compared to Peruvian Spanish (PS) listeners who took part in the same XAB task as reported in Escudero and Wanrooij . As listIf listeners' L1 vowel inventory size affects non-native discrimination difficulty, AusE listeners are predicted to outperform PS listeners overall in their discrimination of the Dutch vowel contrasts. This is in contrast to acoustic comparisons, where comparable perceptual difficulties across both listener groups would be expected. All of the aforementioned studies that used LDAs as a means of testing the predictive nature of listeners' L2 perception only tested listeners whose L1 vowel inventory is smaller than that of the L2. Thus, we further used LDA models to test whether acoustic similarities are predictive of categorization patterns by listeners with a smaller and larger vowel inventory compared to the target language , /ε/, /ʉ:/ and /ɪ/ (F2), and /ε/, /ɪ/, /ʊ/ and /ʉ:/ (F3). Thus, in line with these parameters, AusE listeners are predicted to encounter the Subset Scenario by categorizing Dutch /ʏ-y/ across multiple native categories, namely AusE /ε/, /ɪ/, /ʊ/, and /ʉ:/. PS listeners are predicted to not face difficulties in their discrimination of Dutch /ʏ-y/ and are expected to encounter Similar Scenario by perceiving both sounds in line with a distinct native phoneme namely PS /e/ and /i/.Dutch /i-y/In line with overall perceptual similarity, PS listeners are predicted to categorize Dutch /i-y/ as PS /i/. However, based on the stepwise classifications, Dutch /y/ also exhibits some F2 and F3 similarity to PS /u/. While minimal, these differences between backness and rounding are predicted to aid PS listeners when discerning these two sounds. Similarly, AusE listeners are predicted to exploit all three acoustic parameters in their perception of Dutch /i-y/. That is, based on height (F1), Dutch /i/ is acoustically most similar to AusE /ɪ/ and /ʉ:/, while Dutch /y/ is acoustically most similar to AusE /ʉ:/. However, based on the first two formants Dutch /i/ is closest to AusE /i/, while Dutch /y/ is most similar to AusE /ʉ:/. Therefore, AusE listeners are predicted to face Similar Scenario by categorizing Dutch /i-y/ as AusE /ɪ/ and /ʉ:/. This learning scenario is also predicted by the cross-linguistic DA classification patterns.Dutch /ʏ-ɪ/Based on overall LDA classifications, both listener groups are expected to encounter Similar Scenario when differentiating Dutch /ʏ-ɪ/, as these vowels are acoustically similar to distinct native phonemes. However, based on the stepwise DAs both Dutch /ʏ/ and /ɪ/ bear F1 similarity to AusE /ɪ/, F2 similarity to AusE /ʉ:/ and /ɪ/, and F3 similarity to AusE /ɪ/, /ʉ:/ and /ʊ/. However, based on classification percentages , it is predicted that AusE listeners, facing Subset Scenario, should differentiate these two phonemes due to the due to the low acoustic overlap between the F2 and F3 cues. In contrast, PS listeners are predicted to exhibit lower discrimination accuracy compared to AusE listeners due to a higher acoustic overlap across all three acoustic dimensions and are further expected to classify both vowels as PS /i/, /e/, and /u/.Dutch /a-ɑ/In line with the overall DA and stepwise models, AusE listeners are predicted to find the Dutch /a-ɑ/ somewhat challenging to discern as these vowels were classified across two or more AusE vowel categories, while PS listeners are predicted to encounter New Scenario by predominantly mapping the sounds in line with PS /a/.Based on the cross-validation classification sets in Tables In sum, if predictions based on listeners' L1 vowel inventories size are borne out, AusE listeners, whose vowel inventory is larger than that of PS, are expected to have higher discrimination accuracy than PS listeners for all five Dutch contrasts. Cases of New Scenario are predicted for PS and that of Similar Scenario for AusE listeners. Alternatively and following L2LP's acoustic hypothesis, if acoustic differences between L1 and L2 influence non-native sound perception, both listener groups' discrimination difficulties should yield comparable results. That is, both listener groups are expected to face the New, Similar, and Subset Scenarios. To test these contrastive hypotheses, naïve AusE listeners' XAB discrimination and categorization of five Dutch vowel contrasts  was compared to those of naïve PS listeners reported in previous studies  participated for course credit at Western Sydney University. Participants were born and raised in Greater Western Sydney, and reported no experience with Dutch or any hearing impairment.Twenty-two monolingual AusE students aged 18–45 years  born and raised in Lima, Peru their entire life and reported no knowledge of Dutch.Non-native vowel categorization data from the same AusE listeners were compared to non-native vowel categorization data from 40 PS monolinguals  from Lima, Peru reported in Escudero and Williams . ParticiParticipant data collection for the present study was carried out in accordance with the Human Research Ethics Committee (HREC), Western Sydney University, approval number H9373.Both groups of participants first completed a two-alternative forced choice XAB discrimination task followed by a nonnative categorization task. The auditory stimuli for the XAB discrimination task were 20 naturally produced tokens of each of the five Dutch vowels /a/, /a/, /ɪ/, /i/, /y/, and /ʏ/, extracted from recordings produced by 20 native Standard Northern Dutch speakers  in monosyllabic utterances in a neutral non-word /sVs/ consonantal context embedded within a carrier sentence . There was a between-trial interval of 1 s and listeners could take a short break after every 24 trials.Stimuli for the non-native vowel categorization task were 20 naturally produced tokens of each of the 12 Dutch monophthongal vowels, /ɑ, a, e, ε, ɪ, i, ɔ, o, ø, u, ʏ, y/, extracted from the same speakers and context as in the XAB task. As the present study compares non-native discrimination and categorization, we report categorization results only for the same vowels presented in the XAB task. The task consisted of 240 randomized test trials and participants completed 12 practice trials prior to beginning. In each trial, PS listeners were asked to categorize one Dutch vowel token to one of the nine PS  and 12 AusE  vowels presented orthographically on the screen. According to the Orthographic Depth Hypothesis  stepwise classification models were then trained and tested. Each step in the model contained the same acoustic parameters, vocalic duration as well as the same six target L2 Dutch vowels as the LDA models. The AusE stepwise DA yielded 33.9% for F1 and duration, 71.3% for F1, F2, and duration, as well as 73.3% for F1, F2, F3, and duration correct classification. Whereas, the PS model yielded 55.1 for F1, 87.7% for F1, F2, and duration, in addition to 90.1% for F1, F2, and F3 correct classification.Table p = < 0.001]. While there was no main effect of language background [p = 0.738], there was an interaction of vowel contrast and language background [p = 0.002]. Fishers' LSD-corrected post-hoc pairwise comparisons revealed that PS listeners had marginally more correct responses than AusE listeners for /ɪ-i/ , whereas AusE participants were marginally more correct for /ʏ-ɪ/ than PS listeners . Figure To determine whether discrimination differed between participants whose native language had more (AusE) or fewer (PS) vowels compared to Dutch, we compared performance between AusE and PS listeners. A mixed-effects binary logistic model analyzing participants' correct responses, with participant, speaker, and XAB trial as random effects, and vowel contrast and language background as fixed effects revealed a main effect of contrast [χ4, N = 7,6002 = post-hoc pairwise comparisons revealed that AusE participants had more correct responses for /i-y/ than /ɪ-i/, , /a-ɑ/, , /ʏ-y/, , and /ʏ-ɪ/ ; for /ʏ-ɪ/ than /ɪ-i/ ; and for /a-ɑ/ than /ɪ-i/ . PS participants had more correct responses for /ʏ-y/ than /a-ɑ/  and /ɪ-ʏ/ ; for /i-y/ than /a-ɑ/ , and /ʏ-ɪ/ ; and for /ɪ-i/ than /ʏ-ɪ/ , and trended toward more correct responses for /ɪ-i/ than /a-ɑ/ . Table Fisher's LSD-corrected We examined whether the size and/or acoustic properties of native vowel inventories relative to the target language aid or impede L2 perceptual difficulties by directly comparing L2 vowel discrimination and categorization patterns by two listener groups with varying vowel inventory sizes. The study also tested whether cross-linguistic LDA and stepwise models were predictive of AusE and PS listeners' vowel classification patterns, which in turn should predict their discrimination patterns. Based on a larger and more complex native vowel inventory, AusE listeners were predicted to perform better overall than PS listeners due to their vowel inventory being larger than PS. While there was no effect of language background, an interaction of language background and contrast was observed, with results suggesting that vowel inventory size does not fully explain non-native vowel discrimination. In fact, PS listeners were marginally better than AusE listeners in their discrimination of Dutch /ɪ-i/, while there was a trend for AusE listeners having a higher accuracy in only one contrast, namely /ʏ-ɪ/.Our findings are in line with our acoustic predictions that further support L2LP's tenet that L1–L2 acoustic proximities predict listeners' initial perception and discrimination patterns. That is, both listener groups appear to employ perceptual cues from their L1 when perceiving non-native sounds. Specifically, AusE and PS listeners' perceptual patterns were influenced by L1–L2 acoustic differences and both listener groups faced comparable difficulty in their perception of non-native Dutch vowels. As predicted by our cross-language LDA models, AusE listeners found Dutch /ʏ-y/ their second most challenging contrast. AusE listeners mapped this contrast across multiple native categories, Dutch /ʏ/ as AusE /ε-ʊ-ʉː/ and Dutch /y/ as AusE /ʉː-ʊ-ɪ/, leading to an overall lower discrimination performance compared to PS listeners who mapped these vowels across across two acoustically distinct native categories, PS /e/ and /i/, respectively. Further, evidence for acoustic L1–L2 overlap across multiple native categories affecting listeners' perceptual patterns, irrelevant of their L1 vowel inventory size, can be observed for Dutch /ɪ-i/. Even though AusE has two and PS one /i/ vowel, both listener groups had difficulty when discriminating this contrast. As predicted by the stepwise DAs, PS listeners employed F2 and F3 to classify Dutch /ɪ/ across two native categories, namely /i/ and /e/ while Dutch /i/ was classified solely as PS /i/. On the other hand, AusE listeners mapped Dutch /ɪ-i/ predominantly as AusE /ɪ/ but also /ε/, /i/, /ʉ:/, and /ʊ/.These results are in line with those of earlier studies that show English listeners' initial perceptual patterns are primarily influenced by spectral cues when perceiving the Dutch tense-lax /i-ɪ/ contrast, providing further evidence that acoustic properties influence listeners' perceptual patterns of non-native sounds ; Drafting the work or revising it critically for important intellectual content; ; Final approval of the version to be published; ; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved .This research was supported by MARCS Institute start-up funds (PE), Australian Research Council grants DP130102181 , and ARC Centre of Excellence for the Dynamics of Language project , CE140100041 .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Diagnosis of cancer invasion depth is crucial for selecting the optimal treatment strategy in patients with gastrointestinal cancers. We conducted a meta-analysis to determine the utilities of different endoscopic modalities for diagnosing invasion depth of esophageal squamous cell carcinoma (SCC).We conducted a comprehensive search of MEDLINE, Cochrane Central, and Ichushi databases to identify studies evaluating the use of endoscopic modalities for diagnosing invasion depth of superficial esophageal SCC. We excluded case reports, review articles, and studies in which the total number of patients or lesions was <10.Fourteen studies fulfilled our criteria. Summary receiver operating characteristic curves showed that magnified endoscopy (ME) and endoscopic ultrasonography (EUS) performed better than non-ME. ME was associated with high sensitivity and a very low (0.08) negative likelihood ratio (NLR), while EUS had high specificity and a very high (17.6) positive likelihood ratio (PLR) for the diagnosis of epithelial or lamina propria cancers. NLR <0.1 provided strong evidence to rule out disease, and PLR >10 provided strong evidence of a positive diagnosis.EUS and ME perform better than non-ME for diagnosing invasion depth in SCC. ME has a low NLR and is a reliable modality for confirming deep invasion of cancer, while EUS has a high PLR and can reliably confirm that the cancer is limited to the surface. Effective use of these two modalities should be considered in patients with SCC.42015024462.PROSPERO ; number  Esophageal squamous cell carcinoma (SCC) is one of the common causes of cancer-related mortality worldwide . AlthougMany factors, e.g. the patient’s condition, metastatic status, cancer invasion depth, and size of the lesion, must be taken into account when choosing the appropriate treatment. Among these factors, cancer invasion depth correlates well with the risk of metastasis and the curability by ER , 11. DiaMany modalities, e.g. non-magnified endoscopy (non-ME), magnified endoscopy (ME), and endoscopic ultrasound (EUS) are currently used for diagnosing the invasion depth of superficial esophageal SCC. Non-ME is a conventional diagnostic modality for invasion depth, and the diagnosis is usually based on the protrusion, depression, thickness, and hardness of the esophageal wall. However, diagnosis by non-ME is subjective and may be subject to inter-observer variability. ME allows clear observation of the microvascular architecture, which is closely associated with the development of esophageal cancer. Diagnosis of esophageal cancer invasion depth using ME was introduced in the 1990s , 13. ThiWe searched the MEDLINE, Cochrane Central, and Ichushi databases from January 1995 to June 2015 using the following search terms:  AND (“diagnosis” OR “endosonography” OR “staining and labeling” OR “iodine” OR “magnifying endoscopy OR “chromoendoscopy” OR “NBI” OR “avascular area” OR “endoscopic ultrasound” OR “imaging” OR “pathology” OR “esophagoscopy”) AND (“neoplasm invasiveness” OR “[T1a and EP]” OR “M1” OR “Tis” OR “[T1a and LPM]” OR “M2” OR “T1a” OR “(T1a and MM)” OR “M3” OR “T1b” OR “[pT1a and MM]” OR “T1b” OR “SM” OR “SM1” OR “SM2” OR “SM3” OR “[T1b and SM] OR “vascular involvement” OR invasion OR “infiltration” OR “depth”). Our search was restricted to English- or Japanese-language studies of human subjects. Two reviewers (R.I. and N.M.) independently screened the titles and abstracts of all the articles according to the defined inclusion and exclusion criteria. The final complete report of all selected articles was then retrieved and reviewed by the same two reviewers (R.I. and N.M.). We also manually screened the reference lists of the selected articles for any potential related articles that were not identified by the initial search . Discrepancies were resolved by discussions. The protocol for this meta-analysis was registered in PROSPERO , in accordance with the most recently published guidelines .The study population consisted of patients with esophageal SCC based on endoscopic biopsy and endoscopic examination. The intervention was endoscopic diagnosis  of cancer invasion depth for superficial SCC. The reference standard was histologic diagnosis of cancer invasion depth by ER, or from surgically resected specimens. Acceptable study designs were retrospective or prospective studies with sufficient data to allow reconstruction of a diagnostic 2 × 2 table . We excluded case reports, review articles, and studies in which the total number of patients or lesions was <10. We also excluded studies that did not provide any predefined criteria to diagnose invasion depth and studies with imaging modalities that are not used in daily practice.Histologic diagnosis of cancer invasion depth was divided into six categories, based on the findings: EP (cancer limited to the epithelium); LPM (cancer invading into the lamina propria); MM (cancer invading into the muscularis mucosa); SM1 ; SM2 ; SM3  .Endoscopic diagnosis of cancer invasion depth was divided into three categories: EP/LPM, MM/SM1, and ≥ SM2, because these categories correspond well with the risk of metastasis  and indiTwo independent reviewers (R.I. and N.M) extracted the following data from the selected studies and added them to standardized data forms: design; country; year of publication; setting; sample size; reference standard; operating frequencies of endoscope and/or probe; number of endoscopic imaging modalities used; and numbers of true-positive, true-negative, false-positive and false-negative values.Study quality and potential bias were assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool , which iWe constructed 2 × 2 tables for EP/LPM and ≥ MM, and for EP-SM1 and ≥ SM2 for each study, based on comparisons between the endoscopic diagnosis and final histologic diagnosis by ER or esophagectomy. The true-positive, false-positive, true-negative, and false-negative values were then calculated based on the 2 × 2 tables. A summary receiver operating characteristic curve (SROC) was constructed . A SROC 2 measure of inconsistency [P value of <0.10 was considered suggestive of significant heterogeneity [2 index describes the percentage of total variation among studies attributed to heterogeneity rather than chance. A value of 0% indicates no observed heterogeneity, and larger values show increasing heterogeneity. Higgins et al. [2 indexes of 25%, 50%, and 75% represented low, moderate, and high heterogeneity, respectively. For all statistical methods, except for Cochran’s Q test, P <0.05 was regarded as significant. Data were analyzed using Meta-Disc (version 1.4) and Review Manager.The pooled sensitivity, specificity, PLR, NLR, and diagnostic odds ratio were estimated using a fixed-effect model (Mantel–Haenszel method). Forest plots were used to show the effect size of each study. Heterogeneity was assessed using Cochran’s Q test and the Isistency –23. The ogeneity . The I2 s et al.  suggesteA total of 359 articles were initially identified using the search strategy, and 18 additional records were identified through manual searching of references  and ME (P = 0.002 for sensitivity and P < 0.001 for specificity), between EP-SM1 and ≥ SM2 by ME (P < 0.001 for specificity). The I2 index identified moderate to high heterogeneities for differentiating between EP/LPM and ≥ MM by non-ME (61.2% for sensitivity and 83.5% for specificity) and ME (91.3% for sensitivity and 87.7% for specificity), between EP-SM1 and ≥ SM2 by ME (91.2% for specificity). Sensitivity analysis was not performed because of the limited number of studies of each modality. However, heterogeneity for differentiating between EP/LPM and ≥ MM by non-ME was resolved by excluding one study [The qualities of the included studies evaluated according to the QUADAS-2 criteria are shown in Fig. ne study , and hetne study .Fig. 4QuThe current meta-analysis analyzed the performances of non-ME, ME, and EUS for diagnosing superficial esophageal SCC. SROCs showed a trade-off between sensitivity and specificity. Given that an AUC of 1 indicated an excellent test, EUS and ME demonstrated very high diagnostic accuracies. EUS and ME had different characteristics according to our analysis. ME showed high sensitivities for the diagnosis of EP/LPM and EP-SM1 cancers and very low NLRs for the diagnosis of EP/LPM and EP-SM1 cancers. The NLR assesses the ability of the test to exclude the disease in question. An NLR <0.1 provides strong evidence to rule out the disease , indicatAlthough the current meta-analysis analyzed the diagnostic abilities of the individual modalities, they are usually used in combination in clinical practice. Non-ME is conducted as an initial examination in most cases, usually followed by EUS, ME, or both. However, there are currently no guidelines or consensus on how best to combine these modalities, and further studies are therefore needed to clarify the additional benefits of combinations of these modalities.All the selected articles in the current study were reported from Japan and 11 of them were written in Japanese. This is probably because we limited the disease to SCC, and the cancer invasion depth categories to EP/LPM, MM/SM1 and ≥ SM2. This is one of the limitations of this meta-analysis and may raise some concern about generalizability of the result. This point should be confirmed by further studies outside Japan.Classification of the invasion depth of superficial esophageal SCCs into three categories  is relatively uncommon, but nevertheless practical. It can stratify the risk of metastasis , and is There were some limitations of this meta-analysis. Non-ME and ME demonstrated heterogeneity for differentiating between EP/LPM and ≥ MM, and ME for differentiating between EP-SM1 and ≥ SM2. We were unable to perform sensitivity analyses because of the limited number of studies for each modality. However, heterogeneities for differentiating between EP/LPM and ≥ MM by non-ME , and betEUS and ME are preferable to non-ME for diagnosing invasion depth in esophageal SCC. ME demonstrated very low NLR, and is thus a reliable modality for confirming deep cancer invasion, while EUS showed a high PLR, and is thus a suitable modality for confirming that a cancer is limited to the surface. Combined use of these two modalities should thus be considered for determining cancer invasion depth in patients with esophageal SCC.