dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, while there is clear evidence that prenatal MMS and LNS can improve birth outcomes in LMICs, the evidence remains inconclusive for child development outcomes. The objective of this study was to assess the effect of prenatal MMS and LNS on child development outcomes as compared to routine prenatal IFA supplementation. We analyzed data from a 3-arm cluster-randomized controlled trial of prenatal nutritional supplements in rural Niger that assessed child development with the Bayley Scales of Infant and Toddler Development III (BSID-III) and WHO gross motor milestones during the first 2 years of life as secondary outcomes.
Study design A randomized, double-blind, placebo-controlled trial to assess the safety and efficacy of Rotasiil (SerumAU : PleasenotethatasperPLOSstyle; donotuseInc:; Ltd:; etc:exceptasappropriateintheaffiliati Institute of India), a live, oral rotavirus vaccine for infants, was conducted in Madarounda, Niger (clinicaltrials. gov identifier: NCT02145000)  [22, 23]. The rotavirus vaccine trial protocol and the primary results and adverse events have been published elsewhere  [22]. Given the low immunogenicity of oral vaccines in high mortality settings, a nested cluster-randomized controlled trial of nutritional interventions in pregnancy (referred hereafter as the nutrition-immunogenicity trial) was conducted to evaluate whether prenatal MMS and LNS can increase the immunogenicity of rotavirus vaccine as compared to prenatal IFA, which is standard of care in Niger and most LMIC settings  [24].
Data collection and child development assessment At the time of enrollment in the nutrition-immunogenicity trial, study midwives administered a standardized questionnaire to pregnant women, which assessed maternal and household sociodemographic characteristics and conducted a physical exam, and assessed maternal anthropometry (height, weight, and mid-upper arm circumference (MUAC)). Maternal Hb concentration was assessed from a finger-prick blood sample (Hemocue Hb 301, Angelholm, Sweden), and pregnant women received a malaria rapid diagnostic test (Biolin Malaria Ag Pf (HRP-2), Abbott Diagnostics, Scarborough, USA). Food security was assessed using the household hunger scale  [26]. Improved sanitation was defined as a household having access to a flush toilet, improved pit latrine, or slab latrine.
To maintain functional equivalence, replacement stimuli were selected to be of similar size, style, and complexity to the original stimuli. Eight female research nurses were selected and trained as dedicated BSID-III data collectors. Recruitment for the BSID-III assessment cohort was stopped on September 17, 2017, primarily due to resource constraints; the cohort of infants who turned 6 months of age after this date did not receive BSID-III assessments. Field-based supervision and weekly staff meetings were used to prevent assessor drift and ensure the continued quality of implementation. The BSID-III was administered in quiet and dedicated evaluation rooms at each health facility. The BSID-III showed high internal consistency for all domains (Cronbach's alphas �0. 85) (Table B in S1 Appendix).
Multilevel models were constructed with the GLIMMIX procedure in SAS version 9. 3 to take into account correlation within village clusters and correlation within children who were nested within the village clusters with random intercepts. GLMMs included randomized treatment arm, child age at assessment (6-, 9-, 12-, 15-, 18-, 21-, and 24-month time bins), an interaction term between treatment arm and infant age, and used a compound symmetry structure for within-subject correlation. If the overall test for difference in the trajectory of development domain scores between randomized groups was statistically significant, then differences in mean scores between groups at each child age were assessed using least-square means with Tukey-Kramer adjustment for multiple comparisons. In addition, Cox proportional hazard models with cluster-robust standard errors were used to assess the time to acquisition of each of the 6 WHO gross motor milestones; hazard ratios (HRs) >1.
Second, our study was conducted in rural Niger, which provides much-needed evidence in the context of a population of mothers and children at high risk for undernutrition and a population that has limited data on child development. In terms of limitations, we assessed child development to 2 years of age and therefore may not have captured beneficial effects that may be easier to detect or emerge at older child ages. For example, the positive effects in MMS trials in Indonesia and China were detected in mid-childhood and early adolescence  [14, 15]. In addition, the use of the BSID-III in the context of rural Niger, despite care in adaptation to the setting, may result in some degree of nondifferential misclassification of children's developmental abilities across randomized groups if some BSID-III items were not fully capturing children's cognitive, language, motor development and would therefore bias results to the null.
In brain imaging research, prior studies have applied structural covariance analysis to elucidate underlying coordinated morphological changes in brain aging and various brain diseases,  1  but have had several limitations. They often relied on pre-defined neuroanatomical ROIs to construct inter-and intra-individual structural covariance networks. These a priori ROIs might not optimally reflect the molecular-functional characteristics of the brain. In addition, most population-based studies have investigated brain structural covariance within a relatively limited scope, such as within relatively small samples, over a relatively narrow age window (e. g. , adolescence  2  ), within a single disease (e. g. , Parkinson's disease  6  ), or within datasets lacking sufficient diversity in cohort characteristics or MRI scanner protocols.
Results We summarize this work in three units (I to III) outlined in Fig. 1. In Unit I (Fig. 1A ), we present the stochastic orthogonally projective non-negative matrix factorization (sopNMF) algorithm (Method 1), optimized for large-scale multivariate structural covariance analysis. The sopNMF algorithm decomposes large-scale imaging data through online learning to overcome the memory limitations of opNMF. A subgroup of participants with multiple disease diagnoses and healthy controls (ages 5-97, training population, N=4000, Method 2) were sampled from the discovery set (N=32,440, Method 2); their MRI underwent a standard imaging processing pipeline (Method 3A). The processed images were then fit to sopNMF to derive the multi-scale PSCs (N=2003) from the loadings of the factorization (Method 1).
BRIDGEPORT: bridging knowledge across patterns of structural covariance, genomics, and clinical phenotypes We integrated our experimental results and the MuSIC atlas into the BRIDGEPORT online web portal. This online tool allows researchers to interactively browse the MuSIC atlas in 3D, query our experimental results via variants or PSCs, and download the GWAS summary statistics for further analyses. In addition, we allow users to search via conventional brain anatomical terms (e. g. , the right thalamus proper) by automatically annotating traditional anatomic atlas ROIs, specifically from the MUSE atlas  37  (Supplementary eTable 5), to MuSIC PSCs based on their degree of overlaps (Supplementary eFigure 5). Open-source software dedicated to image processing,  37  genetic quality check protocols, MuSIC generation with sopNMF, and machine learning  34  is also publicly available (see Code Availability for details).
All experimental results and code are encapsulated and publicly available in BRIDGEPORT for dissemination: https://www. cbica. upenn. edu/bridgeport/, in order to enable various neuroscience studies to investigate these patterns of structural covariance in diverse contexts. Together, the current study highlighted the adoption of machine learning methods in brain imaging genomics and deepened our understanding of the genetic architecture of the human brain. Our findings reveal new insights into genetic underpinnings that influence patterns of structural covariance in the human brain. Brain morphological development and changes are largely polygenic and heritable, and previous neuroimaging GWAS has not fully uncovered this genetic landscape. In contrast, genetic variants, as well as environmental, aging, and disease effects, exert pleiotropic effects in shaping morphological changes in different brain regions through specific biological pathways.
Methods Method 1: Structural covariance patterns via stochastic orthogonally projective nonnegative matrix factorization The sopNMF algorithm is a stochastic approximation built and extended based on opNMF  9, 48. We consider a dataset of 𝑛 MR images and 𝑑 voxels per image. We represent the data as a matrix X each column corresponds to a flattened image: 𝑿 = [𝑥 1 , 𝑥 2 , … , 𝑥 𝑛 ], 𝑿 ∈ ℝ ≥0 𝑑×𝑛. The sopNMF algorithm factorizes X into two low-rank (𝑟) matrices 𝑾 ∈ ℝ ≥0 𝑑×𝑟 and 𝑯 ∈ ℝ ≥0 𝑟×𝑛 under the constraints of non-negativity and column-orthonormality. Using the Frobenius norm, the loss of this factorization problem can be formulated as ‖𝑿 -𝑾𝑯‖ 𝐹 2 subject to 𝑯 = 𝑾 𝑻 𝑿 , 𝑾 ≥ 0 and 𝑾 𝑻 𝑾 = 𝑰 (1)   where I stands for the identity matrix. The columns 𝑤 𝑖 ∈ ℝ 𝑑 , ‖𝑤 𝑖 ‖ 2 = 1, ∀ 𝑖 ∈ {1. 𝑟} of the socalled component matrix 𝑾 = [𝑤 1 , 𝑤 2 , … , 𝑤 𝑟 ] are part-based representations promoting sparsity in data in this lower-dimensional subspace.
From this perspective, the loading coefficient matrix 𝑯 represents the importance (weights) of each of the features above for a given image. Instead of optimizing the non-convex problem in a batch learning paradigm (i. e. , reading all images into memory) as opNMF,  9  sopNMF subsamples the number of images at each iteration, thereby significantly reducing its memory demand, by randomly drawing data batches 𝑿 𝒃 ∈ ℝ ≥0 𝑑×𝑏 of 𝑏 ≤ 𝑛 images (b is the batch size); this is done without replacement so that all data goes through the model once (⌈𝑛/𝑏⌉). In this case, the updating rule can be rewritten as 𝑾 𝑡+1 = 𝑾 𝑡 (𝑿 𝒃 𝑿 𝒃 𝑻 𝑾) 𝑡 (𝑾𝑾 𝑇 𝑿 𝒃 𝑿 𝒃 𝑻 𝑾) 𝑡 (2) We calculate the loss on the entire dataset at the end of each epoch (i. e. , the loss is incremental across all batches) with the following expression ∑ ‖𝑿 𝒃_𝒊 -𝑾𝑾 𝑻 𝑿 𝒃𝒊 ‖ 𝐹 2 ⌈𝑛/𝑏⌉ 𝑖=1 (3) We evaluated the training loss and the sparsity of W at the end of each iteration.
Method 4: Genetic analyses Genetic analyses were restricted to the discovery and replication set from UKBB (Method 2). We processed the array genotyping and imputed genetic data (SNPs). The two data sets went through a "best-practice" imaging-genetics quality check (QC) protocol (Method 4A) and were restricted to participants of European ancestry. This resulted in 18,052 participants and 8,430,655 SNPs for the discovery set, and 15,243 participants and 8,470,709 SNPs for the replication set. We reperformed the genetic QC and genetic analyses for the combined populations for BRIDGEPORT, resulting in 33,541 participants and 8,469,833 SNPs. Method 4G details the correction for multiple comparisons throughout our analyses. (A): Genetic data quality check protocol. First, we excluded related individuals (up to 2 nddegree) from the complete UKBB sample (N=488,377) using the KING software for family relationship inference.
66  We then removed duplicated variants from all 22 autosomal chromosomes. We also excluded individuals for whom either imaging or genetic data were not available. Individuals whose genetically-identified sex did not match their self-acknowledged sex were removed. Other excluding criteria were: i) individuals with more than 3% of missing genotypes; ii) variants with minor allele frequency (MAF) of less than 1%; iii) variants with larger than 3% missing genotyping rate; iv) variants that failed the Hardy-Weinberg test at 1x10 -10. To adjust for population stratification,  67  we derived the first 40 genetic principle components (PC) using the FlashPCA software  68. The genetic pipeline was described elsewhere,  69  and documented online: https://www. cbica. upenn. edu/bridgeport/data/pdf/BIGS_genetic_protocol.
Acknowledgments The  iSTAGING  consortium is a multi-institutional effort funded by  NIA  by  RF1 AG054409. The  Baltimore Longitudinal Study of Aging neuroimaging study  is funded by the  Intramural Research Program ,  National Institute on Aging ,  National Institutes of Health  and by and Bioengineering, and through generous contributions from the following:  AbbVie ,  Alzheimer's Association ;  Alzheimer's Drug Discovery Foundation ;  Araclon Biotech ;  BioClinica, Inc. ;  Biogen ;  Bristol-Myers Squibb Company ;  CereSpir, Inc. ;  Cogstate ;  Eisai Inc. ;  Elan Pharmaceuticals, Inc. ;  Eli Lilly  and  Company ;  EuroImmun ;  F. Hoffmann-La Roche Ltd  and its affiliated company  Genentech, Inc. ;  Fujirebio ;  GE Healthcare ;  IXICO Ltd. ;  Janssen Alzheimer Immunotherapy Research & Development, LLC.
Data Availability The GWAS summary statistics corresponding to this study are publicly available on the BRIDGEPORT web portal (https://www. cbica. upenn. edu/bridgeport/). The GWAS summary statistics used in the genetic correlation analyses were fetched from the GWAS Catalog platform (https://www. ebi. ac. uk/gwas), although each study provided the original links; The GWAS Catalog platform was used to query if the SNPs identified by MuSIC were previously reported.
PSCs were fit into a linear support vector machine (SVM) to derive SPARE-AD, MCI, SCZ, DM, HTN, MDD, and ASD. Specifically, the SVM aims to classify the patient group (e. g. , AD) from the control group and outputs a decision function that indicates how close each participant is to the hyperplane. The samples selected for each task are presented in Supplementary eTable 2. No statistical methods were used to predetermine sample size. The experiments were not randomized, and the investigators were not blinded to allocation during experiments and outcome assessment.  BRIDGEPORT: https://www. cbica. upenn. edu/bridgeport/, (developed for this study)  BIGS: https://www. cbica. upenn. edu/bridgeport/data/pdf/BIGS_genetic_protocol. pdf, genetic processing protocol (developed for this study)  MLNI: https://pypi.
org/project/mlni/, machine learning (developed for this study)  MUSE: https://www. med. upenn. edu/sbia/muse. html, image preprocessing  Clinica: http://www. clinica. run/, machine learning and image preprocessing  PLINK: https://www. cog-genomics. org/plink/, GWAS  GCTA: https://yanglab. westlake. edu. cn/software/gcta/#Overview, heritability estimates  LDSC: https://github. com/bulik/ldsc, genetic correlation estimates  MAGMA: https://ctg. cncr. nl/software/magma, gene analysis  GWASRAPIDD: https://rmagno. eu/gwasrapidd/articles/gwasrapidd. html, GWAS Catalog query  MsigDB: https://www. gsea-msigdb. org/gsea/msigdb/, gene sets database.
Cell Lines HEK293T/17 cell line (ATCC, Cat#CRL-11268) was cultured in Dulbecco's Modified Eagle Medium (DMEM, Life Technologies) supplemented with 10% (v/v) heat-inactivated FBS (Sigma-Aldrich), 100 U/ml penicillin and 100 mg/ml streptomycin (Sigma-Aldrich) and used for the generation of lentivirus. Sf9 insect cell line (CVCL_0549) was used to produce high titer of baculovirus stocks cultured in Sf-900 II medium (Life Technologies) supplemented with 10% (v/v) heat-inactivated FBS, (fetal bovine serum Sigma-Aldrich), 100 U/ml penicillin, 100 mg/ml streptomycin (Sigma-Aldrich) and 1% (v/v) Lglutamine (Life Technologies). Hi5 insect cell line (CVCL_C190) was used for KIR2DL-Fc fusion protein production and cultured in Express Five serum free medium (Life Technologies) supplemented with 1% (v/v) L-glutamine (Life Technologies).
The HLA class I deficient B cell line 721. 221 (RRID: CVCL_6263)  (38)  was used for expressing HLA-C1 and -C2 allotypes (Table  1 ). For this, the extracellular domains of HLA-C allotypes were obtained from GeneSynthesis (Thermo Fisher) and cloned into a lentiviral transfer vector (pSIP-ZsGreen) with a puromycin resistance. For the production of lentivirus, Lipofectamine 3000 (Life Technology) was used to transfect HEK293T/17 cells with the lentiviral transfer vector with the gene of interest, a VSV-G envelope vector (pHEF-VSVF; NIH HIV Reagent Program) and a HIV-1 Gag-Pol packaging vector (psPAX2; NIH HIV Reagent Program). After 48 h, the supernatant containing the lentivirus was harvested and used for the transduction of the 721. 221 cells. 3 days post-transduction, HLA-C + 721. 221 cells were selected with 1 mg/ml puromycin (Sigma-Aldrich) and later sorted for high HLA-C expression by fluorescence-activated cell sorting.
Vpu Sequence Analysis Raw paired-end reads were aligned to indexed reference sequence AF324493. 2 from NCBI GenBank using bwa mem command  (40). Resulting SAM alignment files were converted to BAM files, sorted and indexed using samtools  (41). Variants were detected using GATK HaplotypeCaller (42) using "-dontuse-soft-clipped-bases true" option, and to identify variant with The symbol "*" represents a separator between the gene and the allele group. In this instance the allele group of the respective KIR2DL1, L2 and L3 proteins. maximum frequency "-max-alternate-alleles 1" option was used. In downstream process low quality variants were filtered out using following parameters from GATK Best Practices recommendations: "DP < 25 || QD < 2. 0 || MQ < 30. 0 || FS > 60. 0 || MQRankSum < -12. 5 || ReadPosRankSum < -8.
Data Analysis and Statistics The acquisition of flow cytometric data was performed with a BD LSRFortessa (BD Bioscience) in the core facility Fluorescence Cytometry at the Leibniz Institute of Virology and analyzed using FlowJo software 10. 7. 1 (BD Life Sciences). Graphical and statistical analyses were performed using GraphPad Prism 9. 0. 1 software (GraphPad Software, La Jolla, CA, USA). Multiple linear regression analysis was performed to assess the impact of HIV-1 status, age and sex on the expression of NK cell receptors (Figure  2C ). One-way ANOVA, test for linear trend was used to test for gene-dose effects (Figure  2A , B, D, E). A nonparametric statistical test (Mann-Whitney) was applied to test for differences between two groups. Adjustment for multiplicity was applied to comparisons of interest using false discovery rate (FDR) with Q = 5% (Benjamini/Krieger/Yekutieli).
Acknowledgements We are particularly grateful to the following people:  Alain Townsend ,  Tiong Kit Tan ,  Pramila Rijal , who contributed the various materials detailed in the above table, and to  Carol Featherstone  for copy editing. We also gratefully acknowledge the contributions of  Marianne Navarra , for her help in setting up the agreement with the hospital;  Emmanuelle Naser  and  Pénélope Viana  from the IPBS flow cytometry facility for their assistance; the staff of the  Toulouse EFS  ( Etablissement Français du Sang ) for the provision of O-blood samples;  Florence Abravanel  and  Jacques Izopet  for providing immune sera from convalescent Covid patients, and  Eric Vallée  for the experiments he carried out very early on in the course of the pandemic to compare the performance of HAT with those of the lateral flow assays.
To classify the different samples, we constructed a 15-layer CNN (Figure  2E , see legend for details). We started by imaging IBV and a virus-negative control consisting of only SrCl 2 and DNA. The two samples were independently imaged four times each over a three-day period. Imaging over 3 days allowed any potential heterogeneity in the image procurement process (such as small differences in temperature on different days) to be captured, in order to enhance the ability of the trained models to classify data from future datasets. The resulting BBXs obtained for each sample were then randomly divided into a training dataset (70%) and a validation dataset (30%). The training dataset was used to train the CNN to differentiate IBV from negative signals, using ∼3000 BBXs per sample. The trained network was validated using the remaining 30% of the data (that the network had never seen before).
5% for IBV vs Udorn (Figure  3C ) and 94. 3% for IBV vs PR8 (Figure  3D ). The network was also able to differentiate between closely related strains of influenza (WSN vs PR8), albeit with a slightly lower accuracy of 79. 6% (Figure  3E ), perhaps reflecting the greater homogeneity between H1N1 strains of the same virus. The ability to distinguish between different influenza viruses that were grown in the same cell line (i. e. , WSN and PR8 were both grown in MDCK cells) established that our classification is not host-cell dependent. The network was also able to distinguish between IBV and a pooled dataset consisting of the virus-negative control and three influenza strains (92. 2%) (Figure  3F ) and, importantly, was able to distinguish three viruses from each other in a multiclassifier experiment (the coronavirus IBV and two influenza strains, PR8 and WSN; 81.
78 kW/cm 2 for the red (640 nm) and 1. 09 kW/cm 2 for the green (532 nm) laser. To automate the task and ensure no bias in the selection of FOVs, the whole sample was scanned using the multiple acquisition capability of the microscope; 81 FOVs were imaged in 2 min. Defocusing was carefully controlled using an automated autofocus mechanism to bring the sample to a pre-defined axial position before each field of view was exposed to the excitation lasers. This was achieved by imaging the reflection of a near-IR laser off the glass/sample medium interface and matching the image to a prerecorded reference image. Data in Sup. Figure  6  was acquired using a Zeiss Elyra 7 microscope equipped with two pco. edge sCMOS (version 4. 2 CL HS) cameras. TIRF images were acquired using the alpha Plan-Apochromat 63×/1.
After incubation for 30 min at room temperature the slide was washed thrice in 1× PBS before imaging in 50 mM MEA and an enzymatic oxygen scavenging system consisting of 1 mg/mL glucose oxidase, 40 μg/mL catalase, and 1. 0% (wt/vol) glucose. Super-resolution localizations were extracted using the built-in Nanoimager software and analyzed further in Matlab. Localizations were clustered with DBScan using a minimum cluster size of 50 and an epsilon of 30 nm, followed by computing the convex hull to find the area of the clustered points. Data Segmentation. Each FOV in the red channel was turned into a binary image using MATLAB's built-in imbinarize function with adaptive filtering sensitivity set to 0. 5. Adaptive filtering uses statistics about the neighborhood of each pixel it operates on to determine whether the pixel is foreground or background.
The network was built using Matlab 2020b and trained using the computer's GPU (specifications: NVIDIA 2080Ti, 32 GB RAM, i7 processor). The network had 3 convolutional layers in total, with kernels of 2×2 for the first two convolutions and 3×3 for the last one. The learning rate was set to 0. 01, and the learning schedule rate remained constant throughout the training. The hyperparameters remained the same throughout the training process for all models; the mini batch size was set to 1000, the maximum number of epochs to 100, and the validation frequency to 20 iterations. In the classification layer, trainNetwork took the values from the softmax function and assigned each input to one of K mutually exclusive classes using the cross entropy function for a 1-of-K coding scheme,  24.
ACKNOWLEDGMENTS We are grateful to  Micron Oxford , funded by  Wellcome Strategic Awards  ( 091911  and  107457 ;  PI Ilan Davis ), for their loan of their microscope and to  Nadia Halidi  for her help with the instrument. Special thanks to  Dr. Rebecca Moore  and  Prof. William James  ( Sir William Dunn   School of Pathology, Oxford, UK ) for providing laboratory-grown SARS-CoV-2. We thank the microbiology laboratory staff at the  John Radcliffe Hospital, Oxford.