Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm, and has quite a few effective implementations such as XGBoost and pGBRT. Although many engineering optimizations have been adopted in these implementations, the efficiency and scalability are still unsatisfactory when the feature dimension is high and data size is large. A major reason is that for each feature, they need to scan all the data instances to estimate the information gain of all possible split points, which is very time consuming. To tackle this problem, we propose two novel techniques: Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). With GOSS, we exclude a significant proportion of data instances with small gradients, and only use the rest to estimate the information gain. We prove that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size. With EFB, we bundle mutually exclusive features (i.e., they rarely take nonzero values simultaneously), to reduce the number of features. We prove that finding the optimal bundling of exclusive features is NP-hard, but a greedy algorithm can achieve quite good approximation ratio (and thus can effectively reduce the number of features without hurting the accuracy of split point determination by much). We call our new GBDT implementation with GOSS and EFB LightGBM. Our experiments on multiple public datasets show that, LightGBM speeds up the training process of conventional GBDT by up to over 20 times while achieving almost the same accuracy. <<<

Leore T Geller, Michal Barzily-Rokni, Tal Danino, Oliver H Jonas, Noam Shental, Deborah Nejman, Nancy Gavert, Yaara Zwang, Zachary A Cooper, Kevin Shee, Christoph A Thaiss, Alexandre Reuben, Jonathan Livny, Roi Avraham, Dennie T Frederick, Matteo Ligorio, Kelly Chatman, Stephen E Johnston, Carrie M Mosher, Alexander Brandis, Garold Fuks, Candice Gurbatri, Vancheswaran Gopalakrishnan, Michael Kim, Mark W Hurd, Matthew Katz, Jason Fleming, Anirban Maitra, David A Smith, Matt Skalak, Jeffrey Bu, Monia Michaud, Sunia A Trauger, Iris Barshack, Talia Golan, Judith Sandbank, Keith T Flaherty, Anna Mandinova, Wendy S Garrett, Sarah P Thayer, Cristina R Ferrone, Curtis Huttenhower, Sangeeta N Bhatia, Dirk Gevers, Jennifer A Wargo, Todd R Golub, Ravid Straussman <<<

Growing evidence suggests that microbes can influence the efficacy of cancer therapies. By studying colon cancer models, we found that bacteria can metabolize the chemotherapeutic drug gemcitabine (2',2'-difluorodeoxycytidine) into its inactive form, 2',2'-difluorodeoxyuridine. Metabolism was dependent on the expression of a long isoform of the bacterial enzyme cytidine deaminase (CDD), seen primarily in Gammaproteobacteria. In a colon cancer mouse model, gemcitabine resistance was induced by intratumor Gammaproteobacteria, dependent on bacterial CDD expression, and abrogated by cotreatment with the antibiotic ciprofloxacin. Gemcitabine is commonly used to treat pancreatic ductal adenocarcinoma (PDAC), and we hypothesized that intratumor bacteria might contribute to drug resistance of these tumors. Consistent with this possibility, we found that of the 113 human PDACs that were tested, 86 (76%) were positive for bacteria, mainly Gammaproteobacteria. <<<

Gram-negative bacteria harness multiple protein secretion systems and secrete a large proportion of the proteome. Proteins can be exported to periplasmic space, integrated into membrane, transported into extracellular milieu, or translocated into cytoplasm of contacting cells. It is important for accurate, genome-wide annotation of the secreted proteins and their secretion pathways. In this review, we systematically classified the secreted proteins according to the types of secretion systems in Gram-negative bacteria, summarized the known features of these proteins, and reviewed the algorithms and tools for their prediction. <<<

BACKGROUND: To investigate the genotype distribution of human papillomavirus (HPV) in infected Uygur and Han women in Xinjiang, China; analyze the HPV16 E6 gene polymorphism site and relationship with the development of cervical cancer.METHODS: The HPV16 E6 sequence was analyzed using the European standard prototype to perform an evolutionary tree. HPV16 E6-T295/T350, G295/G350, and T295/G350 GV230 vectors were stably transfected into cervical cancer C33A cells to analyze the cell proliferation, migration and invasion, apoptosis by CCK8 and clonogenic assays, transwell and cell scratch assays, FACS experiments.RESULTS: The total HPV infection rate was 26.390% (760/2879), whereas the Uygur 22.87% (196/857) and the Han was 27.89% (564/2022) (P < 0.05). Among 110 mutations, 65 cases of E6 genes were mutated at nucleotide 350 (T350G) with the leucine changing to valine (L83V). Moreover, there were 7 cases of E6 gene mutated at nucleotide 295 (T295G) with aspartic changing to glutamic (D64E). When E6 vector(s) of mutations sites were transfected into C33A cells, they were found to promote cellular proliferation, migration, invasion, and inhibit apoptosis. T295/G350-E6 was significantly stronger than G295/G350 and T295/T350, G295/G350 was significantly stronger than T295/T350 (P < 0.05). The T295/G350 had the strongest effect on C33A cells and G295/G350 was significantly stronger than T295/T350 (P < 0.05).CONCLUSIONS: The positive HPV infection rates differed between the Uygur and Han in Xinjiang, China, and the genotype distribution of infection was different. After transfecting C33A cells with different eukaryotic expression vectors, the T295/G350 mutation site promoted the proliferation, migration, and invasion of C33A cells to a greater extent than G295/G350; however, G295/G350 had a stronger effect than T295/T350. <<<

Mohammed Alser, Jeremy Rotman, Dhrithi Deshpande, Kodi Taraszka, Huwenbo Shi, Pelin Icer Baykal, Harry Taegyun Yang, Victor Xue, Sergey Knyazev, Benjamin D Singer, Brunilda Balliu, David Koslicki, Pavel Skums, Alex Zelikovsky, Can Alkan, Onur Mutlu, Serghei Mangul <<<

Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today's diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology. <<<

Longxing Cao, Brian Coventry, Inna Goreshnik, Buwei Huang, William Sheffler, Joon Sung Park, Kevin M Jude, Iva Marković, Rameshwar U Kadam, Koen H G Verschueren, Kenneth Verstraete, Scott Thomas Russell Walsh, Nathaniel Bennett, Ashish Phal, Aerin Yang, Lisa Kozodoy, Michelle DeWitt, Lora Picton, Lauren Miller, Eva-Maria Strauch, Nicholas D DeBouver, Allison Pires, Asim K Bera, Samer Halabiya, Bradley Hammerson, Wei Yang, Steffen Bernard, Lance Stewart, Ian A Wilson, Hannele Ruohola-Baker, Joseph Schlessinger, Sangwon Lee, Savvas N Savvides, K Christopher Garcia, David Baker <<<

The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge. Here we describe a general solution to this problem that starts with a broad exploration of the vast space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and, following experimental optimization, bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder-target complexes, and all five closely match the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein-protein interactions, and should guide improvements of both. Our approach enables the targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications. <<<

Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, Sameer Velankar, Gerard J Kleywegt, Alex Bateman, Richard Evans, Alexander Pritzel, Michael Figurnov, Olaf Ronneberger, Russ Bates, Simon A A Kohl, Anna Potapenko, Andrew J Ballard, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Ellen Clancy, David Reiman, Stig Petersen, Andrew W Senior, Koray Kavukcuoglu, Ewan Birney, Pushmeet Kohli, John Jumper, Demis Hassabis <<<

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective. <<<

In a dual-factor model of mental health (cf. Greenspoon & Saklofske, 2001), assessments of positive indicators of wellness (i.e., subjective well-being—SWB) are coupled with traditional negative indicators of illness (i.e., psychopathology) to comprehensively measure mental health. The current study examined the existence and utility of a dual-factor model in early adolescence. The SWB, psychopathology, academic functioning, social adjustment, and physical health of a general sample of 349 middle school students was assessed via self-report scales, school records, and teacher reports regarding students' externalizing psychopathology. The existence of a dual-factor model was supported through the identification of four mental health groups: 57% of the sample had complete mental health, 13% was vulnerable, 13% was symptomatic but content, and 17% was troubled. The means of the four groups differed significantly in terms of academic outcomes, physical health, and social functioning. Results support the importance of high SWB to optimal functioning during adolescence, as students with complete mental health (i.e., high SWB, low psychopathology) had better reading skills, school attendance, academic self-perceptions, academic-related goals, social support from classmates and parents, self-perceived physical health, and fewer social problems than their vulnerable peers also without clinical levels of mental illness but with low SWB. Among students with clinical levels of psychopathology, students with high SWB (symptomatic but content youth) perceived better social functioning and physical health. <<<

Bulk-tissue DNA methylomes represent an average over many different cell types, hampering our understanding of cell-type-specific contributions to disease development. As single-cell methylomics is not scalable to large cohorts of individuals, cost-effective computational solutions are needed, yet current methods are limited to tissues such as blood. Here we leverage the high-resolution nature of tissue-specific single-cell RNA-sequencing datasets to construct a DNA methylation atlas defined for 13 solid tissue types and 40 cell types. We comprehensively validate this atlas in independent bulk and single-nucleus DNA methylation datasets. We demonstrate that it correctly predicts the cell of origin of diverse cancer types and discovers new prognostic associations in olfactory neuroblastoma and stage 2 melanoma. In brain, the atlas predicts a neuronal origin for schizophrenia, with neuron-specific differential DNA methylation enriched for corresponding genome-wide association study risk loci. In summary, the DNA methylation atlas enables the decomposition of 13 different human tissue types at a high cellular resolution, paving the way for an improved interpretation of epigenetic data. <<<

《法界宝藏论》是藏传佛教大圆满法的精髓。论中以不住于相的语言,以清净自在、光明无相的胜义菩提心来阐述法界要义,界定了法界作为一切法的本初根源、一切因之因、一切果之果的地位,在此基础上建立起无修之道。论中以"任运成就"的修行之道,揭示了佛教的自由观、中道观;以"自然智慧"淋漓尽致地展现了佛教的智慧观;以自然圆满、轮涅一味的大圆满果位展现了佛教的生活观、解脱观、成就观。 <<<

Enormous progress has been made in the last half-century in the management of diseases closely integrated with excess body weight, such as hypertension, adult-onset diabetes and elevated cholesterol. However, the treatment of obesity itself has proven largely resistant to therapy, with anti-obesity medications (AOMs) often delivering insufficient efficacy and dubious safety. Here, we provide an overview of the history of AOM development, focusing on lessons learned and ongoing obstacles. Recent advances, including increased understanding of the molecular gut-brain communication, are inspiring the pursuit of next-generation AOMs that appear capable of safely achieving sizeable and sustained body weight loss. <<<

Kai Dührkop, Louis-Félix Nothias, Markus Fleischauer, Raphael Reher, Marcus Ludwig, Martin A Hoffmann, Daniel Petras, William H Gerwick, Juho Rousu, Pieter C Dorrestein, Sebastian Böcker <<<

Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level. <<<

虽然空气污染是由污染物排放到大气中造成的,但是由于气象条件会影响污染物的扩散,因而实际观测到的污染水平会受到气象条件的影响.因此, 有效的空气质量管理要求污染评估指标和统计方法不受气象因素的干扰, 并能准确客观地反映污染物浓度的变化.为了评估北京地区潜在污染物排放的变化, 本文提出一种消除气象干扰的时空调整方法.通过控制气象条件, 调整后的污染物时空平均浓度可以捕捉到潜在排放量的变化.本文提出具体调整均值的方法, 并进行理论和数值分析,将此方法应用于北京地区的空气质量评估, 揭示一些有趣的模式和趋势, 这些结果可以用于空气质量评估和管理. <<<

The concept of updating (or conditioning or revising) a probability distribution is fundamental in (machine) learning and in predictive coding theory. The two main approaches for doing so are called Pearl's rule and Jeffrey's rule. Here we make, for the first time, mathematically precise what distinguishes them: Pearl's rule increases validity (expected value) and Jeffrey's rule decreases (Kullback-Leibler) divergence. This forms an instance of a more general distinction between learning from what's right and learning from what's wrong. The difference between these two approaches is illustrated in a mock cognitive scenario. <<<

John B Kisiel, Brian A Dukek, Reddappa V S R Kanipakam, Hassan M Ghoz, Tracy C Yab, Calise K Berger, William R Taylor, Patrick H Foote, Nasra H Giama, Kristeen Onyirioha, Mohamed A Abdallah, Kelli N Burger, Seth W Slettedahl, Douglas W Mahoney, Thomas C Smyrk, Jason T Lewis, Maria Giakoumopoulos, Hatim T Allawi, Graham P Lidgard, Lewis R Roberts, David A Ahlquist <<<

Early detection improves hepatocellular carcinoma (HCC) outcomes, but better noninvasive surveillance tools are needed. We aimed to identify and validate methylated DNA markers (MDMs) for HCC detection. Reduced representation bisulfite sequencing was performed on DNA extracted from 18 HCC and 35 control tissues. Candidate MDMs were confirmed by quantitative methylation-specific PCR in DNA from independent tissues (74 HCC, 29 controls). A phase I plasma pilot incorporated quantitative allele-specific real-time target and signal amplification assays on independent plasma-extracted DNA from 21 HCC cases and 30 controls with cirrhosis. A phase II plasma study was then performed in 95 HCC cases, 51 controls with cirrhosis, and 98 healthy controls using target enrichment long-probe quantitative amplified signal (TELQAS) assays. Recursive partitioning identified best MDM combinations. The entire MDM panel was statistically cross-validated by randomly splitting the data 2:1 for training and testing. Random forest (rForest) regression models performed on the training set predicted disease status in the testing set; median areas under the receiver operating characteristics curve (AUCs; and 95% confidence interval [CI]) were reported after 500 iterations. In phase II, a six-marker MDM panel (homeobox A1 [HOXA1], empty spiracles homeobox 1 [EMX1], AK055957, endothelin-converting enzyme 1 [ECE1], phosphofructokinase [PFKP], and C-type lectin domain containing 11A [CLEC11A]) normalized by beta-1,3-galactosyltransferase 6 (B3GALT6) level yielded a best-fit AUC of 0.96 (95% CI, 0.93-0.99) with HCC sensitivity of 95% (88%-98%) at specificity of 92% (86%-96%). The panel detected 3 of 4 (75%) stage 0, 39 of 42 (93%) stage A, 13 of 14 (93%) stage B, 28 of 28 (100%) stage C, and 7 of 7 (100%) stage D HCCs. The AUC value for alpha-fetoprotein (AFP) was 0.80 (0.74-0.87) compared to 0.94 (0.9-0.97) for the cross-validated MDM panel (P < 0.0001). Conclusion: MDMs identified in this study proved to accurately detect HCC by plasma testing. Further optimization and clinical testing of this promising approach are indicated. <<<

Epithelial-mesenchymal plasticity (EMP) contributes to tumor progression, promoting therapy resistance and immune cell evasion. Definitive molecular features of this plasticity have largely remained elusive due to the limited scale of most studies. Leveraging single-cell RNA sequencing data from 266 tumors spanning eight different cancer types, we identify expression patterns associated with intratumoral EMP. Integrative analysis of these programs confirmed a high degree of diversity among tumors. These diverse programs are associated with combinations of various common regulatory mechanisms initiated from cues within the tumor microenvironment. We show that inferring regulatory features can inform effective therapeutics to restrict EMP. <<<

When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test. <<<

BACKGROUND: Diffusion MRI is the preferred non-invasive in vivo modality for the study of brain white matter connections. Tractography datasets contain 3D streamlines that can be analyzed to study the main brain white matter tracts. Fiber clustering methods have been used to automatically group similar fibers into clusters. However, due to inter-subject variability and artifacts, the resulting clusters are difficult to process for finding common connections across subjects, specially for superficial white matter.METHODS: We present an automatic method for labeling of short association bundles on a group of subjects. The method is based on an intra-subject fiber clustering that generates compact fiber clusters. Posteriorly, the clusters are labeled based on the cortical connectivity of the fibers, taking as reference the Desikan-Killiany atlas, and named according to their relative position along one axis. Finally, two different strategies were applied and compared for the labeling of inter-subject bundles: a matching with the Hungarian algorithm, and a well-known fiber clustering algorithm, called QuickBundles.RESULTS: Individual labeling was executed over four subjects, with an execution time of 3.6 min. An inspection of individual labeling based on a distance measure showed good correspondence among the four tested subjects. Two inter-subject labeling were successfully implemented and applied to 20 subjects and compared using a set of distance thresholds, ranging from a conservative value of 10 mm to a moderate value of 21 mm. Hungarian algorithm led to a high correspondence, but low reproducibility for all the thresholds, with 96 s of execution time. QuickBundles led to better correspondence, reproducibility and short execution time of 9 s. Hence, the whole processing for the inter-subject labeling over 20 subjects takes 1.17 h.CONCLUSION: We implemented a method for the automatic labeling of short bundles in individuals, based on an intra-subject clustering and the connectivity of the clusters with the cortex. The labels provide useful information for the visualization and analysis of individual connections, which is very difficult without any additional information. Furthermore, we provide two fast inter-subject bundle labeling methods. The obtained clusters could be used for performing manual or automatic connectivity analysis in individuals or across subjects. <<<