Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making. <<<

Christopher G Bell, Robert Lowe, Peter D Adams, Andrea A Baccarelli, Stephan Beck, Jordana T Bell, Brock C Christensen, Vadim N Gladyshev, Bastiaan T Heijmans, Steve Horvath, Trey Ideker, Jean-Pierre J Issa, Karl T Kelsey, Riccardo E Marioni, Wolf Reik, Caroline L Relton, Leonard C Schalkwyk, Andrew E Teschendorff, Wolfgang Wagner, Kang Zhang, Vardhman K Rakyan <<<

Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual. <<<

Handwritten Chinese text recognition (HCTR) has been an active research topic for decades. However, most previous studies solely focus on the recognition of cropped text line images, ignoring the error caused by text line detection in real-world applications. Although some approaches aimed at page-level text recognition have been proposed in recent years, they either are limited to simple layouts or require very detailed annotations including expensive line-level and even character-level bounding boxes. To this end, we propose PageNet for end-to-end weakly supervised page-level HCTR. PageNet detects and recognizes characters and predicts the reading order between them, which is more robust and flexible when dealing with complex layouts including multi-directional and curved text lines. Utilizing the proposed weakly supervised learning framework, PageNet requires only transcripts to be annotated for real data; however, it can still output detection and recognition results at both the character and line levels, avoiding the labor and cost of labeling bounding boxes of characters and text lines. Extensive experiments conducted on five datasets demonstrate the superiority of PageNet over existing weakly supervised and fully supervised page-level methods. These experimental results may spark further research beyond the realms of existing methods based on connectionist temporal classification or attention. The source code is available at https://github.com/shannanyinxiang/PageNet. <<<

Katharine C Simon, Gregory D Clemenson, Jing Zhang, Negin Sattari, Alessandra E Shuster, Brandon Clayton, Elisabet Alzueta, Teji Dulai, Massimiliano de Zambotti, Craig Stark, Fiona C Baker, Sara C Mednick <<<

Sleep facilitates hippocampal-dependent memories, supporting the acquisition and maintenance of internal representation of spatial relations within an environment. In humans, however, findings have been mixed regarding sleep's contribution to spatial memory and navigation, which may be due to task designs or outcome measurements. We developed the Minecraft Memory and Navigation (MMN) task for the purpose of disentangling how spatial memory accuracy and navigation change over time, and to study sleep's independent contributions to each. In the MMN task, participants learned the locations of objects through free exploration of an open field computerized environment. At test, they were teleported to random positions around the environment and required to navigate to the remembered location of each object. In study 1, we developed and validated four unique MMN environments with the goal of equating baseline learning and immediate test performance. A total of 86 participants were administered the training phases and immediate test. Participants' baseline performance was equivalent across all four environments, supporting the use of the MMN task. In study 2, 29 participants were trained, tested immediately, and again 12 h later after a period of sleep or wake. We found that the metric accuracy of object locations, i.e., spatial memory, was maintained over a night of sleep, while after wake, metric accuracy declined. In contrast, spatial navigation improved over both sleep and wake delays. Our findings support the role of sleep in retaining the precise spatial relationships within a cognitive map; however, they do not support a specific role of sleep in navigation. <<<

Yan Li, Yang Chen, Lu Zhou, Shengjie You, Heng Deng, Ya Chen, Saleh Alseekh, Yong Yuan, Rao Fu, Zixin Zhang, Dan Su, Alisdair R Fernie, Mondher Bouzayen, Tao Ma, Mingchun Liu, Yang Zhang <<<

Tomato (Solanum lycopersicum) is a major horticultural crop worldwide and has emerged as a preeminent model for metabolic research. Although many research efforts have focused on the analysis of metabolite differences between varieties and species, the dynamics of metabolic changes during the tomato growth cycle and the regulatory networks that underlie these changes are poorly understood. In this study, we integrated high-resolution spatio-temporal metabolome and transcriptome data to systematically explore the metabolic landscape across 20 major tomato tissues and growth stages. In the resulting MicroTom Metabolic Network, the 540 detected metabolites and their co-expressed genes could be divided into 10 distinct clusters based on their biological functions. Using this dataset, we constructed a global map of the major metabolic changes that occur throughout the tomato growth cycle and dissected the underlying regulatory network. In addition to verifying previously well-established regulatory networks for important metabolites, we identified novel transcription factors that regulate the biosynthesis of important secondary metabolites such as steroidal glycoalkaloids and flavonoids. Our findings provide insights into spatio-temporal changes in tomato metabolism and generate a valuable resource for the study of metabolic regulatory processes in model plants. <<<

Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has been applied to macromolecular structures. This review provides an overview of the recent applications of geometric deep learning in bioorganic and medicinal chemistry, highlighting its potential for structure-based drug discovery and design. Emphasis is placed on molecular property prediction, ligand binding site and pose prediction, and structure-based de novo molecular design. The current challenges and opportunities are highlighted, and a forecast of the future of geometric deep learning for drug discovery is presented. <<<

A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization. <<<

Stefan C. Dentro , Ignaty Leshchiner , Kerstin Haase , Maxime Tarabichi , Jeff Wintersinger , Amit G. Deshwar , Kaixian Yu , Yulia Rubanova , Geoff Macintyre , Jonas Demeulemeester , Ignacio Vázquez-García , Kortine Kleinheinz , Dimitri G. Livitz , Salem Malikic , Nilgun Donmez , Subhajit Sengupta , Pavana Anur , Clemency Jolly , Marek Cmero , Daniel Rosebrock , Steven E. Schumacher , Yu Fan , Matthew Fittall , Ruben M. Drews , Xiaotong Yao , Thomas B.K. Watkins , Juhee Lee , Matthias Schlesner , Hongtu Zhu , David J. Adams , Nicholas McGranahan , Charles Swanton , Gad Getz , Paul C. Boutros , Marcin Imielinski , Rameen Beroukhim , S. Cenk Sahinalp , Yuan Ji , Martin Peifer , Inigo Martincorena , Florian Markowetz , Ville Mustonen , Ke Yuan , Moritz Gerstung , Paul T. Spellman , Wenyi Wang , Quaid D. Morris , David C. Wedge , Peter Van Loo , Stefan C. Dentro , Ignaty Leshchiner , Moritz Gerstung , Clemency Jolly , Kerstin Haase , Maxime Tarabichi , Jeff Wintersinger , Amit G. Deshwar , Kaixian Yu , Santiago Gonzalez , Yulia Rubanova , Geoff Macintyre , Jonas Demeulemeester , David J. Adams , Pavana Anur , Rameen Beroukhim , Paul C. Boutros , David D. Bowtell , Peter J. Campbell , Shaolong Cao , Elizabeth L. Christie , Marek Cmero , Yupeng Cun , Kevin J. Dawson , Nilgun Donmez , Ruben M. Drews , Roland Eils , Yu Fan , Matthew Fittall , Dale W. Garsed , Gad Getz , Gavin Ha , Marcin Imielinski , Lara Jerman , Yuan Ji , Kortine Kleinheinz , Juhee Lee , Henry Lee-Six , Dimitri G. Livitz , Salem Malikic , Florian Markowetz , Inigo Martincorena , Thomas J. Mitchell , Ville Mustonen , Layla Oesper , Martin Peifer , Myron Peto , Benjamin J. Raphael , Daniel Rosebrock , S. Cenk Sahinalp , Adriana Salcedo , Matthias Schlesner , Steven E. Schumacher , Subhajit Sengupta , Ruian Shi , Seung Jun Shin , Lincoln D. Stein , Oliver Spiro , Ignacio Vázquez-García , Shankar Vembu , David A. Wheeler , Tsun-Po Yang , Xiaotong Yao , Ke Yuan , Hongtu Zhu , Wenyi Wang , Quaid D. Morris , Paul T. Spellman , David C. Wedge , Peter Van Loo <<<

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data. <<<

Acute lymphoblastic leukemia (ALL) is a cancer-specific lymphoid cell. Induction and consolidation chemotherapy alone or in combination with different therapeutic approaches remain the main treatment. Although complete or partial remission of the disease can be achieved, the risk of relapse or refractory leukemia is still high. More effective and safe therapy options are yet unmet needs. In recent years' new therapeutic approaches have been widely used. Hematopoietic Stem Cell Transplantation (HSCT) presents significant limitations and the outcome of the consolidation treatment is patient dependent. Side effects such as Graft versus Host Disease (GvHD) in allogeneic hematopoietic stem cell transplantation are extremely common, therefore, using alternative methods to address these challenges for treatment seems crucial. In the last decade, T cells genetically engineered with Chimeric Antigen Receptor (CAR) treatment for the ALL are largely studied and represent the new era of strategy. According to the Phase I/II clinical trials, this technology results seem very promising and can be used in the next future as an effective and safe treatment for ALL treatment. In this review different generations, challenges, and clinical studies related to chimeric antigen receptor (CAR) T-cells for ALL treatment are discussed. <<<

Lactic acid production has been regarded as a mechanism by which malignant cells escape immunosurveillance. Recent technological advances in mass spectrometry and the use of cell culture media with a physiological nutrient composition have shed new light on the role of lactic acid and its conjugate lactate in the tumor microenvironment. Here, we review novel work identifying lactate as a physiological carbon source for mammalian tumors and immune cells. We highlight evidence that its use as a substrate is distinct from the immunosuppressive acidification of the extracellular milieu by lactic acid protons. Together, data suggest that neutralizing the effects of intratumoral acidity while maintaining physiological lactate metabolism in cytotoxic CD8 T cells should be pursued to boost anti-tumor immunity. <<<

Producing spatial transformations that are diffeomorphic has been a central problem in deformable image registration. As a diffeomorphic transformation should have positive Jacobian determinant |J| everywhere, the number of voxels with |J|<0 has been used to test for diffeomorphism and also to measure the irregularity of the transformation. For digital transformations, |J| is commonly approximated using central difference, but this strategy can yield positive |J|'s for transformations that are clearly not diffeomorphic -- even at the voxel resolution level. To show this, we first investigate the geometric meaning of different finite difference approximations of |J|. We show that to determine diffeomorphism for digital images, use of any individual finite difference approximations of |J| is insufficient. We show that for a 2D transformation, four unique finite difference approximations of |J|'s must be positive to ensure the entire domain is invertible and free of folding at the pixel level. We also show that in 3D, ten unique finite differences approximations of |J|'s are required to be positive. Our proposed digital diffeomorphism criteria solves several errors inherent in the central difference approximation of |J| and accurately detects non-diffeomorphic digital transformations. <<<

Kutsev Bengisu Ozyoruk, Sermet Can, Berkan Darbaz, Kayhan Başak, Derya Demir, Guliz Irem Gokceler, Gurdeniz Serin, Uguray Payam Hacisalihoglu, Emirhan Kurtuluş, Ming Y Lu, Tiffany Y Chen, Drew F K Williamson, Funda Yılmaz, Faisal Mahmood, Mehmet Turan <<<

Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments during surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue provides higher quality slides, but the process for obtaining them is laborious (typically lasting 12-48 h) and hence unsuitable for intra-operative use. Here we report the development and performance of a deep-learning model that improves the quality of cryosectioned whole-slide images by transforming them into the style of whole-slide FFPE tissue within minutes. The model consists of a generative adversarial network incorporating an attention mechanism that rectifies cryosection artefacts and a self-regularization constraint between the cryosectioned and FFPE images for the preservation of clinically relevant features. Transformed FFPE-style images of gliomas and of non-small-cell lung cancers from a dataset independent from that used to train the model improved the rates of accurate tumour subtyping by pathologists. <<<

Carlos Taboada, Jesse Delia, Maomao Chen, Chenshuo Ma, Xiaorui Peng, Xiaoyi Zhu, Laiming Jiang, Tri Vu, Qifa Zhou, Junjie Yao, Lauren O'Connell, Sönke Johnsen <<<

Transparency in animals is a complex form of camouflage involving mechanisms that reduce light scattering and absorption throughout the organism. In vertebrates, attaining transparency is difficult because their circulatory system is full of red blood cells (RBCs) that strongly attenuate light. Here, we document how glassfrogs overcome this challenge by concealing these cells from view. Using photoacoustic imaging to track RBCs in vivo, we show that resting glassfrogs increase transparency two- to threefold by removing ~89% of their RBCs from circulation and packing them within their liver. Vertebrate transparency thus requires both see-through tissues and active mechanisms that "clear" respiratory pigments from these tissues. Furthermore, glassfrogs' ability to regulate the location, density, and packing of RBCs without clotting offers insight in metabolic, hemodynamic, and blood-clot research. <<<

Natalie I Mazur, Jonne Terstappen, Ranju Baral, Azucena Bardají, Philippe Beutels, Ursula J Buchholz, Cheryl Cohen, James E Crowe, Clare L Cutland, Linda Eckert, Daniel Feikin, Tiffany Fitzpatrick, Youyi Fong, Barney S Graham, Terho Heikkinen, Deborah Higgins, Siddhivinayak Hirve, Keith P Klugman, Leyla Kragten-Tabatabaie, Philippe Lemey, Romina Libster, Yvette Löwensteyn, Asuncion Mejias, Flor M Munoz, Patrick K Munywoki, Lawrence Mwananyanda, Harish Nair, Marta C Nunes, Octavio Ramilo, Peter Richmond, Tracy J Ruckwardt, Charles Sande, Padmini Srikantiah, Naveen Thacker, Kody A Waldstein, Dan Weinberger, Joanne Wildenbeest, Dexter Wiseman, Heather J Zar, Maria Zambon, Louis Bont <<<

Respiratory syncytial virus is the second most common cause of infant mortality and a major cause of morbidity and mortality in older adults (aged >60 years). Efforts to develop a respiratory syncytial virus vaccine or immunoprophylaxis remain highly active. 33 respiratory syncytial virus prevention candidates are in clinical development using six different approaches: recombinant vector, subunit, particle-based, live attenuated, chimeric, and nucleic acid vaccines; and monoclonal antibodies. Nine candidates are in phase 3 clinical trials. Understanding the epitopes targeted by highly neutralising antibodies has resulted in a shift from empirical to rational and structure-based vaccine and monoclonal antibody design. An extended half-life monoclonal antibody for all infants is likely to be within 1 year of regulatory approval (from August, 2022) for high-income countries. Live-attenuated vaccines are in development for older infants (aged >6 months). Subunit vaccines are in late-stage trials for pregnant women to protect infants, whereas vector, subunit, and nucleic acid approaches are being developed for older adults. Urgent next steps include ensuring access and affordability of a respiratory syncytial virus vaccine globally. This review gives an overview of respiratory syncytial virus vaccines and monoclonal antibodies in clinical development highlighting different target populations, antigens, and trial results. <<<

The relationship between mental health and social media has received significant research and policy attention. However, there is little population representative data about who social media users are which limits understanding of confounding factors between mental health and social media. Here we profile users of Facebook, Twitter, Instagram, Snapchat and YouTube from the Avon Longitudinal Study of Parents and Children population cohort (N=4,083). We provide estimates of demographics and mental health and well-being outcomes by platform. We find that users of different platforms and frequencies are not homogeneous. User groups differ primarily by sex and YouTube users are the most likely to have poorer mental health outcomes. Instagram and Snapchat users tend to have higher well-being than the other social media sites considered. Relationships between use-frequency and well-being differ depending on the specific well-being construct measured. The reproducibility of future research may be improved by stratifying by sex and being specific about the well-being constructs used. <<<

Humans display astonishing skill in learning about the environment in which they operate. They assimilate a rich set of affordances and interrelations among different elements in particular contexts, and form flexible abstractions (i.e., concepts) that can be generalised and leveraged with ease. To capture these abilities, we present a deep hierarchical Active Inference model of goal-directed behaviour, and the accompanying belief update schemes implied by maximising model evidence. Using simulations, we elucidate the potential mechanisms that underlie and influence concept learning in a spatial foraging task. We show that the representations formed-as a result of foraging-reflect environmental structure in a way that is enhanced and nuanced by Bayesian model reduction, a special case of structure learning that typifies learning in the absence of new evidence. Synthetic agents learn associations and form concepts about environmental context and configuration as a result of inferential, parametric learning, and structure learning processes-three processes that can produce a diversity of beliefs and belief structures. Furthermore, the ensuing representations reflect symmetries for environments with identical configurations. <<<

8-Oxoguanine (8oxoG) in DNA is a major oxidized base that poses a severe threat to genome stability. To counteract the mutagenic effect generated by 8oxoG in DNA, cells have evolved 8oxoG DNA glycosylase (OGG) that can excise this oxidized base from DNA. Currently, OGG enzymes have been divided into three families: OGG1, OGG2 and AGOG (archaeal 8oxoG DNA glycosylase). Due to the limited reports, our understanding on AGOG enzymes remains incomplete. Herein, we present evidence that an AGOG from the hyperthermophilic euryarchaeon Ch5 (Tb-AGOG) excises 8oxoG from DNA at high temperature. The enzyme displays maximum efficiency at 75°C-95°C and at pH 9.0. As expected, Tb-AGOG is a bifunctional glycosylase that harbors glycosylase activity and AP (apurinic/apyrimidinic) lyase activity. Importantly, we reveal for the first time that residue D41 in Tb-AGOG is essential for 8oxoG excision and intermediate formation, but not essential for DNA binding or AP cleavage. Furthermore, residue E79 in Tb-AGOG is essential for 8oxoG excision and intermediate formation, and is partially involved in DNA binding and AP cleavage, which has not been described among the reported AGOG members to date. Overall, our work provides new insights into catalytic mechanism of AGOG enzymes. <<<

The immune system responds to cancer in two main ways. First, there are prewired responses involving myeloid cells, innate lymphocytes and innate-like adaptive lymphocytes that either reside in premalignant tissues or migrate directly to tumours, and second, there are antigen priming-dependent responses, in which adaptive lymphocytes are primed in secondary lymphoid organs before homing to tumours. Transforming growth factor-β (TGFβ) - one of the most potent and pleiotropic regulatory cytokines - controls almost every stage of the tumour-elicited immune response, from leukocyte development in primary lymphoid organs to their priming in secondary lymphoid organs and their effector functions in the tumour itself. The complexity of TGFβ-regulated immune cell circuitries, as well as the contextual roles of TGFβ signalling in cancer cells and tumour stromal cells, necessitates the use of rigorous experimental systems that closely recapitulate human cancer, such as autochthonous tumour models, to uncover the underlying immunobiology. The diverse functions of TGFβ in healthy tissues further complicate the search for effective and safe cancer therapeutics targeting the TGFβ pathway. Here we discuss the contextual complexity of TGFβ signalling in tumour-elicited immune responses and explain how understanding this may guide the development of mechanism-based cancer immunotherapy. <<<

Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license ( https://github.com/marbl/mash ). <<<

Importance: Childhood poverty has been associated with increased internalizing and externalizing problems in adolescence, a period of peak onset for psychiatric problems. The underlying neural mechanisms remain unclear because longitudinal studies of poverty, brain structure, and changes in psychiatric symptoms are lacking.Objective: To examine whether structural differences in cortical regions mediate the association between household poverty and change in psychiatric symptoms in early adolescence.Design, Setting, and Participants: This longitudinal cohort study used baseline and 1-year follow-up data from the Adolescent Brain Cognitive Development Study. Children aged 9 to 10 years in the US were enrolled between September 1, 2016, and October 15, 2018. Data analysis was performed from August 13, 2021, to September 30, 2022.Exposures: Household poverty as measured by income-to-needs ratio, which incorporates family income and adjusts for family size as a percentage of the federal poverty level.Main Outcomes and Measures: Mediators were children's cortical surface area, thickness, and volume, obtained using magnetic resonance imaging. Internalizing and externalizing problems at 1-year follow-up were outcomes measured by maternal report using the Child Behavior Checklist. Analyses were adjusted for baseline psychiatric problems and sociodemographic variables, including sex, race and ethnicity, parental educational level, and study site.Results: Of the 7569 children (mean [SD] age, 9.91 [0.62] years; 3970 boys [52.5%]) included in the analysis, 1042 children (13.8%) lived below the poverty threshold between 2016 and 2018. Poverty was associated with increased externalizing symptoms score at 1-year follow-up (b = 1.57; 95% CI, 1.14-1.99), even after adjustment for baseline externalizing symptoms (b = 0.35; 95% CI, 0.06-0.64). The longitudinal associations of poverty with increases in externalizing problems over time were mediated by reductions in surface area in multiple cortical regions that support executive functioning (middle frontal gyrus), decision-making (lateral orbitofrontal cortex), visual processing (fusiform gyrus), auditory processing (transverse temporal gyrus), and emotion and language processing (superior temporal gyrus).Conclusions and Relevance: The findings of this study suggest that childhood poverty is associated with increases in externalizing problems, but not internalizing problems, over time in early adolescence. This association is mediated by reductions in cortical surface area across numerous brain regions. These findings highlight potential neurobiological mechanisms underlying the link between poverty and the emergence of externalizing problems during early adolescence. <<<