Yufeng Lin, Harry Cheuk-Hay Lau, Yali Liu, Xing Kang, Yiwei Wang, Nick Lung-Ngai Ting, Thomas Ngai-Yeung Kwong, Jing Han, Weixin Liu, Changan Liu, Junjun She, Sunny Hei Wong, Joseph Jao-Yiu Sung, Jun Yu <<<

BACKGROUND & AIMS: The enteric mycobiota is a major component of the human gut microbiota, but its role in colorectal cancer (CRC) remains largely elusive. We conducted a meta-analysis to uncover the contribution of the fungal mycobiota to CRC.METHODS: We retrieved fecal metagenomic data sets from 7 previous publications and established an additional in-house cohort, totaling 1329 metagenomes (454 with CRC, 350 with adenoma, and 525 healthy individuals). Mycobiota composition and microbial interactions were analyzed. Candidate CRC-enriched fungal species (Aspergillus rambellii) was functionally validated in vitro and in vivo.RESULTS: Multicohort analysis revealed that the enteric mycobiota was altered in CRC. We identified fungi that were associated with patients with CRC or adenoma from multiple cohorts. Signature CRC-associated fungi included 6 enriched (A rambellii, Cordyceps sp. RAO-2017, Erysiphe pulchra, Moniliophthora perniciosa, Sphaerulina musiva, and Phytophthora capsici) and 1 depleted species (A kawachii). Co-occurrent interactions among CRC-enriched fungi became stronger in CRC compared with adenoma and healthy individuals. Moreover, we reported the transkingdom interactions between enteric fungi and bacteria in CRC progression, of which A rambellii was closely associated with CRC-enriched bacteria Fusobacterium nucleatum. A rambellii promoted CRC cell growth in vitro and tumor growth in xenograft mice. We further identified that combined fungal and bacterial biomarkers were more accurate than panels with pure bacterial species to discriminate patients with CRC from healthy individuals (the area under the curve relative change increased by 1.44%-10.60%).CONCLUSIONS: This study reveals enteric mycobiota signatures and pathogenic fungi in stages of colorectal tumorigenesis. Fecal fungi can be used, in addition to bacteria, for noninvasive diagnosis of patients with CRC. <<<

One of the buzzwords in the technological world is “Hyperautomation”. Hyperautomation is the new technological phenomenon in which it can bring intelligent automation processes using Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML) and other technologies. It has a good amount of business applications; that is why many tech giants and start-ups are putting huge investments to reap the fruits of Hyperautomation. The data was collected from different secondary sources from 1-4-2020 to 31-7-2021. The rationale of this conceptual manuscript is to explain the definitions, concepts, technologies and models behind Hyperautomation. This study also emphasis in-depth benefits of hyper-automation specifically in Banking & Finance sector. Moreover, the study also presents some of the industry test cases to explain the Hyperautomation adoption level by different countries across the globe. <<<

As a major event in human civilization, wild plants were successfully domesticated to be crops, largely owing to continuing artificial selection. Here, we summarize new discoveries made during the past decade in crop domestication and breeding. The construction of crop genome maps and the functional characterization of numerous trait genes provide foundational information. Approaches to read, interpret, and write complex genetic information are being leveraged in many plants for highly efficient de novo or re-domestication. Understanding the underlying mechanisms of crop microevolution and applying the knowledge to agricultural productions will give possible solutions for future challenges in food security. <<<

Genomic alterations (GA) in PIK3CA leads to the hyper-activation of the phosphatidylinositol-4, 5-bisphosphate 3-kinase (PI3K) pathway in more than 20% of ovarian cancer (OC) patients. Therefore, PI3K therapies are under clinical evaluation for this subset of patients. Evidently, in clinical trials testing the efficacy of isoform-specific inhibitors of PI3K (PI3Ki), patients having a stable disease eventually relapse, as tumors become resistant to treatment. Hence, there is an urgent clinical need to develop new therapeutic combinations to improve the efficacy of PI3Ki in PIK3CA-driven OC patients. Here we identified the molecular mechanism that limits the efficacy of the beta-sparing PI3Ki, Taselisib (GDC0032), in PIK3CA-mutated OC cell lines (IGROV1 and OAW42) that acquired resistance to GDC0032. By comparing the molecular profile of GDC0032-sensitve and -resistant OC cell lines, we found that AKT/mTOR inhibition is required for GDC0032 efficacy. In resistant cells, the sustained activation of AKT/mTOR was regulated by the upregulation of the insulin growth factor 1 receptor (IGF1R). Knockdown of IGF1R re-sensitized cells to GDC0032 in vitro, and the combination of AEW541, an IGF1R inhibitor, with GDC0032 exhibited potent anti-tumor activity in vitro and in vivo. We further demonstrated that IGF1R regulates tumor cell proliferation in IGROV1 cells, whereas in OAW42, it determines autophagy as well. Overall, our findings suggest that the dual inhibition of PI3K and IGF1R may be considered as a new therapeutic strategy in PIK3CA-driven OC. <<<

David E Frankhouser, Denis O'Meally, Sergio Branciamore, Lisa Uechi, Lianjun Zhang, Ying-Chieh Chen, Man Li, Hanjun Qin, Xiwei Wu, Nadia Carlesso, Guido Marcucci, Russell C Rockne, Ya-Huei Kuo <<<

MicroRNAs (miRNAs) have been shown to hold prognostic value in acute myeloid leukemia (AML); however, the temporal dynamics of miRNA expression in AML are poorly understood. Using serial samples from a mouse model of AML to generate time-series miRNA sequencing data, we are the first to show that the miRNA transcriptome undergoes state-transition during AML initiation and progression. We modeled AML state-transition as a particle undergoing Brownian motion in a quasi-potential and validated the AML state-space and state-transition model to accurately predict time to AML in an independent cohort of mice. The critical points of the model provided a framework to align samples from mice that developed AML at different rates. Our mathematical approach allowed discovery of dynamic processes involved during AML development and, if translated to humans, has the potential to predict an individual's disease trajectory. <<<

PURPOSE: The study aimed to determine the diagnostic yield, optimal timing, and methodology of next generation sequencing data reanalysis in suspected Mendelian disorders.METHODS: We conducted a systematic review and meta-analysis of studies that conducted data reanalysis in patients with suspected Mendelian disorders. Random effects model was used to pool the estimated outcome with subgroup analysis stratified by timing, sequencing methodology, sample size, segregation, use of research validation, and artificial intelligence (AI) variant curation tools.RESULTS: A search of PubMed, Embase, Scopus, and Web of Science between 2007 and 2021 yielded 9327 articles, of which 29 were selected. Significant heterogeneity was noted between studies. Reanalysis had an overall diagnostic yield of 0.10 (95% CI = 0.06-0.13). Literature updates accounted for most new diagnoses. Diagnostic yield was higher after 24 months, although this was not statistically significant. Increased diagnoses were obtained with research validation and data sharing. AI-based tools did not adversely affect reanalysis diagnostic rate.CONCLUSION: Next generation sequencing data reanalysis can improve diagnostic yield. Owing to the heterogeneity of the studies, the optimal time to reanalysis and the impact of AI-based tools could not be determined with confidence. We propose standardized guidelines for future studies to reduce heterogeneity and improve the quality of the conclusions. <<<

A fast, subcortical pathway to the amygdala is thought to have evolved to enable rapid detection of threat. This pathway's existence is fundamental for understanding nonconscious emotional responses, but has been challenged as a result of a lack of evidence for short-latency fear-related responses in primate amygdala, including humans. We recorded human intracranial electrophysiological data and found fast amygdala responses, beginning 74-ms post-stimulus onset, to fearful, but not neutral or happy, facial expressions. These responses had considerably shorter latency than fear responses that we observed in visual cortex. Notably, fast amygdala responses were limited to low spatial frequency components of fearful faces, as predicted by magnocellular inputs to amygdala. Furthermore, fast amygdala responses were not evoked by photographs of arousing scenes, which is indicative of selective early reactivity to socially relevant visual information conveyed by fearful faces. These data therefore support the existence of a phylogenetically old subcortical pathway providing fast, but coarse, threat-related signals to human amygdala. <<<

A crucial role for both insulin and mTOR in the regulation of milk protein synthesis is emerging. Bovine mammary biopsies harvested during late-pregnancy through end of subsequent lactation were used to evaluate via quantitative PCR the expression of 44 genes involved in pathways of insulin, mTOR, AMPK, and Jak2-Stat5 signalling and also glucose and amino acid (AA) transporters. We observed an increased expression during lactation of ELF5, AA and glucose transporters, insulin signaling pathway components, MAPK14, FRAP1, EIF4EBP2, GSK3A and TSC1 among mTOR signaling-related genes. Among ribosomal components RPL22 was down-regulated. The overall data support a central role of AA and glucose transporters and insulin signaling through mTOR for the regulation of protein synthesis in bovine mammary gland. Furthermore, the existence of translational competition favoring the translation of milk protein transcripts was inferred from the combined dataset. <<<

Animals can navigate vast distances and often display behaviours or activities that indicate a detailed, internal spatial representation of their surrounding environment or a 'cognitive map'. Over a century of behavioural research on spatial navigation in humans and animals has greatly increased our understanding of how this highly complex feat is achieved. In turn this has inspired half a century of electrophysiological spatial navigation and memory research which has further advanced our understanding of the brain. In particular, three functional cell types have been suggested to underlie cognitive mapping processes; place cells, head direction cells and grid cells. However, there are numerous other spatially modulated neurons in the brain. For a more complete understanding of the electrophysiological systems and behavioural processes underlying spatial navigation we must also examine these lesser understood neurons. In this review we will briefly summarise the literature surrounding place cells, head direction cells, grid cells and the evidence that these cells collectively form the neural basis of a cognitive map. We will then review literature covering many other spatially modulated neurons in the brain that perhaps further augment this cognitive map. <<<

Importance: Certain psychiatric and immune-related disorders are reciprocal risk factors. However, the nature of these associations is unclear.Objective: To characterize the pleiotropy between psychiatric and immune-related traits, as well as risk factors of hypothesized relevance.Design, Setting, and Participants: This genetic association study was conducted from July 10, 2020, to January 15, 2022. Analyses used genome-wide association (GWA) statistics related to 14 psychiatric traits; 13 immune-related phenotypes, ie, allergic, autoimmune, and inflammatory disorders; and 15 risk factors related to health-related behaviors, social determinants of health, and stress response. Genetically correlated psychiatric-immune pairs were assessed using 2-sample mendelian randomization (MR) with sensitivity analyses and multivariable adjustment for genetic associations of third variables. False discovery rate correction (Q value < .05) was applied for each analysis.Exposures: Genetic associations.Main Outcomes and Measures: Genetic correlations and MR association estimates with SEs and P values. A data-driven approach was used that did not test a priori planned hypotheses.Results: A total of 44 genetically correlated psychiatric-immune pairs were identified, including 31 positive correlations (most consistently involving asthma, Crohn disease, hypothyroidism, and ulcerative colitis) and 13 negative correlations (most consistently involving allergic rhinitis and type 1 diabetes). Correlations with third variables were especially strong for psychiatric phenotypes. MR identified 7 associations of psychiatric phenotypes on immune-related phenotypes that were robust to multivariable adjustment, including the positive association of (1) the psychiatric cross-disorder phenotype with asthma (odds ratio [OR], 1.04; 95% CI, 1.02-1.06), Crohn disease (OR, 1.09; 95% CI, 1.05-1.14), and ulcerative colitis (OR, 1.09; 95% CI, 1.05-1.14); (2) major depression with asthma (OR, 1.25; 95% CI, 1.13-1.37); (3) schizophrenia with Crohn disease (OR, 1.12; 95% CI, 1.05-1.18) and ulcerative colitis (OR, 1.14; 95% CI, 1.07-1.21); and a negative association of risk tolerance with allergic rhinitis (OR, 0.77; 95% CI, 0.67-0.92).Conclusions and Relevance: Results of this genetic association study suggest that genetic liability for psychiatric disorders was associated with liability for several immune disorders, suggesting that vertical pleiotropy related to behavioral traits (or correlated third variables) contributes to clinical associations observed in population-scale data. <<<

In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes an upper bound on the joint entropy of the distribution that generates the joint image data. Further, we derive the congealing method for groupwise registration by optimizing the profile likelihood in closed form, and using coordinate ascent, or iterative model refinement. We also describe a method for feature based registration in the same framework and demonstrate it on groupwise tractographic registration. In the second part of the article, we propose an approach to deep metric registration that implements maximum likelihood registration using deep discriminative classifiers. We show further that this approach can be used for maximum profile likelihood registration to discharge the need for well-registered training data, using iterative model refinement. We demonstrate that the method succeeds on a challenging registration problem where the standard mutual information approach does not perform well. <<<

With the aging population trend, there is a great demand for dysphagia diet as the elderly suffering from dysphagia is increasing rapidly. 3D printing is capable of processing mashed and not attractive dysphagia diet into appetizing foods with appealing appearance. Black fungus (Auricularia auricula) illustrates many health-promotion effects, but its elastic texture and great chewing efforts making it unfeasible for the elderly. In this work, we studied the feasibility to develop 3D printed visually appealing texture modified black fungus-based food as potential dysphagia diet, with addition of gums (0.3%, 0.6%, 0.9%, w/w), i.e. k-carrageenan gum (KG), xanthan gum (XG) and arabic gum (AG). Results indicated that KG and XG addition significantly increased the mechanical strength (yield stress and elasticity), viscosity, hardness and gumminess of ink samples by reducing water mobility and facilitating hydrogen bond formation, while AG addition showed an opposite effect. International dysphagia diet standardization initiative (IDDSI) tests indicated that AG and KG containing sample failed the spoon tilt test within IDDSI framework, while XG containing sample could be classified as level 5-minced and moist dysphagia diet. 3D printed samples using control or AG containing ink illustrated poor self-supporting capability. KG containing ink was not easy for extrusion with defective points in printed samples. In contrast, XG-0.9% samples demonstrated high printing precision with great self-supporting capability and smooth surface texture. This work provides insights for the development of visually appealing dysphagia diet using 3D printing. <<<

Jungeun Kim, Sang-Ho Kang, Sin-Gi Park, Tae-Jin Yang, Yi Lee, Ok Tae Kim, Oksung Chung, Jungho Lee, Jae-Pil Choi, Soo-Jin Kwon, Keunpyo Lee, Byoung-Ohg Ahn, Dong Jin Lee, Seung-Il Yoo, In-Gang Shin, Yurry Um, Dae Young Lee, Geum-Soog Kim, Chang Pyo Hong, Jong Bhak, Chang-Kug Kim <<<

Triterpenoid saponins (TSs) are common plant defense phytochemicals with potential pharmaceutical properties. (Campanulaceae) has been traditionally used to treat bronchitis and asthma in East Asia. The oleanane-type TSs, platycosides, are a major component of the root extract. Recent studies show that platycosides exhibit anti-inflammatory, antiobesity, anticancer, antiviral, and antiallergy properties. However, the evolutionary history of platycoside biosynthesis genes remains unknown. In this study, we sequenced the genome of and investigated the genes involved in platycoside biosynthesis. The draft genome of is 680.1 Mb long and contains 40,017 protein-coding genes. Genomic analysis revealed that the family genes play a major role in platycoside oxidation. The gene family of was much larger than that of other Asterid species. Orthologous gene annotation also revealed the expansion of () in , which was confirmed by tissue-specific gene expression. In these expanded gene families, we identified key genes showing preferential expression in roots and association with platycoside biosynthesis. In addition, whole-genome bisulfite sequencing showed that and genes are hypomethylated in , suggesting that epigenetic modification of these two gene families affects platycoside biosynthesis. Thus whole-genome, transcriptome, and methylome data of provide novel insights into the regulation of platycoside biosynthesis by and gene families. <<<

Antonella Amore, Brandon C Knott, Nitin T Supekar, Asif Shajahan, Parastoo Azadi, Peng Zhao, Lance Wells, Jeffrey G Linger, Sarah E Hobdey, Todd A Vander Wall, Todd Shollenberger, John M Yarbrough, Zhongping Tan, Michael F Crowley, Michael E Himmel, Stephen R Decker, Gregg T Beckham, Larry E Taylor <<<

In nature, many microbes secrete mixtures of glycoside hydrolases, oxidoreductases, and accessory enzymes to deconstruct polysaccharides and lignin in plants. These enzymes are often decorated with N- and O-glycosylation, the roles of which have been broadly attributed to protection from proteolysis, as the extracellular milieu is an aggressive environment. Glycosylation has been shown to sometimes affect activity, but these effects are not fully understood. Here, we examine N- and O-glycosylation on a model, multimodular glycoside hydrolase family 7 cellobiohydrolase (Cel7A), which exhibits an O-glycosylated carbohydrate-binding module (CBM) and an O-glycosylated linker connected to an N- and O-glycosylated catalytic domain (CD)-a domain architecture common to many biomass-degrading enzymes. We report consensus maps for Cel7A glycosylation that include glycan sites and motifs. Additionally, we examine the roles of glycans on activity, substrate binding, and thermal and proteolytic stability. N-glycan knockouts on the CD demonstrate that N-glycosylation has little impact on cellulose conversion or binding, but does have major stability impacts. O-glycans on the CBM have little impact on binding, proteolysis, or activity in the whole-enzyme context. However, linker O-glycans greatly impact cellulose conversion via their contribution to proteolysis resistance. Molecular simulations predict an additional role for linker O-glycans, namely that they are responsible for maintaining separation between ordered domains when Cel7A is engaged on cellulose, as models predict α-helix formation and decreased cellulose interaction for the nonglycosylated linker. Overall, this study reveals key roles for N- and O-glycosylation that are likely broadly applicable to other plant cell-wall-degrading enzymes. <<<

ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40-60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com . <<<

The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed. <<<

The study of short association fibers is still an incomplete task due to their higher inter-subject variability and the smaller size of this kind of fibers in comparison to known long association bundles. However, their description is essential to understand human brain dysfunction and better characterize the human brain connectome. In this work, we present a multi-subject atlas of short association fibers, which was computed using a superficial white matter bundle identification method based on fiber clustering. To create the atlas, we used probabilistic tractography from one hundred subjects from the HCP database, aligned with non-linear registration. The method starts with an intra-subject clustering of short fibers (30-85 mm). Based on a cortical atlas, the intra-subject cluster centroids from all subjects are segmented to identify the centroids connecting each region of interest (ROI) of the atlas. To reduce computational load, the centroids from each ROI group are randomly separated into ten subgroups. Then, an inter-subject hierarchical clustering is applied to each centroid subgroup, followed by a second level of clustering to select the most-reproducible clusters across subjects for each ROI group. Finally, the clusters are labeled according to the regions that they connect, and clustered to create the final bundle atlas. The resulting atlas is composed of 525 bundles of superficial short association fibers along the whole brain, with 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI. The reproducibility of the bundles was verified using automatic segmentation on three different tractogram databases. Results for deterministic and probabilistic tractography data show high reproducibility, especially for probabilistic tractography in HCP data. In comparison to previous work, our atlas features a higher number of bundles and greater cortical surface coverage. <<<

Yuechen Luo, Changlu Xu, Bing Wang, Qing Niu, Xiuhua Su, Yingnan Bai, Shuxian Zhu, Chunxiao Zhao, Yunyan Sun, Jiali Wang, Maolan Liu, Xiaolei Sun, Ge Song, Haidong Cui, Xiaoli Chen, Huifang Huang, Haikun Wang, Mingzhe Han, Erlie Jiang, Lihong Shi, Xiaoming Feng <<<

Human FOXP3 regulatory T (T) cells are central to immune tolerance. However, their heterogeneity and differentiation remain incompletely understood. Here we use single-cell RNA and T cell receptor sequencing to resolve T cells from healthy individuals and patients with or without acute graft-versus-host disease (aGVHD) who undergo stem cell transplantation. These analyses, combined with functional assays, separate T cells into naïve, activated, and effector stages, and resolve the HLA-DR, LIMS1, highly suppressive FOXP3, and highly proliferative MKI67 effector subsets. Trajectory analysis assembles T subsets into two differentiation paths (I/II) with distinctive phenotypic and functional programs, ending with the FOXP3 and MKI67 subsets, respectively. Transcription factors FOXP3 and SUB1 contribute to some Path I and Path II phenotypes, respectively. These FOXP3 and MKI67 subsets and two differentiation pathways are conserved in transplanted patients, despite having functional and migratory impairments under aGVHD. These findings expand the understanding of T cell heterogeneity and differentiation and provide a single-cell atlas for the dissection of T complexity in health and disease. <<<

Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space. <<<

To identify sequences with a role in microbial pathogenesis, we assessed the adequacy of their annotation by existing controlled vocabularies and sequence databases. Our goal was to regularize descriptions of microbial pathogenesis for improved integration with bioinformatic applications. Here, we review the challenges of annotating sequences for pathogenic activity. We relate the categorization of more than 2,750 sequences of pathogenic microbes through a controlled vocabulary called Functions of Sequences of Concern (FunSoCs). These allow for an ease of description by both humans and machines. We provide a subset of 220 fully annotated sequences in the supplemental material as examples. The use of this compact (∼30 terms), controlled vocabulary has potential benefits for research in microbial genomics, public health, biosecurity, biosurveillance, and the characterization of new and emerging pathogens. <<<