Miracle-dz/UniFAR-FTUs huggingface dataset:
Helicobacter pylori Chronic Infection Selects for Effective Colonizers of Metaplastic Glands

Chronic infection with Helicobacter pylori is the primary risk factor for developing stomach cancer. As disease progresses H. pylori must adapt to a changing host tissue environment that includes induction of new cell fates in the cells that line the stomach. ABSTRACT Chronic gastric infection with Helicobacter pylori can lead to progressive tissue changes that culminate in cancer, but how H. pylori adapts to the changing tissue environment during disease development is not fully understood. In a transgenic mouse gastric metaplasia model, we found that strains from unrelated individuals differed in their ability to infect the stomach, to colonize metaplastic glands, and to alter the expression of the metaplasia-associated protein TFF3. H. pylori isolates from different stages of disease from a single individual had differential ability to colonize healthy and metaplastic gastric glands. Exposure to the metaplastic environment selected for high gastric colonization by one of these strains. Complete genome sequencing revealed a unique alteration in the frequency of a variant allele of the putative adhesin sabB, arising from a recombination event with the related sialic acid binding adhesin (SabA) gene. Mutation of sabB in multiple H. pylori strain backgrounds strongly reduced adherence to both normal and metaplastic gastric tissue, and highly attenuated stomach colonization in mice. Thus, the changing gastric environment during disease development promotes bacterial adhesin gene variation associated with enhanced gastric colonization. IMPORTANCE Chronic infection with Helicobacter pylori is the primary risk factor for developing stomach cancer. As disease progresses H. pylori must adapt to a changing host tissue environment that includes induction of new cell fates in the cells that line the stomach. We tested representative H. pylori isolates collected from the same patient during early and later stages of disease in a mouse model where we can rapidly induce disease-associated tissue changes. Only the later-stage H. pylori strains could robustly colonize the diseased stomach environment. We also found that the ability to colonize the diseased stomach was associated with genetic variation in a putative cell surface adhesin gene called sabB. Additional experiments revealed that SabB promotes binding to stomach tissue and is critical for stomach colonization by the late-stage strains. Thus, H. pylori diversifies its genome during disease progression and these genomic changes highlight critical factors for bacterial persistence.
Distinct hippocampal and cortical contributions in the representation of hierarchies

Humans generate complex hierarchies across a variety of domains, including language and music, and this capacity is often associated with activity in inferior frontal gyrus (IFG). Non-human animals have also been shown to represent simple hierarchies in spatial navigation, and human neuroimaging work has implicated the hippocampus in the encoding of items-in-contexts representations, which constitute 2-level hierarchical dependencies. These fields of research use distinct paradigms, leading to disjoint models and precluding adequate cross-species comparisons. In this study, we developed a paradigm to bring together these two areas of research and show that anterior hippocampus and medial prefrontal cortex encode hierarchical context, mimicking findings from animal spatial navigation. Additionally, we replicated classic neurolinguistic findings of 1) left IFG and posterior temporal cortex in the representation of hierarchies and 2) the association between IFG and processing automaticity. We propose that mammals share an evolutionary ancient system for the generation of simple hierarchies which is complemented in humans by additional capacities. Highlights HPC and mPFC activity is specifically modulated by hierarchical context Syntax-related regions in the left hemisphere encode for hierarchy in general IFGop activity is maintained in later trials for hierarchies but not sequences These findings mimic those from animal spatial navigation and neurolinguistics
TMbed – Transmembrane proteins predicted through Language Model embeddings

Background Despite the immense importance of transmembrane proteins (TMP) for molecular biology and medicine, experimental 3D structures for TMPs remain about 4-5 times underrepresented compared to non-TMPs. Today’s top methods such as AlphaFold2 accurately predict 3D structures for many TMPs, but annotating transmembrane regions remains a limiting step for proteome-wide predictions. Results Here, we present TMbed, a novel method inputting embeddings from protein Language Models (pLMs, here ProtT5), to predict for each residue one of four classes: transmembrane helix (TMH), transmembrane strand (TMB), signal peptide, or other. TMbed completes predictions for entire proteomes within hours on a single consumer-grade desktop machine at performance levels similar or better than methods, which are using evolutionary information from multiple sequence alignments (MSAs) of protein families. On the per-protein level, TMbed correctly identified 94±8% of the beta barrel TMPs (53 of 57) and 98±1% of the alpha helical TMPs (557 of 571) in a non-redundant data set, at false positive rates well below 1% (erred on 30 of 5654 non-membrane proteins). On the per-segment level, TMbed correctly placed, on average, 9 of 10 transmembrane segments within five residues of the experimental observation. Our method can handle sequences of up to 4200 residues on standard graphics cards used in desktop PCs (e.g., NVIDIA GeForce RTX 3060). Conclusions Based on embeddings from pLMs and two novel filters (Gaussian and Viterbi), TMbed predicts alpha helical and beta barrel TMPs at least as accurately as any other method but at lower false positive rates. Given the few false positives and its outstanding speed, TMbed might be ideal to sieve through millions of 3D structures soon to be predicted, e.g., by AlphaFold2. Availability Our code, method, and data sets are freely available in the GitHub repository, https://github.com/BernhoferM/TMbed.
High-Coverage Four-Dimensional Data-Independent Acquisition Proteomics and Phosphoproteomics Enabled by Deep Learning-Driven Multi-Dimensional Prediction

Four-dimensional (4D) data-independent acquisition (DIA)-based proteomics is an emerging technology that has been proven to have high precursor ion sampling efficiency and higher precursor identification specificity. However, the current 4D DIA proteomics is still dependent on the building of project-specific experimental library which is time-consuming and limits the coverage for identification/quantification. Herein, a workflow of 4D DIA proteomics by using the predicted multi-dimensional in silico library was established. A deep learning model Deep4D that could high-accurately predict the CCS and RT of both the unmodified and phosphorylated peptides was developed. By using an integrated 4D in silico library containing millions of peptides, we have identified 25% more protein than using experimental libraries in the DIA proteomics analysis of HeLa cells. We further demonstrate that the introduction of in silico prediction library can greatly complement the experimental library of directly obtained phosphorylated peptides, resulting in a greater increase in the identification of phosphorylated peptides and phosphorylated proteins.
Integrated Analysis of Tissue-Specific Gene Expression in Diabetes by Tensor Decomposition Can Identify Possible Associated Diseases

In the field of gene expression analysis, methods of integrating multiple gene expression profiles are still being developed and the existing methods have scope for improvement. The previously proposed tensor decomposition-based unsupervised feature extraction method was improved by introducing standard deviation optimization. The improved method was applied to perform an integrated analysis of three tissue-specific gene expression profiles (namely, adipose, muscle, and liver) for diabetes mellitus, and the results showed that it can detect diseases that are associated with diabetes (e.g., neurodegenerative diseases) but that cannot be predicted by individual tissue expression analyses using state-of-the-art methods. Although the selected genes differed from those identified by the individual tissue analyses, the selected genes are known to be expressed in all three tissues. Thus, compared with individual tissue analyses, an integrated analysis can provide more in-depth data and identify additional factors, namely, the association with other diseases.
Comparing the XGBoost machine learning algorithm to polygenic scoring for the prediction of intelligence based on genotype data

A polygenic score (PGS) is a linear combination of effects from a GWAS that represents and can be used to predict genetic predisposition to a particular phenotype. A key limitation of the PGS method is that it assumes additive and independent SNP effects, when it is known that epistasis (gene interactions) can contribute to complex traits. Machine learning methods can potentially overcome this limitation by virtue of their ability to capture nonlinear interactions in high dimensional data. Intelligence is a complex trait for which PGS prediction currently explains up to 5.2% of the variance, a relatively small proportion of the heritability estimate of 50% obtained from twin studies. Here, we use gradient boosting, a machine learning technique based on an ensemble of weak prediction models, to predict intelligence from genotype data. We found that while gradient boosting did not outperform the PGS method in predicting intelligence based on SNP data, it was capable of achieving similar predictive performance with less than a quarter of the SNPs with the top SNPs identified as being important for predictive performance being biologically meaningful. These results indicate that ML methods may be useful in interpreting the biological meaning underpinning SNP-phenotype associations due to the smaller number of SNPs required in the ML model as opposed to the standard PGS method based on GWAS.
A Cloud Detector for Automated Telescopes

Abstract An instrument is described that can detect clouds at night-time by sensing their infrared emission. The device can readily detect clouds that are difficult to see with the unaided eye on a moon-lit night. It can be used to provide an indication of how photometric the conditions are, to terminate exposures when cloud forms, and to close the dome when conditions become unsuitable for observing. The detector also has applications as an astronomical site-surveying instrument.
A lysosomal surveillance response (LySR) that reduces proteotoxicity and extends healthspan

Lysosomes are cytoplasmic organelles central for the degradation of macromolecules to maintain cellular homeostasis and health. Here, we discovered an adaptive lysosomal transcriptional response that we termed the Lysosomal Surveillance Response (LySR). Typified by the induction of a large group of transcripts involved in lysosomal function and proteolysis, the LySR can be triggered by silencing of specific vacuolar H+-ATPase subunits in Caenorhabditis elegans. Notably, LySR activation enhances the clearance of protein aggregates in worm models of Alzheimer’s and Huntington’s disease and amyotrophic lateral sclerosis, thereby boosting fitness and extending lifespan. The GATA transcription factor, ELT-2, regulates the LySR program as well as its associated beneficial effects. In mammalian cells, overexpression of GATA4/GATA6, the mammalian orthologs of ELT-2, is sufficient to induce the expression of multiple lysosome-specific proteases and alleviate proteotoxicity. Activating the LySR pathway may therefore represent an attractive mechanism to reduce proteotoxicity and, as such, potentially extend healthspan. Highlights RNAi of specific v-ATPase subunits extends C. elegans lifespan and activates LySR GATA transcription factor ELT-2 regulates LySR and LySR-associated lifespan extension LySR activation reduces protein aggregates and extends worm healthspan Overexpression of GATA4/GATA6 alleviates amyloid-β proteotoxicity in mammalian cells
Allele-specific gene regulation, phenotypes, and therapeutic vulnerabilities in estrogen receptor alpha mutant endometrial cancer

Activating estrogen receptor alpha (ER) mutations are present in primary endometrial and metastatic breast cancers, promoting estrogen-independent activation of the receptor. Functional characterizations in breast cancer have established unique molecular and phenotypic consequences of the receptor, yet the impact of ER mutations in endometrial cancer has not been fully explored. In this study, we used CRISPR-Cas9 to model the clinically prevalent ER-Y537S mutation and compared results to ER-D538G to discover allele-specific differences between ER mutations in endometrial cancer. We found that constitutive activity of mutant ER resulted in changes in the expression of thousands of genes, stemming from combined alterations to ER binding and chromatin accessibility. The unique gene expression programs resulted in ER mutant cells developing increased cancer associated phenotypes, including migration, invasion, anchorage independent growth, and growth in vivo. To uncover potential treatment strategies, we identified ER associated proteins via Rapid Immunoprecipitation and Mass Spectrometry of Endogenous Proteins (RIME) and interrogated two candidates, CDK9 and NCOA3. Inhibition of these regulatory proteins resulted in decreased growth and migration, representing potential novel treatment strategies for ER mutant endometrial cancer. Implications This study provides insight into mutant ER activity in endometrial cancer and identifies potential therapies for women with ER mutant endometrial cancer. STATEMENT OF SIGNIFICANCE Activating estrogen receptor alpha (ER) mutations promote ligand-independent activity of the receptor. This study evaluates ER-Y537S and ER-D538G mutations in primary endometrial cancer, revealing their effects on gene regulation and cancer-associated phenotypes. By identifying ER associated proteins, we also uncover potential novel treatments for women with ER mutant endometrial cancer.
Rapid Development of a Mucosal Nanoparticle Flu Vaccine by Genetic Engineering of Bacteriophage T4 using CRISPR-Cas

Mucosal vaccines that can induce local mucosal immune responses and combat the pathogens at entry sites are considered to be the most effective way to prevent infection. A universal platform that can be customized for development of mucosal vaccines against any given pathogen is therefore highly desired. Here, we demonstrate an efficient approach to develop nasal mucosal vaccines through genetic engineering of T4 phage to generate antigen-decorated nanoparticles. The antigen coding sequence was inserted into T4 genome in-frame at the C terminus of Soc (small outer capsid protein) using the CRISPR-Cas phage editing technology. During the propagation of recombinant T4 phages in E. coli, the Soc-antigen fusion proteins self-assemble on T4 capsids to form antigen-decorated nanoparticles that have intrinsic adjuvant activity and mucosal adhesive property. As a proof of concept, we showed that intranasal immunization with Flu viral M2e-decorated T4 nanoparticles efficiently induced local mucosal as well as systemic immune responses and provided complete protections against divergent influenza viruses in a mouse model. Potentially, our platform can be customized for any respiratory pathogen to rapidly generate mucosal vaccines against future emerging epidemics and pandemics.
Modelos constitutivos para análise tridimensional de estruturas de concreto armado através do método dos elementos finitos

BONO, G.F. F. Modelos Constitutivos para Analise Tridimensional de Estruturas de Concreto Armado atraves do Metodo dos Elementos Finitos. 2008. Tese (Doutorado em Engenharia) – Programa de Pos-Graduacao em Engenharia Civil, UFRGS, Porto Alegre. In this work, a 3D finite element model for analyses of reinforced concrete structures subjected to monotonic and cyclic loads is presented. The constitutive model proposed for the concrete is an orthotropic model that follows the nonlinear elasticity theory. In this model, the concept of equivalent uniaxial strain is applied for determination of the triaxial behavior of the concrete through three equivalent uniaxial stress-strain curves, which makes it easier to simulate the concrete’s behavior and allows a good approximation with experimental results. The model describes well the response of concrete when subjected to several loading types, being capable of representing crushing and cracking of concrete. The implementation in finite elements is based upon the consideration of smeared cracks with them rotating according to principal stresses’ directions. For simulation of the concrete’s behavior under cyclic loads, the modified Popovics-Saenz curve has been considered, with some changes in the coordinates of the origin and of the peak of this equivalent uniaxial stress-strain curve, when the concrete is subjected to unloading and reloading processes. The behavior of the steel is described by an uniaxial constitutive model. To model this behavior when subjected by monotonic loads, an elastic bilinear stress-strain curve with strain hardening is considered. For simulation of the cyclic response of the steel, a nonlinear constitutive model is used. This model can well represent the cyclic behavior of the steel, simulating its nonlinear response after load reversals. The computational code uses linear and quadratic isoparametric hexahedral finite elements for the concrete. To represent the reinforcing bars, an embedded model is used. Finally, to validate the efficiency of the proposed model, several numerical simulations are carried out in plain and reinforced concrete. The comparisons made with test results show an excellent agreement between the model predictions and experimental data.
Inhibiting Spinal Cord-Specific Hsp90 Isoforms Reveals a Novel Strategy to Improve the Therapeutic Index of Opioid Treatment

Opioid drugs like morphine are the gold standard for the treatment of chronic pain, but are limited by adverse side effects, such as tolerance, constipation, and reward/addiction. In our earlier work, we showed that Heat shock protein 90 (Hsp90) has a crucial role in regulating opioid signaling that differs between brain and spinal cord; Hsp90 inhibition in brain blocks opioid pain relief, while inhibition in the spinal cord enhances it. Building on these findings here, we injected the non-selective Hsp90 inhibitor KU-32 directly into the spinal cord of male and female CD-1 mice, showing that morphine anti-nociceptive potency was boosted by 1.9-3.5 fold in the pain models of tail flick, post-surgical paw incision, and HIV peripheral neuropathy. At the same time, morphine tolerance was reduced from 21 fold to 2.9 fold and established tolerance was rescued, while the potency of constipation and reward (as measured by conditioned place preference) was unchanged. These results demonstrate that spinal Hsp90 inhibition can improve the therapeutic index of morphine. However, we also found that systemic non-selective Hsp90 inhibition resulted in a brain-like effect, blocking opioid pain relief. We thus sought a way to circumvent the effects of brain Hsp90 inhibition by investigating the molecular Hsp90 isoforms active in regulating opioid signaling in both regions. Using selective small molecule inhibitors and CRISPR gene editing, we found that 3 Hsp90 isoforms regulated spinal cord opioid signaling (Hsp90α, Hsp90β, and Grp94) while our previous work showed only Hsp90α was active in brain. We thus hypothesized that a systemically delivered selective inhibitor to Hsp90β or Grp94 could selectively inhibit spinal cord Hsp90 activity, resulting in enhanced opioid pain relief and decreased side effects. We tested this hypothesis using intravenous delivery of KUNB106 (Hsp90β) and KUNG65 (Grp94), showing that both drugs enhanced morphine potency in tail flick and paw incision pain while rescuing anti-nociceptive tolerance. We also found that intravenous KUNA115 (Hsp90α) fully blocked morphine anti-nociception. Together, these results suggest that selective inhibition of spinal cord Hsp90 isoforms is a novel, translationally feasible strategy to improve the therapeutic index of opioids.
The neural architecture of theory-based reinforcement learning

Humans learn internal models of the environment that support efficient planning and flexible generalization in complex, real-world domains. Yet it remains unclear how such internal models are represented and learned in the brain. We approach this question within the framework of theory-based reinforcement learning, a strong form of model-based reinforcement learning in which the model is an intuitive theory – a rich, abstract, causal model of the environment built on a natural ontology of physical objects, intentional agents, relations, and goals. We used a theory-based reinforcement learning model to analyze brain data from human participants learning to play different Atari-style video games while undergoing functional MRI. Theories inferred by the theory-based model explained the signal in inferior frontal gyrus and other prefrontal areas better than several alternative models. Brain activity increased in response to theory update events in inferior frontal gyrus, occipital cortex, and fusiform gyrus, with separate learning signals for different theory components. This corresponded with a transient strengthening of theory representations in those regions. Finally, the effective connectivity pattern during theory updating suggests that information flows top-down from theory-coding regions in the prefrontal cortex to theory updating regions in occipital and temporal cortex. These results are consistent with a neural architecture in which top-down theory representations originating in prefrontal regions shape sensory predictions in visual areas, where factorized theory prediction errors are computed and in turn trigger bottom-up updates of the theory. This initial sketch provides a foundation for understanding of the neural representations and computations that support efficient theory-based reinforcement learning in complex, naturalistic environments.
Water-Quality analysis and Fish diversity of Southern West Part of the West-Bengal

Marine fishes are one of the important factors to stabilize the local aquatic ecosystem and regulating socio-economy of local fishermen. In this study we emphasized upon finding out the interrelationship in the water quality, anthropogenic activity, and fish habitat through the 31km stretch of East Medinipur coast in West Bengal, India which is known for its tourist destinations. The study was conducted monthly on different trawler fish landing sites from Dec 2018 to Dec 2021. During this period, we took fish samples and identified them. We took water quality data such as SST, Concentration of Chl-a, Turbidity, and DO for further correlation between the water quality and species diversity. Total 154 numbers of commercially important marine fish species were documented. Among them, 12 species are from Elasmobranch and the rest is Actinopterygii. As per the IUCN database, 13% of the total fish species are under the red list category and 16% of the species showing decreasing population trend. After analyzing the water quality data, we found out that DO, SST, Turbidity, and Chlorophyll-A correlate with the species richness and it also differs during the seasons.