Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissecting Mismatch Negativity: Early and Late Subcomponents for Detecting Deviants in Local and Global Sequence Regularities

Mismatch negativity (MMN) is commonly recognized as a neural signal of prediction error evoked by deviants in the expected pattern of sensory input. Studies show that MMN diminishes when a sequence pattern becomes more predictable over a longer timescale. This implies that MMN is comprised of multiple subcomponents, each responding to different levels of temporal regularities. To probe the hypothesized subcomponents in MMN, we record human electroencephalography during an auditory local-global oddball paradigm where the tone-to-tone transition probability (local regularity) and the overall sequence probability (global regularity) are manipulated to control temporal predictabilities at two hierarchical levels. We find that the size of MMN is correlated with both probabilities and the spatiotemporal structure of MMN can be decomposed into two distinct subcomponents. Both subcomponents appear as negative waveforms which peak early in the central-frontal area and late in a more frontal area, respectively. With a quantitative predictive coding model, we map the early and late subcomponents to the prediction errors that are tied to local and global regularities, respectively. Our study highlights the hierarchical complexity of MMN and offers an experimental and analytical platform for developing a multi-tiered neural marker, applicable in clinical settings.
PROTACable is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning to Automate the De Novo Design of PROTACs

Proteolysis-targeting chimeras (PROTACs) that engages two biological targets at once is a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than those of a small molecule drug, we explored a combination of computational chemistry and deep learning strategies to forecast PROTAC activity and enable automated design. A new method named PROTACable was developed for de novo design of PROTACs, which includes a robust 3-D modeling workflow to model PROTAC ternary complexes using a library of E3 ligase and linker and an SE(3)-equivariant graph transformer network to predict the activity of newly designed PROTACs. PROTACable is available at https://github.com/giaguaro/PROTACable/.
Ammonia inhibits antitumor activity of NK cells by decreasing mature perforin

Immunotherapy revolutionized cancer treatment in the last decade. Natural killer (NK) cells are one of the key host immunity components against malignant cells. Thus, they are currently extensively investigated in the field of immunotherapy of cancer. Different approaches have been developed to improve the antitumor activity of NK cells. Nonetheless, tumor microenvironment remains an obstacle to effective NK cell-based therapies. Here, we demonstrated that a cancer-conditioned medium suppresses the anti-tumor activity of NK cells. Further, we found that ammonia, a by-product of cancer cell metabolism, accumulates in the cancer-conditioned medium and tumor microenvironment. We identified that ammonia impairs the cytotoxicity of NK cells as well as the effectiveness of antibody-based and chimeric antigen receptor (CAR)-NK-based therapies in vitro. Inhibited activity of NK cells was caused by decreased levels of perforin. This effect was dependent on the lysosomotropic features of ammonia and its ability to increase pH in acidic compartments. In consequence, upon contact with ammonia the mature form of perforin was decreased in NK cells leading to their dysfunction. Our findings demonstrate that in addition to its previously described role of promoting tumor growth as a nitrogen source for tumor biomass ammonia could promote tumor escape as an NK cells immune checkpoint. Graphical abstract Highlights Cancer-conditioned medium suppresses the antitumor activity of NK cells Ammonia accumulates in conditioned medium and in the tumor microenvironment Impaired cytotoxicity of NK cells is caused by ammonia that decreases perforin levels Ammonia causes NK cell dysfunction
XCP-D: A Robust Pipeline for the post-processing of fMRI data

Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they tend not to support output from disparate pre-processing pipelines, may have limited documentation, and may not follow BIDS best practices. Here we present XCP-D, which presents a solution to these issues. XCP-D is a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University at Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Singularity image. XCP-D generates denoised BOLD images and functional derivatives from resting-state data in either NifTI or CIFTI files, following pre-processing with fMRIPrep, HCP, and ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded >3,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.
Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome

IgA, the most highly produced human antibody, is continually secreted into the gut to shape the intestinal microbiota. Methodological limitations have critically hindered defining which microbial strains are targeted by IgA and why. Here, we develop a new technique, Metagenomic Immunoglobulin Sequencing (MIG-Seq), and use it to determine IgA coating levels for thousands of gut microbiome strains in healthy humans. We find that microbes associated with both health and disease have higher levels of coating, and that microbial genes are highly predictive of IgA binding levels, with mucus degradation genes especially correlated with high binding. We find a significant reduction in replication rates among microbes bound by IgA, and demonstrate that IgA binding is more correlated with host immune status than traditional microbial abundance measures. This study introduces a powerful technique for assessing strain-level IgA binding in human stool, paving the way for deeper understanding of IgA-based host microbe interactions.
Gain of function in linc-133 compensates for daf-18/PTEN loss and rescues survival

DAF-18, homology of PTEN, possess tumor suppressor activity. Loss of daf-18 causes cell growth in L1 diapause C. elegans is well studied; however, the reason why survival is dramatically shortened is not well elaborated. We found that linc-133 gain of function can fully restore the shortened survival caused by daf-18 loss. When lipid phosphatase activity of DAF-18 is defective, the linc-133 gain of function interacts with 14-3-3 proteins to obstruct DAF-16 translocation from the nucleus to the cytoplasm. However, the dysfunction of protein phosphatase activity of DAF-18 caused high levels of aggregated proteins. The linc-133 gain of function can induce HSP chaperones to select and process the aggregated proteins for degradation through ubiquitination. Our work demonstrates that protein homeostasis controlled by the protein phosphatase activity of DAF-18 is the main factor affecting survival and identifies a new function of the linc-133 gene, which can compensate for the loss of daf-18.
Analysis of different strains of the turquoise killifish Nothobranchius furzeri identifies transcriptomic signatures associated with heritable lifespan differences

The killifish Nothobranchius furzeri is the shortest-lived vertebrate model organism and is characterized by laboratory strains differing in lifespan by more than a factor of two. The genetic architecture underlying this difference is complex and the pathways responsive remain elusive. We performed an analysis in N. furzeri of public transcritomic datasets comprehensive of four different tissues and one embryonic stage in two strains: the shorter-lived GRZ and the longer-lived MZM0410. Remarkably, the two strains differ in their transcriptome profile already at the embryonic stage. The short-lived strain GRZ shows an anticipated aging profile consistently in all tissues investigated, with differences in expression of aging-related genes detected already at sexual maturity. In addition, analysis of a longitudinal dataset revealed that genes whose expression is prognostic of longer lifespan at the individual level are also differentially expressed between strains at an early adult age, suggesting antagonistic pleiotropism.
CslA and GlxA from Streptomyces lividans form a functional cellulose synthase complex

Filamentous growth of streptomycetes coincides with the synthesis and deposition of an uncharacterized protective glucan at hyphal tips. Synthesis of this glucan depends on the integral membrane protein CslA and the radical copper oxidase GlxA, which are part of a presumably large multiprotein complex operating at growing tips. Here, we show that CslA and GlxA interact by forming a protein complex that is sufficient to synthesize cellulose in vitro. Mass spectrometry analysis revealed that the purified complex produces cellulose chains with a degree of polymerization of at least 80 residues. Truncation analyses demonstrated that the removal of a significant extracellular segment of GlxA had no impact on complex formation, but significantly diminished activity of CslA. Altogether, our work demonstrates that CslA and GlxA form the active core of the cellulose synthase complex and provides molecular insights into a unique cellulose biosynthesis system that is conserved in streptomycetes. Significance Cellulose stands out as the most abundant polysaccharide on Earth. While the synthesis of this polysaccharide has been extensively studied in plants and Gram-negative bacteria, the mechanisms in Gram-positive bacteria have remained largely unknown. Our research unveils a novel cellulose synthase complex formed by the interaction between the cellulose synthase-like protein CslA and the radical copper oxidase GlxA from Streptomyces lividans, a soil-dwelling Gram-positive bacterium. This discovery provides molecular insights into the distinctive cellulose biosynthesis machineries. Beyond expanding our understanding of cellulose biosynthesis, this study also opens avenues for exploring biotechnological applications and ecological roles of cellulose in Gram-positive bacteria, thereby contributing to the broader field of microbial cellulose biosynthesis and biofilm research.
Allosteric modulation of the CXCR4:CXCL12 axis by targeting receptor nanoclustering via the TMV-TMVI domain

CXCR4 is a ubiquitously expressed chemokine receptor that regulates leukocyte trafficking and arrest in homeostatic and pathological states, and also participates in organogenesis, HIV-1 infection and tumor development. Despite the potential therapeutic benefit of CXCR4 antagonists, so far only one, plerixafor (AMD3100), which blocks the ligand-binding site, has reached the clinic. Recent advances in imaging and biophysical techniques have provided a richer understanding of the membrane organization and dynamics of this receptor. CXCL12 activation of CXCR4 reduces the number of CXCR4 monomers/dimers at the cell membrane and increases the formation of large nanoclusters, which are largely immobile and are required for correct cell orientation towards chemoattractant gradients. Mechanistically, CXCR4 activation involves a structural motif defined by residues on TMV and TMVI of CXCR4. Using this structural motif as a template, we performed in silico molecular modeling followed by in vitro screening of a small compound library to search for allosteric antagonists of CXCR4 that do not affect CXCL12 binding. We identified AGR1.137, a small compound that abolishes CXCL12-mediated receptor nanoclustering and dynamics and blocks the ability of cells to sense CXCL12 gradients both in vitro and in vivo without altering ligand binding or receptor internalization. Significance Statement CXCR4 is a ubiquitous chemokine receptor that regulates leukocyte trafficking and arrest in homeostatic and pathological states. Yet, the only commercial CXCR4 antagonist approved for clinical use is plerixafor (AMD3100), a small compound that blocks the ligand-binding site. Unfortunately, its clinical application is limited by poor pharmacokinetics and adverse effects associated with long-term administration. Here, we performed in silico analyses of a small aromatic compound library followed by in vitro screening to identify allosteric CXCR4 antagonists that abrogate the ability of cells to sense chemoattractant gradients without altering other ligand-mediated functions such as blockade of cAMP production or receptor internalization. The selected compounds also acted in vivo, as demonstrated by reduced tumorigenesis and metastasis in a zebrafish tumor model. Our study describes a new approach to selectively alter some GPCR functions without the need for abolishing all receptor functionality.
Decoding the chemical language of Suillus fungi: genome mining and untargeted metabolomics uncover terpene chemical diversity

Ectomycorrhizal fungi establish mutually beneficial relationships with trees, trading nutrients for carbon. Suillus are ectomycorrhizal fungi that are critical to the health of boreal and temperate forest ecosystems. Comparative genomics has identified a high number of non-ribosomal peptide synthetase and terpene biosynthetic gene clusters (BGC) potentially involved in fungal competition and communication. However, the functionality of these BGCs is not known. This study employed co-culture techniques to activate BGC expression and then used metabolomics to investigate the diversity of metabolic products produced by three Suillus species (S. hirtellus EM16, S. decipiens EM49, and S. cothurnatus VC1858), core members of the Pine microbiome. After 28 days of growth on solid media, liquid chromatography–tandem mass spectrometry identified a diverse range of extracellular metabolites (exometabolites) along the interaction zone between Suillus co-cultures. Prenol lipids were among the most abundant chemical classes. Out of the 62 unique terpene BGCs predicted by genome mining, 116 putative terpenes were identified across the three Suillus species using metabolomics. Notably, some terpenes were significantly more abundant in co-culture conditions. For example, we identified a metabolite matching to isomers isopimaric acid, sandaracopimaric acid, and abietic acid, which can be found in pine resin and play important roles in host defense mechanisms and Suillus spore germination. This research highlights the importance of combining genomics and metabolomics to advance our understanding of the chemical diversity underpinning fungal signaling and communication. Importance Using a combination of genomics and metabolomics, this study’s findings offer new insights into the signaling and communication of Suillus fungi, which serve a critical role in forest ecosystems.
Creating 3D Diorama from Single Image with Deep Learning

Creating 3D scenes is a time-consuming task that requires experience with modeling software. This paper presents a novel approach that combines neural models for panoptic segmentation and monocular depth estimation to construct dioramas. While previous research has explored generating dioramas from single images, to the best of our knowledge, there is no research utilizing deep learning techniques for the task. This paper provides an analysis of existing approaches to diorama generation. We then describe the construction of the diorama, where objects identified by segmentation are separated into distinct images with transparent backgrounds. These images are then placed in a 3D scene, arranged to reflect the estimated depth of each object. We also address several challenges that had to be overcome. Specifically, we employed fine-tuning to address the limitations of the available depth model when applied to outdoor scenes. Our method has been implemented as an add-on for the open-source 3D software Blender, utilizing neural models in the ONNX format for depth and segmentation inferences.
Influence of Oxygen Minimum Zone on Macrobenthic Community Structure in the 1 Northern Benguela Upwelling System: A Macro-Nematode Perspective

11 Macrobenthic samples were collected offshore Namibia on board R/V Mirabilis during the 3 rd 12 RGNO training and the National Marine Information and Research Centre's (NatMIRC's) plankton 13 survey from 13 th May to 17 th May 2016. Two transects, Cape Frio (20º S) and Walvis Bay (23º S), 14 hosted three stations each, while the third transect, Luderitz (26º S), hosted only one station. From 15 the results, three oxygen zones were identified, namely Microxic (<0.1 ml l -1 ), Dysoxic (0.1-1.0 16 ml l -1 ), and Oxic (>1.0 ml l -1 ). A total of 20 Macrobenthic taxa were identified; Nematoda, 17 Polychaeta, Cumacea, and Oligochaeta were the most dominant taxa and recorded abundances in 18 all oxygen zones. Eighteen genera of macro-Nematoda were identified; Desmolaimus and 19 Paracomesoma dominated in all oxygen zones, Metoncholaimus recorded higher abundance in the 20 Dysoxic stations, and no abundance in the Oxic stations, and the opposite was observed for 21 Halanonchus and Dorylaimopsis . H′ Diversity values for both the general Macrofauna and Macro-22 Nematoda were higher in the Oxic stations and lower in both the Dysoxic and Microxic stations, 23 while an opposite trend was observed for Dominance values. Density values were lower in 24 Microxic stations and higher in Dysoxic stations, while at the Oxic stations, the density values fell 25 in between the two hypoxic zones.
Current Trends in OMICS (CTO)

Heavy metals have an enduring presence, risky characteristics, and the propensity to accumulate in the environment. This is why heavy metal toxics are widely acknowledged as harmful environmental pollutants. Heavy metals damage both aquatic and terrestrial ecosystems, posing a major risk to the environment and human health. Four freshwater fish species namely Labeo rohita, Catla catla, Hypophthalmichthys molitrix, and Ctenopharyngodon idella were the focus of this investigation. This study investigated the potential genotoxic effects of lead (Pb), copper (Cu), and cadmium (Cd) on the above fish species through the application of comet assay test. The fish were exposed to these metals at four distinct concentrations (19%, 24%, 31%, and 50% of the LC 50 ) over the course of 40 days. All four fish species were exposed to metals to varying degrees, according to the genetic damage index, cumulative tail length of comets, and the proportion of damaged cells. In contrast to Catla catla, Hypophthalmichthys molitrix had the highest prevalence of DNA damage. The current study suggests that the presence of these particular metals in Pakistan's aquatic ecosystems may have an adverse effect on the DNA of the country's fish species. Metals cause damage to DNA in fibroblast cells through distinct mechanisms when present in water, air, and soil. Comet assay test has a remarkable sensitivity that helps to identify extremely low amounts of DNA damage. Out of the four fish species, Ctenopharyngodon idella showed higher levels of damaged cells, a higher genetic damage index,
MINING INFREQUENT ITEMSETS USING APRIORI ALGORITHM

: The aim of Association Rule Mining is to find the correlation between data Items based on frequency of occurrence. Infrequent Item set mining is a variation of frequent item set mining where it finds the uninteresting patterns i.e., it finds the data items which occurs very rarely. Considering weight for each distinct item in a transaction independent manner adds effectiveness for finding frequent item set mining. Several articles related to frequent and weighted infrequent item set mining were proposed. This paper focus on reviewing various Existing Algorithms related to frequent and infrequent item set mining which creates a path for future researches in the field of Association Rule Mining.
SKIPJACK TUNA CPUE TRENDS USING ALTERNATIVE INDICES FROM THE FRENCH PURSE SEINE LOGBOOKS

We used the French purse seine data to examine the trend of skipjack CPUEs for the period 1984-2013. Only sets made on drifting objects (logs and fish aggregating devices –FADs) are used in this analysis. Sensitivity tests were performed to evaluate the effect of the size of the core area in the north equatorial area (0-13N/45’70E) as well as effort minimal thresholds, on CPUE estimates (filtered nominal indices based on the catch per set). No significant effect was noted suggesting that the north equatorial area, as a whole, is a pertinent skipjack core area. A standardization procedure was applied to datasets based on CPUEs aggregated by 1°longitude/0.33° latitude and by month, to which corresponding environmental data such as the depth of the mixed layer, the sea surface anomaly, the speed of the current and the chlorophyll concentration were added. Generalized additive models (GAMs) were used to explore the shape of relationships with fishery-derived and environmental covariates, and undertake transformation in the dataset used by generalized linear models (GLMs). Seven models with various assumptions were tested. They denote a clear skipjack CPUE decline since 2002, with a steeper rate of decline since 2009. We suggest that the massive deployment of FADs during the 1990s and the development of efficient FAD fishing tactics have likely contributed to such decline, as more than 60% of skipjack is caught on FADs. However, environmental fluctuations are also at stake in such decline and notably the chlorophyll concentration which reflects the productivity level at the base of the food chain. The surface chlorophyll concentration has been anomalously low in the Somali Basin since 2006 and this could lead to detrimental foraging conditions for the upper trophic levels, or trigger spatial shift of the population in search of more productive areas. The biological and ecological processes associated to this decline would deserve more research. However, the fact that a sustained CPUE decline for skipjack has been occurring since 2002 could become a concern for the conservation of this stock.
Deep CNN Model for Multiclass Classification of Human Protein Atlas Images

Protein, which serves as a service provider for the cell to exert specific function, is ubiquitously distributed in a biological system. After the transcription of genomic information, protein starts to form as the product of RNA translation. Therefore, in order to facilitate medical research in molecular biology, we need to develop complete and accurate models to identify and classify organelles in cells from a large number of images produced by microscopy. But the existing machine learning architectures are failed to provide the highest classification accuracy. Thus, this work majorly focused on implementation of Human Protein Atlas Image Classification using Deep learning Convolutional Neural Networks (DLCNN). The simulation results show that the propose method gives the highest classification accuracy compared to the state of art approaches.