Miracle-dz/UniFAR-FTUs huggingface dataset:
NPZfc: An ecological relation based fish catch prediction model using Artificial Neural Network

Quantifying interactions of organisms of the various trophic levels is very important in understanding the dynamics of aquatic ecosystems. With regards to fish, as both ecologically and commercially important parts of an ecosystem, predicting their catch in relation to primary producers provides insight into sustainable management. The present paper describes a novel model NPZfc, for nutrients, phytoplankton, zooplankton and fishes, which can predict planktivorous fish catch. Unlike the existing models which deal with the interactions within the system through mathematical equilibrium, the proposed model uses artificial neural network (ANN) to automatically learn inter-dependencies between different related variables and predict the fish catch of a water body. The efficiency of the model was increased by refining the input variables. Here biomass of plankton species population (phyto-plankton and zooplankton) were specifically selected from feeding ecology studies of target fish species as input variable. The study of two of the commercially important fish species, Etroplus suratensis and Nematalosa nasus in Chilika lagoon showed that the model can predict with high accuracy from limited input data. The root mean square error (RMSE) is found to be very satisfactory, ranging from 3.53% to 11.5% for E. suratensis and from 1.63% to 2.22% for N. nasus. Higher accuracy and better predictive ability with a smaller dataset makes this ANN-based NPZfc model more conducive.
Cortical high gamma power relates to movement speed and is disproportionately reduced after stroke

Motor cortical high gamma oscillations (60 to 90 Hz) occur at the onset of movement and are spatially focused over the contralateral primary motor cortex. They have been shown to be prokinetic in nature and contain information on various movement parameters. Although high gamma oscillations are widely recognized for their significance in human motor control, their precise function on a cortical level remains elusive. Further, their relevance in human stroke pathophysiology is unknown. Here, we aim to investigate how cortical high gamma oscillations relate to motor performance and motor skill acquisition, and test for possible alterations after ischemic stroke. We performed an observational study investigating the relationship between cortical high gamma oscillations and motor function. We recorded magnetoencephalography data during a thumb movement speed task in 14 chronic stroke patients (age = 65.4 ± 9.3 years, 7 females), 15 age-matched control participants (age = 64.5 ± 8.4 years, 7 females) and 29 healthy young participants (age = 25.4 ± 4.6 years, 13 females). Motor cortical high gamma oscillations showed a strong relation with movement speed as trials with higher movement speed were associated with greater high gamma power. Stroke patients showed reduced cortical high gamma power, surpassing the effect attributable to decreased movement speed in these patients. The decrease of high gamma power, however, did not relate to residual motor function or structural disconnection. In slower movements, we observed distinct high gamma peaks for each movement, whereas in faster movements high gamma activation smeared to fewer distinct peaks. Even though motor skill acquisition was evident in all groups, it was not linked to high gamma power. Our study is the first to quantify high gamma oscillations after stroke, revealing a reduction in movement-related high gamma power. Moreover, we provide strong evidence for a pivotal role of motor cortical high gamma oscillations in encoding movement speed. Our findings underscore the potential for exploring non-invasive brain stimulation within the high gamma frequency range to enhance motor rehabilitation after stroke.
flowVI: Flow Cytometry Variational Inference

Single-cell flow cytometry stands as a pivotal instrument in both biomedical research and clinical practice, not only offering invaluable insights into cellular phenotypes and functions but also significantly advancing our understanding of various patient states. However, its potential is often constrained by factors such as technical limitations, noise interference, and batch effects, which complicate comparison between flow cytometry experiments and compromise its overall impact. Recent advances in deep representation learning have demonstrated promise in overcoming similar challenges in related fields, particularly in the context of single-cell transcriptomic sequencing data analysis. Here, we propose flowVI, a multimodal deep generative model, tailored for integrative analysis of multiple massively parallel cytometry datasets from diverse sources. By effectively modeling noise variances, technical biases, and batch-specific heterogeneity using probabilistic data representation, we demonstrate that flowVI not only excels in the imputation of missing protein markers but also seamlessly integrates data from distinct cytometry panels. FlowVI thus emerges as a potent tool for constructing comprehensive flow cytometry atlases and enhancing the precision of flow cytometry data analyses. The source code for replicating these findings is hosted on GitHub, ‘theislab/flowVI’
Control of a gene transfer agent cluster in Caulobacter crescentus by transcriptional activation and anti-termination

Gene Transfer Agents (GTAs) are domesticated prophages that cannot self-multiply and be infectious but might have been co-opted to perform biological functions for the host bacteria. Caulobacter crescentus, a bacterium best known as a model organism to study bacterial cell biology and cell cycle regulation, has recently been demonstrated to produce bona fide GTA particles (CcGTA). Two direct activators of the CcGTA biosynthetic gene cluster, GafY and GafZ, have been identified, however, it is unknown how GafYZ controls transcription mechanistically or how they coordinate gene expression of the CcGTA gene cluster with other accessory genes elsewhere on the genome for complete CcGTA production. Here, we show that the CcGTA gene cluster is transcriptionally co-activated by GafY, integration host factor (IHF), and by GafZ-mediated transcription anti-termination. We present evidence that GafZ is a novel transcription anti-terminator that likely forms an anti-termination complex with RNA polymerase, NusA, NusG, and NusE to bypass transcription terminators within the 14 kb CcGTA cluster. Overall, we reveal a two-tier regulation that coordinates the synthesis of GTA particles in C. crescentus.
Unsaturated fatty acid alters the immune response in non-small cell lung adenocarcinoma through regulation of HMGB1 trafficking

Cancer cell evasion of the immune response is critical to cancer development and metastases. The ability of clinicians to kickstart the immune system to target these rogue cells is an ever-growing area of research and medicine. In this study, we delved into the relationship between lipid metabolism, High Mobility Group Box 1 protein (HMGB1), and immune regulation within non-small cell lung adenocarcinoma (NSCLC), shedding light on novel therapeutic avenues and potential personalized approaches for patients. We found that the expression of stearoyl CoA desaturase 1 (SCD1) was decreased in NSCLC tumors compared to normal tissues. This emphasized the critical role of lipid metabolism in tumor progression. Interestingly, monounsaturated fatty acid (MUFA) availability impacted the expression of programmed death receptor ligand −1 (PD-L1), a pivotal immune checkpoint target in lung cancer cells and immune cells, suggesting a novel approach to modulating the immune response. This study uncovered a complex interplay between HMGB1, SCD1, and PD-L1, influencing the immunological sensitivity of tumors. Our work underscores the importance of understanding the intricate relationships between lipid metabolism and immune modulation to develop more effective NSCLC treatments and personalized therapies. As we continue to explore these connections, we hope to contribute to the ever-evolving field of cancer research, improving patient outcomes and advancing precision medicine in NSCLC.
Rho/ROCK activity tunes cell compartment segregation and differentiation in nephron-forming niches

Controlling the time and place of nephron formation in vitro would improve nephron density and connectivity in next-generation kidney replacement tissues. Recent developments in kidney organoid technology have paved the way to achieving self-sustaining nephrogenic niches in vitro. The physical and geometric structure of the niche are key control parameters in tissue engineering approaches. However, their relationship to nephron differentiation is unclear. Here we investigate the relationship between niche geometry, cell compartment mixing, and nephron differentiation by targeting the Rho/ROCK pathway, a master regulator of the actin cytoskeleton. We find that the ROCK inhibitor Y-27632 increases mixing between nephron progenitor and stromal compartments in native mouse embryonic kidney niches, and also increases nephrogenesis. Similar increases are also seen in reductionist mouse primary cell and human induced pluripotent stem cell (iPSC)-derived organoids perturbed by Y-27632, dependent on the presence of stromal cells. Our data indicate that niche organization is a determinant of nephron formation rate, bringing renewed focus to the spatial context of cell-cell interactions in kidney tissue engineering efforts.
Memory control deficits in the sleep-deprived human brain

Sleep disturbances are associated with intrusive memories, but the neurocognitive mechanisms underpinning this relationship are poorly understood. Here, we show that an absence of sleep disrupts prefrontal inhibition of memory retrieval, and that the overnight restoration of this inhibitory mechanism is predicted by time spent in rapid eye movement (REM) sleep. The functional impairments arising from sleep loss are linked to a behavioural deficit in the ability to suppress unwanted memories, and coincide with a deterioration of deliberate patterns of self-generated thought. We conclude that sleep deprivation gives rise to intrusive memories via the disruption of neural circuits governing mnemonic inhibitory control, which may rely on REM sleep.
FBXO24 ensures male fertility by preventing abnormal accumulation of membraneless granules in sperm flagella

Ribonucleoprotein (RNP) granules are membraneless electron-dense structures rich in RNAs and proteins, and involved in various cellular processes. Two RNP granules in male germ cells, intermitochondrial cement and the chromatoid body (CB), are associated with PIWI-interacting RNAs (piRNAs) and are required for transposon silencing and spermatogenesis. Other RNP granules in male germ cells, the reticulated body and CB remnants, are also essential for spermiogenesis. In this study, we disrupted FBXO24, a testis-enriched F-box protein, in mice and found numerous membraneless electron-dense granules accumulated in sperm flagella. Fbxo24 knockout (KO) mice exhibited malformed flagellar structures, impaired sperm motility, and male infertility, likely due to the accumulation of abnormal granules. The amount and localization of known RNP granule-related proteins were not disrupted in Fbxo24 KO mice, suggesting that the accumulated granules were distinct from known RNP granules. Further studies revealed that RNAs and two importins, IPO5 and KPNB1, abnormally accumulated in Fbxo24 KO spermatozoa. In addition, IPO5 and KPNB1 were recruited to stress granules, RNP complexes, when cells were treated with oxidative stress or a proteasome inhibitor. These results suggest that FBXO24 plays a critical role in preventing the accumulation of importins and RNP granules in sperm flagella.
Axonal PLC-γ activity is required for BDNF long-distance signaling

Brain-derived neurotrophic factor (BDNF) and its receptor tyrosine kinase receptor B (TrkB) are important signaling proteins that regulate dendritic growth and maintenance in the central nervous system (CNS). After binding of BDNF, TrkB is endocytosed into endosomes and continues signaling within the cell soma, dendrites, and axon. We showed that BDNF signaling initiated in axons triggers long-distance signaling, inducing dendritic arborization in a CREB-dependent manner in cell bodies, processes that depend on axonal dynein and TrkB activities. The binding of BDNF to TrkB triggers the activation of different signaling pathways, including the ERK1/2, PLC-γ and PI3K-mTOR pathways, to induce dendritic growth and synaptic plasticity. How TrkB downstream pathways regulate long-distance signaling is unclear. Here, we studied the role of PLC-γ-Ca2+ in BDNF/TrkB-induced long-distance signaling using compartmentalized microfluidic cultures. We found that dendritic branching and CREB phosphorylation induced by axonal BDNF stimulation require the activation of PLC-γ in the axons of cortical neurons. Locally on axons, BDNF increases PLC-γ phosphorylation and induces intracellular Ca2+ waves in a PLC-γ-dependent manner. Moreover, the transport of BDNF-containing signaling endosomes to the cell body was dependent on PLC-γ activity and intracellular Ca2+ stores. Furthermore, the activity of PLC-γ is required for BDNF-dependent TrkB endocytosis, suggesting a role for the TrkB/PLC-γ signaling pathway in axonal signaling endosome generation.
Cleavage of protein kinase c δ by caspase-3 mediates pro-inflammatory cytokine-induced apoptosis in pancreatic islets

In type 1 diabetes (T1D), autoreactive immune cells infiltrate the pancreas and secrete pro-inflammatory cytokines that initiate cell death in insulin producing islet β-cells. Protein kinase C δ (PKCδ) plays a role in mediating cytokine-induced β-cell death; however, the exact mechanisms are not well understood. To address this, we utilized an inducible β-cell specific PKCδ KO mouse as well as a small peptide specific inhibitor of PKCδ. We identified a role for PKCδ in mediating cytokine-induced β-cell death and have shown that inhibiting PKCδ protects pancreatic β-cells from cytokine-induced apoptosis in both mouse and human islets. We determined that cytokines induced nuclear translocation and activity of PKCδ and that caspase-3 cleavage of PKCδ may be required for cytokine-mediated islet apoptosis. Further, cytokine-activated PKCδ increases activity both of pro-apoptotic Bax with acute treatment and JNK with prolonged treatment. Overall, our results suggest that PKCδ mediates cytokine-induced apoptosis via nuclear translocation, cleavage by caspase-3, and upregulation of pro-apoptotic signaling in pancreatic β-cells. Combined with the protective effects of PKCδ inhibition with δV1-1, the results of this study will aid in the development of novel therapies to prevent or delay β-cell death and preserve β-cell function in T1D.
Weed Detection and Treatment by RGB System - Case of the FPL Smart Farm

Strawberry is one of the most important crops in the Loukkos area in Morocco; it exceeds 80% of the national production. This crop is very sensitive and requires well-controlled climatic conditions, specific inputs and meticulous monitoring to avoid any uncertainty in yield. Our work consists in treating the side related to the weeds, which can cause considerable losses of yield and disturb plant development. The danger of these weeds in this culture is the strong competition with the needs of the culture (water, nutrients, light.). The objective of our work is to make a state of the art by bibliographic systemic analysis and propose intelligent methods for detection and treatment of weeds based on RGB images. Most of the works are based on image acquisition mainly by smartphones or unmanned aerial vehicles (UAVs), and some by professional cameras. The image processing can be done by deep learning: through convolutional neural networks (CNN), support vector machine (SVM), or local binary pattern (LBP). Other methods mentionned allow detecting and controlling weeds using a robot that determines the undesirable plants by image processing (via deep learning), records their location and proceeds to a weeding or a localized treatment using herbicides. These methods allow decreasing labor dependency, a better treatment efficiency, and a lower herbicide use up to 90%. We thus propose a best suitable method to detect and treat weeds in an intelligent way in the "Smart Farm" of the Polydisciplinary Faculty in Larache. This method consists in using fluorescent paint at the stem base of the strawberry plant in order to differentiate the main crop from any undesirable plant. The robot that takes RGB images under UV rays considers as "weed" any plant that does not contain fluorescent paint.
PROSPER: Extracting Protocol Specifications Using Large Language Models

We explore the application of Large Language Models (LLMs) (specifically GPT-3.5-turbo) to extract specifications and automating understanding of networking protocols from Internet Request for Comments (RFC) documents. LLMs have proven successful in specialized domains like medical and legal text understanding, and this work investigates their potential in automatically comprehending RFCs. We develop Artifact Miner, a tool to extract diagram artifacts from RFCs. We then couple extracted artifacts with natural language text to extract protocol automata using GPT-turbo 3.5 (chatGPT) and present our zero-shot and few-shot extraction results. We call this framework for FSM extraction 'PROSPER: Protocol Specification Miner'. We compare PROSPER with existing state-of-the-art techniques for protocol FSM state and transition extraction. Our experiments indicate that employing artifacts along with text for extraction can lead to lower false positives and better accuracy for both extracted states and transitions. Finally, we discuss efficient prompt engineering techniques, the errors we encountered, and pitfalls of using LLMs for knowledge extraction from specialized domains such as RFC documents.
Productivity and nutrient export in a maize and forage grasses intercropping under semiarid conditions

ABSTRACT The aim was to evaluate the productive performance and nutrient export of maize intercropped with drought-tolerant forage grasses. The experimental design was a randomized blocks in a 2x2x2+1 factorial scheme (two forage grasses species – Massai and Buffel; two forage sowing methods - in the furrow and broadcast; and two forage sowing times - anticipated and simultaneous; and monoculture maize as an additional control), with three replicates. The maize+forage intercropping promoted greater competition for resources, especially water, limiting N uptake, chlorophyll synthesis, and biomass production by maize under semiarid conditions. Early sowing gave forage plants a competitive advantage, favoring forage establishment before or immediately after the sowing of maize. Thus, the simultaneous sowing of forage grasses in a semiarid environment is a more appropriate option considering the importance of high levels of chlorophyll to ensure high photosynthetic activity in maize plants and greater capacity of biomass synthesis. Massai intercropped with maize exhibits a greaterpotential for biomass production than Buffel under semiarid conditions. Maize+Buffel result in highest macronutrient export by maize, while maize+Massai result in highest macronutrient exports by total forage biomass under a semiarid environment. The choice of the forage species and its sowing time in relation to maize are the main determinants of successful intercropping systems under semiarid conditions.
Unleashing the Power of NLP and Transformers: A Game-Changer in Medical Research and Clinical Practice and a revolution of Medical Text Analysis.: Case Study: Cancer report classification by priority

The integration of (NLP) and transformers, such as BERT, in the medical field has revolutionized text processing and analysis, offering remarkable opportunities for advancements in research and clinical practice. This article presents a comprehensive exploration of the application of NLP and transformers in the medical domain, specifically focusing on their utilization in cancer-related radiological reports' classification by priority. The study employs BERT, a bidirectional transformer-based language model, to convert radiological reports into embedding vectors, capturing contextual relationships between words. An attention technique is employed to aggregate word-embedding vectors, emphasizing important words within the report. next, an analysis using the gated rectified unit (GRU) is performed on a collection of radiological reports pertaining to an individual patient. This analysis aims to derive a patient embedding vector, which encapsulates the essential information from the multiple reports and represents the overall condition of the patient. Finally, a classifier is implemented to classify the reports based on their priority levels, enabling doctors to treat patients in an ordered and timely manner. Experimental results on a dataset consisting of 1,000 cancer-related radiological reports demonstrate the efficacy of the proposed approach. The dataset includes reports with varying degrees of severity, allowing for the classification of reports into priority categories. Evaluation metrics such as precision, recall, and F1 score showcase the model's accuracy and robustness, achieving high performance across different priority levels.
An Advanced IoT-Based Architecture for Healthcare Systems: A Focus on Blockchain-based Edge Computing for Diabetes Management

The healthcare industry has undergone a significant transformation with the emergence of Internet of Things (IoT) technology. Various computing paradigms, including cloud computing, fog computing, and edge computing, have emerged to address challenges in processing, storing, and analyzing data in modern systems. This paper proposes an advanced architecture for healthcare systems based on our research team's previous work. Our research focuses on developing a smart healthcare system to assist diabetes patients in managing their illness. Our novel architecture builds on the previous one, which included blockchain with IoT, by incorporating a blockchain-based edge layer to address scalability and security challenges that were previously encountered.
Bimodal Emotional Recognition based on Long Term Recurrent Convolutional Network

Determining a person’s emotional state remains a non-trivial task relating to the ambiguous definition of the emotion itself and the different tools used to identify emotional aspects. In this study, we propose an Emotional Recognition System by adopting a multimodal approach that combines the based speech emotion features and the based facial expressions features. Accordingly, the proposed recognition system contains three parts. The first component will be reserved to extract the facial expressions features using a deep learning-based network called Long-Term Recurrent Convolutional Network (LRCN). The second component will be set aside to extract speech emotion features using the Alex-Net deep learning-based network. Finally, we dedicated the last component to combine the information learned from the two previous modalities through the late fusion. In order to evaluate the performance of the proposed approach, we tested it with the Ryerson Audio Visual Database of Emotional Speech and Song (RAVDESS) human emotions. Obtained results show that the accuracy rate was improved by using the fusion strategy. Indeed, the accuracy rate increased from 78.82 (for facial modality) and 76.39 (for speech modality) to 85.76.