Miracle-dz/UniFAR-FTUs huggingface dataset:
Chemical cues from beetle larvae trigger proliferation and virulence of a plant pathogen

Pest insects and microbial pathogens threaten agricultural crop productivity and global forest biomes, and insect-microbe interactions can increase the severity of plant decline. Whilst bacterial chemicals have been shown to influence insect behaviour, the impact of insect-derived chemistries on phytopathogens is barely understood. Here, we investigated beetle larvae-bacteria interactions in acute oak decline (AOD), a decline disease of oak characterised by inner bark necrosis caused by a polymicrobial consortium including Brenneria goodwinii and larval galleries of Agrilus biguttatus. To test the hypothesis that larval chemicals influence the activity of B. goodwinii, we extracted A. biguttatus larvae and tested the effect of extracts on the growth and gene expression of B. goodwinii in vitro. We demonstrate that larval chemicals elicit increased bacterial growth rate and final cell density, as well as differential expression of almost 800 genes, including the Type III Secretion System and its effectors, major virulence factors in other plant pathogens. Contrastingly, gallery woody-tissue extracts induced no such effects. Our work highlights the importance of multi-kingdom interactions in plant disease and demonstrate a novel mode-of-action for insect-derived chemical elicitors in facilitating the virulence of phytopathogens, which may be important in understanding plant diseases globally.
Leveraging a large language model to predict protein phase transition: a physical, multiscale and interpretable approach

Protein phase transitions (PPTs) from the soluble state to a dense liquid phase (forming droplets via liquid-liquid phase separation) or solid aggregates (such as amyloid) play key roles in pathological processes associated with age-related diseases such as Alzheimer’s disease (AD). Several computational frameworks are capable of separately predicting the formation of protein droplets or amyloid aggregates based on protein sequences, yet none have tackled the prediction of both within a unified framework. Recently, large language models (LLMs) have exhibited great success in protein structure prediction; however, they have not yet been used for PPTs. Here, we fine-tune a LLM for predicting PPTs and demonstrate its superior performance compared to suitable classical benchmarks. Due to the “black-box” nature of the LLM, we also employ a classical random forest model along with biophysical features to facilitate interpretation. Finally, focusing on AD-related proteins, we demonstrate that greater aggregation is associated with reduced gene expression in AD, suggesting a natural defense mechanism. Significance Statement The protein phase transition is a physical mechanism associated with both physiological processes and age-related diseases. Here, we present a modeling approach for predicting a specific protein sequence’s propensity to undergo phase transitions directly from its sequence. Our methodology involves utilizing a large language model to analyze the likelihood of a given protein sequence existing in a particular material state. Additionally, for enhanced interpretability, we incorporate a classical knowledge-based model. Our results suggest the potential for accurately predicting the propensity to form either liquid or solid condensates. Furthermore, our findings indicate the potential regulation of this propensity by gene expression under pathological conditions to prevent aggregation.
Solving the time-dependent protein distributions for autoregulated bursty gene expression using spectral decomposition

In this study, we obtain an exact time-dependent solution of the chemical master equation (CME) of an extension of the two-state telegraph model describing bursty or non-bursty protein expression in the presence of positive or negative autoregulation. Using the method of spectral decomposition, we show that the eigenfunctions of the generating function solution of the CME are Heun functions, while the eigenvalues can be determined by solving a continued fraction equation. Our solution generalizes and corrects a previous time-dependent solution for the CME of a gene circuit describing non-bursty protein expression in the presence of negative autoregulation [“Exact time-dependent solutions for a self-regulating gene. ” Phys. Rev. E 83: 062902 (2011)]. In particular, we clarify that the eigenvalues are generally not real as previously claimed. We also investigate the relationship between different types of dynamic behavior and the type of feedback, the protein burst size, and the gene switching rate.
Mono-methylated histones control PARP-1 in chromatin and transcription

PARP-1 is central to transcriptional regulation under both normal and stress conditions, with the governing mechanisms yet to be fully understood. Our biochemical and ChIP-seq-based analyses showed that PARP-1 binds specifically to active histone marks, particularly H4K20me1. We found that H4K20me1 plays a critical role in facilitating PARP-1 binding and the regulation of PARP-1-depenednt loci during both development and heat shock stress. Here we report that the sole H4K20 mono-methylase, pr-set7, and parp-1 Drosophila mutants undergo developmental arrest. RNA-seq analysis showed an absolute correlation between PR-SET7- and PARP-1-dependent loci expression, confirming co-regulation during developmental phases. PARP-1 and PR-SET7 are both essential for activating hsp70 and other heat shock genes during heat stress, with a notable increase of H4K20me1 at their gene body. Mutating pr-set7 disrupts monometylation of H4K20 along heat shock loci and abolish PARP-1 binding there. These data strongly suggest that H4 monometylation is a key triggering point in PARP-1 dependent processes in chromatin.
Boosting performance of generative diffusion model for molecular docking by training on artificial binding pockets

This study introduces the PocketCFDM generative diffusion model, aimed at improving the prediction of small molecule poses in the protein binding pockets. The model utilizes a novel data augmentation technique, involving the creation of numerous artificial binding pockets that mimic the statistical patterns of non-bond interactions found in actual protein-ligand complexes. An algorithmic method was developed to assess and replicate these interaction patterns in the artificial binding pockets built around small molecule conformers. It is shown that the integration of artificial binding pockets into the training process significantly enhanced the model’s performance. Notably, PocketCFDM surpassed DiffDock in terms of non-bond interaction quality, number of steric clashes, and inference speed. Future developments and optimizations of the model are discussed. Availability The inference code and final model weights of PocketCFDM are accessible publicly via the GitHub repository: https://github.com/vtarasv/pocket-cfdm.git.
Transwell-based microphysiological platform for high-resolution imaging of airway tissues

Transwell-based airway models have become increasingly important to study the effects of respiratory diseases and drug treatment at the air-liquid interface of the lung epithelial barrier. However, the underlying mechanisms at tissue and cell level often remain unclear, as transwell inserts feature limited live-cell imaging compatibility. Here, we report on a novel microphysiological platform for the cultivation of transwell-based lung tissues providing the possibility to alternate between air-liquid and liquid-liquid interfaces. While the air-liquid interface recapitulates physiological conditions for the lung model, the liquid-liquid interface enables live-imaging of the tissue at high spatiotemporal resolution. The plastics-based microfluidic platform enables insertion and recuperation of the transwell inserts, which allows for tissue cultivation and analysis under standardized well plate conditions. We used the device to monitor infections of Pseudomonas aeruginosa in human stem-cell-derived bronchial epithelial tissue. We continuously imaged the progression of a P. aeruginosa infection in real time at high resolution, which provided insights into bacterial spreading and invasion on the apical tissue surface, as well as insights into tissue breaching and destruction over time. The airway tissue culture system is a powerful tool to visualize and elucidate key processes of developing respiratory diseases and to facilitate drug testing and development.
Nephron progenitors rhythmically alternate between renewal and differentiation phases that synchronize with kidney branching morphogenesis

The mammalian kidney achieves massive parallelization of function by exponentially duplicating nephron-forming niches during development. Each niche caps a tip of the ureteric bud epithelium (the future urinary collecting duct tree) as it undergoes branching morphogenesis, while nephron progenitors within niches balance self-renewal and differentiation to early nephron cells. Nephron formation rate approximately matches branching rate over a large fraction of mouse gestation, yet the nature of this apparent pace-maker is unknown. Here we correlate spatial transcriptomics data with branching ‘life-cycle’ to discover rhythmically alternating signatures of nephron progenitor differentiation and renewal across Wnt, Hippo-Yap, retinoic acid (RA), and other pathways. We then find in human stem-cell derived nephron progenitor organoids that Wnt/β-catenin-induced differentiation is converted to a renewal signal when it temporally overlaps with YAP activation. Similar experiments using RA activation indicate a role in setting nephron progenitor exit from the naive state, the spatial extent of differentiation, and nephron segment bias. Together the data suggest that nephron progenitor interpretation of consistent Wnt/β-catenin differentiation signaling in the niche may be modified by rhythmic activity in ancillary pathways to set the pace of nephron formation. This would synchronize nephron formation with ureteric bud branching, which creates new sites for nephron condensation. Our data bring temporal resolution to the renewal vs. differentiation balance in the nephrogenic niche and inform new strategies to achieve self-sustaining nephron formation in synthetic human kidney tissues.
Fast and accurate disulfide bridge detection

Recombinant expression of proteins, propelled by therapeutic antibodies, has evolved into a multi-billion-dollar industry. Essential here is quality control assessment of critical attributes such as sequence fidelity, proper folding, and post-translational modifications (PTMs). Errors can lead to diminished bioactivity and, in the context of therapeutic proteins, an elevated risk for immunogenicity. Over the years, many techniques were developed and applied to validate proteins in a standardized and high-throughput fashion. One parameter has, however, so far been challenging to assess. Disulfide bridges, covalent bonds linking two Cysteine residues, assist in the correct folding and stability of proteins and thus have a major influence on their efficacy. Mass spectrometry promises to be an optimal technique to uncover them in a fast and accurate fashion. In this work, we present a unique combination of sample preparation, data acquisition and analysis facilitating the rapid and accurate assessment of disulfide bridges in purified proteins. Through microwave-assisted acid hydrolysis (MAAH), the proteins are digested rapidly and artifact-free into peptides, with a substantial degree of overlap over the sequence. The nonspecific nature of this procedure, however, introduces chemical background which is efficiently removed by integrating ion mobility preceding the mass spectrometric measurement. The nonspecific nature of the digestion step additionally necessitates new developments in data analysis, for which we extended the XlinkX node in Proteome Discoverer (XlinkX/PD) to efficiently process the data and ensure correctness through effective false discovery rate correction. The entire workflow can be completed within one hour, allowing for high-throughput, high-accuracy disulfide mapping.
The CD36 scavenger receptor Bez regulates lipid redistribution from fat body to oocytes in Drosophila

Class B scavenger receptors of the CD36 family are important for lipid mobilization from mammalian adipose tissue. This protein family has 3 members in mammals, but 14 in Drosophila melanogaster. Lipid distribution in Drosophila is mediated by homologs of the LDL receptors, while little is known about the function of scavenger receptors for this process in invertebrates. Here we unravel a role for the so far uncharacterized scavenger receptor Bez in lipid export from Drosophila adipocytes. Bez shares the lipid binding residue with CD36 and is expressed at the plasma membrane of the embryonic, larval and adult fat body. Bez loss-of-function lowers the organismal content of storage lipids while they accumulate in the fat body, concomitant with female sterility and degeneration of ovaries. Using an alkyne-labeled fatty acid tracer, we demonstrate that Bez interacts with the apoB homolog Lipophorin at the plasma membrane of adipocytes, thereby enabling lipid transfer. Our study shows how a scavenger receptor interacts with lipoproteins to distribute storage lipids from the fat body to the ovaries and thereby contributes to the metabolic control of development.
Moderate confirmation bias enhances collective decision-making in reinforcement-learning agents

Humans tend to give more weight to information confirming their beliefs than to information that disconfirms them. Nevertheless, this apparent irrationality has been shown to improve individual decision-making under uncertainty. However, little is known about this bias’ impact on collective decision-making. Here, we investigate the conditions under which confirmation bias is beneficial or detrimental to collective decision-making. To do so, we develop a Collective Asymmetric Reinforcement Learning (CARL) model in which artificial agents observe others’ actions and rewards, and update this information asymmetrically. We use agent-based simulations to study how confirmation bias affects collective performance on a two-armed bandit task, and how resource scarcity, group size and bias strength modulate this effect. We find that a confirmation bias benefits group learning across a wide range of resource-scarcity conditions. Moreover, we discover that, past a critical bias strength, resource abundance favors the emergence of two different performance regimes, one of which is suboptimal. In addition, we find that this regime bifurcation comes with polarization in small groups of agents. Overall, our results suggest the existence of an optimal, moderate level of confirmation bias for collective decision-making. AUTHOR SUMMARY When we give more weight to information that confirms our existing beliefs, it typically has a negative impact on learning and decision-making. However, our study shows that moderate confirmation bias can actually improve collective decision-making when multiple reinforcement learning agents learn together in a social context. This finding has important implications for policymakers who engage in fighting against societal polarization and the spreading of misinformation. It can also inspire the development of artificial, distributed learning algorithms. Based on our research, we recommend not directly targeting confirmation bias but instead focusing on its underlying factors, such as group size, individual incentives, and the interactions between bias and the environment (such as filter bubbles).
Eﬀect of some chemical and natural preservative solutions on vase life, water relations and some chemical composition of Dianthus caryophyllus L. cut ﬂowers

: To investigate the eﬀect of some pulsing and holding solutions on the quality of carnation cv. Turbo cut ﬂowers, a laboratory experiment was con­ ducted in the Agricultural Research Center and Cairo University, Egypt during 2020 and 2021 seasons. In this regard distilled water, silver thiosulfate (STS) at 0.4 ppm + sucrose 10% (PS1) and AgNO 3 at 10.0 ppm + sucrose 10% (PS2) were employed as a pulsing solution for 15 min while distilled water, sucrose 4% (HS1), boric acid (BoA) at 200 ppm + sucrose 4% (HS2), 8­hydroxyquinoline sul­ fate (8­HQS) at 300 ppm + sucrose 4% (HS3), Amino­oxyacetic acid (AOA) at 250 ppm + sucrose 4% (HS4), 8­HQS + AOA + sucrose 4% (HS5), BoA + 8­HQS + sucrose 4% (HS6), BoA + AOA + sucrose 4% (HS7), BoA + 8­HQS + AOA + sucrose 4% (HS8)
Plant Leaf Species Identi�cation Using LBHPG Feature Extraction and Machine Learning Classi�er Technique

The paper presents the identi�cation and classi�cation of Indian agricultural crop species using a novel combined Local Binary Histogram Pattern of Gradient (LBHPG) image feature extraction technique. Initially, a partition of the leaf image background is done through the newly developed Fast Adaptive Fuzzy C-mean Clustering (FAFCM) technique. After that, leaf objects within the image are identi�ed using the LBHPG method. For the classi�cation, KNN, PNN, and SVM shallow machine learning classi�ers are used for crop species identi�cation. The performance evaluation is done by using LBP and HOG individually along with the new proposed LBHPG technique for classi�cation using KNN, PNN, and SVM Classi�ers. The performance evaluation is based on six metrics parameters of the confusion matrix, viz., accuracy, sensitivity, speci�city, precision, recall, and F-measure. The experimental results show that the proposed novel LBHP feature extraction technique with PNN Classi�er gives the highest accuracy of 94.58%.
A semi-immersive visualisation system for model-based participatory forest design and decision support

There is an increasing interest in tools that facilitate forest design, both for the forest professional and as part of a public process. This thesis advocates the development of integrated design tools that allow a broader range of forest criteria (including social and aesthetic values) to be included in the decision making process and enable a broad range of people to participate in this process. A series of criteria for designbased decision-support tools is developed, a prototype computer visualisation system was implemented, and is described in this thesis. This prototype system uses a semi-immersive large-screen theatre, innovative user interfaces and real-time three dimensional graphics to provide an intuitive environment for designing a forest. The system is evaluated against the described criteria. This evaluation shows that such systems can be implemented and show promise in assisting forestry based decisions, although many questions remain on the context in which such tools would be applied. Future research directions are suggested for developing forest design software and introducing their usage into public processes.
A Multi-label Classiﬁcation Algorithm Combining Feature Screening and Label Correlation

—Multi-label classiﬁcation is a hot topic in the ﬁeld of data mining. It has important applications in text classiﬁcation, image, video annotation, music emotion classi-ﬁcation, and other ﬁelds. In the past, most papers only used label correlation or feature screening to improve the accuracy of the multi-label classiﬁcation and paid one-sided attention to feature screening while ignoring the correlation between labels. Therefore, in this paper, a multi-classiﬁcation algorithm (MIRD) combining feature screening and label correlation is proposed, which not only combines the correlation between labels and features and design thresholds to screen features, but also uses association rules to update the label set to realize the correlation between labels, making full use of the correlation for multi-label classiﬁcation. Finally, the proposed algorithm is compared with other multi-label algorithms, and the results show that it can achieve better results from most data sets, which proves that the proposed algorithm is better than the comparison algorithm.
TaggedH1 Wealth and Sedentary Time Are Associated With Dietary Patterns Among Preadolescents in Nairobi City, Kenya

Objective: The study aimed to compare dietary patterns in preadolescents in urban areas with different physical activity and socioeconomic proﬁles in Nairobi, Kenya. Design: Cross-sectional. Participants: Preadolescents aged 9 − 14 years (n = 149) living in low-or middle-income areas in Nairobi. Variables Measured: Sociodemographic characteristics were collected using a validated questionnaire. Weight and height were measured. Diet was assessed using a food frequency questionnaire and physical activity by accelerometer. Analysis: Dietary patterns (DP) were formed through principal component analysis. Associations of age, sex, parental education, wealth, body mass index, physical activity, and sedentary time with DPs were analyzed with linear regression. Results: Three DPs explained 36% of the total variance in food consumption: (1) snacks, fast food, and meat; (2) dairy products and plant protein; and (3) vegetables and reﬁned grains. Higher wealth was associated with higher scores of the ﬁrst DP ( P < 0.05). Conclusions and Implications: Consumption of foods often deemed unhealthy (eg, snacks and fast food) was more frequent among preadolescents whose families were wealthier. Interventions that seek ways to promote healthy lifestyles among families residing in urban areas of Kenya are warranted.
SPAC INTERFACE WATER FLUX CONTROL AND ITS APPLICATION TO WATER-SAVING IN AGRICULTURE

Authors have conducted theoretical and practical studies on water saving in agriculture over the last decade.This paper dealt with the connotation of "Five Water Types"(air,soil,plant,surface water and groundwater) interactions in farmlands.Based on this connotation,authors discussed water interactions in soil,plant and atmosphere,as coupling system of soil-plant-atmosphere continuum(SPAC). Considering field water saving,authors presented an approach to control water fluxes through SPAC interface.Based on system integration,models for water saving were suggested for application.Data show that decreasing water consumption while increasing yield can be achieved