Miracle-dz/UniFAR-FTUs huggingface dataset:
Rv0687 a Putative Short-Chain Dehydrogenase is indispensable for pathogenesis of Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb), a successful human pathogen, resides in host sentinel cells and combats the stressful intracellular environment induced by reactive oxygen and nitrogen species during infection. Mtb employs several evasion mechanisms in the face of the host as a survival strategy, including detoxifying enzymes as short-chain dehydrogenases/ reductases (SDRs) to withstand host-generated insults. In this study, using specialized transduction we have generated a Rv0687 deletion mutant and its complemented strain and investigated the functional role of Rv0687, a member of SDRs family genes in Mtb pathogenesis. Wildtype (WT) and mutant Mtb strain lacking Rv0687 (RvΔ0687) were tested for in-vitro stress response and in-vivo survival in macrophages and mice models of infection. The study demonstrates that Rv0687 is crucial for sustaining bacterial growth in nutrition-limited conditions. The deletion of Rv0687 elevated the sensitivity of Mtb to oxidative and nitrosative stress-inducing agents. Furthermore, the lack of Rv0687 compromised the survival of Mtb in primary bone marrow macrophages and led to an increase in the levels of the secreted proinflammatory cytokines TNF-α, and MIP-1α. Interestingly, the growth of WT and RvΔ0687 was similar in the lungs of infected immunocompromised mice however, a significant reduction in RvΔ0687 growth was observed in the spleen of immunocompromised Rag-/- mice at 4 weeks post-infection. Moreover Rag-/- mice infected with RvΔ0687 survived longer compared to WT Mtb strain. Additionally, we observed significant reduction in bacterial burden in spleens and lungs of immunocompetent C57BL/6 mice infected with RvΔ0687 compared to complemented and WT Mtb strains. Collectively, this study reveals that Rv0687 plays a role in Mtb pathogenesis.
Enzyme fluctuations data improve inference of metabolic interaction networks with an inverse differential Jacobian approach

The development of next-generation sequencing and single-cell technology has generated vast genome-scale multi-omics datasets. Dedicated mathematical algorithms are required to dissect intricate molecular causality within metabolic networks using these datasets. Based on the network analysis, recent research has introduced the inverse differential Jacobian algorithm, which combines metabolic interaction network construction and covariance matrix analysis of genome-scale metabolomics data to elucidate system regulatory factors near steady-state dynamics. Traditionally, these studies assumed metabolomics variations solely resulted from metabolic system fluctuations, acting independently on each metabolite. However, emerging evidence highlights the role of internal network fluctuations, particularly from the gene expression fluctuations, leading to correlated perturbations on metabolites. In this article, we propose a novel approach that exploits these correlations to reconstruct relevant metabolic interactions. Thereby, enzymes exhibiting significant variances in activity values serve as indicators of large fluctuations in their catalyzed reactions. By integrating this information in an inverse Jacobian algorithm, we are able to exploit the underlying reaction network structure to improve the construction of the fluctuation matrix required in the inverse Jacobian algorithm. After a comprehensive assessment of three critical factors affecting the algorithm’s accuracy, we conclude that using the enzyme fluctuation data significantly enhances the inverse Jacobian algorithm’s performance. We applied this approach to a breast cancer dataset with two different cell lines, which highlighted metabolic interactions where fluctuations in enzyme gene expression yield a relevant difference between the cell lines.
Compared with pure forest, mixed forest alters microbial diversity and increases the complexity of interdomain networks in arid areas

ABSTRACT Soil microbial communities are key to material cycling and energy exchange in forest soils. As the Tianshan Mountains in Xinjiang are rich in tree species, it is important to understand the impact of tree species composition on soil microbial community structure and co-occurrence networks in arid mountain habitats. In this study, soil microbial community structure of five typical vegetation types in the Tianshan Mountains of Xinjiang, China, was investigated using bacterial 16S rRNA and fungal ITS sequences. The results showed that the relationship between the fungal community and tree species was strong, while the bacterial community was mainly affected by soil carbon. The dominant bacterial composition was Ascomycota, which had the highest relative abundance in herbaceous soils (27%–41%); the dominant fungal phylum was Ascomycota (50.22%–63.95% of the total sequences), followed by Stramonium (8.99%–28.09%). The fungal community structure of mixed coniferous and broadleaf forests was similar to that of conifers, while the bacterial community structure was similar to that of pure broadleaf forests. Mixed forests (mean connectivity: 812.50, clustering coefficient: 8.89) showed a more complex co-occurrence network and a significant increase in microbial structure related to soil carbon and nitrogen than pure forests (broad: mean connectivity: 710.69, clustering coefficient: 6.94; coniferous: mean connectivity: 730.79, clustering coefficient: 7.76). The results contribute to the understanding of the interactions between soil microorganisms and soil factors in different forest ecosystems and provide new insights into the interactions among forest microorganisms in arid zones. IMPORTANCE The results provide a comparative study of the response of soil microbial ecology to the afforestation of different tree species and deepen the understanding of the factors controlling soil microbial community structure. The results provide a comparative study of the response of soil microbial ecology to the afforestation of different tree species and deepen the understanding of the factors controlling soil microbial community structure.
Bridge-like lipid transfer protein family member 2 suppresses ciliogenesis

Bridge-like lipid transfer protein family member 2 (BLTP2) is an evolutionary conserved protein with unknown function(s). The absence of BLTP2 in Drosophila melanogaster results in impaired cellular secretion and larval death, while in mice (Mus musculus), it causes preweaning lethality. Structural predictions propose that BLTP2 belongs to the repeating β-groove domain-containing (also called the VPS13) protein family, forming a long tube with a hydrophobic core, suggesting that it operates as a lipid transfer protein (LTP). We establish BLTP2 as a negative regulator of ciliogenesis in RPE-1 cells based on a strong genetic interaction with WDR44, a gene that also suppresses ciliogenesis. Like WDR44, BLTP2 localizes to membrane contact sites involving the endoplasmic reticulum and the tubular endosome network in HeLa cells and that BLTP2 depletion enhanced ciliogenesis by serum-fed RPE-1 cells, a condition where ciliogenesis is normally suppressed. This study establishes human BLTP2 as a putative lipid transfer protein acting between tubular endosomes and ER that regulates primary cilium biogenesis. Significance statement We show the involvement of an ER-localized bridge-like lipid transfer protein, BLTP2, in ciliogenesis and establish that BLTP2 is enriched at organelle-organelle membrane contact sites involving the endoplasmic reticulum (ER) and the tubular endosome network (TEN). These results implicate, for the first time, the involvement of bulk lipid transfer between the ER and TEN in regulating ciliogenesis.
Pre-ciliated tubal epithelial cells are prone to initiation of high-grade serous ovarian carcinoma

The distal region of the uterine (Fallopian) tube is commonly associated with high-grade serous carcinoma (HGSC), the predominant and most aggressive form of ovarian or extra-uterine cancer. Specific cell states and lineage dynamics of the adult tubal epithelium (TE) remain insufficiently understood, hindering efforts to determine the cell of origin for HGSC. Here, we report a comprehensive census of cell types and states of the mouse uterine tube. We show that distal TE cells expressing the stem/progenitor cell marker Slc1a3 can differentiate into both secretory (Ovgp1+) and ciliated (Fam183b+) cells. Inactivation of Trp53 and Rb1, whose pathways are commonly altered in HGSC, leads to elimination of targeted Slc1a3+ cells by apoptosis, thereby preventing their malignant transformation. In contrast, pre-ciliated cells (Krt5+, Prom1+, Trp73+) remain cancer-prone and give rise to serous tubal intraepithelial carcinomas and overt HGSC. These findings identify transitional pre-ciliated cells as a previously unrecognized cancer-prone cell state and point to pre-ciliation mechanisms as novel diagnostic and therapeutic targets.
In Silico Evaluation of Cell Therapy in Acute versus Chronic Infarction: Role of Automaticity, Heterogeneity and Purkinje in Human

Human-based modelling and simulation offer an ideal testbed for novel medical therapies to guide experimental and clinical studies. Myocardial infarction (MI) is a common cause of heart failure and mortality, for which novel therapies are urgently needed. Although cell therapy offers promise, electrophysiological heterogeneity raises pro-arrhythmic safety concerns, where underlying complex spatio-temporal dynamics cannot be investigated experimentally. After demonstrating credibility of the modelling and simulation framework, we investigate cell therapy in acute versus chronic MI, and the role of cell heterogeneity, scar size and the Purkinje system. Simulations agreed with experimental and clinical recordings from ionic to ECG dynamics in acute and chronic infarction. Following cell delivery, spontaneous beats were facilitated by heterogeneity in cell populations, chronic MI due to tissue depolarisation, and slow sinus rhythm. Subsequent re-entrant arrhythmias occurred, in some instances with Purkinje involvement, and their susceptibility was enhanced by impaired Purkinje-myocardium coupling, large scars, and acute infarction. We conclude that homogeneity in injected cell populations minimises their spontaneous beating, which is enhanced by chronic MI, whereas a healthy Purkinje-myocardium coupling is key to prevent subsequent re-entrant arrhythmias, particularly for large scars.
A cyclic dipeptide for salinity stress alleviation and the trophic flexibility of an endophyte reveal niches in salt marsh plant-microbe interactions

In response to climate change, the nature of endophytes and their applications in sustainable agriculture has attracted the attention of academia and agro-industries. We focused on the endophytic halophiles of the endangered Taiwanese salt marsh plant, Bolboschoenus planiculmis, and evaluated the functions of the isolates through in planta salinity stress alleviation assay using Arabidopsis. An endophytic strain Priestia megaterium BP01R2 that could promote plant growth and salinity tolerance was further characterised through multi-omics approaches. The transcriptomics results suggested that BP01R2 could function by tuning hormone signal transduction, energy-producing metabolism, multiple stress responses, etc. In addition, a cyclodipeptide, cyclo(L-Ala-Gly), identified by metabolomics analysis was later confirmed to contribute to salinity stress alleviation in stressed plants by exogenous supplementation. Here we provide a new perspective on host-microbe interactions in the wetland biome based on the multi-omics investigation and mixotrophic character of BP01R2. This study revealed a biostimulant-based plant-endophyte symbiosis with potential application in sustainable agriculture and facilitated our understanding of those enigmatic cross-kingdom relationships.
Adaptive LDA classifier enhances real-time control of an EEG Brain-computer interface for imagined-speech decoding

Brain-Computer Interfaces (BCI) aim to establish a pathway between the brain and an external device without the involvement of the motor system, relying exclusively on neural signals. Such systems have the potential to provide a means of communication for patients who have lost the ability to speak due to a neurological disorder. Traditional methodologies for decoding imagined speech directly from brain signals often deploy static classifiers, that is, decoders that are computed once at the beginning of the experiment and that remain unchanged throughout the BCI use. However, this approach might be inadequate in effectively handling the non-stationary nature of electroencephalography (EEG) signals and the learning that accompanies BCI use as parameters are expected to change, all the more in a real-time setting. To address this limitation, we have developed an adaptive classifier that updates its parameters based on the incoming data in real time. We first identified optimal parameters (the update coefficient, UC) to be used in an adaptive Linear Discriminant Analysis (LDA) classifier, using a previously recorded EEG dataset, acquired while healthy participants controlled a binary BCI based on imagined syllable decoding. We subsequently tested the effectiveness of this optimization in a real-time BCI-control setting. Twenty healthy participants performed two BCI-control sessions based on the imagery of two syllables, using a static LDA and the other the adaptive LDA classifier, in randomized order. In this real-time BCI-control task, the adaptive classifier led to better performances than the static one. Furthermore, the optimal parameters for the adaptive classifier were closely aligned in both datasets acquired with the same syllable imagery task. These findings highlight the effectiveness and re-liability of adaptive LDA classifiers for real-time imagined speech decoding, and its interest for non-invasive EEG-based BCI notably characterized by low decoding accuracies.
Catalytically inactive dKbCas12d guided by sgRNA and new insights into its binding through a single-molecule approach

CRISPR-Cas12d is a distinct V-D type system discovered in the metagenomes of Candidate Phyla Radiation bacteria. It stands out from most closely related systems due to its 17-19 nucleotide short spacer region and specialized stabilizing scoutRNAs. We made significant improvements to this system by modifying its scoutRNA to create sgRNA, which greatly simplifies its use. We found mutations in the RuvC domain of the effector protein KbCas12d that resulted in loss of nuclease activity. We obtained two catalytically inactive dKbCas12d variants: D827A and E913A. Using the optical tweezers technique, we demonstrated the high specificity of dKbCas12d in binding targets on individual DNA molecules. Engineered sgRNA and catalytically inactive dKbCas12d variants have promising applications in biotechnology for the precise regulation of gene expression and molecular diagnostics.
Proceedings of the 2 nd International Conference of the UISPP Commission on Flint Mining in Pre-and Protohistoric Times ( Madrid , 14-17 October 2009 )

In archaeological research it is common practice to infer the function of prehistoric tools from their typology and the context in which they were recovered. However, analysis of function based on the inspection of macroand microscopic use-wear evidence is the only way to provide credible support for such inferences. The present work reports data gathered during the use-wear analysis of a sample of antler and stone artefacts found in the Neolithic mine of Casa Montero. These data certify that the antler tool recovered were not mining tools, while the knapped stone tools provide new information regarding activities undertaken in this fl int mining context, as well as the taphonomic processes occurring at the site. The proposed uses of these tools indicate human habitation near the site.
What is Heterodox Economics? Insights from Interviews with Leading Thinkers

Abstract This article investigates heterodox economics, drawing on data from interviews we conducted with leading economists, using thematic analysis to identify three core concerns of our respondents: realism, power, and pluralism. Heterodox economists appear to value realism (as a philosophical position) and realisticness (as a property of theories and models). They share a concern for power in the world and in the economics profession and recognize the need to get it, for their own agency. They value pluralism of theory and method and insist that this must include heterodox approaches. Moreover, power and pluralism to some extent serve their pursuit of realisticness and truth.
Kinome focused CRISPR-Cas9 screens in African ancestry patient-derived breast cancer organoids identifies essential kinases and synergy of EGFR and FGFR1 inhibition

Precision medicine approaches to cancer treatment aim to exploit genomic alterations that are specific to individual patients to tailor therapy strategies. These alterations are usually revealed via next generation sequencing of the tumor tissue. Yet, it is clear that some targetable genes and pathways are essential for tumor cell viability even in the absence of direct genomic alterations. This is especially important in under-represented populations, whose mutational landscape and determinants of response to existing therapies are poorly characterized due to limited inclusion in clinical trials and studies. One way to reveal tumor essential genes is with genetic screens. Most screens are conducted on cell lines that bear little resemblance to patient tumors, after years of culture in non-physiological conditions. To address this problem, we aimed to develop a CRISPR screening pipeline in 3D-grown patient-derived tumor organoid (PDTO) models. We focused on identifying essential kinases that may translate to options for targeted therapies, including combination therapies. We first established a breast cancer PDTO biobank focused on underrepresented populations, including West African patients. We then performed a negative selection kinome-focused CRISPR screen to identify kinases essential for organoid growth and potential targets for combination therapy with EGFR or MEK inhibitors. We identified several previously unidentified kinase targets and showed that combination of FGFR1 and EGFR inhibitors synergizes to block organoids proliferation. Together these data demonstrate feasibility of CRISPR-based genetic screens in patient-derived tumor models, including PDTOs from under-represented cancer patients, and identify new targets for cancer therapy.
Fruit Image Classiﬁcation Model Based on MobileNetV2 with Deep Transfer Learning Technique

: Due to the rapid emergence and evolution of AI applications, the utilization of smart imaging devices has increased signiﬁcantly. Researchers have started using deep learning models, such as CNN, for image classiﬁcation. Unlike the traditional models, which require a lot of features to perform well, CNN does not require any handcrafted features to perform well. It uses numerous ﬁlters, which extract required features from images automatically for classiﬁcation. One of the issues in the horticulture industry is fruit classiﬁcation, which requires an expert with a lot of experience. To overcome this issue an automated system is required which can classify different types of fruits without the need for any human effort. In this study, a dataset of a total of 26,149 images of 40 different types of fruits was used for experimentation. The training and test set were randomly recreated and divided into the ratio of 3:1. The experiment introduces a customized head of ﬁve different layers into MobileNetV2 architecture. The classiﬁcation layer of the MobileNetV2 model is replaced by the customized head, which produced the modiﬁed version of MobileNetV2 called TL-MobileNetV2. In addition, transfer learning is used to retain the pre-trained model. TL-MobileNetV2 achieves an accuracy of 99%, which is 3% higher than MobileNetV2, and the equal error rate of TL-MobileNetV2 is just 1%. Compared to AlexNet, VGG16, InceptionV3, and ResNet, the accuracy is better by 8, 11, 6, and 10%, respectively. Furthermore, the TL-MobileNetV2 model obtained 99% precision, 99% for recall, and a 99% F1-score. It can be concluded that transfer learning plays a big part in achieving better results, and the dropout technique helps to reduce the overﬁtting in transfer learning.
Measuring the Performance of User Traﬀic in Home Wireless Networks

This paper studies how home wireless performance characteristics affect the performance of user trafﬁc in real homes. Previous studies have focused either on wireless metrics exclusively, without connection to the performance of user trafﬁc; or on the performance of the home network at higher layers. In contrast, we deploy a passive measurement tool on commodity access points to correlate wireless performance metrics with TCP performance of user trafﬁc. We implement our measurement tool, deploy it on commodity routers in 66 homes for one month, and study the relationship between wireless metrics and TCP performance of user trafﬁc. We ﬁnd that, most of the time, TCP ﬂows from devices in the home achieve only a small fraction of available access link throughput; as the throughput of user trafﬁc approaches the access link throughput, the characteristics of the home wireless network more directly affect performance. We also ﬁnd that the 5 GHz band offers users better performance better than the 2.4 GHz band, and although the performance of devices varies within the same home, many homes do not have multiple devices sending high trafﬁc volumes, implying that certain types of wireless contention may be uncommon in practice.
THE EPIDEMIOLOGY OF RUGBY INJURIES IN A SOUTH AFRICAN HIGH SCHOOL OVER A ONE-YEAR PERIOD

Adolescent rugby players have a high risk for injuries; however, injury prevention programmes have reduced the incidence of injuries amongst this group. The purpose of this retrospective analysis was to evaluate the epidemiology of rugby injuries at a South African high school over one season. There were 99 participants over a total of 98 matches and the participants were aged between 13 and 18 years. Information on the type of injuries was documented and the injury proportion (IP), injury incidence and 95% confidence intervals (CI) were calculated, with 1000 player-hours as the denominator. Ninety-nine injuries were recorded, with a total injury incidence of 34.1 injuries/1000 player-hours (95% CI 27.4–40.8). Injuries were predominantly of moderate severity (IP: 25.3%) with a time-loss period of 8 to 28 days. Injuries to the head, neck and spine were the most common (IP: 38.4%; incidence: 13.1; 95% CI 8.9–17.2). The findings on the nature of injuries were largely consistent with current local and international research on high school rugby, with the head, neck and spine remaining at a significant risk of injury. It is recommended that injury prevention programmes further enforce safety guidelines for the protection of the head, neck and spine amongst high school athletes.