Miracle-dz/UniFAR-FTUs huggingface dataset:
Between a rock and a hard place: Effects of land-use change on rock-dwelling animals of lateritic plateaus in the northern Western Ghats

Open natural ecosystems like lateritic plateaus, are undergoing rapid transformation with very poor understanding of these impacts on the threatened and endemic biodiversity. The unprotected, low-elevation lateritic plateaus of the northern Western Ghats are case to the point, as they have high endemicity but remain unprotected under Indian law. We aimed to understand the impact of the conversion of the natural lateritic plateaus to agroforestry and paddy cultivation on biodiversity. We compared the prevalence of two species of endemic herpetofauna of the northern Western Ghats (Gegeneophis seschachari and Hemidactylus albofasciatus) and a widespread snake (Echis carinatus) and the composition of other rock-dwelling animals across 12 undisturbed plateau sites and 10 sites each in agroforestry plantations and abandoned paddies on plateaus using time-constrained searches. We had 5738 encounters with 38 different animal species/groups. We found that the abundance of large rocks, which were the most-preferred size class of rocks by animals, was higher in abandoned paddy compared to plateaus and orchards. However, the prevalence of H. albofasciatus and E. carinatus was highest on undisturbed plateaus. Contrastingly, the prevalence of G. seshachari was significantly higher in abandoned paddy than undisturbed plateau or orchards. Non-metric multi-dimensional analysis showed that the assemblage of rock-dwelling fauna differed significantly across the three land-use types. Despite being adapted to persist in extremely variable climates on lateritic plateaus, we find that multiple species/groups are vulnerable to land-use change. However, G. seshachari and a few other taxa appear to benefit from certain kinds of land-use change, highlighting the context-specificity in species responses. While multiple studies have determined the impacts of forest conversion to other land-uses, this is one of the first studies to determine the impacts of the conversion of rocky outcrops, thereby highlighting the conservation value of habitats that are often classified as wastelands.
Spike substitution T813S increases Sarbecovirus fusogenicity by enhancing the usage of TMPRSS2

SARS-CoV Spike (S) protein shares considerable homology with SARS-CoV-2 S, especially in the conserved S2 subunit (S2). S protein mediates coronavirus receptor binding and membrane fusion, and the latter activity can greatly influence coronavirus infection. We observed that SARS-CoV S is less effective in inducing membrane fusion compared with SARS-CoV-2 S. We identify that S813T mutation is sufficient in S2 interfering with the cleavage of SARS-CoV-2 S by TMPRSS2, reducing spike fusogenicity and pseudoparticle entry. Conversely, the mutation of T813S in SARS-CoV S increased fusion ability and viral replication. Our data suggested that residue 813 in the S was critical for the proteolytic activation, and the change from threonine to Serine at 813 position might be an evolutionary feature adopted by SARS-2-related viruses. This finding deepened the understanding of Spike fusogenicity and could provide a new perspective for exploring Sarbecovirus’ evolution. Author Summary The Spike strain of SARS-CoV-2 has accumulated many mutations during its time in circulation, most of which have occurred in the S1 region, and more specifically in the RBD, in an effort to either improve the virus’s affinity for the receptor ACE2 or to enhance its ability to evade the immune system. Mutations in the Spike S2 region have more far-reaching effects than those in the S1 region because it is more conserved across sarbecoviruses. By comparing SARS and SARS2, we found that an important substitution at amino acid position 813 in the S2 region (T813S) disrupts the utilization of TMPRSS2 and can significantly influence viral entry into cells. This discovery deepens our knowledge of S proteins and provides new prospects for tracing the evolution of Sarbecoviruses.
Scalable Gaussian process inference of neural responses to natural images

Predicting the responses of sensory neurons is a long-standing neuroscience goal. However, while there has been much progress in modeling neural responses to simple and/or artificial stimuli, predicting responses to natural stimuli remains an ongoing challenge. One the one hand, deep neural networks perform very well on certain data-sets, but can fail when data is limited. On the other hand, gaussian processes (GPs) perform well on limited data, but are generally poor at predicting responses to high-dimensional stimuli, such as natural images. Here we show how structured priors, e.g. for local and smooth receptive fields, can be used to scale up GPs to high-dimensional stimuli. We show that when we do this, a GP model largely outperforms a deep neural network trained to predict retinal responses to natural images, with largest differences observed when both models are trained on a very small data-set. Further, since GPs compute the uncertainty in their predictions, they are well-suited to closed-loop experiments, where stimuli are chosen actively so as to collect ‘informative’ neural data. We show how this can be done in practice on our retinal data-set, so as to: (i) efficiently learn a model of retinal responses to natural images, using little data, and (ii) rapidly distinguish between competing models (e.g. a linear vs a non-linear model). In the future, our approach could be applied to other low-level sensory areas, beyond the retina.
Natural evolution of SARS-CoV-2 variants in K18-ACE2 mice gives rise to more virulent virus and variant alleles associated with treatment resistance. (preprint)

Background: The continuing COVID-19 pandemic is partially due to viral evolution decreasing vaccine and treatment efficacies. Predicting viral evolution is difficult. Others have found that serial infections of the original SARS-CoV-2 isolate in non transgenic mice is accompanied with selection of alleles (such as Spike 417N, 493H and 501Y) conferring adaptation and greater binding affinity for the murine ACE2 receptor, enhanced infectivity, and lethal phenotype in mice. We designed a study to investigate long-term viral evolution's impact in K18-ACE2 mice with virus closer related to the Omicron sub-lineage. Methods: We serially infected SARS-CoV-2-naive mice with either the B.1. 351 (Beta) or the B.1. 617.2 (Delta) variant across twenty passages without selective pressures. We sequenced virus across passages, annotating variant alleles changing in frequency using published tools, tracking alleles suspected of decreasing vaccine/treatment efficacy. We evaluated virulence in animals infected with virus isolated at study completion and determined antibody neutralization sensitivity using sera from vaccinated individuals. Results and Conclusions: We observed variant alleles with documented roles in vaccine-elicited immunity evasion. This included the Omicron-associated mutation spike S371F that arose de-novo during our study. Passage 20 (P20) viruses were more virulent that their P0 counterpart with P20 Delta lineage being significantly more resistant to antibody neutralization. These developments occurred within two months of the study starting, suggesting that our model can rapidly emulate pandemic progression in mammals. While our model lacked selective pressures, such as pre-existing SARS-CoV-2 immunity, these conditions could readily be adapted. Such modeling could enable development of more evolution-resistant treatments.
Recognition of Plant Leaf Diseases Based on Convolutional Neural Network

Crop diseases cause huge yield and economic losses in agricultural production. Monitoring the health of crops and identifying diseases is essential for sustainable agriculture. In this paper, we propose a convolutional neural network based on the Inception module of super-large convolution kernel and depthwise separable convolution, in which an attention module (CBAM) is embedded, aiming to improve the classification of plant leaf diseases and prevent the disease propagation to ensure normal plant growth. In this experiment, the proposed method achieves a recognition accuracy of 99.55% on the test set. Experimental results show that our model has fewer parameters, shorter training time and higher recognition accuracy than existing image classification models.
Optimization of extracellular matrix for primary human hepatocyte cultures using mixed collagen-Matrigel matrices

Loss of differentiation of primary human hepatocytes (PHHs) ex vivo is a known problem of in vitro liver models. Culture optimizations using collagen type I and Matrigel reduce the dedifferentiation process but are not able to prevent it. While neither of these extracellular matrices (ECMs) on their own correspond to the authentic hepatic ECM, a combination of them could more closely resemble the in vivo situation. Our study aimed to systematically analyze the influence of mixed matrices composed of collagen type I and Matrigel on the maintenance and reestablishment of hepatic functions. Therefore, PHHs were cultured on mixed collagen-Matrigel matrices in monolayer and sandwich cultures and viability, metabolic capacity, differentiation markers, cellular arrangement and the cells' ability to repolarize and form functional bile canaliculi were assessed by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR), functional assays and immunofluorescence microscopy. Our results show that mixed matrices were superior to pure matrices in maintaining metabolic capacity and hepatic differentiation. In contrast, Matrigel supplementation can impair the development of a proper hepatocytic polarization. Our systematic study helps to compose an optimized ECM to maintain and reestablish hepatic differentiation on cellular and multicellular levels in human liver models.
Test–retest reliability of a simple bedside-quantitative sensory testing battery for chronic neuropathic pain

Supplemental Digital Content is Available in the Text. In addition to the previously described good criterion validity, most bedside-quantitative sensory testing parameters showed satisfactory test–retest reliability and divergent validity, allowing their use in clinical practice/studies. Abstract Introduction: The sensory phenotype is believed to provide information about the underlying pathophysiological mechanisms and to be used in the diagnosis and treatment of chronic neuropathic pain. However, the use of standardized quantitative sensory testing (QST) protocols is limited due to high expenditures of time and costs. Thus, a simple bedside-QST battery was recently developed showing good agreement when compared with laboratory QST. The aim of this study was to preliminary validate this bedside-QST protocol. Methods: Patients experiencing chronic pain with neuropathic features (n = 60) attended 3 visits. During the first visit, laboratory QST and bedside-QST were performed by the same trained investigator. Three hours and 3 weeks later, bedside-QST was repeated. Patients completed questionnaires regarding their pain (intensity, quality), depression/anxiety, and quality of life. Test–retest reliability and convergent/divergent validity were investigated. Results: Most of the bedside-QST parameters, including also those recommended in our first study as being indicative for sensory phenotypes, revealed a moderate to excellent test–retest reliability. Overall, results for short-term reliability and interval-scaled parameters were slightly better. Most of the bedside-QST parameters did not correlate with the depression and anxiety score, suggesting a good divergent validity. Conclusions: Bedside-QST has good criterion and divergent validity as well as reliability. This battery consists of 5 low-cost devices that can be quickly and easily used to characterize the sensory phenotype of patients with neuropathic pain. A combination of bedside-QST parameters can be used to investigate patients' subgroups with specific pathophysiological mechanisms and to identify treatment responders.
A PARADOXICAL EFFECT OF NUISANCE PARAMETERS ON EFFICIENCY OF ESTIMATORS

This paper is concerned with parameter estimation in the presence of nuisance parameters. Usually, an estimator with known nuisance parameters is better than that with unknown nuisance parameters in reference to the asymptotic variance. However, it has been noted that the opposite can occur in some situations. In this paper we elucidate when and how this phenomenon occurs using the orthogonal decomposition of estimating functions. Most of the examples of this phenomenon are found in the case of semiparametric models, but this phenomenon can also occur in parametric models. As an example, we consider the estimation of the dispersion parameter in a generalized linear model.
A novel deep learning-based brain tumor detection using the Bagging ensemble with K-nearest neighbor

Abstract In the case of magnetic resonance imaging (MRI) imaging, image processing is crucial. In the medical industry, MRI images are commonly used to analyze and diagnose tumor growth in the body. A number of successful brain tumor identification and classification procedures have been developed by various experts. Existing approaches face a number of obstacles, including detection time, accuracy, and tumor size. Early detection of brain tumors improves options for treatment and patient survival rates. Manually segmenting brain tumors from a significant number of MRI data for brain tumor diagnosis is a tough and time-consuming task. Automatic image segmentation of brain tumors is required. The objective of this study is to evaluate the degree of accuracy and simplify the medical picture segmentation procedure used to identify the type of brain tumor from MRI results. Additionally, this work suggests a novel method for identifying brain malignancies utilizing the Bagging Ensemble with K-Nearest Neighbor (BKNN) in order to raise the KNN’s accuracy and quality rate. For image segmentation, a U-Net architecture is utilized first, followed by a bagging-based k-NN prediction algorithm for classification. The goal of employing U-Net is to improve the accuracy and uniformity of parameter distribution in the layers. Each decision tree is fitted on a little different training dataset during classification, and the bagged decision trees are effective since each tree has minor differences and generates slightly different skilled predictions. The overall classification accuracy was up to 97.7 percent, confirming the efficiency of the suggested strategy for distinguishing normal and pathological tissues from brain MR images; this is greater than the methods that are already in use.
Penetration and Development of Meloidogyne Javanica on Four Pistachio Rootstocks and their Defense Responses

Abstract Pistachio yield is annually reduced due to root-knot nematode infections. In order to evaluate its resistance to Meloidogyne javanica, three domestic pistachio rootstocks, namely, Badami, Ghazvini and Sarakhs, and a wild pistachio, Baneh (Pistacia atlantica subsp. mutica), were selected. Their response to the nematode infection was evaluated based on different plant and nematode indexes, 120 days post-inoculation (dpi). The penetration and development rate of nematode in roots of these four pistachio rootstocks were evaluated at different time points by acid fuchsin staining. Based on the measured indexes, Badami, Ghazvini, Sarakhs, and Baneh rootstocks ranked as susceptible, moderately resistant, moderately resistant, and resistant, respectively. The penetration rate of second-stage nematode juveniles (J2) into four rootstocks was discussed. The first “midstage” or swollen juveniles appeared at 4 dpi but to a lesser extent in Ghazvini, Sarakhs, and Baneh cultivars. The first females were seen in Badami at 21 dpi, in Ghazvini and Sarakhs at 35 dpi, and in Baneh at 45 dpi. Three types of defense responses were distinguished and characterized in the examined pistachio rootstocks: (i) a hypersensitive response (HR)-like reaction in the cortex in Ghazvini, Sarakhs, and Baneh root tips at 4 dpi and 6 dpi; (ii) an HR response, degrading J2 which induce giant cells in the vascular cylinder of all rootstocks, at 6 dpi and 10 dpi; and (iii) an HR response, degrading females and giant cells in the vascular cylinder of all rootstocks at 15 dpi onward. These observations open new fields of study in breeding programs of this crop.
Vancomycin Resistance in Enterococcus faecium from the Dallas, Texas, Area Is Conferred Predominantly on pRUM-Like Plasmids

Vancomycin is an antibiotic used to treat infections caused by multidrug-resistant Gram-positive bacteria. Vancomycin resistance is common in clinical isolates of the Gram-positive pathogen Enterococcus faecium. ABSTRACT Vancomycin-resistant E. faecium (VREfm) is a significant public health concern because of limited treatment options. Genomic surveillance can be used to monitor VREfm transmission and evolution. Genomic analysis of VREfm has not been reported for the Dallas/Fort Worth/Arlington, TX, area, which is currently the 4th largest metropolitan area in the United States. Our study aimed to address this gap in knowledge by analyzing the genomes of 46 VREfm strains and 1 vancomycin-sensitive comparator collected during routine fecal surveillance of high-risk patients upon admission to a Dallas, TX, hospital system (August to October 2015). Thirty-one complete and 16 draft genome sequences were generated. The closed VREfm genomes possessed up to 12 extrachromosomal elements each. Overall, 251 closed putative plasmid sequences assigned to previously described and newly defined rep family types were obtained. Phylogenetic analysis identified 10 different sequence types (STs) among the isolates, with the most prevalent being ST17 and ST18. Strikingly, all but three of the VREfm isolates encoded vanA-type vancomycin resistance within Tn1546-like elements on a pRUM-like (rep17) plasmid backbone. Relative to a previously reported typing scheme for the vanA-carrying Tn1546, new variants of the Tn1546 were identified that harbored a combination of 7 insertion sequences (IS), including 3 novel IS elements reported here (ISEfa16, ISEfa17, and ISEfa18). We conclude that pRUM-like plasmids are important vectors for vancomycin resistance in the Dallas, TX, area and should be a focus of plasmid surveillance efforts. IMPORTANCE Vancomycin is an antibiotic used to treat infections caused by multidrug-resistant Gram-positive bacteria. Vancomycin resistance is common in clinical isolates of the Gram-positive pathogen Enterococcus faecium. Among E. faecium strains, vancomycin resistance genes can be disseminated by plasmids with different host ranges and transfer efficiencies. Surveillance of resistance plasmids is critical to understanding antibiotic resistance transmission. This study analyzed the genome sequences of VREfm isolates collected from the Dallas, TX, area, with particular focus on the mobile elements associated with vancomycin resistance genes. We found that a single plasmid family, the pRUM-like family, was associated with vancomycin resistance in the majority of isolates sampled. Our work suggests that the pRUM-like plasmids should continue to be studied to understand their mechanisms of maintenance, transmission, and evolution in VREfm.
Theoretical models for gas separation prediction of mixed matrix membranes: effects of the shape factor of nanofillers and interface voids

Abstract In this work, a new model is developed by modifying the existing Maxwell–Wagner–Sillars (MWS) model to predict the gas separation properties of mixed matrix membranes (MMMs). The new modified MWS model, for the first time, provides the simultaneous exploration of the role of nanofillers/matrix interface voids and the exact geometrical shape of nanofillers in predicting the gas separation properties of MMMs. To unveil the crucial role of nanofillers/matrix interface voids, a mixed matrix membrane is considered a three-component system composed of the polymer matrix as the continuous component, nanofillers as the dispersed component and the interface voids between the two components. Moreover, the new model elucidates the role of the exact ellipsoidal shape of nanofillers within the membrane on the gas separation of MMMs by considering the shape factor of nanofillers. The newly developed modified MWS model is accurately able to predict the gas permeation of MMMs with a lower average absolute relative error (%AARE) of around 8% compared with the around 30% for conventional models such as the Maxwell model, Bruggeman model, Lewis–Nielsen model and Pal model and even compared with the modified Maxwell model (∼24%).
Transform coding of digital images using variable block-size DCT with adaptive thresholding and quantization

Discrete cosine transform (DCT) coding has been emerging as a key element for image data compression. Conventional DCT coding algorithms which treat all the image areas indiscriminately unfortunately give nonuniform image quality for various image contents. This motivates work on DCT schemes adaptive to the image contents so that a better tradeoff between bit rate and image quality can be achieved. In this paper a DCT algorithm with adaptive thresholding and quantization is combined with variable block size segmentation to further improve the coding performance. A new segmentation criterion is proposed. Some simulation results are given to illustrate the superiority of this adaptive DCT algorithm with segmented blocks. It is also shown that this algorithm poses itself as a promising compression method to deal with the composite images consisting of text/graphics and natural scenes.
The complete chloroplast genome of an endangered plant Artemisia borotalensis (Asteraceae) and phylogenetic analysis

Abstract Artemisia borotalensis Poljakov is an endemic and endangered herb in China. In this study, we sequenced and analyzed the complete chloroplast genome of this species. Sequencing revealed the genome to be 151,179 bp in length, containing a large single copy region (82,862 bp), a small single copy region (18,377 bp), and a pair of inverted repeat regions (24,970 bp each). Our analyses demonstrated that it contained 133 genes, including 87 protein-coding genes, 37 transfer RNA genes, eight ribosomal RNA genes, and one pseudogene (ycf1). Furthermore, we found the genome to have an overall GC content of 37.4%. A phylogenetic analysis indicated that A. borotalensis and A. maritima clustered together as sister group to A. annua and A. fukudo clade.
Effects of acute and chronic disease on cell junctions in mouse liver

Cell junctions, including anchoring, occluding and communicating junctions, play an indispensable role in tissue architecture and homeostasis. Consequently, malfunctioning of cell junctions is linked with a wide range of disorders, including in liver. The present study was set up to investigate the effects of acute and chronic disease induced by chemical compounds on hepatic cell junctions in mice. Mice were either overdosed with paracetamol or repeatedly administered carbon tetrachloride followed by sampling at 24 hours or 8 weeks, respectively. mRNA and protein expression levels of adherens, gap and tight junction components were measured in liver using reverse transcription quantitative real-time polymerase chain reaction analysis and immunoblot techniques, respectively. It was found that protein levels of the adherens junction building blocks β-catenin and γ-catenin, the gap junction components Cx26 and Cx32, and the tight junction constituent zonula occludens 2 were decreased, while mRNA levels of the adherens junction building block E-cadherin, and the tight junction constituent zonula occludens 2 and claudin 1 were upregulated following paracetamol overdosing. Repeated administration of carbon tetrachloride increased protein levels of E-cadherin, β-catenin, Cx26, Cx32, Cx43 and claudin 1. The latter was reflected at the mRNA level. In conclusion, acute and chronic liver disease have different effects on cell junctions in liver.