Miracle-dz/UniFAR-FTUs huggingface dataset:
Co-expression Networks From Gene Expression Variability Between Genetically Identical Seedlings Can Reveal Novel Regulatory Relationships

Co-expression networks are a powerful tool to understand gene regulation. They have been used to identify new regulation and function of genes involved in plant development and their response to the environment. Up to now, co-expression networks have been inferred using transcriptomes generated on plants experiencing genetic or environmental perturbation, or from expression time series. We propose a new approach by showing that co-expression networks can be constructed in the absence of genetic and environmental perturbation, for plants at the same developmental stage. For this we used transcriptomes that were generated from genetically identical individual plants that were grown in the same conditions and for the same amount of time. Twelve time points were used to cover the 24h light/dark cycle. We used variability in gene expression between individual plants of the same time point to infer a co-expression network. We show that this network is biologically relevant and use it to suggest new gene functions and to identify new targets for the transcription factors GI, PIF4 and PRR5. Moreover, we find different co-regulation in this network based on changes in expression between individual plants, compared to the usual approach requiring environmental perturbation. Our work shows that gene co-expression networks can be identified using variability in gene expression between individual plants, without the need for genetic or environmental perturbations. It will allow further exploration of gene regulation in contexts with subtle differences between plants, which could be closer to what individual plants in a population might face in the wild. Author summary Plant development and response to changes in the environment are strongly regulated at the level of gene expression. That is why understanding how gene expression is regulated is key, and transcriptome approaches have allowed the analysis of transcription for all genes of the genome. Extracting useful information from the high amount of data generated by transcriptomes is a challenge, and gene co-expression networks are a powerful tool to do this. The principle is to find genes that co-vary in expression in different conditions and to pair them together. Communities of genes that are more closely linked are then identified and this is the starting point to look for their implication in the same pathway. Co-expression networks have been used to identify new regulation and function of genes involved in plant development and their response to the environment. They were constructed using transcriptomes generated on plants experiencing genetic or environmental perturbation. We show that co-expression networks can in fact be constructed in the absence of genetic and environmental perturbation. Our work will allow further exploration of gene co-regulation in contexts with subtle differences between plants, which could be closer to what individual plants in a population might face in the wild.
Relationship of Sitona discoideus (Coleoptera: Curculionidae) larval density to September-October potential soil moisture deficits

Abstract The relationship between maximum potential soil moisture deficit (D max.) during Silona discoideus Gyllenhal oviposition and subsequent peak larval establishment in young lucerne (Medicago sativa L.) is described. Regression analysis showed that variation in larval establish- . ment from year to year was best correlated with the values of D max. attained during September and October. The best fit was hyperbolic, implying a precipitous reduction in peak larval numbers when D max. exceeded about 50 mm. After this reduction. larval populations were stable at about 1000/m2. This pattern is probably a result of rhizobial nodule availability for neonate larval populations. The eventual size of larval populations is related to the prevailing soil moisture conditions in September-October. This period has previously been found to be the most important for sitona weevil egg-laying within New Zealand, in terms of resultant larval establishment.
Genome-Wide Identification of Long Non-coding RNAs in the Gravid Ectoparasite Varroa destructor

Long non-coding RNAs (lncRNAs) emerge as critical regulators with various biological functions in living organisms. However, to date, no systematic characterization of lncRNAs has been investigated in the ectoparasitic mite Varroa destructor, the most severe biotic threat to honey bees worldwide. Here, we performed an initial genome-wide identification of lncRNAs in V. destructor via high-throughput sequencing technology and reported, for the first time, the transcriptomic landscape of lncRNAs in the devastating parasite. By means of a lncRNA identification pipeline, 6,645 novel lncRNA transcripts, encoded by 3,897 gene loci, were identified, including 2,066 sense lncRNAs, 2,772 lincRNAs, and 1,807 lncNATs. Compared with protein-coding mRNAs, V. destructor lncRNAs are shorter in terms of full length, as well as of the ORF length, contain less exons, and express at lower level. GO term and KEGG pathway enrichment analyses of the lncRNA target genes demonstrated that these predicted lncRNAs are likely to play key roles in cellular processes, genetic information processing and environmental responses. To our knowledge, this is the first catalog of lncRNA profile in the parasitiformes species, providing a valuable resource for genetic and genomic studies. Understanding the characteristics and features of lncRNAs in V. destructor would promote sustainable pest control.
Does competition for existence pushed the evolution of the 'once' saprophytic fungi to parasitic life?

Aims: The current study was planned to understand how the ‘once’ saprophytic fungi would have adapted/equipped themselves to be pathogens in human environment and what environmental events that would have compelled/ facilitated certain fungi to become human pathogens. Methodology and results: Antibiosis of soil fungi such as Chrysosporium keratinophillum, Aspergillus niger, Aspergillus fumigatus, Aspergillus flavus, Penicillium sp. , Rhizopus oryzae and Curvularia lunata on different test fungi viz. different species of dermatophytes, Malassezia furfur, Cryptococcus neoformans and Histoplasma capsulatum was studied. In vitro susceptibility testing of secretory and intracellular substances of soil fungi was also tested on the test fungi of clinical significance. The ability of the test fungi for saprophytic survivability was tested on sterile and un-sterile soil. M. furfur and the anthrophilic dermatophytes were susceptible to the secretory substance and intracellular substances of the soil fungi. Conclusion, significance and impact study: The anthropophilic dermatophytes are not capable of existing in soil as saprophytes. The antagonistic effect of C. keratinophillum (sharing the same nutritional preference viz keratinophilic nature and ecological niche) could be one among the possible early events that formed the basis for the evolution of obligate parasitism in certain dermatophytes. Other obligate anthropophilic fungi, M. furfur is also not capable of existing even in sterile soil as a saprophyte. Most of the soil fungi tested show inhibition on M. furfur in vitro. The ability of fungi to cause disease in humans appears to be an accidental phenomenon With the exception of a few dermatophytes, pathogenicity among the molds is not necessary for the maintenance of dissemination of the species (Rippon, 1988). Further, the fungi that are able to cause disease seem to do so because of some peculiar trait of their metabolism that is not shared by taxonomically similar species. The survival and growth of fungi at the elevated temperature of the body, the reduced oxidation-reduction environment of tissue and the ability to overcome the host’s defence mechanisms clearly sets ‘mere’ saprophytic and pathogenic fungi apart from each other. It is always of greater interest to know how these organisms would have adapted/equipped themselves to be pathogens in human environment and what environmental events that would have compelled/facilitated certain fungi to become human pathogens. This study reports the possible fungus-fungus interaction which would have been one of the driving forces of fungal evolution resulting in obligate anthropization of certain species.
Ghrelin signaling regulates feeding behavior, metabolism, and memory through the vagus nerve

Vagal afferent neuron (VAN) signaling sends information from the gut to the brain and is fundamental in the neural control of feeding behavior and metabolism. Recent findings reveal that VAN signaling also plays a critical role in cognitive processes, including hippocampus (HPC)-dependent memory. VANs, located in nodose ganglia, express receptors for various gut-derived endocrine signals, however, the function of these receptors with regards to feeding behavior, metabolism, and memory control is poorly understood. We hypothesized that VAN-mediated processes are influenced by ghrelin, a stomach-derived orexigenic hormone, via communication to its receptor (growth hormone secretagogue receptor [GHSR]) expressed on gut-innervating VANs. To examine this hypothesis, rats received nodose ganglia injections of an adeno-associated virus (AAV) expressing short hairpin RNAs targeting GHSR (or a control AAV) for RNA interference-mediated VAN-specific GHSR knockdown. Results reveal that VAN GHSR knockdown induced various feeding and metabolic disturbances, including increased meal frequency, impaired glucose tolerance, delayed gastric emptying, and increased body weight compared to controls. Additionally, VAN-specific GHSR knockdown impaired HPC-dependent episodic contextual memory and reduced HPC brain-derived neurotrophic factor expression, but did not affect anxiety-like behavior or levels of general activity. A functional role for endogenous VAN GHSR signaling was further confirmed by results revealing that VAN signaling is required for the hyperphagic effects of ghrelin administered at dark onset, and that gut-restricted ghrelin-induced increases in VAN firing rate require intact VAN GHSR expression. Collective results reveal that VAN GHSR signaling is required for both normal feeding and metabolic function as well as HPC-dependent memory.
Pervasive RNA secondary structure in the genomes of SARS-CoV-2 and other coronaviruses – an endeavour to understand its biological purpose

The ultimate outcome of the COVID-19 pandemic is unknown and is dependent on a complex interplay of its pathogenicity, transmissibility and population immunity. In the current study, SARS coronavirus 2 (SARS-CoV-2) was investigated for the presence of large scale internal RNA base pairing in its genome. This property, termed genome scale ordered RNA structure (GORS) has been previously associated with host persistence in other positive-strand RNA viruses, potentially through its shielding effect on viral RNA recognition in the cell. Genomes of SARS-CoV-2 were remarkably structured, with minimum folding energy differences (MFEDs) of 15%, substantially greater than previously examined viruses such as HCV (MFED 7-9%). High MFED values were shared with all coronavirus genomes analysed created by several hundred consecutive energetically favoured stem-loops throughout the genome. In contrast to replication-association RNA structure, GORS was poorly conserved in the positions and identities of base pairing with other sarbecoviruses – even similarly positioned stem-loops in SARS-CoV-2 and SARS-CoV rarely shared homologous pairings, indicative of more rapid evolutionary change in RNA structure than in the underlying coding sequences. Sites predicted to be base-paired in SARS-CoV-2 showed substantially less sequence diversity than unpaired sites, suggesting that disruption of RNA structure by mutation imposes a fitness cost on the virus which is potentially restrictive to its longer evolution. Although functionally uncharacterised, GORS in SARS-CoV-2 and other coronaviruses represent important elements in their cellular interactions that may contribute to their persistence and transmissibility.
Molecular underpinning of intracellular pH regulation on TMEM16F

TMEM16F, a dual functional phospholipid scramblase and ion channel, is important in blood coagulation, skeleton development, HIV infection and cell fusion. Despite the advances in understanding its structure and activation mechanism, how TMEM16F is regulated by intracellular factors remains largely elusive. Here we report that TMEM16F lipid scrambling and ion channel activities are strongly influenced by intracellular pH (pHi). We find that low pHi attenuates whereas high pHi potentiates TMEM16F activation. Our biophysical characterizations pinpoint that the pHi regulatory effects on TMEM16F stem from protonation and deprotonation of the Ca2+ binding sites, which in turn reduces and enhances Ca2+ binding affinity, respectively. We further demonstrate that intracellular alkalization of 0.5 pHi can significantly promote TMEM16F activities in a choriocarcinoma cell line. Our findings thus uncover a regulatory mechanism of TMEM16F by pHi and shine light on understanding the pathophysiological roles of TMEM16F in diseases with dysregulated pHi including cancer.
Juvenile production and culture of the silver-lip pearl oyster, Pinctada maxima (Jameson)

The silver-lip (or gold-lip) pearl oyster, Pinctada maxima (Jameson) is the most highly prized of all pearl oysters. Through production of "south sea pearls", P. maxima is the basis of Australia's most lucrative aquaculture industry and is of great economic importance throughout south east Asia. In recent years there has been a rapid expansion in the cultivation of P. maxima throughout the region brought about by improvements in hatchery technology. Despite this, there is limited published information concerning rearing of P. maxima. This study aims to address this problem by establishing appropriate culture criteria for P. maxima in the specific areas of collection (settlement) and maintenance of spat in the hatchery, and the early nursery rearing and the grow-out of P. maxima at sea. In the first of a series of settlement experiments, the influence of pediveliger stocking density on settlement and subsequent growth of spat was investigated. Pediveligers were placed in 15 L vessels at densities of 0.5, 1.0, 1.5 or 2.0 larvae per mL. The number of spat per 100 cm2 in the 1.0 larva per mL treatment was over double, and significantly greater (P< 0.05) than that in the 0.5 larva per rid, treatment. Increases in the number of spat were not significant (P >0.05) between larval densities of 1.0 and 1.5 larvae per mL and 1.5 and 2.0 larvae per mL. Stocking density did not significantly influence survival (P>0.05). By day 44, spat from the 0.5 larva per mL treatment were significantly larger (P <0.05) than spat from all other treatments and spat in the 1.0 larva
Universal probabilistic programming: a powerful new approach to statistical phylogenetics

Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabilistic graphical models, but this formalism can only partly express phylogenetic problems. Here we show that universal probabilistic programming languages (PPLs) solve the modeling language expressivity problem, while still supporting automated generation of efficient inference algorithms. To prove the latter point, we develop automated generation of sequential Monte Carlo (SMC) algorithms for PPL descriptions of arbitrary biological diversification (birth-death) models. SMC is a new inference strategy for these problems, supporting both parameter inference and efficient Bayesian model testing. We then automatically generate SMC algorithms for several recent diversification models that have been difficult or impossible to tackle previously. Finally, applying these algorithms to 40 bird phylogenies, we show that models with slowing diversification, constant turnover and many small shifts generally explain the data best. Our work opens up several related problem domains to PPL approaches, and shows that few hurdles remain before these techniques can be effectively applied to the full range of phylogenetic models.
Fluctuating asymmetry and mentum gaps in populations of the midge Chironomus riparius (diptera: Chironomidae) from a metal‐contaminated river

The developmental stability of both metal‐exposed and nonexposed Chironomus riparius populations from the lowland River Dommel was investigated using fluctuating asymmetry (FA) and the incidence of mentum gaps. It was hypothesized that larval development was affected by the influx of metals, directly by chemical stress, as well as through inbreeding of metal‐adapted and nonadapted specimens. Morphological parameters were therefore assessed in field‐collected larvae and in clean, laboratory‐cultured, first‐generation (F1) larvae. Fluctuating asymmetry values and mentum gap incidence at contaminated field sites were significantly higher than at clean, upstream locations. Furthermore, FA values of clean, laboratory‐cultured F1 larvae generally fell to reference values, indicating the direct effect of metal pollution on developmental aberrations. Mentum gaps were not observed in clean F1 cultures. Slightly elevated FA values were, however, still observed in clean F1 larvae from polluted locations downstream from the metal input. This residual disturbance was thought to reflect genetic stress emerging from interbreeding between metal‐adapted and nonadapted specimens. Fluctuating asymmetry and mentum gaps together serve as a useful ecotoxicological marker for metal stress and, when combined with in situ studies and F1 cultures, allow for analysis of the response of animal populations to spatial and temporal gradients in metal exposure.
Fermentable fiber-induced hepatocellular carcinoma in mice recapitulates gene signatures found in human liver cancer

Hepatocellular carcinoma (HCC), the most malignant form of primary liver cancer, is the fourth most prevalent cause of cancer mortality globally. It was recently discovered that the dietary fermentable fiber, inulin, can reprogram the murine liver to favor HCC development in a gut microbiota-dependent manner. Determining the molecular pathways that are either over expressed or repressed during inulin-induced HCC would provide a platform of potential therapeutic targets. In the present study, we have combined analysis of the novel inulin-induced HCC murine model and human HCC samples to identify differentially expressed genes (DEGs) in hepatocarcinogenesis. Hepatic transcriptome profiling revealed that there were 674 DEGs in HCC mice compared to mice safeguarded from HCC. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis uncovered enrichment in ECM-receptor interaction, steroid hormone biosynthesis, PPAR signaling pathway, focal adhesion and protein digestion and absorption during inulin-induced HCC. Tandem mass tag based quantitative, multiplexed proteomic analysis delineated 57 differentially expressed proteins, where the over-expressed proteins were associated with cell adhesion molecules, valine, leucine and isoleucine degradation and ECM-receptor interaction. After obtaining the human orthologs of the mouse genes, we did a comparison analysis to level 3 RNA-seq data found in the Cancer Genome Atlas (TCGA) database, corresponding to human HCC (n = 361) and healthy liver (n = 50) samples. Out of the 549 up-regulated and 68 down-regulated human orthologs identified, 142 genes (137 significantly over-expressed and 5 significantly under-expressed) were associated with human HCC. Using univariate survival analysis, we found 27 over-expressed genes involved in cell-cell adhesion and cell division that were associated with poor HCC patient survival. Overall, the genetic and proteomics signatures highlight potential underlying mechanisms in inulin-induced HCC and support that this murine HCC model is human relevant.
Photothermal Quotient Specifications to Improve Wheat Cultivar Yield Component Models

Previous research has demonstrated the importance and statistical significance of the photothermal quotient (PTQ) to predict and explain wheat (Triticum aestivum L.) yields. The objective of this study is to respecify PTQ to enhance the explanatory power of statistical models used to explain grains per square meter (GM ―2 ), and increase understanding of weather's impact on yields. The primary objective is to identify and quantify potential gains from including separate components of solar radiation (Solar) and temperature (Temp) in place of PTQ (Solar/Temp) to improve wheat yield model explanatory ability. The study also determines the optimal time interval that defines PTQ by varying the number of days before and after 50% anthesis. Using wheat test plot data from Centro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT) in Mexico's Yaqui Valley, a multivariate regression-based stochastic model of wheat yields was used to estimate the impact of altering PTQ definition and specification. Results support previous research: the maximum F-test value of 66.02 and adjusted R 2 value of 0.446 were obtained for 31 d before to 1 d after 50% anthesis. Interpretation and analysis were also enhanced by disaggregating PTQ into separate variables Solar and Temp. A 1 MJ m ―2 d ―1 increase in Solar increased GM ―2 by 1.25%, whereas a 1°C increase in Temp decreased GM ―2 by 2.8%. This difference in yield responsiveness to weather components results in greater statistical significance, explanatory power, and interpretation of GM ―2 models. Future research that builds on these results will better explain, predict, and forecast crop yields.
Computers in Biology and Medicine

With the recent outbreak of COVID-19, fast diagnostic testing has become one of the major challenges due to the critical shortage of test kit. Pneumonia, a major effect of COVID-19, needs to be urgently diagnosed along with its underlying reasons. In this paper, deep learning aided automated COVID-19 and other pneumonia detection schemes are proposed utilizing a small amount of COVID-19 chest X-rays. A deep convolutional neural network (CNN) based architecture, named as CovXNet, is proposed that utilizes depthwise convolution with varying dilation rates for efficiently extracting diversified features from chest X-rays. Since the chest X-ray images corresponding to COVID-19 caused pneumonia and other traditional pneumonias have significant similarities, at first, a large number of chest X-rays corresponding to normal and (viral/bacterial) pneumonia patients are used to train the proposed CovXNet. Learning of this initial training phase is transferred with some additional fine-tuning layers that are further trained with a smaller number of chest X-rays corresponding to COVID-19 and other pneumonia patients. In the proposed method, different forms of CovXNets are designed and trained with X-ray images of various resolutions and for further optimization of their predictions, a stacking algorithm is employed. Finally, a gradient-based discriminative localization is integrated to distinguish the abnormal regions of X-ray images referring to different types of pneumonia. Extensive experimentations using two different datasets provide very satisfactory detection performance with accuracy of 97.4% for COVID/Normal, 96.9% for COVID/Viral pneumonia, 94.7% for COVID/Bacterial pneumonia, and 90.2% for multiclass COVID/normal/Viral/Bacterial pneumonias. Hence, the proposed schemes can serve as an efficient tool in the current state of COVID-19 pandemic. All the architectures are made publicly available at: https: //github.com/Perceptron21/CovXNet.