Exon-Mediated Activation of Transcription Starts.

The processing of RNA transcripts from mammalian genes occurs in proximity to their transcription. Here, we describe a phenomenon affecting thousands of genes that we call exon-mediated activation of transcription starts (EMATS), in which the splicing of internal exons impacts promoter choice and the expression level of the gene. We observed that evolutionary gain of internal exons is associated with gain of new transcription start sites (TSSs) nearby and increased gene expression. Inhibiting exon splicing reduced transcription from nearby promoters, and creation of new spliced exons activated transcription from cryptic promoters. The strongest effects occurred for weak promoters located proximal and upstream of efficiently spliced exons. Together, our findings support a model in which splicing recruits transcription machinery locally to influence TSS choice and identify exon gain, loss, and regulatory change as major contributors to the evolution of alternative promoters and gene expression in mammals.

Interplay between the transcription preinitiation complex and the +1 nucleosome.

Eukaryotic transcription starts with the assembly of a preinitiation complex (PIC) on core promoters. Flanking this region is the +1 nucleosome, the first nucleosome downstream of the core promoter. While this nucleosome is rich in epigenetic marks and plays a key role in transcription regulation, how the +1 nucleosome interacts with the transcription machinery has been a long-standing question. Here, we summarize recent structural and functional studies of the +1 nucleosome in complex with the PIC. We specifically focus on how differently organized promoter-nucleosome templates affect the assembly of the PIC and PIC-Mediator on chromatin and result in distinct transcription initiation.

The Biochemistry and Physiology of A Disintegrin and Metalloproteinases (ADAMs and ADAM-TSs) in Human Pathologies.

Metalloproteinases are a group of proteinases that plays a substantial role in extracellular matrix remodeling and its molecular signaling. Among these metalloproteinases, ADAMs (a disintegrin and metalloproteinases) and ADAM-TSs (ADAMs with thrombospondin domains) have emerged as highly efficient contributors mediating proteolytic processing of various signaling molecules. ADAMs are transmembrane metalloenzymes that facilitate the extracellular domain shedding of membrane-anchored proteins, cytokines, growth factors, ligands, and their receptors and therefore modulate their biological functions. ADAM-TSs are secretory, and soluble extracellular proteinases that mediate the cleavage of non-fibrillar extracellular matrix proteins. ADAMs and ADAM-TSs possess pro-domain, metalloproteinase, disintegrin, and cysteine-rich domains in common, but ADAM-TSs have characteristic thrombospondin motifs instead of the transmembrane domain. Most ADAMs and ADAM-TSs are activated by cleavage of pro-domain via pro-protein convertases at their N-terminus, hence directing them to various signaling pathways. In this article, we are discussing not only the structure and regulation of ADAMs and ADAM-TSs, but also the importance of these metalloproteinases in various human pathophysiological conditions like cardiovascular diseases, colorectal cancer, autoinflammatory diseases (sepsis/rheumatoid arthritis), Alzheimer's disease, proliferative retinopathies, and infectious diseases. Therefore, based on the emerging role of ADAMs and ADAM-TSs in various human pathologies, as summarized in this review, these metalloproteases can be considered as critical therapeutic targets and diagnostic biomarkers.

COVID-19-Related Multi-systemic Inflammatory Syndrome in Children (MIS-C).

Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infections in children. This syndrome manifests about a month after the initial viral infection and is characterized by fever, multiorgan dysfunction, and systemic inflammation. This chapter will review the emergence, epidemiology, clinical characteristics, diagnosis, pathophysiology, immunomodulatory treatment, prognosis, outcomes, and prevention of MIS-C. While the pathophysiology of MIS-C remains to be defined, it is a post-infection, hyperinflammatory syndrome of childhood with elevated inflammatory cytokines.

Daphnia magna digestive activity is differentially altered when exposed to equally turbid waters caused by either suspended sediment or suspended microplastics.

Turbidity can be a result of suspended natural particles, such as sediment, or anthropogenic particles such as microplastics. This study assessed whether Daphnia magna, a pelagic filter feeder known to ingest suspended particles, have an altered response to equally turbid environments caused by the presence of either suspended bentonite or suspended polyethylene microplastics. Compared to controls, daphnids exposed to suspended bentonite maintained their feeding efficiency and increased their digestive activity, as measured by mandibular movement, peristalsis, and expulsion, to pass bentonite through the digestive tract. The same effects were not seen in microplastic-exposed individuals, in which feeding efficiency was decreased and only peristaltic movement was increased but without a coordinated increase in expulsion, suggesting that microplastics do not have the same ability as bentonite to pass through the digestive tract. This study highlights the need to discern the identities of particulates contributing to turbid environments as different particles, even of the same size, can have different effects on filter feeders, which inherently ingest suspended particles.

Magnetotransport Signatures of Superconducting Cooper Pairs Carried by Topological Surface States in Bismuth Selenide.

As topological insulators (TIs) are becoming increasingly intriguing, the community is exploring transformative applications that require interfacing TIs with other materials such as ferromagnets or superconductors. Herein, we report on the manifestations of superconducting electrons carried by topological surface states (TSS) in Bi2Se3 films. As key signatures of TSS-carried Cooper pairs, we uncover the hysteresis of magnetoresistance (MR) and the switching behavior of anisotropic magnetoresistance (AMR). For in-plane fields perpendicular to the injected current, AMR shows negative switching (resistance drop) when the contacts become superconducting, which is consistent with a cooperative Zeeman effect enabled by the spin-momentum locking of TSS. The MR and AMR behaviors are robust, occurring reliably in multiple samples, from different sources, and with different defect concentrations. Our findings can guide novel developments in superconductor/TI quantum devices relying on supercurrent detection as well as lead to more refined transport signatures of Majorana zero-modes in the future.

Increased MYB alternative promoter usage is associated with relapse in acute lymphoblastic leukemia.

Therapy-resistant disease is a major cause of death in patients with acute lymphoblastic leukemia (ALL). Activation of the MYB oncogene is associated with ALL and leads to uncontrolled neoplastic cell proliferation and blocked differentiation. Here, we used RNA-seq to study the clinical significance of MYB expression and MYB alternative promoter (TSS2) usage in 133 pediatric ALLs. RNA-seq revealed that all cases analyzed overexpressed MYB and demonstrated MYB TSS2 activity. qPCR analyses confirmed the expression of the alternative MYB promoter also in seven ALL cell lines. Notably, high MYB TSS2 activity was significantly associated with relapse (p = 0.007). Moreover, cases with high MYB TSS2 usage showed evidence of therapy-resistant disease with increased expression of ABC multidrug resistance transporter genes (e.g., ABCA2, ABCB5, and ABCC10) and enzymes catalyzing drug degradation (e.g., CYP1A2, CYP2C9, and CYP3A5). Elevated MYB TSS2 activity was further associated with augmented KRAS signaling (p < 0.05) and decreased methylation of the conventional MYB promoter (p < 0.01). Taken together, our results suggest that MYB alternative promoter usage is a novel potential prognostic biomarker for relapse and therapy resistance in pediatric ALL.

Computational annotation of miRNA transcription start sites.

MOTIVATION: MicroRNAs (miRNAs) are small noncoding RNAs that play important roles in gene regulation and phenotype development. The identification of miRNA transcription start sites (TSSs) is critical to understand the functional roles of miRNA genes and their transcriptional regulation. Unlike protein-coding genes, miRNA TSSs are not directly detectable from conventional RNA-Seq experiments due to miRNA-specific process of biogenesis. In the past decade, large-scale genome-wide TSS-Seq and transcription activation marker profiling data have become available, based on which, many computational methods have been developed. These methods have greatly advanced genome-wide miRNA TSS annotation. RESULTS: In this study, we summarized recent computational methods and their results on miRNA TSS annotation. We collected and performed a comparative analysis of miRNA TSS annotations from 14 representative studies. We further compiled a robust set of miRNA TSSs (RSmirT) that are supported by multiple studies. Integrative genomic and epigenomic data analysis on RSmirT revealed the genomic and epigenomic features of miRNA TSSs as well as their relations to protein-coding and long non-coding genes. CONTACT: xiaoman@mail.ucf.edu, haihu@cs.ucf.edu.

Non-canonical transcriptional start sites in E. coli O157:H7 EDL933 are regulated and appear in surprisingly high numbers.

Analysis of genome wide transcription start sites (TSSs) revealed an unexpected complexity since not only canonical TSS of annotated genes are recognized by RNA polymerase. Non-canonical TSS were detected antisense to, or within, annotated genes as well new intergenic (orphan) TSS, not associated with known genes. Previously, it was hypothesized that many such signals represent noise or pervasive transcription, not associated with a biological function. Here, a modified Cappable-seq protocol allows determining the primary transcriptome of the enterohemorrhagic E. coli O157:H7 EDL933 (EHEC). We used four different growth media, both in exponential and stationary growth phase, replicated each thrice. This yielded 19,975 EHEC canonical and non-canonical TSS, which reproducibly occurring in three biological replicates. This questions the hypothesis of experimental noise or pervasive transcription. Accordingly, conserved promoter motifs were found upstream indicating proper TSSs. More than 50% of 5,567 canonical and between 32% and 47% of 10,355 non-canonical TSS were differentially expressed in different media and growth phases, providing evidence for a potential biological function also of non-canonical TSS. Thus, reproducible and environmentally regulated expression suggests that a substantial number of the non-canonical TSSs may be of unknown function rather than being the result of noise or pervasive transcription.

"Spice"-related deaths in and around Munich, Germany: A retrospective look at the role of synthetic cannabinoid receptor agonists in our post-mortem cases over a seven-year period (2014-2020).

Synthetic cannabinoid receptor agonists (SCRAs, "Spice") are a diverse group of recreational drugs, with their structural and pharmacological variability still evolving. Forensic toxicologists often rely on previous reports to assess their role in intoxication cases. This work provides detailed information on the "Spice"-related fatalities around Munich, Germany, from 2014 to 2020. All cases underwent an autopsy. Pharmaceutical and illicit drugs were detected and quantified in post-mortem peripheral blood or liver by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Based on circumstantial evidence, only those cases for which a prior consumption was suspected underwent additional analyses for SCRAs and other new psychoactive substances in post-mortem blood, liver or antemortem specimens. Drug concentrations, pathological findings at autopsy and case histories were considered to assess and rank the SCRAs' involvement in each death. Concentration ranges for the individual substances in blood were defined and their distribution patterns over the investigated period were determined and correlated with their legal status and local police seizures. We identified 41 different SCRAs among 98 fatalities. 91.8% were male, at a median age of 36 years. SCRAs played a causative role in 51%, contributory role in 26%, and an insignificant role in 23% of cases. In correlation with local police seizures and legal status, 5F-ADB was the most prevalent in our cases, followed by 5F-MDMB-PICA and AB-CHMINACA. Cumyl-CBMICA and 5F-MDMB-P7AICA were among the least frequently detected SCRAs. "Spice"-related fatalities and SCRAs' causative role have significantly decreased among our cases since the German New Psychoactive Substances Act.

Estimation of Sugar Content in Wine Grapes via In Situ VNIR-SWIR Point Spectroscopy Using Explainable Artificial Intelligence Techniques.

Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR-SWIR spectrum (350-2500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR-SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination (R2), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm (R2>0.8, RPIQ≥4), while a good fit was attained for the Chardonnay variety from SVR (R2=0.63, RMSE=2.10, RPIQ=2.24), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way.

Rapid Determination of Wine Grape Maturity Level from pH, Titratable Acidity, and Sugar Content Using Non-Destructive In Situ Infrared Spectroscopy and Multi-Head Attention Convolutional Neural Networks.

In the pursuit of enhancing the wine production process through the utilization of new technologies in viticulture, this study presents a novel approach for the rapid assessment of wine grape maturity levels using non-destructive, in situ infrared spectroscopy and artificial intelligence techniques. Building upon our previous work focused on estimating sugar content (∘Brix) from the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, this research expands its scope to encompass pH and titratable acidity, critical parameters determining the grape maturity degree, and in turn, wine quality, offering a more representative estimation pathway. Data were collected from four grape varieties-Chardonnay, Malagouzia, Sauvignon Blanc, and Syrah-during the 2023 harvest and pre-harvest phenological stages in the vineyards of Ktima Gerovassiliou, northern Greece. A comprehensive spectral library was developed, covering the VNIR-SWIR spectrum (350-2500 nm), with measurements performed in situ. Ground truth data for pH, titratable acidity, and sugar content were obtained using conventional laboratory methods: total soluble solids (TSS) (∘Brix) by refractometry, titratable acidity by titration (expressed as mg tartaric acid per liter of must) and pH by a pH meter, analyzed at different maturation stages in the must samples. The maturity indicators were predicted from the point hyperspectral data by employing machine learning algorithms, including Partial Least Squares regression (PLS), Random Forest regression (RF), Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), in conjunction with various pre-processing techniques. Multi-output models were also considered to simultaneously predict all three indicators to exploit their intercorrelations. A novel multi-input-multi-output CNN model was also proposed, incorporating a multi-head attention mechanism and enabling the identification of the spectral regions it focuses on, and thus having a higher interpretability degree. Our results indicate high accuracy in the estimation of sugar content, pH, and titratable acidity, with the best models yielding mean R2 values of 0.84, 0.76, and 0.79, respectively, across all properties. The multi-output models did not improve the prediction results compared to the best single-output models, and the proposed CNN model was on par with the next best model. The interpretability analysis highlighted that the CNN model focused on spectral regions associated with the presence of sugars (i.e., glucose and fructose) and of the carboxylic acid group. This study underscores the potential of portable spectrometry for real-time, non-destructive assessments of wine grape maturity, thereby providing valuable tools for informed decision making in the wine production industry. By integrating pH and titratable acidity into the analysis, our approach offers a holistic view of grape quality, facilitating more comprehensive and efficient viticultural practices.

Exogenous Thymosin Beta 4 Suppresses IPF-Lung Cancer in Mice: Possibly Associated with Its Inhibitory Effect on the JAK2/STAT3 Signaling Pathway.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, fibrotic interstitial lung disease of unknown etiology. At present, the mortality rate of the deadly disease is still very high, while the existing treatments only delay the progression of the disease and improve the quality of life of patients. Lung cancer (LC) is the most fatal disease in the world. In recent years, IPF has been considered to be an independent risk factor for the development of LC. The incidence of lung cancer is increased in the patients with IPF and the mortality is also significantly increased in the patients inflicted with the two diseases. In this study, we evaluated an animal model of pulmonary fibrosis complicated with LC by implanting LC cells orthotopically into the lungs of mice several days after bleomycin induction of the pulmonary fibrosis in the same mice. In vivo studies with the model showed that exogenous recombinant human thymosin beta 4 (exo-rhTß4) alleviated the impairment of lung function and severity of damage of the alveolar structure by the pulmonary fibrosis and inhibited the proliferation of LC tumor growth. In addition, in vitro studies showed that exo-rhTß4 inhibited the proliferation and migration of A549 and Mlg cells. Furthermore, our results also showed that rhTß4 could effectively inhibit the JAK2-STAT3 signaling pathway and this might exert an anti-IPF-LC effect. The establishment of the IPF-LC animal model will be helpful for the development of drugs for the treatment of IPF-LC. Exogenous rhTß4 can be potentially used for the treatment of IPF and LC.

Application of near-infrared spectroscopy for the estimation of volatile compounds in Tempranillo Blanco grape berries during ripening.

BACKGROUND: The knowledge of volatile compounds concentration in grape berries is very valuable information for the winemaker, since these compounds are strongly involved in the final wine quality, and in consumer acceptance. In addition, it would allow to set the harvest date according to aromatic maturity, to classify grape berries according to their quality and to make wines with different characteristics, among other implications. However, so far, there are no tools that allow the volatile composition to be measured directly on intact berries, either in the vineyard or in the winery. RESULTS: In this work, the use of near-infrared (NIR) spectroscopy to estimate the aromatic composition and total soluble solids (TSS) of Tempranillo Blanco grape berries during ripening was evaluated. For this purpose, the spectra in the NIR range (1100-2100 nm) of 240 intact berry samples were acquired in the laboratory. From these same samples, the concentration of volatile compounds was analyzed by thin film-solid-phase microextraction-gas chromatography-mass spectrometry (TF-SPME-GC-MS), and the TSS were quantified by refractometry. These two methods were used as reference methods for model building. Calibration, cross-validation and prediction models were built from spectral data using partial least squares (PLS). Determination coefficients of cross-validation (R2 CV ) above 0.5 were obtained for all volatile compounds, their families, and TSS. CONCLUSIONS: These findings support that NIR spectroscopy can be successfully use to estimate the aromatic composition as well as the TSS of intact Tempranillo Blanco berries in a non-destructive, fast, and contactless form, allowing simultaneous determination of technological and aromatic maturities. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Multi-criteria decision analysis integrated with GIS to determine land suitability for cultivation and best harvest time of vine.

BACKGROUND: In this study, the quality of land suitability for vine cultivation in south-western Iran was evaluated and a land suitability map for vine cultivation was developed using the fuzzy-analytic hierarchy process (AHP). The best harvest time of vines was determined based on the relationship between total soluble solids (TSS) and titratable acidity (TA) with fruit colour (red, green, and blue or RGB) in ten points and also the growing degree-days (GDD) maps from April to December. The relationship between GDD and effective parameters in vine cultivation was determined using principal component analysis (PCA) and Pearson correlation methods. RESULTS: The results illustrated that the maximum temperature and relative humidity (RH) have the greatest effect on vine cultivation and its yield (weight 0.24). The results of the land suitability map showed that central regions have better conditions for growing vines (32%). The measurements of TA and TSS depicted that vines of the northern parts have higher TA and lower TSS and there is a significant relationship between them and fruit colour. The results of GDD maps showed that the harvest time of ruby vine and Gezel-azm is July and August, respectively. Also, the temperature (Tmean , Tmax and Tmin ), wind speed, and GDD were the most important parameters to determine the best location for vine cultivation. CONCLUSION: Determining land suitability for cultivation, the best harvest time, the time of grape ripening, and following the exact time of export and import of vine has a vital role to increase its productivity and services. © 2022 Society of Chemical Industry.

Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel 2 MSI: the importance of considering model functional form.

Anthropogenic eutrophication is a global environmental problem threatening the ecological functions of many inland freshwaters and diminishing their abilities to meet their designated uses. Water authorities worldwide are being pressed to improve their abilities to monitor, predict, and manage the incidence of harmful algal blooms (HABs). While most water quality management decisions are still based on conventional monitoring programs that lack the needed spatio-temporal resolution for effective lake/reservoir management, recent advances in remote sensing are providing new opportunities towards better understanding water quality variability in these important freshwater systems. This study assessed the potential of using the Sentinel 2 Multispectral Instrument to predict and assess the spatio-temporal variability in the water quality of the Qaraoun Reservoir, a poorly monitored Mediterranean hypereutrophic monomictic reservoir that is subject to extensive periods of HABs. The work first evaluated the ability to transfer and recalibrate previously developed reservoir-specific Landsat 7 and 8 water quality models when used with Sentinel 2 data. The results showed poor transferability between Landsat and Sentinel 2, with most models experiencing a significant drop in their predictive skill even after recalibration. Sentinel 2 models were then developed for the reservoir based on 153 water quality samples collected over 2 years. The models explored different functional forms, including multiple linear regressions (MLR), multivariate adaptive regression splines (MARS), random forests (RF), and support vector regressions (SVR). The results showed that the RF models outperformed their MLR, MARS, and SVR counterparts with regard to predicting chlorophyll-a, total suspended solids, Secchi disk depth, and phycocyanin. The coefficient of determination (R2) for the RF models varied between 85% for TSS up to 95% for SDD. Moreover, the study explored the potential of quantifying cyanotoxin concentrations indirectly from the Sentinel 2 MSI imagery by benefiting from the strong relationship between cyanotoxin levels and chlorophyll-a concentrations.

Comparison of Characteristics and Outcomes of Multisystem Inflammatory Syndrome, Kawasaki Disease and Toxic Shock Syndrome in Children.

Background and Objectives: Since the first cases of multisystem inflammatory syndrome in children (MIS-C) in April 2020, the diagnostic challenge has been to recognize this syndrome and to differentiate it from other clinically similar pathologies such as Kawasaki disease (KD) and toxic shock syndrome (TSS). Our objective is to compare clinical signs, laboratory data and instrumental investigations between patients with MIS-C, KD and TSS. Materials and Methods: This retrospective observational study was conducted at the Children's Clinical University Hospital, Latvia (CCUH). We collected data from all pediatric patients <18 years of age, who met the Centers for Disease Control and Prevention case definition for MIS-C, and who presented to CCUH between December 2020 and December 2021. We also retrospectively reviewed data from inpatient medical records of patients <18 years of age diagnosed as having KD and TSS at CCUH between December 2015 and December 2021. Results: In total, 81 patients were included in this study: 39 (48.1%) with KD, 29 (35.8%) with MIS-C and 13 (16.1%) with TSS. In comparison with TSS and KD, patients with MIS-C more often presented with gastrointestinal symptoms (abdominal pain (p < 0.001), diarrhea (p = 0.003)), shortness of breath (p < 0.02) and headache (p < 0.003). All MIS-C patients had cardiovascular involvement and 93.1% of MIS-C patients fulfilled KD criteria, showing higher prevalence than in other research. Patients with KD had higher prevalence of cervical lymphadenopathy (p < 0.006) and arthralgias (p < 0.001). In comparison with KD and TSS, MIS-C patients had higher levels of ferritin (p < 0.001), fibrinogen (p = 0.04) and cardiac biomarkers, but lower levels of platelets and lymphocytes (p < 0.001). KD patients tended to have lower peak C-reactive protein (CRP) (p < 0.001), but higher levels of platelets. Acute kidney injury was more often observed in TSS patients (p = 0.01). Pathological changes in electrocardiography (ECG) and echocardiography were significantly more often observed in MIS-C patients (p < 0.001). Conclusions: This research shows that MIS-C, KD and TSS have several clinical similarities and additional investigations are required for reaching final diagnosis. All the patients with suspected MIS-C diagnosis should be examined for possible cardiovascular involvement including cardiac biomarkers, ECG and echocardiography.

Nonlinear physics opens a new paradigm for accurate transcription start site prediction.

There is evidence that DNA breathing (spontaneous opening of the DNA strands) plays a relevant role in the interactions of DNA with other molecules, and in particular in the transcription process. Therefore, having physical models that can predict these openings is of interest. However, this source of information has not been used before either in transcription start sites (TSSs) or promoter prediction. In this article, one such model is used as an additional information source that, when used by a machine learning (ML) model, improves the results of current methods for the prediction of TSSs. In addition, we provide evidence on the validity of the physical model, as it is able by itself to predict TSSs with high accuracy. This opens an exciting avenue of research at the intersection of statistical mechanics and ML, where ML models in bioinformatics can be improved using physical models of DNA as feature extractors.

DeepTSS: multi-branch convolutional neural network for transcription start site identification from CAGE data.

BACKGROUND: The widespread usage of Cap Analysis of Gene Expression (CAGE) has led to numerous breakthroughs in understanding the transcription mechanisms. Recent evidence in the literature, however, suggests that CAGE suffers from transcriptional and technical noise. Regardless of the sample quality, there is a significant number of CAGE peaks that are not associated with transcription initiation events. This type of signal is typically attributed to technical noise and more frequently to random five-prime capping or transcription bioproducts. Thus, the need for computational methods emerges, that can accurately increase the signal-to-noise ratio in CAGE data, resulting in error-free transcription start site (TSS) annotation and quantification of regulatory region usage. In this study, we present DeepTSS, a novel computational method for processing CAGE samples, that combines genomic signal processing (GSP), structural DNA features, evolutionary conservation evidence and raw DNA sequence with Deep Learning (DL) to provide single-nucleotide TSS predictions with unprecedented levels of performance. RESULTS: To evaluate DeepTSS, we utilized experimental data, protein-coding gene annotations and computationally-derived genome segmentations by chromatin states. DeepTSS was found to outperform existing algorithms on all benchmarks, achieving 98% precision and 96% sensitivity (accuracy 95.4%) on the protein-coding gene strategy, with 96.66% of its positive predictions overlapping active chromatin, 98.27% and 92.04% co-localized with at least one transcription factor and H3K4me3 peak. CONCLUSIONS: CAGE is a key protocol in deciphering the language of transcription, however, as every experimental protocol, it suffers from biological and technical noise that can severely affect downstream analyses. DeepTSS is a novel DL-based method for effectively removing noisy CAGE signal. In contrast to existing software, DeepTSS does not require feature selection since the embedded convolutional layers can readily identify patterns and only utilize the important ones for the classification task. This study highlights the key role that DL can play in Molecular Biology, by removing the inherent flaws of experimental protocols, that form the backbone of contemporary research. Here, we show how DeepTSS can unleash the full potential of an already popular and mature method such as CAGE, and push the boundaries of coding and non-coding gene expression regulator research even further.

Assessing the Impact of Wildlife on Vegetation Cover Change, Northeast Namibia, Based on MODIS Satellite Imagery (2002-2021).

Human-wildlife conflict in the Zambezi region of northeast Namibia is well documented, but the impact of wildlife (e.g., elephants) on vegetation cover change has not been adequately addressed. Here, we assessed human-wildlife interaction and impact on vegetation cover change. We analyzed the 250 m MODIS and ERA5 0.25° × 0.25° drone and GPS-collar datasets. We used Time Series Segmented Residual Trends (TSS-RESTREND), Mann-Kendall Test Statistics, Sen's Slope, ensemble, Kernel Density Estimation (KDE), and Pearson correlation methods. Our results revealed (i) widespread vegetation browning along elephant migration routes and within National Parks, (ii) Pearson correlation (p-value = 5.5 × 10-8) showed that vegetation browning areas do not sustain high population densities of elephants. Currently, the Zambezi has about 12,008 elephants while these numbers were 1468, 7950, and 5242 in 1989, 1994, and 2005, respectively, (iii) settlements and artificial barriers have a negative impact on wildlife movement, driving vegetation browning, and (iv) vegetation greening was found mostly within communal areas where intensive farming and cattle grazing is a common practice. The findings of this study will serve as a reference for policy and decision makers. Future studies should consider integrating higher resolution multi-platform datasets for detailed micro analysis and mapping of vegetation cover change.