Miracle-dz/UniFAR-FTUs huggingface dataset:
Double Mass Extinctions and the Volcanogenic Dark Matter Scenario

A few of the major mass extinctions of paleontology have recently been found to consist of two distinct extinction peaks at higher resolution. A viable explanation for this remains elusive. In this paper it is shown that the recently proposed volcanogenic dark matter model can explain this puzzling characteristic of these extinctions. The accumulation and annihilation of dark matter in the center of the Earth due to the passage of a clump leads to excess heat generation with the consequent ejection of superplumes, followed by massive volcanism and attendant mass extinctions. This is preceded by an extinction pulse due to carcinogenesis arising from the direct interaction of the clumped dark matter with living organisms.
Management of Enamel Defects with Resin Infiltration Techniques: Two Years Follow Up Retrospective Study

Background: Developmental Defects of Enamel (DDE) represent an aesthetic, functional, and often psychological problem, especially in young patients. Infiltrative treatment with resin (Icon-DMG, Hamburg, Germany) is a minimally invasive technique based on the infiltration of high viscosity resin inside the hypomineralized enamel, modifying its optical properties. The aim of this paper is to evaluate the clinical efficacy of superficial infiltration in the treatment of white enamel defects of the anterior sector with pre-eruptive etiology and its stability over time. Methods: Thirty-three patients affected by DDE associated with mild and moderate Molar Incisor Hypomineralization (MIH), mild and moderate fluorosis, and post-traumatic hypomineralization treated with resin infiltration were retrospectively retrieved. Results: In all cases an improvement in aesthetic appearance was achieved, and the 24-month follow-up confirmed the stability of the results. However, in the cases of traumatic hypomineralization the results were not completely satisfactory. Conclusions: The superficial infiltration technique can be considered a valid minimally invasive alternative to traditional treatment of mild or moderate fluorosis and mild MIH.
Laboratory Analyses Used to Define the Nutritional Parameters and Quality Indexes of Some Unusual Forages

Simple Summary In the present study, laboratory analyses conducted on unusual forages have been used to calculate various parameters and define some quality indexes using different equations. It was found that the quality of unusual forages decreased during the growth but maintained a high level. Different indexes were used to evaluate the forage quality. The Relative Feed Value and the Relative Forage Quality indexes, when calculated with the formula for legumes, correctly summarized the trend of the nutritional characteristics of the unusual forages at different maturity stages. Abstract The quality of a forage influences the production of animals, and it can be defined in many ways. Laboratory analyses are important tools because they can be used to indicate the quality of the forages, and they represent a relatively quick way of defining their nutritive values. However, specific quality indexes are necessary to evaluate and rank forages. The quality of conventional forages is predicted by different indexes, according to whether they are legumes or grasses. However, no indications are given about what formulae should be used for unusual forages. In the present study, laboratory analyses have been conducted on three unusual crops belonging to three different botanical families (amaranth, borage, and camelina) at four growth stages, and conventional quality indexes have been calculated and applied to establish their quality. The obtained results have shown that the nutritive value of the unusual forages modified during the growth, although they always maintained a high quality. Hence, the Relative Feed Value of unusual forages can be measured using the ADF content or digestibility value. The Relative Forage Quality, calculated with the legume formula, seems more appropriate for the considered unusual forages as it was able to reveal any changes that took place during maturity.
Unsteadiness of shock-boundary layer interactions in a Mach 2.0 supersonic turbine cascade

The physics of shock-boundary layer interactions (SBLIs) in a supersonic turbine cascade at Mach 2.0 and Reynolds number 395,000, based on the axial chord, is investigated through a wall-resolved large eddy simulation. Special attention is given to the characterization of the low-frequency dynamics of the separation bubbles using flow visualization, spectral analysis, space-time cross correlations, and flow modal decomposition. The mean flowfield shows different shock structures formed on both sides of the airfoil. On the suction side, an oblique shock impinges on the turbulent boundary layer, whereas a Mach reflection interacts with the pressure side boundary layer. The interactions taking place in the present turbine cascade show similarities and discrepancies with respect to more canonical cases. For example, the characteristic frequencies of the shock/bubble motions are comparable to those described in the literature of canonical cases. However, the suction side bubble leads to compression waves that do not coalesce into a separation shock, and a thin bubble forms on the pressure side despite the strong normal shock from the Mach reflection. Instantaneous flow visualizations illustrate elongated streamwise structures on the incoming boundary layers and their interactions with the shocks and separation bubbles. The space-time cross-correlations reveal that the near-wall streaks drive the motion of the suction side separation bubble, which in turn promotes oscillations of the reattachment shock and shear layer flapping. Organized motions in the SBLIs and their corresponding characteristic frequencies and spatial support are identified using proper orthogonal decomposition.
Effects of Dietary Nitrate Supplementation on Performance and Muscle Oxygenation during Resistance Exercise in Men

The purpose of the current study was to assess the effects of acute and short-term nitrate (NO3−)-rich beetroot juice (BR) supplementation on performance outcomes and muscle oxygenation during bench press and back squat exercise. Fourteen recreationally active males were assigned in a randomized, double-blind, crossover design to supplement for 4 days in two conditions: (1) NO3−-depleted beetroot juice (PL; 0.10 mmol NO3− per day) and (2) BR (11.8 mmol NO3− per day). On days 1 and 4 of the supplementation periods, participants completed 2 sets of 2 × 70%1RM interspersed by 2 min of recovery, followed by one set of repetitions-to-failure (RTF) at 60%1RM for the determination of muscular power, velocity, and endurance. Quadriceps and pectoralis major tissue saturation index (TSI) were measured throughout exercise. Plasma [NO3−] and nitrite ([NO2−]) were higher after 1 and 4 days of supplementation with BR compared to PL (p < 0.05). Quadriceps and pectoralis major TSI were not different between conditions (p > 0.05). The number of RTF in bench press was 5% greater after acute BR ingestion compared to PL (PL: 23 ± 4 vs. BR: 24 ± 5, p < 0.05). There were no differences between BR and PL for RTF for back squat or power and velocity for back squat or bench press (p > 0.05). These data improve understanding on the ergogenic potential of BR supplementation during resistance exercise.
Experimental Demonstration of an Autonomous Distributed-Consensus Network for Underwater Passive-Acoustic Detection

In this article an autonomous distributed-consensus system is evaluated with regard to its efficacy for establishing a common detection statistic among a network of passive acoustic hydrophones. The notion of consensus among sensors is briefly reviewed, and an algorithm motivated by diffusion of information among network neighbors is developed within the context of spectral graph theory. Relative entropy derived at each node provides information that the network averages to reach a joint detection decision. As the expected value of a log-likelihood ratio, relative entropy is the appropriate detection statistic for this application because it is nondimensional, additive, and insensitive to calibration. These properties facilitate the fusion of information among heterogeneous sensors. Seven nodes of an acoustical receiver network were moored on the New Jersey coastal shelf in May 2019, while towed and autonomous sources provided target signals at 900–1050 Hz. The convergence rate of the system to a detection statistic was found to be limited by slow acoustic communications, and convergence to a global consensus was often disrupted by communication breakdowns. When failures occurred, the network successfully demonstrated robustness by dissociating into viable subnetworks, each of which converged to a valid detection statistic.
Fractional-Order Vectorial Halanay-Type Inequalities With Applications for Stability and Synchronization Analyses

The Halanay inequality is widely used in various time-delayed dynamical systems analyses and its vectorial form has become available recently. In this article, the integer-order vectorial Halanay-type inequality is further extended to fractional-order ones in both time-invariant and time-varying forms. It is shown that the fractional-order vectorial Halanay-type inequalities hold under the derived conditions in the form of $M$ -matrices. In addition, the time-invariant inequalities are applied to analyzing the stability and synchronization of fractional-order systems with two numerical examples to substantiate the theoretical results.
Effects of Left Atrial Appendage Closure on Neuroendocrine Function in Patients with Nonvalvular Atrial Fibrillation

Background The left atrial appendage (LAA) is an organ with neuroendocrine function. It remains unclear whether left atrial appendage closure (LAAC) has physiological effects on neuroendocrine function in patients with nonvalvular atrial fibrillation (NVAF). In the present study, we aimed to investigate the effects of LAAC on neuroendocrine function in patients with NVAF. Material/Methods We enrolled 20 patients with NVAF treated by LAAC in Jiangsu Taizhou People’s Hospital from October 2019 to October 2020. Blood samples were collected 1 day before LAAC and 12 months after LAAC. Plasma concentrations of adrenaline, aldosterone, pro-atrial natriuretic peptide (NT-proANP), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) were measured. Results LAAC was successfully performed in all patients, without serious complications. Compared with the preoperative levels, there was no significant difference in the levels of NT-proANP, NT-proBNP, and epinephrine at 12 months after LAAC (P>0.05). However, there was a significant decrease in aldosterone level at 12 months post-procedure (209.04±132.98 pg/ml) compared with pre-procedure baseline (279.08±166.88 pg/ml, P=0.04). There was no correlation between the compression rate of the occlusion and the reduction of aldosterone (Kendall’s Tau-b=0.159, P=0.351). Conclusions LAAC can be safely and effectively performed in NVAF patients, and showed no significant effect on the adrenergic system and natriuretic peptides, but had an influence on the RAAS.
Practical Considerations for the Management of Cancer-Associated Venous Thromboembolism: A Guide for the General Oncology Practitioner

Cancer-associated venous thromboembolism is a devastating complication of cancer and is associated with significant morbidity and mortality. The cornerstone of cancer-associated venous thromboembolism treatment is anticoagulation, and in recent years, there have been notable randomized clinical trials that have revealed insights into the efficacy and safety of direct oral anticoagulants and low-molecular-weight heparin in the treatment of cancer-associated thrombosis. Deciding on the ideal anticoagulation treatment plan for a patient with a cancer-associated thrombosis is a complex task that requires an understanding of clinical trial data, society guidelines, and, most importantly, consideration of many cancer-related, treatment-related, and patient-related factors. This article summarizes important factors to consider when deciding on anticoagulation therapy for a patient with cancer-associated thrombosis.
THE ROLE OF IT CAPABILITIES AND IT GOVERNANCE ON ACCOUNTABILITY AND PERFORMANCE OF HIGHER EDUCATION INSTITUTIONS DURING THE COVID-19 PANDEMIC

The COVID-19 pandemic has urged higher education institutions (HEIs) to improve their information technology (IT) capabilities due to online demands in almost all activities. However, a large IT investment does not guarantee added value for an organisation if the effectiveness of IT governance does not accompany it. Since studies on this issue are still lacking, this study examined the effect of IT capabilities and perceived effectiveness of IT governance on the accountability and performance of HEIs during the COVID-19 pandemic. This study also tested the role of IT governance as a mediator. This study employed a survey method with hypothesis testing using the Partial Least Squares (PLS) technique. The sample comprised 129 HEIs located on Java Island, Indonesia. Cluster sampling was utilised to ensure the representativeness of the sample between the provinces located in Java, covering Banten, the Special Capital Region of Jakarta, West Java, Central Java, East Java, and the Special Region of Yogyakarta. The results showed that when IT capabilities positively affected IT governance, either IT capabilities or IT governance positively affected HEIs accountability and performance during the COVID-19 pandemic. Further, IT governance was found to be a mediating variable.
Learning to find order in disorder

We introduce the use of neural networks as classifiers on classical disordered systems with no spatial ordering. In this study, we propose a framework of design objectives for learning tasks on disordered systems. Based on our framework, we implement a convolutional neural network trained to identify the spin-glass state in the three-dimensional Edwards–Anderson Ising spin-glass model from an input of Monte Carlo sampled configurations at a given temperature. The neural network is designed to be flexible with the input size and can accurately perform inference over a small sample of the instances in the test set. We examine and discuss the use of the neural network in classifying instances from three-dimensional Edwards–Anderson Ising spin-glass in a (random) field.
A Study on the Prediction of Cancer Using Whole-Genome Data and Deep Learning

The number of patients diagnosed with cancer continues to increasingly rise, and has nearly doubled in 20 years. Therefore, predicting cancer occurrence has a significant impact on reducing medical costs, and preventing cancer early can increase survival rates. In the data preprocessing step, since individual genome data are used as input data, they are classified as individual genome data. Subsequently, data embedding is performed in character units, so that it can be used in deep learning. In the deep learning network schema, using preprocessed data, a character-based deep learning network learns the correlation between individual feature data and predicts cancer occurrence. To evaluate the objective reliability of the method proposed in this study, various networks published in other studies were compared and evaluated using the TCGA dataset. As a result of comparing various networks published in other studies using the same data, excellent results were obtained in terms of accuracy, sensitivity, and specificity. Thus, the superiority of the effectiveness of deep learning networks in predicting cancer occurrence using individual whole-genome data was demonstrated. From the results of the confusion matrix, the validity of the model for predicting the cancer using an individual’s whole-genome data and the deep learning proposed in this study was proven. In addition, the AUC, which is the area under the ROC curve, which judges the efficiency of diagnosis as a performance evaluation index of the model, was found to be 90% or more, good classification results were derived. The objectives of this study were to use individual genome data for 12 cancers as input data to analyze the whole genome pattern, and to not separately use reference genome sequence data of normal individuals. In addition, several mutation types, including SNV, DEL, and INS, were applied.
Eye Movement Control in Tibetan Reading: The Roles of Word Length and Frequency

We investigated the effects of word length and frequency on eye movement control during Tibetan reading through two experiments. A preliminary experiment examined the predictive effect of word length and frequency on fixation duration and landing position using multiple linear regression analysis. In the formal experiment, we manipulated the length and frequency of target words simultaneously to investigate the effects of word length and frequency on fixation duration and landing position in Tibetan reading. In this study, we found that: (1) there were significant word-length and word-frequency effects affecting all lexical processing in Tibetan reading; (2) there are preferred viewing locations in Tibetan reading; specifically, for short words, it is the end, whereas for long words, it spans from the center to the beginning of the word; (3) word frequency does not affect preferred viewing location in Tibetan reading; (4) the preferred viewing position and the interaction of word length and viewing position found in this study supported the “strategy-tactics” approach.
Bioefficacy of insecticides against Thrips palmi in cotton (Gossypium hirsutum)

Various species of arthropod have been recorded as cotton pests at various stages of its growth as defoliators, tissue borers and sap-suckers, causing considerable yield losses. Among these, thrips are one of the most devastating early-season sucking pests limiting cotton (Gossypium hirsutum L.) productivity directly by feeding and transmitting viruses. So far, scanty information is available on the efficacy of insecticide against thrips in cotton. Thus the present study was carried out at Research farm of CCS HAU, Hisar, Haryana during rainy (kharif) season 2019–20 and 2020–21. The efficacy of 14 label-claimed insecticides against Thrips palmi was evaluated under field and laboratory conditions. Under field conditions, profenofos was the most effective insecticide against thrips at three days after spray, while spinetoram recorded the highest thrips reduction after seven days of spray. The following efficacious chemistries against thrips were fipronil and cypermethrin, whereas flonicamid and buprofezin recorded higher thrips population than other treatments. Under laboratory studies, the leaf dip bioassay was conducted to revalidate the field results, and it was found that profenofos, fipronil and spinetoram were highly effective, causing maximum thrips knockdown, whereas lower mortality was recorded in flonicamid. The study provided baseline data on thrips susceptibility to different insecticides, which can be used to develop an effective management strategy by altering different chemistries to avoid selection pressure.
Identification of Drug Combination Therapies for SARS-CoV-2: A Molecular Dynamics Simulations Approach

Purpose The development of effective treatments for coronavirus infectious disease 19 (COVID-19) caused by SARS-Coronavirus-2 was hindered by the little data available about this virus at the start of the pandemic. Drug repurposing provides a good strategy to explore approved drugs’ possible SARS-CoV-2 antiviral activity. Moreover, drug synergism is essential in antiviral treatment due to improved efficacy and reduced toxicity. In this work, we studied the effect of approved and investigational drugs on one of SARS-CoV-2 essential proteins, the main protease (Mpro), in search of antiviral treatments and/or drug combinations. Methods Different possible druggable sites of Mpro were identified and screened against an in-house library of more than 4000 chemical compounds. Molecular dynamics simulations were carried out to explore conformational changes induced by different ligands’ binding. Subsequently, the inhibitory effect of the identified compounds and the suggested drug combinations on the Mpro were established using a 3CL protease (SARS-CoV-2) assay kit. Results Three potential inhibitors in three different binding sites were identified; favipiravir, cefixime, and carvedilol. Molecular dynamics simulations predicted the synergistic effect of two drug combinations: favipiravir/cefixime, and favipiravir/carvedilol. The in vitro inhibitory effect of the predicted drug combinations was established on this enzyme. Conclusion In this work, we could study one of the promising SARS-CoV-2 viral protein targets in searching for treatments for COVID-19. The inhibitory effect of several drugs on Mpro was established in silico and in vitro assays. Molecular dynamics simulations showed promising results in predicting the synergistic effect of drug combinations.
Complexity Penalized M-Estimation

We present very fast algorithms for the exact computation of estimators for time series, based on complexity penalized log-likelihood or M-functions. The algorithms apply to a wide range of functionals with morphological constraints, in particular to Potts or Blake–Zisserman functionals. The latter are the discrete versions of the celebrated Mumford–Shah functionals. All such functionals contain model parameters. Our algorithms allow for optimization not only for each separate parameter, but even for all parameters simultaneously. This allows for the examination of the models in the sense of a family approach. The algorithms are accompanied by a series of illustrative examples from molecular biology.
Serum Phospholipid Profile Changes in Gaucher Disease and Parkinson’s Disease

Alterations in the levels of serum sphingolipids and phospholipids have been reported in Gaucher disease and in Parkinson’s disease, suggesting a potential role of these lipids as biomarkers. This project’s objective is to detect novel associations and novel candidate biomarkers in the largest Spanish Gaucher and Parkinson diseases of the Iberian Peninsula. For that, 278 participants were included: 100 sporadic Parkinson’s patients, 70 Gaucher patients, 15 GBA1-mutation-carrier Parkinson’s patients and 93 controls. A serum lipidomics array including 10 phospholipid groups, 368 species, was performed using high-performance liquid chromatography–mass spectrometry. Lipid levels were compared between groups via multiple-regression analyses controlling for clinical and demographic parameters. Additionally, lipid levels were compared within the Gaucher and Parkinson’s groups controlling for medication and/or disease severity. Results were controlled for robustness by filtering of non-detectable lipid values. There was an increase in the levels of phosphatidylcholine, with a simultaneous decrease in lyso-phosphatidylcholine, in the Gaucher, Parkinson’s and GBA1-mutation-carrier Parkinson’s patients vs. controls. Phosphatidylethanolamine, lyso- and plasmalogen-phosphatidylethanolamine were also increased in Gaucher and Parkinson’s. Gaucher patients also showed an increase in lyso-phosphatidylserine and phosphatidylglycerol. While in the Gaucher and Parkinson’s groups, velaglucerase alpha and dopamine agonists, respectively, showed positive associations with the lipid changes, miglustat treatment in Gaucher patients normalized the altered phosphatidylcholine/lyso-phosphatidylcholine ratio. In conclusion, Gaucher and Parkinson’s patients showed changes in various serum phospholipid levels when compared with healthy controls, further supporting the role of such lipids in disease development and, possibly, as putative biomarkers. This hypothesis was reinforced by the normalizing effect of miglustat, and by controlling for data robustness, even though the limited number of participants, especially in the sub-distribution by treatment groups in GD requires validation in a larger number of patients.