Miracle-dz/UniFAR-FTUs huggingface dataset:
Chemistry of Fluorinated Pyrimidines in the Era of Personalized Medicine

We review developments in fluorine chemistry contributing to the more precise use of fluorinated pyrimidines (FPs) to treat cancer. 5-Fluorouracil (5-FU) is the most widely used FP and is used to treat > 2 million cancer patients each year. We review methods for 5-FU synthesis, including the incorporation of radioactive and stable isotopes to study 5-FU metabolism and biodistribution. We also review methods for preparing RNA and DNA substituted with FPs for biophysical and mechanistic studies. New insights into how FPs perturb nucleic acid structure and dynamics has resulted from both computational and experimental studies, and we summarize recent results. Beyond the well-established role for inhibiting thymidylate synthase (TS) by the 5-FU metabolite 5-fluoro-2′-deoxyuridine-5′-O-monophosphate (FdUMP), recent studies have implicated new roles for RNA modifying enzymes that are inhibited by 5-FU substitution including tRNA methyltransferase 2 homolog A (TRMT2A) and pseudouridylate synthase in 5-FU cytotoxicity. Furthermore, enzymes not previously implicated in FP activity, including DNA topoisomerase 1 (Top1), were established as mediating FP anti-tumor activity. We review recent literature summarizing the mechanisms by which 5-FU inhibits RNA- and DNA-modifying enzymes and describe the use of polymeric FPs that may enable the more precise use of FPs for cancer treatment in the era of personalized medicine.
Descriptive Analysis of the Varroa Non-Reproduction Trait in Honey Bee Colonies and Association with Other Traits Related to Varroa Resistance

Simple Summary Honey bees, Apis mellifera, are currently facing drastic colony losses, some of which lies partially on the infestation by a parasite, varroa destructor. To maintain healthy honey bee colonies breeding for resistance to this infestation seems promising. However, measuring resistance is a tedious task as it is a complex process. One can consider a honey bee colony resistant to varroa infestation when the parasite fails reproducing and thus its population does not grow in the colony, this is called mite non reproduction. In this study we dissected the performance of a scoring method for mite non reproduction and compared it to other known methods to measure resistance. Although the mite non reproduction method is preferred for field studies because of its relative simplicity it appears to lack correspondence with other methods and it should be interpreted cautiously because it is variable. Even though resistance to varroa infestation is desired for breeding decision it should be done carefully when the way to measure such resistance is using the mite non reproduction method. Abstract In the current context of worldwide honey bee colony losses, among which the varroa mite plays a major role, the hope to improve honey bee health lies in part in the breeding of varroa resistant colonies. To do so, methods used to evaluate varroa resistance need better understanding. Repeatability and correlations between traits such as mite non-reproduction (MNR), varroa sensitive hygiene (VSH), and hygienic behavior are poorly known, due to practical limitations and to their underlying complexity. We investigate (i) the variability, (ii) the repeatability of the MNR score, and (iii) its correlation with other resistance traits. To reduce the inherent variability of MNR scores, we propose to apply an empirical Bayes correction. In the short-term (ten days), MNR had a modest repeatability of 0.4, whereas in the long-term (a month), it had a low repeatability of 0.2, similar to other resistance traits. Within our dataset, there was no correlation between MNR and VSH. Although MNR is amongst the most popular varroa resistance estimates in field studies, its underlying complex mechanism is not fully understood. Its lack of correlation with better described resistance traits and low repeatability suggest that MNR needs to be interpreted cautiously, especially when used for selection.
Estimation and Validation of Arterial Blood Pressure Using Photoplethysmogram Morphology Features in Conjunction With Pulse Arrival Time in Large Open Databases

Although various predictors and methods for BP estimation have been proposed, differences in study designs have led to difficulties in determining the optimal method. This study presents analyses of BP estimation methods using 2.4 million cardiac cycles of two commonly used non-invasive biosignals, electrocardiogram (ECG) and photoplethysmogram (PPG), from 1376 surgical patients. Feature selection methods were used to determine the best subset of predictors from a total of 42 including PAT, heart rate (HR), and various PPG morphology features, and BP estimation models constructed using linear regression (LR), random forest (RF), artificial neural network (ANN), and recurrent neural network (RNN) were evaluated. 28 features out of 42 were determined as suitable for BP estimation, in particular two PPG morphology features outperformed PAT, which has been conventionally seen as the best non-invasive indicator of BP. By modelling the low frequency component of BP using ANN and the high frequency component using RNN with the selected predictors, mean errors of 0.05 ± 6.92 mmHg for systolic BP, and -0.05 ± 3.99 mmHg for diastolic BP were achieved. External validation of the model using another biosignal database consisting of 334 intensive care unit patients led to similar results, satisfying three standards for accuracy of BP monitors. The results indicate that the proposed method can contribute to the realization of ubiquitous non-invasive continuous BP monitoring.
Is Antioxidant Therapy a Useful Complementary Measure for Covid-19 Treatment? An Algorithm for Its Application

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes the corona virus disease-19 which is accompanied by severe pneumonia, pulmonary alveolar collapses and which stops oxygen exchange. Viral transmissibility and pathogenesis depend on recognition by a receptor in the host, protease cleavage of the host membrane and fusion. SARS-CoV-2 binds to the angiotensin converting enzyme 2 receptor. Here, we discuss the general characteristics of the virus, its mechanism of action and the way in which the mechanism correlates with the comorbidities that increase the death rate. We also discuss the currently proposed therapeutic measures and propose the use of antioxidant drugs to help patients infected with the SARS-CoV-2. Oxidizing agents come from phagocytic leukocytes such as neutrophils, monocytes, macrophages and eosinophils that invade tissue. Free radicals promote cytotoxicity thus injuring cells. They also trigger the mechanism of inflammation by mediating the activation of NFkB and inducing the transcription of cytokine production genes. Release of cytokines enhances the inflammatory response. Oxidative stress is elevated during critical illnesses and contributes to organ failure. In corona virus disease-19 there is an intense inflammatory response known as a cytokine storm that could be mediated by oxidative stress. Although antioxidant therapy has not been tested in corona virus disease-19, the consequences of antioxidant therapy in sepsis, acute respiratory distress syndrome and acute lung injury are known. It improves oxygenation rates, glutathione levels and strengthens the immune response. It reduces mechanical ventilation time, the length of stay in the intensive care unit, multiple organ dysfunctions and the length of stay in the hospital and mortality rates in acute lung injury/acute respiratory distress syndrome and could thus help patients with corona virus disease-19.
Standardized Saponin Extract from Baiye No.1 Tea (Camellia sinensis) Flowers Induced S Phase Cell Cycle Arrest and Apoptosis via AKT-MDM2-p53 Signaling Pathway in Ovarian Cancer Cells

Ovarian cancer is considered to be one of the most serious malignant tumors in women. Natural compounds have been considered as important sources in the search for new anti-cancer agents. Saponins are characteristic components of tea (Camellia sinensis) flower and have various biological activities, including anti-tumor effects. In this study, a high purity standardized saponin extract, namely Baiye No.1 tea flower saponin (BTFS), which contained Floratheasaponin A and Floratheasaponin D, were isolated from tea (Camellia sinensis cv. Baiye 1) flowers by macroporous resin and preparative liquid chromatography. Then, the component and purity were detected by UPLC-Q-TOF/MS/MS. This high purity BTFS inhibited the proliferation of A2780/CP70 cancer cells dose-dependently, which is evidenced by the inhibition of cell viability, reduction of colony formation ability, and suppression of PCNA protein expression. Further research found BTFS induced S phase cell cycle arrest by up-regulating p21 proteins expression and down-regulating Cyclin A2, CDK2, and Cdc25A protein expression. Furthermore, BTFS caused DNA damage and activated the ATM-Chk2 signaling pathway to block cell cycle progression. Moreover, BTFS trigged both extrinsic and intrinsic apoptosis—BTFS up-regulated the expression of death receptor pathway-related proteins DR5, Fas, and FADD and increased the ratio of pro-apoptotic/anti-apoptotic proteins of the Bcl-2 family. BTFS-induced apoptosis seems to be related to the AKT-MDM2-p53 signaling pathway. In summary, our results demonstrate that BTFS has the potential to be used as a nutraceutical for the prevention and treatment of ovarian cancer.
Video Snapshot: Single Image Motion Expansion via Invertible Motion Embedding

Unlike images, finding the desired video content in a large pool of videos is not easy due to the time cost of loading and watching. Most video streaming and sharing services provide the video preview function for a better browsing experience. In this paper, we aim to generate a video preview from a single image. To this end, we propose two cascaded networks, the motion embedding network and the motion expansion network. The motion embedding network aims to embed the spatio-temporal information into an embedded image, called video snapshot. On the other end, the motion expansion network is proposed to invert the video back from the input video snapshot. To hold the invertibility of motion embedding and expansion during training, we design four tailor-made losses and a motion attention module to make the network focus on the temporal information. In order to enhance the viewing experience, our expansion network involves an interpolation module to produce a longer video preview with a smooth transition. Extensive experiments demonstrate that our method can successfully embed the spatio-temporal information of a video into one “live” image, which can be converted back to a video preview. Quantitative and qualitative evaluations are conducted on a large number of videos to prove the effectiveness of our proposed method. In particular, statistics of PSNR and SSIM on a large number of videos show the proposed method is general, and it can generate a high-quality video from a single image.
Ectodomain Pulling Combines with Fusion Peptide Inserting to Provide Cooperative Fusion for Influenza Virus and HIV

Enveloped viruses include the most dangerous human and animal pathogens, in particular coronavirus, influenza virus, and human immunodeficiency virus (HIV). For these viruses, receptor binding and entry are accomplished by a single viral envelope protein (termed the fusion protein), the structural changes of which trigger the remodeling and merger of the viral and target cellular membranes. The number of fusion proteins required for fusion activity is still under debate, and several studies report this value to range from 1 to 9 for type I fusion proteins. Here, we consider the earliest stage of viral fusion based on the continuum theory of membrane elasticity. We demonstrate that membrane deformations induced by the oblique insertion of amphipathic fusion peptides mediate the lateral interaction of these peptides and drive them to form into a symmetric fusion rosette. The pulling force produced by the structural rearrangements of the fusion protein ectodomains gives additional torque, which deforms the membrane and additionally stabilizes the symmetric fusion rosette, thus allowing a reduction in the number of fusion peptides needed for fusion. These findings can resolve the large range of published cooperativity indices for HIV, influenza, and other type I fusion proteins.
Clinical Significance of Electronegative Low-Density Lipoprotein Cholesterol in Atherothrombosis

Despite the numerous risk factors for atherosclerotic cardiovascular diseases (ASCVD), cumulative evidence shows that electronegative low-density lipoprotein (L5 LDL) cholesterol is a promising biomarker. Its toxicity may contribute to atherothrombotic events. Notably, plasma L5 LDL levels positively correlate with the increasing severity of cardiovascular diseases. In contrast, traditional markers such as LDL-cholesterol and triglyceride are the therapeutic goals in secondary prevention for ASCVD, but that is controversial in primary prevention for patients with low risk. In this review, we point out the clinical significance and pathophysiological mechanisms of L5 LDL, and the clinical applications of L5 LDL levels in ASCVD can be confidently addressed. Based on the previously defined cut-off value by receiver operating characteristic curve, the acceptable physiological range of L5 concentration is proposed to be below 1.7 mg/dL. When L5 LDL level surpass this threshold, clinically relevant ASCVD might be present, and further exams such as carotid intima-media thickness, pulse wave velocity, exercise stress test, or multidetector computed tomography are required. Notably, the ultimate goal of L5 LDL concentration is lower than 1.7 mg/dL. Instead, with L5 LDL greater than 1.7 mg/dL, lipid-lowering treatment may be required, including statin, ezetimibe or PCSK9 inhibitor, regardless of the low-density lipoprotein cholesterol (LDL-C) level. Since L5 LDL could be a promising biomarker, we propose that a high throughput, clinically feasible methodology is urgently required not only for conducting a prospective, large population study but for developing therapeutics strategies to decrease L5 LDL in the blood.
Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification

The scarcity of labelled time-series data can hinder a proper training of deep learning models. This is especially relevant for the growing field of ubiquitous computing, where data coming from wearable devices have to be analysed using pattern recognition techniques to provide meaningful applications. To address this problem, we propose a transfer learning method based on attributing sensor modality labels to a large amount of time-series data collected from various application fields. Using these data, our method firstly trains a Deep Neural Network (DNN) that can learn general characteristics of time-series data, then transfers it to another DNN designed to solve a specific target problem. In addition, we propose a general architecture that can adapt the transferred DNN regardless of the sensors used in the target field making our approach in particular suitable for multichannel data. We test our method for two ubiquitous computing problems—Human Activity Recognition (HAR) and Emotion Recognition (ER)—and compare it a baseline training the DNN without using transfer learning. For HAR, we also introduce a new dataset, Cognitive Village-MSBand (CogAge), which contains data for 61 atomic activities acquired from three wearable devices (smartphone, smartwatch, and smartglasses). Our results show that our transfer learning approach outperforms the baseline for both HAR and ER.
Neurologic Safety of Etomidate-Based Sedation during Upper Endoscopy in Patients with Liver Cirrhosis Compared with Propofol: A Double-Blind, Randomized Controlled Trial

(1) Background: Although etomidate-based sedation is an effective and safe protocol in endoscopic procedures, there is a lack of evidence regarding the safety of etomidate in patients with liver cirrhosis (LC). This study aimed to compare the neurologic safety and efficacy of etomidate and propofol for endoscopic sedation in patients with LC. (2) Methods: From December 2017 to December 2019, consecutive cirrhotic patients who underwent sedative endoscopy using either etomidate or propofol were randomly recruited. The primary endpoint was the number connection test (NCT), and the secondary endpoints included factors for the safety of sedatives during endoscopy. (3) Results: 63 patients were enrolled in each of the etomidate and propofol groups. The NCT times were significantly lower in the etomidate group than in the propofol group. Furthermore, severe or very severe degree of encephalopathy was higher in the propofol group but was not significantly different. Pharmacological properties and the overall incidence of respiratory and cardiovascular events did not differ significantly between the groups. (4) Conclusion: Etomidate-based sedation exacerbates neither subclinical nor overt hepatic encephalopathy. It guarantees efficacies similar to those of propofol regarding rapid sedation, fast recovery, and early discharge, with no increased risk of adverse respiratory or cardiovascular events in patients with LC.
Prevalence of Potential Drug–Drug Interaction Risk among Chronic Kidney Disease Patients in a Spanish Hospital

Chronic kidney disease (CKD) is a major health problem worldwide and, in Spain, it is present in 15.1% of individuals. CKD is frequently associated with some comorbidities and patients need to be prescribed multiple medications. Polypharmacy increases the risk of adverse drug reactions (ADRs). There are no published studies evaluating the prevalence of potential drug–drug interactions (pDDIs) among CKD patients in any European country. This study was aimed to determine the prevalence, pattern, and factors associated with pDDIs among CKD patients using a drug interactions program. An observational cross-sectional study was carried out at Plasencia Hospital, located in Spain. Data were collected among patients with CKD diagnoses and pDDIs were assessed by the Lexicomp® Drug Interactions platform. Data were obtained from 112 CKD patients. A total number of 957 prescribed medications were acknowledged, and 928 pDDIs were identified in 91% of patients. Age and concomitant drugs were significantly associated with the number of pDDIs (p < 0.05). According to the results, the use of programs for the determination of pDDIs (such as Lexicomp®) is recommended in the clinical practice of CKD patients in order to avoid serious adverse effects, as is paying attention to contraindicated drug combinations.
Cooperative Set Homomorphic Proofs for Data Possession Checking in Clouds

Outsourcing an increasing amount of data to a third party raises a number of security and privacy challenges, namely remote data integrity verification. Indeed, proofs for data possession checking address the verification that some previously outsourced data blocks across multiple storing nodes are correctly stored and fully available. In this paper, we propose a new set homomorphic proof of data possession, referred to as SHoPS, supporting several operations like aggregation of proofs. SHoPS is a deterministic Proof of Data Possession (PDP) scheme, based on an interactive proof protocol. Our approach has several advantages. First, it enables several proofs to be aggregated and a subset of data files’ proofs to be verified, while providing an attractive communication overhead. Second, it supports public verifiability where the verification process can be delegated to another entity, thus releasing the data owner from the cumbersome task of periodical verifications. Third, SHoPS is efficient and provably secure, as it is resistant to the fraudulence of the prover and the leakage of verified data. Finally, a theoretical performances’ analysis shows that SHoPS performs better in terms of functionality, communication and computation overhead compared to closely related works and experimental results point out the applicability of the proposed scheme in real world scenarios.
Insights on 3D Structures of Potential Drug‐targeting Proteins of SARS‐CoV‐2: Application of Cavity Search and Molecular Docking

The emergence of the COVID‐19 has caused public health problems worldwide and there is no effective pharmacological treatment for this disease. Research on 3D models of proteins and the search for active molecular sites are important tools to assist in the discovery of effective antiviral drugs to combat COVID‐19. To address this problem, the 3D protein structures of SARS‐CoV 2 were analyzed and submitted to cavities research, evaluation of their druggabillity and liganbility, and applied to molecular docking studies with potential ligand candidates actually assayed against COVID‐19. Eight druggable potential cavity sites were determined in model structures’ PDB code, 6W4B, 6VWW, 6W01, 6M3M, and 6VYO, and these are the good alternatives to be characterized as targets for antiviral compounds. The good cavity model of the protease 3D structure was used in molecular docking, and this allowed verifying the theoric interactions of this protein and lopinavir and ritonavir antiviral drugs. These results may assist in the use of 3D protein models in drug design studies aiming to develop drugs against the COVID‐19 pandemic.
A Comparison of Empirical Correlations of Viscosity and Thermal Conductivity of Water-Ethylene Glycol-Al2O3 Nanofluids

Because of their superb thermal conductivity, nanofluids are seen as new generation of cooling mediums in many engineering applications. It is well established that even a small amount of nanoparticles mixed with a base fluid may result in distinct thermal conductivity enhancement. On the other hand, addition of nanoparticles to the base fluid results in its substantial viscosity increase. Therefore, it is very difficult to evaluate the relative importance of viscosity and thermal conductivity of the nanofluid on convective heat transfer performance. In order to estimate such resultant impact properly, it is necessary to develop reliable correlation equations for predictions of these two thermophysical properties of nanofluids. In this paper, the thermal conductivity and dynamic viscosity of five fluids, i.e., pure water, ethylene glycol (EG) and three mixtures of water and EG with volume ratio of 40:60, 50:50 and 60:40 have been experimentally determined. The aforementioned fluids served as base fluids in nanofluids with Al2O3 nanoparticles at the concentration of 0.01%, 0.1% and 1% by weight. A set of 20 correlations for prediction of thermal conductivity and dynamic viscosity of base fluids and corresponding nanofluids has been developed. Moreover, present results have been confronted with literature data and predictions made by use of carefully selected recognized literature correlations.
The BovMAS Consortium: identification of QTL for milk yield and milk protein percent on chromosome 14 in the Brown Swiss breed

Riassunto Identificazione di QTL per quantità di latte e tenore proteico sul cromosoma 14 nella razza Bruna. Numerosi progetti di mappaggio di QTL hanno permesso di identificare diverse regioni cromosomiche associate a caratteri di interesse produttivo. In questo lavoro sono state identificate 2 possibili regioni QTL sul cromosoma 14 (0-20 cM e 70-110) nelle quali sono stati trovati marcatori in associazione con latte (kg) e proteina (%). Nella regione centromerica è stato inoltre studiato il genotipo per i geni DGAT1 e TG, il primo dei quali è stato recentemente proposto come causativo di un maggior contenuto lipidico nel latte.