Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduced-switch SCALDO: an extra low-frequency DC–DC converter technique for VRM applications

State-of-the-art microprocessors require very low DC-voltages at sub-1 V levels. Many processors draw high-current at low voltages and require low-noise DC-power rails. Switched-mode power supply (SMPS) topologies are the common approach to design voltage regulator modules (VRMs). Fast operations of switches in SMPSs allow the use of smaller inductors but ultimately result in radio frequency and electromagnetic interference issues. Compared to SMPSs, linear regulators have lower noise, high quality DC output, and faster response to the load high-current slew rates; however, with the serious disadvantage of low efficiency. Supercapacitor assisted low-dropout (SCALDO) regulator is a technique to achieve high end-to-end efficiency (ETEE) for linear regulator-based converters. Though, the switch-operation frequency is extremely low, number of switches required for SCALDO configuration is three times larger than that of supercapacitors. Reduced-switch SCALDO (RS-SCALDO) is a topological variation of SCALDO that requires fewer switches. By designing an alternately operated high-current LDO pair, RS-SCALDO can handle high load currents, allowing development of a linear VRM. This study presents a proof of concept prototype of 3.5-to-1.5 V RS-SCALDO for a maximum 5 A load with digitally adjustable output voltages. The prototype achieved an ETEE better than 80%, and required half the switch count of an equivalent SCALDO circuit.
Random survival forests for competing risks with multivariate longitudinal endogenous covariates

: Predicting the individual risk of a clinical event using the complete patient history is still a major challenge for personalized medicine. Among the methods developed to compute individual dynamic predictions, the joint models have the assets of using all the available information while accounting for dropout. However, they are restricted to a very small number of longitudinal predictors. Our objective was to propose an innovative alternative solution to predict an event probability using a possibly large number of longitudinal predictors. We developed DynForest , an extension of random survival forests for competing risks that handles endogenous longitudinal predictors. At each node of the trees, the time-dependent predictors are translated into time-ﬁxed features (using mixed models) to be used as candidates for splitting the subjects into two subgroups. The individual event probability is estimated in each tree by the Aalen-Johansen estimator of the leaf in which the subject is classiﬁed according to his/her history of predictors. The ﬁnal individual prediction is given by the average of the tree-speciﬁc individual event probabilities. We carried out a simulation study to demonstrate the performances of DynForest both in a small dimensional context (in comparison with joint models) and in a large dimensional context (in comparison with
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity

This paper concerns the central issues of model robustness and sample efficiency in offline reinforcement learning (RL), which aims to learn to perform decision making from history data without active exploration. Due to uncertainties and variabilities of the environment, it is critical to learn a robust policy -- with as few samples as possible -- that performs well even when the deployed environment deviates from the nominal one used to collect the history dataset. We consider a distributionally robust formulation of offline RL, focusing on tabular robust Markov decision processes with an uncertainty set specified by the Kullback-Leibler divergence in both finite-horizon and infinite-horizon settings. To combat with sample scarcity, a model-based algorithm that combines distributionally robust value iteration with the principle of pessimism in the face of uncertainty is proposed, by penalizing the robust value estimates with a carefully designed data-driven penalty term. Under a mild and tailored assumption of the history dataset that measures distribution shift without requiring full coverage of the state-action space, we establish the finite-sample complexity of the proposed algorithms. We further develop an information-theoretic lower bound, which suggests that learning RMDPs is at least as hard as the standard MDPs when the uncertainty level is sufficient small, and corroborates the tightness of our upper bound up to polynomial factors of the (effective) horizon length for a range of uncertainty levels. To the best our knowledge, this provides the first provably near-optimal robust offline RL algorithm that learns under model uncertainty and partial coverage.
Quantized Adaptive Subgradient Algorithms and Their Applications

Data explosion and an increase in model size drive the remarkable advances in large-scale machine learning, but also make model training time-consuming and model storage difficult. To address the above issues in the distributed model training setting which has high computation efficiency and less device limitation, there are still two main difficulties. On one hand, the communication costs for exchanging information, e.g., stochastic gradients among different workers, is a key bottleneck for distributed training efficiency. On the other hand, less parameter model is easy for storage and communication, but the risk of damaging the model performance. To balance the communication costs, model capacity and model performance simultaneously, we propose quantized composite mirror descent adaptive subgradient (QCMD adagrad) and quantized regularized dual average adaptive subgradient (QRDA adagrad) for distributed training. To be specific, we explore the combination of gradient quantization and sparse model to reduce the communication cost per iteration in distributed training. A quantized gradient-based adaptive learning rate matrix is constructed to achieve a balance between communication costs, accuracy, and model sparsity. Moreover, we theoretically find that a large quantization error brings in extra noise, which influences the convergence and sparsity of the model. Therefore, a threshold quantization strat-egy with a relatively small error is adopted in QCMD adagrad and QRDA adagrad to improve the signal-to-noise ratio and preserve the sparsity of the model. Both theoretical analyses and empirical results demonstrate the efficacy and efficiency of the proposed algorithms.
ROC: A New Paradigm for Lyric-to-Melody Generation

Lyric-to-melody generation is an important task in songwriting, and is also quite challenging due to its distinctive characteristics: the generated melodies should not only follow good musical patterns, but also align with features in lyrics such as rhythms and structures. These characteristics cannot be well handled by neural generation models that learn lyric-to-melody mapping in an end-to-end way, due to several issues: (1) lack of aligned lyric-melody training data to sufficiently learn lyric-melody feature alignment; (2) lack of controllability in generation to explicitly guarantee the lyric-melody feature alignment. In this paper, we propose ROC, a new paradigm for lyric-to-melody generation that addresses the above issues through a generation-retrieval pipeline. Specifically, our paradigm has two stages: (1) creation stage, where a huge amount of music pieces are generated by a neural-based melody language model and indexed in a database through several key features (e.g., chords, tonality, rhythm, and structural information including chorus or verse); (2) re-creation stage, where melodies are recreated by retrieving music pieces from the database according to the key features from lyrics and concatenating best music pieces based on composition guidelines and melody language model scores. Our ROC paradigm has several advantages: (1) It only needs unpaired melody data to train melody language model, instead of paired lyric-melody data in previous models. (2) It achieves good lyric-melody feature alignment in lyric-to-melody generation. Experiments on English and Chinese datasets demonstrate that ROC outperforms previous neural based lyric-to-melody generation models on both objective and subjective metrics. We provide our code in supplementary
Protective manifestation of herbonanoceuticals as antifungals: A possible drug candidate for dermatophytic infection

Fungal dermatophytosis or Tinea is a predominance in about 20%–25% of all total world populations. Dermatophyte infections are mainly caused by fungi belonging to Trichophyton, Epidermophyton, and Microsporum genera along with some other fungi. This epidemiological distribution may change with migration, lifestyle, immunosuppressive state, drug therapy, and socioeconomic conditions.
The expression of aminoglycoside resistance genes in integron cassettes is not controlled by riboswitches

Abstract Regulation of gene expression is a key factor influencing the success of antimicrobial resistance determinants. A variety of determinants conferring resistance against aminoglycosides (Ag) are commonly found in clinically relevant bacteria, but whether their expression is regulated or not is controversial. The expression of several Ag resistance genes has been reported to be controlled by a riboswitch mechanism encoded in a conserved sequence. Yet this sequence corresponds to the integration site of an integron, a genetic platform that recruits genes of different functions, making the presence of such a riboswitch counterintuitive. We provide, for the first time, experimental evidence against the existence of such Ag-sensing riboswitch. We first tried to reproduce the induction of the well characterized aacA5 gene using its native genetic environment, but were unsuccessful. We then broadened our approach and analyzed the inducibility of all AgR genes encoded in integrons against a variety of antibiotics. We could not observe biologically relevant induction rates for any gene in the presence of several aminoglycosides. Instead, unrelated antibiotics produced mild but consistently higher increases in expression, that were the result of pleiotropic effects. Our findings rule out the riboswitch control of aminoglycoside resistance genes in integrons.
Advances in the Biological Functions and Mechanisms of miRNAs in the Development of Osteosarcoma

Osteosarcoma is one of the most common primary malignant bone tumors, mainly occurring in children and adolescents, and is characterized by high morbidity and poor prognosis. MicroRNAs, a class of noncoding RNAs consisting of 19 to 25 nucleotides, are involved in cell proliferation, invasion, metastasis, and apoptosis to regulate the development and progression of osteosarcoma. Studies have found that microRNAs are closely related to the diagnosis, treatment, and prognosis of osteosarcoma patients and have an important role in improving drug resistance in osteosarcoma. This paper reviews the role of microRNAs in the pathogenesis of osteosarcoma and their clinical value, aiming to provide a new research direction for diagnosing and treating osteosarcoma and achieving a better prognosis.
Behavioral avoidance of contagious and non-contagious adults

Evolutionary theories of disease avoidance propose that humans have a set of universal psychological processes to detect environmental cues indicative of infectious disease. These processes then initiate cognitive, emotional, and behavioral responses that function to limit contact with harmful pathogens. Here, we study the conditions under which people exhibit behavioral avoidance of others with a contagious illness or a physical injury (i.e., a broken leg), and the potential mechanisms that underlie this behavior. Across three studies, participants were given the option of sitting at one of two workstations previously occupied by two confederates, one of whom either showed visible symptoms of a cold (contagion condition), wore a lower-leg orthopedic boot and used crutches (broken leg condition), or showed no signs of illness or physical injury (control). We found strong evidence that adults explicitly avoid contact with individuals who show symptoms of a contagious illness. Further, we provide some evidence that adults also avoid individuals with a physical injury, but that this behavior might be driven by implicit, unconscious processes. The findings are discussed in terms of implications for the healthy avoidance of contagion, and the risk for potential stigmatization of non-contagious groups.
Downregulation of SETD5 Suppresses the Tumorigenicity of Hepatocellular Carcinoma Cells

Hepatocellular carcinoma (HCC) is an aggressive and incurable cancer. Although understanding of the molecular pathogenesis of HCC has greatly advanced, therapeutic options for the disease remain limited. In this study, we demonstrated that SETD5 expression is positively associated with poor prognosis of HCC and that SETD5 depletion decreased HCC cell proliferation and invasion while inducing cell death. Transcriptome analysis revealed that SETD5 loss downregulated the interferon-mediated inflammatory response in HCC cells. In addition, SETD5 depletion downregulated the expression of a critical glycolysis gene, PKM (pyruvate kinase M1/2), and decreased glycolysis activity in HCC cells. Finally, SETD5 knockdown inhibited tumor growth in xenograft mouse models. These results collectively suggest that SETD5 is involved in the tumorigenic features of HCC cells and that targeting SETD5 may suppress HCC progression.
Occupational risk factors for hypertension

The identification and characterization of the modifiable risk factors of hypertension is of great value for public health and clinical medicine to achieve primary prevention. A large amount of literature on the effects of occupational factors on blood pressure and hypertension has been published. In this review, I summarize current evidence regarding the role of occupational factors in hypertension. The results of this review suggest that there is sufficient evidence to support the association between increased risk of hypertension and job stress, shift work, occupational lead exposure, and noise exposure. The association of hypertension with physical inactivity, sedentary behavior, and occupational exposure to mercury, arsenic, cadmium, or carbon disulfide remains inconclusive, although several studies have reported this finding. This review will serve as a step toward future research and provide baseline information for developing strategic interventions to prevent hypertension in the working population.
The complexity landscape of viral genomes

Abstract Background Viruses are among the shortest yet highly abundant species that harbor minimal instructions to infect cells, adapt, multiply, and exist. However, with the current substantial availability of viral genome sequences, the scientific repertory lacks a complexity landscape that automatically enlights viral genomes’ organization, relation, and fundamental characteristics. Results This work provides a comprehensive landscape of the viral genome’s complexity (or quantity of information), identifying the most redundant and complex groups regarding their genome sequence while providing their distribution and characteristics at a large and local scale. Moreover, we identify and quantify inverted repeats abundance in viral genomes. For this purpose, we measure the sequence complexity of each available viral genome using data compression, demonstrating that adequate data compressors can efficiently quantify the complexity of viral genome sequences, including subsequences better represented by algorithmic sources (e.g., inverted repeats). Using a state-of-the-art genomic compressor on an extensive viral genomes database, we show that double-stranded DNA viruses are, on average, the most redundant viruses while single-stranded DNA viruses are the least. Contrarily, double-stranded RNA viruses show a lower redundancy relative to single-stranded RNA. Furthermore, we extend the ability of data compressors to quantify local complexity (or information content) in viral genomes using complexity profiles, unprecedently providing a direct complexity analysis of human herpesviruses. We also conceive a features-based classification methodology that can accurately distinguish viral genomes at different taxonomic levels without direct comparisons between sequences. This methodology combines data compression with simple measures such as GC-content percentage and sequence length, followed by machine learning classifiers. Conclusions This article presents methodologies and findings that are highly relevant for understanding the patterns of similarity and singularity between viral groups, opening new frontiers for studying viral genomes’ organization while depicting the complexity trends and classification components of these genomes at different taxonomic levels. The whole study is supported by an extensive website (https://asilab.github.io/canvas/) for comprehending the viral genome characterization using dynamic and interactive approaches.
Glial fibrillary acidic protein in cerebrospinal fluid of patients with spinal muscular atrophy

Activated astroglia is involved in the pathophysiology of neurodegenerative diseases and has also been described in animal models of spinal muscular atrophy (SMA). Given the urgent need of biomarkers for treatment monitoring of new RNA‐modifying and gene replacement therapies in SMA, we examined glial fibrillary acidic protein concentrations in cerebrospinal fluid (cGFAP) as a marker of astrogliosis in SMA.
Risk stratification and prognostic value of prothrombin time and activated partial thromboplastin time among COVID-19 patients

Background COVID-19 is a viral disease caused by a new strain of corona virus. Currently, prognosis and risk stratification of COVID-19 patients is done by the disease’s clinical presentation. Therefore, identifying laboratory biomarkers for disease prognosis and risk stratification of COVID-19 patients is critical for prompt treatment. Therefore, the main objective of this study was to assess the risk stratification and prognostic value of basic coagulation parameters and factors associated with disease severity among COVID-19 patients at the Tibebe Ghion Specialized Hospital, COVID-19 treatment center, Northwest Ethiopia. Methods A follow-up study was conducted among conveniently recruited COVID-19 patients attended from March to June 2021. Socio-demographic and clinical data were collected using a structured questionnaire and checklist, respectively. Prothrombin time (PT) and activated partial thromboplastin time (APTT) were analyzed by the HUMACLOT DUE PLUS® machine. Descriptive statistics were used to summarize the socio-demographic and clinical characteristics of study participants. Kruskal Wallis tests were used to compare the difference between parametric and non-parametric continuous variables, respectively. The area under the receiver operating characteristic curve (AUC) was used to evaluate the value of PT and APTT in the risk stratification and disease prognosis of COVID-19 patients. Ordinal logistic regression was used to identify the factors associated with disease severity and prognosis. A P-value < 0.05 was defined as statistically significant for all results. Result Baseline PT at a cut-off value ≥ 16.25 seconds differentiated severe COVID-19 patients from mild and moderate patients (AUC: 0.89, 95% CI: 0.83–0.95). PT also differentiated mild COVID-19 patients from moderate and severe patients at a cut-off value ≤ 15.35 seconds (AUC: 0.90, 95% CI: 0.84–0.96). Moreover, alcohol drinkers were a 3.52 times more likely chance of having severe disease than non-drinkers (95% CI: 1.41–8.81). A one-year increment in age also increased the odds of disease severity by 6% (95% CI: 3–9%). An increment of ≥ 0.65 seconds from the baseline PT predicted poor prognosis (AUC: 0.93, 0.87–0.99). Conclusions and recommendations Prolonged baseline PT was observed in severe COVID-19 patients. Prolonged baseline PT was also predicted to worsen prognosis. An increase from the baseline PT was associated with worsen prognosis. Therefore, PT can be used as a risk stratification and prognostic marker in COVID-19 patients.
Visual Feedback in Augmented Reality to Walk at Predefined Speed Cross-Sectional Study Including Children With Cerebral Palsy

In an augmented reality environment, the range of possible real-time visual feedback is extensive. This study aimed to compare the impact of six scenarios in augmented reality combining four visual feedback characteristics on achieving a target walking speed. The six scenarios have been developed for Microsoft Hololens augmented reality headset. The four feedback characteristics that we have varied were: Color; Spatial anchoring; Speed of the feedback, and Persistence. Each characteristic could have different values (for example, the color could be unicolor, bicolor, or gradient). Participants had to walk for two consecutive walking trials for each scenario: at their maximal speed and an intermediate speed. Mean speed, percentage of time spent above or around target speed, and time to reach target speed were compared between scenarios using mixed linear models. A total of 25 children with disabilities have been included. The feasibility and user experience were excellent. Mean speed during scenario 6, which displayed feedback with gradient color, attached to the world, with a speed relative to the player equal to his speed, and that disappeared over time, was significantly higher than other scenarios and control (p =0.003). Participants spent 80.98% of time above target speed during scenario 6. This scenario mixed the best combination of feedback characteristics to exceed the target walking speed (p=0.0058). Scenarios 5 and 6, which shared the same feedback characteristics for spatial anchoring (world-locked) and feedback speed (equal to the player speed), decreased the time to reach the target speed (p=0.019). Delivering multi-modal feedback has been recognized as more effective for improving motor performance. Therefore, our results showed that not all visual feedback had the same impact on performance. Further studies are required to test the weight of each feedback characteristic and their possible interactions inside each scenario. This study was registered in the ClinicalTrials.gov database (NCT04460833).
Robotic colectomy with CME versus laparoscopic colon resection with or without CME for colon cancer: a systematic review and meta-analysis.

INTRODUCTION This systematic review with meta-analysis aimed to compare the robotic complete mesocolon excision (RCME) to laparoscopic colectomy (LC) with (LCME) or without CME (LC non-CME) in postoperative outcomes, harvested lymph nodes and disease-free survival. METHODS We performed a systematic review with meta-analysis according to PRISMA 2020 and AMSTAR 2 guidelines. RESULTS The literature search yielded seven comparative studies including 677 patients: 269 patients in the RCME group and 408 in the LC group. The pooled analysis concluded to a lower conversion rate in the RCME group (OR=0.17; 95% CI [0.04, 0.74], p=0.02). There was no difference between the two groups in terms of morbidity (OR=1.03; 95% CI [0.70, 1.53], p=0.87), anastomosis leakage (OR=0.83; 95% CI [0.18, 3.72], p=0.81), bleeding (OR=1.90; 95% CI [0.64, 5.58], p=0.25), wound infection (OR=1.37; 95% CI [0.51, 3.68], p=0.53), operative time (mean difference (MD)=36.32; 95% CI [-24.30, 96.93], p=0.24), hospital stay (MD=-0.94; 95% CI [-2.03, 0.15], p=0.09) and disease-free survival (OR=1.29; 95% CI [0.71, 2.35], p=0.41). In the subgroup analysis, the operative time was significantly shorter in the LCME group than RCME group (MD=50.93; 95% CI [40.05, 61.81], p<0.01) and we noticed a greater number of harvested lymph nodes in the RCME group compared with LC non-CME group (MD=8.96; 95% CI [5.98, 11.93], p<0.01). CONCLUSIONS The robotic approach for CME ensures a lower conversion rate than the LC. RCME had a longer operative time than the LCME subgroup and a higher number of harvested lymph nodes than the LC non-CME group.