Miracle-dz/UniFAR-FTUs huggingface dataset:
New synonymy in the genus Coomaniella (Coleoptera: Buprestidae), with remarks on another species

Coomaniella aureopilosa Thery, 1931 is considered a new junior subjective synonym of Coomaniella (Coomaniella) violaceipennis Bourgoin, 1924. Chukrasia tabularis A. H. L. Jussieu (Meliaceae) is recorded as a host plant of Coomaniella purpurascens Baudon, 1966. Taxonomy, synonymy, host plant, Coleoptera, Buprestidae, Coomaniella, Oriental Region
Identification of the Key Genes Involved in the Effect of Folic Acid on Endothelial Progenitor Cell Transcriptome of Patients with Type 1 Diabetes

Type 1 diabetes (T1D) is one of the most common autoimmune diseases in children. Previous studies have suggested that endothelial progenitor cells (EPCs) might be engaged in the regulating of the biological processes in T1D and folic acid (FA) might be engaged in regulating EPC function. The present study has identified 716 downregulated genes and 617 upregulated genes in T1D EPC cases after treated with FA. Bioinformatics analysis has shown that these DEGs were engaged in regulating metabolic processes, cell proliferation-related processes, bone marrow development, cell adhesion, platelet degranulation, and cellular response to growth factor stimulus. Furthermore, we have conducted and identified hub PPI networks. Importantly, we have identified 6 upregulated genes (POLR2A, BDNF, CDC27, LTN1, RAB1A, and CUL2) and 8 downregulated genes (SHC1, GRIN2B, TTN, GNAL, GNB2, PTK2, TF, and TLR9) as key regulators involved in the effect of FA on endothelial progenitor cell transcriptome of patients with T1D. We think that this study could provide novel information to understand the roles of FA in regulating EPCs of T1D patients.
Venous thromboembolism prevention protocol for adapting prophylaxis recommendations to the potential risk post total knee replacement: a randomized controlled trial

Background: Total knee replacement (TKR) is a major orthopedic surgery that is considered high risk for the development of venous thromboembolism (VTE). Objective: The aim of this study is to evaluate the clinical outcomes that resulted from the use of a new proposed VTE risk stratification protocol for selecting a suitable extended VTE prophylaxis for post TKR surgery patients administered in conjunction with patient education programs. Method: A randomized controlled trial was conducted in two medical centers in Saudi Arabia. A total of 242 patients were enrolled in the study, 121 patients in each group. The experimental group (A) was assessed by using the proposed VTE risk stratification protocol and also took part in patient education programs about TKR and its complications. The control group (B) was assessed by using the 2005 Caprini risk assessment tool and no education programs were given to this group. Both groups were followed for 35 days post operation. Results: The mean age of the participants was 65.86 (SD 8.67) and the majority of them were female 137 (56.6%). The mean body mass index of the study sample was 32.46 (SD 5.51). There were no significant differences between the two groups except for surgery type; the proportion of bilateral TKR in group A was higher than in group B (69/121 (28.5%) vs. 40/121(16.5%), p<0.05). There were no confirmed pulmonary embolism cases in the study sample and diagnosis of deep-vein thrombosis was confirmed in 12/242 (5.0%) of patients: 1/121 (0.8%) in group A and 11/121 (9.1%) in group B (p<0.05). The readmission rate for all patients was 2.5% (6/242), all of whom were in group B (p<0.05). Conclusion: The proposed VTE risk stratification protocol that was applied in conjunction with patient education programs reduced VTE complications and readmission events, post TKR surgery. Trial Registration: ClinicalTrials.gov: Identifier: NCT04031859.
Characterization of the C-terminal tail of the Arc protein

The activity-regulated cytoskeleton-associate protein Arc (or Arg3.1) is specifically linked to memory formation and a number of cognitive disorders, including Alzheimer’s disease and schizophrenia. Since the discovery of Arc in 1995, extensive research has been conducted on the protein to identify its function and mechanisms of action, with solving the structure of Arc as a major goal. However, the Arc protein tends to self-oligomerize in vitro, and is difficult to crystallize. These properties have hindered efforts to obtain the structure of the full-length, whole protein Arc. As an alternative approach, we and others, have sought to solve the structures of various subdomain proteins of Arc, including the N-lobe, C-lobe, and capsid domain (N-lobe + C-lobe). In this study, we characterized the C-terminal tail of Arc using integrated bioinformatic and structural biology techniques. We compared the sequences of Arc proteins in different mammal species and found that the amino-acid composition in the C-terminal tail region has a significantly higher degree of variation rate than the rest of the protein. Structural prediction programs suggested that the C-terminal tail is structurally disordered. Chemical shift analysis based on solution NMR spectra confirmed that the C-terminal tail has a random coil (disordered) structure, and the tail starts from the residue D357. Furthermore, the NMR spectra showed that the C-terminal tail has minimum (if any) interaction with its neighboring capsid domain in Arc. This study fills gaps in our specific understanding of the structural nature and functional contributions of the Arc C-terminus.
Radiofrequency ablation using internally cooled wet electrodes in bipolar mode for the treatment of recurrent hepatocellular carcinoma after locoregional treatment: A randomized prospective comparative study

Objective This study aimed to compare the efficacy between bipolar radiofrequency ablation (RFA), using twin internally cooled wet (TICW) electrodes, and switching monopolar RFA, using separable clustered (SC) electrodes, in the treatment of recurrent hepatocellular carcinoma (HCC) after locoregional treatment. Materials and methods In this single-center, two-arm, parallel-group, randomized controlled study, we performed a 1:1 random allocation on eligible patients with recurrent HCC after locoregional treatment, to receive TICW-RFA or SC-RFA. The primary endpoint was the minimum diameter of the ablation zone per unit ablation time. Secondary endpoints included other technical parameters, complication rate, technical success and technique efficacy, and clinical outcomes. Results Enrolled patients were randomly assigned to the TICW-RFA group (n = 40) or SC-RFA group (n = 37). The two groups did not show significant differences in the primary endpoint, the minimum diameter of the ablation zone per unit ablation time was 2.71 ± 0.98 mm/min and 2.61 ± 0.96 mm/min in the TICW-RFA and SC-RFA groups, respectively (p = 0.577). Total RF energy delivery (11.75 ± 9.04 kcal vs. 22.61 ± 12.98 kcal, p < 0.001) and energy delivery per unit time (0.81 ± 0.49 kcal/min vs. 1.45 ± 0.42 kcal/min, p < 0.001) of the TICW-RFA group were less than those of the SC-RFA group. No procedure-related death or major complications occurred. Technical success was achieved in all patients in both groups, and technique efficacy rates were 100% (46/46) in the TICW-RFA group and 95.0% (38/40) in the SC-RFA group (p = 0.213). The 1-year and 2-year cumulative LTP rates were 11.8% and 24.2%, respectively, in the TICW-RFA group, and 8.6% and 18.1%, respectively, in the SC-RFA group (p = 0.661). Conclusion In this single-center randomized controlled study from a Korean tertiary referral hospital, TICW-RFA demonstrated similar therapeutic efficacy and safety profile for recurrent HCC after locoregional treatment compared with SC-RFA. Trial registration ClinicalTrials.gov (NCT03806218)
Keep CALM and Explore: Language Models for Action Generation in Text-based Games

Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces. In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state. Our key insight is to train language models on human gameplay, where people demonstrate linguistic priors and a general game sense for promising actions conditioned on game history. We combine CALM with a reinforcement learning agent which re-ranks the generated action candidates to maximize in-game rewards. We evaluate our approach using the Jericho benchmark, on games unseen by CALM during training. Our method obtains a 69% relative improvement in average game score over the previous state-of-the-art model. Surprisingly, on half of these games, CALM is competitive with or better than other models that have access to ground truth admissible actions. Code and data are available at this https URL.
Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference

Inference on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering. The path-based reasoning models can leverage much information over paths other than pure triples in the KG, which face several challenges: all the existing path-based methods are data-driven, lacking explainability for path representation. Besides, some methods either consider only relational paths or ignore the heterogeneity between entities and relations both contained in paths, which cannot capture the rich semantics of paths well. To address the above challenges, in this work, we propose a novel joint semantics and data-driven path representation that balances explainability and generalization in the framework of KG embedding. More specifically, we inject horn rules to obtain the condensed paths by the transparent and explainable path composition procedure. The entity converter is designed to transform the entities along paths into the representations in the semantic level similar to relations for reducing the heterogeneity between entities and relations, in which the KGs both with and without type information are considered. Our proposed model is evaluated on two classes of tasks: link prediction and path query answering task. The experimental results show that it has a significant performance gain over several different state-of-the-art baselines.
A framework for predicting, interpreting, and improving Learning Outcomes

It has long been recognized that academic success is a result of both cognitive and non-cognitive dimensions acting together. Consequently, any intelligent learning platform designed to improve learning outcomes (LOs) must provide actionable inputs to the learner in these dimensions. However, operationalizing such inputs in a production setting that is scalable is not trivial. We develop an Embibe Score Quotient model (ESQ) to predict test scores based on observed academic, behavioral and test-taking features of a student. ESQ can be used to predict the future scoring potential of a student as well as offer personalized learning nudges, both critical to improving LOs. Multiple machine learning models are evaluated for the prediction task. In order to provide meaningful feedback to the learner, individualized Shapley feature attributions for each feature are computed. Prediction intervals are obtained by applying non-parametric quantile regression, in an attempt to quantify the uncertainty in the predictions. We apply the above modelling strategy on a dataset consisting of more than a hundred million learner interactions on the Embibe learning platform. We observe that the Median Absolute Error between the observed and predicted scores is 4.58% across several user segments, and the correlation between predicted and observed responses is 0.93. Game-like what-if scenarios are played out to see the changes in LOs, on counterfactual examples. We briefly discuss how a rational agent can then apply an optimal policy to affect the learning outcomes by treating the above model like an Oracle.
Joint Turn and Dialogue level User Satisfaction Estimation on Mulit-Domain Conversations

Dialogue level quality estimation is vital for optimizing data driven dialogue management. Current automated methods to estimate turn and dialogue level user satisfaction employ hand-crafted features and rely on complex annotation schemes, which reduce the generalizability of the trained models. We propose a novel user satisfaction estimation approach which minimizes an adaptive multi-task loss function in order to jointly predict turn-level Response Quality labels provided by experts and explicit dialogue-level ratings provided by end users. The proposed BiLSTM based deep neural net model automatically weighs each turn’s contribution towards the estimated dialogue-level rating, implicitly encodes temporal dependencies, and removes the need to hand-craft features. On dialogues sampled from 28 Alexa domains, two dialogue systems and three user groups, the joint dialogue-level satisfaction estimation model achieved up to an absolute 27% (0.43 -> 0.70) and 7% (0.63 -> 0.70) improvement in linear correlation performance over baseline deep neural net and benchmark Gradient boosting regression models, respectively.
Improving Nonparametric Density Estimation with Tensor Decompositions

While nonparametric density estimators often perform well on low dimensional data, their performance can suffer when applied to higher dimensional data, owing presumably to the curse of dimensionality. One technique for avoiding this is to assume no dependence between features and that the data are sampled from a separable density. This allows one to estimate each marginal distribution independently thereby avoiding the slow rates associated with estimating the full joint density. This is a strategy employed in naive Bayes models and is analogous to estimating a rank-one tensor. In this paper we investigate whether these improvements can be extended to other simplified dependence assumptions which we model via nonnegative tensor decompositions. In our central theoretical results we prove that restricting estimation to low-rank nonnegative PARAFAC or Tucker decompositions removes the dimensionality exponent on bin width rates for multidimensional histograms. These results are validated experimentally with high statistical significance via direct application of existing nonnegative tensor factorization to histogram estimators.
Spectrum of extensive multiclusters in the Kuramoto model with simplicial interaction.

Globally coupled ensembles of phase oscillators serve as useful tools for modeling synchronization and collective behavior in a variety of applications. As interest in the effects of simplicial interactions (i.e., non-additive, higher-order interactions between three or more units) continues to grow we study an extension of the Kuramoto model where oscillators are coupled via three-way interactions that exhibits novel dynamical properties including clustering, multistability, and abrupt desynchronization transitions. Here we provide a rigorous description of the stability of various multicluster states by studying their spectral properties in the thermodynamic limit. Specifically, we show that, similar to the classical Kuramoto model, the drifting subpopulation of any state has a continuous spectra confined to the imaginary axis and the negative real axis.
Comparison of outcome between liver resection, radiofrequency ablation, and transarterial therapy for multiple small hepatocellular carcinoma within the Milan criteria

Purpose Although surgical resection is usually considered for a single tumor, several reports have suggested that resection can be considered for multiple tumors. The objective of this study was to determine whether resection could provide better long-term outcome for patients with multiple hepatocellular carcinomas (HCCs) within Milan criteria. Methods A total of 276 patients with multiple HCCs within Milan criteria with liver function preserved who underwent resection, radiofrequency ablation (RFA), or transarterial chemoembolization (TACE) between 2009 and 2013 were analyzed. Propensity-score (PS) matching was conducted. Results Five-year overall survival (OS) and recurrence-free survival (RFS) were better in the resection group than that in the RFA or TACE group. Patients who underwent resection had more preserved liver function and different tumor characteristics compared to those received RFA or TACE. With similar baseline characteristics generated in the PS model, there was no difference in 5-year OS among 3 groups (79.5% vs. 72.3% or 62.0%, P = 0.232), but the 5-year RFS was better for patients who received resection than those who received RFA or TACE (51.9% vs. 22.0% or 0.0%, P < 0.001). Although the major complication rate was slightly higher than RFA or TACE, there was no significant difference between the 3 groups before and after PS matching. Conclusion Resection was associated with better RFS than RFA or TACE and showed comparable OS in multiple HCC patients within the Milan criteria, but at a cost of slightly increased risk of complication. Resection can be considered as a first-line option if selected appropriately.
Projection Mapping Implementation: Enabling Direct Externalization of Perception Results and Action Intent to Improve Robot Explainability

Existing research on non-verbal cues, e.g., eye gaze or arm movement, may not accurately present a robot's internal states such as perception results and action intent. Projecting the states directly onto a robot's operating environment has the advantages of being direct, accurate, and more salient, eliminating mental inference about the robot's intention. However, there is a lack of tools for projection mapping in robotics, compared to established motion planning libraries (e.g., MoveIt). In this paper, we detail the implementation of projection mapping to enable researchers and practitioners to push the boundaries for better interaction between robots and humans. We also provide practical documentation and code for a sample manipulation projection mapping on GitHub: this http URL.
How Not To Be in the Wrong Place at the Wrong Time: An Education Primer for Use with “Deposition of Centromeric Histone H3 Variant CENP-A/Cse4 into Chromatin Is Facilitated by Its C-Terminal Sumoylation”

Recent work by Kentaro Ohkuni and colleagues exemplifies how a series of molecular mechanisms contribute to a cellular outcome—equal distribution of chromosomes. Failure to maintain structural and numerical integrity of chromosomes is one contributing factor in genetic diseases such as cancer. Specifically, the authors investigated molecular events surrounding centromeric histone H3 variant Cse4 deposition—a process important for chromosome segregation, using Saccharomyces cerevisiae as a model organism. This study illustrates an example of a post-translational modification—sumoylation—regulating a cellular process and the concept of genetic interactions (e.g., synthetic dosage lethality). Furthermore, the study highlights the importance of using diverse experimental approaches in answering a few key research questions. The authors used molecular biology techniques (e.g., qPCR), biochemical experiments (e.g., Ni-NTA/8His protein purification), as well as genetic approaches to understand the regulation of Cse4. At a big-picture level, the study reveals how genetic changes can lead to subsequent molecular and cellular changes.
Assessment of genetic variability in Hill cattle of Garhwal region of Uttarakhand by microsatellite markers

Knowledge of livestock genetic diversity is a crucial step to respond to commercial requirements and achieve production objectives in different environments and production systems. We investigated population genetic diversity in a hitherto unexplored Hill cattle population of Garhwal region of Uttarakhand state using microsatellite markers. A total of 48 individuals belonging to the Garhwal Hill cattle population were typed with 21 microsatellites. A mean of 12.61±4.80 alleles per locus was typed, indicating high allelic diversity in the population. The mean number of effective alleles per locus was 5.66±2.64. The studied population had high gene diversity indicated by high mean observed and expected heterozygosities across the 21 loci, i.e. 0.699±0.19 and 0.721±0.10 respectively. Mean PIC across the investigated loci was 0.782±0.10. The mean F1’s value was 0.118±0.17 indicating inbreeding in the population. Genetic bottleneck evaluated by IAM, TPM and SMM methods as well as frequency distribution was found to be absent in the evaluated population.
Metabolic Engineering of Microorganisms for the Production of Flavonoids

Flavonoids are a class of secondary metabolites found in plant and fungus. They have been widely used in food, pharmaceutical, and nutraceutical industries owing to their significant biological activities, such as antiaging, antioxidant, anti-inflammatory, and anticancer. However, the traditional approaches for the production of flavonoids including chemical synthesis and plant extraction involved hazardous materials and complicated processes and also suffered from low product titer and yield. Microbial synthesis of flavonoids from renewable biomass such as glucose and xylose has been considered as a sustainable and environmentally friendly method for large-scale production of flavonoids. Recently, construction of microbial cell factories for efficient biosynthesis of flavonoids has gained much attention. In this article, we summarize the recent advances in microbial synthesis of flavonoids including flavanones, flavones, isoflavones, flavonols, flavanols, and anthocyanins. We put emphasis on developing pathway construction and optimization strategies to biosynthesize flavonoids and to improve their titer and yield. Then, we discuss the current challenges and future perspectives on successful strain development for large-scale production of flavonoids in an industrial level.
Social-Ecological Resilience Moderates the Effectiveness of Avoidant Coping in Children Exposed to Adversity: An Exploratory Study in Lithuania

Background Against the high prevalence of adverse childhood experiences in Lithuania, the government testified a lack of effective ways to address the problem. A crucial endeavor for intervention planners is to identify the risk and protective factors whose interaction may lead at risk children to achieve greater levels of functioning. Internal qualities and external resources can act independently or interactively to reduce the damaging effects of adversities, and to enhance resilience process. In particular, both coping strategies and social resources have been shown to have a consistent influence on trauma-related outcomes. Objective The aim of this study was to investigate the potential interaction of coping strategies with external resources in predicting trauma-related outcomes in children exposed to adversities. Participants and Setting A sample of 372 Lithuanian children (mean age = 13.03; range: 7–17) with a history of traumatic experiences has been involved. Methods The Child and Youth Resilience Measure-Revised (CYRM-R), the Children Coping Strategy Checklist (CCSC), and the Trauma Symptom Checklist for Children (TSCC) have been administered to participants. A moderation analysis was performed to test whether social-ecological resilience moderate the relationship between each coping strategy and trauma-related outcomes. Results: When controlling for sex, age, other coping strategies, and social-ecological resilience, only active coping was found to significantly predict each of the trauma-related symptoms. Furthermore, social-ecological resilience has a negatively moderating effect on the relationship between avoidant coping strategies and depression. Conclusion MHPSS professionals who design and implement interventions to enhance the likelihood of resilience among vulnerable children, should take in considerations the multiple interaction between social-ecological resilience and avoidant coping strategies in the children adjustment.