Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities

How to learn an effective reinforcement learning-based model for control tasks from high-level visual observations is a practical and challenging problem. A key to solving this problem is to learn low-dimensional state representations from observations, from which an effective policy can be learned. In order to boost the learning of state encoding, recent works are focused on capturing behavioral similarities between state representations or applying data augmentation on visual observations. In this paper, we propose a novel meta-learner-based framework for representation learning regarding behavioral similarities for reinforcement learning. Specifically, our framework encodes the high-dimensional observations into two decomposed embeddings regarding reward and dynamics in a Markov Decision Process (MDP). A pair of meta-learners are developed, one of which quantifies the reward similarity and the other quantifies dynamics similarity over the correspondingly decomposed embeddings. The meta-learners are self-learned to update the state embeddings by approximating two disjoint terms in on-policy bisimulation metric. To incorporate the reward and dynamics terms, we further develop a strategy to adaptively balance their impacts based on different tasks or environments. We empirically demonstrate that our proposed framework outperforms state-of-the-art baselines on several benchmarks, including conventional DM Control Suite, Distracting DM Control Suite and a self-driving task CARLA.
Label Encoding for Regression Networks

Deep neural networks are used for a wide range of regression problems. However, there exists a significant gap in accuracy between specialized approaches and generic direct regression in which a network is trained by minimizing the squared or absolute error of output labels. Prior work has shown that solving a regression problem with a set of binary classifiers can improve accuracy by utilizing well-studied binary classification algorithms. We introduce binary-encoded labels (BEL), which generalizes the application of binary classification to regression by providing a framework for considering arbitrary multi-bit values when encoding target values. We identify desirable properties of suitable encoding and decoding functions used for the conversion between real-valued and binary-encoded labels based on theoretical and empirical study. These properties highlight a tradeoff between classification error probability and error-correction capabilities of label encodings. BEL can be combined with off-the-shelf task-specific feature extractors and trained end-to-end. We propose a series of sample encoding, decoding, and training loss functions for BEL and demonstrate they result in lower error than direct regression and specialized approaches while being suitable for a diverse set of regression problems, network architectures, and evaluation metrics. BEL achieves state-of-the-art accuracies for several regression benchmarks. Code is available at https://github.com/ubc-aamodt-group/BEL_regression.
HCMUS at MediaEval2021: Content-Based Misinformation Detection Using ContextualizedWord Embedding from BERT

The FakeNews task in MediaEval2021 explores the challenge of building accurate and high-performance algorithms. Despite the dominance of deep learning approaches in fake news detection, in this paper, we propose different approaches leveraging the advantages of using pretrained BERT family transformers in extracting word embedding. The result from experiments shows that averaging ensemble methods using machine learning classifiers as estimators can achieve up to 0.6478 Matthew Correlation Coefficient(MCC) in the run submission’s test set.
Planning in Stochastic Environments with a Learned Model

Model-based reinforcement learning has proven highly successful. However, learning a model in isolation from its use during planning is problematic in complex environments. To date, the most effective techniques have instead combined value-equivalent model learning with powerful tree-search methods. This approach is exempliﬁed by MuZero , which has achieved state-of-the-art performance in a wide range of domains, from board games to visually rich environments, with discrete and continuous action spaces, in online and ofﬂine settings. However, previous instantiations of this approach were limited to the use of deterministic models. This limits their performance in environments that are inherently stochastic, partially observed, or so large and complex that they appear stochastic to a ﬁnite agent. In this paper we extend this approach to learn and plan with stochastic models. Specifically, we introduce a new algorithm, Stochastic MuZero , that learns a stochastic model incorporating afterstates, and uses this model to perform a stochastic tree search. Stochastic MuZero matched or exceeded the state of the art in a set of canonical single and multi-agent environments, including 2048 and backgammon, while maintaining the superhuman performance of standard MuZero in the game of Go.
Quantum dense coding without entanglement

We describe a quantum cryptography protocol with up to twenty four-dimensional ($\mathcal{D} =4$) states generated by a polarization-, phase- and time-encoding transmitter. This protocol can be experimentally realized with existing technology, drawing from time-encoded and polarization-encoded systems. The protocol is error tolerant and has a quantum bit-rate of 2 per transmission/detection, which when combined with state detection efficiency yields a qubit efficiency of up to 1 or double that of BB84-{\it like} protocols.
The effect of percutaneouS vs. cutdoWn accEss in patients after Endovascular aorTic repair (SWEET): Study protocol for a single-blind, single-center, randomized controlled trial

Background Endovascular abdominal aortic repair (EVAR) and thoracic endovascular aortic repair (TEVAR) have become the first-line treatment for aortic diseases, but current evidence is uncertain regarding whether a percutaneous approach has better outcomes than cutdown access, especially for patient-centered outcomes (PCOs). This study is designed to compare these outcomes of percutaneous access vs. cutdown access after endovascular aortic repair. Method The SWEET study is a randomized, controlled, single-blind, single-center non-inferiority trial with two parallel groups in two cohorts respectively. After eligibility screening, subjects who meet the inclusion criteria will be divided into Cohort EVAR or Cohort TEVAR according to clinic interviews. And then participants in two cohorts will be randomly allocated to either intervention groups receiving percutaneous access endovascular repair or controlled groups receiving cutdown access endovascular repair separately. Primary clinician-reported outcome (ClinRO) is access-related complication, and primary patient-centered outcome (PCO) is time back to normal life. Follow-up will be conducted at 2 weeks, 1 month, 3 months postoperatively. Discussion The choice of either percutaneous or cutdown access may not greatly affect the success of EVAR or TEVAR procedures, but can influence the quality of life and patient-centered experience. Given the very low evidence for ClinROs and few data for PCOs, comparison of the percutaneous vs. cutdown access EVAR and TEVAR is essential for both patient-centered care and clinical decision making in endovascular aortic repair. Trial registration Chinese Clinical Trial Registry ChiCTR2100053161 (registered on 13th November, 2021).
Transcriptome changes in DM1 patients’ tissues are governed by the RNA interference pathway

Myotonic dystrophy type 1 (DM1) is a multisystemic disease caused by pathogenic expansions of CTG repeats. The expanded repeats are transcribed to long RNA and induce cellular toxicity. Recent studies suggest that the CUG repeats are processed by the RNA interference (RNAi) pathway to generate small interfering repeated RNA (siRNA). However, the effects of the CTG repeat-derived siRNAs remain unclear. We hypothesize that the RNAi machinery in DM1 patients generates distinct gene expression patterns that determine the disease phenotype in the individual patient. The abundance of genes with complementary repeats that are targeted by siRNAs in each tissue determines the way that the tissue is affected in DM1. We integrated and analyzed published transcriptome data from muscle, heart, and brain biopsies of DM1 patients, and revealed shared, characteristic changes that correlated with disease phenotype. These signatures are overrepresented by genes and transcription factors bearing endogenous CTG/CAG repeats and are governed by aberrant activity of the RNAi machinery, miRNAs, and a specific gain-of-function of the CTG repeats. Computational analysis of the DM1 transcriptome enhances our understanding of the complex pathophysiology of the disease and may reveal a path for cure.
The intriguing dose-dependent effect of selected amphiphilic compounds on insulin amyloid aggregation: Focus on a cholesterol-based detergent, Chobimalt

The amyloidogenic self-assembly of many peptides and proteins largely depends on external conditions. Among amyloid-prone proteins, insulin attracts attention because of its physiological and therapeutic importance. In the present work, the amyloid aggregation of insulin is studied in the presence of cholesterol-based detergent, Chobimalt. The strategy to elucidate the Chobimalt-induced effect on insulin fibrillogenesis is based on performing the concentration- and time-dependent analysis using a combination of different experimental techniques, such as ThT fluorescence assay, CD, AFM, SANS, and SAXS. While at the lowest Chobimalt concentration (0.1 µM; insulin to Chobimalt molar ratio of 1:0.004) the formation of insulin fibrils was not affected, the gradual increase of Chobimalt concentration (up to 100 µM; molar ratio of 1:4) led to a significant increase in ThT fluorescence, and the maximal ThT fluorescence was 3-4-fold higher than the control insulin fibril’s ThT fluorescence intensity. Kinetic studies confirm the dose-dependent experimental results. Depending on the concentration of Chobimalt, either (i) no effect is observed, or (ii) significantly, ∼10-times prolonged lag-phases accompanied by the substantial, ∼ 3-fold higher relative ThT fluorescence intensities at the steady-state phase are recorded. In addition, at certain concentrations of Chobimalt, changes in the elongation-phase are noticed. An increase in the Chobimalt concentrations also triggers the formation of insulin fibrils with sharply altered morphological appearance. The fibrils appear to be more flexible and wavy-like with a tendency to form circles. SANS and SAXS data also revealed the morphology changes of amyloid fibrils in the presence of Chobimalt. Amyloid aggregation requires the formation of unfolded intermediates, which subsequently generate amyloidogenic nuclei. We hypothesize that the different morphology of the formed insulin fibrils is the result of the gradual binding of Chobimalt to different binding sites on unfolded insulin. A similar explanation and the existence of such binding sites with different binding energies was shown previously for the nonionic detergent. Thus, the data also emphasize the importance of a protein partially-unfolded state which undergoes the process of fibrils formation; i.e., certain experimental conditions or the presence of additives may dramatically change not only kinetics but also the morphology of fibrillar aggregates.
Editorial: Managing chronic obstruction pulmonary disease: From translational research to public health practice

COPYRIGHT © 2022 Han, Ho, Ho and Chuang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Managing chronic obstruction pulmonary disease: From translational research to public health practice
Efficacy and safety of biological agents for the treatment of pediatric patients with psoriasis: A bayesian analysis of six high-quality randomized controlled trials

Background Biological agents have been used with extreme caution in children because of their possible adverse effects. Objectives This study used high-quality randomized controlled trials (RCTs) to provide high-level evidence to assess the effectiveness and safety of biological agents for treating children with psoriasis. Methods We searched PubMed, Embase, Cochrane, and Web of Science databases through October 31, 2021. We included trials reporting at least one adverse event after treatment with biological agents of patients less than 18-year-old diagnosed with psoriasis. RevMan 5.3 and Stata 15.0 software were used for meta and Bayesian analyses. Results Six trials with 864 participants were included in the analysis. The results showed a 2.37-fold higher response rate in all biologics groups than in the control group for psoriasis area and severity index 75 (PASI75) (RR= 2.37, P-value < 0.01, 95% confidence interval [CI] [1.22, 4.62]). Compared with placebo, the PASI75 response rates of etanercept (RR= 2.82, 95% [CI] [1.10, 7.21]), ustekinumab low dose (RR= 7.45, 95%[CI] [1.25, 44.58]), and ustekinumab high dose (RR= 7.25, 95%[CI] [1.21, 43.41]) were superior. Additionally, the incidence of total adverse reactions was 1.05 times higher for biologics than for controls, indicating a good safety profile (RR= 1.05, P-value = 0.53, 95%[CI] [0.92, 1.19]). Overall, these six high-quality randomized controlled trials suggest that biologics are effective and safe for pediatric patients with psoriasis. Limitations Inclusion of few relevant, high-quality RCTs. Conclusion The results of this study indicate that biologics can be used to treat children with moderate-to-severe psoriasis without the risk of adverse effects. Ustekinumab showed the best efficacy and the fewest adverse effects.
A retrospective study of post-operative complications and cost analysis in robotic rectal resection versus laparoscopic rectal resection

Background Robotic rectal cancer surgery has proven to be a viable alternative to laparoscopic surgery in treating rectal cancer. This study assessed the short-term operative measures of robotic versus laparoscopic surgery. Material Data was obtained retrospectively from July 2019 to November 2021. Patient demographics, pre-and post-operative features, initial bowel movement, length of hospital stay, and short-term postoperative outcomes such as harvested lymph node, sepsis, Clavien–Dindo Classification, and cost were evaluated. Results A total of 155 patients were treated for colorectal cancer, with 64 receiving robotic surgery and 91 receiving laparoscopic surgery. According to the Clavien–Dindo classification, there is a significant P < 0.05 between robotic and laparoscopic rectal surgery, with robotic having fewer patients in grade III-IV than laparoscopic. Despite this, laparoscopic surgery is associated with more sepsis patients (P < 0.05), and harvested lymph nodes are likewise associated with significant results. Conclusion With respect to post-operative complication and cost analysis, our finding imply that robotic rectal resection achieves better-quality short-term outcome but more costly than laparoscopic as well as Clavien–Dindo classification plays a crucial role in assessing postoperative rectal cancer complications and considerably impacts the quality of life.
Maize diversification and nitrogen fertilization effects on soil nitrous oxide emissions in irrigated mediterranean conditions

Maize is a major irrigated crop in Mediterranean areas and its typical intensive management may impact soil nitrous oxide (N2O) emissions. In these irrigated continuous maize systems, the legumes incorporation as well as adjusted nitrogen (N) fertilization might be interesting strategies to reduce soil N2O emissions. The objective of this study was to assess the impact of cropping diversification and different N rates on soil N2O emissions in flooded irrigated maize under Mediterranean conditions. To achieve this, two cropping systems (maize monoculture system, MC; and pea -maize rotation, MP) and 3N rates (unfertilized, 0N; medium rate, MN; and high rate, HN) were evaluated in a field experiment established in NE Spain during 2 years (2019; 2020). During the studied period, the N rate had a significant effect on soil N2O emissions, with a non-linear positive response of cumulative soil N2O emissions to N rates. In both systems, quick and high increases of soil N2O fluxes were observed immediately after the N application reaching 55 and 100 mg N2O-N m−2 day−1 in MC and MP, respectively. Both years, the pea phase of the MP rotation showed greater cumulative N2O emissions than the fallow of MC. However, N2O losses in the maize phase were similar (2019) or even higher (2020) in MC than in MP. Moreover, in both seasons, the MN treatments showed lower yield-scaled N2O emissions and N emission factor than the HN treatments, being this last lower than 1% in all cases. The results obtained showed that in irrigated Mediterranean conditions the replacement of a fallow by a legume, together with an adjusted N fertilization are favourable strategies to mitigate soil N2O emissions in high-yielding maize systems.
Untargeted serum metabolomic profiling for early detection of Schistosoma mekongi infection in mouse model

Mekong schistosomiasis is a parasitic disease caused by blood flukes in the Lao People’s Democratic Republic and in Cambodia. The standard method for diagnosis of schistosomiasis is detection of parasite eggs from patient samples. However, this method is not sufficient to detect asymptomatic patients, low egg numbers, or early infection. Therefore, diagnostic methods with higher sensitivity at the early stage of the disease are needed to fill this gap. The aim of this study was to identify potential biomarkers of early schistosomiasis using an untargeted metabolomics approach. Serum of uninfected and S. mekongi-infected mice was collected at 2, 4, and 8 weeks post-infection. Samples were extracted for metabolites and analyzed with a liquid chromatography-tandem mass spectrometer. Metabolites were annotated with the MS-DIAL platform and analyzed with Metaboanalyst bioinformatic tools. Multivariate analysis distinguished between metabolites from the different experimental conditions. Biomarker screening was performed using three methods: correlation coefficient analysis; feature important detection with a random forest algorithm; and receiver operating characteristic (ROC) curve analysis. Three compounds were identified as potential biomarkers at the early stage of the disease: heptadecanoyl ethanolamide; picrotin; and theophylline. The levels of these three compounds changed significantly during early-stage infection, and therefore these molecules may be promising schistosomiasis markers. These findings may help to improve early diagnosis of schistosomiasis, thus reducing the burden on patients and limiting spread of the disease in endemic areas.
Role of endometrial microRNAs in repeated implantation failure (mini-review)

MicroRNAs (miRNAs) play various roles in the implantation and pregnancy process. Abnormal regulation of miRNAs leads to reproductive disorders such as repeated implantation failure (RIF). During the window of implantation, different miRNAs are released from the endometrium, which can potentially reflect the status of the endometrium for in vitro fertilization (IVF). The focus of this review is to determine whether endometrial miRNAs may be utilized as noninvasive biomarkers to predict the ability of endometrium to implant and provide live birth during IVF cycles. The levels of certain miRNAs in the endometrium have been linked to implantation potential and pregnancy outcomes in previous studies. Endometrial miRNAs could be employed as non-invasive biomarkers in the assisted reproductive technology (ART) cycle to determine the optimal time for implantation. Few human studies have evaluated the association between ART outcomes and endometrial miRNAs in RIF patients. This review may pave the way for more miRNA transcriptomic studies on human endometrium and introduce a specific miRNA profile as a multivariable prediction model for choosing the optimal time in the IVF cycle.
Vibration therapy as an effective approach to improve bone healing in diabetic rats

Objective To investigate the effects of vibration therapy on fracture healing in diabetic and non-diabetic rats. Methods 148 rats underwent fracture surgery and were assigned to four groups: (1) SHAM: weight-matched non-diabetic rats, (2) SHAM+VT: non-diabetic rats treated with vibration therapy (VT), (3) DM: diabetic rats, and (4) DM+VT: diabetic rats treated with VT. Thirty days after diabetes induction with streptozotocin, animals underwent bone fracture, followed by surgical stabilization. Three days after bone fracture, rats began VT. Bone healing was assessed on days 14 and 28 post-fracture by serum bone marker analysis, and femurs collected for dual-energy X-ray absorptiometry, micro-computed tomography, histology, and gene expression. Results Our results are based on 88 animals. Diabetes led to a dramatic impairment of bone healing as demonstrated by a 17% reduction in bone mineral density and decreases in formation-related microstructural parameters compared to non-diabetic control rats (81% reduction in bone callus volume, 69% reduction in woven bone fraction, 39% reduction in trabecular thickness, and 45% in trabecular number). These changes were accompanied by a significant decrease in the expression of osteoblast-related genes (Runx2, Col1a1, Osx), as well as a 92% reduction in serum insulin-like growth factor I (IGF-1) levels. On the other hand, resorption-related parameters were increased in diabetic rats, including a 20% increase in the callus porosity, a 33% increase in trabecular separation, and a 318% increase in serum C terminal telopeptide of type 1 collagen levels. VT augmented osteogenic and chondrogenic cell proliferation at the fracture callus in diabetic rats; increased circulating IGF-1 by 668%, callus volume by 52%, callus bone mineral content by 90%, and callus area by 72%; and was associated with a 19% reduction in circulating receptor activator of nuclear factor kappa beta ligand (RANK-L). Conclusions Diabetes had detrimental effects on bone healing. Vibration therapy was effective at counteracting the significant disruption in bone repair induced by diabetes, but did not improve fracture healing in non-diabetic control rats. The mechanical stimulus not only improved bone callus quality and quantity, but also partially restored the serum levels of IGF-1 and RANK-L, inducing bone formation and mineralization, thus creating conditions for adequate fracture repair in diabetic rats.