Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrinsic Auricular Muscle Zone Stimulation Improves Walking Parameters of Parkinson's Patients Faster Than Levodopa in the Motion Capture Analysis: A Pilot Study

It has been demonstrated that intrinsic auricular muscles zone stimulation (IAMZS) can improve the motor symptoms of Parkinson's disease (PD) patients who are examined with the Unified Parkinson's Disease Rating Scale (UPDRS) motor scores. In the present pilot study, using motion capture technology, we aimed to investigate the efficacy of IAMZS compared to medication alone or in combination with medication. Ten PD patients (mean age: 54.8 ± 10.1 years) were enrolled. Each participant participated in three different sessions: sole medication, sole stimulation-20 min of IAMZS, and combined IAMZS (20 min) and medication. Each session was performed on different days but at the same time to be aligned with patients' drug intake. Motion capture recording sessions took place at baseline, 20, 40, and 60 min. Statistical analysis was conducted using one-way repeated measures ANOVA. Bonferroni correction was implemented for pairwise comparisons. The sole medication was ineffective to improve gait-related parameters of stride length, stride velocity, stance, swing, and turning speed. In the sole-stimulation group, pace-related gait parameters were significantly increased at 20 and 40 min. These improvements were observed in stride length at 20 (p = 0.0498) and 40 (p = 0.03) min, and also in the normalized stride velocity at 40 min (p-value = 0.02). Stride velocity also tended to be significant at 20 min (p = 0.06) in the sole-stimulation group. Combined IAMZS and medication demonstrated significant improvements in all the time segments for pace-related gait parameters [stride length: 20 min (p = 0.04), 40 min (p = 0.01), and 60 min (p < 0.01); stride velocity: 20 min (p < 0.01), 40 min (p = 0.01), and 60 min (p < 0.01)]. These findings demonstrated the fast action of the IAMZS on PD motor symptoms. Moreover, following the termination of IAMZS, a prolonged improvement in symptoms was observed at 40 min. The combined use of IAMZS with medication showed the most profound improvements. The IAMZS may be particularly useful during medication off periods and may also postpone the long-term side effects of high-dose levodopa. A large scale multicentric trial is required to validate the results obtained from this pilot study. Clinical Trial Registration: www.ClinicalTrials. gov, identifier NCT03907007.
Antibacterial activity of Cinnamomum cassia L. essential oil in a carbapenem- and polymyxin-resistant Klebsiella aerogenes strain

Abstract INTRODUCTION: Essential oils can serve as novel sources of antibiotics for multidrug-resistant bacteria. METHODS: The multidrug-resistance profile of a Klebsiella aerogenes strain was assessed by PCR and sequencing. The antibacterial activity of Cinnamomum cassia essential oil (CCeo) against K. aerogenes was assessed by broth microdilution and time-kill methods. RESULTS: K. aerogenes showed high antibiotic resistance. The genes bla KPC-2, ampC, bla CTX-M-15, bla OXA-1, and bla TEM were present. CCeo exhibited an inhibitory effect with a minimum inhibitory concentration of 17.57 μg/mL. CONCLUSIONS: The antibacterial activity of CCeo makes it a potential candidate for treating carbapenem- and polymyxin-resistant K. aerogenes strains.
Psychometric properties of the International Trauma Questionnaire (ITQ) examined in a Norwegian trauma-exposed clinical sample

ABSTRACT Background The International Trauma Questionnaire (ITQ) is a self-report measure for post-traumatic stress disorder (PTSD) and complex post-traumatic stress disorder (CPTSD), corresponding to the diagnostic criteria in the International Classification of Diseases, 11th Revision (ICD-11). A 12-item version of the ITQ based on samples from English-speaking countries has been presented, and the wider generalizability to other languages needs to be examined. Objective The current study examines the psychometric properties of scores from a longer, preliminary 22-item version of the ITQ and the current reduced 12-item version by means of generalizability theory (G-theory) and confirmatory factor analysis (CFA). Method The 22-item version of the ITQ was translated into Norwegian and administered to patients in two trauma treatment trials (total N = 202). A generalizability study was used to investigate the psychometric properties of scores reflecting CPTSD. G-theory was also used to investigate alternative measurement designs to optimize the sufficient number of items that provide acceptable generalizability and dependability of scores. Model fit to the theoretical factor structure was then examined by CFA, both for the 22-item version and for the 12-item version of the ITQ. Results The two subscales negative self-concept and relational disturbances had acceptable generalizability coefficients. We found substantial measurement error related to affective dysregulation, mainly attributable to affective hyperactivation. A latent factor structure model with two separate affective dysregulation factors: hyperactivation and deactivation, represented the data well in the 22-item version. The proposed confirmatory structure model for the 12-item short form did not converge in the CFA. Conclusion This study supports the applicability of the ITQ in a non-English-speaking country and provides support for the validity of the Norwegian translation. Further research is needed to improve the psychometric properties of the affective dysregulation subscale.
Parallax Motion Effect Generation Through Instance Segmentation And Depth Estimation

Stereo vision is a growing topic in computer vision due to the innumerable opportunities and applications this technology offers for the development of modern solutions, such as virtual and augmented reality applications. To enhance the user’s experience in three-dimensional virtual environments, the motion parallax estimation is a promising technique to achieve this objective. In this paper, we propose an algorithm for generating parallax motion effects from a single image, taking advantage of state-of-the-art instance segmentation and depth estimation approaches. This work also presents a comparison against such algorithms to investigate the trade-off between efficiency and quality of the parallax motion effects, taking into consideration a multi-task learning network capable of estimating instance segmentation and depth estimation at once. Experimental results and visual quality assessment indicate that the PyD-Net network (depth estimation) combined with Mask R-CNN or FBNet networks (instance segmentation) can produce parallax motion effects with good visual quality.
Reward Propagation Using Graph Convolutional Networks

Potential-based reward shaping provides an approach for designing good reward functions, with the purpose of speeding up learning. However, automatically finding potential functions for complex environments is a difficult problem (in fact, of the same difficulty as learning a value function from scratch). We propose a new framework for learning potential functions by leveraging ideas from graph representation learning. Our approach relies on Graph Convolutional Networks which we use as a key ingredient in combination with the probabilistic inference view of reinforcement learning. More precisely, we leverage Graph Convolutional Networks to perform message passing from rewarding states. The propagated messages can then be used as potential functions for reward shaping to accelerate learning. We verify empirically that our approach can achieve considerable improvements in both small and high-dimensional control problems.
Finding Top-k Nodes for Temporal Closeness in Large Temporal Graphs

The harmonic closeness centrality measure associates, to each node of a graph, the average of the inverse of its distances from all the other nodes (by assuming that unreachable nodes are at infinite distance). This notion has been adapted to temporal graphs (that is, graphs in which edges can appear and disappear during time) and in this paper we address the question of finding the top-k nodes for this metric. Computing the temporal closeness for one node can be done in O(m) time, where m is the number of temporal edges. Therefore computing exactly the closeness for all nodes, in order to find the ones with top closeness, would require O(nm) time, where n is the number of nodes. This time complexity is intractable for large temporal graphs. Instead, we show how this measure can be efficiently approximated by using a “backward” temporal breadth-first search algorithm and a classical sampling technique. Our experimental results show that the approximation is excellent for nodes with high closeness, allowing us to detect them in practice in a fraction of the time needed for computing the exact closeness of all nodes. We validate our approach with an extensive set of experiments.
Effects of germline and somatic events in candidate BRCA-like genes on breast-tumor signatures

Background Mutations in BRCA1 and BRCA2 cause deficiencies in homologous recombination repair (HR), resulting in repair of DNA double-strand breaks by the alternative non-homologous end-joining pathway, which is more error prone. HR deficiency of breast tumors is important because it is associated with better responses to platinum salt therapies and PARP inhibitors. Among other consequences of HR deficiency are characteristic somatic-mutation signatures and gene-expression patterns. The term “BRCAness” describes tumors that harbor an HR defect but have no detectable germline mutation in BRCA1 or BRCA2. A better understanding of the genes and molecular events associated with BRCAness could provide mechanistic insights and guide development of targeted treatments. Methods Using data from The Cancer Genome Atlas (TCGA) for 1101 breast-cancer patients, we identified individuals with a germline mutation, somatic mutation, homozygous deletion, and/or hypermethylation event in BRCA1, BRCA2, and 59 other cancer-predisposition genes. Based on the assumption that BRCAness events would have similar downstream effects on tumor biology as BRCA1/BRCA2 germline mutations, we quantified these effects based on somatic-mutation signatures and gene-expression profiles. We reduced the dimensionality of the somatic-mutation signatures and expression data and used a statistical resampling approach to quantify similarities between patients who had a BRCA1/BRCA2 germline mutation, another type of aberration in BRCA1 or BRCA2, or any type of aberration in one of the other genes. Results Somatic-mutation signatures of tumors having a somatic mutation, homozygous deletion, or hypermethylation event in BRCA1/BRCA2 (n = 80) were generally similar to each other and to tumors from BRCA1/BRCA2 germline carriers (n = 44). Additionally, somatic-mutation signatures of tumors with germline or somatic events in ATR (n = 16) and BARD1 (n = 8) showed high similarity to tumors from BRCA1/BRCA2 carriers. Other genes also showed high similarity but only for a small number of events or for a single event type. Tumors with germline mutations or hypermethylation of BRCA1 had relatively similar gene-expression profiles and overlapped considerably with the Basal-like subtype; but the transcriptional effects of the other events lacked consistency. Conclusions Our findings confirm previously known relationships between molecular signatures and germline or somatic events in BRCA1/BRCA2 and suggest additional genes that may be considered for inclusion in the definition of BRCAness.
Dimensional Synthesis for Multi-Linkage Robots Based on a Niched Pareto Genetic Algorithm

The dimensional synthesis of multi-linkage robots has great significance for improving flexibility and efficiency. With the increase of the degree of freedom and restrictions on special occasions, the solution of dimensional synthesis becomes complicated and time-consuming. Theory of workspace density function, maneuverability, and energy expenditure had been studied. With high flexibility and low energy consumption as the design goal, the method for dimensional and joint angle synthesis of multi-linkage robots was proposed based on a niched Pareto genetic algorithm. The Pareto solution set has been obtained. The method was verified by two application examples, which is occlusion of the solar salt evaporation pool and the secondary scattering of solid 2,2′-azobis(2,4-dimethylvaleronitrile). Through the application of NPGA (niched Pareto genetic algorithm) compared with KPCA (kernel principal component analysis), it can save 12.37% time in occlusion of one evaporating pool and reduce energy consumption by 3.85%; it can save 9.96% time in scattering of remain materials per barrel and reduce energy consumption by 1.77%. The study reduces the labor intensity of manual workers in the salt making industry, ensures the safe production of dangerous chemicals, and provides new ideas and methods for the dimensional synthesis of multi-linkage robots.
Joint coded aperture optimization and compressive hyperspectral image classification using 3D coded neural network.

Hyperspectral image classification (HIC) is an active research topic in remote sensing. However, the huge volume of three-dimensional (3D) hyperspectral images poses big challenges in data acquisition, storage, transmission and processing. To overcome these limitations, this paper develops a novel deep learning HIC approach based on the compressive measurements of coded-aperture snapshot spectral imaging (CASSI) system, without reconstructing the complete hyperspectral data cube. A new kind of deep learning strategy, namely 3D coded convolutional neural network (3D-CCNN) is proposed to efficiently solve for the HIC problem, where the hardware-based coded aperture is regarded as a pixel-wise connected network layer. An end-to-end training method is developed to jointly optimize the network parameters and the coded aperture pattern with periodic structure. The accuracy of HIC approach is effectively improved by involving the degrees of optimization freedom from the coded aperture. The superiority of the proposed method is assessed on some public hyperspectral datasets over the state-of-the-art HIC methods.
Antioxidant Activity, Enzyme Inhibition Potentials, and Phytochemical Profiling of Premna serratifolia L. Leaf Extracts

Premna serratifolia, commonly known as Arogo in Tentena-Sulawesi, is a popular vegetable. As a promising herbal tea and food ingredient, further investigation is required to find the best knowledge for medicinal use of P. serratifolia leaves. This research investigated the antioxidant activity of the ethanol (EEPS) and water (WEPS) extracts of P. serratifolia leaves, based on their scavenging activities on DPPH radicals and their reducing capacities (CuPRAC, total antioxidant/phosphomolybdenum, and ferric thiocyanate reducing power assays). The DNA-protecting effect by EEPS was tested using pBR322 plasmid DNA against •OH radical-induced damage. The inhibition potentials of both extracts against several enzymes related to metabolic diseases (α-glucosidase, α-amylase, xanthine oxidase, and protease) were evaluated. The phytochemical analysis was conducted by an LC-QTOF-MS/MS technique. EEPS proved to be a better antioxidant and had higher phenolic content compared to WEPS. EEPS demonstrated a protective effect on DNA with recovery percentage linearly correlated with EEPS concentrations. Strong inhibition on α-glucosidase and α-amylase was observed for EEPS; however, EEPS and WEPS showed weak inhibitions on xanthine oxidase and protease. LC-QTOF-MS/MS analysis identified seven main components in EEPS, namely scroside E, forsythoside A and forsythoside B, lavandulifolioside, diosmin, nobilin D, campneoside I, and isoacteoside. These components may be responsible for the observed enzymes inhibitions and antioxidant properties. Premna serratifolia leaves can be an appropriate choice for the development of nutraceutical and drug preparations.
Effect of Exogenous Melatonin Administration in Critically Ill Patients on Delirium and Sleep: A Randomized Controlled Trial

Introduction Sleep deprivation is a contributor for delirium in intensive care. Melatonin has been proposed as a pharmacological strategy to improve sleep, but studies have shown that the increase in plasma levels of melatonin do not correlate to a beneficial clinical effect; in addition, melatonin's short half-life may be a major limitation to achieving therapeutic levels. This study applies a previously published novel regimen of melatonin with proven sustained levels of melatonin during a 12 h period. In this study, the aim is to determine if such melatonin dosing positively influences on the sleep architecture and the incidence of delirium in intensive care. Methods Single center, randomized control trial with consecutive recruitment over 5 years. Medical and surgical patients were in a recovery phase, all weaning from mechanical ventilation. Randomized allocation to placebo or enteral melatonin, using a previously described regimen (loading dose of 3 mg at 21 h, followed by 0.5 mg hourly maintenance dose until 03am through a nasogastric tube). Sleep recordings were performed using polysomnogram at baseline (prior to intervention) and the third night on melatonin (postintervention recording). Delirium was assessed using the Richmond Agitation and the Confusion Assessment Method Scales. Environmental light and noise levels were recorded using a luxmeter and sound meter. Results 80 patients were screened, but 33 were recruited. Sleep studies showed no statistical differences on arousal index or length of sleep. Baseline delirium scores showed no difference between groups when compared to postintervention scores. RASS scores were 1 in both groups at baseline, compared to zero (drug group) and 0.5 (placebo group) posttreatment. CAM scores were zero (drug group) and 1 (placebo group) at baseline, compared to zero (in both groups) postintervention. Conclusion High levels of plasma melatonin during the overnight period of intensive care cohort patients did not improve sleep nor decreased the prevalence of delirium. This trial is registered with Anzctr.org. au/ACTRN12620000661976.aspx.
Analysis of factors influencing expressway speeding behavior in China

Based on the characteristics of expressway driving behavior, a punishment avoidance variable is introduced in this study to modify the theory of planned behavior (TPB), and the analysis model of expressway speeding behavior is improved and verified through survey data. The mechanism of the effects of attitude to behavior, subjective norm, perceived behavioral control, and punishment avoidance on expressway speeding behavior is analyzed. The results show that drivers lack a correct understanding of expressway speeding behavior and that punishment avoidance has a significant effect on expressway speeding behavior. Younger drivers (25–34), men, High income earners, and those who received more penalty points are considered prone to speeding. The study provides valuable contributions to the development of the Chinese version of the expressway speeding analysis model.
Does the Objective Matter? Comparing Training Objectives for Pronoun Resolution

Hard cases of pronoun resolution have been used as a long-standing benchmark for commonsense reasoning. In the recent literature, pre-trained language models have been used to obtain state-of-the-art results on pronoun resolution. Overall, four categories of training and evaluation objectives have been introduced. The variety of training datasets and pre-trained language models used in these works makes it unclear whether the choice of training objective is critical. In this work, we make a fair comparison of the performance and seed-wise stability of four models that represent the four categories of objectives. Our experiments show that the objective of sequence ranking performs the best in-domain, while the objective of semantic similarity between candidates and pronoun performs the best out-of-domain. We also observe a seed-wise instability of the model using sequence ranking, which is not the case when the other objectives are used.
Joint COCO and Mapillary Workshop at ICCV 2019: COCO Instance Segmentation Challenge Track

In this report, we present our object detection/instance segmentation system, MegDetV2, which works in a two-pass fashion, first to detect instances then to obtain segmentation. Our baseline detector is mainly built on a new designed RPN, called RPN++. On the COCO-2019 detection/instance-segmentation test-dev dataset, our system achieves 61.0/53.1 mAP, which surpassed our 2018 winning results by 5.0/4.2 respectively. We achieve the best results in COCO Challenge 2019 and 2020.
Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start

A standard way to address different NLP problems is by first constructing a problem-specific dataset, then building a model to fit this dataset. To build the ultimate artificial intelligence, we desire a single machine that can handle diverse new problems, for which task-specific annotations are limited. We bring up textual entailment as a unified solver for such NLP problems. However, current research of textual entailment has not spilled much ink on the following questions: (i) How well does a pretrained textual entailment system generalize across domains with only a handful of domain-specific examples? and (ii) When is it worth transforming an NLP task into textual entailment? We argue that the transforming is unnecessary if we can obtain rich annotations for this task. Textual entailment really matters particularly when the target NLP task has insufficient annotations. Universal NLP can be probably achieved through different routines. In this work, we introduce Universal Few-shot textual Entailment (UFO-Entail). We demonstrate that this framework enables a pretrained entailment model to work well on new entailment domains in a few-shot setting, and show its effectiveness as a unified solver for several downstream NLP tasks such as question answering and coreference resolution when the end-task annotations are limited. Code: this https URL
Tactical Patterns for Grassroots Urban Repair

The process of revitalizing cities in the United States suffers from balky and unresponsive processes---de jure egalitarian but de facto controlled and mediated by city officials and powerful interests, not residents. We argue that, instead, our goal should be to put city planning in the hands of the people, and to that end, give ordinary residents pattern-based planning tools to help them redesign (and repair) their urban surrounds. Through this, residents can explore many disparate ideas, try them, and, if successful, replicate them, enabling bottom-up city planning through direct action. We describe a prototype for such a tool that leverages classic patterns to enable city planning by residents, using case studies from Los Angeles as guides for both the problem and potential solution.
Unsupervised Hierarchical Concept Learning

Discovering concepts (or temporal abstractions) in an unsupervised manner from demonstration data in the absence of an environment is an important problem. Organizing these discovered concepts hierarchically at different levels of abstraction is useful in discovering patterns, building ontologies, and generating tutorials from demonstration data. However, recent work to discover such concepts without access to any environment does not discover relationships (or a hierarchy) between these discovered concepts. In this paper, we present a Transformer-based concept abstraction architecture UNHCLE (pronounced uncle) that extracts a hierarchy of concepts in an unsupervised way from demonstration data. We empirically demonstrate how UNHCLE discovers meaningful hierarchies using datasets from Chess and Cooking domains. Finally, we show how UNHCLE learns meaningful language labels for concepts by using demonstration data augmented with natural language for cooking and chess. All of our code is available at this https URL