Miracle-dz/UniFAR-FTUs huggingface dataset:
Stereotypic Repetitive Hand Flapping Movement Detector for Children with Autism

The prevalence of autism spectrum disorder (asd) has increased at a significant rate in recent years, especially in developed nations such as the united states, japan, and south korea. Children affected with autism tend to exhibit a spectrum of symptoms depending on the severity of the disease. These behaviors are primarily repetitive, self-stimulatory, and stereotypic behaviors such as hand flapping (i.e., repeatedly shaking their hands), body rocking (i.e., swaying their body back and forth), drumming, walking around in a confined space, and walking with shortened steps on toes, etc. in this research project, a simple reminder system is proposed using wearable sensors to automatically detect significant hand flapping movements. The reminder system consists of a speaker encoded with the voice of mother, father, or any other custom designed voice. an algorithm was designed to trigger the audio device when the child showed significant hand flapping activity (i.e., hand flapping more than 10 times) within a given time frame. the time frame and the hand flapping count can be customized per severity of the child’s symptoms. The device was successfully tested in the laboratory by simulating the hand flapping movement shown by the child. It is hoped that this system provides a valuable service to parents and the child by alerting the child to autistic movements, preventing the habit from turning into self-injurious behavior, and taking away the burden from parents and caregivers.
The effect of environmental contamination on the community structure and fructification of ectomycorrhizal fungi

Ectomycorrhizal fungi are an essential component of forest ecosystems, most of which can form edible and medical fruiting bodies. Although many studies have focused on the fructification of ectomycorrhizal fungi in phenology, the impact of environmental contamination, especially living garbage, on the formation of fruiting body is still unknown. A field investigation, combined with a high‐throughput sequencing method, was used to study the effect of living garbage pollution on the fructification and hypogeous community structure of ectomycorrhizal fungi symbiosing with cedar (Cedrus deodara (Roxb.) G. Don). The results showed that garbage significantly altered soil abiotic and biotic properties, increasing soil urease activity, decreasing the soil exchangeable metal content and phosphatase activity, and ultimately inhibiting the formation of fruiting bodies. The pollution of garbage also changed the community structure of hypogeous ectomycorrhizal fungi where ectomycorrhizal ascomycetes dominated. In unpolluted sites, the relative abundance of ectomycorrhizal ascomycetes and basidiomycetes were almost equal. Although no fruiting bodies were observed in that soil polluted by living garbage, the sequencing result showed that various ectomycorrhizal fungi were present underground, suggesting that these taxonomic fungi had the potential to cope with adverse conditions. This study not only provided a deeper understanding of the relationship between ectomycorrhizal fungal communities and prevailing environmental conditions, but provided a new pathway for the excavation and utilization of the resource of antistress ectomycorrhizal fungi.
Conduction processes in the nerve cord of the moth Manduca sexta in relation to its ultrastructure and haemolymph ionic composition.

1. Connectives of the ventral nerve cord of Manduca sexta consist of glia-ensheathed axons surrounded by a perineuriurn and an acellular neural lamella, which is greatly expanded on the dorsal surface. The glial cells are linked to one another by desmosomea and tight junctions; the latter also occur between adjacent perineurial cells. There no continuous circum-neural fat-body sheath. 2. A ten-fold change in the external potassium concentration results in a 43 mV change in the resting potential of de-sheathed connectives. Action potentials of such exposed axons are rapidly blocked in low-sodium or sodium-free saline and under these conditions neither calcium nor magnesium is able to maintain conduction. Spikes from de-sheathed preparations are rapidly abolished on exposure to 10 -6 M tetrodotoxin. These iindmgs indicate a conventional ionic basis of excitation for the axonal membrane of this insect. 3. Analyses of the haemolymph reveal a mean sodium concentration of 25.4 (s.E. ± 0.98) mM/1 and a mean potassium concentration of 25.1 (s.E. ± 1.74) mM/. 1 4. Action potentials recorded from sheathed connectives are maintained for extended periods in sodium-free saline. 5. Exposure of most sheathed connectives to elevated potassium concentrations results in a two-stage depolarization. A rapid, single-stage, apparently extraneuronal potential change is, however, observed in some preparations. 6. These results on sheathed connectives indicate the presence of some peripheral barrier to the movements of sodium and potassium; the tight junctions between adjacent perineurial cells are considered to be possible sites of this restriction.
Scene Completion Using Top-1 Similar Image

Scene Completion is an interesting Image Processing problem that has recently been studied in the context of data, i.e. by using large repositories of data. One of the requirements for such a data intensive approach is that the completion has to be done without human intervention. This is rather challenging as it may not be clear that what could be the most suitable image for the completion purpose in the data repository that potentially contains millions of images. We propose a methodology for finding the top-1 image in a data repository that could be the best candidate for scene completion. We do so by computing a representative set of features namely Gist, Texture and Colour, and then give an algorithm for scene completion. To obtain the top-1 image, we consider a ranking scheme that satisfies the value-invariance property and thus, is not affected by the individual feature scores. The scene completion algorithm completes the input image with the constraint that the completion has to be seamless. The approach is data-driven and there is no need of labelling by the user. Although, the completion process is automated, we also allow the user to select a completion image from the top-k matches in order to have a completion that is semantically valid. The experimental results show that we are able to find a matching image that is able to complete the input image seamlessly.
T-Cell Autophagy Deficiency Increases Mortality and Suppresses Immune Responses after Sepsis

Background Although the role of autophagy in sepsis has been characterized in several organs, its role in the adaptive immune system remains to be ascertained. This study aimed to investigate the role of autophagy in sepsis-induced T cell apoptosis and immunosuppression, using knockout mice with T cell specific deletion of autophagy essential gene Atg7. Methods and Results Sepsis was induced in a cecal ligation and puncture (CLP) model, with T-cell-specific Atg7-knockout mice compared to control mice. Autophagic vacuoles examined by electron microscopy were decreased in the spleen after CLP. Autophagy proteins LC3-II and ATG7, and autophagosomes and autolysosomes stained by Cyto-ID Green and acridine orange were decreased in CD4+ and CD8+ splenocytes at 18 h and 24 h after CLP. This decrease in autophagy was associated with increased apoptosis of CD4+ and CD8+ after CLP. Moreover, mice lacking Atg7 in T lymphocytes showed an increase in sepsis-induced mortality, T cell apoptosis and loss of CD4+ and CD8+ T cells, in comparison to control mice. This was accompanied by suppressed cytokine production of Th1/Th2/Th17 by CD4+ T cells, reduced phagocytosis in macrophages and decreased bacterial clearance in the spleen after sepsis. Conclusion These results indicated that sepsis led to down-regulation of autophagy in T lymphocytes, which may result in enhanced apoptosis induction and decreased survival in sepsis. Autophagy may therefore play a protective role against sepsis-induced T lymphocyte apoptosis and immunosuppression.
Image Matting via Local Tangent Space Alignment

Image matting refers to the problem of accurately extracting foreground objects in images and video. The most recent work by Levin, Lischinski and Weiss (2008) in natural image matting relies on the local smoothness assumptions on foreground and background colors on which a cost function is established. The closed-form solution has been derived based on certain degree of user inputs. In this paper, we present a framework of formulating new cost function from the manifold learning perspective based on the so-called Local Tangential Space Alignment algorithm (Zhang and Zha, 2004) where the local smoothness assumptions have been replaced by implicit manifold structure defined in local color spaces. We illustrate our new algorithm using the standard benchmark images and very comparable results have been obtained.
Comparing the UCREL Semantic Annotation Scheme with Lexicographical Taxonomies

Annotation schemes for semantic field analysis use abstract concepts to classify words and phrases in a given text. The use of such schemes within lexicography is increasing. Indeed, our own UCREL semantic annotation system (USAS) is to form part of a web-based ‘intelligent’ dictionary (Herpio 2002). As USAS was originally designed to enable automatic content analysis (Wilson and Rayson 1993), we have been assessing its usefulness in a lexicographical setting, and also comparing its taxonomy with schemes developed by lexicographers. This paper initially reports the comparisons we have undertaken with two dictionary taxonomies: the first was designed by Tom McArthur for use in the Longman Lexicon of Contemporary English, and the second by Collins Dictionaries for use in their Collins English Dictionary. We then assess the feasibility of mapping USAS to the CED tagset, before reporting our intentions to also map to WordNet (a reasonably comprehensive machine-useable database of the meanings of English words) via WordNet Domains (which augments WordNet 1.6 with 200+ domains). We argue that this type of research can provide a practical guide for tagset mapping and, by so doing, bring lexicographers one-step closer to using the semantic field as the organising principle for their general-purpose dictionaries.
All‐trans retinoic acid‐induced expression of bactericidal/permeability‐increasing protein (BPI) in human myeloid cells correlates to binding of C/EBPβ and C/EBPε to the BPI promoter

Bactericidal/permeability‐increasing protein (BPI) neutralizes the proinflammatory effects of lipopolysaccharide and is of potential clinical use in the treatment of fulminant Gram‐negative infections. BPI is a cationic protein with antibacterial activity stored in azurophil (primary) granules of neutrophil granulocytes. However, the absence of BPI in patients with specific granule deficiency indicates a transcriptional control of BPI, which is distinct from that of other azurophil granule proteins. Accordingly, we demonstrate in vivo that the BPI mRNA level peaks, together with mRNA for specific granule proteins, during the myelocytic and metamyelocytic stage of granulocytic maturation. The human promyelocytic cell line NB4 expresses several azurophil granule proteins, but expression of BPI is undetectable. We show that treatment of NB4 cells with all‐trans retinoic acid (ATRA) induces BPI expression at mRNA and at protein level. The induction is dependent on de novo protein synthesis, as judged by sensitivity to cycloheximide. Previous investigations have indicated a potential role of CCAAT/enhancer‐binding protein (C/EBP) transcription factors in the regulation of BPI expression. Here, we show that induction of NB4 cells with ATRA correlates to direct binding of C/EBPβ and C/EBPε to the proximal BPI promoter, as determined by electrophoretic mobility shift analysis and chromatin immunoprecipitation. The dependency on C/EBPβ and C/EBPε provides an explanation for delayed BPI mRNA expression, as compared with mRNA of other azurophil granule proteins.
Real-time rendering and dynamic updating of 3-d volumetric data

A dense 3-d terrain model obtained using reconstruction methods from aerial images is represented in a probabilistic volumetric framework. The choice of probabilistic representation is to represent inherent ambiguity in reconstruction of surface from images. Such probabilistic representation handles the ambiguity very well but leads to expensive dense volumetric storage. The area coverage required for building 3-d models varies from half a square kilometer to thousands of kilometers. Extensive computational resources are required for rendering and building such large models. Existing methods for rendering 3-d models typically cater to mesh models only and also lack strategies to dynamically update the models due to memory intensive operations conventionally better suited for CPUs. This paper proposes a novel OpenCL implementation catering to both GPUs and CPUs for real-time visualizing as well as updating, and dynamic restructuring of dense volumetric models for 3-d terrain. The major contributions of this paper are hybrid representation of grid and octrees, bit-based representation of octrees, randomization of data to enable parallelization of an otherwise serial strategy for subdividing octrees, real-time rendering of dense volumetric data and segmentation algorithm for minimizing global memory access in GPUs. The ideas and implementations proposed could potentially be used in different applications.
Circulating Cell Free DNA as the Diagnostic Marker for Ovarian Cancer: A Systematic Review and Meta-Analysis

Background Quantitative analyses of circulating cell-free DNA (cfDNA) are potential methods for the detection of ovarian cancer. Many studies have evaluated these approaches, but the results were too inconsistent to be conclusive. This study is the first to systematically evaluate the accuracy of circulating cfDNA for the diagnosis of ovarian cancer by conducting meta-analysis. Methods We searched PubMed, Embase, Cochrane Library and the Chinese National Knowledge Infrastructure (CNKI) databases systematically for relevant literatures up to December 10, 2015. All analyses were conducted using Meta-DiSc1.4 and Stata 12.0 software. Sensitivity, specificity and other measures of accuracy of circulating cfDNA for the diagnosis of ovarian cancer were pooled. Meta-regression was performed to identify the sources of heterogeneity. Results This meta-analysis included a total of 9 studies, including 462 ovarian cancer patients and 407 controls. The summary estimates for quantitative analysis of circulating cfDNA in ovarian cancer screen were as follows: sensitivity, 0.70 (95% confidence interval (CI), 0.65–0.74); specificity, 0.90 (95% CI, 0.87–0.93); positive likelihood ratio, 6.60 (95% CI, 3.90–11.17); negative likelihood ratio, 0.34 (95% CI, 0.25–0.47); diagnostic odds ratio, 26.05 (95% CI, 14.67–46.26); and area under the curve, 0.89 (95% CI, 0.83–0.95), respectively. There was no statistical significance for the evaluation of publication bias. Conclusions Current evidence suggests that quantitative analysis of cfDNA has unsatisfactory sensitivity but acceptable specificity for the diagnosis of ovarian cancer. Further large-scale prospective studies are required to validate the potential applicability of using circulating cfDNA alone or in combination with conventional markers as diagnostic biomarker for ovarian cancer and explore potential factors that may influence the accuracy of ovarian cancer diagnosis.
Validation of Inter-Subject Training for Hidden Markov Models Applied to Gait Phase Detection in Children with Cerebral Palsy

Gait-phase recognition is a necessary functionality to drive robotic rehabilitation devices for lower limbs. Hidden Markov Models (HMMs) represent a viable solution, but they need subject-specific training, making data processing very time-consuming. Here, we validated an inter-subject procedure to avoid the intra-subject one in two, four and six gait-phase models in pediatric subjects. The inter-subject procedure consists in the identification of a standardized parameter set to adapt the model to measurements. We tested the inter-subject procedure both on scalar and distributed classifiers. Ten healthy children and ten hemiplegic children, each equipped with two Inertial Measurement Units placed on shank and foot, were recruited. The sagittal component of angular velocity was recorded by gyroscopes while subjects performed four walking trials on a treadmill. The goodness of classifiers was evaluated with the Receiver Operating Characteristic. The results provided a goodness from good to optimum for all examined classifiers (0 < G < 0.6), with the best performance for the distributed classifier in two-phase recognition (G = 0.02). Differences were found among gait partitioning models, while no differences were found between training procedures with the exception of the shank classifier. Our results raise the possibility of avoiding subject-specific training in HMM for gait-phase recognition and its implementation to control exoskeletons for the pediatric population.
Comparison of Enoxaparin and Rivaroxaban in Balance of Anti-Fibrinolysis and Anticoagulation Following Primary Total Knee Replacement: A Pilot Study

Background This study aimed to assess whether the efficacy of tranexamic acid (TXA) would be altered when rivaroxaban or enoxaparin was used for thromboprophylaxis in primary total knee replacement (TKR). It was hypothesized that the hemostatic effect of TXA would be better with the use of enoxaparin. Material/Methods A randomized clinical trial was conducted on 194 patients undergoing primary TKR for osteoarthritis. An intravenous dose of 15 mg/kg (TXA) and 1 g topical TXA were used. Patients randomly received enoxaparin or rivaroxaban prophylaxis when the drainage was less than 30 ml/h 6–8 h postoperatively. The primary endpoint was hidden blood loss (HBL). Indexes of total blood loss drainage, hemoglobin drop, transfusion, range of motion (ROM), HSS score, VAS pain score, knee swelling, length of hospital stay (LOHS), incidence of venous thromboembolism, major/minor bleeding, and wound complications were also compared between the groups. Results More than 80% of patients initiated anticoagulation within 6 h postoperatively. No statistically significance difference was detected in terms of HBL (679.0±205.6 vs. 770.5±206.1, p=.062) or other bleeding index, ROM, or LOHS. The motion VAS pain score and knee swelling (16.7% vs. 6.1%, p=.021) were significantly lower, and HSS score at discharge was higher in the enoxaparin group. The rivaroxaban group had less asymptomatic deep venous (4.1% vs. 0%, p=.121) and muscular venous thrombosis (2.1% vs. 9.2%, p=.033); more ecchymosis (13.5% vs. 10.2%, p=.472), and wound complications (13.5% vs. 6.1%, p=.082). No episodes of transfusion, pulmonary embolism, or major bleeding occurred in either group. Conclusions More attention should be paid to the increased risk of wound complications and knee swelling associated with rivaroxaban, although the hidden blood loss was similar in both groups.
Convergence Problems of General-Sum Multiagent Reinforcement Learning

Stochastic games are a generalization of MDPs to multiple agents, and can be used as a framework for investigating multiagent learning. Hu and Wellman (1998) recently proposed a multiagent Q-learning method for general-sumstochastic games. In addition to describing the algorithm, they provide a proof that the method will converge to a Nash equilibrium for the game under specified conditions. The convergence depends on a lemma stating that the iteration used by this method is a contraction mapping. Unfortunately the proof is incomplete. In this paper we present a counterexample and flaw to the lemma’s proof. We also introduce strengthened assumptions under which the lemma holds, and examine how this affects the classes of games to which the theoretical result can be applied.
A Reinforcement Learning Approach for Interdomain Routing with Link Prices

In today’s Internet, the commercial aspects of routing are gaining importance. Current technology allows Internet Service Providers (ISPs) to renegotiate contracts online to maximize profits. Changing link prices will influence interdomain routing policies that are now driven by monetary aspects as well as global resource and performance optimization. In this article, we consider an interdomain routing game in which the ISP’s action is to set the price for its transit links. Assuming a cheapest path routing scheme, the optimal action is the price setting that yields the highest utility (i.e., profit) and depends both on the network load and the actions of other ISPs. We adapt a continuous and a discrete action learning automaton (LA) to operate in this framework as a tool that can be used by ISP operators to learn optimal price setting. In our model, agents representing different ISPs learn only on the basis of local information and do not need any central coordination or sensitive information exchange. Simulation results show that a single ISP employing LAs is able to learn the optimal price in a stationary environment. By introducing a selective exploration rule, LAs are also able to operate in nonstationary environments. When two ISPs employ LAs, we show that they converge to stable and fair equilibrium strategies.
Dynamic feature extraction of epileptic EEG using recurrence quantification analysis

Detecting the reliable transition point embedded in the electroencephalograms (EEGs) is a challenge in the field of epileptic research. In this research, a recurrence quantification analysis (RQA) is proposed to help medical doctors to reveal dynamical characteristics in EEGs of patients suffering from epilepsy. In contrast with traditional chaos methods, the merits of RQA method is that it can measure the complexity of a short and non-stationary signal without any assumptions such as linear, stationary and noiseless noise. In this study, EEGs with generalized epilepsy were collected in Epilepsy Center of Renji Hospital. The test results show that three RQA measurements, i.e. recurrence rate, determinism and entropy can track the complexity changes of brain electrical activity. RQA variables show a large fluctuation in pre-ictal stage, which reflects a transitional state leading to seizure activity. On the contrary, RQA variables fluctuate in relatively small bounds in ictal stage, which is due to organized and self-sustained rhythmic discharge. Therefore, RQA could be a promising approach in prediction and diagnosis for epileptic seizures.