Miracle-dz/UniFAR-FTUs huggingface dataset:
Haptic Device System For Upper Limb Motor Impairment Patients: Developing And Handling In Healthy Subjects

Based on an aging society with fewer children, an explosive increase in the number of older adults and patients with motor dysfunctions and a reduction in the number of caregivers are anticipated. Patients with motor dysfunctions can benefit from movement therapies. They may in some cases gain social independence and improvements in daily living and quality of life. When a stroke damages the motor pathways for the arm and leg, the prospects for functional recovery are better for the leg than for the arm. It has been reported that 25 of 33 individuals with spinal cord injuries learned to walk independently within 3 to 20 weeks (median 10.5) with partial body weight support treadmill training, known as Lauband therapy (Wernig & Muller, 1992). However, this treatment presents specific challenges for the therapist. The passive moving of a disabled person's legs is ergonomically difficult since the individual being treated cannot independently support and/or move his legs. In order to improve this therapy, a driven gait orthosis was developed for use on patients with varying degrees of paresis or spasticity for up to one-half hour (Colombo et al., 2000). For the sake of basic human movements, e.g., standing upright with stability or maintaining a steady gait, lower limb rehabilitation is an important aspect of regaining function. From the viewpoint of assistance with daily activities, however, it is obvious that upper limb rehabilitation is also important, especially for stroke patients. There are several rehabilitation protocols: increased intensity of treatment (Lincoln et al., 1999), device-enhanced treatment (Sunderland et al., 1994; Feys et al., 1998), and specially focused sensorimotor activity for the affected limb, known as "constraint-induced movement therapy" (Miltner et al., 1999). In the case of constraint-induced movement therapy, a clinical trial with acute rehabilitation is feasible and is associated with less arm impairment at the end of treatment (Dromerick et al., 2000; Taub et al., 1993). In this respect, increased sensorimotor activity involving a paralyzed upper limb after a stroke leads to improved motor performance and enhanced activities of daily living. Several groups are approaching upper limb rehabilitation with the use of robotics and information technology to control sensorimotor activity delivered to an upper limb
Necessity of Genetic Evaluation of Metachronous Metastases of Colorectal Cancer: Quantitative Analysis of Genetic Discordance Between Metachronous Metastases and Radically Resected Primary Colorectal Cancers Using Next-Generation Sequencing.

BACKGROUND Mutation analyses provide the basis of selecting an appropriate target agent for the treatment of metastatic colorectal cancer. However, metachronous metastases developed after the treatment of primary tumor could create significant opportunities for different genetic profiles relative to the primary tumors. OBJECTIVE The purpose of this study was to assess the necessity of genetic evaluation of metachronous metastases; we performed a quantitative analysis of genetic discordance between metachronous metastases and radically resected primary colorectal cancers using next-generation sequencing. DESIGN This was a retrospective study. SETTINGS Patients from a single-institution tertiary care center were studied. PATIENTS We enrolled 33 patients who underwent resection of metachronous metastases between January 2014 and December 2016, ≥6 months after radical resection of primary colorectal cancer and whose tissue was available for analysis. MAIN OUTCOME MEASURES Tumor samples were analyzed by next-generation sequencing. The mutant allele frequency was analyzed to evaluate the proportion of mutations in the tumor tissue. RESULTS The mutant allele frequency of KRAS in metachronous metastases was higher in 6 cases (mean difference =% 25.5% (range, 9.5%-58.0%)) and lower in 3 cases (mean difference = 9.3% (range, 8.0-10.0%) compared with each of their primary tumors. In 1 case, the KRAS mutant-type (mutant allele frequency = 22.6%) metachronous metastasis had developed from the KRAS wild-type primary tumor. LIMITATIONS Tumor sample may not represent perfectly the whole tumor of the patient because of heterogeneity. CONCLUSIONS Genetic discordance can exist between metachronous metastases and radically resected primary colorectal cancers. For appropriate target therapy, genetic evaluation of metachronous metastases needs to be considered when possible. See Video Abstract at http://links.lww. com/DCR/A932.
A New Light on Vitamin D in Obesity: A Novel Association with Trimethylamine-N-Oxide (TMAO)

Vitamin D deficiency and obesity are two public health problems extensively exacerbated over the last years. Among the several mechanisms proposed to account for the complex interplay between vitamin D and obesity, one that has gained particular attention is related to the emerging role of obesity-related changes in gut microbiota and gut-derived metabolites, such as Trimethylamine-N-oxide (TMAO). Vitamin D deficiency and high circulating TMAO levels are associated with body weight and the severity of non-alcoholic fatty liver disease (NAFLD). Considering the link of obesity with vitamin D on the one hand and obesity with TMAO on the other hand, and the central role of the liver in both the vitamin D and TMAO metabolism, the aim of this cross-sectional observational study was first, to confirm the possible inverse association between vitamin D and TMAO across different body mass index (BMI) classes and second, to investigate if this association could be influenced by the presence of NAFLD. One hundred and four adult subjects (50 males and 54 females; 35.38 ± 7.49 years) were enrolled. The fatty liver index (FLI) was used as a proxy for the diagnosis of NAFLD. Vitamin D deficiency was found in 65 participants (62.5%), while 33 subjects (31.7%) had insufficient levels, and the remaining subjects had sufficient levels of vitamin D. Subjects with both vitamin D deficiency and FLI-NAFLD had the highest TMAO levels (p < 0.001). By stratifying the sample population according to the BMI classes, vitamin D levels decreased significantly along with the increase of plasma TMAO concentrations, with the lowest vitamin D levels and highest TMAO, respectively, in class III obesity. Vitamin D levels showed significant opposite associations with circulating levels of TMAO (r = −0.588, p < 0.001), but this association was no longer significant after the adjustment for FLI values. The highest values of TMAO were significantly associated with the severity of obesity (OR 7.92; p < 0.001), deficiency of vitamin D (OR 1.62; p < 0.001), and FLI-NAFLD (OR 3.79; p < 0.001). The most sensitive and specific cut-off for vitamin D to predict the circulating levels of TMAO was ≤19.83 ng/mL (p < 0.001). In conclusion, our study suggests that high TMAO levels are associated with vitamin D deficiency and NAFLD. Further studies are required to investigate if there is a causality link or whether all of them are simply the consequence of obesity.
Parasite-mediated anorexia and nutrition modulate virulence evolution.

Temporary but substantial reductions in voluntary food intake routinely accompany parasite infection in hosts ranging from insects to humans. This 'parasite-mediated anorexia' drives dynamic nutrient-dependent feedbacks within and among hosts, which should alter the fitness of both hosts and parasites. Yet, few studies have examined the evolutionary and epidemiological consequences of this ubiquitous but overlooked component of infection. Moreover, numerous biomedical, veterinary, and farming practices (e.g., rapid biomass production via high-calorie or high-fat diets, low-level antibiotics to promote growth, nutritional supplementation, nonsteroidal anti-inflammatory drugs like Ibuprofen) directly or indirectly alter the magnitude of host anorexia - while also controlling host diet and therefore the nutrients available to hosts and parasites. Here, we show that anorexia can enhance or diminish disease severity, depending on dietary context; some nutrients bolster immune function, others inhibit it. Feedbacks driven by nutrition-mediated competition between host immune function and parasite production can create a unimodal relationship between anorexia and parasite fitness. Subsequently, depending on the host's diet, medical or husbandry practices that suppress anorexia could backfire, and inadvertently select for more virulent parasites and larger epidemics. These findings carry implications for the development of integrated treatment programs that consider links between host feeding behavior, nutrition, and disease severity.
Meaningful Gamification and Students' Motivation: A Strategy for Scaffolding Reading Material.

Gamification is an innovative pedagogical strategy where digital games are used in an educational setting and as an aid to learning. Recent publications on gamification in the classroom investigate the concept of meaningful gamification, where, in line with Ryan and Deci’s self-determination theory, competency, autonomy, and relatedness are prioritized. The paradigm of meaningful gamification works well as a catalyst in motivating students to read background material and grasp key concepts that facilitate a flipped classroom. This study measures the impact of meaningful gaming on students’ motivation in a higher education setting. The context for this study is the module Women in Film, which is part of the Ideas and Exposition Programme at the Centre for English Language Communication, National University of Singapore.
SMALL AND PSEUDO-SMALL SETS FOR MARKOV CHAINS

In this paper we examine the relationship between small sets and their generalisation, pseudo-small sets. We consider conditions which imply the equivalence of the two notions, and give examples where they are definitely different. We give further examples where sets are both pseudo-small and small, but the minorisation constants implied by the two notions are different. Applications of recent computable bounds results are given and extended. We also give a result linking the ideas of monotonicity and minorisation. Specifically we demonstrate that if a non-monotone chain satisfies a minorisation condition, and furthermore is stochastically dominated by a monotone chain which satisfies a Lyapunov drift condition, then a probability construction exists which incorporates both the bounding process and the minorisation condition.
Unrest in Long Valley Caldera, California, 1978–2004

Abstract Long Valley Caldera and the Mono-Inyo Domes volcanic field in eastern California lie in a left-stepping offset along the eastern escarpment of the Sierra Nevada, at the northern end of the Owens Valley and the western margin of the Basin and Range Province. Over the last 4 Ma, this volcanic field has produced multiple volcanic eruptions, including the caldera-forming eruption at 760 000 a bp and the recent Mono-Inyo Domes eruptions 500–660 a bp and 250 a bp. Beginning in the late 1970s, the caldera entered a sustained period of unrest that persisted through the end of the century without culminating in an eruption. The unrest has included recurring earthquake swarms; tumescence of the resurgent dome by nearly 80 cm; the onset of diffuse magmatic carbon dioxide emissions around the flanks of Mammoth Mountain on the southwest margin of the caldera; and other indicators of magma transport at mid- to upper-crustal depths. Although we have made substantial progress in understanding the processes driving this unrest, many key questions remain, including the distribution, size, and relation between magma bodies within the mid-to-upper crust beneath the caldera, Mammoth Mountain, and the Inyo Mono volcanic chain, and how these magma bodies are connected to the roots of the magmatic system in the lower crust or upper mantle.
Models that learn how humans learn: The case of decision-making and its disorders

Popular computational models of decision-making make specific assumptions about learning processes that may cause them to underfit observed behaviours. Here we suggest an alternative method using recurrent neural networks (RNNs) to generate a flexible family of models that have sufficient capacity to represent the complex learning and decision- making strategies used by humans. In this approach, an RNN is trained to predict the next action that a subject will take in a decision-making task and, in this way, learns to imitate the processes underlying subjects’ choices and their learning abilities. We demonstrate the benefits of this approach using a new dataset drawn from patients with either unipolar (n = 34) or bipolar (n = 33) depression and matched healthy controls (n = 34) making decisions on a two-armed bandit task. The results indicate that this new approach is better than baseline reinforcement-learning methods in terms of overall performance and its capacity to predict subjects’ choices. We show that the model can be interpreted using off-policy simulations and thereby provides a novel clustering of subjects’ learning processes—something that often eludes traditional approaches to modelling and behavioural analysis.
Tapadoir: developing a statistical machine translation engine and associated resources for Irish

Tapadoir (from the Irish ´ tapa ‘fast’ and the nominal suffix -oir ´ ) is a statistical machine translation (SMT) project, funded by the Irish government. This work was commissioned to help government translators meet the translation demands which have arisen from the Irish language’s status as an official EU and national language. The development of this system, which translates English into Irish (a morphologically rich, low-resourced minority language), has produced an interesting set of challenges. These challenges have inspired a creative response to the lack of data and NLP tools available for the Irish language and have also resulted in the development of new resources for the Irish linguistic and NLP community. We show that our SMT system out-performs Google TranslateTM (a widely used general-domain SMT system) as a result of steps we have taken to tailor translation output to the user’s specific needs.
Provenance Summaries for Answers and Non-Answers

Explaining why an answer is (not) in the result of a query has proven to be of immense importance for many applications. However, why-not provenance, and to a lesser degree also why-provenance, can be very large, even for small input datasets. The resulting scalability and usability issues have limited the applicability of provenance. We present PUG , a system for why and why-not provenance that applies a range of novel techniques to overcome these challenges. Specifically, PUG limits provenance capture to what is relevant to explain a (missing) result of interest and uses an efficient sampling-based summarization method to produce compact explanations for (missing) answers. Using two real-world datasets, we demonstrate how a user can draw meaningful insights from explanations produced by PUG.
Some Recent Applications of Reinforcement Learning

Five relatively recent applications of reinforcement learning methods are described. These examples were chosen to illustrate a diversity of application types, the engineering needed to build applications, and most importantly, the impressive results that these methods are able to achieve. This paper is based on a case-study chapter of the forthcoming second edition of Sutton and Barto’s 1998 book “Reinforcement Learning: An Introduction” [7].
GBA1-associated parkinsonism: new insights and therapeutic opportunities.

PURPOSE OF REVIEW GBA1 mutations, which result in the lysosomal disorder Gaucher disease, are the most common known genetic risk factor for Parkinson disease and Dementia with Lewy Bodies (DLB). The pathogenesis of this association is not fully understood, but further elucidation of this link could lead to new therapeutic options. RECENT FINDINGS The characteristic clinical phenotype of GBA1-PD resembles sporadic Parkinson disease, but with an earlier onset and more severe course. Many different GBA1 mutations increase the risk of Parkinson disease, some primarily detected in specific populations. Glucocerebrosidase deficiency appears to be associated with increased α-synuclein aggregation and accumulation, mitochondrial dysfunction because of impaired autophagy, and increased endoplasmic reticulum stress. SUMMARY As our understanding of GBA1-associated Parkinson disease increases, new treatment opportunities emerge. MicroRNA profiles are providing examples of both up-regulated and down-regulated proteins related to GBA1 and may provide new therapeutic targets. Chaperone therapy, directed at either misfolded glucocerebrosidase or α-synuclein aggregation, is currently under development and there are several early clinical trials ongoing. Substrate reduction therapy, aimed at lowering the accumulation of metabolic by-products, especially glucosylsphingosine, is also being explored. Basic science insights from the rare disorder Gaucher disease are serving to catapult drug discovery for parkinsonism.
Non supervised perceptual model for target recognition in UAVs

Today, drones play an interesting role in the so-called Revolution 4.0. One of the problems studied by various companies and research groups are the precision landing techniques since this drone feature can be used in applications such as package delivery or object tracking. In this paper, we propose a non-supervised model that allows to detect and recognize a set of landing targets using the Gestalt principles. This proposed method is capable to recognize different coded landing targets in a robust way under outdoor non-controlled light conditions. Comparing to thresholding techniques and other methods, this work deals with image degradations caused by shadows, change of scale, noise and camera target deformation.
Spectral Total-Variation Local Scale Signatures for Image Manipulation and Fusion

We propose a unified framework for isolating, comparing, and differentiating objects within an image. We rely on the recently proposed total-variation transform, yielding a continuous, multi-scale, fully edge-preserving, and local descriptor, referred to as spectral total-variation local scale signatures. We show and analyze several useful merits of this framework. Signatures are sensitive to size, local contrast, and composition of structures; are invariant to translation, rotation, flip, and linear illumination change; and texture signatures are robust to the underlying structures. We prove exact conditions in the 1D case. We propose several applications for this framework: saliency map extraction for fusion of thermal and optical images or for medical imaging, clustering of vein-like features, and size-based image manipulation.
Iterative Entity Alignment with Improved Neural Attribute Embedding

Entity alignment (EA) aims to detect equivalent entities in different knowledge graphs (KGs), which can facilitate the integration of knowledge from multiple sources. Current EA methods usually harness KG embeddings to project entities in various KGs into the same low-dimensional space, where equivalent entities are placed close to each other. Nevertheless, most methods fail to take fully advantage of other sources of information, e.g., attribute information, and overlook the negative impact brought by lack of labelled data. To overcome these deficiencies, in this paper, we propose to generate neural attribute representation by considering both local and global signals. Besides, entity representations are refined via an iterative training process on the neural network. We evaluate our proposal on real-life datasets against state-of-the-art methods, and the results demonstrate the effectiveness of our solution.
Exploration of User-Centered Agile Development Practices

d design, regard proposed integration models and perceive multidisciplinary cooperation and integration. The impression is that fundamental principles of agile and user-centered methodologies are not necessarily merged in integrated approaches. The developers confirm the UX-designers notion that development is emphasized over design work, and that user-centered work is given the least importance – thus the success of merged models currently rests heavily on comfortable project constraints. Findings suggest proposed parallel integration models are generally well received among developers, however the success of merged models seems to rest heavily on comfortable project constraints. An interesting insight from the study is how proposed parallel models may be disadvantageous to interdisciplinary collaboration in multidisciplinary teams.
Keeping a positive carbon balance under adverse conditions: responses of photosynthesis and respiration to water stress

Drought and salinity (i.e. soil water stress) are the main environmental factors limiting photosynthesis and respiration and, consequently, plant growth. This review summarizes the current status of knowledge on photosynthesis and respiration under water stress. It is shown that diffusion limitations to photosynthesis under most water stress conditions are predominant, involving decreased mesophyll conductance to CO 2 , an important but often neglected process. A general failure of photochemistry and biochemistry, by contrast, can occur only when daily maximum stomatal conductance (g s ) drops below 0.05-0.10 mol H 2 O m -2 s -1 . Because these changes are preceded by increased leaf antioxidant activities (g s below 0.15-0.20 mol H 2 O m -2 s -1 ), it is suggested that metabolic responses to severe drought occur indirectly as a consequence of oxidative stress, rather than as a direct response to water shortage. As for respiration, it is remarkable that the electron partitioning towards the alternative respiration pathway sharply increases at the same g s threshold, although total respiration rates are less affected. Despite the considerable improvement in the understanding of plant responses to drought, several gaps of knowledge are highlighted which should become research priorities for the near future. These include how respiration and photosynthesis interact at severe stress, what are the boundaries and mechanisms of photosynthetic acclimation to water stress and what are the factors leading to different rates of recovery after a stress period.
Absorption-Based Query Entailment Checking for Expressive Description Logics

Conjunctive query answering is an important reasoning task for logicbased knowledge representation formalisms, such as Description Logics, to query for instance data that is related in certain ways. Although there exist many knowledge bases that use language features of more expressive Description Logics, there are hardly any systems that support full conjunctive query answering for these logics. In fact, existing systems usually impose restrictions on the queries or only compute incomplete results. In this paper, we present a new approach for conjunctive query entailment checking that can directly be integrated into existing reasoning systems for expressive Description Logics and serves as basis for conjunctive query answering. The approach reminds of absorption, a well-known preprocessing step that rewrites axioms such that they can be handled more efficiently. In this sense, we rewrite the query such that entailment can dynamically be checked in the dominantly used tableau calculi with minor extensions. Our implementation in the reasoning system Konclude shows encouraging results.