Miracle-dz/UniFAR-FTUs huggingface dataset:
Using mindfulness to improve quality of life in caregivers of individuals with intellectual disabilities and autism spectrum disorder

Objectives Caring for individuals with intellectual disabilities (ID) or autism spectrum disorder (ASD) can be gratifying as well as stressful. Professional staff employed as caregivers often report compromised mental and physical wellbeing due to the stressful nature of working with clients who exhibit aggressive and destructive behaviors. Prolonged work-related stress results in diminished quality of life for the caregivers. The aim of the present study was to evaluate the comparative effects of three programs—mindfulness program, psychoeducational program, and inservice training-as-usual—on the quality of life of professional caregivers who provide services to adolescent and adult clients with ID and ASD Methods Professional caregivers (N = 216) were randomized into three experimental conditions, and trained in mindfulness, psychoeducation, and inservice training-as-usual. The effects of the training on the caregivers’ quality of life were assessed in terms of perceived stress, compassion satisfaction, compassion fatigue (i.e. burnout, secondary traumatic stress), and symptoms of depression at the end of 32 weeks of implementation. Results Perceived stress, burnout, and secondary traumatic stress decreased significantly in the mindfulness condition, followed by psychoeducation, but not in the inservice training-as-usual condition. Compassion satisfaction increased significantly in the mindfulness condition, followed by psychoeducation, but not in the inservice training-as-usual condition. Symptoms of depression, which were rated in the borderline clinical range prior to intervention, decreased significantly to within normal levels in the mindfulness condition, decreased minimally in the psychoeducation condition, and showed no change in the training-as usual condition. Conclusions A 3-day training in mindfulness meditations and associated contemplative practices provides a better basis for enhancing caregivers’ quality of life than psychoeducation or inservice training-as-usual.
A Severe Thunderstorm Outbreak North of 70°N Over the Canadian Arctic Islands with Unusual Lightning Characteristics

ABSTRACT This study explores the causes of a severe thunderstorm outbreak north of 70°N on 24–25 July 2014 and provides the first characterization of lightning over the Canadian Arctic Islands. Lightning data were obtained from the Global Lightning Dataset (GLD360) network. Convective available potential energy calculated using representative soundings and surface conditions indicated high instability that, combined with large vertical wind shear and storm-relative helicity, likely caused severe thunderstorms to form over Victoria Island. These storms subsequently drifted northeastward over Parry Channel, where they transitioned into elevated storms and travelled as far north as and passed close to Grise Fiord (76.4°N). Satellite imagery suggested that overshooting tops reached 11.6 km. The GLD360 network detected more than 15,000 strokes north of the Arctic Circle and an unusually high ratio of positive strokes during this outbreak.
Compilation of MATLAB computations to CPU/GPU via C/OpenCL generation

In order to take advantage of the processing power of current computing platforms, programmers typically need to develop software versions for different target devices. This task is time‐consuming and requires significant programming and computer architecture expertise. A possible and more convenient alternative is to start with a single high‐level description of a program with minimum implementation details, and generate custom implementations according to the target platform. In this paper, we use MATLAB as a high‐level programming language and propose a compiler that targets CPU/GPU computing platforms by generating customized implementations in C and OpenCL. We propose a number of compiler techniques to automatically generate efficient C and OpenCL code from MATLAB programs. One of such compiler techniques relies on heuristics to decide when and how to use Shared Virtual Memory (SVM). The experimental results show that our approach is able to generate code that provides significant speedups (eg, geometric mean speedup of 11× for a set of simple benchmarks) using a discrete GPU over equivalent sequential C code executing on a CPU. With more complex benchmarks, for which only some code regions can be parallelized, and are thus offloaded, the generated code achieved speedups of up to 2.2×. We also show the impact of using SVM, specifically fine‐grained buffers, and the results show that the compiler is able to achieve significant speedups, both over the versions without SVM and with naïve aggressive SVM use, across three CPU/GPU platforms.
Characterization of Dinophysis acuminata from the Yellow Sea, China, and its response to different temperatures and Mesodinium prey

Abstract Dinophysis species are distributed worldwide and cause diarrheic shellfish poisoning (DSP). This paper documents the first successful culture of a Dinophysis (DAYS01) strain taken off Xiaoping Island, the Yellow Sea, China. The strain was identified as Dinophysis acuminata Claparède & Lachmann by morphological and phylogenetic analysis. The effects of temperature and different Mesodinium prey on the physiological and toxigenic characteristics of D. acuminata DAYS01 were also investigated. The results showed that the toxin concentration (toxin amount per ml of culture) increased with an increase in cell densities. Okadaic acid (OA), dinophysistoxin 1 (DTX1) and pectenotoxin 2 (PTX2) were the major toxin components of DAYS01, with the highest recorded toxin content of 5.66, 0.56 and 192.87 ng ml-1 culture medium, respectively. The prey type significantly influenced the growth yield in Dinophysis cultures. There was an effect of prey on the total toxin content, suggesting that the origin or strain of ciliate directly impacted the D. acuminata blooms and the overall toxin concentration in the system
Improved region convolutional neural network for ship detection in multiresolution synthetic aperture radar images

Effectively obtaining the location and direction of the ship target is an important prerequisite for maritime traffic management and marine accident rescue. Thanks to the rapid development of the target detection methods based on deep learning, this article proposed a ship target detection method for multiresolution synthetic aperture radar (SAR) images based on improved region convolution neural network (R‐CNN). According to the characteristics of ship target in the SAR images, we make several improvements such as enlarging the input, proposal optimization, database target categorization, and weight balance on the basis of the standard Faster R‐CNN. The experimental results proved that the proposed method can detect target effectively and precisely in complicated scenes of multiresolution SAR images, such as in‐shore and dense targets. It has a good potential in practical application.
Gathering Intelligence on Student Information Behavior Using Data Mining

Abstract:In this paper, we present a novel machine-learning approach that analyzes student assessment scores across a teaching period to predict their final exam performance. One challenge for many universities around the world is identifying the students who are at risk of failing a subject sufficiently early enough to provide proactive interventions that aim to minimize the risk of failure due to several reasons such as the volume of (big) data. We propose a data-driven strategy using machine learning, an application of artificial intelligence that has become popular for extracting knowledge from data by combining strategies and processes from statistics and computer science. By being able to predict what a student's exam performance is ahead of time, interventions can occur, and students can be provided with extra support from their teachers to aid them in achieving the best result possible. In this research, we collected data from a popular information-technology subject at an Australian university and applied a machine-learning algorithm to the data to predict a few hundred students' exam scores. We also developed a framework of learningsupport activities that would be of most benefit to at-risk students to achieve maximum impact before their exam would be conducted. We discovered through our approach that we can accurately predict the bottom 20–30 percent of students at risk, enabling a large cohort of students to be helped through our intervention framework, which we believe can have a positive impact on their future results.
The Polyhedral-Surface Cutting-Plane Method for Linear Combinatorial Optimization

For linear optimization on combinatorial sets inscribed into a convex surface, a Polyhedral-Surface Cutting-Plane Method (PSCM) is offered. It essentially uses representability of such sets as an intersection of a polytope with a circum surface. Three versions of PSCM are formulated depending on the choice of surface involved. A justification of applicability of PSCM for linear permutation-based and Boolean optimization problems is given, which opens perspectives to solve in a reasonable time a significantly wider class of realworld tasks modelled as combinatorial optimization problems.
Electrical stimulation for seizure induction during SEEG exploration: a useful predictor of postoperative seizure recurrence?

Objective Direct electrical stimulations of cerebral cortex are a traditional part of stereoelectroencephalography (SEEG) practice, but their value as a predictive factor for seizure outcome has never been carefully investigated. Patients and method We retrospectively analysed a cohort of 346 patients operated on for drug-resistant focal epilepsy after SEEG exploration. As potential predictors we included: aetiology, MRI data, age of onset, duration of epilepsy, age at surgery, topography of surgery and whether a seizure was induced by either low frequency electrical stimulation (LFS) or high frequency electrical stimulation. Results Of 346 patients, 63.6% had good outcome (no seizure recurrence, Engel I). Univariate analysis demonstrated significant correlation with favourable outcome (Engel I) for: aetiology, positive MRI and seizure induced by stimulation. At multivariate analysis, informative MRI, type II focal cortical dysplasia and tumour reduced the risk of seizure recurrence (SR) by 47%, 58% and 81%, respectively. Compared with the absence of induced seizures, the occurrence of ictal events after LFS significantly predicts a favourable outcome on seizures, with only 44% chance of disabling SR at last follow-up. Conclusion Among the already known predictors outcome, seizure induction by LFS therefore represents a positive predictive factor for seizure outcome after surgery.
Determinants of total factor productivity in Pakistan: a time series analysis using ARDL approach

ABSTRACT This study examines key determinants of total factor productivity (TFP) in Pakistan for the period 1970–2018 using the auto-regressive distributive lag (ARDL) bound testing approach. The study conducts a time series analysis on macro-economic indicators namely foreign direct investment (FDI), trade openness and human capital and aims to identify whether these factors impact significantly on TFP. TFP is a measure of the productive capabilities of factors of production (capital and labour), and is crucial in relation to economic efficiency and economic growth. The study applied an ARDL bound test to analyse the long-run and short-run relationship among the variables, and also a Granger causality test to consider causal relationships between the explanatory variables and TFP. The results suggest that TFP has a long-run relationship with FDI and human capital.
Destructive populism as “perverted containing”: A psychoanalytical look at the attraction of Donald Trump

ABSTRACT The author examines the appearances and formation processes of destructive populist developments in the Western world from a psychoanalytical perspective, using the example of Donald Trump and his voters. She draws on the concepts of Sigmund Freud, Melanie Klein, Wilfred Bion, Herbert Rosenfeld and Otto Kernberg, which build on each other, and drafts the basic features of a psychoanalytic explanatory model of destructive populist social processes. She illustrates the phenomenon of the »destructive populist fit« between Trump and his followers by means of an analysis of Donald Trump's well-documented election campaigns in 2016 and 2020, supplemented by his actions during the COVID 19 crisis in 2020. On the psychoanalytical understanding of the effectiveness of his methods and the susceptibility of voters to destructive populism, she applies Bion's model of containing in conjunction with Herbert Rosenfeld's destructive narcissism and develops the concept of »perverted containing«: in the course of regression to a paranoid-schizoid level, aggressively destructive and omnipotent affects are idealised and the container function of democratic society is increasingly decomposed, distorted and finally twisted. Beta-elements are not detoxified and digested but, on the contrary, are fueled. This dynamic implies permanent escalation through ever new destructive-exciting acts in order to maintain the symbiotic-destructive fit in the mode of destructive narcissism.
Towards an Automated Writing Assistant for Online Reviews

Online reviews play a critical role in persuading or dissuad-ing users when making purchase decisions. And yet very few users take the time to write helpful reviews. Encourag-ingly, recent advances in AI offer good potential to produce review-like natural language content. However, The main challenge is a lack of large, high-quality labeled data at both the aspect and sentiment level for training the automated review generators. Hence, we study the feasibility of a writing assistant framework in order to help users post online reviews and introduce a data-driven approach to label data required for training. We evaluate the effectiveness of our approach by launching a user study on how end-users perceive the quality of the labels and automated generated reviews.
On planning in a knowledge-hypergraph

Autonomous robots that operate in indoor, uncertain environments move around from one location to the next to fulﬁll a set of ordered task. To do so, they need a ﬂoor plan of the building to plan their route. In case the building is unfamiliar to a robot it can employ online mapping techniques, though these techniques are resource intensive and time consuming. In addition, processing such geometric maps in an automated planner to determine a feasible route is resource demanding as well. The solution proposed in this article is an ontology that combines knowledge of ﬂoor plans and actual information of a building with concepts in automated planning, so that robots become more efﬁcient in route planning. Moreover, the ontology is implemented as a hypergraph to beneﬁt from its advances in creating elegant inference-rules, e.g., to infer route-alternatives while preparing the operation.
Inhibition of microRNA‐9‐5p and microRNA‐128‐3p can inhibit ischemic stroke‐related cell death in vitro and in vivo

Ischemic stroke is the major form of stroke and is accentuated by multiple comorbidities. It has been previously shown that different microRNAs (miRNAs) regulate separate aspects of ischemic stroke. Differential miRNA expression analysis in cerebrospinal fluid of stroke patients had revealed upregulation of miR‐124‐3p, miR‐9‐3p, miR‐9‐5p, and miR‐128‐3p. However, whether the overexpression is correlative or causative was not known. Here, using an in vitro oxygen–glucose deprivation/reoxygenation (OGD/R) neuronal cell model, we saw OGD/R‐induced injury was associated with significant upregulation of the aforementioned four miRNAs. Target gene prediction using in situ algorithms and gene set enrichment analysis revealed significant enrichment of FOXO and Relaxin signaling pathways and regulatory processes associated with endothelial cell migration, which are all known to associate with apoptotic pathways. In situ protein–protein interaction network analysis confirmed the findings of gene set enrichment analysis. TUNEL analysis showed that OGD/R‐induced injury resulted in significant apoptosis, which was significantly inhibited in neuronal cells pretransfected with inhibitors of either miR‐9‐5p or miR‐128‐3p. Further testing in an in vivo middle cerebral artery occlusion (MCAO) mouse model of ischemic stroke showed that inhibiting miR‐9‐5p or miR‐128‐3p significantly decreases MCAO‐induced infraction volume and inhibited apoptotic response as revealed by decreased cleaved Caspase‐3 protein expression in immunohistochemical analysis. Combined inhibition of miR‐9‐5p and miR‐128‐3p resulted in a synergistic decrease in cell death and infraction volume in vitro and in vivo, respectively. Cumulatively, our results provide critical knowledge about the mechanism by which elevated miR‐9‐5p and miR‐128‐3p causes brain damage in ischemic stroke and provides evidence of them being attractive therapeutic targets.
Endoplasmic Reticulum Associated Protein Degradation (ERAD) in the Pathology of Diseases Related to TGFβ Signaling Pathway: Future Therapeutic Perspectives

The transforming growth factor signaling pathway (TGFβ) controls a wide range of cellular activities in adulthood as well as during embryogenesis including cell growth, differentiation, apoptosis, immunological responses and other cellular functions. Therefore, germline mutations in components of the pathway have given rise to a heterogeneous spectrum of hereditary diseases with variable phenotypes associated with malformations in the cardiovascular, muscular and skeletal systems. Our extensive literature and database searches revealed 47 monogenic diseases associated with germline mutations in 24 out of 41 gene variant encoding for TGFβ components. Most of the TGFβ components are membrane or secretory proteins and they are therefore expected to pass through the endoplasmic reticulum (ER), where fidelity of proteins folding is stringently monitored via the ER quality control machineries. Elucidation of the molecular mechanisms of mutant proteins’ folding and trafficking showed the implication of ER associated protein degradation (ERAD) in the pathogenesis of some of the diseases. For example, hereditary hemorrhagic telangiectasia types 1 and 2 (HHT1 and HHT2) and familial pulmonary arterial hypertension (FPAH) associated with mutations in Endoglin, ALK1 and BMPR2 components of the signaling pathway, respectively, have all exhibited loss of function phenotype as a result of ER retention of some of their disease-causing variants. In some cases, this has led to premature protein degradation through the proteasomal pathway. We anticipate that ERAD will be involved in the mechanisms of other TGFβ signaling components and therefore warrants further research. In this review, we highlight advances in ER quality control mechanisms and their modulation as a potential therapeutic target in general with particular focus on prospect of their implementation in the treatment of monogenic diseases associated with TGFβ components including HHT1, HHT2, and PAH. In particular, we emphasis the need to establish disease mechanisms and to implement such novel approaches in modulating the molecular pathway of mutant TGFβ components in the quest for restoring protein folding and trafficking as a therapeutic approach.
Integrative Models of Histopathological Image Features and Omics Data Predict Survival in Head and Neck Squamous Cell Carcinoma

Background Both histopathological image features and genomics data were associated with survival outcome of cancer patients. However, integrating features of histopathological images, genomics and other omics for improving prognosis prediction has not been reported in head and neck squamous cell carcinoma (HNSCC). Methods A dataset of 216 HNSCC patients was derived from the Cancer Genome Atlas (TCGA) with information of clinical characteristics, genetic mutation, RNA sequencing, protein expression and histopathological images. Patients were randomly assigned into training (n = 108) or validation (n = 108) sets. We extracted 593 quantitative image features, and used random forest algorithm with 10-fold cross-validation to build prognostic models for overall survival (OS) in training set, then compared the area under the time-dependent receiver operating characteristic curve (AUC) in validation set. Results In validation set, histopathological image features had significant predictive value for OS (5-year AUC = 0.784). The histopathology + omics models showed better predictive performance than genomics, transcriptomics or proteomics alone. Moreover, the multi-omics model incorporating image features, genomics, transcriptomics and proteomics reached the maximal 1-, 3-, and 5-year AUC of 0.871, 0.908, and 0.929, with most significant survival difference (HR = 10.66, 95%CI: 5.06–26.8, p < 0.001). Decision curve analysis also revealed a better net benefit of multi-omics model. Conclusion The histopathological images could provide complementary features to improve prognostic performance for HNSCC patients. The integrative model of histopathological image features and omics data might serve as an effective tool for survival prediction and risk stratification in clinical practice.
Presence Promotes Performance on a Virtual Spatial Cognition Task: Impact of Human Factors on Virtual Reality Assessment

The use of virtual reality in spatial cognition evaluation has been growing rapidly, mainly because of its potential applications in the training and diagnosis of cognitive impairment and its ability to blend experimental control and ecological validity. However, there are still many gray areas on virtual reality, notably on the sense of presence and its complex relationship to task performance. Performance in VR is often suggested to be influenced by other human factors including, amongst others, cybersickness, gender, video game experience, and field dependence. Would an individual experiencing more presence systematically show better performance? This study aimed to be part of a framework of virtual reality as this question is fundamental for rigorous assessment and diagnostics, and particularly in the spatial cognition field. Forty-eight healthy young subjects were recruited to take part in a virtual spatial cognition evaluation. Spatial cognition performance, along with their level of presence, cybersickness, video game experience, gender and field dependence, were measured. Matrix correlations were used, along with linear regressions and mediation analysis. Results show that presence promoted performance on the spatial cognition evaluation, while cybersickness symptoms hindered it, notably among women. The presence—performance relationship was not mediated by other human factors. Video game experience significantly predicted both sense of presence and cybersickness, the latter two being negatively correlated. Even if women experienced more negative symptoms than men, gender appears less informative than cybersickness and video game experience. Field dependence was not associated with any other variable. Results are discussed by confronting two theories of cognition (representational vs. ecological), highlighting that virtual reality is not a simple transposition of reality but truly a new paradigm with its own biases favoring some individuals more than others, and that some human factors have to be controlled for rigorous uses of virtual environments, particularly for spatial cognition evaluation.