Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Body Motion Synchrony in Distance Education

The concept of well-being has spread to communication and education dimensions. In recent year, there has been remarkable progress in research on distance education. The interactive and communication factors have been considered important in distance education. In particular, body motion synchrony well represents the characteristics of social and interpersonal interaction during face-to-face communication process. This article investigated how the spatiotemporal separation as distance education factors affects body motion synchrony during the communication process. As a result, there were significant differences in the synchronization characteristics among three conditions. We suggest that body motion synchrony is useful to evaluate communication factors in distance education. In addition, the detection method of the algorithm can apply to the communication technology on well-being.
A Construction-Grammar Approach to Computationally Solving the Winograd Schema: Implementation and Evaluation

This paper describes an original method of solving the Wino-grad Schema, a procedure presenting a challenge for Arti-ﬁcial Intelligence implementations. “Solving” refers to correctly answering the Winograd Schema question. The method has been computationally implemented by utilizing Construction Grammar, a formalism for designing and processing data structures, aka constructions, that incorporate information on the text form and the meaning corresponding to that form. Incorporating the needed syntactic, semantic and pragmatic data has resulted in answering accuracy comparable to that of the human. Moreover, the effort to develop the construction as light and ﬂexible as practically possible has resulted in the capability to use data from the knowledge base, such as ConceptNet, and to reuse knowledge for the text with similar form and meaning structure. Evaluation experiments have demonstrated that the proposed method implementation out-performs baseline ones that utilize state-of-the-art techniques of anaphora resolution, a procedure indispensable for solving the Winograd Schema. The method implementation also features rather high processing speed. The paper includes an account of the way constructions that store various related knowledge are automatically combined into networks, a process similar to human understanding of the text. The paper also describes the way the question communicative function is recognized and the information needed to answer it is retrieved from the network formed by combining constructions.
Right to Sign: Safeguarding data immutability in blockchain systems with cryptographic signatures over a broad range of available consensus finding scenarios

The choice of the consensus method ultimately determines throughput, scalability, tamper resistance, and consistency of a blockchain system. However, across all the types of blockchain (private, semi-private, consortium, or public), there is no consensus method that uniformly addresses all these traits. Verifiable lottery algorithms (Proof of .) increase tamper resistance but show weakness in throughput and scalability, while established methods like PAXOS and RAFT provide no additional protection against tampering. In this paper, we introduce Right to Sign which aims to provide additional tamper resistance by cryptographic signatures over a broad range of available consensus finding methods.
Body Mass Index and Mortality in Elderly Subjects from the Moli-Sani Study: A Possible Mediation by Low-Grade Inflammation?

ABSTRACT The association between obesity and mortality in the elderly remains controversial. To test the association between BMI and mortality, with the hypothesis of a mediation by low-grade inflammation (LGI), a prospective study design (median follow-up 7.8 years) was used on a sample of 4,970 elderly subjects (age ≥ 65 years) from the Moli-sani Study cohort. The association between BMI categories and overall or cause-specific mortality (hazard ratio, HR) was calculated by multivariable Cox regression. Dose-response relationship was tested using restricted cubic splines. Interaction between BMI and LGI, assessed through high-sensitivity C-reactive protein (hs-CRP) and INFLA-score, was also tested. In comparison with normal-weight, overweight was significantly associated with a 20% (adjusted HR = 0.80; 95%CI 0.67–0.95) reduced risk of total mortality, while severe obesity (BMI > 40) with an increased risk (HR = 1.81; 95%CI 1.13–2.93). Cubic spline curves showed a U-shaped relationship between BMI and total mortality (p value for nonlinear relationship = 0.001). Similar results were found for cardio-cerebrovascular and other causes mortality. Hs-CRP and INFLA-score were associated with an increased risk of total mortality in adjusted analyses. Mediation analysis did not show any effect of LGI on the association between BMI and mortality. However, after stratification for LGI under or below the population median, greater LGI increased the risk of mortality in obese elderly more than expected (p for interaction = 0.04). A U-shaped association between BMI and mortality was observed in Italian elderly subjects. While the association was independent of LGI levels, there was a significant interaction between BMI and LGI in increasing mortality risk in obese elderly individuals.
Establishing a new medical school: A contemporary approach to personalizing medical education

Abstract Objective: To borrow a public health concept, there has been a global outbreak, perhaps a pandemic, of new medical schools during the last 20 years, resulting in a diverse range of programs in many different contexts. The question posed was: how should the task of establishing a new medical program be approached in 2018? Methods: Based on involvement with several new medical programs, this paper presents a highly idealistic commentary on what a new medical program might look like. The paper adopts the organizational structure of the World Federation of Medical Education Basic Medical Education standards as a scaffold, because accreditation both locally and globally is intended. Results: The program design reflects both progress in learning technology and the challenges faced in a changing world, where disruption appears inevitable, and innovation may be necessary to produce the medical graduates needed to improve the health status of an expanding, ageing and ailing global population. Conclusion: The program model described represents a combination of educational design, emerging technology and a focus on future health care needs.
Effects of metformin treatment on radiotherapy efficacy in patients with cancer and diabetes: a systematic review and meta-analysis

Purpose Metformin is a key pharmaceutical for patients with diabetes mellitus (DM). Metformin also can enhance tumor radiosensitivity in vitro and in vivo. Some retrospective cohort studies have indicated that metformin can improve the efficacy of radiotherapy in patients with cancer and DM. The aim of this systematic review was to evaluate the radiotherapy efficacy of metformin in patients with cancer and DM. Methods Multiple databases were queried for studies that address the efficacy of metformin in radiotherapy of patients with cancer and DM. Studies were included that involved comparisons of the short-term tumor responses and long-term survival outcomes of these patients who were managed with or without metformin as well as of nondiabetic patients without metformin. The OR and HR with accompanying 95% CI were assessed in a random effects model. The main endpoints were 2-year and 5-year overall survival (2y-OS and 5y-OS, respectively). Results The database search yielded 17 cohort studies that met the inclusion criteria. The results indicated that the tumor response was higher in patients who also were treated with metformin than in those who were not (OR, 0.48; 95% CI, 0.22–1.07; P=0.07) and nondiabetic (OR, 0.27; 95% CI, 0.07–0.98; P=0.05). Moreover, patients who received metformin had survival benefits compared with patients not treated with metformin (2y-OS: OR, 0.48; 95% CI, 0.29–0.80; P=0.005; 5y-OS: OR, 0.38; 95% CI, 0.25–0.56; P<0.00001). The metformin-related HRs of OS values were not significantly different. Conclusion Metformin appears to improve the tumor response to radiotherapy in patients with cancer and DM and partly yield survival benefits. Despite the apparent advantages provided by metformin treatment on 2y-OS and 5y-OS, these retrospective data are at risk of bias and should be interpreted with caution.
A Framework Using Machine Vision and Deep Reinforcement Learning for Self-Learning Moving Objects in a Virtual Environment

In recent artificial intelligence (AI) research, convolutional neural networks (CNNs) can create artificial agents capable of self-learning. Self-learning autonomous moving objects utilize machine vision techniques based on processing and recognizing objects in digital images. Afterwards, deep reinforcement learning (Deep-RL) is applied to understand and learn intelligent actions and controls. The objective of our research is to study meth-ods and designs on how machine vision and deep machine learning algorithms can be implemented in a virtual world (e.g., a computer game) for moving objects (e.g., vehicles or aircrafts) to improve their navigation and detection of threats in real life. In this paper, we create a framework for generating and using data from computer games to be used in CNNs and Deep-RL to perform intelligent actions. We show the initial results of applying the framework and identify various military applications that may benefit from this research.
The Powerful People’s Biographies Analysis for Modern Leaders’ Identity Construction

In the article, the factors which influenced the world’s most powerful people’s biographies have been analysed, taking into consideration life’s main features such as tendencies and risks of society’s development, economy, globalization, politics, a material life model and a social and cultural environment. The aim of this research is to prove that the generalization of the world's most powerful people's life experiences is outlined as the tool of challenge for a modern leader's identity construction. A reconstruction of the person’s biography has been created in the process of anthropological research. Analysing the life experience of many leaders, the authors of the article have declared that, despite the leaders’ individual self-development strategy, attitude, behaviour and motivation of the specific character traits, it is possible to verify essential common features which make leaders special and unique. In the process of research, the generalization of the world’s most powerful peoples’ life experiences is outlined as the tool for the construction of the modern leader’s identity. The study of the modern society’s development reveals the necessity for the wider use of biography analysis methodology in anthropological and sociological researches. The method of biography analysis operates with data on a person’s objective events and personal experiences. In the common world’s context, a person is considered as a part of his/her lifetime, and his/her life “culmination” and “finish” depend on its “start’ and “distance” running quality. Analysing biographies of great personalities and studying principles of their behaviour and major traits, contemporary leaders can apply the method of biographical analysis as a tool for constructing their identity. The experience and skills gained will become a vector for selfgrowth, self-empowerment, and transformation.
Green synthesis of selenium nanoparticles with extract of hawthorn fruit induced HepG2 cells apoptosis

Abstract Context: Selenium nanoparticles (SeNPs) have attracted worldwide attention due to their unique properties and potential bioactivities. Considering that hawthorn is both a traditional medicine and a common edible food, hawthorn fruit extract (HE) was chosen as a reductant to prepare SeNPs. Objective: SeNPs were synthesized by using an aqueous HE as a reductant and stabilizer. The antitumor activities and potential mechanisms of SeNPs were explored by using a series of cellular assays. Materials and methods: The HE mediated SeNPs (HE-SeNPs) were examined using various characterisation methods. The cytotoxicity was measured against HepG2 cells after treated with 0, 5, 10 and 20 μg/mL of HE-SeNPs for 24 h. Annexin V-FITC/PI staining analysis was performed to observe the apoptosis of HepG2 cells. Additionally, mitochondrial membrane potential (MMP), intracellular reactive oxygen species (ROS) levels were evaluated. Finally, the protein expression levels of caspase-9 and Bcl-2 were identified by Western blot. Results: The mono-dispersed and stable SeNPs were prepared with an average size of 113 nm. HE-SeNPs showed obvious antitumor activities towards HepG2 cells with an IC50 of 19.22 ± 5.3 μg/mL. Results from flow cytometry revealed that both early and total apoptosis rates increased after treating with HE-SeNPs. After cells were treated with various concentrations of HE-SeNPs (5, 10 and 20 μg/mL) for 24 h, the total rate increased to 7.3 ± 0.5, 9.7 ± 1.7 and 19.2 ± 1.6%, respectively. Meanwhile, treatment of HE-SeNPs up-regulated intracellular ROS levels and reduced the MMP. In addition, HE-SeNPs induced the up-regulation of caspase-9 and down-regulation of Bcl-2. Discussion and conclusions: HE-SeNPs induced intracellular oxidative stress and mitochondrial dysfunction to initiate HepG2 cell apoptosis through the mitochondrial pathway. Therefore, HE-SeNPs may be a candidate for further evaluation as a chemotherapeutic agent for human liver cancer.
Design implications for Designing with a Collaborative AI

This paper proposes a framework for a collaborative designing system from an interaction design perspective. Using the agent-based model from the mixed-initiative interaction framework as a starting point, an ideal interaction scenario in a web design context is described and implications for designing collaborative systems are presented. Previous work on machine learning and artificial intelligence for interaction design has already looked at recognition of designers’ intent and combinatorial problem-solving in design. This paper, in contrast, focuses on the interaction design perspective of designing such a system, and introduces a framework that highlights requirements in this context. The framework uses the notion of task model and world model from agent-based models as a frame, and the resulting implications call for a stronger involvement of designers in the process.
Social adjustment and family function after drug switch from IR-methylphenidate to OROS-methylphenidate in patients with attention-deficit/hyperactivity disorder

Purpose This prospective, single-arm, open-label, 8-week, multicenter study investigated the effectiveness of switching from immediate-release methylphenidate (IR-MPH) to osmotic controlled-release methylphenidate (OROS-MPH) in patients with attention-deficit/hyperactivity disorder (ADHD). Patients and methods Overall, 296 patients with ADHD (mean age: 9.5 years) already on IR-MPH treatment were enrolled. Upon enrollment, a flexible dose of OROS-MPH was administered, replacing IR-MPH. Patients were assessed at baseline and weeks 2, 4, and 8 using the Swanson, Nolan, and Pelham version IV scale (SNAP-IV) and the Clinical Global Impression for ADHD symptoms. The Social Adjustment Inventory for Children and Adolescents assessed social functions, and the Chinese Health Questionnaire (CHQ) and Family Adaptation, Partnership, Growth, Affection, and Resolve evaluated parental and family functions. Results Switching from IR-MPH to OROS-MPH yielded significant improvements in all ADHD symptoms, as rated by parents, teachers (SNAP-IV), and study investigators (Clinical Global Impression). CHQ scores and all Social Adjustment Inventory for Children and Adolescents subscores except spare time scores improved significantly. Patients with poor IR-MPH adherence had greater improvements in teacher-rated SNAP-IV and mothers’ mental health (CHQ) after switching. Conclusion Switching from IR-MPH to OROS-MPH improved patients’ behavioral ADHD symptoms and social adjustment, and mental health of patients’ mothers. This was most evident in patients who previously exhibited poor IR-MPH adherence.
Beta Chaotic Map Based Image Encryption Using Genetic Algorithm

In this paper, an efficient image encryption technique using beta chaotic map, nonsubsampled contourlet transform, and genetic algorithm is proposed. Initially, the nonsubsampled contourlet transform is utilized to decompose the input image into subbands. The beta chaotic map is used to develop pseudo-random key that encrypts the coefficients of subbands. However, it requires certain parameters to encrypt these coefficients. A multiobjective fitness function for genetic algorithm is designed to find the optimal parameter of beta chaotic map. The inverse of nonsubsampled contourlet transform is performed to obtain a ciphered image. The performance of the proposed technique is compared with recently developed well-known meta-heuristic based image encryption techniques. Experimental results reveal that the proposed technique provides better computational speed and high encryption intensity. The comparative analyses show effectiveness of the proposed image encryption technique.
Gene expression analyses identify a relationship between stanniocalcin 2 and the malignant behavior of colorectal cancer

Background Colorectal cancer (CRC) is one of the main causes of cancer-related death worldwide. Stanniocalcin 2 (STC2), a secreted glycoprotein, has been suggested to exert various functions in progression of many cancers. However, the precise biological role in CRC is not fully understood. Therefore, this study based on several public datasets aims at investigating the roles of STC2 in CRC. Methods We used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to evaluate the STC2 expression and its clinical significance in CRC. Cell migration and invasion by STC2 overexpression and knockdown were assessed using Transwell migration and Matrigel invasion assays. We next performed RNAseq analysis on SW480 cells with or without STC2 overexpression. Differentially expressed genes were selected by using fold-change >5 and P-value <0.05. Results In this study, we found that STC2 level was significantly higher in CRC than that in adjacent noncancerous tissues from TCGA and GEO. Tumors with high mRNA levels of STC2 were more common in patients with rectal cancer, left-sided CRC, advanced T-stage (T3–T4), positive lymph node involvement and advanced AJCC-stage (III–IV) from TCGA. STC2 displayed the negative correlation with the expressions of epithelial cell markers, while it was positively correlated with the expressions of mesenchymal cell markers, MMPs and the epithelial-mesenchymal transition (EMT)-related transcriptional factors. Furthermore, we found that STC2 promoted cell migration and invasion in vitro. And a group of differentially expressed genes, which were modulated by STC2, were identified from RNAseq analyses. Conclusion Our study demonstrates that STC2 is overexpressed in CRC compared with normal tissues, and promotes CRC cell migration and invasion. Our data suggest that STC2 may be used as a potential biomarker for clinical application and target therapy in future.
Barnaba: software for analysis of nucleic acid structures and trajectories

RNA molecules are highly dynamic systems characterized by a complex interplay between sequence, structure, dynamics, and function. Molecular simulations can potentially provide powerful insights into the nature of these relationships. The analysis of structures and molecular trajectories of nucleic acids can be non-trivial because it requires processing very high-dimensional data that are not easy to visualize and interpret. Here we introduce Barnaba, a Python library aimed at facilitating the analysis of nucleic acids structures and molecular simulations. The software consists of a variety of analysis tools that allow the user to i) calculate distances between three-dimensional structures using different metrics, ii) back-calculate experimental data from three-dimensional structures, iii) perform cluster analysis and dimensionality reductions, iv) search three-dimensional motifs in PDB structures and trajectories and v) construct elastic network models (ENM) for nucleic acids and nucleic acids-protein complexes. In addition, Barnaba makes it possible to calculate torsion angles, pucker conformations and to detect base-pairing/base-stacking interactions. Barnaba produces graphics that conveniently visualize both extended secondary structure and dynamics for a set of molecular conformations. The software is available as a command-line tool as well as a library, and supports a variety of 1le formats such as PDB, dcd and xtc 1les. Source code, documentation and examples are freely available at https://github.com/srnas/barnaba under GNU GPLv3 license.
Mutual Information Better Quantifies Brain Network Architecture in Children with Epilepsy

Purpose Metrics of the brain network architecture derived from resting-state fMRI have been shown to provide physiologically meaningful markers of IQ in children with epilepsy. However, traditional measures of functional connectivity (FC), specifically the Pearson correlation, assume a dominant linear relationship between BOLD time courses; this assumption may not be valid. Mutual information is an alternative measure of FC which has shown promise in the study of complex networks due to its ability to flexibly capture association of diverse forms. We aimed to compare network metrics derived from mutual information-defined FC to those derived from traditional correlation in terms of their capacity to predict patient-level IQ. Materials and Methods Patients were retrospectively identified with the following: (1) focal epilepsy; (2) resting-state fMRI; and (3) full-scale IQ by a neuropsychologist. Brain network nodes were defined by anatomic parcellation. Parcellation was performed at the size threshold of 350 mm2, resulting in networks containing 780 nodes. Whole-brain, weighted graphs were then constructed according to the pairwise connectivity between nodes. In the traditional condition, edges (connections) between each pair of nodes were defined as the absolute value of the Pearson correlation coefficient between their BOLD time courses. In the mutual information condition, edges were defined as the mutual information between time courses. The following metrics were then calculated for each weighted graph: clustering coefficient, modularity, characteristic path length, and global efficiency. A machine learning algorithm was used to predict the IQ of each individual based on their network metrics. Prediction accuracy was assessed as the fractional variation explained for each condition. Results Twenty-four patients met the inclusion criteria (age: 8–18 years). All brain networks demonstrated expected small-world properties. Network metrics derived from mutual information-defined FC significantly outperformed the use of the Pearson correlation. Specifically, fractional variation explained was 49% (95% CI: 46%, 51%) for the mutual information method; the Pearson correlation demonstrated a variation of 17% (95% CI: 13%, 19%). Conclusion Mutual information-defined functional connectivity captures physiologically relevant features of the brain network better than correlation. Clinical Relevance Optimizing the capacity to predict cognitive phenotypes at the patient level is a necessary step toward the clinical utility of network-based biomarkers.