Miracle-dz/UniFAR-FTUs huggingface dataset:
The Therapeutic Potential of Metformin in Neurodegenerative Diseases

The search for treatments for neurodegenerative diseases is a major concern in light of today's aging population and an increasing burden on individuals, families, and society. Although great advances have been made in the last decades to understand the underlying genetic and biological cause of these diseases, only some symptomatic treatments are available. Metformin has long since been used to treat Type 2 Diabetes and has been shown to be beneficial in several other conditions. Metformin is well-tested in vitro and in vivo and an approved compound that targets diverse pathways including mitochondrial energy production and insulin signaling. There is growing evidence for the benefits of metformin to counteract age-related diseases such as cancer, cardiovascular disease, and neurodegenerative diseases. We will discuss evidence showing that certain neurodegenerative diseases and diabetes are explicitly linked and that metformin along with other diabetes drugs can reduce neurological symptoms in some patients and reduce disease phenotypes in animal and cell models. An interesting therapeutic factor might be how metformin is able to balance survival and death signaling in cells through pathways that are commonly associated with neurodegenerative diseases. In healthy neurons, these overarching signals keep energy metabolism, oxidative stress, and proteostasis in check, avoiding the dysfunction and neuronal death that defines neurodegenerative disease. We will discuss the biological mechanisms involved and the relevance of neuronal vulnerability and potential difficulties for future trials and development of therapies.
One Size Does Not Fit All: The Case for Chunking Configuration in Backup Deduplication

Data backup is regularly required by both enterprise and individual users to protect their data from unexpected loss. There are also various commercial data deduplication systems or software that help users to eliminate duplicates in their backup data to save storage space. In data deduplication systems, the data chunking process splits data into small chunks. Duplicate data is identified by comparing the fingerprints of the chunks. The chunk size setting has significant impact on deduplication performance. A variety of chunking algorithms have been proposed in recent studies. In practice, existing systems often set the chunking configuration in an empirical manner. A chunk size of 4KB or 8KB is regarded as the sweet spot for good deduplication performance. However, the data storage and access patterns of users vary and change along time, as a result, the empirical chunk size setting may not lead to a good deduplication ratio and sometimes results in difficulties of storage capacity planning. Moreover, it is difficult to make changes to the chunking settings once they are put into use as duplicates in data with different chunk size settings cannot be eliminated directly. In this paper, we propose a sampling-based chunking method and develop a tool named SmartChunker to estimate the optimal chunking configuration for deduplication systems. Our evaluations on real-world datasets demonstrate the efficacy and efficiency of SmartChunker.
Individual performance in team-based online games

Complex real-world challenges are often solved through teamwork. Of special interest are ad hoc teams assembled to complete some task. Many popular multiplayer online battle arena (MOBA) video-games adopt this team formation strategy and thus provide a natural environment to study ad hoc teams. Our work examines data from a popular MOBA game, League of Legends, to understand the evolution of individual performance within ad hoc teams. Our analysis of player performance in successive matches of a gaming session demonstrates that a player’s success deteriorates over the course of the session, but this effect is mitigated by the player’s experience. We also find no significant long-term improvement in the individual performance of most players. Modelling the short-term performance dynamics allows us to accurately predict when players choose to continue to play or end the session. Our findings suggest possible directions for individualized incentives aimed at steering the player’s behaviour and improving team performance.
An optimization of the K-Nearest Neighbor using Dynamic Time Warping as a measurement similarity for facial expressions recognition

At the present time, the facial expressions recognition system from video sequences is a huge challenging problem to face in human and computer interaction. Emotions are important information to retrieve and treat. With the developed system, we were able to decode facial expressions and deduce human emotions, all in keeping a low error rate. The aim of this paper is to bring together two areas of which are K-Nearest Neighbor (KNN) and Dynamic Time Warping (DTW) applying for facial expressions classification. The proposed system constructs the robust features based on Active Shape Models (ASM), computes distance similarity between feature vectors by DTW, then uses KNN as a classifier. Our system achieved 90.1% of recognition accuracy using the Cohn-Kanade (CK+) facial expressions database.
Conversation-Based Complex Event Management in Smart-Spaces

Smart space management can be done in many ways. On one hand, there are conversational assistants such as the Google Assistant or Amazon Alexa that enable users to comfortably interact with smart spaces with only their voice, but these have limited functionality and are usually limited to simple commands. On the other hand, there are visual interfaces such as IBM's Node-RED that enable complex features and dependencies between different devices. However, these are limited since they require users to have a technical knowledge of how the smart devices work and the system's interface is more complicated and harder to use since they require a computer. This project proposes a new conversational assistant - Jarvis - that combines the ease of use of current assistants with the operational complexity of the visual platforms. The goal of Jarvis is to make it easier to manage smart spaces by providing intuitive commands and useful features. Jarvis integrates with already existing user interfaces such as the Google Assistant, Slack or Facebook Messenger, making it very easy to integrate with existing systems. Jarvis also provides an innovative feature - causality queries - that enable users to ask it why something happened. For example, a user can ask "why did the light turn on? " to understand how the system works.
Soy Protein Isolate Suppresses Bone Resorption and Improves Trabecular Microarchitecture in Spontaneously Hyperphagic, Rapidly Growing Male OLETF Rats

Abstract Background Traditionally, milk proteins have been recommended for skeletal health; recently, soy proteins have emerged as popular alternatives. Excess adiposity appears detrimental to skeletal health, as obese adolescents have increased fracture rates compared with healthy controls. However, soy protein effects on skeletal health during excess adiposity remain unknown. Objective The study objective was to examine the effects of isocaloric diets containing milk protein isolate (MPI), soy protein isolate (SPI), or a 50/50 combination (MIX) as the sole protein source on metabolic health indicators and bone outcomes in rapidly growing, hyperphagic, male Otsuka Long Evans Tokushima Fatty (OLETF) rats. Methods OLETF rats, aged 4 wk, were randomly assigned to 3 treatment groups (MPI, SPI, or MIX, n = 20 per group) and provided with access to experimental diets ad libitum for 16 wk. Results Body mass did not differ between the groups, but SPI had lower percentage body fat than MPI (P = 0.026). Insulin was lower in MPI than in MIX (P = 0.033) or SPI (P = 0.044), but fasting blood glucose was not different between the groups. SPI significantly reduced serum cholesterol compared with MPI (P = 0.001) and MIX (P = 0.002). N-terminal propeptide of type I collagen (P1NP) was higher in MIX than MPI (P = 0.05); C-terminal telopeptide of type 1 collagen (CTx) was higher in MPI than SPI (P < 0.001) and MIX (P < 0.001); the P1NP to CTx ratio was significantly higher in SPI and MIX than in MPI (P < 0.001). Trabecular separation was reduced in SPI compared with MPI (P = 0.030) and MIX (P = 0.008); trabecular number was increased in SPI compared with MIX (P = 0.038). No differences were seen in cortical geometry and biomechanical properties. Conclusions In the context of excess adiposity, soy- and milk-based proteins have comparable effects on cortical bone geometry and biomechanical properties, whereas soy-based proteins favorably affect the trabecular microarchitecture, and the combination of both proteins may offer additional benefits to bone remodeling in rapidly growing male OLETF rats.
3D skeletal muscle fascicle engineering is improved with TGF-β1 treatment of myogenic cells and their co-culture with myofibroblasts

Background Skeletal muscle wound healing is dependent on complex interactions between fibroblasts, myofibroblasts, myogenic cells, and cytokines, such as TGF-β1. This study sought to clarify the impact of TGF-β1 signaling on skeletal muscle cells and discern between the individual contributions of fibroblasts and myofibroblasts to myogenesis when in co-culture with myogenic cells. 3D tissue-engineered models were compared to equivalent 2D culture conditions to assess the efficacy of each culture model to predictively recapitulate the in vivo muscle environment. Methods TGF-β1 treatment and mono-/co-cultures containing human dermal fibroblasts or myofibroblasts and C2C12 mouse myoblasts were assessed in 2D and 3D environments. Three culture systems were compared: cell monolayers grown on 2D dishes and 3D tissues prepared via a self-assembly method or collagen 1-based hydrogel biofabrication. qPCR identified gene expression changes during fibroblast to myofibroblast and myoblast differentiation between culture conditions. Changes to cell phenotype and tissue morphology were characterized via immunostaining for myosin heavy chain, procollagen, and α-smooth muscle actin. Tissue elastic moduli were measured with parallel plate compression and atomic force microscopy systems, and a slack test was employed to quantify differences in tissue architecture and integrity. Results TGF-β1 treatment improved myogenesis in 3D mono- and co-cultures containing muscle cells, but not in 2D. The 3D TGF-β1-treated co-culture containing myoblasts and myofibroblasts expressed the highest levels of myogenin and collagen 1, demonstrating a greater capacity to drive myogenesis than fibroblasts or TGF-β1-treatment in monocultures containing only myoblasts. These constructs possessed the greatest tissue stability, integrity, and muscle fiber organization, as demonstrated by their rapid and sustained shortening velocity during slack tests, and the highest Young’s modulus of 6.55 kPA, approximate half the stiffness of in situ muscle. Both self-assembled and hydrogel-based tissues yielded the most multinucleated, elongated, and aligned muscle fiber histology. In contrast, the equivalent 2D co-culture model treated with TGF-β1 completely lacked myotube formation through suppression of myogenin gene expression. Discussion These results show skeletal muscle regeneration can be promoted by treating myogenic cells with TGF-β1, and myofibroblasts are superior enhancers of myogenesis than fibroblasts. Critically, both TGF-β1 treatment and co-culturing skeletal muscle cells with myofibroblasts can serve as myogenesis accelerators across multiple tissue engineering platforms. Equivalent 2D culture systems cannot replicate these affects, however, highlighting a need to continually improve in vitro models for skeletal muscle development, discovery of therapeutics for muscle regeneration, and research and development of in vitro meat products.
Higher breast cancer prevalence associated with higher socioeconomic status in the South Korean population; Has it resulted from overdiagnosis?

Recently, breast cancer prevalence has increased in South Korea. In this study, we investigated the correlation between breast cancer prevalence and socioeconomic status. This study enrolled 27,331 people who participated in the Korean National Health and Nutrition Examination Survey (KNHANES) IV–VI (2007–2015). In addition, we obtained data from the Korean Statistical Information Service (KSIS) on the breast cancer age-standardized incidence rate (AIR), the age-standardized mortality rate (AMR), the number of women screened, and the number of newly diagnosed patients. The KHANES data showed that breast cancer prevalence was significantly associated with educational level (odds ratio [OR], 2.02; 95% confidence interval [CI], 1.08–3.77 for 10–12 vs. ≤ 6 years of education, and OR, 2.36; 95% CI, 1.10–5.06 for ≥ 13 vs. ≤ 6 years of education). However, there was no significant association of breast cancer prevalence with monthly household income. In a separate analysis of the AIR, AMR, and number of women screened for breast cancer, the AIR increased with the number of women screened, whereas the AMR did not. Furthermore, the number of newly diagnosed patients in all age groups increased over time. The present results demonstrate that the recently increased breast cancer prevalence documented in South Korea may be attributable to earlier detection rather than to a real increase in prevalence, and that breast cancer may be overdiagnosed.
Towards a theory of software development expertise

Software development includes diverse tasks such as implementing new features, analyzing requirements, and fixing bugs. Being an expert in those tasks requires a certain set of skills, knowledge, and experience. Several studies investigated individual aspects of software development expertise, but what is missing is a comprehensive theory. We present a first conceptual theory of software development expertise that is grounded in data from a mixed-methods survey with 335 software developers and in literature on expertise and expert performance. Our theory currently focuses on programming, but already provides valuable insights for researchers, developers, and employers. The theory describes important properties of software development expertise and which factors foster or hinder its formation, including how developers' performance may decline over time. Moreover, our quantitative results show that developers' expertise self-assessments are context-dependent and that experience is not necessarily related to expertise.
Path-ZVA: general, efficient and automated importance sampling for highly reliable Markovian systems

We introduce Path-ZVA: an efficient simulation technique for estimating the probability of reaching a rare goal state before a regeneration state in a (discrete-time) Markov chain. Standard Monte Carlo simulation techniques do not work well for rare events, so we use importance sampling; i.e., we change the probability measure governing the Markov chain such that transitions `towards' the goal state become more likely. To do this we need an idea of distance to the goal state, so some level of knowledge of the Markov chain is required. In this paper, we use graph analysis to obtain this knowledge. In particular, we focus on knowledge of the shortest paths (in terms of `rare' transitions) to the goal state. We show that only a subset of the (possibly huge) state space needs to be considered. This is effective when the high dependability of the system is primarily due to high component reliability, but less so when it is due to high redundancies. For several models we compare our results to well-known importance sampling methods from the literature and demonstrate the large potential gains of our method.
Real-Time Graph Partition and Embedding of Large Network

Recently, large-scale networks attract significant attention to analyze and extract the hidden information of big data. Toward this end, graph embedding is a method to embed a high dimensional graph into a much lower dimensional vector space while maximally preserving the structural information of the original network. However, effective graph embedding is particularly challenging when massive graph data are generated and processed for real-time applications. In this paper, we address this challenge and propose a new real-time and distributed graph embedding algorithm (RTDGE) that is capable of distributively embedding a large-scale graph in a streaming fashion. Specifically, our RTDGE consists of the following components: (1) a graph partition scheme that divides all edges into distinct subgraphs, where vertices are associated with edges and may belong to several subgraphs; (2) a dynamic negative sampling (DNS) method that updates the embedded vectors in real-time; and (3) an unsupervised global aggregation scheme that combines all locally embedded vectors into a global vector space. Furthermore, we also build a real-time distributed graph embedding platform based on Apache Kafka and Apache Storm. Extensive experimental results show that RTDGE outperforms existing solutions in terms of graph embedding efficiency and accuracy.
Distinct Oceanic Microbiomes From Viruses to Protists Located Near the Antarctic Circumpolar Current

Microbes occupy diverse ecological niches and only through recent advances in next generation sequencing technologies have the true microbial diversity been revealed. Furthermore, lack of perceivable marine barriers to genetic dispersal (i.e., mountains or islands) has allowed the speculation that organisms that can be easily transported by currents and therefore proliferate everywhere. That said, ocean currents are now commonly being recognized as barriers for microbial dispersal. Here we analyzed samples collected from a total of six stations, four located in the Indian Ocean, and two in the Southern Ocean. Amplicon sequencing was used to characterize both prokaryotic and eukaryotic plankton communities, while shotgun sequencing was used for the combined environmental DNA (eDNA), microbial eDNA (meDNA), and viral fractions. We found that Cyanobacteria dominated the prokaryotic component in the South-West Indian Ocean, while γ-Proteobacteria dominated the South-East Indian Ocean. A combination of γ- and α-Proteobacteria dominated the Southern Ocean. Alveolates dominated almost exclusively the eukaryotic component, with variation in the ratio of Protoalveolata and Dinoflagellata depending on station. However, an increase in haptophyte relative abundance was observed in the Southern Ocean. Similarly, the viral fraction was dominated by members of the order Caudovirales across all stations; however, a higher presence of nucleocytoplasmic large DNA viruses (mainly chloroviruses and mimiviruses) was observed in the Southern Ocean. To our knowledge, this is the first that a statistical difference in the microbiome (from viruses to protists) between the subtropical Indian and Southern Oceans. We also show that not all phylotypes can be found everywhere, and that meDNA is not a suitable resource for monitoring aquatic microbial diversity.
Describing Local Reference Frames for 3-D Motion Trajectory Recognition

Motion trajectories tracked from the points of interest can provide the key relevant features for characterizing the motion patterns in video. As the increasing number of 3-D vision sensors rises, the 3-D motion trajectories that serve as motion representations have been applied successfully to video retrieval and analysis, scene understanding, motion recognition, and so on, in existing works. Most of these works use raw data of motion trajectories directly or draw simple geometric quantities to describe the motion trajectories, whereas these simple descriptions are not intrinsically complete as they cannot feature the orientation changes of moving points along the 3-D motion trajectories. In principle, orientation changes of a single moving point in 3-D space have to been obtained by resorting to high-order derivatives, but the high-order derivatives would result in high sensitivity to noise. This paper tackles the problem by describing the local reference frames along 3-D motion trajectories, while we consider a motion trajectory as a temporal sequence of local reference frames. The maximal blurred segment of the noisy discrete curves is employed to estimate the local reference frames without high-order derivatives involved, and the local reference frame contains complete information of positions and orientations in the 3-D Euclidean space. To describe such local reference frames, we use the rotations and local square root velocities of local reference frames as the proposed descriptor to characterize the position and orientation changes of the moving points along the motion trajectories. In the experiments, we evaluate the effectiveness of the proposed descriptor by applying it to the gesture recognition on two large benchmark data sets that contain hand motion trajectories. The results show that our proposed descriptor can achieve superior performance compared to the existing descriptors and state-of-the-art methods in the 3-D motion trajectory recognition.
Effect of knee positions on cardiac compression variables in cardiopulmonary resuscitation of rescuer; Manikin study

The aim of the study was to analyze the effect of knee positions on cardiac compression variables in cardiopulmonary resuscitation (CPR) using the manikin. Adult with career of CPR instructor (n=9; mean age, 27.11±6.60 years; mean heights, 177.39±4.40 cm; mean weights, 69.45±14.85 kg) participated in the experiment, and each participant performed cardiac compression from two different knee positions. Cardiac compression was 30 times per minute for each position with order of position randomized. The results obtained from variables of cardiac compression force were composed of compression velocity, elapsed time, decay rate, and loading rate in maximum and minimum medial-lateral, anterior-posterior (AP), vertical direction respectively. The above variables in 20.3 cm of knee position showed effective result than that of 50 cm of knee position, while maximum AP compression force increased. Given the often predictable setting of sports and exercise rehabilitation related with cardiac arrest, CPR relative to change of knee position were significantly associated with more efficient cardiac compression variables. These data have significant implications for health services program in fields of sports and exercise rehabilitation.
A geo-aware server assignment problem for mobile edge computing

ABSTRACT As mobile devices have become the preferred tool for communication, work, and entertainment, traffic at the edge of the network is growing more rapidly than ever. To improve user experience, commodity servers are deployed in the edge to form a decentralised network of mini datacenters each serving a localised region. A challenge is how to place these servers geographically to maximise the offloading benefit and be close to the users they respectively serve. We introduce a formulation for this problem to serve applications that involve pairwise communication between mobile devices at different geolocations. We explore several heuristic solutions and compare them in an evaluation using both real-world and synthetic datasets. GRAPHICAL ABSTRACT