Miracle-dz/UniFAR-FTUs huggingface dataset:
Mortality from scale drop disease in farmed Lates calcarifer in Southeast Asia.

In Southeast Asia, a new disease called scale drop disease (SDD) caused by a novel Megalocytivirus (SDDV) has emerged in farmed Asian sea bass (Lates calcarifer) in Singapore, Malaysia and Indonesia. We received samples from an Eastern Thai province that also showed gross signs of SDD (loss of scales). Clinical samples of 0.2-1.1 kg L. calcarifer collected between 2016 and 2018 were examined for evidence of SDDV infection. Histopathology was similar to that in the first report of SDDV from Singapore including necrosis, inflammation and nuclear pyknosis and karyorrhexis in the multiple organs. Intracytoplasmic inclusion bodies were also observed in the muscle tissue. In a density-gradient fraction from muscle extracts, TEM revealed enveloped, hexagonal megalocytiviral-like particles (~100-180 nm). By PCR using primers derived from the Singaporean SDDV genome sequence, four different genes were amplified and sequenced from the Thai isolate revealing 98.7%-99.9% identity between the two isolates. Since viral inclusions were rarely observed, clinical signs and histopathology could not be used to easily distinguish between SDD caused by bacteria or SDDV. We therefore recommend that PCR screening be used to monitor broodstock, fry and grow-out fish to estimate the current impact of SDDV in Southeast Asia and to prevent its spread.
Improving Hand Function of Severely Impaired Chronic Hemiparetic Stroke Individuals Using Task-Specific Training With the ReIn-Hand System: A Case Series

Purpose: In this study, we explored whether improved hand function is possible in poststroke chronic hemiparetic individuals with severe upper limb motor impairments when they participate in device-aided task-specific practice. Subjects: Eight participants suffering from chronic stroke (>1-year poststroke, mean: 11.2 years) with severely impaired upper extremity movement (Upper Extremity Subscale of the Fugl-Meyer Motor Assessment (UEFMA) score between 10 and 24) participated in this study. Methods: Subjects were recruited to participate in a 20-session intervention (3 sessions/7 weeks). During each session, participants performed 20–30 trials of reaching, grasping, retrieving, and releasing a jar with the assistance of a novel electromyography-driven functional electrical stimulation (EMG-FES) system. This EMG-FES system allows for Reliable and Intuitive use of the Hand (called ReIn-Hand device) during multi-joint arm movements. Pre-, post-, and 3-month follow-up outcome assessments included the UEFMA, Cherokee McMaster Stroke Assessment, grip dynamometry, Box and Blocks Test (BBT), goniometric assessment of active and passive ranges of motion (ROMs) of the wrist and the metacarpophalangeal flexion and extension (II, V fingers), Nottingham Sensory Assessment–Stereognosis portion (NSA), and Cutaneous Sensory Touch Threshold Assessment. Results: A nonparametric Friedman test of differences found significant changes in the BBT scores (χ2 = 10.38, p < 0.05), the passive and active ROMs (χ2 = 11.31, p < 0.05 and χ2 = 12.45, p < 0.01, respectively), and the NSA scores (χ2 = 6.42, p < 0.05) following a multi-session intervention using the ReIn-Hand device. Conclusions: These results suggest that using the ReIn-Hand device during reaching and grasping activities may contribute to improvements in gross motor function and sensation (stereognosis) in individuals with chronic severe UE motor impairment following stroke.
STAR: Scaling Transactions through Asymmetric Replication

In this paper, we present STAR, a new distributed in-memory database with asymmetric replication. By employing a single-node non-partitioned architecture for some replicas and a partitioned architecture for other replicas, STAR is able to efficiently run both highly partitionable workloads and workloads that involve cross-partition transactions. The key idea is a new phase-switching algorithm where the execution of single-partition and cross-partition transactions is separated. In the partitioned phase, single-partition transactions are run on multiple machines in parallel to exploit more concurrency. In the single-master phase, mastership for the entire database is switched to a single designated master node, which can execute these transactions without the use of expensive coordination protocols like two-phase commit. Because the master node has a full copy of the database, this phase-switching can be done at negligible cost. Our experiments on two popular benchmarks (YCSB and TPC-C) show that high availability via replication can coexist with fast serializable transaction execution in distributed in-memory databases, with STAR outperforming systems that employ conventional concurrency control and replication algorithms by up to one order of magnitude.
The Role of Attachment Insecurity and Big Five Traits on Sensory Processing Sensitivity

Abstract This study examined the interplay between behavioral inhibition/activation systems (BIS/BAS) sensitivity, attachment insecurity (i.e., anxiety and avoidance), and Big Five personality traits in predicting sensory processing sensitivity (SPS). We have specifically tested three alternative theoretical models to explain the process through which BIS/BAS sensitivity link to SPS; unique effects of attachment dimensions and personality traits, as well as moderating and mediating role of these variables. Participants (N = 494) completed the highly sensitive Person scale, BIS/BAS scales, experiences in close relationships-revised scale, and big five inventory. The findings revealed the complex role of attachment dimensions and personality traits on SPS. Attachment avoidance, but not attachment anxiety, moderated the effect of BIS activity on SPS indicating that, compared to those with high BIS sensitivity, those with low levels of both BIS and attachment avoidance reported lower level of SPS. Attachment anxiety, neuroticism, extraversion, and openness partially mediated the effects of BIS on SPS. Conceptual implications of the findings and suggestions for future research are discussed.
Countering Android Malware: A Scalable Semi-Supervised Approach for Family-Signature Generation

Reducing the effort required by humans in countering malware is of utmost practical value. We describe a scalable, semi-supervised framework to dig into massive data sets of Android applications and identify new malware families. Until 2010, the industrial standard for the detection of malicious applications has been mainly based on signatures; as each tiny alteration in malware makes them ineffective, new signatures are frequently created –- a task that requires a considerable amount of time and resources from skilled experts. The framework we propose is able to automatically cluster applications in families and suggest formal rules for identifying them with 100% recall and quite high precision. The families are used either to safely extend experts’ knowledge on new samples or to reduce the number of applications requiring thorough analyses. We demonstrated the effectiveness and the scalability of the approach running experiments on a database of 1.5 million Android applications. In 2018, the framework has been successfully deployed on Koodous, a collaborative anti-malware platform.
Effectiveness and postoperative wound infection of preperitoneal pelvic packing in patients with hemodynamic instability caused by pelvic fracture

Introduction Despite the use of a multidisciplinary treatment approach, the mortality rate of hemodynamic instability due to severe pelvic fracture remains 40–60%. Several recent studies have shown that preperitoneal pelvic packing (PPP) was useful for achieving hemostasis in these patients in the acute phase. However, few studies have examined postoperative complications. The purpose of the present study was to evaluate clinical outcomes and wound infections of PPP in these patients. Materials and methods We retrospectively reviewed the medical charts of 62 patients with hemorrhagic shock due to pelvic fracture between March 2011 and May 2017. Excluding four patients (two with other major hemorrhage sites and two who experienced cardiac arrest in the emergency room), the patients were divided into PPP (n = 30) and non-PPP (n = 28) groups according to PPP application. Clinical outcomes including early-stage mortality, transfusion amount, and surgical site infection (SSI) were compared between the two groups. Results The overall mortality rate was 48.3% and the mean Injury Severity Score (ISS) was 39 ± 9. The 30 patients in the PPP group had a significantly lower hemorrhage-induced mortality rate than the 28 patients in the non-PPP group (16.7% vs 50%, p = 0.019), although both groups had similar patient characteristics (age, ISS, and initial serum lactate level). Independent factors associated with hemorrhage-induced mortality were PPP and the requirement of packed red blood cells for 4 h. In the PPP group, SSI occurred in 5 of 25 (20%) patients. Conclusions PPP may be considered as a hemostatic modality for hemodynamic instability due to pelvic fracture because it reduces the hemorrhage-induced mortality rate. However, wound infections after the procedure should be considered.
Edge Caching Architecture for Media Delivery over P2P Networks

Peer to Peer (P2P) systems, have been identified as one of the main contributors to the exponential growth of internet traffic. File sharing applications such as BitTorrent clients, are the most popular among P2P systems and tend to consume great amounts of network resources, stretching infrastructure capabilities to their limits. This causes interdomain links to be overloaded with traffic, created from content traversing the network, which is most of the times redundant. Cloud computing has been an empowering force towards alleviating this issue and many research approaches have been proposed. With cloud orchestration frameworks and Software Defined Networks (SDN) as our empowering technologies, we propose a cross-layered edge-caching mechanism to alleviate content and service providers of link-saturation problems, caused by identical content requested and downloaded by multiple users in the same network vicinity. The proposed mechanism identifies and classifies P2P data-in-transit, caches it locally and successively acts as a peer by sharing it with all other requesting users. This way, redundant traffic in the backhaul link is minimized, since requests for content cached by the mechanism, will be served by the target edge-node and not from the content provider. To validate the performance and overall efficacy of the proposed mechanism, simulation experiments were conducted under a controlled-conditions environment. Results indicated that the proposed mechanism can reduce redundant network traffic up to 22%.
Cache-centric video recommendation: an approach to improve the efficiency of YouTube caches

In this paper, we take advantage of the user behavior of requesting videos from the related list provided by YouTube and the user behavior of requesting videos from the top of this related list to improve the performance of YouTube's caches. We recommend a related list reordering approach which modifies the order of the videos shown on the related list based on the content in the cache. The main goal of our reordering approach is to push the contents already in the cache to the top of the related list and push non-cached contents towards the bottom, which increases the likelihood that the already cached content will be chosen by the viewer. We analyze the benefits of our approach by an investigation that is based on two traces collected from an university campus. Our analysis shows that the proposed reordering approach for related list would lead to a 2 to 5 times increase in cache hit rate compared to an approach without reordering the related list. The increase in hit rate would lead to a 5.12% to 18.19% reduction in server load or back-end bandwidth usage. This increase in hit rate and reduction in back-end bandwidth reduces the latency in streaming the video requested by the viewer and has the potential to improve the overall performance of YouTube's content distribution system. An analysis of YouTube's recommendation system reveals that related lists are created from a small pool of videos, which increases the potential for caching content from related lists and reordering based on the content in the cache.
Low rank approximation and regression in input sparsity time

We design a new distribution over poly(r ε<sup>-1</sup>) x n matrices S so that for any fixed n x d matrix A of rank r, with probability at least 9/10, SAx<sub>2</sub> = (1 pm ε)Ax<sub>2</sub> simultaneously for all x ∈ R<sup>d</sup>. Such a matrix S is called a <i>subspace embedding</i>. Furthermore, SA can be computed in O(nnz(A)) + ~O(r<sup>2</sup>ε<sup>-2</sup>) time, where nnz(A) is the number of non-zero entries of A. This improves over all previous subspace embeddings, which required at least Ω(nd log d) time to achieve this property. We call our matrices S <i>sparse embedding matrices</i>. Using our sparse embedding matrices, we obtain the fastest known algorithms for overconstrained least-squares regression, low-rank approximation, approximating all leverage scores, and l<sub>p</sub>-regression: to output an x' for which Ax'-b<sub>2</sub> ≤ (1+ε)min<sub>x</sub> Ax-b<sub>2</sub> for an n x d matrix A and an n x 1 column vector b, we obtain an algorithm running in O(nnz(A)) + ~O(d<sup>3</sup>ε<sup>-2</sup>) time, and another in O(nnz(A)log(1/ε)) + ~O(d<sup>3</sup>log(1/ε)) time. (Here ~O(f) = f ⋅ log<sup>O(1)</sup>(f). ) to obtain a decomposition of an n x n matrix A into a product of an n x k matrix L, a k x k diagonal matrix D, and a n x k matrix W, for which F{A - L D W} ≤ (1+ε)F{A-A<sub>k</sub>}, where A<sub>k</sub> is the best rank-k approximation, our algorithm runs in O(nnz(A)) + ~O(nk<sup>2</sup> ε<sup>-4</sup>log n + k<sup>3</sup>ε<sup>-5</sup>log<sup>2</sup>n) time. to output an approximation to all leverage scores of an n x d input matrix A simultaneously, with constant relative error, our algorithms run in O(nnz(A) log n) + ~O(r<sup>3</sup>) time. to output an x' for which Ax'-b<sub>p</sub> ≤ (1+ε)min<sub>x</sub> Ax-b<sub>p</sub> for an n x d matrix A and an n x 1 column vector b, we obtain an algorithm running in O(nnz(A) log n) + poly(r ε<sup>-1</sup>) time, for any constant 1 ≤ p < ∞. We optimize the polynomial factors in the above stated running times, and show various tradeoffs. Finally, we provide preliminary experimental results which suggest that our algorithms are of interest in practice.
SOLVENT

Scientific discoveries are often driven by finding analogies in distant domains, but the growing number of papers makes it difficult to find relevant ideas in a single discipline, let alone distant analogies in other domains. To provide computational support for finding analogies across domains, we introduce SOLVENT, a mixed-initiative system where humans annotate aspects of research papers that denote their background (the high-level problems being addressed), purpose (the specific problems being addressed), mechanism (how they achieved their purpose), and findings (what they learned/achieved), and a computational model constructs a semantic representation from these annotations that can be used to find analogies among the research papers. We demonstrate that this system finds more analogies than baseline information-retrieval approaches; that annotators and annotations can generalize beyond domain; and that the resulting analogies found are useful to experts. These results demonstrate a novel path towards computationally supported knowledge sharing in research communities.
Successful Encoding during Natural Reading Is Associated with Fixation-Related Potentials and Large-Scale Network Deactivation

Abstract Reading literature (e.g., an entire book) is an enriching experience that qualitatively differs from reading a single sentence; however, the brain dynamics of such context-dependent memory remains unclear. This study aimed to elucidate mnemonic neural dynamics during natural reading of literature by performing electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI). Brain activities of human participants recruited on campus were correlated with their subsequent memory, which was quantified by semantic correlation between the read text and reports subsequently written by them based on state of the art natural language processing procedures. The results of the EEG data analysis showed a significant positive relationship between subsequent memory and fixation-related EEG. Sentence-length and paragraph-length mnemonic processes were associated with N1-P2 and P3 fixation-related potential (FRP) components and fixation-related θ-band (4–8 Hz) EEG power, respectively. In contrast, the results of fMRI analysis showed a significant negative relationship between subsequent memory and blood oxygenation level-dependent (BOLD) activation. Sentence-length and paragraph-length mnemonic processes were associated with networks of regions forming part of the salience network and the default mode network (DMN), respectively. Taken together with the EEG results, these memory-related deactivations in the salience network and the DMN were thought to reflect the reading of sentences characterized by low mnemonic load and the suppression of task-irreverent thoughts, respectively. It was suggested that the context-dependent mnemonic process during literature reading requires large-scale network deactivation, which might reflect coordination of a range of voluntary processes during reading.
Effect of personal factors to use ICTs on e-learning adoption: comparison between learner and instructor in developing countries

ABSTRACT This research investigated some key factors influencing an individual’s behavior to use e-learning in developing countries through social cognitive theory for examining the adoption of e-learning by instructors and learners, who are the key e-learning users. This paper focused on the personal capacity of Information and Communications Technologies and also investigated the mediating effects of computer self-efficacy (CSE) and performance expectation on intention to use e-learning. The results indicate that performance expectation and CSE were crucial factors influencing behavior intention to use e-learning systems for both instructors and learners. This study also found that computer experience and personal innovativeness in the domain of information technology were prominent factors for instructors. In addition, some differences and similarities were identified in the results for instructors and learners. This study used structure equation model, partial least squares, for data analysis, and data were collected from universities in Indonesia.
Carbon sequestration in riparian forests: A global synthesis and meta‐analysis

Restoration of deforested and degraded landscapes is a globally recognized strategy to sequester carbon, improve ecological integrity, conserve biodiversity, and provide additional benefits to human health and well‐being. Investment in riparian forest restoration has received relatively little attention, in part due to their relatively small spatial extent. Yet, riparian forest restoration may be a particularly valuable strategy because riparian forests have the potential for rapid carbon sequestration, are hotspots of biodiversity, and provide numerous valuable ecosystem services. To inform this strategy, we conducted a global synthesis and meta‐analysis to identify general patterns of carbon stock accumulation in riparian forests. We compiled riparian biomass and soil carbon stock data from 117 publications, reports, and unpublished data sets. We then modeled the change in carbon stock as a function of vegetation age, considering effects of climate and whether or not the riparian forest had been actively planted. On average, our models predicted that the establishment of riparian forest will more than triple the baseline, unforested soil carbon stock, and that riparian forests hold on average 68–158 Mg C/ha in biomass at maturity, with the highest values in relatively warm and wet climates. We also found that actively planting riparian forest substantially jump‐starts the biomass carbon accumulation, with initial growth rates more than double those of naturally regenerating riparian forest. Our results demonstrate that carbon sequestration should be considered a strong co‐benefit of riparian restoration, and that increasing the pace and scale of riparian forest restoration may be a valuable investment providing both immediate carbon sequestration value and long‐term ecosystem service returns.
Blocking H2A.Z Incorporation via Tip60 Inhibition Promotes Systems Consolidation of Fear Memory in Mice

Abstract Memory formation is a protracted process that initially involves the hippocampus and becomes increasingly dependent on the cortex over time, but the mechanisms of this transfer are unclear. We recently showed that hippocampal depletion of the histone variant H2A.Z enhances both recent and remote memories, but the use of virally mediated depletion reduced H2A.Z levels throughout testing, making its temporally specific function unclear. Given the lack of drugs that target histone variants, we tested existing drugs for efficacy against H2A.Z based on their targeting of known H2A.Z regulators. The Tip60 (part of H2A.Z deposition complex) inhibitor Nu9056 reduced H2A.Z binding, whereas the histone deacetylase (HDAC) inhibitor Trichostatin-A increased H2A.Z acetylation without influencing total H2A.Z in cultured hippocampal neurons. Tip60 (but not HDAC) inhibition 23 h after learning enhanced remote (tested at 7 d) and not recent (tested at 24 h) contextual fear memory in mice. In contrast, Tip60 inhibition 30 d after learning impaired recall of remote memory after 1 h, but protected the memory from further decline 24 h later. These data provide the first evidence of a delayed postlearning role for histone variants in supporting memory transfer during systems consolidation.
Antitumor Action of Amygdalin on Human Breast Cancer Cells by Selective Sensitization to Oxidative Stress

Abstract The treatment of MCF-7 and T47D human breast cancer cell lines with amygdalin was able to reduce the growth of both cells, in concentration and time-dependent manners. The potency of this inhibition against MCF-7 and T47D cells produced IC50 values of 39 and 45 mM, respectively. To investigate the correlation of this inhibition with oxidative stress, an amygdalin treatment of both cell lines was capable of inducing the generation of malondialdehyde (MDA) and oxidized glutathione levels. Also, this treatment caused the decrease of total glutathione and glutathione reductase activity. The proportional survival of tumor cells from this inhibition was positively correlated with the total glutathione, but it was inversely correlated with amygdalin or MDA levels (P < 0.001). In MCF-7 cells, the production of total glutathione was six times higher in the untreated than in amygdalin-treated cells, whereas this difference was reduced to 2.1 times in the T47D cells. Similarly, the production of MDA in MCF-7 cells was 2.4 times higher in the amygdalin-treated than in the untreated cultures, which were lowered to 1.3 times in the T47D cells. These data support a mechanism of amygdalin antitumor action against breast cancer cells based on the induction of oxidative stress.