Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Cardiac Activity by Capacitive Electrodes from a Single Point on the Wrist

Ballistocardiography (BCG) is the measurement of body movement by forces associated with heart contraction that can be used for monitoring cardiac activity. It has already been measured by force sensor and accelerometer. In this research, we developed a capacitive wristband that provides a method for single point, continuous BCG measurement, which has the potential to become a new type of sensor for wearable health care. The aim of this paper is to validate that the signal detected by capacitive electrodes is actually the BCG signal. Signals from four healthy subjects were acquired by a capacitive wristband together with Electrocardiogram (ECG). The capacitive signal was validated by both morphology matching analysis and wave occurrence time matching analysis to show that it is indeed BCG signal. JJ intervals extracted from BCG were shown to have potential to be surrogate of ECG RR series in heart rate variability analysis.
Collecting and Analyzing Digital Proof Material to Detect Cybercrimes

Nowadays it is crucial for maintaining security defence on devices against cybercrimes over the Internet. None of the existing systems proved to be flawless, though various approaches have been used to counter cybercrime activities. This paper proposes a real time model for identifying and collecting cybercrime evidences. In particular, the proposed model comprises three main components, rules-based IDS for cybercrimes monitoring and identifying their features. Database for collecting and storing features of new cybercrimes and BASE for result viewing in real time. Features installed on Raspberry Pi, were dedicated for data collection process. The proposed model is efficient in identifying and collecting network cybercrimes evidences in real time based on the result demonstrated.
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons

We analyze linear independence of rank one tensors produced by tensor powers of randomly perturbed vectors. This enables efficient decomposition of sums of high-order tensors. Our analysis builds upon [BCMV14] but allows for a wider range of perturbation models, including discrete ones. We give an application to recovering assemblies of neurons. Assemblies are large sets of neurons representing specific memories or concepts. The size of the intersection of two assemblies has been shown in experiments to represent the extent to which these memories co-occur or these concepts are related; the phenomenon is called association of assemblies. This suggests that an animal's memory is a complex web of associations, and poses the problem of recovering this representation from cognitive data. Motivated by this problem, we study the following more general question: Can we reconstruct the Venn diagram of a family of sets, given the sizes of their $\ell$-wise intersections? We show that as long as the family of sets is randomly perturbed, it is enough for the number of measurements to be polynomially larger than the number of nonempty regions of the Venn diagram to fully reconstruct the diagram.
Overexpression of long non-coding RNA n346372 in bladder cancer tissues is associated with a poor prognosis

Accumulating evidence has confirmed that dysregulated long non-coding RNAs (lncRNAs) participate in the initiation and progression of a number of solid tumors and have potential applications for early diagnosis, targeted therapy, and the prognosis of patients with bladder cancer. In the present study, via high-throughput sequencing technology and bioinformatics analysis, a total of 169 lncRNAs with significantly differential expression between bladder cancer tissues and paired adjacent normal tissues (n=10) were initially identified by screening. Reverse-transcription-quantitative polymerase chain reaction was carried out to validate the expression levels of lncRNA-n346372 in 60 pairs of tissue samples from bladder cancer patients. The results indicated that lncRNA-n346372 was upregulated in bladder cancer tissues compared with the matched adjacent normal tissues (P<0.05). In addition, the results of fluorescence in situ hybridization analysis of bladder cancer cells and tissues demonstrated that lncRNA-n346372 is located in the cytoplasm, and the expression of lncRNA-n346372 in bladder cancer tissues was significantly increased compared with the paired normal tissues. Following a χ2 test with common clinical variables among the patients, the expression level of lncRNA-n346372 was demonstrated to be positively associated with advanced tumor stage and poor histological differentiation of bladder cancer. Kaplan-Meier survival analysis revealed that patients with high expression of n346372 were more likely to have a poor prognosis compared with patients with low n346372 expression. Finally, univariate and multivariate analyses indicated that the relative level of n346372, apart from tumor stage and histological grade, may serve as an independent prognostic factor of bladder cancer. To the best of the authors' knowledge, this is the first study to verify the dysregulated expression of lncRNA-n346372 in bladder cancer; an association of this lncRNA with overall survival of bladder cancer patients was also uncovered in the present study, suggesting that lncRNA-n346372 may contribute to the initiation and/or progression of bladder cancer with potential applications in the clinic.
Application of Machine Learning Techniques for Hate Speech Detection in Mobile Applications

The proliferation of data through various platforms and applications is in constant increase. The versatility of data and its omnipresence makes it very hard to detect the trustworthiness and intention of the source. This is very evident in dynamic environments such as mobile applications. As a result, designing mobile applications that will monitor, control and block any type of malintents is important. This paper makes an attempt in this direction by implementing a lightweight machine learning classification scheme for hate speech detection in Albanian Language for mobile applications. Initial testing and evaluations indicate good classifier accuracy in mobile environments where frequent and real-time training of the algorithm is required.
Macrophage-specific MHCII expression is regulated by a remote Ciita enhancer controlled by NFAT5

MHCII in antigen-presenting cells (APCs) is a key regulator of adaptive immune responses. Expression of MHCII genes is controlled by the transcription coactivator CIITA, itself regulated through cell type–specific promoters. Here we show that the transcription factor NFAT5 is needed for expression of Ciita and MHCII in macrophages, but not in dendritic cells and other APCs. NFAT5-deficient macrophages showed defective activation of MHCII-dependent responses in CD4+ T lymphocytes and attenuated capacity to elicit graft rejection in vivo. Ultrasequencing analysis of NFAT5-immunoprecipitated chromatin uncovered an NFAT5-regulated region distally upstream of Ciita. This region was required for CIITA and hence MHCII expression, exhibited NFAT5-dependent characteristics of active enhancers such as H3K27 acetylation marks, and required NFAT5 to interact with Ciita myeloid promoter I. Our results uncover an NFAT5-regulated mechanism that maintains CIITA and MHCII expression in macrophages and thus modulates their T lymphocyte priming capacity.
Cyber Security Challenges in Organisations: A Case Study in Malaysia

This paper presents challenges in cyber security implementation at organisational level. This research is based on a case study on government sector in Malaysia deploying qualitative approach using semi-structured interviews with key officers in the organization. The data were analysed using Grounded Theory. The findings clustered under three pillars of success namely People, Process and Technology. Under the pillar of People, the challenges are lack of skills, cyber security is everyone’s responsibilities and human error, whilst under Process, challenges identified are lack of implementation plan, wrongly placed human resource and lack of budget. The challenge in Technology is that it moves too fast. The findings are useful for the cyber security policy makers and implementers.
Budget Impact of Appropriate Low-Dose Aspirin Use for Primary and Secondary Cardiovascular Event Prevention in the Managed Care Setting

BACKGROUND: Cardiovascular disease remains the leading cause of death in adults in the United States and constitutes a substantial portion of overall national health expenditures. Aspirin is generally recommended for primary cardiovascular event prevention based on a given patient’s underlying cardiovascular event risk profile, particularly for those aged 50-69 years with a 10-year risk of coronary heart disease of ≥ 10%. Evidence-based clinical guidelines are in agreement for secondary prevention consisting of lifelong, low-dose aspirin therapy following a cardiovascular event. Despite these recommendations, research suggests suboptimal concordance between guidelines and clinical practice. OBJECTIVE: To evaluate the budget impact of appropriate low-dose aspirin use for primary and secondary cardiovascular event prevention compared with current rates of low-dose aspirin use. METHODS: An economic model measuring budget spend for cardiovascular events, aspirin, and aspirin-related adverse events was developed from the perspective of a U.S. payer. The model compared current rates of aspirin use to appropriate rates of aspirin use according to guideline recommendations for both primary and secondary cardiovascular event prevention. RESULTS: For a hypothetical plan with 1 million members, an estimated 18,026 patients were on aspirin therapy for primary cardiovascular event prevention, while guidelines recommend that 55,788 patients should have been on aspirin therapy for this indication. Optimal aspirin use in the primary cardiovascular event prevention population reduced the number of nonfatal myocardial infarctions (MIs; -367), ischemic strokes (-232), and deaths (-60), with an increase in the number of gastrointestinal bleeds (169) and hemorrhagic strokes (98). Evidence-based guideline-compliant use of aspirin for primary cardiovascular event prevention resulted in total cost savings of approximately $4.2 million over a 5-year time horizon. For secondary cardiovascular event prevention, an estimated 48,663 patients were on aspirin, while clinical guidelines recommend that 71,316 patients should have been on aspirin therapy for this indication. Optimal aspirin use in secondary cardiovascular event prevention reduced the number of nonfatal MIs (-515), ischemic strokes (-375), and deaths (-217), with an increase in the number of gastrointestinal bleeds (98) and hemorrhagic strokes (58). Evidence-based guideline-compliant use of aspirin for secondary cardiovascular event prevention resulted in total cost savings of approximately $11 million over a 5-year time horizon. CONCLUSIONS: Appropriate low-dose aspirin use for primary and secondary cardiovascular event prevention can result in improved patient outcomes with significant cost savings for U.S. payers. As a simple and inexpensive prophylactic measure for cardiovascular event prevention, aspirin use should be carefully considered in all appropriate at-risk adult patients.
Downregulation of miR-505-3p predicts poor bone metastasis-free survival in prostate cancer

The principal issue deriving from prostate cancer (PCa) is its propensity to metastasize to bone. To date, bone metastasis remains incurable, and therapeutic strategies are limited. Therefore, it is of paramount importance to explore predictive markers for bone metastasis of PCa. In the present study, we reported that miR-505-3p was significantly downregulated in bone metastatic PCa tissues compared with that in non-bone metastatic PCa tissues, but there was no significant difference in miR-505-3p expression between PCa and adjacent normal tissues. miR-505-3p expression was inversely associated with serum PSA levels, Gleason grade, N and M classification, and short bone metastasis-free survival in PCa patients, but had no effect on overall survival in PCa patients. Furthermore, upregulation of miR-505-3p suppressed the activity of TGF-β signaling by directly targeting downstream effectors of TGF-β signaling, SMAD2 and SMAD3, further inhibiting the invasion and migration abilities of PCa cells. Therefore, our findings unraveled a novel mechanism by which miR-505-3p inhibits bone metastasis of PCa, supporting the notion that miR-505-3p may serve as a predictive marker for bone metastasis of PCa.
Kinetics of the OH+HCl→H2O+Cl reaction: Rate determining roles of stereodynamics and roaming and of quantum tunneling

The OH + HCl → H2O + Cl reaction is one of the most studied four‐body systems, extensively investigated by both experimental and theoretical approaches. Here, as a continuation of our previous work on the OH + HBr and OH + HI reactions, which manifest an anti‐Arrhenius behavior that was explained by stereodynamic and roaming effects, we extend the strategy to understand the transition to the sub‐Arrhenius behavior occurring for the HCl case. As previously, we perform first‐principles on‐the‐fly Born–Oppenheimer molecular dynamics calculations, thermalized at four temperatures (50, 200, 350, and 500 K), but this time we also apply a high‐level transition‐state‐theory, modified to account for tunneling conditions. We find that the theoretical rate constants calculated with Bell tunneling corrections are in good agreement with extensive experimental data available for this reaction in the ample temperature range: (i) simulations show that the roles of molecular orientation in promoting this reaction and of roaming in finding the favorable path are minor than in the HBr and HI cases, and (ii) dominating is the effect of quantum mechanical penetration through the energy barrier along the reaction path on the potential energy surface. The discussion of these results provides clarification of the origin on different non‐Arrhenius mechanisms observed along this series of reactions. © 2018 Wiley Periodicals, Inc.
P-MCGS: Parallel Monte Carlo Acyclic Graph Search

Recently, there have been great interests in Monte Carlo Tree Search (MCTS) in AI research. Although the sequential version of MCTS has been studied widely, its parallel counterpart still lacks systematic study. This leads us to the following questions: \emph{how to design efficient parallel MCTS (or more general cases) algorithms with rigorous theoretical guarantee? Is it possible to achieve linear speedup? } In this paper, we consider the search problem on a more general acyclic one-root graph (namely, Monte Carlo Graph Search (MCGS)), which generalizes MCTS. We develop a parallel algorithm (P-MCGS) to assign multiple workers to investigate appropriate leaf nodes simultaneously. Our analysis shows that P-MCGS algorithm achieves linear speedup and that the sample complexity is comparable to its sequential counterpart.
Test–Retest Reproducibility of 18F-FDG PET/CT Uptake in Cancer Patients Within a Qualified and Calibrated Local Network

Calibration and reproducibility of quantitative 18F-FDG PET measures are essential for adopting integral 18F-FDG PET/CT biomarkers and response measures in multicenter clinical trials. We implemented a multicenter qualification process using National Institute of Standards and Technology–traceable reference sources for scanners and dose calibrators, and similar patient and imaging protocols. We then assessed SUV in patient test–retest studies. Methods: Five 18F-FDG PET/CT scanners from 4 institutions (2 in a National Cancer Institute–designated Comprehensive Cancer Center, 3 in a community-based network) were qualified for study use. Patients were scanned twice within 15 d, on the same scanner (n = 10); different but same model scanners within an institution (n = 2); or different model scanners at different institutions (n = 11). SUVmax was recorded for lesions, and SUVmean for normal liver uptake. Linear mixed models with random intercept were fitted to evaluate test–retest differences in multiple lesions per patient and to estimate the concordance correlation coefficient. Bland–Altman plots and repeatability coefficients were also produced. Results: In total, 162 lesions (82 bone, 80 soft tissue) were assessed in patients with breast cancer (n = 17) or other cancers (n = 6). Repeat scans within the same institution, using the same scanner or 2 scanners of the same model, had an average difference in SUVmax of 8% (95% confidence interval, 6%–10%). For test–retest on different scanners at different sites, the average difference in lesion SUVmax was 18% (95% confidence interval, 13%–24%). Normal liver uptake (SUVmean) showed an average difference of 5% (95% confidence interval, 3%–10%) for the same scanner model or institution and 6% (95% confidence interval, 3%–11%) for different scanners from different institutions. Protocol adherence was good; the median difference in injection-to-acquisition time was 2 min (range, 0–11 min). Test–retest SUVmax variability was not explained by available information on protocol deviations or patient or lesion characteristics. Conclusion: 18F-FDG PET/CT scanner qualification and calibration can yield highly reproducible test–retest tumor SUV measurements. Our data support use of different qualified scanners of the same model for serial studies. Test–retest differences from different scanner models were greater; more resolution-dependent harmonization of scanner protocols and reconstruction algorithms may be capable of reducing these differences to values closer to same-scanner results.
Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images

Accurate delineation of heart substructures is a prerequisite for abnormality detection, for making quantitative and functional measurements, and for computer-aided diagnosis and treatment planning. Late Gadolinium-Enhanced Cardiac MRI (LGE-CMRI) is an emerging imaging technology for myocardial infarction or scar detection based on the differences in the volume of residual gadolinium distribution between scar and healthy tissues. While LGE-CMRI is a well-established non-invasive tool for detecting myocardial scar tissues in the ventricles, its application to left atrium (LA) imaging is more challenging due to its very thin wall of the LA and poor quality images, which may be produced because of motion artefacts and low signal-to-noise ratio. As the LGE-CMRI scan is designed to highlight scar tissues by altering the gadolinium kinetics, the anatomy among different heart substructures has less distinguishable boundaries. An accurate, robust and reproducible method for LA segmentation is highly in demand because it can not only provide valuable information of the heart function but also be helpful for the further delineation of scar tissue and measuring the scar percentage. In this study, we proposed a novel deep learning framework working on LGE-CMRI images directly by combining sequential learning and dilated residual learning to delineate LA and pulmonary veins fully automatically. The achieved results showed accurate segmentation results compared to the state-of-the-art methods. The proposed framework leads to an automatic generation of a patient-specific model that can potentially enable an objective atrial scarring assessment for the atrial fibrillation patients.
On the Impact of Muscle Shortening on Non-Negative Matrix Factorization of Dynamic Surface Electromyograms

We analyzed the sensitivity of Non-negative Matrix Factorization (NMF) of dynamic surface electromyograms (EMG) to muscle shortening. We first identified Motor unit action potentials (MUAPs) by decomposing experimentally recorded EMG signals during slow shortening of biceps brachii muscle in five young healthy males. We then used these MUAPs to generate different synthetic EMG signals with different muscle shortening and excitation profiles. Afterwards, we applied NMF to the synthetic EMG signals and calculated Pearson correlation coefficient (CC) between the extracted NMF components and a) muscle shortening and b) muscle excitation profiles. The results demonstrated good match between NMF components and muscle excitation profiles, but only when the muscle excitation level changed for at least 10 % during the muscle shortening. During constant muscle excitation, the resulting NMF components correlated significantly with the muscle shortening profiles. These results demonstrate that NMF components reflect not only the muscle excitation profiles but also muscle shortening profiles. Therefore, the results of NMF analysis of dynamic EMG signals need to be interpreted with caution.
Morphological Reinflection in Context: CU Boulder’s Submission to CoNLL–SIGMORPHON 2018 Shared Task

This paper describes two systems for the second subtask of CoNLL-SIGMORPHON 2018 shared task on universal morphological re-inﬂection submitted by the University of Colorado Boulder team. Both systems are implementations of RNN encoder-decoder models with soft attention. The ﬁrst system is similar to the baseline system with minor differences in architecture and parameters, and is implemented using PyTorch. It works for both track 1 and track 2 of the subtask and generally outperforms the baseline at low data settings in both tracks. The second system predicts the morphosyntactic description (MSD) of the lemma to be inﬂected using an MSD prediction model. The data for subtask 2 is processed and reformatted to subtask 1 data format to train an inﬂection model. Then the inﬂection model predicts the inﬂected form for the target lemma given the predicted MSD. This sys-tem achieves higher accuracies than the ﬁrst system when the training data is the most limited, though it does not perform better when the training data is abundant.
Quantifying Co-Oligomer Formation by α-Synuclein

Small oligomers of the protein α-synuclein (αS) are highly cytotoxic species associated with Parkinson’s disease (PD). In addition, αS can form co-aggregates with its mutational variants and with other proteins such as amyloid-β (Aβ) and tau, which are implicated in Alzheimer’s disease. The processes of self-oligomerization and co-oligomerization of αS are, however, challenging to study quantitatively. Here, we have utilized single-molecule techniques to measure the equilibrium populations of oligomers formed in vitro by mixtures of wild-type αS with its mutational variants and with Aβ40, Aβ42, and a fragment of tau. Using a statistical mechanical model, we find that co-oligomer formation is generally more favorable than self-oligomer formation at equilibrium. Furthermore, self-oligomers more potently disrupt lipid membranes than do co-oligomers. However, this difference is sometimes outweighed by the greater formation propensity of co-oligomers when multiple proteins coexist. Our results suggest that co-oligomer formation may be important in PD and related neurodegenerative diseases.