Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoding English Alphabet Letters Using EEG Phase Information

Increasing evidence indicates that the phase pattern and power of the low frequency oscillations of brain electroencephalograms (EEG) contain significant information during the human cognition of sensory signals such as auditory and visual stimuli. Here, we investigate whether and how the letters of the alphabet can be directly decoded from EEG phase and power data. In addition, we investigate how different band oscillations contribute to the classification and determine the critical time periods. An English letter recognition task was assigned, and statistical analyses were conducted to decode the EEG signal corresponding to each letter visualized on a computer screen. We applied support vector machine (SVM) with gradient descent method to learn the potential features for classification. It was observed that the EEG phase signals have a higher decoding accuracy than the oscillation power information. Low-frequency theta and alpha oscillations have phase information with higher accuracy than do other bands. The decoding performance was best when the analysis period began from 180 to 380 ms after stimulus presentation, especially in the lateral occipital and posterior temporal scalp regions (PO7 and PO8). These results may provide a new approach for brain-computer interface techniques (BCI) and may deepen our understanding of EEG oscillations in cognition.
Rapid and simple method by combining FTA™ card DNA extraction with two set multiplex PCR for simultaneous detection of non‐O157 Shiga toxin‐producing Escherichia coli strains and virulence genes in food samples

The aim of this research was to optimize two multiplex polymerase chain reaction (PCR) assays that could simultaneously detect six non‐O157 Shiga toxin‐producing Escherichia coli (STEC) as well as the three virulence genes. We also investigated the potential of combining the FTA™ card‐based DNA extraction with the multiplex PCR assays. Two multiplex PCR assays were optimized using six primer pairs for each non‐O157 STEC serogroup and three primer pairs for virulence genes respectively. Each STEC strain specific primer pair only amplified 155, 238, 321, 438, 587 and 750 bp product for O26, O45, O103, O111, O121 and O145 respectively. Three virulence genes were successfully multiplexed: 375 bp for eae, 655 bp for stx1 and 477 bp for stx2. When two multiplex PCR assays were validated with ground beef samples, distinctive bands were also successfully produced. Since the two multiplex PCR examined here can be conducted under the same PCR conditions, the six non‐O157 STEC and their virulence genes could be concurrently detected with one run on the thermocycler. In addition, all bands clearly appeared to be amplified by FTA card DNA extraction in the multiplex PCR assay from the ground beef sample, suggesting that an FTA card could be a viable sampling approach for rapid and simple DNA extraction to reduce time and labour and therefore may have practical use for the food industry.
Combinatorial antitumor effects of indoleamine 2,3-dioxygenase inhibitor NLG919 and paclitaxel in a murine B16-F10 melanoma model

Indoleamine 2,3-dioxygenase (IDO) is involved in tumor immune escape and resistance to chemotherapy, and is clinically correlated with tumor progression. IDO inhibitors show marginal efficacy as single agents; therefore, combinations of these inhibitors with other therapies hold promise for cancer therapy. The aim of this study was to investigate the synergistic antitumor effects of IDO inhibitor NLG919 in combination with different regimens of paclitaxel in a murine B16-F10 melanoma model. NLG919 increased the cytotoxic activity of paclitaxel toward B16-F10 cells in the presence of pretreatment with interferon (IFN)-γ in vitro. In B16-F10 tumor-bearing mice, NLG919 was uniformly distributed throughout tumors and decreased kynurenine levels and kynurenine/tryptophan ratios in tumors and plasma for 6–12 h. NLG919 suppressed tumor growth in a dose-dependent manner and exhibited maximum efficacy at 100 mg/kg. In combination with different regimens of paclitaxel, NLG919 displayed synergistic antitumor effects, and NLG919 did not increase the side effects of paclitaxel. Within the tumors, the percentage of CD3+, CD8+, and CD4+ T cells and secretion of IFN-γ and interleukin-2 were synergistically increased, whereas the percentage of CD4+CD25+ regulatory T cells was decreased. NLG919 can potentiate the antitumor efficacy of paclitaxel without increasing its side effects, suggesting that the combination of IDO inhibitor-based immunotherapy with chemotherapy could be a potential strategy for cancer treatment.
The prevalence of diagnosed α1-antitrypsin deficiency and its comorbidities: results from a large population-based database

α1-Antitrypsin deficiency (AATD) is a genetically determined disorder that is associated with different clinical manifestations. We aimed to assess the prevalence of diagnosed AATD and its comorbidities using a large healthcare database. In this retrospective longitudinal observational study, we analysed data from 4 million insurants. Using International Classification of Diseases revision 10 (ICD-10) codes, we assessed the prevalence, comorbidities and healthcare utilisation of AATD patients (E88.0 repeatedly coded) relative to non-AATD patients with chronic obstructive pulmonary disease (COPD), emphysema or asthma. In our study population, we identified 673 AATD patients (590 aged ≥30 years), corresponding to a prevalence of 23.73 per 100 000 in all age groups and 29.36 per 100 000 in those ≥30 years. Based on the number of AATD cases detected in the sample size (673 out of 2 836 585), we extrapolated that there were 19 162 AATD cases in Germany during the years studied. AATD patients had a higher prevalence of arterial hypertension, chronic kidney disease and diabetes relative to non-AATD asthma or emphysema patients. When compared to non-AATD COPD patients, AATD patients had significantly more consultations and more frequent and longer hospitalisations. Our data strengthen the assumption that AATD is associated with a variety of other diseases. Healthcare utilisation appears to be higher among AATD patients as compared to patients with non-AATD-related obstructive lung diseases. AATD patients have higher healthcare utilisation relative to non-AATD COPD, emphysema and asthma patients http://ow.ly/pHHh303m025
Competing for Relationships: Markets and Informal Institutions in Sierra Leone

A body of literature suggests that relationships aect contractual and market outcomes, but how does market structure aect the economics of relationships? This paper provides microeconometric evidence that upstream market structure aects the value of downstream relationships between retailers and buyers. In our setting, a monopoly ice manufacturer sells through independent retailers to shermen buyers in Sierra Leone. We demonstrate that a shock that increases upstream competition among manufacturers improves the contractual terms oered by retailers to buyers. Under the monopolistic manufacturer, we document that late deliveries are common due to outside demand shocks. To help mitigate this uncertainty, retailers prioritize loyal customers when faced with shortages, and buyers respond by rarely switching retailers. When manufacturers compete, prices fall, quantities increase and services improve with fewer late deliveries. Entry upstream also disrupts collusion among retailers by increasing the value of competing for buyer relationships. Competing retailers expand trade credit provision as a new basis for loyalty, and stable buyer relationships reemerge after a period of intense switching. The ndings suggest that market structure shapes informal contractual institutions, and that competition can increase the value of relationships.
Introducing ICD-resistant mortality as an end point to evaluate the clinical efficacy of an implantable cardioverter-defibrillator in ischaemic cardiomyopathy

Abstract Objective: A new end point called ICD-resistant mortality was evaluated to assess the clinical efficacy of ICD implantations. Methods and results: In 302 ICD patients with ischaemic cardiomyopathy, we investigated which clinical parameters predicted useful ICD implantations using cumulative incidence competing risk analysis. Implantation was deemed clinically useful when the ICD provided appropriate therapy and the patient survived implantation by 1 year and the first shock by 30 days. ICD-resistant mortality (ICDRM) was defined as death within 30 days after the first shock, within 1 year of implantation or without previous appropriate ICD therapy. After 5 years, ICDRM occurred in 23% of implantations, while 36% were clinically useful. After multivariable analysis, ICDRM was associated with LVEF <35% (HR: 2.63; p = .005), beta-blocker dose <50% (HR: 2.0; p = .01) and anterior or diffuse infarct location (HR: 3.61; p = .001 and HR: 2.89; p = .02). Useful ICD implantations were associated with beta-blocker dose <50% (HR: 1.64; p = .02) and non-anterior infarct location (HR: 3.22 vs anterior and 1.59 vs diffuse; combined p<.001). Conclusions: Five years after implantation, an ICD could be classified as useful in 1 out of 3, while ICDRM occurred in one out of four patients. At 10 years, up to 80% of implantations could be categorized. Lower LVEF was related with significantly higher incidence of ICDRM. Anterior infarcts were associated with more ICDRM and less useful implantations than non-anterior infarcts. Future risk stratification for ICD should focus more on the discrimination between arrhythmic risk, probably preventable by ICDs and ICD-resistant mortality risk.
CoRAL: Confined Recovery in Distributed Asynchronous Graph Processing

Existing distributed asynchronous graph processing systems employ checkpointing to capture globally consistent snapshots and rollback all machines to most recent checkpoint to recover from machine failures. In this paper we argue that recovery in distributed asynchronous graph processing does not require the entire execution state to be rolled back to a globally consistent state due to the relaxed asynchronous execution semantics. We define the properties required in the recovered state for it to be usable for correct asynchronous processing and develop CoRAL, a lightweight checkpointing and recovery algorithm. First, this algorithm carries out confined recovery that only rolls back graph execution states of the failed machines to affect recovery. Second, it relies upon lightweight checkpoints that capture locally consistent snapshots with a reduced peak network bandwidth requirement. Our experiments using real-world graphs show that our technique recovers from failures and finishes processing 1.5x to 3.2x faster compared to the traditional asynchronous checkpointing and recovery mechanism when failures impact 1 to 6 machines of a 16 machine cluster. Moreover, capturing locally consistent snapshots significantly reduces intermittent high peak bandwidth usage required to save the snapshots -- the average reduction in 99th percentile bandwidth ranges from 22% to 51% while 1 to 6 snapshot replicas are being maintained.
Detection of Exocometary CO within the 440 Myr Old Fomalhaut Belt: A Similar CO+CO2 Ice Abundance in Exocomets and Solar System Comets

Recent Atacama Large Millimeter/submillimeter Array observations present mounting evidence for the presence of exocometary gas released within Kuiper Belt analogs around nearby main-sequence stars. This represents a unique opportunity to study their ice reservoir at the younger ages when volatile delivery to planets is most likely to occur. We here present the detection of CO J = 2-1 emission colocated with dust emission from the cometary belt in the 440 Myr old Fomalhaut system. Through spectrospatial filtering, we achieve a 5. 4σ detection and determine that the ring’s sky-projected rotation axis matches that of the star. The CO mass derived ( ) is the lowest of any circumstellar disk detected to date and must be of exocometary origin. Using a steady-state model, we estimate the CO+CO2 mass fraction of exocomets around Fomalhaut to be between 4.6% and 76%, consistent with solar system comets and the two other belts known to host exocometary gas. This is the first indication of a similarity in cometary compositions across planetary systems that may be linked to their formation scenario and is consistent with direct interstellar medium inheritance. In addition, we find tentative evidence that % of the detected flux originates from a region near the eccentric belt’s pericenter. If confirmed, the latter may be explained through a recent impact event or CO pericenter glow due to exocometary release within a steady-state collisional cascade. In the latter scenario, we show how the azimuthal dependence of the CO release rate leads to asymmetries in gas observations of eccentric exocometary belts.
A Nonparametric Statistical Approach to Clustering via Mode Identification

A new clustering approach based on mode identification is developed by applying new optimization techniques to a nonparametric density estimator. A cluster is formed by those sample points that ascend to the same local maximum (mode) of the density function. The path from a point to its associated mode is efficiently solved by an EM-style algorithm, namely, the Modal EM (MEM). This method is then extended for hierarchical clustering by recursively locating modes of kernel density estimators with increasing bandwidths. Without model fitting, the mode-based clustering yields a density description for every cluster, a major advantage of mixture-model-based clustering. Moreover, it ensures that every cluster corresponds to a bump of the density. The issue of diagnosing clustering results is also investigated. Specifically, a pairwise separability measure for clusters is defined using the ridgeline between the density bumps of two clusters. The ridgeline is solved for by the Ridgeline EM (REM) algorithm, an extension of MEM. Based upon this new measure, a cluster merging procedure is created to enforce strong separation. Experiments on simulated and real data demonstrate that the mode-based clustering approach tends to combine the strengths of linkage and mixture-model-based clustering. In addition, the approach is robust in high dimensions and when clusters deviate substantially from Gaussian distributions. Both of these cases pose difficulty for parametric mixture modeling. A C package on the new algorithms is developed for public access at http://www.stat.psu.edu/∼jiali/hmac.
Influence of Biofield Treatment on Physical, Structural and Spectral Properties of Boron Nitride

Boron nitride (BN) is known for high hardness, thermal stability, thermal conductivity, and catalytic action. The aim of this study was to investigate the effect of biofield treatment on physical, structural and spectral properties of BN powder. The control and treated sample of BN powder were characterized by X-ray diffraction (XRD), surface area analysis and Fourier transform infrared spectroscopy (FT-IR). XRD results indicated that biofield treatment had substantially changed the crystallinity of BN powder as compared to control. Apart from the crystallinity, significant changes were also observed in lattice parameter, density and molecular weight of the treated BN powder as compared to control sample. The XRD data confirmed 33.30% increase crystallite size in treated BN powder as compared to control. The surface area data showed 10.33% increment in surface area of treated BN as compared to control. Furthermore, FT-IR spectra revealed that some part of BN may be transformed from hexagonal BN (h-BN) to rhombohedral boron nitride (r-BN), which was corroborated by emergence of new prominent peaks at 1388 cm-1 in treated BN as compared to control sample. These findings suggest that biofield treatment has substantially altered the structural properties and surface area of treated BN powder.
Cultural adaptation, reliability and validity of Japanese Orthopaedic Association Back Pain Evaluation Questionnaire to Brazilian Portuguese

ABSTRACT Objective To describe the translation and cultural adaptation of the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire into Brazilian Portuguese, and verifies the reliability and validity of this new version. Methods A cross-cultural adaptation of the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire was performed using the following steps: translation, back-translation, committee review, and pre-testing phase (50 subjects). The psychometric properties were evaluated by application of the questionnaire to 102 patients. Reliability was assessed by homogeneity and stability of measures. The criterion-related validity was tested by comparing scores of Japanese Orthopaedic Association Back Pain Evaluation Questionnaire to Oswestry and Medical Outcomes Study 36 - Item Short questionnaires. Results Excellent internal consistency was found in both test (Cronbach’s α of 0.90) and re-test (Cronbach’s α of 0.91). The Japanese Orthopaedic Association Back Pain Evaluation Questionnaire showed good reliability and the correlations ranged from reasonable (0.64) to very good (r=0.91). Conclusion The Brazilian Portuguese version of Medical Outcomes Study 36 - Item Short was easy to apply and understand. The questionnaire had a great impact on assessment and multidimensional care of patients with low back pain.
A drift-diffusion checkpoint model predicts a highly variable and growth-factor-sensitive portion of the cell cycle G1 phase

Even among isogenic cells, the time to progress through the cell cycle, or the intermitotic time (IMT), is highly variable. This variability has been a topic of research for several decades and numerous mathematical models have been proposed to explain it. Previously, we developed a top-down, stochastic drift-diffusion+threshold (DDT) model of a cell cycle checkpoint and showed that it can accurately describe experimentally-derived IMT distributions [Leander R, Allen EJ, Garbett SP, Tyson DR, Quaranta V. Derivation and experimental comparison of cell-division probability densities. J. Theor. Biol. 2014;358:129–135]. Here, we use the DDT modeling approach for both descriptive and predictive data analysis. We develop a custom numerical method for the reliable maximum likelihood estimation of model parameters in the absence of a priori knowledge about the number of detectable checkpoints. We employ this method to fit different variants of the DDT model (with one, two, and three checkpoints) to IMT data from multiple cell lines under different growth conditions and drug treatments. We find that a two-checkpoint model best describes the data, consistent with the notion that the cell cycle can be broadly separated into two steps: the commitment to divide and the process of cell division. The model predicts one part of the cell cycle to be highly variable and growth factor sensitive while the other is less variable and relatively refractory to growth factor signaling. Using experimental data that separates IMT into G1 vs. S, G2, and M phases, we show that the model-predicted growth-factor-sensitive part of the cell cycle corresponds to a portion of G1, consistent with previous studies suggesting that the commitment step is the primary source of IMT variability. These results demonstrate that a simple stochastic model, with just a handful of parameters, can provide fundamental insights into the biological underpinnings of cell cycle progression.
Beta blockers in patients with end‐stage renal disease—Evidence‐based recommendations

For patients who require hemodialysis, beta blockers offer a simultaneous opportunity and challenge in the treatment of cardiovascular disease. Beta blockers are well supported by data from nondialysis populations and directly mitigate the sympathetic overactivity that links chronic kidney disease with cardiovascular sequelae. However, the evidence supporting their use in patients receiving hemodialysis is sparse and the heterogeneity of the beta blocker class makes it difficult to prescribe these medications with confidence. Despite these limitations, both trial and observational data exist that can help guide the use of these medications. In this review, we outline the reasons to consider beta blockers for patients receiving hemodialysis, discuss the barriers to their use, and provide specific evidence‐based recommendations for beta blocker use in patients with heart failure, hypertension, ischemic heart disease and arrhythmia.
Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis

Objective 1) To develop a machine learning approach for detecting stroke cases and subtypes from hospitalization data, 2) to assess algorithm performance and predictors on real-world data collected by a large-scale epidemiology study in the US; and 3) to identify directions for future development of high-precision stroke phenotypic signatures. Materials and methods We utilized 8,131 hospitalization events (ICD-9 codes 430–438) collected from the Greater Cincinnati/Northern Kentucky Stroke Study in 2005 and 2010. Detailed information from patients’ medical records was abstracted for each event by trained research nurses. By analyzing the broad list of demographic and clinical variables, the machine learning algorithms predicted whether an event was a stroke case and, if so, the stroke subtype. The performance was validated on gold-standard labels adjudicated by stroke physicians, and results were compared with stroke classifications based on ICD-9 discharge codes, as well as labels determined by study nurses. Results The best performing machine learning algorithm achieved a performance of 88.57%/93.81%/92.80%/93.30%/89.84%/98.01% (accuracy/precision/recall/F-measure/area under ROC curve/area under precision-recall curve) on stroke case detection. For detecting stroke subtypes, the algorithm yielded an overall accuracy of 87.39% and greater than 85% precision on individual subtypes. The machine learning algorithms significantly outperformed the ICD-9 method on all measures (P value<0.001). Their performance was comparable to that of study nurses, with better tradeoff between precision and recall. The feature selection uncovered a subset of predictive variables that could facilitate future development of effective stroke phenotyping algorithms. Discussion and conclusions By analyzing a broad array of patient data, the machine learning technologies held promise for improving detection of stroke diagnosis, thus unlocking high statistical power for subsequent genetic and genomic studies.