Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Multivitamin and Multimineral Supplementation on Blood Pressure: A Meta-Analysis of 12 Randomized Controlled Trials

Previous studies have not drawn a consistent conclusion about effect of multivitamin and multimineral supplementation (MVMS) on blood pressure. A comprehensive search of PubMed, Embase and Cochrane Library (up to May 2018) and references of relevant articles was undertaken. The present meta-analysis included 12 randomized controlled trials (RCTs), of which eight RCTs in 2011 subjects evaluated the effect of MVMS on blood pressure and four RCTs in 21,196 subjects evaluated the effect of MVMS on the risk of hypertension. MVMS had a lowering effect on systolic blood pressure (SBP) and diastolic blood pressure (DBP): the weighted mean difference (WMD) was −1.31 mmHg (95% CI, −2.48 to −0.14 mmHg) and −0.71 mmHg (95% CI, −1.43 to 0.00 mmHg), respectively. Subgroup analysis indicated that the lowering effect of MVMS on blood pressure was only significant in 134 subjects with chronic disease but not in 1580 healthy subjects, and the WMD for systolic blood pressure (SBP) and DBP in subjects with chronic disease was −6.29 mmHg (95% CI, −11.09 to −1.50 mmHg) and −2.32 mmHg (95% CI, −4.50 to −0.13 mmHg), respectively. The effect size of MVMS on SBP in 58 hypertensive subjects (WMD, −7.98 mmHg; 95% CI, −14.95 to −1.02 mmHg) was more than six times of that in 1656 normotensive subjects (WMD, −1.25 mmHg; 95% CI, −2.48 to −0.02 mmHg). However, no significant effect on DBP was observed in both hypertensive and normotensive subgroups. There was no significant effect of MVMS on risk of hypertension in 22,852 subjects with a normal blood pressure at baseline. In conclusion, although MVMS had a significant lowering effect on blood pressure in normotensive subjects, the lowering effect was too small to effectively prevent future hypertension. MVMS may be an effective method for blood pressure control in subjects with chronic disease including hypertension, but the sample size of subjects with hypertension or other chronic disease was too small, and more well-designed RCTs are needed to confirm this result.
GLSE Precoders for Massive MIMO Systems: Analysis and Applications

This paper proposes the class of generalized least-square-error (GLSE) precoders for multiuser massive multiple-input multiple-output (MIMO) systems. For a generic transmit constellation, the GLSE precoders minimize the interference at user terminals assuring that some given constraints on the transmit signals are satisfied. The general form of these precoders enables us to impose multiple restrictions at the transmit signal, such as limited peak power and restricted number of active transmit antennas. The performance of these precoders is analyzed in the large-system limit. It is shown that the output symbols are identically distributed, and their statistics are described with an equivalent scalar GLSE precoder. To demonstrate the applications of the proposed framework, we employ the GLSE precoding to form transmit signals over a discrete alphabet and to select an effective subset of transmit antennas. Our investigations show that a computationally efficient GLSE precoder requires 41% less active transmit antennas than the conventional selection protocols in order to achieve a given level of input–output distortion.
Multi-Center Pre-clinical Consortia to Enhance Translation of Therapies and Biomarkers for Traumatic Brain Injury: Operation Brain Trauma Therapy and Beyond

Current approaches have failed to yield success in the translation of neuroprotective therapies from the pre-clinical to the clinical arena for traumatic brain injury (TBI). Numerous explanations have been put forth in both the pre-clinical and clinical arenas. Operation Brain Trauma Therapy (OBTT), a pre-clinical therapy and biomarker screening consortium has, to date, evaluated 10 therapies and assessed three serum biomarkers in nearly 1,500 animals across three rat models and a micro pig model of TBI. OBTT provides a unique platform to exploit heterogeneity of TBI and execute the research needed to identify effective injury specific therapies toward precision medicine. It also represents one of the first multi-center pre-clinical consortia for TBI, and through its work has yielded insight into the challenges and opportunities of this approach. In this review, important concepts related to consortium infrastructure, modeling, therapy selection, dosing and target engagement, outcomes, analytical approaches, reproducibility, and standardization will be discussed, with a focus on strategies to embellish and improve the chances for future success. We also address issues spanning the continuum of care. Linking the findings of optimized pre-clinical consortia to novel clinical trial designs has great potential to help address the barriers in translation and produce successes in both therapy and biomarker development across the field of TBI and beyond.
Scale development and psychometric properties of the Cardiac Self-Blame Attributions scale in patients with cardiovascular disease

Patients with cardiovascular disease may attribute their cardiovascular disease to their behaviors (behavioral self-blame) or to their dispositions (characterological self-blame). However, findings are mixed on the effects of behavioral self-blame and characterological self-blame on health outcomes, possibly because there are no validated, multiple-item measures. This study developed and tested an 11-item Cardiac Self-Blame Attributions scale via questionnaire data from 121 patients with cardiovascular disease. Results yielded a two-factor structure that explained 65 percent of the variance, with good reliability and discriminant validity. Findings suggest that the scale is reliable and valid and can be used to understand the cardiac attributions patients create.
Connecting Paper to Digitization: a Homework Data Processing System with Data Labeling and Visualization

How to monitor students' learning in daily learning behavior and guide teachers to teach more rationally are very important problems. While the digital teaching resources are popular, we can not ignore the information of traditional paper media. We design a homework processing system to solve the problems. First of all, we begin the operation from the acquisition side, by photo taking and image preprocessing, we make the homework picture with semantic meanings. Then we construct an answer reference system, which shows the answers of different kinds of incorrectbess as samples for teachers to refer to when correcting. Semantic pictures are dispatched to teachers handheld devices for intelligent and precise correct activities. We divide correct process into determining the degree of error and giving comments to evaluate and inspire students. Finally, we design a web page for homework data management and correction statistics to help teachers understand the homework completion status of students. The design of homework correction scheme is easy to deploy and can be used in most teaching environments.
The Human Cytomegalovirus, from Oncomodulation to Oncogenesis

Besides its well-described impact in immunosuppressed patients, the role of human cytomegalovirus (HCMV) in the pathogenesis of cancer has been more recently investigated. In cancer, HCMV could favor the progression and the spread of the tumor, a paradigm named oncomodulation. Although oncomodulation could account for part of the protumoral effect of HCMV, it might not explain the whole impact of HCMV infection on the tumor and the tumoral microenvironment. On the contrary cases have been reported where HCMV infection slows down the progression and the spread of the tumor. In addition, HCMV proteins have oncogenic properties per se, HCMV activates pro-oncogenic pathways in infected cells, and recently the direct transformation of cells following HCMV infection has been described, which gave rise to tumors when injected in mice. Thus, beyond the oncomodulation model, this review will assess the direct transforming role of HMCV-infected cells and the potential classification of HCMV as an oncovirus.
Pixel-level Semantics Guided Image Colorization

While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from the problems of context confusion and edge color bleeding. To address context confusion, we propose to incorporate the pixel-level object semantics to guide the image colorization. The rationale is that human beings perceive and distinguish colors based on the object's semantic categories. We propose a hierarchical neural network with two branches. One branch learns what the object is while the other branch learns the object's colors. The network jointly optimizes a semantic segmentation loss and a colorization loss. To attack edge color bleeding we generate more continuous color maps with sharp edges by adopting a joint bilateral upsamping layer at inference. Our network is trained on PASCAL VOC2012 and COCO-stuff with semantic segmentation labels and it produces more realistic and finer results compared to the colorization state-of-the-art.
Combining Frequency and Spatial Diversities to Calibrate Ranging over WLAN

Channel state information (CSI) is now popular in many emerging applications over WLAN, such as gesture recognition, indoor localization, line-of-sight (LOS) identification and ranging. Traditional power-based ranging approaches usually utilize received signal strength indicator (RSSI) because of its low cost and wide applicability. However, the coarse-grained RSSI is sensitive to the multipath effect and performs poorly in the non-line-of-sight (NLOS) situation. Compared with RSSI, CSI is a finer-grained value, and it depicts the amplitude and phase of each frequency-domain subcarrier in an OFDM channel. Besides, MIMO technology leveraged in 802.11 infrastructures extends the spatial diversity of WiFi signals and guarantees reliable communications. To obtain more stable and precise distance of direct path (DDP) between the transmitter and the receiver, we employ both the frequency diversity and spatial diversity of CSI and propose a new ranging system over WLAN. We consider multiple influence factors when implementing the system on commodity WiFi devices, and we evaluate its performance in diverse indoor environments. The results demonstrate that our system outperforms the baselines in all the pre-set scenarios, especially under the conditions of NLOS and dynamic multipaths.
Histopathological Evaluation of the Exposure by Cyanobacteria Cultive Containing [d-Leu1]Microcystin-LR on Lithobates catesbeianus Tadpoles

This study evaluated the effects of [d-Leu1]Microcystin-LR variant by the exposure of Lithobates catesbeianus tadpole to unialgal culture Microcystis aeruginosa NPLJ-4 strain. The Tadpole was placed in aquariums and exposed to Microcystis aeruginosa culture or disrupted cells. For 16 days, 5 individuals were removed every 2 days, and tissue samples of liver, skeletal muscle, and intestinal tract were collected for histopathology and bioaccumulation analyses. After exposure, those surviving tadpoles were placed in clean water for 15 days to evaluate their recovery. A control without algae and toxins was maintained in the same conditions and exhibited normal histology and no tissue damage. In exposed tadpoles, samples were characterized by serious damages that similarly affected the different organs, such as loss of adhesion between cells, nucleus fragmentation, necrosis, and hemorrhage. Samples showed signs of recovery but severe damages were still observed. Neither HPLC-PDA nor mass spectrometry analysis showed any evidence of free Microcystins bioaccumulation.
Sensor Agnostic Photoplethysmogram Signal Quality Assessment using Morphological Analysis

In this article, we propose a method to assess the clinical usability of fingertip Photoplethysmogram (PPG) waveform, collected from medical grade oximeter (train data) and smartphone (test data). We introduce a set of novel Signal Quality Indices (SQIs) to represent the noise characteristics of the PPG waveform. The SQIs are presented to a random forest classifier to discriminate between clean and noisy signals. The proposed method was evaluated on datasets annotated by four experts, resulting into a sensitivity and specificity of (92 ± 4.7 %, 95 ± 3 %) and (82.6 ± 4.6 %, 95.4 ± 3.1 %) on train and test data respectively. Further we applied the proposed method on PPG waveform of clinically proven control and disease population of Coronary Artery Disease (CAD), which resulted into (77 %, 77 %) of sensitivity and specificity respectively.
Anti-α-galactoside and Anti-β-glucoside Antibodies are Partially Occupied by Either of Two Albumin-bound O-glycoproteins and Circulate as Ligand-binding Triplets

ABSTRACT Two heavily O-glycosylated proteins and albumin co-purified with anti-α-galactoside (anti-Gal), the chief xenograft-rejecting antibody and anti-β-glucan (ABG) antibody isolated from human plasma by affinity chromatography on respective ligand-bearing matrices. Both antibodies and O-glycoproteins co-purified with plasma albumin eluted from albumin-specific matrix. Using components of affinity-purified antibody samples separated by electrophoresis binding of either albumin or antibody to the affinity matrix of the other or binding of O-glycoprotein to either matrix was ruled out. Enzyme-linked immunoassay and ligand-induced fluorescence enhancement of fluorolabeled antibody showed that O-glycoproteins occupied sugar-binding sites of anti-Gal and ABG. Neither antibody recognized albumin. O-Glycoprotein-albumin complexes free in plasma, or released from antibodies by specific sugars, were captured on microwell-coated O-glycan-specific lectin jacalin and detected using labeled anti-albumin. We conclude that circulating anti-Gal and ABG form protein triplets in which either O-glycoprotein bridges between antibody and albumin by binding simultaneously to both. Bound albumin restricted O-glycoprotein occupation on antibodies enabling triplets to bind other ligands using spared binding sites. Free anti-Gal and ABG were undetectable in plasma. Jacalin treatment, but not de-O-glycosylation of O-glycoproteins abolished their recognition by anti-Gal or ABG indicating that antibodies recognized serine- and threonine-rich peptide sequences that underlie the O-glycans and are reported surrogate ligands for anti-Gal. The albumin- and antibody-binding O-glycoproteins AOP1 and AOP2 were single polypeptide proteins of size 107 kDa and 98 kDa, containing 54% and 51% carbohydrate respectively and conformed to no known plasma protein in properties. Prospects of triplet-mediated modulations in autologous tissues expressing antibody ligands are discussed.
Spatial Deep Learning for Wireless Scheduling

The optimal scheduling of multiple interfering links in a densely deployed wireless network with full frequency reuse is a well-known challenging problem. The classical optimization approaches to this problem typically operate under the paradigm of first estimating all the interfering channel strengths then finding an optimum solution using the model. However, traditional scheduling methods are computationally and resource intensive, because channel estimation is expensive especially in dense networks, and further the optimization of link scheduling is typically a nonconvex problem. This paper takes a novel deep spatial learning approach to the scheduling problem. We show that it is possible to bypass the channel estimation stage altogether and to use a deep neural network to produce a near optimal schedule based solely on geographic locations of the transmitters and receivers in the network. This is accomplished by taking advantage of the recent advances in fractional programming that allows us to generate high- quality local optimum solutions to the scheduling problem for randomly deployed device-to-device networks as training data, and by using a novel neural network architecture that takes the geographic spatial convolutions of the interfering and interfered neighboring nodes as input over multiple feedback stages to learn the optimum solution.
Evaluation of IV to Enteral Benzodiazepine Conversion Calculations in a Pediatric Intensive Care Setting

Objectives: To evaluate if institutionally established calculations for transitioning continuous IV midazolam to enteral benzodiazepines maintain Withdrawal Assessment Tool—Version 1 scores equal to or less than preconversion values. Design: Retrospective cohort study evaluating the effectiveness and safety of benzodiazepine conversion calculations embedded within an institution-specific clinical pathway for sedation and weaning of mechanically ventilated pediatric patients. Setting: A 55-bed, mixed-medical, noncardiac surgical PICU in a tertiary care children’s hospital. Patients: All patients age 6 months to 18 years who received continuous midazolam for 5 days or longer while mechanically ventilated for 5–21 days and were then converted to either enteral diazepam or lorazepam following extubation (or return to baseline ventilator settings in tracheostomy-dependent patients) between January 1, 2015, and June 30, 2016. Interventions: Benzodiazepine conversion calculations were applied according to institutional clinical pathway guidance. Measurements and Main Results: Withdrawal Assessment Tool—Version 1 scores were compared pre and post benzodiazepine conversion. Patient demographics, benzodiazepine dose escalations, as needed benzodiazepine requirements, and severe adverse events within 48 hours of conversion were assessed. Seventy-one patient encounters were analyzed (median age, 2.5 yr; interquartile range, 1.2–5.3). The median Withdrawal Assessment Tool—Version 1 scores pre conversion and post conversion were not significantly different (1 [interquartile range, 0.75–2] and 1 [interquartile range, 0.25–2], respectively, p = 0.1). As needed benzodiazepine doses were administered in 38% of encounters post conversion, but escalation of a scheduled enteral benzodiazepine regimen was only required in 2.8% of encounters. Post conversion, one patient (1.4%) had increased seizure activity, and four patients (5.6%) required fluid boluses secondary to tachycardia or dehydration, but not hypotension. Conclusions: These findings suggest that standardized benzodiazepine conversions successfully achieved consistent Withdrawal Assessment Tool—Version 1 scores compared with preconversion values. Severe adverse events associated with oversedation and/or withdrawal were minimal and confounded by underlying disease states.
Path tracing in production

The last few years have seen a decisive move of the movie making industry towards rendering using physically based methods, mostly implemented in terms of path tracing. While path tracing reached most VFX houses and animation studios at a time when a physically based approach to rendering and especially material modelling was already firmly established, the new tools brought with them a whole new balance, and many new workflows have evolved to find a new equilibrium. Letting go of instincts based on hard-learned lessons from a previous time has been challenging for some, and many different takes on a practical deployment of the new technologies have emerged. While the language and toolkit available to the technical directors keep closing the gap between lighting in the real world and the light transport simulations ran in software, an understanding of the limitations of the simulation models and a good intuition of the tradeoffs and approximations at play are of fundamental importance to make efficient use of the available resources. In this course, the novel workflows emerged during the transitions at a number of large facilities are presented to a wide audience including technical directors, artists, and researchers.
Genome-Wide Identification and Analysis of Polygalacturonase Genes in Solanum lycopersicum

Polygalacturonase (PG), a large hydrolase family in plants, is involved in pectin disassembly of the cell wall in plants. The present study aims to characterize PG genes and investigate their expression patterns in Solanum lycopersicum. We identified 54 PG genes in the tomato genome and compared their amino acid sequences with their Arabidopsis counterpart. Subsequently, we renamed these PG genes according to their Arabidopsis homologs. Phylogenetic and evolutionary analysis revealed that these tomato PG genes could be classified into seven clades, and within each clade the exon/intron structures were conserved. Expression profiles analysis through quantitive real-time polymerase chain reaction (qRT-PCR) revealed that most SlPGs had specific or high expression patterns in at least one organ, and particularly five PG genes (SlPG14, SlPG15, SlPG49, SlPG70, and SlPG71) associated with fruit development. Promoter analysis showed that more than three cis-elements associated with plant hormone response, environmental stress response or specific organ/tissue development exhibited in each SlPG promoter regions. In conclusion, our results may provide new insights for the further study of PG gene function during plant development.
Assessing the Impact of Network Events with User Feedback

User feedback data, generated when users call customer care agents with problems, is a valuable source of data for understanding network problems from users' perspectives. However, this data is extremely noisy. In this paper, we design a framework, LOTUS, to assess the user impact of network events from the user feedback, through a novel algorithmic composition of co-training and spatial scan statistics. Through experimental analysis on synthetic and real data, we show the accuracy and practical nature of LOTUS.
Polyraptor: Embracing Path and Data Redundancy in Data Centres for Efficient Data Transport

In this paper, we introduce Polyraptor, a novel data transport protocol that uses RaptorQ (RQ) codes and is tailored for one-to-many and many-to-one data transfer patterns, which are extremely common in modern data centres. Polyraptor builds on previous work on fountain coding-based transport and provides excellent performance, by exploiting native support for multicasting in data centres and data resilience provided by data replication.
Integrated Genomic Comparison of Mouse Models Reveals Their Clinical Resemblance to Human Liver Cancer

Hepatocellular carcinoma (HCC) is a heterogeneous disease. Mouse models are commonly used as preclinical models to study hepatocarcinogenesis, but how well these models recapitulate molecular subtypes of human HCC is unclear. Here, integration of genomic signatures from molecularly and clinically defined human HCC (n = 11) and mouse models of HCC (n = 9) identified the mouse models that best resembled subtypes of human HCC and determined the clinical relevance of each model. Mst1/2 knockout (KO), Sav1 KO, and SV40 T antigen mouse models effectively recapitulated subtypes of human HCC with a poor prognosis, whereas the Myc transgenic model best resembled human HCCs with a more favorable prognosis. The Myc model was also associated with activation of β-catenin. E2f1, E2f1/Myc, E2f1/Tgfa, and diethylnitrosamine (DEN)-induced models were heterogeneous and were unequally split into poor and favorable prognoses. Mst1/2 KO and Sav1 KO models best resemble human HCC with hepatic stem cell characteristics. Applying a genomic predictor for immunotherapy, the six-gene IFNγ score, the Mst1/2 KO, Sav1 KO, SV40, and DEN models were predicted to be the least responsive to immunotherapy. Further analysis showed that elevated expression of immune-inhibitory genes (Cd276 and Nectin2/Pvrl2) in Mst1/2 KO, Sav1 KO, and SV40 models and decreased expression of immune stimulatory gene (Cd86) in the DEN model might be accountable for the lack of predictive response to immunotherapy. Implication: The current genomic approach identified the most relevant mouse models to human liver cancer and suggests immunotherapeutic potential for the treatment of specific subtypes. Mol Cancer Res; 16(11); 1713–23. ©2018 AACR.