Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Determination of Valproic Acid and Its Major Metabolites by UHPLC-MS/MS in Chinese Patients: Application to Therapeutic Drug Monitoring.

A specific and sensitive Ultra-high Performance Liquid Chromatography-tandem Mass spectrometry (UHPLC-MS/MS) method was developed for the simultaneous determination of the concentrations of valproic acid (VPA) and its clinically relevant metabolites (4-ene-VPA, 2,4-diene-VPA and 2-ene-VPA) in human serum. After solid-phase extraction, VPA, its metabolites and the internal standard were subjected to chromatographic separation by gradient elution of acetonitrile and 10 mM ammonium acetate as mobile phase at a flow rate of 0.6 mL/min on an EC-C18 column. The method was validated over the concentration ranges of 1-200 μg/mL for VPA, 0.5-10 μg/mL for 2-ene-VPA, 10-500 ng/mL for 4-ene-VPA and 25-500 ng/mL for 2,4-diene-VPA. The inter-day and intra-day accuracy and precision were within the acceptable limits of <15 %. The recoveries and matrix effects met the requirement for the analysis of biological samples. No obvious degradation was observed under various storage conditions including room temperature for 12 h, three freeze-thaw cycles and -80°C for 1 month. The assay method was successfully applied to monitor the concentration of VPA and its three metabolites in epileptic patients. The UHPLC-MS/MS method demonstrated a good analytical performance essential for therapeutic drug monitoring, which would potentially lead to clinically relevant improvements in VPA dosage and patient management.
Sharpness-preservation video upscaling

Video upscaling is a common task performing the adaption of the actual video resolution to the screen display area. The extensively used interpolation methods are easily implemented but usually result in some artifacts, such as blurring and aliasing effects. This paper proposes to combine sharpening with interpolation to reduce the blurring effects of enlarged videos, in which videos are firstly preprocessed with sharpening to enhance profiles so as to compensate the loss of high-frequency components during interpolation. To tune sharpness degree in sharpening procedure, the blurriness metric considering image gradients as well as pixel intensities is particularly suggested and the generation rule for sharpening strength is accordingly regulated. In practice, three representative interpolation methods, namely, Bilinear, Bicubic and Lanczos, are experimentally incorporated into our framework to carry out validation. Results with respect to both original and decoded videos show the significant objective and subjective improvements against the respective regular interpolation.
Effects of prophylactic ankle and knee braces on leg stiffness during hopping

During human movement, the leg can be represented as a mechanical spring, with its stiffness potentially contributing to sports performance and injury prevention. Although many individuals perform athletic activities with joint stabilizers, little is known about the effects of prophylactic lower extremity braces on leg stiffness. The objective of this study was to investigate the effect of ankle and/or knee braces on leg stiffness measured during one-legged hopping at a range of frequencies. Thirteen male participants performed one-legged hopping with their dominant leg at frequencies of 2.2, 2.6, and 3.0 Hz. All participants were randomly tested under the following four brace conditions: 1) no brace (control), 2) prophylactic ankle brace, 3) prophylactic knee brace, and 4) prophylactic ankle and knee braces. Based on a spring–mass model, leg stiffness was calculated using data from an accelerometer. It was found that leg stiffness increased with increasing hopping frequency for each brace condition. However, there were no significant differences in leg stiffness among the four brace conditions at the three hopping frequencies. Since some level of leg stiffness is needed for optimal athletic performance and training, these results suggest that ankle and knee braces do not significantly interfere with dynamic hopping activities.
Distributional Semantics for Medical Information Extraction

This report describes two methods implemented for the CLEF eHealth 2016 Task 1 challenge. They consist of: a) a feed forward neural network; and b) a random forest for classiﬁcation and a feed forward neural net, applied to automatically ﬁll in medical handover forms using synthetic medical records as inputs. Both approaches are interesting because they rely on word embeddings, are domain independent, and are feature engineering free. We discuss the complexity of the task, and the impact in our models, having too many output classes and a limited amount of training data. The performance of the methods are based on traditional classiﬁcation metrics (e.g. precision, recall, and f1-score) on the macro and micro averaged level, and focus on two sets of labels: a) the ”NA” tag, which recognize data that is irrelevant and therefore should be excluded from the form; and b) all other tags, which account for the diﬀerent ﬁelds of the form. The neural network achieved an F1-score of 0.8 (for the ”NA” tag) and a macro-averaged F1-score of 0.308 and a micro-averaged result of 0.514 (for the remaining categories), while the ensemble pipeline got 0.813 (for the ”NA” tag) and 0.345 and 0.503 for the macro- and micro-averaged rates on the rest of the labels.
Maternal pre‐pregnancy obesity and child neurodevelopmental outcomes: a meta‐analysis

This review examined evidence of the association between maternal pre‐pregnancy overweight/obesity status and child neurodevelopmental outcomes. PubMed and PsycINFO databases were systematically searched for empirical studies published before April 2017 using keywords related to prenatal obesity and children's neurodevelopment. Of 1483 identified papers, 41 were included in the systematic review, and 32 articles representing 36 cohorts were included in the meta‐analysis. Findings indicated that compared with children of normal weight mothers, children whose mothers were overweight or obese prior to pregnancy were at increased risk for compromised neurodevelopmental outcomes (overweight: OR = 1.17, 95% CI [1.11, 1.24], I2 = 65.51; obese: OR = 1.51; 95% CI [1.35, 1.69], I2 = 79.63). Pre‐pregnancy obesity increased the risk of attention deficit–hyperactivity disorder (OR = 1.62; 95% CI [1.23, 2.14], I2 = 70.15), autism spectrum disorder (OR = 1.36; 95% CI [1.08, 1.70], I2 = 60.52), developmental delay (OR = 1.58; 95% CI [1.39, 1.79], I2 = 75.77) and emotional/behavioural problems (OR = 1.42; 95% CI [1.26, 1.59], I2 = 87.74). Given the current obesity prevalence among young adults and women of childbearing age, this association between maternal obesity during pregnancy and atypical child neurodevelopment represents a potentially high public health burden.
Matrix metalloproteinase activity and prostaglandin E2 are elevated in the synovial fluid of meniscus tear patients

ABSTRACT Purpose: Meniscus tears are a common knee injury and are associated with the development of post-traumatic osteoarthritis (OA). The purpose of this study is to evaluate potential OA mediators in the synovial fluid and serum of meniscus tear subjects compared to those in the synovial fluid of radiographic non-OA control knees. Materials and methods: Sixteen subjects with an isolated unilateral meniscus injury and six subjects who served as reference controls (knee Kellgren-Lawrence grade 0-1) were recruited. Twenty-one biomarkers were measured in serum from meniscus tear subjects and in synovial fluid from both groups. Meniscus tear subjects were further stratified by tear type to assess differences in biomarker levels. Results: Synovial fluid total matrix metalloproteinase (MMP) activity and prostaglandin E2 (PGE2) were increased 25-fold and 290-fold, respectively, in meniscus tear subjects as compared to reference controls (p < 0.05). Synovial fluid MMP activity and PGE2 concentrations were positively correlated in meniscus tear subjects (R = 0.83, p < 0.0001). In meniscus tear subjects, synovial fluid levels of MMP activity, MMP-2, MMP-3, sGAG, COMP, IL-6, and PGE2 were higher than serum levels (p < 0.05). Subjects with complex meniscus tears had higher synovial fluid MMP-10 (p < 0.05) and reduced serum TNFα and IL-8 (p < 0.05) compared to other tear types. Conclusions: Given the degradative and pro-inflammatory roles of MMP activity and PGE2, these molecules may alter the biochemical environment of the joint. Our findings suggest that modulation of PGE2 signaling, MMP activity, or both following a meniscus injury may be targets to promote meniscus repair and prevent OA development.
MRMR based feature selection for the classification of stress using EEG

Mental stress is a social concern causing functional disability during work routines. The evaluation of stress using electroencephalogram signals is a topic of contemporary research. EEG provides several different features and the selection of appropriate features becomes a question. This study presents the utilization of feature selection using maximum relevance and minimum redundancy (MRMR) based on mutual information (MI) on the obtained features from electroencephalogram (EEG) signals during stress and control tasks. We moved forward in recording EEG during stress which was induced by taking up an eminent experimental model based on the Montreal Imaging Stress Task (MIST). The induced stress was endorsed by the performance during the task and the response of the subjects. The methodology consist of EEG feature extraction such as the absolute power and relative power, feature selection (MI) and classification using the support vector machine. The results of the proposed methodology showed a maximum accuracy of 93.75% and above 85% accuracy throughout the experiment. The performance is better than the existing studies in the literature. In conclusion, the MRMR criterion of feature selection using MI gives reliable and consistent results for the classification of stress.
An ultralow-power high-gain biopotential amplifier for electromyogram signal recording

This paper introduces a design for an ultralow-power electromyogram (EMG) signal amplifier with low noise operation. The design consists of two stages, the first stage is highly efficient but supply-sensitive single ended amplifier and the second stage is differential, to improve the supply rejection ratio and common mode rejection ratio. Each stage is configured with cascode MOSFET transistors to increase the gain value. The proposed design is simulated by 130 nm CMOS, and its results are reported. The design achieves 60.62 dB mid-band gain with bandwidth of 1.72kHz. Using a supply voltage of 1.1 V, the amplifier consumes 1.03 μA of current. Input referred noise is 3.006 μVrms. The common mode and power supply rejection ratios are above 49.05 dB and 55.72 dB respectively.
Digital Circulating Tumor Cell Analyses for Prostate Cancer Precision Oncology.

In this issue of Cancer Discovery, Miyamoto and colleagues adapted their microfluidic CTC-iChip isolation platform with a digital RNA-PCR readout for eight prostate-specific transcripts and two assays for the androgen receptor mRNA splice variant ARV7 and the TMPRSS2-ERG translocation transcript. In patients with metastatic castrate-resistant prostate cancer at initiating abiraterone therapy in a first-line setting, the resulting RNA-based digital circulating tumor cell signatures identified patients with a shorter overall survival, and in patients with clinically localized disease, the signatures identified those with seminal vesicle invasion and pelvic lymph node involvement. Cancer Discov; 8(3); 269-71. ©2018 AACR.See related article by Miyamoto et al., p. 288.
An Evolutionary-Based Method for Reconstructing Conversation Threads in Email Corpora

Email is a type of Web data which is produced in enormous quantities. It is beneficial to detect conversation threads contained in the email corpora for various applications, including discussion search, expert finding and even email clustering and classification. Conversation thread in email corpora can be defined as a cluster of exchanged emails among the same group of people by reply or forwarding on the same topic. According to this definition, we can define parent-child relation between emails, so email conversation threads seem to demonstrate tree structure. This paper presents a new approach based on genetic programming for reconstruction of conversation threads in emails data. This approach considers finding email conversation threads as an optimization problem, and exploits genetic programming to search intelligently in the space of possible solutions. Rather than several studies that have been conducted on this problem, this work concentrates on detecting accurate structure of conversation threads in high recall. This paper provides a comprehensive evaluation on the BC3 data set. Preliminary results suggest that our method provides acceptable precision and higher recall than existing methods.
Partial Diversity Generates Effector Immunity Specificity of the Bac41-Like Bacteriocins of Enterococcus faecalis Clinical Strains

ABSTRACT Bacteriocin 41 (Bac41) is the plasmid-encoded bacteriocin produced by the opportunistic pathogen Enterococcus faecalis. Its genetic determinant consists of bacL1 (effector), bacL2 (regulator), bacA (effector), and bacI (immunity). The secreted effectors BacL1 and BacA coordinate to induce the lytic cell death of E. faecalis. Meanwhile, the immunity factor BacI provides self-resistance to the Bac41 producer, E. faecalis, against the action of BacL1 and BacA. In this study, we demonstrated that more than half of the 327 clinical strains of E. faecalis screened had functional Bac41 genes. Analysis of the genetic structure of the Bac41 genes in the DNA sequences of the E. faecalis strains revealed that the Bac41-like genes consist of a relatively conserved region and a variable region located downstream from bacA. Based on similarities in the variable region, the Bac41-like genes could be classified into type I, type IIa, and type IIb. Interestingly, the distinct Bac41 types had specific immunity factors for self-resistance, BacI1 or BacI2, and did not show cross-immunity to the other type of effector. We also demonstrated experimentally that the specificity of the immunity was determined by the combination of the C-terminal region of BacA and the presence of the unique BacI1 or BacI2 factor. These observations suggested that Bac41-like bacteriocin genes are extensively disseminated among E. faecalis strains in the clinical environment and can be grouped into at least three types. It was also indicated that the partial diversity results in specificity of self-resistance which may offer these strains a competitive advantage. IMPORTANCE Bacteriocins are antibacterial effectors produced by bacteria. In general, a bacteriocin-coding gene is accompanied by a cognate immunity gene that confers self-resistance on the bacteriocin-producing bacterium itself. We demonstrated that one of the bacteriocins, Bac41, is disseminated among E. faecalis clinical strains and the Bac41 subtypes with partial diversity. The Bac41-like bacteriocins were found to be classified into type I, type IIa, and type IIb by variation of the cognate immunity factors. The antibacterial activity of the respective effectors was specifically inhibited by the immunity factor from the same type of Bac41 but not the other types. This specificity of effector-immunity pairs suggests that bacteriocin genes might have evolved to change the immunity specificity to acquire an advantage in interbacterial competition.
Spiking Neural Classifier with Lumped Dendritic Nonlinearity and Binary Synapses: A Current Mode VLSI Implementation and Analysis

We present a neuromorphic current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown previously in software simulations that such a system with binary synapses can be trained with structural plasticity algorithms to achieve comparable classification accuracy with fewer synaptic resources than conventional algorithms. We show that even in real analog systems with manufacturing imperfections (CV of 23.5% and 14.4% for dendritic branch gains and leaks respectively), this network is able to produce comparable results with fewer synaptic resources. The chip fabricated in m complementary metal oxide semiconductor has eight dendrites per cell and uses two opposing cells per class to cancel common-mode inputs. The chip can operate down to a V and dissipates 19 nW of static power per neuronal cell and 125 pJ/spike. For two-class classification problems of high-dimensional rate encoded binary patterns, the hardware achieves comparable performance as software implementation of the same with only about a 0.5% reduction in accuracy. On two UCI data sets, the IC integrated circuit has classification accuracy comparable to standard machine learners like support vector machines and extreme learning machines while using two to five times binary synapses. We also show that the system can operate on mean rate encoded spike patterns, as well as short bursts of spikes. To the best of our knowledge, this is the first attempt in hardware to perform classification exploiting dendritic properties and binary synapses.
Serum Stability and Physicochemical Characterization of a Novel Amphipathic Peptide C6M1 for SiRNA Delivery

The efficient delivery of nucleic acids as therapeutic agents is a major challenge in gene therapy. Peptides have recently emerged as a novel carrier for delivery of drugs and genes. C6M1 is a designed amphipathic peptide with the ability to form stable complexes with short interfering RNA (siRNA). The peptide showed a combination of random coil and helical structure in water but mainly adopted a helical conformation in the presence of anions or siRNA. Revealed by dynamic light scattering (DLS) and microscopy techniques, the interaction of C6M1 and siRNA in water and HEPES led to complexes of ∼70 and ∼155 nm in size, respectively, but showed aggregates as large as ∼500 nm in PBS. The time-dependent aggregation of the complex in PBS was studied by DLS and fluorescence spectroscopy. At molar ratio of 15∶1, C6M1 was able to completely encapsulate siRNA; however, higher molar ratios were required to obtain stable complexes. Naked siRNA was completely degraded in 4 h in the solution of 50% serum; however C6M1 protected siRNA against serum RNase over the period of 24 h. Western blotting experiment showed ∼72% decrease in GAPDH protein level of the cells treated with C6M1-siRNA complexes while no significant knockdown was observed for the cells treated with naked siRNA.
Anisotropic Discrete Dual-Tree Wavelet Transform for Improved Classification of Trabecular Bone

This paper deals with a new anisotropic discrete dual-tree wavelet transform (ADDTWT) to characterize the anisotropy of bone texture. More specifically, we propose to extend the conventional discrete dual-tree wavelet transform (DDTWT) by using the anisotropic basis functions associated with the hyperbolic wavelet transform instead of isotropic spectrum supports. A texture classification framework is adopted to assess the performance of the proposed transform. The generalized Gaussian distribution is used to model the distribution of the sub-band coefficients. The estimated vector of parameters for each image is then used as input for the support vector machine classifier. Experiments were conducted on synthesized anisotropic fractional Brownian motion fields and on a real database composed of osteoporotic patients and control cases. Results show that the ADDTWT outperforms most of the competing anisotropic transforms with an area under curve rate of 93%.
Robust principal component analysis via joint ℓ2,1-norms minimization

Principal Component Analysis (PCA) is the most widely used unsupervised subspace learning method, and lots of its variants have been developed. With so many proposed PCA-like methods, it is still not clear that which features are better or worse for principal components, especially when the data suffers from outliers. To this end, we propose Robust Principal Component Analysis via joint ℓ2,1-norms minimization, which provides new insights into two crucial issues of PCA: feature selection and robustness to outliers. Unlike other PCA-like methods, the proposed method is able to select effective features for reconstruction by using the ℓ2,1-norm regularization term. More specific, we first use a ℓ2,1-norm based transformation matrix to select effective features that can effectively characterize key components (e.g., the eyes and the nose in a face image), and then use an orthogonal transformation matrix to recover the original data from the selected data representation. In this way, the key components can be well recovered by using the effective features selected by a learned transformation matrix. On the other hand, we also impose ℓ2,1-norm on a loss term to select clean samples to recover its same class samples but with outliers. A simple yet effective optimization algorithm is proposed to solve the resulting optimization problem. Experiments on six datasets demonstrate the effectiveness of the proposed method.
Intravitreal Anti-vascular Endothelial Growth Factor for Typical Exudative Age-related Macular Degeneration in Eyes with Good Baseline Visual Acuity

Purpose To investigate 12-month treatment outcomes of anti-vascular endothelial growth factor therapy in eyes with typical exudative age-related macular degeneration with good baseline visual acuity. Methods This retrospective observational case series included 18 eyes (18 patients) with typical exudative age-related macular degeneration with a baseline best-corrected visual acuity of 20 / 25 or better. Patients were treated with anti-vascular endothelial growth factor monotherapy during the 12-month follow-up period. Baseline visual acuity and central foveal thickness were compared to the values at 12 months. Results Patients received an average of 4.4 ± 1.3 intravitreal anti-vascular endothelial growth factor injections. The mean logarithm of minimum angle of resolution visual acuity was 0.08 ± 0.04, 0.08 ± 0.07, 0.12 ± 0.09, and 0.16 ± 0.11 at baseline, three months, six months, and 12 months, respectively. Visual acuity at 12 months was significantly worse than the baseline value at diagnosis (p = 0.017), and the mean central foveal thickness at the defined time points was 270.2 ± 55.6, 204.4 ± 25.4, 230.1 ± 56.3, and 216.8 ± 48.7 µm, respectively. The central foveal thickness at 12 months was significantly less than the baseline value at diagnosis (p = 0.042). Conclusions Deterioration in visual acuity was noted in eyes with typical exudative age-related macular degeneration with good baseline visual acuity, suggesting the need for close patient monitoring and prompt treatment even in patients with good baseline visual acuity.