Miracle-dz/UniFAR-FTUs huggingface dataset:
Pregestational overweight and obesity are associated with differences in gut microbiota composition and systemic inflammation in the third trimester

The obesity epidemic is a global challenge, and the velocity of propagation is high in the population at reproductive age. Overweight and obesity during pregnancy have been associated with high birth weight and an increased risk of childhood obesity, reinforcing the risk of other non-communicable diseases. Obesity involves chronic low-grade systemic inflammation. New biomarkers for early detection of obesity risk are urgently required. The aim of this study was to identify the connection between pregestational BMI (pre-BMI) status and inflammatory biomarkers during the third trimester of pregnancy and their association with intestinal microbiota composition. Fifty-four pregnant women were classified according to pre-pregnancy BMI as normoweight, overweight, or obese. Weight gain, inflammatory biomarkers (hs_CRP, haptoglobin, and suPAR), and microbiota composition were assessed during the third trimester. A significant lower weight gain for obese mothers and a positive correlation between pre-BMI and inflammatory biomarkers were detected (Spearman’s correlation). Haptoglobin levels were significantly higher in overweight and obese mothers. Higher Firmicutes levels and a higher ratio Firmicutes/Bacteroidetes were observed in the overweight and obese subjects. High hs_CRP and haptoglobin levels were also correlated with decreased microbiota diversity (Shannon index), whereas haptoglobin and hs_CRP values were correlated with several microbiota components, such as Ruminococcus gnavus and Faecalibacterium, and with specific phyla in the normoweight and overweight mothers; no significant associations with microbiota were found for suPAR. In conclusion, haptoglobin and hs_CRP reflected pregestational BMI status and related microbiota components, but haptoglobin was a better biomarker for microbiota associated with overweight. suPAR was associated with low grade inflammation dependent on pre-pregnancy BMI, but it was not related to deviated microbiota profiles.
Neural measures of the causal role of observers’ facial mimicry on visual working memory for facial expressions

Abstract Simulation models of facial expressions propose that sensorimotor regions may increase the clarity of facial expressions representations in extrastriate areas. We monitored the event-related potential marker of visual working memory (VWM) representations, namely the sustained posterior contralateral negativity (SPCN), also termed contralateral delay activity, while participants performed a change detection task including to-be-memorized faces with different intensities of anger. In one condition participants could freely use their facial mimicry during the encoding/VWM maintenance of the faces while in a different condition participants had their facial mimicry blocked by a gel. Notably, SPCN amplitude was reduced for faces in the blocked mimicry condition when compared to the free mimicry condition. This modulation interacted with the empathy levels of participants such that only participants with medium-high empathy scores showed such reduction of the SPCN amplitude when their mimicry was blocked. The SPCN amplitude was larger for full expressions when compared to neutral and subtle expressions, while subtle expressions elicited lower SPCN amplitudes than neutral faces. These findings provide evidence of a functional link between mimicry and VWM for faces and further shed light on how this memory system may receive feedbacks from sensorimotor regions during the processing of facial expressions.
Pattern of road traffic injuries in Yemen: a hospital-based study

Introduction Road traffic injuries (RTIs) are the eighth leading cause of death globally and the leading cause of death for young people. This study aimed to present time-limited trial surveillance in two referral hospitals to describe the pattern of RTIs in Sana'a, Yemen and determine road traffic crashes (RTCs) associated factors. Methods All RTIs presented to Emergency Departments (ED) of the two Sanaa city hospitals between August and October, 2015 were studied and described. Data were collected everyday by trained data collectors. A pretested questionnaire modified from WHO injury surveillance form was used for data collection. Results A total of 156 casualties from 128 RTCs had attended the two study hospitals during the study period. About 73% of victims were less than 30 years old. Only 13% of casualties were transported by ambulance. None of the victims wore the seat belt in case of 4-wheeled vehicles' users nor the helmet in case of 2-wheeled vehicles' users. Poor driving skills were involved in 133 (85%) casualties. Factors related to the vehicles contributed to 12% of RTCs. Of the 156 casualties, 17% had severe injuries and needed ICU admission. After 48 hours of the accident, 38% of patients ended with disability due to fractured limbs, 29% were not improving and their conditions were deteriorating, 18% had recovered and 5% died. Conclusion Several personal, behavioral, environmental and vehicles related factors had contributed to RTIs in Yemen. The burden of RTIs in Yemen in terms of disability and mortality is high.
Dual-Surface Modification of AlGaN/GaN HEMTs Using TMAH and Piranha Solutions for Enhancing Current and 1/f-Noise Characteristics

We demonstrated dual-surface modification of GaN/AlGaN/GaN high-electron mobility transistors using tetramethylammonium hydroxide (TMAH) and piranha solutions prior to gate metallization. The TMAH-treated device exhibits improved performances with lower I-V hysteresis, in off-state leakage current and gate leakage current. The device performances were further significantly improved with applies additional piranha solution treatment right after the TMAH treatment, especially in hysteresis and 1/f-noise characteristics. It is found that the Schottky barrier height is high and ideality factor is low measured from I-V characteristics for the TMAH and piranha solution treated device. Reasonable gate leakage mechanisms were also discussed using Poole–Frenkel and Schottky emissions. In addition, it is observed that the magnitude of interface state density for the TMAH treatment after the piranha solution treated device shows significantly low compared to other devices. These excellent device-performances are observed due to the reason of dual-surface treatment which effectively decreases the surface trap density with an appropriate etching and passivation of the device surface exposed prior to the gate metallization.
Bone Regeneration of Peri-Implant Defects Using a Collagen Membrane as a Carrier for Recombinant Human Bone Morphogenetic Protein-2

This study is designed to determine the effect of collagen membrane (CM) soaked with bone morphogenetic protein-2 (rhBMP-2) for the treatment of peri-implant dehiscence defects. Material and Methods. Three treatment groups were allocated at each defect in 5 dogs: (i) collagenated synthetic bone (OC) and CM soaked with rhBMP-2 (BMP group), (ii) OC and CM soaked with saline (nonBMP group), and (iii) no further treatment (control group). Titanium pins were used to stabilize the membranes in two dogs. Radiographic and histomorphometric analyses were performed 4 weeks later. Results. The median augmented volumes were 4.27 mm3, 6.24 mm3, and 2.75 mm3 in the BMP, nonBMP, and control groups, respectively; the corresponding median first bone-to-implant contact (fBIC) distances were 3.25 mm, 3.08 mm, and 2.56 mm (P > 0.05). The placement of pins (with the BMP and nonBMP groups pooled) significantly improved bone regeneration: the augmented volumes were 17.60 mm3 with pins and 3.68 mm3 without pins (P = 0.024), with corresponding fBIC distances of 2.25 mm and 3.31 mm, respectively (P < 0.001). Conclusions. The addition of rhBMP-2 to CM failed to improve bone regeneration of peri-implant dehiscence defects compared to using an unsoaked CM after 4 weeks. However, the stabilization of CMs using pins positively influenced the outcomes.
The effects of bisphosphonates on osteoporotic patients after lumbar fusion: a meta-analysis

Purpose We conducted a meta-analysis of controlled clinical trials to evaluate the efficacy of bisphosphonates in lumbar fusion. Introduction Bisphosphonates reduce bone resorption and remodeling by osteoclast activity inhibition, inactivation, and apoptosis. However, it remains controversial whether bisphosphonate therapy affects spinal fusion. Methods We searched MEDLINE, Cochrane CENTRAL, ScienceDirect, EMBASE, and Google Scholar to identify studies reporting the effects of bisphosphonates on osteoporotic patients after lumbar fusion. Secondary sources were identified from the references of the included literature. Pooled data were analyzed using RevMan 5.1. Results Seven studies met the inclusion criteria. There were significant differences in solid intervertebral fusion (RD=0.07, 95% CI: −0.00 to 0.15, P=0.05), subsequent VCFs (RD=−0.21, 95% CI: −0.30 to −0.12, P<0.00001), pedicle screw loosening (RD=−0.17, 95% CI: −0.28 to −0.05, P=0.006), and cage subsidence (RD=−0.25, 95% CI: −0.42 to −0.07, P=0.005) between two groups. No significant differences between two groups were found regarding implant fixation failure (RD=−0.06, 95% CI: −0.22 to 0.10, P=0.48). Conclusion This meta-analysis showed that bisphosphonates may increase solid intervertebral fusion and decrease subsequent VCFs, pedicle screw loosening, and cage subsidence.
Compiling Database Application Programs

There is a trend towards increased specialization of data management software for performance reasons. In this paper, we study the automatic specialization and optimization of database application programs -- sequences of queries and updates, augmented with control flow constructs as they appear in database scripts, UDFs, transactional workloads and triggers in languages such as PL/SQL. We show how to build an optimizing compiler for database application programs using generative programming and state-of-the-art compiler technology. We evaluate a hand-optimized low-level implementation of TPC-C, and identify the key optimization techniques that account for its good performance. Our compiler fully automates these optimizations and, applied to this benchmark, outperforms the manually optimized baseline by a factor of two. By selectively disabling some of the optimizations in the compiler, we derive a clinical and precise way of obtaining insight into their individual performance contributions.
Propensity Score Estimation Using Classification and Regression Trees in the Presence of Missing Covariate Data

Abstract Data mining and machine learning techniques such as classification and regression trees (CART) represent a promising alternative to conventional logistic regression for propensity score estimation. Whereas incomplete data preclude the fitting of a logistic regression on all subjects, CART is appealing in part because some implementations allow for incomplete records to be incorporated in the tree fitting and provide propensity score estimates for all subjects. Based on theoretical considerations, we argue that the automatic handling of missing data by CART may however not be appropriate. Using a series of simulation experiments, we examined the performance of different approaches to handling missing covariate data; (i) applying the CART algorithm directly to the (partially) incomplete data, (ii) complete case analysis, and (iii) multiple imputation. Performance was assessed in terms of bias in estimating exposure-outcome effects among the exposed, standard error, mean squared error and coverage. Applying the CART algorithm directly to incomplete data resulted in bias, even in scenarios where data were missing completely at random. Overall, multiple imputation followed by CART resulted in the best performance. Our study showed that automatic handling of missing data in CART can cause serious bias and does not outperform multiple imputation as a means to account for missing data.
Tear Film Break-Up Time Measurement Using Deep Convolutional Neural Networks for Screening Dry Eye Disease

Tear film instability is one of the major characteristics of dry eye syndrome. However, traditional diagnostic methods, such as the fluorescein tear film break-up time (FTBUT) test, are limited by the subjective interpretation of results. The test needs to manually identify break-up areas in the fluorescent image, thus producing variable diagnosis results. This paper proposes an automatic method to detect the fluorescent tear film break-up area using a deep convolutional neural network (CNN) model and to define its appearance as CNN-BUT. A digital slit-lamp recorded the standard FTBUT measurement for each of 80 study participants. Fifty participants were used to train the CNN model to identify the tear film break-up area, while the remaining 30 were used to validate the proposed CNN-BUT test. Among six normal controls and 24 dry eye patients enrolled in this paper, CNN-BUT was significantly lower in dry eye patients (<inline-formula> <tex-math notation="LaTeX">${p} <0.05$ </tex-math></inline-formula>). The correlation between CNN-BUT and FTBUT was also significant (<inline-formula> <tex-math notation="LaTeX">${r} =0.9$ </tex-math></inline-formula>; <inline-formula> <tex-math notation="LaTeX">${p} <0.05$ </tex-math></inline-formula>). Using 5 s as the cutoff value, the CNN-BUT offered acceptable sensitivity and specificity to screen dry eye patients (0.83 and 0.95, respectively). These results indicate that CNN-BUT may be used to evaluate tear film stability and to assess the status of dry eye syndrome automatically.
Dual Mode SWIPT: Waveform Design and Transceiver Architecture with Adaptive Mode Switching Policy

In this paper, we propose a dual mode simultaneous wireless information and power transfer (SWIPT) system in which a sensor node monitors the received power and adaptively controls the communication mode. To this end, we design two types of single and multi-tone waveforms and propose a new transceiver architecture that realizes adaptive power management and information decoding (adaptive PM-\&-ID) module. On top of this, we implement adaptive PM-\&-ID policy algorithm which reflects non-linear energy harvesting (EH) model for both single and multi-tone waveforms. Our newly designed SWIPT can be applied to Internet-of-Things (IoT) network with RF EH capability for self-powering, leading to battery-free network. Numerical results show that significant performance gain can be achieved by the proposed dual mode SWIPT over the existing SWIPT design.
A High-Precision and High-Bandwidth MEMS-Based Capacitive Accelerometer

In this paper, we present a capacitive, MEMS-based accelerometer comprising an ultra-low noise CMOS integrated readout-IC and a high-precision bulk micro machined sensing element. The resulting accelerometer reaches an acceleration equivalent noise of only 200 ng/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz, which makes it suitable for seismic measurement that require noise levels significantly below 1 <inline-formula> <tex-math notation="LaTeX">$\mu \text{g}/\surd $ </tex-math></inline-formula>Hz. In addition, a high bandwidth of more than 5 kHz was achieved, which also makes the presented sensor system applicable for high-frequency measurements, e.g., in predictive maintenance applications for rotating machinery. The design of the sensing element and readout IC is presented in detail, and measurement results are shown which demonstrate the performance of the sensor system.
Microenvironment Cell Contribution to Lymphoma Immunity

Lymphoma microenvironment is a complex system composed of stromal cells, blood vessels, immune cells as well as extracellular matrix, cytokines, exosomes, and chemokines. In this review, we describe the function, localization, and interactions between various cellular components. We also summarize their contribution to lymphoma immunity in the era of immunotherapy. Publications were identified from searching Pubmed. Primary literature was carefully evaluated for replicability before incorporating into the review. We describe the roles of mesenchymal stem/stromal cells (MSCs), lymphoma-associated macrophages (LAMs), dendritic cells, cytotoxic T cells, PD-1 expressing CD4+ tumor infiltrating lymphocytes (TILs), T-cells expressing markers of exhaustion such as TIM-3 and LAG-3, regulatory T cells, and natural killer cells. While it is not in itself a cell, we also include a brief overview of the lymphoma exosome and how it contributes to anti-tumor effect as well as immune dysfunction. Understanding the cellular players that comprise the lymphoma microenvironment is critical to developing novel therapeutics that can help block the signals for immune escape and promote tumor surveillance. It may also be the key to understanding mechanisms of resistance to immune checkpoint blockade and immune-related adverse events due to certain types of immunotherapy.
Seeded graph matching via large neighborhood statistics

We study a noisy graph isomorphism problem, where the goal is to perfectly recover the vertex correspondence between two edge‐correlated graphs, with an initial seed set of correctly matched vertex pairs revealed as side information. We show that it is possible to achieve the information‐theoretic limit of graph sparsity in time polynomial in the number of vertices n. Moreover, we show the number of seeds needed for perfect recovery in polynomial‐time can be as low as nϵ in the sparse graph regime (with the average degree smaller than nϵ ) and Ω(logn) in the dense graph regime, for a small positive constant ϵ . Unlike previous work on graph matching, which used small neighborhoods or small subgraphs with a logarithmic number of vertices in order to match vertices, our algorithms match vertices if their large neighborhoods have a significant overlap in the number of seeds.
Structural Brain Network Alteration and its Correlation With Structural Impairments in Patients With Depression in de novo and Drug-Naïve Parkinson's Disease

Purpose: Depression is common in Parkinson's disease (PD) and is correlated with the severity of motor deficits and quality of life. The present study aimed to investigate alterations in the structural brain network related to depression in Parkinson's disease (d-PD) and their correlations with structural impairments of white matter (WM). Materials and Methods: Data were acquired from the Parkinson Progression Markers Initiative (PPMI) database. A total of 84 de novo and drug-naïve PD patients were screened and classified into two groups according to the 15-item Geriatric Depression Scale (GDS-15): d-PD (n = 28) and nondepression in PD (nd-PD, n = 56). Additionally, 37 healthy controls (HC) were screened. All subjects underwent DTI and 3D-T1WI on a 3.0 T MR scanner. Individual structural brain networks were constructed and analyses were performed using graph theory and network-based statistics (NBS) at both global and local levels. Differences in global topological properties were explored among the three groups. The association models between node and edge changes and the GDS-15 were constructed to detect regions that were specifically correlated with d-PD. Tract-based spatial statistics (TBSS) was used to detect structural impairments of WM between the d-PD and nd-PD groups. The correlations between altered global topological properties and structural impairments were analyzed in the d-PD group. Results: The global efficiency and characteristic path length of the structural brain network were impaired in the d-PD group compared with those in the nd-PD and HC groups. Thirteen nodes and 1 subnetwork with 10 nodes and 12 edges specifically correlated with d-PD were detected. The left hippocampus, left parahippocampal, left lingual, left middle occipital, left inferior occipital, left fusiform, left middle temporal, and left inferior temporal regions were all involved in the results of node and edge analysis. No WM microstructural impairments were identified in the d-PD group. Conclusion: Our study suggests that the integration of the structural brain network is impaired with disrupted connectivity of limbic system and visual system in the de novo and drug-naïve d-PD patients.The topological properties assessing integration of the structural brain network can serve as a potential objective neuroimaging marker for early diagnosis of d-PD.
Improved Training with Curriculum GANs

In this paper we introduce Curriculum GANs, a curriculum learning strategy for training Generative Adversarial Networks that increases the strength of the discriminator over the course of training, thereby making the learning task progressively more difficult for the generator. We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation. We also show evidence that this strategy may be broadly applicable to improving GAN training in other data modalities.
Simplex Representation for Subspace Clustering

The self-expressive property of data points, i.e., each data point can be linearly represented by the other data points in the same subspace, has proven effective in leading subspace clustering methods. Most self-expressive methods usually construct a feasible affinity matrix from a coefficient matrix, obtained by solving an optimization problem. However, the negative entries in the coefficient matrix are forced to be positive when constructing the affinity matrix via exponentiation, absolute symmetrization, or squaring operations. This consequently damages the inherent correlations among the data. Besides, the affine constraint used in these methods is not flexible enough for practical applications. To overcome these problems, in this paper, we introduce a scaled simplex representation (SSR) for subspace clustering problem. Specifically, the non-negative constraint is used to make the coefficient matrix physically meaningful, and the coefficient vector is constrained to be summed up to a scalar s<1 to make it more discriminative. The proposed SSR based subspace clustering (SSRSC) model is reformulated as a linear equality-constrained problem, which is solved efficiently under the alternating direction method of multipliers framework. Experiments on benchmark datasets demonstrate that the proposed SSRSC algorithm is very efficient and outperforms state-of-the-art subspace clustering methods on accuracy. The code can be found at this https URL.
Unbounded Output Networks for Classification

We proposed the expected energy-based restricted Boltzmann machine (EE-RBM) as a discriminative RBM method for classification. Two characteristics of the EE-RBM are that the output is unbounded and that the target value of correct classification is set to a value much greater than one. In this study, by adopting features of the EE-RBM approach to feed-forward neural networks, we propose the UnBounded output network (UBnet) which is characterized by three features: (1) unbounded output units; (2) the target value of correct classification is set to a value much greater than one; and (3) the models are trained by a modified mean-squared error objective. We evaluate our approach using the MNIST, CIFAR-10, and CIFAR-100 benchmark datasets. We first demonstrate, for shallow UBnets on MNIST, that a setting of the target value equal to the number of hidden units significantly outperforms a setting of the target value equal to one, and it also outperforms standard neural networks by about 25\%. We then validate our approach by achieving high-level classification performance on the three datasets using unbounded output residual networks. We finally use MNIST to analyze the learned features and weights, and we demonstrate that UBnets are much more robust against adversarial examples than the standard approach of using a softmax output layer and training the networks by a cross-entropy objective.