Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Proteins Associated with the Early Restoration of Insulin Sensitivity After Biliopancreatic Diversion

Abstract Context Insulin resistance (IR) is a risk factor for type 2 diabetes, diabetic kidney disease, cardiovascular disease and nonalcoholic steatohepatitis. Biliopancreatic diversion (BPD) is the most effective form of bariatric surgery for improving insulin sensitivity. Objective To identify plasma proteins correlating with the early restoration of insulin sensitivity after BPD. Design Prospective single-center study including 20 insulin-resistant men with morbid obesity scheduled for BPD. Patient characteristics and blood samples were repeatedly collected from baseline up to 4 weeks postsurgery. IR was assessed by homeostatic model assessment for insulin resistance (HOMA-IR), Matsuda Index, and by studying metabolic profiles during meal tolerance tests. Unbiased proteomic analysis was performed to identify plasma proteins altered by BPD. Detailed plasma profiles were made on a selected set of proteins by targeted multiple reaction monitoring mass spectrometry (MRM/MS). Changes in plasma proteome were evaluated in relation to metabolic and inflammatory changes. Results BPD resulted in improved insulin sensitivity and reduced body weight. Proteomic analysis identified 29 proteins that changed following BPD. Changes in plasma levels of afamin, apolipoprotein A-IV (ApoA4), and apolipoprotein A-II (ApoA2) correlated significantly with changes in IR. Conclusion Circulating levels of afamin, ApoA4, and ApoA2 were associated with and may contribute to the rapid improvement in insulin sensitivity after BPD.
Novel Algorithm for Automated Optic Nerve Sheath Diameter Measurement Using a Clustering Approach.

INTRODUCTION Using ultrasound to measure optic nerve sheath diameter (ONSD) has been shown to be a useful modality to detect elevated intracranial pressure. However, manual assessment of ONSD by a human operator is cumbersome and prone to human error. We aimed to develop and test an automated algorithm for ONSD measurement using ultrasound images and compare it to measurements performed by physicians. MATERIALS AND METHODS Patients were recruited from the Neurological Intensive Care Unit. Ultrasound images of the optic nerve sheath from both eyes were obtained using an ultrasound unit with an ocular preset. Images were processed by two attending physicians to calculate ONSD manually. The images were processed as well using a novel computerized algorithm that automatically analyzes ultrasound images and calculates ONSD. Algorithm-measured ONSD was compared to manually measured ONSD using multiple statistical measures. RESULTS Forty-Four patients with an average/standard deviation (SD) intracranial pressure of 14(9.7) mm Hg were recruited and tested (with a range between 1 and 57 mm Hg). A t-test showed no statistical difference between the ONSD from left and right eyes (P > 0.05). Furthermore, a paired t-test showed no significant difference between the manual and algorithm measure ONSD with a mean difference (SD) of 0.012(0.046) cm (P > 0.05) and percentage error of difference of 6.43% (P = 0.15). Agreement between the two operators was highly correlated (Interclass Correlation coefficient = 0.8, P = 0.26). Bland-Altman analysis revealed mean difference (SD) of 0.012 (0.046) (P = 0.303) and limits of agreement between -0.1 and 0.08. Receiver operator curve analysis yielded an area under the curve of 0.965 (P < 0.0001) with high sensitivity and specificity. CONCLUSIONS The automated image-analysis algorithm calculates ONSD reliably and with high precision when compared to measurements obtained by expert physicians. The algorithm may have a role in computer-aided decision support systems in acute brain injury.
Multiple-shoot Induction and Genetic Transformation of Island Cotton(Gossypium barbadense L.) in Xinjiang

With three island cottons,Xinhai 17,Xinhai 14,85H,shoot organogenesis and genetic transformation were studied in Xinjiang.The results were as follows: in vitro plant regeneration of meristem shoot tip is better,and the seedling rate of 90.5%was obtained.Frequency of inducing shoots reached maximum with 6-BA at 0.5 mg·L-1;whereas number of budding shoots in per explant reached maximum with 6-BA at 1.5 mg·L-1;plantlet from shoot apical meristem repressed with 6-BA at 2.0 mg·L-1;The maximum budding shoots in per explant is four.Isolated meristem shoot tips infected with Agrobacterium tumefacien have strong endurance on infected damnification and kan,of which selective concentration reached 100 mg·L-1.The rate of plantlet from infected shoot tips on selection medium reached to 88.9%.
Sleep and screen exposure across the beginning of life: Deciphering the links using big-data analytics.

STUDY OBJECTIVES Evidence for the association between screen time and insufficient sleep is bourgeoning, and recent findings suggest that these associations may be more pronounced in younger compared to older children, and for portable compared to non-portable devices. However, these effects have yet to be investigated within the beginning of life. Importantly, there are no data for the relationship between screen exposure and objectively measured infant sleep. This study examined the moderating role of age for both touchscreens' and television's relationship with sleep, using auto-videosomnography within a big-data sample of infants. METHODS The sleep of 1,074 infants (46% girls) aged 0-18 months was objectively assessed using computer-vision technology in this cross-sectional study. Sleep was additionally reported by parents in an online survey, as was infant's exposure to screens. RESULTS Age significantly moderated the relationship between daytime touchscreen exposure and sleep with a distinct pattern for younger infants, in which screen exposure was associated with decreased daytime sleep, but with a proposed compensatory increase in nighttime sleep consolidation. Compared to touchscreens, television exposure was less likely to be associated with sleep metrics, and age moderated this relationship only for daytime and 24-h sleep duration. CONCLUSIONS In young infants, a daytime-nighttime sleep 'trade-off' emerged, suggesting that the displacement of daytime sleep by screens may lead to greater accumulation of sleep homeostatic pressure, which in turn facilitates more consolidated nighttime sleep.
Within‐ and Among‐Clutch Variation of Yolk Perfluoroalkyl Acids in a Seabird from the Northern Adriatic Sea

Perfluoroalkyl substances (PFAS) are surface‐active agents used in diverse industrial and commercial applications. They contaminate both freshwater and marine ecosystems, are highly persistent, and accumulate through trophic transfer. Seabirds are exposed to environmental contaminants due to their high trophic position in food webs and relatively long lifespan. We measured levels of 10 perfluoroalkyl acids (PFAAs) in egg yolks of yellow‐legged gulls (Larus michahellis) breeding in the northern Adriatic Sea (Northeast Italy). We examined variations in PFAAs within clutches (between eggs of different laying order) and among clutches. Perfluorooctane sulfonate (PFOS) was the most abundant yolk PFAA (mean = 42.0 ng/g wet wt), followed by perfluorooctanoic acid (PFOA; 3.8 ng/g wet wt) and perfluorododecanoic acid (PFDoDa; 2.8 ng/g wet wt). The ∑PFAAs averaged 57.4 ng/g wet weight, ranging between 26.5 and 115.0 ng/g wet weight. The PFAA levels varied substantially among clutches (0.29–0.79 of the total variation), whereas the effects of laying order were considerably weaker (0.01–0.13). Egg‐laying order effects were detected for ∑PFAAs, PFOS, perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnA), and PFDoDa, whereby the last‐laid eggs exhibited lower PFAA concentrations than early‐laid eggs. Our results indicate that seagulls from the northern Adriatic basin deposit measurable amounts of PFAAs in their eggs. The large among‐clutches differences in PFAAs suggest that exposure of yellow‐legged gull females to these compounds is highly variable. Environ Toxicol Chem 2021;40:744–753. © 2020 SETAC
Effects of Different Forages on BmNPV Resistance and Activities of Three Metabolic Enzymes of Silkworm(Bombyx mori L.)

In order to explore the relationship between forage and occurrence of BmNPV and the relationship between occurrence of BmNPV and activities of metabolic enzymes of the silkworm(Bombyx mori L.),BmNPV resistance and activities of three metabolic enzymes(carboxylesterase,acetylcholine esterase and alkaline phosphatase) of the silkworm larvae feeding on Tricuspid Cudrania[Cudrania tricuspidata(Carr.) Bur. ] leaves and mulberry leaves were investigated.The results showed that the infective dose 50(ID50) and lethal dose 50(LD50) of silkworm feeding on Tricuspid cudrania leaves were 1.319×105 PID/larva and 1.632×105 PID/larva respectively,being only about 1/2 and 1/4 to those of the larvae feeding on mulberry leaves.That is to say,BmNPV resistance of silkworm larvae feeding on Tricuspid cudrania leaves was remarkably weaker than that of the larvae feeding on mulberry leaves.In both larvae feeding on Tricuspid cudrania and mulberry leaves,activities of carboxylesterase had increased significantly,and those of alkaline phosphatase and acetylcholine esterase decreased significantly.Moreover,after BmNPV infection,the activities of alkaline phosphatase and acetylcholine esterase in the larvae feeding on Tricuspid cudrania leaves were lower than those feeding on mulberry leaves.
Performance Evaluation of Routing Protocols under Black Hole Attack in Cognitive Radio Mesh Network

Wireless technology is rapidly proliferating. Devices such as Laptops, PDAs and cell-phones gained a lot of importance due to the use of wireless technology. Nowadays there is also a huge demand for spectrum allocation and there is a need to utilize the maximum available spectrum in efficient manner. Cognitive Radio (CR) Network is one such intelligent radio network, designed to utilize the maximum licensed bandwidth to un-licensed users. Cognitive Radio has the capability to understand unused spectrum at a given time at a specific location. This capability helps to minimize the interference to the licensed users and improves the performance of the network. Routing protocol selection is one of the main strategies to design any wireless or wired networks. In Cognitive radio networks the selected routing protocol should be best in terms of establishing an efficient route, addressing challenges in network topology and should be able to reduce bandwidth consumption. Performance analysis of the protocols helps to select the best protocol in the network. Objective of this study is to evaluate performance of various cognitive radio network routing protocols like Spectrum Aware On Demand Routing Protocol (SORP), Spectrum Aware Mesh Routing in Cognitive Radio Networks (SAMER) and Dynamic Source Routing (DSR) with and without black hole attack using various performance parameters like Throughput, E2E delay and Packet delivery ratio with the help of NS2 simulator.
Short-Term Traffic Flow Prediction Based on the Differential Data Graph and Deep Learning

Traffic flow prediction (TFP) is an very important issue for successfully promoting traffic efficiency and reducing traffic congestion in intelligent transportation system (ITS). In this paper, in order to improve the TFP accuracy from the perspective of traffic flow features extraction, we propose a new method named DG-CNN-SVR. The basic thought behind relies on two aspects: the inherit trend in traffic flow data and the some subtle difference at different weekdays. Therefore, we introduce Differential operation to eliminate the trend component and then construct a traffic data Graph to help interpret the complex features. We use convolutional neural network (CNN) for features learning and support vector regression (SVR) for traffic flow predicting. The experimental results by the test data set show that the proposed TFP method performs more effective than the cases when CNN and SVR are used individually.
An Adaptive Niche Genetic Algorithm for Multimodal Function Optimization

An adaptive niche genetic algorithm is proposed to solve the problems of the inaccurate niche identification and the conflict between quick convergence and population diversity maintaining in niche genetic algorithms.In the proposed algorithm,an improved niche identification method is designed to identify the niches of the population,and a concept of niche entropy is introduced to measure the diversity of the population.The evolutionary parameters of the algorithm can be adjusted adaptively on the basis of the niche entropy of population.And the strategies of selection and crossover are also improved in the algorithm.The strategies divide the operation of crossover into the out-niche crossover and the in-niche crossover to enhance global searching ability and local convergence rate of the algorithm.Experimental results show that the proposed algorithm can solve the multimodal function optimization problems with quick convergence rate,low computational complexity and avoidance of the genetic drift.
(A Little) Ignorance is Bliss: The Effect of Imperfect Model Information on Stealthy Attacks in Power Grids

Stealthy attacks on control systems have the potential to cause serious harm, as they are not readily detectable by any intrusion detection system. However, it is often the case that neither the controller nor the attacker can gather perfect information about the system model. We show that while a small mismatch between model and reality can easily be managed by a robust controller, an attacker's imperfect knowledge of the system can thwart the stealth of the attack. This opens the door to a whole new class of defense mechanisms, which focus on maximizing the attacker's uncertainty about the system while maintaining the controller's uncertainty within the bounds of its robustness. We demonstrate our findings on the simple quadruple tank control process and on a realistic power grid model, showing that the time to detect a stealthy attack depends on the attacker's level of uncertainty about the model.
Research on Cross-Medium Target Detection

This paper designs a type of acoustic buoy for underwater platform to detect the aerial targets crossing the different medium. This cylindrical buoy has a three-dimensional microphone array which is expandable and retractable. In case of storage or launch, the array folds in order to save space and be easy to launch, and the array can expand automatically after the buoy rushes out of the water surface for enlarging the array aperture. This buoy has ability in estimating target direction both in horizontal and vertical. Simulations on the buoy's rising motion are carried out, and the factors influencing launch speed, positive buoyancy effects and ocean currents are analyzed in detail. Experimental results demonstrate the feasibility and superiority of the proposed designing method.
Decoupling Numeric and Analytic Linear Analysis for Modular Flight Control Law Synthesis

Traditional flight control synthesis techniques rely on full-model numerical perturbation to compute a linear model via point and slope differentiation, valid in a small neighborhood around an operating condition of interest. While this approach is valid, insight into the sensitivities of a linear model may be nebulous, and computational efficiency can be gained, as well as reuse of an analytical linear model, by decomposing common analytic terms and platform specific numerical models. In this work, a general set of nonlinear equations of motion are extended for a VTOL (Vertical Take Off and Landing) vehicle with a non-traditional suite of effectors and a heavy rotating duct. The nonlinear equations, presented in both matrix and scalar form, are then decomposed and linearized to promote reuse with modularity by decoupling platform specific data (aerodynamics and control power) with common six degree of freedom inertia and mass properties effects. A sub-optimal infinite horizon state feedback control law, referred to as a servomechanism, is synthesized and simulation results are presented. Finally, the paper concludes with a summary and path forward for future work.
Golay Complementary Waveform Design for Enhanced Doppler Resolution

Golay complementary waveforms are long suffered from dissatisfactory Doppler resolution in target detection. In this paper, we develop a signal processing processor which comparing and pointwise thresholding the radar echo wave of two separate pairs of Golay complementary waveforms. The effectiveness of this pointwise thresholding processor (PTP) in terms of better Doppler resolution than conventional design of Golay complementary waveforms is proved by simulating and analyzing the output Delay-Doppler maps.
Graph Convolutional Network for Visual Question Answering Based on Fine-grained Question Representation

Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Recent research works mainly focus on the bilinear fusion methods and attention mechanism methods to explore the global features and local features, respectively. When an image exists multiple objects, the fine-grained question features can effectively identify the relationship between objects in an image. Therefore we propose a graph convolutional network based on fine-grained question representation (FQ-GCN). The object relation graph is firstly constructed. The fine-grained question features are used to explore the relations between objects in an image and prune unrelated edges between object nodes. Graph convolutional network is used to aggregate all neighborhood information of each object in an object graph. Experiments on the VQA 2.0 dataset show that the performance of our FQ-GCN model improves 2%$\sim 3$% than the classical methods.
Information Model of Radar Communication System and Optimization of Power Allocation

Based on the radar communication integration, this paper considers a scene where the radar performs target-detection and then sends the echo information to the control center via a communication link. According to the law of electromagnetic wave propagation, the echo power is inversely proportional to the fourth power of the distance between targets and the radar, while received power at the control center is inversely proportional to the square of the communication distance. It can be seen that shortening the target-radar distance can effectively improve the system's performance. Different from traditional radar communication integration which mainly focused on the integrated signals' and systems' design, this paper innovatively establishes an information model of radar communication system where the performance is evaluated by the location mutual information. Combining with the Shannon channel capacity theorem, an optimized power allocation method under the limited total power is presented to maximize the sensing information obtained by the radar communication system. Numerical results show that the power can be saved by 4dB to 7.8dB under the optimization of the proposed system.
SAR Parameter Estimation Method for Rectangle Plane Based on Information Geometry

After establishing scattering models, the traditional synthetic aperture radar (SAR) parameter estimation methods usually utilize vanilla gradients to estimate target parameters from SAR echoes or images. However, the vanilla gradients will lead to lower iteration efficiency. Hence, in this paper, to take the rectangle plane as an example, we propose a SAR parameter estimation method based on the information geometry (SPEM-IG) to solve the above problem. The SPEM-IG adopts the natural gradient to estimate the parameters based on the information geometry optimization algorithm. The central objective of SPEM-IG is to project the least square function into probability distribution space and find the optimal solution in the new parameter space. In order to reduce the computational complexity caused by the multi-peak feature of least squares function, SPEM-IG utilizes the range of parameters rather than its exact value in calculating the value of least squares function, and takes the optimal solution in this range as the result of the current iteration process. By considering the geometric structure of manifolds, SPEM-IG calculates the optimal gradient direction called natural gradient to obtain faster convergence rate. The experimental results show that the SPEM-IG is effective for estimating target parameters. Besides, SPEM-IG has good performance even at low signal-to-noise ratios of SAR images.
Detection of Alcoholics using EEG and EMD Domain Statistical Features

Alcoholism is a disease where a person loses control over the mind and start drinking a large amount of alcohol. Detection of alcoholism in person plays a significant role in avoiding an accident in a hazardous area. Alcohol disturbs the neuronal system of the human brain and starts misfiring neurons leads to anonymous message transfer. These neuronal activities captured using Electroencephalogram (EEG) by placing electrodes on the scalp. This paper presents a new real time system for the finding of alcoholism using empirical mode decomposition (EMD) domain statistical features and Gaussian support vector machine. EMD decomposes the signal into different intrinsic mode function (IMF). Statistical parameters of these IMF play a vital role in discriminating alcoholics from a normal healthy subject.
Brain Extraction from Magnetic Resonance Imaging Using Deep Neural Network with Edge Prior

Brain extraction from Magnetic resonance imaging (MRI) is the separation of brain tissue from non-brain tissue in MRI brain images and removal of extra brain tissue. Automatic extraction of brain tissue is an important step in neuroimage processing for clinical applications and scientific research. Accurate delineation of the region can be challenged due to the weak contrast between the brain and non-brain tissue. To overcome this issue, this paper proposed a novel brain extraction approach integrating the boundary information under the deep neural network framework. The boundary of the brain tissues was first detected by an edge detection neural network. The inception module was then utilized in the convolutional stage to capture multi-scale features. A dense decoder shortcut connection structure was finally introduced to leverage the multi-level features in the upsampling stage. The proposed method can achieve Dice overlap coefficients of 98.15% and 98.09%, for the OASIS and LPBA40 datasets respectively. The proposed method outperforms the commonly used brain extraction tools by qualitative and quantitative assessments on two publicly available benchmark datasets.
Gain Enhancement in U-Slotted Broadband Dual Circularly Polarized Antenna for S-Band Radar Applications

Compact dual feed circularly polarized wideband antenna is simulated. Dual circular polarization is achieved by simply adding U shaped ground slot radiator on the bottom side along with asymmetrical patch feed lines on the top of the substrate. This arrangement provide design simplicity in the antenna structure. The U shaped radiator is used to generate one CP mode on the lower frequency band & another CP mode on the higher frequency band is generated by asymmetrical feed line structure. By adding two circular curves one on the middle part as well as other on the outer part of slotted U radiator it is possible to reduce the difference between the two CP modes which further improves the IBW & 3-dB ARBW of antenna. Size of antenna is 48×48×1mm3.The result shows good isolation between the two asymmetrical ports. The impedance bandwidth is 1.99-8.20 GHz (155.2 %) & 3-dB AR bandwidth is 1.93- 6.29 GHz (109 %) also port isolation of > 14.3 dB achieved. Both the ports are matched with their 50 Ω impedances.