Miracle-dz/UniFAR-FTUs huggingface dataset:
A Phytogeographic Divide Along the 500 mm Isohyet in the Qinghai-Tibet Plateau: Insights From the Phylogeographic Evidence of Chinese Alliums (Amaryllidaceae)

The Qinghai-Tibet Plateau (QTP) has been biogeographically divided into the eastern monsoonal and the western continental climatic zones along the 500 mm isohyet. However, this biogeographic hypothesis has been rarely tested using a phylogeographic approach. The members of the genus Allium subgenus Cyathophora coincidentally distribute across this biogeographical divide. Intriguingly, Allium fasciculatum of subgenus Amerallium co-occurs in the distribution range of subgenus Cyathophora. To illuminate the role of this biogeographic divide on the genetic divergence, we genotyped 466 individuals of 52 populations of subgenus Cyathophora and 110 individuals of 19 populations of A. fasciculatum using three chloroplast DNA fragments, whole nrITS and nine nuclear microsatellite loci, supplemented with the present environmental space and paleo-distribution modeling. Our phylogeographical evidence recovered the concordant east–west genetic breaks both for subgenus Cyathophora and A. fasciculatum along the 500 mm isohyet. The divergence time estimations and environmental niche differentiations suggested this east–west genetic breaks could have been triggered by the climatic-induced vicariance during the early Pleistocene. Noticeably, this split within subgenus Cyathophora could have been deepened by the morphological vicariance from the eastern umbel to the western spicate, while that within A. fasciculatum could have been obscured due to the pollen flows from the east to west caused by the postglacial expansion. The genetic structures and ecological niche modelings (ENMs) recovered the distinct responses to the Quaternary climatic oscillations for species constricted to different climatic zones, further highlighting the profound effect of the climatic differences and tectonic uplifts on the genetic diversification. Overall, our findings offer strong evidence for the existence of a biogeographic divide between the eastern monsoonal and the west continental climatic zones of the QTP nearly along the 500 mm isohyet.
Research on the Application of Big Data in Community Smart Service

The development of a new generation of information and communication technologies represented by Big Bata has brought new opportunities for community governance and service innovation. Intelligent governance and intelligent services are the main trends and future directions of community building. This paper mainly studies how the community applies Big Data to promote its development towards smart, and how to use the Big Data idea to build a comprehensive coverage of smart service system, improve the intelligent level of community service to meet the new personalized, humanized and precise service needs of community residents.
Chance Constrained Reserve Scheduling Using Uncertain Controllable Loads Part II: Analytical Reformulation

This paper is Part II of a two-part paper that develops a multi-period chance constrained optimal power flow model to schedule generation and reserves from both generators and aggregations of uncertain but controllable electric loads. Part I developed the formulation and solved the problem using a scenario-based method. In Part II, we assume that all uncertainties follow multivariate normal distributions, allowing us to reformulate the constraints analytically to obtain a deterministic formulation that can be solved faster and with less need for real data than the scenario-based method in Part I. We test the approach on a modified IEEE 30-bus system and compare the solutions, empirical reliabilities, and computational requirements to those of the scenario-based method. We find that the analytical reformulation solved using a cutting plane algorithm requires less computational time than the scenario-based method. Additionally, its solution is less costly and less conservative; however, its empirical reliability is lower, though still close to the desired reliability.
Electrically Programmable On-Chip Equivalent-Phase-Shifted Waveguide Bragg Grating on Silicon

We report an electrically programmable equivalent-phase-shifted (EPS) waveguide Bragg grating implemented on silicon with programmable spectral response. Equivalent phase shift through nonuniform sampling in a Bragg grating is an effective solution to realize a phase-shifted Bragg grating, which significantly reduces the requirement for fabrication accuracy by three orders of magnitude as compared with the fabrication of a conventional phase-shifted Bragg grating. In this paper, an EPS Bragg grating with an equivalent phase shift introduced by increasing one sampling period in the grating center by a half sampling period is proposed, and the tuning of the phase shift is enabled by incorporating two independent PN junctions in each sampling period. Through controlling the bias voltages applied to the PN junctions, the spectral response of the EPS Bragg grating is tuned. The proposed EPS waveguide grating is fabricated and its performance is experimentally evaluated. A multichannel EPS Bragg grating with programmable spectral response is demonstrated. The key advantages of implementing EPS Bragg gratings include largely reduced fabrication constraint and strong multichannel tuning capability, which opens new avenues for on-chip Bragg gratings for programmable multichannel signal processing.
TRP Channels: Current Perspectives in the Adverse Cardiac Remodeling

Calcium is an important second messenger required not only for the excitation-contraction coupling of the heart but also critical for the activation of cell signaling pathways involved in the adverse cardiac remodeling and consequently for the heart failure. Sustained neurohumoral activation, pressure-overload, or myocardial injury can cause pathologic hypertrophic growth of the heart followed by interstitial fibrosis. The consequent heart’s structural and molecular adaptation might elevate the risk of developing heart failure and malignant arrhythmia. Compelling evidences have demonstrated that Ca2+ entry through TRP channels might play pivotal roles in cardiac function and pathology. TRP proteins are classified into six subfamilies: TRPC (canonical), TRPV (vanilloid), TRPM (melastatin), TRPA (ankyrin), TRPML (mucolipin), and TRPP (polycystin), which are activated by numerous physical and/or chemical stimuli. TRP channels participate to the handling of the intracellular Ca2+ concentration in cardiac myocytes and are mediators of different cardiovascular alterations. This review provides an overview of the current knowledge of TRP proteins implication in the pathologic process of some frequent cardiac diseases associated with the adverse cardiac remodeling such as cardiac hypertrophy, fibrosis, and conduction alteration.
An Improved Deep Forest Model for Predicting Self-Interacting Proteins From Protein Sequence Using Wavelet Transformation

Self-interacting proteins (SIPs), whose more than two identities can interact with each other, play significant roles in the understanding of cellular process and cell functions. Although a number of experimental methods have been designed to detect the SIPs, they remain to be extremely time-consuming, expensive, and challenging even nowadays. Therefore, there is an urgent need to develop the computational methods for predicting SIPs. In this study, we propose a deep forest based predictor for accurate prediction of SIPs using protein sequence information. More specifically, a novel feature representation method, which integrate position-specific scoring matrix (PSSM) with wavelet transform, is introduced. To evaluate the performance of the proposed method, cross-validation tests are performed on two widely used benchmark datasets. The experimental results show that the proposed model achieved high accuracies of 95.43 and 93.65% on human and yeast datasets, respectively. The AUC value for evaluating the performance of the proposed method was also reported. The AUC value for yeast and human datasets are 0.9203 and 0.9586, respectively. To further show the advantage of the proposed method, it is compared with several existing methods. The results demonstrate that the proposed model is better than other SIPs prediction methods. This work can offer an effective architecture to biologists in detecting new SIPs.
P2P Image Transmission Realized with a Resource Limited Microcontroller

This paper will design a network camera, use STM32 (MCU Microcontroller), establish P2P (Peer to Peer) network architecture transmission through UDP protocol. We chose a digital camera (OV5640) because it supports JPEG Compression Mode. The purpose is to transmit the image to the remote user, and the client registers with the P2P (Peer to Peer) server to establish a connection request, then achieving UDP traversal across the network through the NAT penetration principle. We use the Cypress WICED N02 platform (STM32F4XX + Wi-Fi) to register with the P2P server over the wireless network, and the client sends a connection request to the P2P server. It is required to establish a connection with the target device (Camera Device), and the image is transmitted to the client through the UDP protocol. The P2P server is used only for establishing connection during transmission, and there is no intervention at all. Compared with the traditional Client-Server network architecture, the transmission cost of the P2P network architecture is very low.
Long-Term Planning of Connected Industrial Microgrids: A Game Theoretical Approach Including Daily Peer-to-Microgrid Exchanges

In this paper, a tool for the long-term (LT) planning, i.e., up to 20 years, of industrial microgrids (IMGs) connected to the distribution network and made of industrial consumers, prosumers, and of the distribution system operator (DSO), is proposed. The DSO assumes here the new role of microgrid energy manager. In order to realize the proper choice of LT investments (e.g., in renewable energy system and energy storage system), a short-term (ST) energy management is performed each day of the planning period. For that purpose, a new system of daily operation including industrial load management and allowing peer-to-microgrid as well as external energy exchanges is implemented. The LT investments and ST operational decisions are coupled via two game theoretical frameworks, which also allow the modeling of the different, even conflicting, objectives of the stakeholders. Different LT and ST pricing schemes are also considered in order to provide general advices concerning the creation of new IMGs. The developed tool is tested on a virtual IMG and the technical and economical outputs are presented.
A New Method to Compute Ratio of Secure Summations and Its Application in Privacy Preserving Distributed Data Mining

Computing the ratio of secure summations (RSS) is one of the most important tools for privacy preserving distributed data mining. It refers to such a problem; given the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> parties and their respective secret values (<inline-formula> <tex-math notation="LaTeX">$x_{i}$ </tex-math></inline-formula>,<inline-formula> <tex-math notation="LaTeX">$y_{i}$ </tex-math></inline-formula>), where <inline-formula> <tex-math notation="LaTeX">$i=1,\ldots,n$ </tex-math></inline-formula>, how can they get the value of <inline-formula> <tex-math notation="LaTeX">$\sum _{i=1}^{n}x_{i}/\sum _{i=1}^{n}y_{i}$ </tex-math></inline-formula> without disclosing (<inline-formula> <tex-math notation="LaTeX">$x_{i}$ </tex-math></inline-formula>,<inline-formula> <tex-math notation="LaTeX">$y_{i}$ </tex-math></inline-formula>), <inline-formula> <tex-math notation="LaTeX">$\sum _{i=1}^{n}x_{i}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\sum _{i=1}^{n}y_{i}$ </tex-math></inline-formula>? In this paper, we propose a new method to solve this problem. Compared with the existing methods, our method is not only secure under the assumption of semi-honest but also can resist collusion attacks—even when all but one party are corrupt. At the same time, the computational complexity and communication complexity of our method are both <inline-formula> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula>. Therefore, it can meet the needs of the practical application in terms of both security and efficiency. In addition to the theoretical guarantee, the practical usability of our method is also verified through experiments by constructing Naive Bayes classifiers in a privacy-preserving distributed environment.
Disease Control With Delayed Salvage Radiotherapy for Macroscopic Local Recurrence Following Radical Prostatectomy

Purpose: To retrospectively assess clinical outcomes and toxicity profile of prostate cancer patients treated with delayed dose-escalated image-guided salvage radiotherapy (SRT) for macroscopic local recurrence after radical prostatectomy (RP). Material and Methods: We report on a cohort of 69 consecutive patients with local recurrence after RP and no evidence of regional or distant metastasis who were referred for salvage radiotherapy between 2007 and 2016. SRT consisted of 64–66 Gy (2 Gy/fraction) to the prostatic bed followed by dose escalation to 72–74 Gy (2Gy/fraction) to the macroscopic disease. All patients received concurrent short-term androgen deprivation therapy (ADT). Biochemical recurrence-free survival (bRFS) and clinical progression-free-survival (cPFS) were depicted using Kaplan-Meier method. Multivariable Cox proportional hazards regression assessed predictors of survival outcomes. Baseline, acute, and late urinary and gastrointestinal (GI) toxicity rates were reported using CTCAE v4.03. Results: Median time from RP to SRT was 66 months (IQR: 32–124). Median pre-SRT prostate-specific antigen (PSA) was 2.7 ng/ml (IQR: 0.9–6.5). Median follow-up after SRT was 38 months (IQR: 24–66). The 3- and 5-year bRFS were 58 and 44%, respectively. The 3- and 5-year cPFS were 91 and 76%, respectively. Median time from SRT to clinical disease progression was 102 months (IQR 77.5–165). At baseline, 3 patients (4%) had grade 3 urinary symptoms. Six patients (9%) developed acute and six patients (9%) developed late grade 3 urinary toxicity. Five patients (7%) had acute grade 2 GI toxicity. No acute grade 3 GI toxicity was reported. Late grade 3 GI toxicity was reported in one patient (1.5%). Conclusions: Delayed dose-escalated SRT combined with short-course ADT for macroscopic LR after RP was associated with 44% bRFS and 76% cPFS at 5 years. Albeit improved patient stratification is warranted, these data suggest that delayed SRT provides inferior tumor control compared to early intervention.
A Modified Benders Decomposition Algorithm to Solve Second-Order Cone AC Optimal Power Flow

This paper proposes to speed up solving large-scale second-order cone ac optimal power flow (SOC-ACOPF) problem by decomposition and parallelization. First, we use spectral factorization to partition large power network to multiple subnetworks connected by tie-lines. Then a modified Benders decomposition algorithm (M-BDA) is proposed to solve the SOC-ACOPF problem iteratively. Taking the total power output of each subnetwork as the complicating variable, we formulate the SOC-ACOPF problem of tie-lines as the master problem and the SOC-ACOPF problems of the subnetworks as the subproblems in the proposed M-BDA. The feasibility and optimality (preserving the original optimal solution of the SOC-ACOPF model) of the proposed M-BDA are analytically and numerically proved. A GAMS grid computing framework is designed to compute the formulated subproblems of M-BDA in parallel. The numerical results show that the proposed M-BDA can solve large-scale SOC-ACOPF problem efficiently. Accelerated M-BDA by parallel computing converges within few iterations. The computational efficiency (reducing computation CPU time and computer RAM requirement) can be improved by increasing the number of partitioned subnetworks.
Topology Optimized Multimaterial Soft Fingers for Applications on Grippers, Rehabilitation, and Artificial Hands

Multimaterial soft robots, composing of integrated soft actuators and a relatively harder body, show great potential to exert higher payloads and support their own weights. This paper proposed a systemic framework to automatically design and fabricate this kind of robots. The multimaterial design problem is mathematically modeled under the framework of topology optimization in which the structure and material distribution are obtained simultaneously. Herein, a multimaterial pneumatic soft finger, modeled as a compliant mechanism, is optimized to achieve its maximal bending deflection and further customized to practical applications on grippers, rehabilitation, and an artificial hand. These optimized multimaterial soft fingers are fabricated by combining molding and three-dimensional printing technique. Experimental results show that the soft gripper can manipulate a large variety of objects with different shapes (from M4 screws to complicated sunglasses) and weights (up to 168 g), the rehabilitation finger can facilitate human safely in two modes, and the artificial hand can perform various gestures. This paper represents an important step toward the realm of high-performance soft robots.
Decentralized Cross-Layer Optimization for Energy-Efficient Resource Allocation in HetNets

In this paper, we develop a joint physical and medium access control (MAC) layer optimization (JPMO) scheme based on game theory for energy-efficient resource allocation and interference management in heterogeneous networks (HetNets). In HetNets, cross-tier interference and co-tier interference significantly limit the network performance. Moreover, when each user only has its own channel state information and chooses its transmission policy independently without any coordination mechanism, it will result in the network collapse or waste of channel resources. To maximize the network efficiency, we develop the JPMO scheme through pricing mechanism from the perspective of game theory. Then we transform this scheme into a two-stage Stackelberg game, in which macrocell determines the transmission policy in MAC layer first, and then SCs perform EE power allocation in physical layer. Simulation results validate the effectiveness of the proposed scheme.
The Superoanterior Fasciculus (SAF): A Novel White Matter Pathway in the Human Brain?

Fiber tractography (FT) using diffusion magnetic resonance imaging (dMRI) is widely used for investigating microstructural properties of white matter (WM) fiber-bundles and for mapping structural connections of the human brain. While studying the architectural configuration of the brain’s circuitry with FT is not without controversy, recent progress in acquisition, processing, modeling, analysis, and visualization of dMRI data pushes forward the reliability in reconstructing WM pathways. Despite being aware of the well-known pitfalls in analyzing dMRI data and several other limitations of FT discussed in recent literature, we present the superoanterior fasciculus (SAF), a novel bilateral fiber tract in the frontal region of the human brain that—to the best of our knowledge—has not been documented. The SAF has a similar shape to the anterior part of the cingulum bundle, but it is located more frontally. To minimize the possibility that these FT findings are based on acquisition or processing artifacts, different dMRI data sets and processing pipelines have been used to describe the SAF. Furthermore, we evaluated the configuration of the SAF with complementary methods, such as polarized light imaging (PLI) and human brain dissections. The FT results of the SAF demonstrate a long pathway, consistent across individuals, while the human dissections indicate fiber pathways connecting the postero-dorsal with the antero-dorsal cortices of the frontal lobe.
Interactions Between Enteroviruses and the Inflammasome: New Insights Into Viral Pathogenesis

Enteroviruses (EVs) have emerged a substantial threat to public health. EVs infection range from mild to severe disease, including mild respiratory illness, diarrhea, poliomyelitis, hand, foot, and mouth disease, aseptic meningitis, and encephalitis. In the Asia-Pacific region, for example, one of the best studied enterovirus 71 (EV71) has been associated with pandemics of hand, foot, and mouth disease (HFMD) in children, particularly those under the age of five. Serious HFMD cases are associated with neurological complications, such as aseptic meningitis, acute flaccid paralysis, brainstem encephalitis, and have been associated with as many as 1000s of deaths in children and infants from 2008 to 2017, in China. More than 90% of laboratory confirmed deaths due to HMFD are associated with EV71. However, little is known about the pathogenesis of EVs. Studies have reported that EVs-infected patients with severe complications show elevated serum concentrations of IL-1β. The secretion of IL-1β is mediated by NLRP3 inflammasome during EV71 and CVB3 infection. Enteroviruses 2B and 3D proteins play an important role in activation of NLRP3 inflammasome, while 3C and 2A play important roles in antagonizing the activation of NLRP3 and the secretion of IL-1β. In this review, we summarize current knowledge regarding the molecular mechanisms that underlie the activation and regulation of the NLRP3 inflammasome, particularly how viral proteins regulate NLRP3 inflammasome activation. These insights into the relationship between the NLRP3 inflammasome and the pathogenesis of EVs infection may ultimately inform the development of novel antiviral drugs.
A Novel Deep Flexible Neural Forest Model for Classification of Cancer Subtypes Based on Gene Expression Data

Classification of cancer subtypes is of paramount importance for diagnosis and prognosis of cancer. In recent years, deep learning methods have gained considerable popularity for cancer subtype classification, however, the structure of the neural network is difficult to determine and the performance of the deep network depends largely on its structure. To address this problem, a flexible neural tree (FNT) may be used. FNT is a special neural network with the advantage of automatic optimization of structure and parameters which cannot be used for multi-class classification. In this paper, a deep flexible neural forest (DFNForest) model is proposed, a novel ensemble of FNT model to aid with the classification of cancer subtypes. The proposed DFNForest model differs from the conventional FNT model because it transforms a multi-classification problem into many binary classification problems for each forest. We explore the cascade structure of DFNForest to deepen the flexible neural tree model so that the depth of the model is increased without introducing additional parameters. In addition to the DFNForest model, this paper proposes a combination of fisher ratio and neighborhood rough set for dimensionality reduction of gene expression data to obtain higher classification performance. The experiments on RNA-seq gene expression data show that our gene selection method has higher accuracy with fewer genes and the proposed DFNForest model has better performance for classification of cancer subtypes as compared to the conventional methods.