Miracle-dz/UniFAR-FTUs huggingface dataset:
AURORAMAP: A BOUNDARY-HOMOGRAPHIC VISUALIZATION FOR MAPPING MULTIVARIATE 2D SPATIAL DISTRIBUTIONS

Visualizing multidimensional spatial data is an essential visual analysis strategy, it helps us interpret and communicate how different variables correlate to geographical information. In this study, we proposed an abstract contextual visualization that encodes data on the boundaries of spatial distributions and developed a new algorithm, AuroraMap. AuroraMap projects the spatial data to the boundaries of the distributions and color-encodes the densities continuously. We further conducted the user experiments, and the results show users can detect the relative locations and scopes of the clusters. Furthermore, users can quantitatively determine the peak value of each cluster’s density. The method provides three contributions: (1) freeing up and saving the graphical visualization space; (2) assisting the users to quantitatively estimate the clusters inside distributions; (3) facilitating the visual comparisons for multiple and multivariate spatial distributions. In the end, we demonstrated two applications with real-world religious infrastructural data by AuroraMap to visualize geospatial data within complex boundaries and compare multiple variables in one graph.
Nanocomplexes of Graphene Oxide and Platinum Nanoparticles against Colorectal Cancer Colo205, HT-29, HTC-116, SW480, Liver Cancer HepG2, Human Breast Cancer MCF-7, and Adenocarcinoma LNCaP and Human Cervical Hela B Cell Lines

Inefficient drug administration into cancer cells is related to the chemoresistance of cancer cells caused by genetic mutations including genes involved in drug transport, enzyme metabolism, and/or DNA damage repair. The objective of the present study was to evaluate the properties of platinum (NP-Pt), graphene oxide (GO), and the nanocomplex of GO functionalized with platinum nanoparticles (GO-NP-Pt) against several genetically, phenotypically, and metabolically different cancer cell lines: Colo205, HT-29, HTC-116, SW480, HepG2, MCF-7, LNCaP, and Hela B. The anticancer effects toward the cancer cell lines were evaluated by 2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxyanilide salt (XTT) and bromodeoxyuridine (BrdU) assays and measurements of cell apoptosis and morphology deformations. The NP-Pt and GO could effectively be introduced to cancer cells, but more effective delivery was observed after GO-NP-Pt treatment. The delivery of the GO-NP-Pt nanocomplex significantly decreased the viability of Colo 205 and HepG2 cells, but did not increase the cytotoxicity of other investigated cancer cells. The nanocomplex GO-NP-Pt also significantly increased the apoptosis of Colo 205 and HepG2 cancer cells. The obtained results suggest that the nanocomplex GO-NP-Pt is a remarkable nanostructure that can improve the delivery of Pt nanoparticles into cancer cells and has potential anticancer applications.
Semisupervised Dictionary Learning with Graph Regularized and Active Points

Supervised Dictionary Learning has gained much interest in the recent decade and has shown significant performance improvements in image classification. However, in general, supervised learning needs a large number of labelled samples per class to achieve an acceptable result. In order to deal with databases which have just a few labelled samples per class, semi-supervised learning, which also exploits unlabelled samples in training phase is used. Indeed, unlabelled samples can help to regularize the learning model, yielding an improvement of classification accuracy. In this paper, we propose a new semi-supervised dictionary learning method based on two pillars: on one hand, we enforce manifold structure preservation from the original data into sparse code space using Locally Linear Embedding, which can be considered a regularization of sparse code; on the other hand, we train a semi-supervised classifier in sparse code space. We show that our approach provides an improvement over state-of-the-art semi-supervised dictionary learning methods.
Cellulose Biomaterials for Tissue Engineering

In this review, we highlight the importance of nanostructure of cellulose-based biomaterials to allow cellular adhesion, the contribution of nanostructure to macroscale mechanical properties, and several key applications of these materials for fundamental scientific research and biomedical engineering. Different features on the nanoscale can have macroscale impacts on tissue function. Cellulose is a diverse material with tunable properties and is a promising platform for biomaterial development and tissue engineering. Cellulose-based biomaterials offer some important advantages over conventional synthetic materials. Here we provide an up-to-date summary of the status of the field of cellulose-based biomaterials in the context of bottom-up approaches for tissue engineering. We anticipate that cellulose-based material research will continue to expand because of the diversity and versatility of biochemical and biophysical characteristics highlighted in this review.
Tax incentive and household saving strategy: A regression discontinuity approach to catch-up contributions

ABSTRACT This study examined how the catch-up provision—a tax-deferral incentive—impacts total retirement saving and household saving strategies. Using the Survey of Consumer Finance from 2004 to 2016, this study found that the catch-up provision does not have a positive impact on total retirement saving in the majority of cases; however, this provision was found to positively impact contributions to defined contribution plans among middle-class households whose income ranges were between $50,00 and $100,000. The study also found that household saving and consumption habits, which include self-control factors, as well as employer-match programs, play an important role in increasing savings for retirement. The results of this study have implications for future work on a broad spectrum of social policies and programs that promote financial security in retirement.
IgA and FcαRI: Pathological Roles and Therapeutic Opportunities

Immunoglobulin A (IgA) is the most abundant antibody class present at mucosal surfaces. The production of IgA exceeds the production of all other antibodies combined, supporting its prominent role in host-pathogen defense. IgA closely interacts with the intestinal microbiota to enhance its diversity, and IgA has a passive protective role via immune exclusion. Additionally, inhibitory ITAMi signaling via the IgA Fc receptor (FcαRI; CD89) by monomeric IgA may play a role in maintaining homeostatic conditions. By contrast, IgA immune complexes (e.g., opsonized pathogens) potently activate immune cells via cross-linking FcαRI, thereby inducing pro-inflammatory responses resulting in elimination of pathogens. The importance of IgA in removal of pathogens is emphasized by the fact that several pathogens developed mechanisms to break down IgA or evade FcαRI-mediated activation of immune cells. Augmented or aberrant presence of IgA immune complexes can result in excessive neutrophil activation, potentially leading to severe tissue damage in multiple inflammatory, or autoimmune diseases. Influencing IgA or FcαRI-mediated functions therefore provides several therapeutic possibilities. On the one hand (passive) IgA vaccination strategies can be developed for protection against infections. Furthermore, IgA monoclonal antibodies that are directed against tumor antigens may be effective as cancer treatment. On the other hand, induction of ITAMi signaling via FcαRI may reduce allergy or inflammation, whereas blocking FcαRI with monoclonal antibodies, or peptides may resolve IgA-induced tissue damage. In this review both (patho)physiological roles as well as therapeutic possibilities of the IgA-FcαRI axis are addressed.
Applicability of Structural Equation Modeling in Practice of Optimal Dietary Intake amongst Recuperating Alcoholics

Background: Structural equation modeling is a methodology for representing, estimating, and testing a network of relationships between measured variables and latent constructs. This statistical approach is used quite readily to test theoretical models and provide overall fit indices that determine whether the model tested actually fits the observed data. Objective: We aimed at providing recuperating alcoholics with the basics of structural equation modeling so they can assimilate evidence from studies that use this statistical tool to incorporate such findings into optimal dietary intake practice. Methods: This cross-sectional study was conducted from August to November 2018 amongst recuperating alcoholics receiving rehabilitation in Asumbi treatment center of Homabay County, Kenya. Structural equation modeling determined the evidence of practice of optimal dietary intake amongst recuperating alcoholics. Results: Structural model parameter estimation showed high values, especially for subjective norm (β=0.62, p<0.01, n=207) that significantly influenced practice of optimal dietary intake. Conclusion: Nutritionist or other health professionals who wish to use SEM to explore such relations should apply all the steps used in SEM and ensure they have a sample that is sufficient.
Estimation of physiological genomic estimated breeding values (PGEBV) combining full hyperspectral and marker data across environments for grain yield under combined heat and drought stress in tropical maize (Zea mays L.)

High throughput phenotyping technologies are lagging behind modern marker technology impairing the use of secondary traits to increase genetic gains in plant breeding. We aimed to assess whether the combined use of hyperspectral data with modern marker technology could be used to improve across location pre-harvest yield predictions using different statistical models. A maize bi-parental doubled haploid (DH) population derived from F1, which consisted of 97 lines was evaluated in testcross combination under heat stress as well as combined heat and drought stress during the 2014 and 2016 summer season in Ciudad Obregon, Sonora, Mexico (27°20” N, 109°54” W, 38 m asl). Full hyperspectral data, indicative of crop physiological processes at the canopy level, was repeatedly measured throughout the grain filling period and related to grain yield. Partial least squares regression (PLSR), random forest (RF), ridge regression (RR) and Bayesian ridge regression (BayesB) were used to assess prediction accuracies on grain yield within (two-fold cross-validation) and across environments (leave-one-environment-out-cross-validation) using molecular markers (M), hyperspectral data (H) and the combination of both (HM). Highest prediction accuracy for grain yield averaged across within and across location predictions (rGP) were obtained for BayesB followed by RR, RF and PLSR. The combined use of hyperspectral and molecular marker data as input factor on average had higher predictions for grain yield than hyperspectral data or molecular marker data alone. The highest prediction accuracy for grain yield across environments was measured for BayesB when molecular marker data and hyperspectral data were used as input factors, while the highest within environment prediction was obtained when BayesB was used in combination with hyperspectral data. It is discussed how the combined use of hyperspectral data with molecular marker technology could be used to introduce physiological genomic estimated breeding values (PGEBV) as a pre-harvest decision support tool to select genetically superior lines.
Protease Activity Profiling of Snake Venoms Using High-Throughput Peptide Screening

Snake venom metalloproteinases (SVMPs) and snake venom serine proteinases (SVSPs) are among the most abundant enzymes in many snake venoms, particularly among viperids. These proteinases are responsible for some of the clinical manifestations classically seen in viperid envenomings, including hemorrhage, necrosis, and coagulopathies. The objective of this study was to investigate the enzymatic activities of these proteins using a high-throughput peptide library to screen for the proteinase targets of the venoms of five viperid (Echis carinatus, Bothrops asper, Daboia russelii, Bitis arietans, Bitis gabonica) and one elapid (Naja nigricollis) species of high medical importance. The proteinase activities of these venoms were each tested against 360 peptide substrates, yielding 2160 activity profiles. A nonlinear regression model that accurately described the observed enzymatic activities was fitted to the experimental data, allowing for the comparison of cleavage rates across species. In this study, previously unknown protein targets of snake venom proteinases were identified, potentially implicating novel human and animal proteins that may be involved in the pathophysiology of viper envenomings. The functional relevance of these targets was further evaluated and discussed. These new findings may contribute to our understanding of the clinical manifestations and underlying biochemical mechanisms of snakebite envenoming by viperid species.
Transcriptome-wide piRNA profiling in human gastric cancer

Piwi-interacting RNAs (piRNAs) comprise the largest class of non-coding RNAs. They represent a molecular feature shared by all non-aging biological systems, including germline and somatic cancer stem cells, which display an indefinite capacity of renewal and proliferation and are potentially immortal. They have been identified in animal stomachs, but their relationship with human gastric cancers remains largely unclear. The present study aimed to identify the piRNAs associated with human gastric cancers across the whole transcriptome. Fresh tumor tissues and adjacent non-tumorous tissues from stomachs were examined using a piRNA microarray (23,677 piRNAs) that was then validated by qPCR. The differential expression of piRNAs between cases and controls was analyzed. The transposable elements (TEs) that are potentially targeted by the risk piRNAs were searched. The expression of the nearest genes that are complementary to the sequences of the piRNAs was examined in the stomach tissue. The regulatory effects of genome-wide significant and replicated cancer-risk DNA variants on the piRNA expression in stomach were tested. Based on the findings, we identified a total of 8,759 piRNAs in human stomachs. Of all, 50 were significantly (P<0.05) and differentially (>2-fold change) expressed between the cases and controls, and 64.7% of the protein-coding genes potentially regulated by the gastric cancer-associated piRNAs were expressed in the human stomach. The expression of many cancer-associated piRNAs was correlated with the genome-wide and replicated cancer-risk SNPs. In conclusion, we conclude that piRNAs are abundant in human stomachs and may play important roles in the etiological processes of gastric cancers.
A Clockless Temperature-Compensated Nanowatt Analog Front-End for Wake-Up Radios Based on a Band-Pass Envelope Detector

This paper presents an Analog Front-End for integrated Wake-Up Radios. The proposed Analog Front-End is composed of an envelope detector, a Schmitt trigger and a biasing block and has three distinctive features: i) clockless solution, which does not require an always-on oscillator; ii) an envelope detector with band-pass response which leads to smaller capacitance, thus easier integration, and low-frequency noise suppression; iii) temperature compensated biasing scheme. An active scheme for the detector is used based on MOSFETs operated in the subthreshold region with a self-biased topology. Advantages and drawbacks of the proposed architecture are analyzed. A prototype was fabricated in the STMicroelectronics 90-nm BCD technology. The overall power consumption, excluding the biasing block, is 36 nW at 1.2 V. A 10 −3 Bit Error Rate is measured with a 771-MHz, 2-kbit/s OOK modulated input signal with −46 dBm power at room temperature and at −20 °C, and with almost −43 dBm power at 60 °C.
Distributed State Estimation for AC Power Systems using Gauss-Newton ALADIN

This paper proposes a structure exploiting algorithm for solving non-convex power system state estimation problems in distributed fashion. Because the power flow equations in large electrical grid networks are non-convex equality constraints, we develop a tailored state estimator based on Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method, which can handle these nonlinearities efficiently. Here, our focus is on using Gauss-Newton Hessian approximations within ALADIN to arrive at an efficient (computationally and communicationally) variant of ALADIN for network maximum likelihood estimation problems. Analyzing the IEEE 30-Bus system we illustrate how the proposed algorithm can be used to solve non-trivial network state estimation problems. We also compare the method with existing distributed parameter estimation codes in order to illustrate its performance.
Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications

Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.
AN IMPROVED CURRENT CONTROL SCHEME FOR THREE-PHASE SHUNT ACTIVE POWER FILTERS TO OVERCOME POWER QUALITY ISSUES

The paper proposes an advanced current control strategy to enhance the performance of shunt Active Power Filter(APF) with the aid of PI-VPI controllers in order to achieve harmonic current compensation and to overcome the various power quality issues that exists in power system. The proposed control scheme requires only two current sensors and does not need a harmonic detector. The absence of harmonic detector not only simplifies the control scheme but also improves accuracy of the APF. Furthermore, the total cost to implement the proposed APF becomes lower, owing to the minimized current sensors and the use of a four-switch three-phase inverter. Keywords— Four-switch three-phase inverter (FSTPI), harmonic current compensation, PI-VPI current controller, power quality, Shunt active power filters
A Compound Computational Model for Filling-In Processes Triggered by Edges: Watercolor Illusions

The goal of our research was to develop a compound computational model with the ability to predict different variations of the “watercolor effects” and additional filling-in effects that are triggered by edges. The model is based on a filling-in mechanism solved by a Poisson equation, which considers the different gradients as “heat sources” after the gradients modification. The biased (modified) contours (edges) are ranked and determined according to their dominancy across the different chromatic and achromatic channels. The color and intensity of the perceived surface are calculated through a diffusive filling-in process of color triggered by the enhanced and biased edges of stimulus formed as a result of oriented double-opponent receptive fields. The model can successfully predict both the assimilative and non-assimilative watercolor effects, as well as a number of “conflicting” visual effects. Furthermore, the model can also predict the classic Craik–O'Brien–Cornsweet (COC) effect. In summary, our proposed computational model is able to predict most of the “conflicting” filling-in effects that derive from edges that have been recently described in the literature, and thus supports the theory that a shared visual mechanism is responsible for the vast variety of the “conflicting” filling-in effects that derive from edges.
Prognositic significance of P-cadherin expression in breast cancer

Background: P-cadherin is a calcium-dependent cell–cell adhesion glycoprotein. It has been implicated in invasiveness and metastasis. However, the clinical prognostic value of overexpression of P-cadherin in patients with breast cancer (BC) remains unsettled. Methods: A systematic literature search will be performed in all available databases to quantitatively review eligible studies and identify all relevant data, which could be used to detect the relationship between overexpression of P-cadherin and overall survival (OS), disease-free survival (DFS), and clinicopathological parameters. Hazard ratio and 95% confidence intervals (CIs) or P value will be employed as effect measures to estimate the correlation between P-cadherin and the oncologic outcomes including overall survival (OS), disease-free survival (DFS). Odds ratios (ORs) and the 95% CIs will be evaluated for the pooled analysis of the correlation between P-cadherin expression and clinicopathological features. We will use the Review Manager (Revman) 5.3. 5 software (Cochrane Community, London, United Kingdom) and STATA 14 software (version 14.0; Stata Corp, College Station, TX) to perform the meta-analysis to calculate the data. Results: The review will provide a high-quality synthesis of current evidence of the prognostic role of P-cadherin in BCs. The results will be published in a peer-reviewed journal. Conclusion: We hope that the results of this study will provide significant evidence to assess whether the expression of P-cadherin is associated with poor prognosis in patients with BC. PROSPERO registration number: This meta-analysis protocol has been registered in the PROSPERO network with registration number: CRD42019119880.
Impact of an Interfering Node on Unmanned Aerial Vehicle Communications

Unlike terrestrial communications, unmanned aerial vehicle (UAV) communications have some advantages such as the line-of-sight (LoS) environment and flexible mobility. However, the interference will be still inevitable. In this paper, we analyze the effect of an interfering node on the UAV communications by considering the LoS probability and different channel fading for LoS and non-line-of-sight (NLoS) links, which are affected by horizontal and vertical distances of the communication link. We then derive a closed-form outage probability in the presence of an interfering node for all the possible scenarios and environments of main and interference links. After discussing the impacts of transmitting and interfering node parameters on the outage probability, we show the existence of the optimal height of the UAV that minimizes the outage probability. We also show the NLoS environment can be better than the LoS environment if the average received power of the interference is more dominant than that of the transmitting signal on UAV communications. Finally, we analyze the network outage probability for the case of multiple interfering nodes using stochastic geometry and the outage probability of the single interfering node case, and show the effect of the interfering node density on the optimal height of the UAV.