Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel rules based classifier using DNA strand displacement for multiple molecular markers detection

The detection of molecular markers such as micro ribonucleic acid (miRNA) expression levels in the cells are essential for diagnosis of a disease, especially cancer. Recently, a huge amount of molecular markers on cell is being extracted by single molecular detection method, as results, excessively detection process and time is required. Meanwhile DNA computing has capabilities to interact with biological nucleic acids and massively parallel processing, we propose parallel rule-based classifier using DNA strand displacement reaction as method of detecting multiple molecular markers. Two DNA reaction of parallel sensing and encoding system of the IF-THEN rules have been developed in our proposed model. In this paper, we compared between the proposed method and the AND gate method for the classification result. Moreover, based on the simulation results show that our method is possible as an alternative solution for the programmable fast big molecular markers detection and diagnosis.
Comparing Mobile Health Strategies to Improve Medication Adherence for Veterans With Coronary Heart Disease (Mobile4Meds): Protocol for a Mixed-Methods Study

Background Adherence to antiplatelet medications is critical to prevent life threatening complications (ie, stent thrombosis) after percutaneous coronary interventions (PCIs), yet rates of nonadherence range from 21-57% by 12 months. Mobile interventions delivered via text messaging or mobile apps represent a practical and inexpensive strategy to promote behavior change and enhance medication adherence. Objective The Mobile4Meds study seeks to determine whether text messaging or a mobile app, compared with an educational website control provided to all Veterans, can improve adherence to antiplatelet therapy among patients following acute coronary syndrome (ACS) or PCI. The three aims of the study are to: (1) determine preferences for content and frequency of text messaging to promote medication adherence through focus groups; (2) identify the most patient-centered app that promotes adherence, through a content analysis of all commercially available apps for medication adherence and focus groups centered on usability; and (3) compare adherence to antiplatelet medications in Veterans after ACS/PCI via a randomized clinical trial (RCT). Methods We will utilize a mixed-methods design that uses focus groups to achieve the first and second aims (N=32). Patients will be followed for 12 months after being randomly assigned to one of three arms: (1) customized text messaging, (2) mobile app, or (3) website-control groups (N=225). Medication adherence will be measured with electronic monitoring devices, pharmacy records, and self-reports. Results Enrollment for the focus groups is currently in progress. We expect to enroll patients for the RCT in the beginning of 2018. Conclusions Determining the efficacy of mobile technology using a Veteran-designed protocol to promote medication adherence will have a significant impact on Veteran health and public health, particularly for individuals with chronic diseases that require strict medication adherence. Trial Registration ClinicalTrials.gov NCT03022669
Magnetoelectric effect in coated fibrous magnetic–piezoelectric composites

In this study, the magnetoelectroelastic behavior is investigated for magnetic–piezoelectric composites with coated reinforcements under antiplane mechanical and in-plane electromagnetic loads. The coating is considered as a layer with properties different from those of the inclusion and the matrix. The developed model is based on the solution of the problem of the magnetoelectroelastic coated inclusion embedded in an infinite homogeneous medium obtained from two different approaches. The first is the complex variable method integrating the Laurent expansion method, and the second is a micromechanical approach based simultaneously on Green’s function techniques and the interfacial operators. The exact solution is obtained and used as background to determine the effective magnetoelectroelastic properties of the composite containing thinly coated inclusions embedded in a matrix through Mori–Tanaka’s model. Numerical investigations into magnetoelectroelastic moduli responsible for the magnetoelectric effect are presented as functions of the volume fraction and characteristics of the coated inclusions. Comparisons with existing models are presented for various fractions of the coated inclusions.
An empirical study of static program slice size

This article presents results from a study of all slices from 43 programs, ranging up to 136,000 lines of code in size. The study investigates the effect of five aspects that affect slice size. Three slicing algorithms are used to study two algorithmic aspects: calling-context treatment and slice granularity. The remaining three aspects affect the upstream dependencies considered by the slicer. These include collapsing structure fields, removal of dead code, and the influence of points-to analysis. The results show that for the most precise slicer, the average slice contains just under one-third of the program. Furthermore, ignoring calling context causes a 50% increase in slice size, and while (coarse-grained) function-level slices are 33% larger than corresponding statement-level slices, they may be useful predictors of the (finer-grained) statement-level slice size. Finally, upstream analyses have an order of magnitude less influence on slice size.
Cooperative Metabolism in a Three-Partner Insect-Bacterial Symbiosis Revealed by Metabolic Modeling

ABSTRACT An important factor determining the impact of microbial symbionts on their animal hosts is the balance between the cost of nutrients consumed by the symbionts and the benefit of nutrients released back to the host, but the quantitative significance of nutrient exchange in symbioses involving multiple microbial partners has rarely been addressed. In this study on the association between two intracellular bacterial symbionts, “Candidatus Portiera aleyrodidarum” and “Candidatus Hamiltonella defensa,” and their animal host, the whitefly Bemisia tabaci, we apply metabolic modeling to investigate host-symbiont nutrient exchange. Our in silico analysis revealed that >60% of the essential amino acids and related metabolites synthesized by “Candidatus Portiera aleyrodidarum” are utilized by the host, including a substantial contribution of nitrogen recycled from host nitrogenous waste, and that these interactions are required for host growth. In contrast, “Candidatus Hamiltonella defensa” retains most or all of the essential amino acids and B vitamins that it is capable of synthesizing. Furthermore, “Candidatus Hamiltonella defensa” suppresses host growth in silico by competition with “Candidatus Portiera aleyrodidarum” for multiple host nutrients, by suppressing “Candidatus Portiera aleyrodidarum” growth and metabolic function, and also by consumption of host nutrients that would otherwise be allocated to host growth. The interpretation from these modeling outputs that “Candidatus Hamiltonella defensa” is a nutritional parasite could not be inferred reliably from gene content alone but requires consideration of constraints imposed by the structure of the metabolic network. Furthermore, these quantitative models offer precise predictions for future experimental study and the opportunity to compare the functional organization of metabolic networks in different symbioses. IMPORTANCE The metabolic functions of unculturable intracellular bacteria with much reduced genomes are traditionally inferred from gene content without consideration of how the structure of the metabolic network may influence flux through metabolic reactions. The three-compartment model of metabolic flux between two bacterial symbionts and their insect host constructed in this study revealed that one symbiont is structured to overproduce essential amino acids for the benefit of the host, but the essential amino acid production in the second symbiont is quantitatively constrained by the structure of its network, rendering it “selfish” with respect to these nutrients. This study demonstrates the importance of quantitative flux data for elucidation of the metabolic function of symbionts. The in silico methodology can be applied to other symbioses with intracellular bacteria.
A framework with updateable joint images re-ranking for Person Re-identification

Person re-identification plays an important role in realistic video surveillance with increasing demand for public safety. In this paper, we propose a novel framework with rules of updating images for person re-identification in real-world surveillance system. First, Image Pool is generated by using mean-shift tracking method to automatically select video frame fragments of the target person. Second, features extracted from Image Pool by convolutional network work together to re-rank original ranking list of the main image and matching results will be generated. In addition, updating rules are designed for replacing images in Image Pool when a new image satiating with our updating critical formula in video system. These rules fall into two categories: if the new image is from the same camera as the previous updated image, it will replace one of assist images; otherwise, it will replace the main image directly. Experiments are conduced on Market-1501, iLIDS-VID and PRID-2011 and our ITSD datasets to validate that our framework outperforms on rank-1 accuracy and mAP for person re-identification. Furthermore, the update ability of our framework provides consistently remarkable accuracy rate in real-world surveillance system.
Current sights for mechanisms of deoxynivalenol‐induced hepatotoxicity and prospective views for future scientific research: A mini review

Deoxynivalenol (DON) belongs to the group B trichothecenes, which are the most common mycotoxins in cereal commodities. It is very stable within the temperature range 170–350 °C, showing no reduction in its concentration after 30 min at 170 °C. This chemical property is a very dangerous factor for human and animal health. Liver is also responsible for the detoxification and formation of DON‐glucuronide in both human and animals. Some studies already demonstrated that DON could induce liver damage remarkably through DON altering expressions of p53, caspase‐3, caspase‐7, caspase‐8 and Bax in different cell lines. At the same time, other publications illustrated some opposite results. At present, a full and systematic discussion of the hepatic toxicity of DON is still lacking. Therefore, the aim of the present review is to summarize and update the prominent evidence, regarding DON effects on liver tissues and cell lines. Moreover, based on the current studies we outline some of the identified molecule targets or pathways involved in DON‐induced hepatotoxicity, and put forward our opinions and suggest an hypothesis for future research. Copyright © 2016 John Wiley & Sons, Ltd.
Graph Bundling by Kernel Density Estimation

We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved graph edges into the height gradient flow. Our technique can be easily and efficiently implemented using standard graphics acceleration techniques and produces graph bundlings of similar appearance and quality to state‐of‐the‐art methods at a fraction of the cost. Additionally, we show how to create bundled layouts constrained by obstacles and use shading to convey information on the bundling quality. We demonstrate our method on several large graphs.
Three-dimensional brain arteriovenous malformation models for clinical use and resident training

Background: To fabricate three-dimensional (3D) models of brain arteriovenous malformation (bAVM) and report our experience with customized 3D printed models of patients with bAVM as an educational and clinical tool for patients, doctors, and surgical residents. Methods: Using computerized tomography angiography (CTA) or digital subtraction angiography (DSA) images, the rapid prototyping process was completed with specialized software and “in-house” 3D printing service. Intraoperative validation of model fidelity was performed by comparing to DSA images of the same patient during the endovascular treatment process. 3D bAVM models were used for preoperative patient education and consultation, surgical planning, and resident training. Results: 3D printed bAVM models were successful made. By neurosurgeons’ evaluation, the printed models precisely replicated the actual bAVM structure of the same patients (n = 7, 97% concordance, range 95%–99% with average of < 2 mm variation). The use of 3D models was associated shorter time for preoperative patient education and consultation, higher acceptable of the procedure for patients and relatives, shorter time between obtaining intraoperative DSA data and the start of endovascular treatment. Thirty surgical residents from residency programs tested the bAVM models and provided feedback on their resemblance to real bAVM structures and the usefulness of printed solid model as an educational tool. Conclusions: Patient-specific 3D printed models of bAVM can be constructed with high fidelity. 3D printed bAVM models were proven to be helpful in preoperative patient consultation, surgical planning, and resident training.
Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction

Recently, a number of approaches to low-dose computed tomography (CT) have been developed and deployed in commercialized CT scanners. Tube current reduction is perhaps the most actively explored technology with advanced image reconstruction algorithms. Sparse data sampling is another viable option to the low-dose CT, and sparse-view CT has been particularly of interest among the researchers in CT community. Since analytic image reconstruction algorithms would lead to severe image artifacts, various iterative algorithms have been developed for reconstructing images from sparsely view-sampled projection data. However, iterative algorithms take much longer computation time than the analytic algorithms, and images are usually prone to different types of image artifacts that heavily depend on the reconstruction parameters. Interpolation methods have also been utilized to fill the missing data in the sinogram of sparse-view CT thus providing synthetically full data for analytic image reconstruction. In this paper, we introduce a deep-neural-network-enabled sinogram synthesis method for sparse-view CT, and show its outperformance to the existing interpolation methods and also to the iterative image reconstruction approach.
Effects of Rotenoids from Indigofera tinctoria (L) R. Br. On Callosobruchus chinensis and Trogoderma granarium

Six rotenoids have isolated from Indigofera tinctoria, characterized by means of their spectral data and/or co-tlc. The total estimation studies revealed that rotenol was major rotenoid in both in vivo and in vitro. Similarly, by elicitation studies, we observed that summatrol synthesis was higher at the concentration of 0.01 mM ergosterol and salicylic acid. Among all sequential fractions, methanol fraction demonstrated modern mortality rate (70%) on 24h against Callosobruchus chinensis and summatrol. the isolated rotenoid, possesses maximum mortality rate (80%) at the concentration of 1.0 and 2.0% after 12h against C. chinensis and 70% mortality rate to Trogoderma granarium.
On Sampling and Greedy MAP Inference of Constrained Determinantal Point Processes

Subset selection problems ask for a small, diverse yet representative subset of the given data. When pairwise similarities are captured by a kernel, the determinants of submatrices provide a measure of diversity or independence of items within a subset. Matroid theory gives another notion of independence, thus giving rise to optimization and sampling questions about Determinantal Point Processes (DPPs) under matroid constraints. Partition constraints, as a special case, arise naturally when incorporating additional labeling or clustering information, besides the kernel, in DPPs. Finding the maximum determinant submatrix under matroid constraints on its row/column indices has been previously studied. However, the corresponding question of sampling from DPPs under matroid constraints has been unresolved, beyond the simple cardinality constrained k-DPPs. We give the first polynomial time algorithm to sample exactly from DPPs under partition constraints, for any constant number of partitions. We complement this by a complexity theoretic barrier that rules out such a result under general matroid constraints. Our experiments indicate that partition-constrained DPPs offer more flexibility and more diversity than k-DPPs and their naive extensions, while being reasonably efficient in running time. We also show that a simple greedy initialization followed by local search gives improved approximation guarantees for the problem of MAP inference from k- DPPs on well-conditioned kernels. Our experiments show that this improvement is significant for larger values of k, supporting our theoretical result.
Lurasidone Dose Escalation in Early Nonresponding Patients With Schizophrenia: A Randomized, Placebo-Controlled Study.

OBJECTIVE To assess the effect of dose increase in adult patients with schizophrenia who demonstrate inadequate initial response to standard-dose lurasidone and to evaluate the efficacy of low-dose lurasidone in adult patients with schizophrenia. METHODS In this randomized, double-blind, placebo-controlled study conducted between May 2013 and June 2014, hospitalized patients with acute schizophrenia (DSM-IV-TR criteria) were randomly assigned to double-blind treatment with lurasidone 20 mg/d (n = 101), lurasidone 80 mg/d (n = 199), or placebo (n = 112). Nonresponders to lurasidone 80 mg/d (Positive and Negative Syndrome Scale [PANSS] score decrease < 20%) at 2 weeks were re-randomized to lurasidone 80 mg/d or 160 mg/d for the remaining 4 weeks of the study. The primary outcome measure was change from baseline to week 6 in PANSS total score. RESULTS In nonresponders to lurasidone 80 mg/d (n = 95), dose increase to 160 mg/d at week 2 significantly reduced PANSS total score at week 6 study endpoint compared with continuing 80 mg/d (-16.6 vs -8.9; P < .05 [effect size = 0.52]). While a comparable magnitude of improvement was observed in Clinical Global Impression-Severity (CGI-S) score from week 2 to week 6 endpoint for lurasidone 160 mg/d versus 80 mg/d (-1.0 vs -0.6; effect size = 0.44), the difference was not statistically significant (P = .052). Patients receiving lurasidone 20 mg/d did not demonstrate significant improvement compared with placebo at week 6 in PANSS total (-17.6 vs -14.5; P = .26) or CGI-S (-0.93 vs -0.73; P = .17) scores. Few dose-related adverse effects associated with lurasidone were observed. CONCLUSIONS In adult patients with schizophrenia demonstrating nonresponse to 2 weeks of treatment with lurasidone 80 mg/d, dose increase to 160 mg/d resulted in significant symptom improvement compared with continuing lurasidone 80 mg/d. Lurasidone 20 mg/d was not associated with significant improvement in psychotic symptoms in adult patients with schizophrenia. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT01821378.
Multi-scale approaches for high-speed imaging and analysis of large neural populations

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover neural activity from data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to “zoom out” by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution. Author Summary The voxel rate of imaging systems ultimately sets the limit on the speed of data acquisition. These limits often mean that only a small fraction of the activity of large neuronal populations can be observed at high spatio-temporal resolution. For imaging of very large populations with single cell resolution, temporal resolution is typically sacrificed. Here we propose a multi-scale approach to achieve single cell precision using fast imaging at reduced spatial resolution. In the first phase the spatial location and shape of each neuron is obtained at standard spatial resolution; in the second phase imaging is performed at much lower spatial resolution. We show that we can apply a demixing algorithm to accurately recover each neuron’s activity from the low-resolution data by exploiting the high-resolution cellular maps estimated in the first imaging phase. Thus by decreasing the spatial resolution in the second phase, we can compress the video data significantly, and potentially acquire images over an order-of-magnitude larger area, or image at significantly higher temporal resolution, with minimal loss in accuracy of the recovered neuronal activity. We evaluate this approach on real data from light-sheet and 2-photon calcium imaging.
Experimental study on the wettability of microtextured surfaces generated by the ultrasonic-assisted face turning

Surface texture technology is a useful method for controlling the wettability. In this paper, ultrasonic-assisted face turning is implemented to create a microscaled texture on the flat faces. The effect of process parameters on the wettability of untextured and textured patterns is investigated by the full factorial method. Experimental results showed that the ultrasonic-assisted face turning is an effective method to create microdimples (periodic ripples) on the flat faces. Considered factors are turning (cutting speed, feed) and ultrasonic (vibration direction and vibration amplitude) parameters, which control the configuration and dimensions of microstructures. Surfaces machined by ultrasonic-assisted face turning have more liquid–solid interfacial areas compared with those processed by conventional turning. By changing the process parameters, expansion of microdimples in the cutting direction changes, hence the ratio between the interfacial and projected area and adsorption between the surface and water increases. So, textured surfaces by the ultrasonic-assisted face turning have better wettability performance in comparison to the untextured surfaces.