Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward real-time endoscopically-guided robotic navigation based on a 3D virtual surgical field model

The challenge is to accurately guide the surgical tool within the three-dimensional (3D) surgical field for roboticallyassisted operations such as tumor margin removal from a debulked brain tumor cavity. The proposed technique is 3D image-guided surgical navigation based on matching intraoperative video frames to a 3D virtual model of the surgical field. A small laser-scanning endoscopic camera was attached to a mock minimally-invasive surgical tool that was manipulated toward a region of interest (residual tumor) within a phantom of a debulked brain tumor. Video frames from the endoscope provided features that were matched to the 3D virtual model, which were reconstructed earlier by raster scanning over the surgical field. Camera pose (position and orientation) is recovered by implementing a constrained bundle adjustment algorithm. Navigational error during the approach to fluorescence target (residual tumor) is determined by comparing the calculated camera pose to the measured camera pose using a micro-positioning stage. From these preliminary results, computation efficiency of the algorithm in MATLAB code is near real-time (2.5 sec for each estimation of pose), which can be improved by implementation in C++. Error analysis produced 3-mm distance error and 2.5 degree of orientation error on average. The sources of these errors come from 1) inaccuracy of the 3D virtual model, generated on a calibrated RAVEN robotic platform with stereo tracking; 2) inaccuracy of endoscope intrinsic parameters, such as focal length; and 3) any endoscopic image distortion from scanning irregularities. This work demonstrates feasibility of micro-camera 3D guidance of a robotic surgical tool.
Glutamine Enhances the Hypoglycemic Effect of Insulin in L6 Cells via Phosphatidylinositol-3-Kinase (PI3K)/Protein Kinase B (AKT)/Glucose Transporter 4 (GLUT4) Signaling Pathway

Background Diabetes mellitus (DM) is characterized by a decreased blood level of glutamine (Gln), which may contribute to the disturbance in the effect of insulin on skeletal muscle. Therefore, it is crucial to study how to improve the effect of insulin on skeletal muscle by increasing Gln. In the present study, we investigated the effect of Gln on the hypoglycemic action of insulin in skeletal muscle L6 cells at high glucose levels through the insulin signaling pathway and glycogen synthesis pathway. Material/Methods The L6 cells were cultured in and stimulated by Gln and insulin. The glutamine analogue, L-Gamma-Glutamyl-p-nitroanilide (GPNA), was used for verifying the effect of Gln. The expression of insulin signaling molecules, including phosphatidylinositol-3-kinase (PI3K), 3-phosphoinositide-dependent protein kinase-1 (PDK1), protein kinase B (AKT), protein kinase C zeta (PKCζ), and glucose transporter 4 (GLUT4), were detected by real-time PCR and Western blot analysis, GLUT4 translocation was observed by immunofluorescence staining, glycogen synthase kinase (GSK) was analyzed by Western blotting, and glucose uptake was measured by glucose oxidase method (GOD). Results The results demonstrated that Gln combined with insulin remarkably up-regulated PI3K and PDK1 and also increased AKT and PKCζ phosphorylation. The present study shows that Gln enhanced the impact of insulin on GLUT4 and its translocation. The results of glucose uptake and GSK phosphorylation further confirmed the hypoglycemic effect of Gln accompanied with insulin. The hypoglycemic effect of Gln was reversed by GPNA. Conclusions These findings suggest that Gln enhances the hypoglycemic role of insulin through the PI3K/AKT/GLUT4 signaling pathway and glycogen synthesis pathway.
An eHealth Intervention to Promote Physical Activity and Social Network of Single, Chronically Impaired Older Adults: Adaptation of an Existing Intervention Using Intervention Mapping

Background Especially for single older adults with chronic diseases, physical inactivity and a poor social network are regarded as serious threats to their health and independence. The Active Plus intervention is an automated computer-tailored eHealth intervention that has been proven effective to promote physical activity (PA) in the general population of adults older than 50 years. Objective The aim of this study was to report on the methods and results of the systematic adaptation of Active Plus to the wishes and needs of the subgroup of single people older than 65 years who have one or more chronic diseases, as this specific target population may encounter specific challenges regarding PA and social network. Methods The Intervention Mapping (IM) protocol was used to systematically adapt the existing intervention to optimally suit this specific target population. A literature study was performed, and quantitative as well as qualitative data were derived from health care professionals (by questionnaires, n=10) and the target population (by focus group interviews, n=14), which were then systematically integrated into the adapted intervention. Results As the health problems and the targeted behavior are largely the same in the original and adapted intervention, the outcome of the needs assessment was that the performance objectives remained the same. As found in the literature study and in data derived from health professionals and focus groups, the relative importance and operationalization of the relevant psychosocial determinants related to these objectives are different from the original intervention, resulting in a refinement of the change objectives to optimally fit the specific target population. This refinement also resulted in changes in the practical applications, program components, intervention materials, and the evaluation and implementation strategy for the subgroup of single, chronically impaired older adults. Conclusions This study demonstrates that the adaptation of an existing intervention is an intensive process in which adopting the IM protocol is an invaluable tool. The study provides a broad insight in adapting interventions aimed at single older adults with a chronic disease. It is concluded that even when the new target population is a sizable segment of the original target population, the adapted intervention still needs considerable changes to optimally fit the needs and situational differences of the narrower target population.
Matrix metalloproteinase 9 is associated with peritoneal membrane solute transport and induces angiogenesis through &bgr;-catenin signaling

Background. For patients using peritoneal dialysis (PD), the peritoneal membrane can develop fibrosis and angiogenesis, leading to ultrafiltration failure, chronic hypervolemia and increased risk of technique failure and mortality. Matrix metalloproteinases (MMPs), and specifically the gelatinases (MMP2 and MMP9), may be involved in peritoneal membrane injury. Methods. From stable PD patients, mesothelial cells were assayed for MMP gene expression. MMP9 was overexpressed in mouse peritoneum by adenovirus, and MMP9−/− mice were subjected to transforming growth factor &bgr; (TGF-&bgr;)–induced peritoneal fibrosis. Results. MMP9 mRNA expression correlated with peritoneal membrane solute transport properties. Overexpression of MMP9 in the mouse peritoneum induced submesothelial thickening and angiogenesis. MMP9 induced mesothelial cell transition to a myofibroblast phenotype measured by increased alpha smooth muscle actin and decreased E-cadherin expression. Angiogenesis was markedly reduced in MMP9−/− mice treated with an adenovirus expressing active TGF-&bgr; compared with wild-type mice. TGF-&bgr;-mediated E-cadherin cleavage was MMP9 dependent, and E-cadherin cleavage led to &bgr;-catenin-mediated signaling. A &bgr;-catenin inhibitor blocked the angiogenic response induced by AdMMP9. Conclusions. Our data suggest that MMP9 is involved in peritoneal membrane injury possibly through cleavage of E-cadherin and induction of &bgr;-catenin signaling. MMP9 is a potential biomarker for peritoneal membrane injury and is a therapeutic target to protect the peritoneal membrane in PD patients.
The risk of use small matrices to measure specialization in host–parasite interaction networks: a comment to Rivera-García et al. (2016)

SUMMARY In the last years, there were a growing number of studies using the metric H 2′ to calculate complementary specialization in host–parasite interaction networks. However, only a few studies have explored the sensitivity of H 2′ to network dimensions (i.e. species richness and number of interactions), which consequently could generate studies that are not comparable among them or lead to biased conclusions. In this study, we used the recent published study conducted by Rivera-García et al. in 2016 involving host–bat fly networks as an example to call attention to the risk of using H 2′ to calculate specialization for small matrices. After conducting analyses based on both empirical and simulated data, we show that H 2′ values are strongly affected by randomly allocation of species interactions to another cell in the matrix for small networks and that therefore the results and conclusions presented in Rivera-García et al. in 2016 are only an artefact of the dataset used. Therefore, we fully recommended taking into account the careful use of small networks to measuring specialization in host–parasite interactions.
Phytochemicals to optimize cognitive function for military mission-readiness: a systematic review and recommendations for the field

Context Optimizing cognitive performance and preventing cognitive impairments that result from exposure to high-stress situations are important to ensure mission-readiness for military personnel. Objective This systematic review assesses the quality of the evidence for plant-based foods and beverages, or their phytochemical constituents, across various outcomes related to cognitive function in healthy adult populations to develop research recommendations for the military. Data Sources PubMed, CINAHL, Embase, PsycInfo, and the Cochrane Library were searched. Study Selection Peer-reviewed randomized controlled trials published in the English language were eligible. Data Extraction Twenty-five trials were included and assessed for methodological quality, and descriptive data were extracted. Data Synthesis The acceptable (n = 16) to high-quality (n = 4) studies produced either no statistically significant effect or mixed results for enhancing cognitive function. Conclusions The evidence suggested that healthy populations do not experience significant changes in cognitive performance when consuming soy- and non-soy-sourced isoflavones or cocoa. Heterogeneity among other interventions precluded reaching formal conclusions surrounding the evidence. Research recommendations are offered, including conducting more studies on the effect of plant-based interventions on populations reflective of military populations when exposed to military-like situations.
Real-time Action Recognition Based on Key Frame Detection

Human action recognition is an important topic in computer vision. Despite the satisfactory accuracies achieved by a number of methods, they often need computationally heavy algorithms to obtain the good accuracies and can only recognize one action in a short video. In this paper, we propose a real-time action recognition method that achieves state-of-the-art accuracy on both single-action and multi-action recognition. A new algorithm is proposed to detect key frames from a temporal window on the video frames. By analyzing only key frames instead of all frames, the recognition speed is improved. Furthermore, in order to guarantee the recognition accuracy, we employ Hidden Markov Model (HMM) to analyze the temporal relationship of the detected key frames. In the experiments, three public datasets, KTH, UCF Sports and s-KTH, are used to demonstrate the good performance of our proposed method on single-action and multi-action recognition.
Progressive Failure Analysis of Thermoplastic PE/PE Composites: Labeling of Clustered AE Signals

Thermoplastic self-reinforced polyethylene (PE/PE) composites were tested under quasi-static tensile load and the failure processes weremonitored by Acoustic Emission (AE) technique. The AE signals were collected and clustered by Unsupervised Pattern Recognition (UPR) scheme. The initiation and progression of the damage mechanisms in the composites can then be reviewed by the cumulative AE hits of each cluster versus strain curves. But the labeling of each cluster is crucial to the failure analysis. The paper focuses on this correlating between the obtained clusters and their specific damage modes. This was carried out by waveform visualization and Fast Fourier Transform analysis. Pure resin and fiber bundles were tested to assist in the labeling of signal classes in the composites (90°, 0° and [±45°] specimens). Typical waveforms of matrix cracking, fiber-matrix debonding, fiber fracture and fiber pullout were indentified respectively. The evolution process of various damage mechanisms in the composites revealed that the correlating method was effective. An objective and repeatable analytical procedure is established for the investigation of progressive failure mechanisms in the thermoplastic composites.
Energy-Saving Resource Management for D2D and Cellular Coexisting Networks Enhanced by Hybrid Multiple Access Technologies

In this paper, we investigate the energy-saving resource management problem for a new device-to-device (D2D) and cellular coexisting network, where D2D users employ orthogonal frequency division multiple access (OFDMA) and cellular users employ sparse code multiple access (SCMA). This hybrid network can support massive connectivity by exploiting the degrees of freedom in code and space domains, however, the complicated spectrum sharing pattern also leads to serious interference, which further boosts the power consumption of mobile devices (MDs). To tackle this problem, we propose a unified resource management scheme to minimize the total transmit power of all MDs by jointly optimizing mode selection, resource allocation, and power control. First, we analytically get the optimal resource-sharing mode (dedicated mode or reuse mode) for cellular users and D2D users based on the mapping rule between SCMA codebooks and OFDMA resource blocks. For each resource-sharing mode, we reformulate the resource management problems as classical problems in graph theory, and then devise efficient algorithms leveraging the special structure of the constructed graphs. Finally, simulation studies indicate that the network capacity is upgraded with the hybrid multiple access technologies, and the energy efficiency performance is also enhanced through the unified resource management.
Determination of the Effects of Safflower Biodiesel and Its Blends with Diesel Fuel on Lubricating Oil in a Single Cylinder Diesel Engine

In the present study, safflower biodiesel (safflower oil methyl ester) was produced through transesterification. Biodiesel fuel obtained from safflower was blended with diesel fuel in ratios of 2% (B2) and 20% (B20). Kinematic viscosity, density, water content, heating value, flash point, clouding point, pour point, ash content, copper strip corrosion and CFPP (cold filter plug point) tests were performed on B2, B20, B100 fuel blends and diesel fuel. A four stroke, single cylinder, water cooled diesel engine with a direct injection fuel system was operated with B2, B20, B100 and diesel fuels at partial load for 100 hours and samples were taken from the engine lubricating oil at certain hours. The wear elements iron, aluminum, lead, copper and chromium contents of the samples taken from the engine lubricating oil were examined.
Creating and Curating a Cross-Language Person-Entity Linking Collection

To stimulate research in cross-language entity linking, we present a new test collection for evaluating the accuracy of cross-language entity linking in twenty-one languages. This paper describes an efficient way to create and curate such a collection, judiciously exploiting existing language resources. Queries are created by semi-automatically identifying person names on the English side of a parallel corpus, using judgments obtained through crowdsourcing to identify the entity corresponding to the name, and projecting the English name onto the non-English document using word alignments. Name projections are then curated, again through crowdsourcing. This technique resulted in the first publicly available multilingual cross-language entity linking collection. The collection includes approximately 55,000 queries, comprising between 875 and 4,329 queries for each of twenty-one non-English languages.
An algorithm for optimal secure signaling over cognitive radio MIMO channels

The multiple-input multiple-output (MIMO) Gaussian wire-tap channel (WTC) model is considered in the cognitive radio (CR) setting, where there is a interference power constraint (IPC), in addition to the total transmit power constraint (TPC). Based on the recently established mini-max characterization of its secrecy capacity, a numerical algorithm for global maximization of secrecy rates over such channels is proposed, which is based on the barrier method in combination with the residual-form Newton method and backtracking line search. Unlike the known algorithms, the proposed algorithm is guaranteed to converge to a global (rather than local) optimum. Its efficient performance is illustrated by examples.
Potential Point-of-Care Testing for Dengue Virus in the Field

ABSTRACT The four serotypes of dengue virus (DENV) cause one of the most important and rapidly emerging mosquito-borne viral diseases in humans. Of the currently available diagnostic tests for dengue, the reverse transcription-PCR (RT-PCR) assay is the most sensitive and specific, and so it is commonly used as the gold standard. However, the requirement of a sophisticated and expensive thermal cycler makes it very difficult to use as a point-of-care diagnostic test in resource-limited regions where dengue is endemic. Tsai et al. (J Clin Microbiol 56:e01865-17, 2018, https://doi.org/10.1128/JCM.01865-17) report the analytical and clinical performances of a reverse transcription-insulated isothermal PCR (RT-iiPCR) assay with a portable nucleic acid analyzer for rapid detection of the four DENV serotypes; its reproducibility and complete agreement on clinical samples with the multiplex RT-PCR assay developed by the Centers for Disease Control and Prevention suggest that the dengue RT-iiPCR is a potential point-of-care test. Compared with other DENV RNA detection methods, the unique isothermal PCR design of RT-iiPCR, together with further improvements, would represent a promising new type of field-deployable diagnostic test for dengue.
Compressive multispectral model for spectrum sensing in cognitive radio networks

Cognitive Radio (CR) is one of the most promising techniques for optimizing the spectrum usage. However, the large amount of data of spectral information that must be processed to identify and assign spectral resources increases the channel assignment times, therefore worsening the quality of service for the devices using the spectrum. Compressive Sensing (CS) is a digital processing technique that allows the reconstruction of sparse or compressible signals using fewer samples than those required traditionally. This paper presents a model that addresses the Spectral Sensing problem in Cognitive Radio using Compressive Sensing as an effective way of decreasing the number of samples required in the sensing process. This model is based on Compressive Spectral Imaging (CSI) architectures where a centralized spectrum manager selects what power data must be delivered by the different wireless devices using binary patterns, and builds a multispectral data cube image with the geographical and spectral data power information. The results show that this multispectral data cube can be built with only a 50% of the samples generated by the devices and, therefore reducing the data traffic dramatically.
Assessing the Psychedelic “After-Glow” in Ayahuasca Users: Post-Acute Neurometabolic and Functional Connectivity Changes Are Associated with Enhanced Mindfulness Capacities

Abstract Background Ayahuasca is a plant tea containing the psychedelic 5-HT2A agonist N,N-dimethyltryptamine and harmala monoamine-oxidase inhibitors. Acute administration leads to neurophysiological modifications in brain regions of the default mode network, purportedly through a glutamatergic mechanism. Post-acutely, ayahuasca potentiates mindfulness capacities in volunteers and induces rapid and sustained antidepressant effects in treatment-resistant patients. However, the mechanisms underlying these fast and maintained effects are poorly understood. Here, we investigated in an open-label uncontrolled study in 16 healthy volunteers ayahuasca-induced post-acute neurometabolic and connectivity modifications and their association with mindfulness measures. Methods Using 1H-magnetic resonance spectroscopy and functional connectivity, we compared baseline and post-acute neurometabolites and seed-to-voxel connectivity in the posterior and anterior cingulate cortex after a single ayahuasca dose. Results Magnetic resonance spectroscopy showed post-acute reductions in glutamate+glutamine, creatine, and N-acetylaspartate+N-acetylaspartylglutamate in the posterior cingulate cortex. Connectivity was increased between the posterior cingulate cortex and the anterior cingulate cortex, and between the anterior cingulate cortex and limbic structures in the right medial temporal lobe. Glutamate+glutamine reductions correlated with increases in the “nonjudging” subscale of the Five Facets Mindfulness Questionnaire. Increased anterior cingulate cortex-medial temporal lobe connectivity correlated with increased scores on the self-compassion questionnaire. Post-acute neural changes predicted sustained elevations in nonjudging 2 months later. Conclusions These results support the involvement of glutamate neurotransmission in the effects of psychedelics in humans. They further suggest that neurometabolic changes in the posterior cingulate cortex, a key region within the default mode network, and increased connectivity between the anterior cingulate cortex and medial temporal lobe structures involved in emotion and memory potentially underlie the post-acute psychological effects of ayahuasca.
Use of OPTICS and Supervised Learning Methods for Database Intrusion Detection

Database security has become a prime concern in today's internet world due to the escalation of various web applications and information systems. Ensuring the security of the back-end databases is highly essential for maintaining the confidentiality and integrity of the stored sensitive information. In this paper, a Density-based clustering technique, namely, OPTICS, has been applied for constructing the normal profile of users. Each incoming transaction either lies within a cluster or is found to deviate from the clusters based on its Local Outlier Factor value. The transactions observed as outliers are further verified by employing various supervised machine learning techniques individually – Naïve Bayes, Decision Tree, Rule Induction, k-Nearest Neighbor and Radial Basis Function Network. The effectiveness of our system is demonstrated by carrying out extensive experimentations and comparative analysis using stochastic models.