Miracle-dz/UniFAR-FTUs huggingface dataset:
Title : Characterization of porcine astrovirus infection in the U . S . pig population – NPB # 12

Many astrovirus (AstV) species are associated with enteric disease. In this study, the prevalence rates of porcine AstV types 1–5 (PAstV1–PAstV5) in the U.S. pig population were investigated using fecal samples from 509 pigs. Specifically, two multiplex differential PCR assays capable of detection and differentiation of all five known PAstV were developed. Among the 509 pigs tested, 488 (95.9 %) came from farms with a history of diarrhea. All of the five known PAstV types were found to circulate in pigs in the U.S., and co-infection of a single pig with two or more PAstV types was frequently observed. A high overall prevalence of 64.0 % (326/509) of PAstV RNA-positive samples was detected, with 97.2 % (317/326) of the PAstV RNA-positive pigs infected with PAstV4. The first complete genome of a PAstV3 isolate was obtained and showed identities of 50.5–55.3 % with mink AstV and the novel human AstVs compared with 38.4–42.7 % with other PAstV types. Further extensive attempts to grow PAstV in vitro were unsuccessful. In vivo inoculation of pigs with PAstV RNA positive material resulted in short duration of PAstV shedding of low magnitude (less than a week) followed by seroconversion. Lesions were not detected in any of the pigs.
06 in the series on “ Dynamics of Knowledge Accumulation , Competitiveness , Regional Cohesion and Economic Policies ”

Using firm-level data from the third Community Innovation Survey, the paper investigates effects of foreign ownership on research and development (R&D) activity carried out by innovative firms in the Czech Republic. It is shown that foreign affiliates tend to engage less in internal R&D compared with domestic-owned firms. The finding is sustained after controlling for a number of other firm, industry and region-specific factors and across different specifications of the model. Only external acquisition of R&D from labs and universities in the host country appears to be complementary to internal R&D activity in foreign affiliates, while sourcing of R&D from abroad is not.
COMPARISON OF FACE RECOGNITION METHODS

: Face recognition has gained its popularity because of its uniqueness among the other biometric methods. It has all the features of an effective security system. But when looking closer we come to know that, there are some limitations in the face recognition system that needs to be researched and studied upon. There are challenges such as variation in illumination, problem of posing, expression and age. In order to eliminate these challenges, we have to use specific algorithms. Combination of one or more algorithms will help in solving these challenges to an extent. The algorithms that will be used in this paper are Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Independent Component Analysis (ICA) and classification performed using Support Vector Machine (SVM). We will be using Matlab codes for implementing the algorithms. We will be comparing the performance of the systems on the basis of the accuracy.
A Review on Multi-label Classification

Multi-label Classification is a form of supervised learning where the classification algorithm is learned from the set of training instances and based on this learned model the instances are classified into a label-set. In multi-label classification an instance is associated with more than one label at the same time which is inherently different from the traditional classification where an instance is classified into a single label. This paper includes the basic introduction of multi-label classification and existing methods for multilabel classification are studied along with their evaluation metrics.
Foundations of Probabilistic Programming ( tentative title )

The quantitative analysis of probabilistic programs answers queries involving the expected values of program variables and expressions involving them, as well as bounds on the probabilities of assertions. In this chapter, we will present the use of concentration of measure inequalities to reason about such bounds. First, we will briefly present and motivate standard concentration of measure inequalities. Next, we survey approaches to reason about quantitative properties using concentration of measure inequalities, illustrating these on numerous motivating examples. Finally, we discuss currently open challenges in this area for future work.
Deep point correlation design

Designing point patterns with desired properties can require substantial effort, both in hand-crafting coding and mathematical derivation. Retaining these properties in multiple dimensions or for a substantial number of points can be challenging and computationally expensive. Tackling those two issues, we suggest to automatically generate scalable point patterns from design goals using deep learning. We phrase pattern generation as a deep composition of weighted distance-based unstructured filters. Deep point pattern design means to optimize over the space of all such compositions according to a user-provided point correlation loss, a small program which measures a pattern's fidelity in respect to its spatial or spectral statistics, linear or non-linear (e. g., radial) projections, or any arbitrary combination thereof. Our analysis shows that we can emulate a large set of existing patterns (blue, green, step, projective, stair, etc.-noise), generalize them to countless new combinations in a systematic way and leverage existing error estimation formulations to generate novel point patterns for a user-provided class of integrand functions. Our point patterns scale favorably to multiple dimensions and numbers of points: we demonstrate nearly 10k points in 10-D produced in one second on one GPU. All the resources (source code and the pre-trained networks) can be found at https://sampling.mpi-inf.mpg. de/deepsampling.html.
An Introduction to the Science and Philosophy of Mental Imagery

Differences from published version. The small amount of text and punctuation that is in brown indicates material that has been added, updated, or otherwise slightly altered since the published version went to press, or else it is material that was cut or editorially altered in the published version of the glossary. Apart from the title, the most significant changes lie in the glossary, that explains some of the more specialized terms. Authors were asked to supply a glossary relating to their articles, but in the published encyclopedia these have been combined into one single glossary for the whole volume set, with some editorial cuts and revisions. My original version, as presented here, remains particularly appropriate to this article. Here I provide links to the glossary within the text: Use your browser's back button to return from the glossary to your place in the text. Also, the sections in the published version are not numbered. Here I not only number the sections hierarchically, I supply within-page links cross-referencing different sections of the article (the published version, on the other hand, provides numerous cross-references, that do not appear here, to other articles within the encyclopedia). One reference citation has been added here, in brown. N.J.T.T.
Optimization of Overlay QoS Constrained Routing and Mapping Algorithm for Virtual Content Aware networks

Multimedia Services including video distribution are increasingly required by the current market and will be also a target of the Future Internet. One method to customize the multi-domain guaranteed transport with several QoS classes of services is to create Virtual Content Aware Networks (VCAN) constructed as overlays over IP networks. The mapping of VCANs onto real multi-domain topologies is needed. This paper develops new optimizations to increase the performances of a previously proposed combined hierarchical multi-domain algorithm performing VCAN mapping with QoS constraints. Keywords-Virtual Content-Aware Networking; Network Aware Applications; Multi-domain; QoS; Management; Constrained routing; Future Internet.
Microhabitat Use, Spawning Behavior, and Spawning Substrate Use of the Tallapoosa Darter (Etheostoma tallapoosae)

Abstract. Habitat use information for many headwater fish species in the southeastern United States is lacking, despite this group's contribution to regional biodiversity. The Tallapoosa Darter Etheostoma tallapoosae is an endemic species to the Tallapoosa River system, found within Georgia and Alabama. In this study collected data from four streams within the upper part of the species' range to provide estimates of habitat use for this rare percid. Our results indicate the Tallapoosa Darter is a microhabitat specialist and typically spawns in areas composed of coarse substrates and characterized by moderate depths (22-31 cm) and current velocities (0.09-0.18 m·s–1). This species also appears to use coarse gravel more than expected for egg-deposition, in relation to other substrate classes. Any future management plans within this river system should feature this endemic given the Tallapoosa Darter is a microhabitat specialist.
Development and Application of Sub-Mitochondrial Targeted Ca2 + Biosensors

Mitochondrial Ca2+ uptake into the mitochondrial matrix is a well-established mechanism. However, the sub-organellar Ca2+ kinetics remain elusive. In the present work we identified novel site-specific targeting sequences for the intermembrane space (IMS) and the cristae lumen (CL). We used these novel targeting peptides to develop green- and red- Ca2+ biosensors targeted to the IMS and to the CL. Based on their distinctive spectral properties, and comparable sensitivities these novel constructs were suitable to visualize Ca2+-levels in various (sub) compartments in a multi-chromatic manner. Functional studies that applied these new biosensors revealed that knockdown of MCU and EMRE yielded elevated Ca2+ levels inside the CL but not the IMS in response to IP3-generating agonists. Knockdown of VDAC1, however, strongly impeded the transfer of Ca2+ through the OMM while the cytosolic Ca2+ signal remained unchanged. The novel sub-mitochondrially targeted Ca2+ biosensors proved to be suitable for Ca2+ imaging with high spatial and temporal resolution in a multi-chromatic manner allowing simultaneous measurements. These informative biosensors will facilitate efforts to dissect the complex sub-mitochondrial Ca2+ signaling under (patho)physiological conditions.
Integrative Analysis Reveals Across-Cancer Expression Patterns and Clinical Relevance of Ribonucleotide Reductase in Human Cancers

Mining cancer-omics databases deepens our understanding of cancer biology and can lead to potential breakthroughs in cancer treatment. Here, we propose an integrative analytical approach to reveal across-cancer expression patterns and identify potential clinical impacts for genes of interest from five representative public databases. Using ribonucleotide reductase (RR), a key enzyme in DNA synthesis and cancer-therapeutic targeting, as an example, we characterized the mRNA expression profiles and inter-component associations of three RR subunit genes and assess their differing pathological and prognostic significance across over 30-types of cancers and their related subtypes. Findings were validated by immunohistochemistry with clinical tissue samples (n = 211) collected from multiple cancer centers in China and with clinical follow-up. Underlying mechanisms were further explored and discussed using co-expression gene network analyses. This framework represents a simple, efficient, accurate, and comprehensive approach for cancer-omics resource analysis and underlines the necessity to separate the tumors by their histological or pathological subtypes during the clinical evaluation of molecular biomarkers.
The Influence of User Interface Component on University Website Towards Student Enrollment

Website interface can be a very effective communication medium between the university and the users such as potential students, current students and other authorities. The purpose of this study is to investigate interface components that represent important information in university website, which led to the enrollment of students. This study is motivated by the user frustration or thwarted by the frequent usability problems of university website and problem in representing important information into required by potential students. The interface component and the important information on university website are identified through a comprehensive literature review. There are several factors that influence the student's decision process such as tuition fees, scholarship, ratings / reputation, vicinity to home and the majors offered. The interface components that are used in this research are metaphor, navigation, mental model, interactions and appearance. This study will produce the interface design guideline based on the most frequent used of interface component. Analysis of 5 universities has been conducted to identify how the important information, which leads to student’s enrollments is presented on the interface.
Ultrastructural variability of the juxtacanalicular tissue along the inner wall of Schlemm’s canal

Purpose Increased resistance of aqueous humor drainage from the eye through Schlemm’s canal (SC) is the basis for elevated intraocular pressure in glaucoma. Experimental evidence suggests that the bulk of outflow resistance lies in the vicinity of the inner wall endothelial lining of SC and the adjacent juxtacanalicular tissue (JCT). However, there is little understanding of how this resistance is generated, and a detailed understanding of the structure-function relationship of the outflow pathway has not been established yet. In the present study, regional variations in the ultrastructure of the JCT and the inner wall of SC were investigated in three dimensions. Methods With the use of serial block face scanning electron microscopy (SBF-SEM), the volume occupied by the electron lucent spaces of the JCT compared to that occupied by the cellular and extracellular matrix was investigated and quantified. The distribution of giant vacuoles (GVs) and pores in the inner wall endothelium of SC was further examined. Results With increasing distance from the inner wall of SC, the volume of the electron lucent spaces increased above 30%. In contrast, the volume of these spaces in immediate contact with the inner wall endothelium was minimal (<10%). Circumferential variability in the type and distribution of GVs was observed, and the percentage of GVs with pores varied between 3% and 27%. Conclusions These studies provide a detailed quantitative analysis of the ultrastructure of JCT and the distribution of GVs along the circumference of SC in three dimensions, supporting the non-uniform or segmental aqueous outflow.
Performance, Security Tradeoffs in Secure Control

This letter summarizes the established vulnerability analysis techniques for cyber physical control software and highlights the possibility of employing lightweight security measures in conjunction with reachability analysis for such systems. We propose a possible cyber physical control system implementation methodology, which leverages the dependence of control performance on the choice of security measure adopted and the resulting platform level vulnerability.
Advanced Symbol-level Precoding Schemes for Interference Exploitation in Multi-antenna Multi-user Wireless Communications

The utilization of multi-antenna transmitters relying on full frequency reuse has proven to be an effective strategy towards fulfilling the constantly increasing throughput requirements of wireless communication systems. As a consequence, in the last two decades precoding has been a prolific research area, due to its ability to handle the interference arising among simultaneous transmissions addressed to different co-channel users. The conventional precoding strategies aim at mitigating the multi-user interference (MUI) by exploiting the knowledge of the channel state information (CSI). More recently, novel approaches have been proposed where the aim is not to eliminate the interference, but rather to control it so as to achieve a constructive interference effect at each receiver. In these schemes, referred to as symbol-level precoding (SLP), the data information (data symbols) is used together with the CSI in the precoding design, which can be addressed following several optimization strategies. In the context of SLP, the work carried out in this thesis is mainly focused on developing more advanced optimization strategies suitable to non-linear systems, where the per-antenna high-power amplifiers introduce an amplitude and phase distortion on the transmitted signals. More specifically, the main objective is to exploit the potential of SLP not only to achieve the constructive interference at the receivers, but also to control the per-antenna instantaneous transmit power, improving the power dynamics of the transmitted waveforms. In fact, a reduction of the power variation of the signals, both in the spatial dimension (across the different antennas) and in the temporal dimension, is particularly important for mitigating the non-linear effects. After a detailed review of the state of the art of SLP, the first part of the thesis is focused on improving the power dynamics of the transmitted signals in the spatial dimension, by reducing the instantaneous power imbalances across the different antennas. First, a SLP per-antenna power minimization scheme is presented, followed by a related max-min fair formulation with per-antenna power constraints. These approaches allow to reduce the power peaks of the signals across the antennas. Next, more advanced SLP schemes are formulated and solved, with the objective of further improving the spatial dynamics of the signals. Specifically, a first approach performs a peak power minimization under a lower bound constraint on the per-antenna transmit power, while a second strategy minimizes the spatial peak-to-average power ratio. The second part of this thesis is devoted to developing a novel SLP method, referred to as spatio-temporal SLP, where the temporal variation of the transmit power is also considered in the SLP optimization. This new model allows to minimize the peak-toaverage power ratio of the transmitted waveforms both in the spatial and in the temporal
MiR‐195 and miR‐497 suppress tumorigenesis in lung cancer by inhibiting SMURF2‐induced TGF‐β receptor I ubiquitination

SMURF2 is a member of the HECT family of E3 ubiquitin ligases that have important roles as a negative regulator of transforming growth factor‐β (TGF‐β) signaling through ubiquitin‐mediated degradation of TGF‐β receptor I. However, the regulatory mechanism of SMURF2 is largely unknown. In this study, we identified that micro(mi)R‐195 and miR‐497 putatively target SMURF2 using several target prediction databases. Both miR‐195 and miR‐497 bind to the 3′‐UTR of the SMURF2 mRNA and inhibit SMURF2 expression. Furthermore, miR‐195 and miR‐497 regulate SMURF2‐dependent TβRI ubiquitination and cause the activation of the TGF‐β signaling pathway in lung cancer cells. Upregulation of miR‐195 and miR‐497 significantly reduced cell viability and colony formation through the activation of TGF‐β signaling. Interestingly, miR‐195 and miR‐497 also reduced the invasion ability of lung cancer cells when cells were treated with TGF‐β1. Subsequent in vivo studies in xenograft nude mice model revealed that miR‐195 and miR‐497 repress tumor growth. These findings demonstrate that miR‐195 and miR‐497 act as a tumor suppressor by suppressing ubiquitination‐mediated degradation of TGF‐β receptors through SMURF2, and suggest that miR‐195 and miR‐497 are potential therapeutic targets for lung cancer.
The effects of omega-3 fatty acids on neuropsychological functioning and brain morphology in mid-life adults: a randomized clinical trial

Abstract Background The diet of most adults is low in fish and, therefore, provides limited quantities of the long-chain, omega-3 fatty acids (LCn-3FAs), eicosapentaenoic and docosahexaenoic acids (EPA, DHA). Since these compounds serve important roles in the brain, we sought to determine if healthy adults with low-LCn-3FA consumption would exhibit improvements in neuropsychological performance and parallel changes in brain morphology following repletion through fish oil supplementation. Methods In a randomized, controlled trial, 271 mid-life adults (30–54 years of age, 118 men, 153 women) consuming ⩽300 mg/day of LCn-3FAs received 18 weeks of supplementation with fish oil capsules (1400 mg/day of EPA and DHA) or matching placebo. All participants completed a neuropsychological test battery examining four cognitive domains: psychomotor speed, executive function, learning/episodic memory, and fluid intelligence. A subset of 122 underwent neuroimaging before and after supplementation to measure whole-brain and subcortical tissue volumes. Results Capsule adherence was over 95%, participant blinding was verified, and red blood cell EPA and DHA levels increased as expected. Supplementation did not affect performance in any of the four cognitive domains. Exploratory analyses revealed that, compared to placebo, fish oil supplementation improved executive function in participants with low-baseline DHA levels. No changes were observed in any indicator of brain morphology. Conclusions In healthy mid-life adults reporting low-dietary intake, supplementation with LCn-3FAs in moderate dose for moderate duration did not affect neuropsychological performance or brain morphology. Whether salutary effects occur in individuals with particularly low-DHA exposure requires further study.
Benford's law in medicinal chemistry: Implications for drug design.

Aim: The explosion of data based technology has accelerated pattern mining. However, it is clear that quality and bias of data impacts all machine learning and modeling. Results & methodology: A technique is presented for using the distribution of first significant digits of medicinal chemistry features: logP, logS, and pKa. experimental and predicted, to assess their following of Benford's law as seen in many natural phenomena. Conclusion: Quality of data depends on the dataset sizes, diversity, and magnitudes. Profiling based on drugs may be too small or narrow; using larger sets of experimentally determined or predicted values recovers the distribution seen in other natural phenomena. This technique may be used to improve profiling, machine learning, large dataset assessment and other data based methods for better (automated) data generation and designing compounds.
Use of EEG for Predicting Treatment Response to Transcranial Magnetic Stimulation in Obsessive Compulsive Disorder

Aim. In this study we assessed the predictive power of quantitative EEG (qEEG) for the treatment response to right frontal transcranial magnetic stimulation (TMS) in obsessive compulsive disorder (OCD) using a machine learning approach. Method. The study included 50 OCD patients (35 responsive to TMS, 15 nonresponsive) who were treated with right frontal low frequency stimulation and identified retrospectively from Uskudar Unversity, NPIstanbul Brain Hospital outpatient clinic. All patients were diagnosed with OCD according to the DSM-IV-TR and DSM-5 criteria. We first extracted pretreatment band powers for patients. To explore the prediction accuracy of pretreatment EEG, we employed machine learning methods using an artificial neural network model. Results. Among 4 EEG bands, theta power successfully discriminated responsive from nonresponsive patients. Responsive patients had more theta powers for all electrodes as compared to nonresponsive patients. Discussion. qEEG could be helpful before deciding about treatment strategy in OCD. The limitations of our study are moderate sample size and limited number of nonresponsive patients and that treatment response was defined by clinicians and not by using a formal symptom measurement scale. Future studies with larger samples and prospective design would show the role of qEEG in predicting TMS response better.