Miracle-dz/UniFAR-FTUs huggingface dataset:
Gut Dysbiosis Clinical Indicators Associate with Body Weight Gain and Cognitive Dysfunction in High-Fat Diet-Induced Pre-obese Mice

Obesity associates with mood and anxiety disorders, cognitive dysfunction, and motor decline, whose major causes are puzzling, since obesogenic mechanisms are complex, including a marked intestinal dysbiosis and a sustained metabolic syndrome encompassing hyperglycemia, hyperinsulinemia, systemic inflammation and oxidative stress in both humans and animal models. We addressed whether the changes occurring in mood, cognitive and motor function in diet-induced pre-obese mice are linked to intestinal dysbiosis and body weight gain. Male and female Swiss mice were made pre-obese by feeding with a reinforced high-fat diet for 7 weeks, along which body weight and food intake were monitored. Then, holeboard, stress-induced hyperthermia, footprint, and vertical pole tests were performed to assess cognitive and motor functions. Animals were sacrificed and intestinal dysbiosis assessed: (i) by determining fecal pH and gram positive bacteria counts; (ii) by assessing bacterial infiltration to the jejunum using tissue gram stain; and (iii) by assessing jejunal tissue histopathological changes (H&E) and the immunohistochemical expression of the sustained inflammation marker CXCL10. Average increases in body weight were observed in males (37%) and females (49%), as well as increases in fecal pH, gram positive counts, bacterial infiltration and CXCL10 expression, particularly marked in females. For both sexes, HFD-fed animals with more marked changes in indicators of intestinal dysbiosis also gained weight faster and displayed more marked mood, cognitive and motor alterations. These results suggest that intestinal dysbiosis is a major driver of obesity-like cognitive and motor alterations in pre-obese mice.
ADD-OPT: Accelerated Distributed Directed Optimization

In this paper, we consider distributed optimization problems where the goal is to minimize a sum of objective functions over a multiagent network. We focus on the case when the interagent communication is described by a strongly connected, <italic>directed</italic> graph. The proposed algorithm, Accelerated Distributed Directed OPTimization (ADD-OPT), achieves the best known convergence rate for this class of problems, <inline-formula> <tex-math notation="LaTeX">$O(\mu ^{k}),0<\mu <1$</tex-math></inline-formula>, given strongly convex, objective functions with globally Lipschitz-continuous gradients, where <inline-formula><tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is the number of iterations. Moreover, ADD-OPT supports a wider and more realistic range of step sizes in contrast to existing work. In particular, we show that ADD-OPT converges for arbitrarily small (positive) step sizes. Simulations further illustrate our results.
Types of Homeschool Environments and Need Support for Children’s Achievement Motivation

ABSTRACT Working within a self-determination theory (SDT) framework, this study used cluster analysis to examine the naturally occurring types of homeschool-learning environments parents (N = 457) have created. Measures of support for student autonomy, mastery goal structure, and use of conditional regard were adapted for a homeschool context and used as constituting variables. Follow-up measures of parental need satisfaction, efficacy, student academic engagement, teaching practices, and demographics were used to identify significant differences among homeschooling motivational profiles. A five-cluster solution best fit the data: a high need support profile, low need support profile and three profiles of mixed need support. In general, the high need and mixed need support profiles were associated with higher student engagement, need satisfaction, efficacy for homeschooling, and frequent use of teaching strategies that promote autonomous motivation and support for student competence. The low need support profile was significantly associated with lower need satisfaction and teaching strategies associated with control. Higher levels of academic engagement were reported for those students homeschooled longer and at higher grade levels. Male teaching parents (N = 29) reported significantly less need satisfaction and were significantly more represented in the low need support profile. These findings point to the utility of self-determination theory for characterizing the motivational environments of homeschools.
Black holes: Dynamics around supermassive black holes

The dynamics of galactic nuclei reflects the presence of supermassive black holes (SBHs) in many ways. Single SBHs act as sinks, destroying a mass in stars equal to their own mass in roughly one relaxation time and forcing nuclei to expand. Formation of binary SBHs displaces a mass in stars roughly equal to the binary mass, creating low-density cores and ejecting hyper-velocity stars. Gravitational radiation recoil can eject coalescing binary SBHs from nuclei, resulting in offset SBHs and lopsided cores. We review recent work on these mechanisms and discuss the observable consequences.
Nonlocal problems for elliptic equations in dihedral angles and the Green formula

Nonlocal problems for higher-order elliptic operators in dihedral and plane angles are considered. The Green formula is obtained, which leads to adjoint problems that take the form of nonlocal transmission problems in dihedral and plane angles. This allows us to establish necessary conditions of Fredholm solvability and sufficient conditions of one-valued solvability for nonlocal problems in dihedral angles.
Exploiting Spatio-Temporal User Behaviors for User Linkage

Cross-device and cross-domain user linkage have been attracting a lot of attention recently. An important branch of the study is to achieve user linkage with spatio-temporal data generated by the ubiquitous GPS-enabled devices. The main task in this problem is twofold, i.e., how to extract the representative features of a user; how to measure the similarities between users with the extracted features. To tackle the problem, we propose a novel model STUL (Spatio-Temporal User Linkage) that consists of the following two components. 1) Extract users - spatial features with a density based clustering method, and extract the users - temporal features with the Gaussian Mixture Model. To link user pairs more precisely, we assign different weights to the extracted features, by lightening the common features and highlighting the discriminative features. 2) Propose novel approaches to measure the similarities between users based on the extracted features, and return the pair-wise users with similarity scores higher than a predefined threshold. We have conducted extensive experiments on three real-world datasets, and the results demonstrate the superiority of our proposed STUL over the state-of-the-art methods.
COCOCLUST : Contour-based Color Clustering for Robust Binarization of Colored Text

This paper proposes COCOCLUST, a contour-based color clustering method which robustly segments and binarizes colored text from complex images. Rather than operating on the entire image, a ‘small’ representative set of color pixels is first identified using the contour information. The method involves the following steps: (i) Identification of prototype colors (ii) A one-pass algorithm to identify color clusters that serve as seeds for the refining step using kmeans clustering (iii) Assignment of pixels in the original image to the nearest color cluster (iv) Identification of potential candidate text regions in individual color layer and (v) Adaptive binarization. We propose a robust binarization technique to threshold the identified text regions, taking into account the presence of inverse texts, such that the output image always has black text on a white background. Experiments on several complex images having large variations in font, size, color, orientation and script illustrate the robustness of the method.
Cultural Psychology A Once And Future Discipline

Thank you very much for reading cultural psychology a once and future discipline. As you may know, people have search hundreds times for their chosen novels like this cultural psychology a once and future discipline, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some malicious virus inside their desktop computer.
An Energy Efficient FPGA Accelerator for Monte Carlo Option Pricing with the Heston Model

Today, pricing of derivates (particularly options) in financial institutions is a challenge. Besides the increasing complexity of the products, obtaining fair prices requires more realistic (and therefore complex) models of the underlying asset behavior. Not only due to the increasing costs, energy efficient and accurate pricing of these models becomes more and more important. In this paper we present - to the best of our knowledge - the first FPGA based accelerator for option pricing with the state-of-the-art Heston model. It is based on advanced Monte Carlo simulations. Compared to an 8-core Intel Xeon Server running at 3.07GHz, our hybrid FPGA-CPU-system saves 89% of the energy and provides around twice the speed. The same system reduces the energy consumption per simulation to around 40% of a fully-loaded Nvidia Tesla C2050 GPU. For a three-Virtex-5 chip only accelerator, we expect to achieve the same simulation speed as a Nvidia Tesla C2050 GPU, by consuming less than 3% of the energy at the same time.
Functional Connectivity and Temporal Variability of Brain Connections in Adults with Attention Deficit/Hyperactivity Disorder and Bipolar Disorder

Objectives: To assess brain functional connectivity and variability in adults with attention deficit/hyperactivity disorder (ADHD) or euthymic bipolar disorder (BD) relative to a control (CT) group. Methods: Electroencephalography (EEG) was measured in 35 participants (BD = 11; ADHD = 9; CT = 15) during an eyes-closed 10-min rest period, and connectivity and graph theory metrics were computed. A coefficient of variation (CV) computed also the connectivity's temporal variability of EEG. Multivariate associations between functional connectivity and clinical and neuropsychological profiles were evaluated. Results: An enhancement of functional connectivity was observed in the ADHD (fronto-occipital connections) and BD (diffuse connections) groups. However, compared with CTs, intrinsic variability (CV) was enhanced in the ADHD group and reduced in the BD group. Graph theory metrics confirmed the existence of several abnormal network features in both affected groups. Significant associations of connectivity with symptoms were also observed. In the ADHD group, temporal variability of functional connections was associated with executive function and memory deficits. Depression, hyperactivity and impulsivity levels in the ADHD group were associated with abnormal intrinsic connectivity. In the BD group, levels of anxiety and depression were related to abnormal frontotemporal connectivity. Conclusions: In the ADHD group, we found that intrinsic variability was associated with deficits in cognitive performance and that connectivity abnormalities were related to ADHD symptomatology. The BD group exhibited less intrinsic variability and more diffuse long-range brain connections, and those abnormalities were related to interindividual differences in depression and anxiety. These preliminary results are relevant for neurocognitive models of abnormal brain connectivity in both disorders.
Endogenous pH-responsive nanoparticles with programmable size changes for targeted tumor therapy and imaging applications

Nanotechnology-based antitumor drug delivery systems, known as nanocarriers, have demonstrated their efficacy in recent years. Typically, the size of the nanocarriers is around 100 nm. It is imperative to achieve an optimum size of these nanocarriers which must be designed uniquely for each type of delivery process. For pH-responsive nanocarriers with programmable size, changes in pH (~6.5 for tumor tissue, ~5.5 for endosomes, and ~5.0 for lysosomes) may serve as an endogenous stimulus improving the safety and therapeutic efficacy of antitumor drugs. This review focuses on current advanced pH-responsive nanocarriers with programmable size changes for anticancer drug delivery. In particular, pH-responsive mechanisms for nanocarrier retention at tumor sites, size reduction for penetrating into tumor parenchyma, escaping from endo/lysosomes, and swelling or disassembly for drug release will be highlighted. Additional trends and challenges of employing these nanocarriers in future clinical applications are also addressed.
A fully integrated Ku-band down-converter front-end for DBS receivers

A fully integrated Ku-band down-converter front-end for digital broadcast satellite (DBS) receiver in a 0.18 μm SiGe BiCMOS technology is presented. To meet the specifications in different areas, the circuit can cover a wide RF range (10.7-13.45 GHz) with four LO frequencies, and down convert the RF signal to L-Band (950-2150 MHz). Compared with previous works, the presented down-converter using a low cost technology to achieve a low noise figure (<; 6 dB), high gain (> 51 dB), and high linearity (OP1dB > 5.5 dBm) under low power consumption (32 mA; 135 mW) with an excellent gain flatness (±1 dB) and a very small chip area (0.6 mm2). This is also the first report of the DBS down-converter covering four different LO frequencies, to the best of our knowledge.
The Effect of Minority Veto Rights on Controller Tunneling

A central challenge in the regulation of controlled firms is curbing controller tunneling. As independent directors and fiduciary duties are widely seen as not up to the task, a number of jurisdictions have given minority shareholders veto rights over these transactions. To assess these rights' efficacy, we exploit a 2011 regulatory reform in Israel that gave the minority the ability to veto pay packages of controllers and their relatives ("controller executives"). We find that the reform curbed the pay of controller executives and led some controller executives to quit their jobs, or work for free, in circumstances suggesting their pay would not have received approval. These findings suggest that minority veto rights can help curb controller tunneling.
Automatic Representation for Lifetime Value Recommender Systems

Many modern commercial sites employ recommender systems to propose relevant content to users. While most systems are focused on maximizing the immediate gain (clicks, purchases or ratings), a better notion of success would be the lifetime value (LTV) of the user-system interaction. The LTV approach considers the future implications of the item recommendation, and seeks to maximize the cumulative gain over time. The Reinforcement Learning (RL) framework is the standard formulation for optimizing cumulative successes over time. However, RL is rarely used in practice due to its associated representation, optimization and validation techniques which can be complex. In this paper we propose a new architecture for combining RL with recommendation systems which obviates the need for hand-tuned features, thus automating the state-space representation construction process. We analyze the practical difficulties in this formulation and test our solutions on batch off-line real-world recommendation data.
The Information Content of Litigation Participation Securities: The Case of Calfed Bancorp

CalFed Bancorp is one of 126 S&Ls suing the U.S. government for breach of contract related to supervisory goodwill, a form of goodwill created by the acquisition of insolvent thrifts during the early 1980s. Before a determination of damages in its lawsuit, CalFed announced and issued a litigation participation security giving shareholders a proportional claim on recovered damages, if any. This announcement generated a positive excess return in part because it made CalFed a more likely acquisition target. This security also reveals important, yet previously unavailable, information about CalFed's lawsuit: its price reveals a market-based estimate of damages while its beta provides information about expected returns and the time until payoff. In a broader context, this security highlights acquisition facilitation as a benefit of issuing targeted stock as well as a series of lawsuits that will set important precedents regarding the determination of liability and the estimation of damages in breach of contract cases.
Improved Multiscale Entropy Technique with Nearest-Neighbor Moving-Average Kernel for Nonlinear and Nonstationary Short-Time Biomedical Signal Analysis

Analysis of biomedical signals can yield invaluable information for prognosis, diagnosis, therapy evaluation, risk assessment, and disease prevention which is often recorded as short time series data that challenges existing complexity classification algorithms such as Shannon entropy (SE) and other techniques. The purpose of this study was to improve previously developed multiscale entropy (MSE) technique by incorporating nearest-neighbor moving-average kernel, which can be used for analysis of nonlinear and non-stationary short time series physiological data. The approach was tested for robustness with respect to noise analysis using simulated sinusoidal and ECG waveforms. Feasibility of MSE to discriminate between normal sinus rhythm (NSR) and atrial fibrillation (AF) was tested on a single-lead ECG. In addition, the MSE algorithm was applied to identify pivot points of rotors that were induced in ex vivo isolated rabbit hearts. The improved MSE technique robustly estimated the complexity of the signal compared to that of SE with various noises, discriminated NSR and AF on single-lead ECG, and precisely identified the pivot points of ex vivo rotors by providing better contrast between the rotor core and the peripheral region. The improved MSE technique can provide efficient complexity analysis of variety of nonlinear and nonstationary short-time biomedical signals.
Minecraft as a sandbox for STEM interest development: Preliminary results

After a brief review of the science of interest and the game of Minecraft, we present a taxonomy of common Minecraft actions and activities and propose that they represent links to specific STEM disciplines. We then discuss the development of a Minecraft survey intended to identify STEM-related interests, and present the results of a pilot study using the survey in three Minecraft camps held in the summer of 2017. We describe the most and least popular Minecraft activities, and report initial analyses of the surveys, revealing potential connections in the earth, biological, and environmental areas of STEM.
Analysis of NFKB2-mediated regulation of mechanisms underlying the development of Hodgkin's lymphoma

Nuclear factor-κB (NF-κB) is widely involved in various lymphoid malignancies. However, its exact functional role and potential regulatory mechanisms in Hodgkin's lymphoma (HL) remains unclear. The present study aimed to investigate the regulatory mechanism of NF-κB in HL by analysis of a gene expression profile that was obtained from HL cells with or without NF-κB subunit 2 (NFKB2) knockdown. The GSE64234 dataset containing 6 HL cell line specimens transfected with small interfering (si)RNA against NFKB2 and 6 control specimens transfected with non-targeting siRNA sequences was downloaded from the Gene Expression Omnibus database. Based on these data, differentially expressed genes (DEGs) were screened for following data preprocessing. Functional enrichment analysis was subsequently conducted among the identified upregulated and downregulated DEGs. Additionally, a protein-protein interaction (PPI) network was constructed and module analyses were performed. Finally, microRNAs (miRNAs/miRs) targeting the identified DEGs were predicted for the construction of a miRNA-target regulatory network. A total of 253 DEGs were identified, consisting of 109 upregulated and 144 downregulated DEGs. Pathway enrichment analysis revealed that B-cell lymphoma 2-like 1 (BCL2L1) was significantly enriched in the NF-κB signaling pathway, and colony-stimulating factor 2 (CSF2) and BCL2L1 were enriched in the Jak-signal transducer and activator of transcription (STAT) signaling pathway. BCL2L1 and CSF2 were determined to be hub genes in the PPI network. A total of 6 miRNAs, including let-7a-5p, miR-9-5p, miR-155-5p, miR-135a-5p, miR-17-5p and miR-375, were identified in the miRNA-target regulatory network. The results of the present study indicated that NFKB2 may be involved in HL development through regulation of BCL2L1, CSF2, miR-135a-5p, miR-155-5p and miR-9-5p expression, as well as the modulation of Jak-STAT and NF-κB signaling pathways.
Geostatistical analysis of the spatial distribution of mycotoxin concentration in bulk cereals

Deoxynivalenol (DON) and ochratoxin A (OTA) in agricultural commodities present hazards to human and animal health. Bulk lots are routinely sampled for their presence, but it is widely acknowledged that designing sampling plans is particularly problematical because of the heterogeneous distribution of the mycotoxins. Previous studies have not explicitly looked at the interactions between the spatial distribution of the mycotoxin and the strategy used to take samples from bulk. Sampling plans are therefore designed on the assumption of random distributions. The objective of this study was to analyse the spatial distribution of DON and OTA in bulk commodities with geostatistics. This study was the first application of geostatistical analysis to data on mycotoxins contamination of bulk commodities. Data sets for DON and OTA in bulk storage were collected from the literature and personal communications, of which only one contained data suitable for geostatistical analysis. This data set represented a 26-tonne truck of wheat with a total of 100 sampled points. The mean concentrations of DON and OTA were 1342 and 0.59 µg kg–1, respectively. The results showed that DON presented spatial structure, whilst OTA was randomly distributed in space. This difference between DON and OTA probably reflected the fact that DON is produced in the field, whereas OTA is produced in storage. The presence of spatial structure for DON implies that sampling plans need to consider the location of sample points in addition to the number of points sampled in order to obtain reliable estimates of quantities such as the mean contamination.
Depth matched transfer function of the modified composite pattern structured light illumination method

Abstract. The use of structured light illumination techniques for three-dimensional (3-D) data acquisition is, in many cases, limited to stationary objects due to the multiple pattern projections needed for depth analysis. High speed N-pattern projections require synchronization between the camera and the projector and have the added expense of these high speed devices. The composite pattern (CP) method allows multiple structured light patterns to be combined via spatial frequency modulation, thereby enabling measurement and rendering of a 3-D surface model of an object using only a single pattern. The capture speed of a single pattern does not require synchronization and is only limited by the camera speed which is N times less than the N-pattern techniques. When used on partially translucent materials such as human skin, the CP weighting is corrupted thereby degrading the 3-D reconstruction. The method described herein, termed modified CP, extends the CP design with the addition of a stripe encoding pattern to be insensitive to the internal scattering of human skin. This stripe pattern, used in conjunction with a new spatial processing method, allows for less contrast sensitivity, less sensitivity to human skin spatial frequency response and thus higher resolution performance. The resolution performance is experimentally measured based on a measure our group has developed, referred to as the depth matched transfer function. Measurements and practical applications are demonstrated.