Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Castration on Expression of Lipid Metabolism Genes in the Liver of Korean Cattle

Castration induces the accumulation of body fat and deposition of intramuscular fat in Korean cattle, resulting in improved beef quality. However, little is known about the metabolic adaptations in the liver following castration. To understand changes in lipid metabolism following castration, hepatic expression levels of lipid metabolism genes were compared between Korean bulls and steers. Steers had higher (p<0.001) hepatic lipids contents and higher (p<0.01) mRNA levels of lipogenic acetyl-CoA carboxylase. This differential gene expression may, in part, contribute to increased hepatic lipid content following the castration of bulls. However, we found no differences in the hepatic expression levels of genes related to triglyceride synthesis (mitochondrial glycerol-3-phosphate acyltransferase, diacylglycerol O-acyltransferase 1 and 2) and fatty acid (FA) oxidation (carnitine palmitoyltransferase 1A, C-4 to C-12 straight chain acyl-CoA dehydrogenase, very long chain acyl-CoA dehydrogenase) between bulls and steers. No differences in gene expression for very-low-density lipoprotein (VLDL) secretion, including apolipoprotein B mRNA and microsomal triglyceride transfer protein (MTTP) protein, were observed in the liver although MTTP mRNA levels were higher in steers compared to bulls. In conclusion, FA synthesis may contribute to increased hepatic lipid deposition in steers following castration. However, hepatic lipid metabolism, including triglyceride synthesis, FA oxidation, and VLDL secretion, was not significantly altered by castration. Our results suggest that hepatic lipid metabolism does not significantly contribute to increased body fat deposition in steers following castration.
Studies on Using the SynFace Talking Head for the Hearing Impaired

SynFace is a lip-synchronized talking agent which is optimized as a visual reading support for the hearing impaired. In this paper we present the large scale hearing impaired user studies carried out for three languages in the Hearing at Home project. The user tests focus on measuring the gain in Speech Reception Threshold in Noise and the effort scaling when using SynFace by hearing impaired people, where groups of hearing impaired subjects with different impairment levels from mild to severe and cochlear implants are tested. Preliminary analysis of the results does not show significant gain in SRT or in effort scaling. But looking at large cross-subject variability in both tests, it is clear that many subjects benefit from SynFace especially with speech with stereo babble.
SLA-Aware Adaptive On-demand Data Broadcasting in Wireless Environments

In mobile and wireless networks, data broadcasting for popular data items enables the efficient utilization of the limited wireless bandwidth. However, efficient data scheduling schemes are needed to fully exploit the benefits of data broadcasting. This motivated the proposal of several broadcast scheduling policies, which have mostly focused on either minimizing response time, or drop rate when requests are associated with hard deadlines. The inherent inaccuracy of hard deadlines in a dynamic mobile environment motivated us to use Service Level Agreements (SLAs) where a user specifies the utility of data as a function of its arrival time. Moreover, SLAs provide the mobile user with an already familiar quality of service specification from wired environments. Hence, in this paper, we propose SAAB which is an SLA-aware adaptive data broadcast scheduling policy for maximizing the system utility under SLA-based performance measures. To achieve this goal, SAAB considers both the characteristics of disseminated data objects as well as the SLAs associated with them. Additionally, SAAB automatically adjusts to the system workload conditions which enables it to constantly outperform existing on-demand broadcast scheduling policies.
A 55‐year‐old natural experiment gives evidence of the effects of changes in fire frequency on ecosystem properties in a seasonal subtropical dry forest

Fire frequency has been highlighted as an important component of fire effects on ecosystems; nevertheless, there is scarce information about how fire modulates changes in ecosystem properties, particularly for subtropical dry forests. A long‐term natural experiment and a multiscale approach were used to analyze changes in ecosystem properties in a subtropical dry forest subjected to contrasting fire regimes. Measurements were taken in two adjacent sites that had contrasting fire regimes (low/high fire frequency), and vegetation and soil analyses were combined with dendrochronological methods. Ten plots (20 × 20 m) were established in each site and soil (depth, chemical, and biological properties), and vegetation (species composition and growth of the dominant tree, Lithraea molleoides) variables were measured. Fire scars in L. molleoides were useful in determining fire history. Shrubs and grasses were more abundant in the high fire frequency site, with a high cover of the exotic grass Melinis repens, whereas trees and vines were more abundant in the low fire frequency site. Soil of the high fire frequency site tended to have lower C and nutrient content as well a significant lower depth than those of the low fire frequency site. Growth of L. molleoides individuals was lower in the high fire frequency site. Our results suggest that the long‐term effects of high fire frequency probably increase both soil loss and fire frequency in the future due to changes in plant composition, indicating that frequent burning is not a sustainable management option for this seasonal subtropical dry forest.
Online cognitive-behavioural treatment of bulimic symptoms: a randomized controlled trial.

BACKGROUND Manualized cognitive-behavioural treatment (CBT) is underutilized in the treatment of bulimic symptoms. Internet-delivered treatment may reduce current barriers. OBJECTIVE This study aimed to assess the efficacy of a new online CBT of bulimic symptoms. METHOD Participants with bulimic symptoms (n = 105) were randomly allocated to online CBT, bibliotherapy or waiting list/delayed treatment condition. Data were gathered at pre-treatment, post-treatment and 1-year follow-up. OUTCOME MEASURES The primary outcome measures were the Eating Disorder Examination Questionnaire (EDE-Q) and the frequency of binge eating and purging episodes. The secondary outcome measure was the Body Attitude Test. RESULTS Dropout from Internet treatment was 26%. Intention-to-treat ANCOVAs of post-test data revealed that the EDE-Q scores and the frequency of binging and purging reduced more in the online CBT group compared with the bibliotherapy and waiting list groups (pooled between-group effect size: d = 0.9). At 1-year follow-up, improvements in the online CBT group had sustained. CONCLUSION This study identifies online CBT as a viable alternative in the treatment of bulimic symptoms.
Orogenic Wedge Evolution of the Central Andes, Bolivia (21°S): Implications for Cordilleran Cyclicity

The Andes are an ideal setting to explore orogenic wedge evolution and the cyclical tectonic processes in Cordilleran convergent‐margin systems. Paleoaltimetry data suggest that the hinterland plateau in southern Bolivia underwent rapid surface uplift at ~16–9 Ma, which is predicted to have induced rapid thrust belt propagation. We integrate fission track and (U‐Th)/He ages from zircon and apatite with a sequentially restored cross section to quantify the timing and rates of thrust belt propagation in southern Bolivia for the last ~43 Myr. These data show that retroarc shortening in the Eastern Cordillera propagated westward from ~43 to 27 Ma as the wedge grew to attain critical taper and steady state. The thrust front then advanced rapidly eastward from ~25 to 17 Ma across the western Interandean zone, where a weak decollement modified the critical taper angle. The thrust front stalled for ~6 Myr but resumed eastward advance into the eastern Interandean zone and Subandean zone by ~11–8 Ma, which we interpret as a response to increased accretionary influx and rapid orogenic wedge expansion induced by eclogitic delamination and corresponding hinterland surface uplift at ~13 Ma. Development of an orographic barrier and wetter climatic conditions resulted in relatively steady state wedge conditions from ~8.5 to 1.5 Ma. Rapid wedge growth after ~1.5 Ma may be attributed to mass accumulation in the orogen interior or a weakened decollement. Our data reveal space‐time variations in orogenic wedge evolution consistent with models of Cordilleran cyclicity and lithospheric removal, with important additional influences of erosion, climate, and rock rheology.
Comparative Study of Frequent Itemset Mining Algorithms Apriori and FP Growth

Frequent itemset mining leads to the discovery of associations among items in large transactional database. In this paper, two algorithms[7] of generating frequent itemsets are discussed: Apriori and FP-growth algorithm. In apriori algorithm candidates are generated and testing is done which is easy to implement but candidate generation and support counting is very expensive in this because database is checked many times. In the fp-growth, there is no candidate generation and requires only 2 passes over the database but in this the generation of fp-tree become very expansive to built and support is counted only when entire dataset is added to fptree. The comparison of these algorithms will tell which algorithm is better to perform.
How Personality Affects our Likes: Towards a Better Understanding of Actionable Images

Messages like "If You Drink Don't Drive", "Each water drop count" or "Smoking causes cancer" are often paired with visual content in order to persuade an audience to perform specific actions, such as clicking a link, retweeting a post or purchasing a product. Despite its usefulness, the current way of discovering actionable images is entirely manual and typically requires marketing experts to filter over thousands of candidate images. To help understand the audience, marketers and social scientists have been investigating for years the role of personality in personalized services by leveraging AI technologies and social network data. In this work, we analyze how personality affects user actions on images in a social network website, and which visual stimuli contained in image content influence actions from users with certain Big Five traits. In order to achieve this goal, we ground this research on psychological studies which investigate the interplay between personality and emotions. Given a public Twitter dataset containing 1.6 million user-image timeline retweet actions, we carried out two extensive statistical analysis, which show significant correlation between personality traits and affective visual concepts in image content. We then proposed a novel model that combines user personality traits and image visual concepts for the task of predicting user actions in advance. This work is the first attempt to integrate personality traits and multimedia features, and moves an important step towards building personalized systems for automatically discovering actionable multimedia content.
A CMOS Contact Imager for Cell Detection in Bio-Sensing Applications

Many experimental procedures in cell biology rely on the use of biochemical light-emitting reporters to enhance structures or processes of interest in a cell sample. The ability to integrate these widely-used protocols on the surface of a CMOS biosensor array, combined with the capacity to perform cell actuation/stimulation, would provide researchers with a valuable tool to conduct high-throughput, high-density, accurate analyses. To this end, a low noise, high-sensitivity CMOS contact imager is presented for the detection of cell cultures coupled directly to the sensor surface. Design considerations are discussed for the application of neural activity recording of neuronal networks cultured in vitro from dissociated neuron cells. A 128 times 128 CMOS imager implemented in 0.18-mum CMOS technology is presented featuring the implementation of the active reset technique with the use of the active column sensor pixel architecture
Evaluating the Effect of Teachers' Performance Incentives on Pupil Achievement

Proposals to use teachers' performance incentives have recently attracted considerable attention. However, there is very little experience with applying incentives in schools. This paper provides evidence on the causal effects of two programs: the first provided the school and its teachers with monetary performance incentives and the second with additional conventional resources. The assignment of schools to the two programs was not random; therefore, identification is a central issue in the empirical analysis. The empirical results suggest that schools' and teachers' group monetary incentives caused significant gains in many dimensions of students' outcomes. Endowing schools with more resources also led to improvement in student performance. However, the comparison based on cost equivalency suggests that the teachers' incentive intervention is much more cost effective.
Real-Time Scheduling in MapReduce Clusters

MapReduce has been widely used as a Big Data processing platform. As it gets popular, its scheduling becomes increasingly important. In particular, since many MapReduce applications require real-time data processing, scheduling real time applications in MapReduce environments has become a significant problem. In this paper, we create a novel real-time scheduler for MapReduce, which overcomes the deficiencies of an existing scheduler. It avoids accepting jobs that will lead to deadline misses and improves the cluster utilization. We implement our scheduler in Hadoop system and experimental results show that our scheduler provides deadline guarantees for accepted jobs and achieves good cluster utilization.
Prediction of Peptide Reactivity with Human IVIg through a Knowledge-Based Approach

The prediction of antibody-protein (antigen) interactions is very difficult due to the huge variability that characterizes the structure of the antibodies. The region of the antigen bound to the antibodies is called epitope. Experimental data indicate that many antibodies react with a panel of distinct epitopes (positive reaction). The Challenge 1 of DREAM5 aims at understanding whether there exists rules for predicting the reactivity of a peptide/epitope, i.e., its capability to bind to human antibodies. DREAM 5 provided a training set of peptides with experimentally identified high and low reactivities to human antibodies. On the basis of this training set, the participants to the challenge were asked to develop a predictive model of reactivity. A test set was then provided to evaluate the performance of the model implemented so far. We developed a logistic regression model to predict the peptide reactivity, by facing the challenge as a machine learning problem. The initial features have been generated on the basis of the available knowledge and the information reported in the dataset. Our predictive model had the second best performance of the challenge. We also developed a method, based on a clustering approach, able to “in-silico” generate a list of positive and negative new peptide sequences, as requested by the DREAM5 “bonus round” additional challenge. The paper describes the developed model and its results in terms of reactivity prediction, and highlights some open issues concerning the propensity of a peptide to react with human antibodies.
Reproductive workers show queenlike gene expression in an intermediately eusocial insect, the buff‐tailed bumble bee Bombus terrestris

Bumble bees represent a taxon with an intermediate level of eusociality within Hymenoptera. The clear division of reproduction between a single founding queen and the largely sterile workers is characteristic for highly eusocial species, whereas the morphological similarity between the bumble bee queen and the workers is typical for more primitively eusocial hymenopterans. Also, unlike other highly eusocial hymenopterans, division of labour among worker subcastes is plastic and not predetermined by morphology or age. We conducted a differential expression analysis based on RNA‐seq data from 11 combinations of developmental stage and caste to investigate how a single genome can produce the distinct castes of queens, workers and males in the buff‐tailed bumble bee Bombus terrestris. Based on expression patterns, we found males to be the most distinct of all adult castes (2411 transcripts differentially expressed compared to nonreproductive workers). However, only relatively few transcripts were differentially expressed between males and workers during development (larvae: 71 and pupae: 162). This indicates the need for more distinct expression patterns to control behaviour and physiology in adults compared to those required to create different morphologies. Among female castes, reproductive workers and their nonreproductive sisters displayed differential expression in over ten times more transcripts compared to the differential expression found between reproductive workers and their mother queen. This suggests a strong shift towards a more queenlike behaviour and physiology when a worker becomes fertile. This contrasts with eusocial species where reproductive workers are more similar to nonreproductive workers than the queen.
Niger’s Child Survival Success, Contributing Factors and Challenges to Sustainability: A Retrospective Analysis

Background Household surveys undertaken in Niger since 1998 have revealed steady declines in under-5 mortality which have placed the country ‘on track’ to reach the fourth Millennium Development goal (MDG). This paper explores Niger’s mortality and health coverage data for children under-5 years of age up to 2012 to describe trends in high impact interventions and the resulting impact on childhood deaths averted. The sustainability of these trends are also considered. Methods and Findings Estimates of child mortality using the 2012 Demographic and Health Survey were developed and maternal and child health coverage indicators were calculated over four time periods. Child survival policies and programmes were documented through a review of documents and key informant interviews. The Lives Saved Tool (LiST) was used to estimate the number of child lives saved and identify which interventions had the largest impact on deaths averted. The national mortality rate in children under-5 decreased from 286 child deaths per 1000 live births (95% confidence interval 177 to 394) in the period 1989–1990 to 128 child deaths per 1000 live births in the period 2011–2012 (101 to 155), corresponding to an annual rate of decline of 3.6%, with significant declines taking place after 1998. Improvements in the coverage of maternal and child health interventions between 2006 and 2012 include one and four or more antenatal visits, maternal Fansidar and tetanus toxoid vaccination, measles and DPT3 vaccinations, early and exclusive breastfeeding, oral rehydration salts (ORS) and proportion of children sleeping under an insecticide-treated bed net (ITN). Approximately 26,000 deaths of children under-5 were averted in 2012 due to decreases in stunting rates (27%), increases in ORS (14%), the Hib vaccine (14%), and breastfeeding (11%). Increases in wasting and decreases in vitamin A supplementation negated some of those gains. Care seeking at the community level was responsible for an estimated 7,800 additional deaths averted in 2012. A major policy change occurred in 2006 enabling free health care provision for women and children, and in 2008 the establishment of a community health worker programme. Conclusion Increases in access and coverage of care for mothers and children have averted a considerable number of childhood deaths. The 2006 free health care policy and health post expansion were paramount in reducing barriers to care. However the sustainability of this policy and health service provision is precarious in light of persistently high fertility rates, unpredictable GDP growth, a high dependence on donor support and increasing pressures on government funding.
What Can I Do With This Tool? Self-Supervised Learning of Tool Affordances From Their 3-D Geometry

The ability to use tools can significantly increase the range of activities that an agent is capable of. Humans start using external objects since an early age to accomplish their goals, learning from interaction and observation the relationship between the objects used, their own actions, and the resulting effects, i.e., the tool affordances. Robots capable of autonomously learning affordances in a similar self-supervised way would be far more versatile and simpler to design than purpose-specific ones. This paper proposes and evaluates an approach to allow robots to learn tool affordances from interaction, and generalize them among similar tools based on their 3-D geometry. A set of actions is performed by the iCub robot with a large number of tools grasped in different poses, and the effects observed. Tool affordances are learned as a regression between tool-pose features and action-effect vector projections on respective self-organizing maps, which enables the system to avoid categorization and keep gradual representations of both elements. Moreover, we propose a set of robot-centric 3-D tool descriptors, and study their suitability for interaction scenarios, comparing also their performance against features derived from deep convolutional neural networks. Results show that the presented methods allow the robot to predict the effect of its tool use actions accurately, even for previously unseen tool and poses, and thereby to select the best action for a particular goal given a tool-pose.
Construction and Application of Bayesian Network in Early Diagnosis of Alzheimer Disease's System

Most of methods in neuroimaging studies are usually based on voxel-wise statistical tests which are performed on functional or structural magnetic resonance (MR) images across or within subjects. Such methods ( i.e. t-test and paired t-test ) are univariate in nature and can only describe the linear association between the brain structure/function and other variables of interest. In this study, we propose a Bayesian network approach combining the data among MR images and clinical/cognitive function variables to assist the early prevention strategy and diagnose the mild cognitive impairment (MCI). The Bayesian network is a powerful knowledge representation and reasoning tool under conditions of uncertainty. Our application of such procedure describes nonlinear multivariate relationships between the human brain structures, function variables and other measurements of interest. Cross-validation approach was utilized in our study for the construction and evaluation of the proposed Bayesian network model. Twenty-five MCI subjects were selected from whom we acquired structural MR data. These MR images were then segmentated and registered to the MNI template and with automated structure volume calculated and made discrete. These volumes included hippocampus, thalamus and perirhinal, etc. With these imaging data and other clinical measurements, we used heuristic search method to construct our Bayesian model and evaluated its reliability using a scoring method. This construction procedure included the training of the structure and estimation of the parameters of the Bayesian network using MR data from 15 these subjects included in this study. To evaluate the use of the proposed model, inference was made with the masked variables using the MR data from the remaining subjects. It was found that MCI was mainly dependent on hippocampus, thalamus and entorhinal. Our method could be used to early diagnose the presence of the mild cognitive impairment from structural MR images and behavioral data.