Miracle-dz/UniFAR-FTUs huggingface dataset:
Establishing sheep as an experimental species to validate ultrasound-mediated blood-brain barrier opening for potential therapeutic interventions

Rationale: Treating diseases of the brain such as Alzheimer's disease (AD) is challenging as the blood-brain barrier (BBB) effectively restricts access of a large number of potentially useful drugs. A potential solution to this problem is presented by therapeutic ultrasound, a novel treatment modality that can achieve transient BBB opening in species including rodents, facilitated by biologically inert microbubbles that are routinely used in a clinical setting for contrast enhancement. However, in translating rodent studies to the human brain, the presence of a thick cancellous skull that both absorbs and distorts ultrasound presents a challenge. A larger animal model that is more similar to humans is therefore required in order to establish a suitable protocol and to test devices. Here we investigated whether sheep provide such a model. Methods: In a stepwise manner, we used a total of 12 sheep to establish a sonication protocol using a spherically focused transducer. This was assisted by ex vivo simulations based on CT scans to establish suitable sonication parameters. BBB opening was assessed by Evans blue staining and a range of histological tests. Results: Here we demonstrate noninvasive microbubble-mediated BBB opening through the intact sheep skull. Our non-recovery protocol allowed for BBB opening at the base of the brain, and in areas relevant for AD, including the cortex and hippocampus. Linear time-shift invariant analysis and finite element analysis simulations were used to optimize the position of the transducer and to predict the acoustic pressure and location of the focus. Conclusion: Our study establishes sheep as a novel animal model for ultrasound-mediated BBB opening and highlights opportunities and challenges in using this model. Moreover, as sheep develop an AD-like pathology with aging, they represent a large animal model that could potentially complement the use of non-human primates.
Overview of Social Cognitive Dysfunctions in Rare Developmental Syndromes With Psychiatric Phenotype

Rare neurodevelopmental syndromes often present social cognitive deficits that may underlie difficulties in social interactions and increase the risk of psychosis or autism spectrum disorders. However, little is known regarding the specificities of social cognitive impairment across syndromes while it remains a major challenge for the care. Our review provides an overview of social cognitive dysfunctions in rare diseases associated with psychiatric symptoms (with a prevalence estimated between 1 in 1,200 and 1 in 25,000 live births: 22q11.2 deletion syndrome, Angelman syndrome, Fragile X syndrome, Klinefelter syndrome, Prader–Willi syndrome, Rett syndrome, Smith–Magenis syndrome, Turner syndrome, and Williams syndrome) and shed some light on the specific mechanisms that may underlie these skills in each clinical presentation. We first detail the different processes included in the generic expression “social cognition” before summarizing the genotype, psychiatric phenotype, and non-social cognitive profile in each syndrome. Then, we offer a systematic review of the social cognitive abilities and the disturbed mechanisms they are likely associated with. We followed the PRISMA process, including the definition of the relevant search terms, the selection of studies based on clear inclusion, and exclusion criteria and the quality appraisal of papers. We finally provide insights that may have considerable influence on the development of adapted therapeutic interventions such as social cognitive training (SCT) therapies specifically designed to target the psychiatric phenotype. The results of this review suggest that social cognition impairments share some similarities across syndromes. We propose that social cognitive impairments are strongly involved in behavioral symptoms regardless of the overall cognitive level measured by intelligence quotient. Better understanding the mechanisms underlying impaired social cognition may lead to adapt therapeutic interventions. The studies targeting social cognition processes offer new thoughts about the development of specific cognitive training programs, as they highlight the importance of connecting neurocognitive and SCT techniques.
Modeling and performance analysis of multiple area power system

Modeling and performance analysis of multiple area power system in S-domain is described in the present article. PID controller is used for load frequency control of the proposed multiple area power system. The use of PID controller ensures the reliability of power flow after a huge load disturbance in the power system network. Optimum values of Proportional, Integral and Differential controller gain has been determined using optimal control algorithm for reliable operation of the proposed multiple area power system. A digital control algorithm is also proposed in place of PID controller to improve the performance of the power system.
A Light Visual Mapping and Navigation Framework for Low-Cost Robots

Abstract We address the problems of localization, mapping, and guidance for robots with limited computational resources by combining vision with the metrical information given by the robot odometry. We propose in this article a novel light and robust topometric simultaneous localization and mapping framework using appearance-based visual loop-closure detection enhanced with the odometry. The main advantage of this combination is that the odometry makes the loop-closure detection more accurate and reactive, while the loop-closure detection enables the long-term use of odometry for guidance by correcting the drift. The guidance approach is based on qualitative localization using vision and odometry, and is robust to visual sensor occlusions or changes in the scene. The resulting framework is incremental, real-time, and based on cheap sensors provided on many robots (a camera and odometry encoders). This approach is, moreover, particularly well suited for low-power robots as it is not dependent on the image processing frequency and latency, and thus it can be applied using remote processing. The algorithm has been validated on a Pioneer P3DX mobile robot in indoor environments, and its robustness is demonstrated experimentally for a large range of odometry noise levels.
Simulation of Graphical Models for Multiagent Probabilistic Inference

Multiply-sectioned Bayesian networks (MSBNs) extend Bayesian networks to graphical models for multiagent probabilistic reasoning. The empirical study of algorithms for manipulations of MSBNs (e.g., verification, compilation, and inference) requires experimental MSBNs. As engineering MSBNs in large problem domains requires significant knowledge and engineering effort, the authors explore automatic simulation of MSBNs. Due to the large domain over which an MSBN is defined and a set of constraints to be satisfied, a generate-and-test approach toward simulation has a high rate of failure. The authors present an alternative approach that treats the simulation process as a sequence of decisions. They constrain the space of each decision so that backtracking is minimized and the outcome is always a legal MSBN. A suite of algorithms that implements this approach is presented, and experimental results are shown.
Original Research: Nurses' Presenteeism and Its Effects on Self-Reported Quality of Care and Costs

ObjectiveAlthough research has been conducted on how nurse staffing levels affect outcomes, there has been little investigation into how the health-related productivity of nurses is related to quality of care. Two major causes of worker presenteeism (reduced on-the-job productivity as a result of health problems) are musculoskeletal pain and mental health issues, particularly depression. This study sought to investigate the extent to which musculoskeletal pain or depression (or both) in RNs affects their work productivity and self-reported quality of care and considered the associated costs. MethodsUsing a cross-sectional survey design, a random sample of 2,500 hospital-employed RNs licensed in North Carolina were surveyed using a survey instrument sent by postal mail. Specific measures included questions on individual and workplace characteristics, self-reported quality of care, and patient safety; a numeric pain rating scale, a depression tool (the Patient Health Questionnaire), and a presenteeism tool (the Work Productivity and Activity Impairment Questionnaire: General Health) were also incorporated. A total of 1,171 completed surveys were returned and used for analysis. ResultsAmong respondents, the prevalence of musculoskeletal pain was 71%; that of depression was 18%. The majority of respondents (62%) reported a presenteeism score of at least 1 on a 0-to-10 scale, indicating that health problems had affected work productivity at least “a little. ” Pain and depression were significantly associated with presenteeism. Presenteeism was significantly associated with a higher number of patient falls, a higher number of medication errors, and lower quality-of-care scores. Baseline cost estimates indicate that the increased falls and medication errors caused by presenteeism are expected to cost $1,346 per North Carolina RN and just under $2 billion for the United States annually. Upper-boundary estimates exceed $9,000 per North Carolina RN and $13 billion for the nation annually. ConclusionMore attention must be paid to the health of the nursing workforce to positively influence the quality of patient care and patient safety and to control costs.
Making Meaning by Seeing Human

On October 3, 2003, entertainers Siegfried and Roy took the stage to perform in their legendary show—a combination of magic, illusions, and stunts performed with live tigers—at the Mirage in Las Vegas. although the duo had been performing on the Vegas strip for almost 30 years, that night they experienced something they never had before. One of the trained tigers attacked Roy, biting him in the neck and causing him massive blood loss. Following the uproar that the event generated, comedian Chris Rock expressed his surprise at the public response to the incident and disdain for the public’s view that “the tiger went crazy. ” Rock stated, “that tiger didn’t go crazy—that tiger went tiger! ” this incident, and Rock’s response, illustrate people’s tendency to turn to anthropomorphic descriptions to explain the actions of nonhumans when things do not go according to plan. given such an unexpected event as this tiger attack, many people sought to explain the tiger’s actions in more mentalistic, human terms. However, as Rock suggested, the actions of the tiger were not psychotic, they were merely the
The Effect of Mobile Learning on Students' Achievement and Conversational Skills

The present study aimed to examine the effect of Mobile Learning, which is a kind of E- learning that uses mobile devices, on the development of the academic achievement and conversational skills of English language specialty students at Najran University. The study used the quasi-experimental approach. Participants consisted of (50) students who were registered in a course called "Strategies of Teaching and Learning" offered by the Department of Curriculum and Instruction at the College of Education to students majoring in English at the Department of English Language at the College of Science and Arts. As usual participant students were divided into experimental and control groups of (25) students in each. The soft copy of the course content was uploaded to the University Blackboard System for students in the experimental group. Another hardcopy of the course content was delivered to students in the control group by hand in the first meeting. Data collection tools included an academic achievement test and conversational skills rating scale. Results showed that mobile learning had quite significant effect on both students' academic achievement and conversational skills. In light of these findings faculty members were recommended to adapt the use of Mobile Learning in their classes.
Invited: Specification and modeling for Systems-on-Chip security verification

This paper describes a methodology for system-level security verification of modern Systems-on-Chip (SoC) designs. These designs comprise interacting firmware and hardware modules which makes verification particularly challenging. These challenges relate to (i) specifying security verification properties, and (ii) verifying these properties across firmware and hardware. We address the latter through raising the level of abstraction of the hardware modules to be similar to that of instructions in software/firmware. This abstraction, referred to as an instruction-level abstraction (ILA), plays a similar role to the instruction set architecture (ISA) definition for general purpose processors and enables high-level analysis of SoC firmware. In particular, the ILA can be used instead of the cycle-accurate bit-precise hardware implementation for scalable verification of system-level security properties in SoCs. We introduce techniques to semi-automatically synthesize the ILA using a template abstraction and directed simulations of the SoC hardware. We describe techniques to ensure that the ILA is a correct abstraction of the underlying hardware implementation. We then show how the ILA can be used for SoC security verification by designing a specification language for security properties and an algorithm based on symbolic execution to verify these properties. Our case studies apply ILA-based verification to an example SoC built out of open source components as well as part of a commercial SoC. The methodology discovers several bugs in the hardware implementation, simulators and firmware.
Cancer risks associated with external radiation from diagnostic imaging procedures

The 600% increase in medical radiation exposure to the US population since 1980 has provided immense benefit, but increased potential future cancer risks to patients. Most of the increase is from diagnostic radiologic procedures. The objectives of this review are to summarize epidemiologic data on cancer risks associated with diagnostic procedures, describe how exposures from recent diagnostic procedures relate to radiation levels linked with cancer occurrence, and propose a framework of strategies to reduce radiation from diagnostic imaging in patients. We briefly review radiation dose definitions, mechanisms of radiation carcinogenesis, key epidemiologic studies of medical and other radiation sources and cancer risks, and dose trends from diagnostic procedures. We describe cancer risks from experimental studies, future projected risks from current imaging procedures, and the potential for higher risks in genetically susceptible populations. To reduce future projected cancers from diagnostic procedures, we advocate the widespread use of evidence‐based appropriateness criteria for decisions about imaging procedures; oversight of equipment to deliver reliably the minimum radiation required to attain clinical objectives; development of electronic lifetime records of imaging procedures for patients and their physicians; and commitment by medical training programs, professional societies, and radiation protection organizations to educate all stakeholders in reducing radiation from diagnostic procedures. CA Cancer J Clin 2012. © 2012 American Cancer Society.
Investigations on End- to-End Audiovisual Fusion

Audiovisual speech recognition (AVSR) is a method to alleviate the adverse effect of noise in the acoustic signal. Leveraging recent developments in deep neural network-based speech recognition, we present an AVSR neural network architecture which is trained end-to-end, without the need to separately model the process of decision fusion as in conventional (e.g. HMM-based) systems. The fusion system outperforms single-modality recognition under all noise conditions. Investigation of the saliency of the input features shows that the neural network automatically adapts to different noise levels in the acoustic signal.
MEXICO’S 1988 PRESIDENTIAL ELECTION∗

This paper disentangles the opportunities for fraud in authoritarian settings by documenting the inflation of the vote returns during the 1988 presidential election in Mexico. In particular, I study how aggregation fraud was the result of an electoral reform that centralized the vote-counting process in the hands of district officials. Moreover, sub-national differences in the levels of this irregularity can be explained by the incentives of the governors to keep officials accountable for delivering the results they expected. Using an original image database of the vote-tally sheets for that election, and applying Convolutional Neural Networks (CNN) to analyze pictures, I find evidence of blatant alterations in almost thirty percent of the tallies in the country. An exploratory analysis on this classification suggests that altered tallies were more prevalent in the incumbent party-strongholds and where governors had both the electoral expertise and political motivations to engage in manipulation. This research has important implications for understanding the ways in which autocrats control elections as well as introducing a new methodology to audit the integrity of the vote tallies in contemporary elections. ∗I am grateful to Danis Boumer, Pippa Norris, Gonzalo Rivero, Jane Sumner, Ricardo Vilalta for their useful feedback. Angélica Alva and Pablo Herández Aparicio provided great research assistance. All errors are my own.
A Jack of All Trades But a Master of None? Evaluating the Performance of the Level of Service Inventory–Revised (LSI-R) in the Assessment of Risk and Need

ABSTRACT We evaluate the performance of the LSI-R in assessing risk and need among 26,001 offenders released from Minnesota prisons between 2003 and 2011. Using multiple performance metrics, the results reveal the LSI-R incorrectly classified 41 percent of the offenders, and its area under the curve (AUC) was 0.628. Drawing on the extensive program evaluation research conducted on Minnesota prisoners over the last decade, we evaluated the LSI-R’s assessment of need by examining the relationship between LSI-R domain scores, participation in effective interventions, and recidivism outcomes. The findings provide, at best, moderate support for the LSI-R’s performance in assessing need. Interventions targeting the education/employment, alcohol/drug, and emotional/personal domains generally yielded better recidivism outcomes among offenders with higher scores in these domains.
Learning from multivariate discrete sequential data using a restricted Boltzmann machine model

A restricted Boltzmann machine (RBM) is a generative neural-network model with many novel applications such as collaborative filtering and acoustic modeling. An RBM lacks the capacity to retain memory, making it inappropriate for dynamic data modeling as in time-series analysis. In this paper we address this issue by proposing the p-RBM model, a generalization of the regular RBM model, capable of retaining memory of p past states. We further show how to train the p-RBM model using contrastive divergence and test our model on the problem of predicting the stock market direction considering 100 stocks of the NASDAQ-100 index. Obtained results show that the p-RBM offer promising prediction potential.
Genome Sequencing and Analysis of the Peanut B-Genome Progenitor (Arachis ipaensis)

Peanut (Arachis hypogaea L.), an important leguminous crop, is widely cultivated in tropical and subtropical regions. Peanut is an allotetraploid, having A and B subgenomes that maybe have originated in its diploid progenitors Arachis duranensis (A-genome) and Arachis ipaensis (B-genome), respectively. We previously sequenced the former and here present the draft genome of the latter, expanding our knowledge of the unique biology of Arachis. The assembled genome of A. ipaensis is ~1.39 Gb with 39,704 predicted protein-encoding genes. A gene family analysis revealed that the FAR1 family may be involved in regulating peanut special fruit development. Genomic evolutionary analyses estimated that the two progenitors diverged ~3.3 million years ago and suggested that A. ipaensis experienced a whole-genome duplication event after the divergence of Glycine max. We identified a set of disease resistance-related genes and candidate genes for biological nitrogen fixation. In particular, two and four homologous genes that may be involved in the regulation of nodule development were obtained from A. ipaensis and A. duranensis, respectively. We outline a comprehensive network involved in drought adaptation. Additionally, we analyzed the metabolic pathways involved in oil biosynthesis and found genes related to fatty acid and triacylglycerol synthesis. Importantly, three new FAD2 homologous genes were identified from A. ipaensis and one was completely homologous at the amino acid level with FAD2 from A. hypogaea. The availability of the A. ipaensis and A. duranensis genomic assemblies will advance our knowledge of the peanut genome.
British Escherichia coli O157 in Cattle Study (BECS): to determine the prevalence of E. coli O157 in herds with cattle destined for the food chain

SUMMARY Escherichia coli O157 are zoonotic bacteria for which cattle are an important reservoir. Prevalence estimates for E. coli O157 in British cattle for human consumption are over 10 years old. A new baseline is needed to inform current human health risk. The British E. coli O157 in Cattle Study (BECS) ran between September 2014 and November 2015 on 270 farms across Scotland and England & Wales. This is the first study to be conducted contemporaneously across Great Britain, thus enabling comparison between Scotland and England & Wales. Herd-level prevalence estimates for E. coli O157 did not differ significantly for Scotland (0·236, 95% CI 0·166–0·325) and England & Wales (0·213, 95% CI 0·156–0·283) (P = 0·65). The majority of isolates were verocytotoxin positive. A higher proportion of samples from Scotland were in the super-shedder category, though there was no difference between the surveys in the likelihood of a positive farm having at least one super-shedder sample. E. coli O157 continues to be common in British beef cattle, reaffirming public health policy that contact with cattle and their environments is a potential infection source.