Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular dosimetry of urinary aflatoxin-DNA adducts in people living in Guangxi Autonomous Region, People's Republic of China.

Hepatocellular carcinoma is one of the five leading human cancers causing at least 250,000 deaths each year. One of the major risk factors for this disease is exposure to dietary aflatoxins, and the development of appropriate molecular dosimetry biomarkers would facilitate the identification of individuals at risk. This study was undertaken to explore the relationship between dietary intake of aflatoxins and the excretion of the major aflatoxin-DNA adduct and other metabolites into the urine of chronically exposed people. The following protocol was developed for this investigation in Guangxi Autonomous Region, People's Republic of China, where the diets of 30 males and 12 females (ages, 25-64 years) were monitored for 1 week and aflatoxin intake levels determined each day. Starting on the fourth day, total urine volumes were obtained in consecutive 12-h fractions for 3 or 4 days. High performance liquid chromatography and competitive radioimmunoassay analyses were done on each of the urine samples, and the relationships between excretion of total aflatoxin metabolites, aflatoxin-N7-guanine, aflatoxin M1, aflatoxin P1, and aflatoxin B1, and aflatoxin B1 intake values were determined. The average intake of aflatoxin B1 by men was 48.4 micrograms/day, giving a total mean exposure during the study period of 276.8 micrograms. The average daily intake by women was 77.4 micrograms/day, resulting in a total average exposure during the 7-day period of 542.6 micrograms aflatoxin B1. Initial efforts to characterize aflatoxin metabolites in urine samples were with an analysis by competitive radioimmunoassay. The analysis by linear regression of the association between aflatoxin B1 intake/day and total aflatoxin metabolite excretion/day showed a correlation coefficient of only 0.26. These findings stimulated the immunoaffinity/analytical high performance liquid chromatography analysis for individual metabolites. When the data were analyzed by linear regression analysis, the aflatoxin N7-guanine excretion and aflatoxin B1 intake from the previous day showed a correlation coefficient of 0.65 and P less than 0.000001. Similar analysis for aflatoxin M1 resulted in a correlation coefficient of 0.55 and P less than 0.00001, whereas there was no positive statistical association between exposure in the diet and aflatoxin P1 excretion, despite aflatoxin P1 being quantitatively a major metabolite. Analysis of the total aflatoxin-N7-guanine excretion in the urine during the complete collection period plotted against the total aflatoxin B1 exposure in the diet for each of the individuals, smoothing the day to day variations, revealed a correlation coefficient of 0.80 and P less than 0.0000001. (ABSTRACT TRUNCATED AT 400 WORDS)
The Amygdala and Anxiety

The amygdala has a central role in anxiety responses to stressful and arousing situations. Pharmacological and lesion studies of the basolateral, central, and medial subdivisions of the amygdala have shown that their activation induces anxiogenic effects, while their inactivation produces anxiolytic effects. Many neurotransmitters and stress mediators acting at these amygdalar nuclei can modulate the behavioral expression of anxiety. These mediators may be released from different brain regions in response to different types of stressors. The amygdala is in close relationship with several brain regions within the brain circuitry that orchestrates the expression of anxiety. Recent developments in optogenetics have begun to unveil details on how these areas interact.
Learning Spatio-Temporal Representation With Local and Global Diffusion

Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for visual recognition problems. Nevertheless, the convolutional filters in these networks are local operations while ignoring the large-range dependency. Such drawback becomes even worse particularly for video recognition, since video is an information-intensive media with complex temporal variations. In this paper, we present a novel framework to boost the spatio-temporal representation learning by Local and Global Diffusion (LGD). Specifically, we construct a novel neural network architecture that learns the local and global representations in parallel. The architecture is composed of LGD blocks, where each block updates local and global features by modeling the diffusions between these two representations. Diffusions effectively interact two aspects of information, i.e., localized and holistic, for more powerful way of representation learning. Furthermore, a kernelized classifier is introduced to combine the representations from two aspects for video recognition. Our LGD networks achieve clear improvements on the large-scale Kinetics-400 and Kinetics-600 video classification datasets against the best competitors by 3.5% and 0.7%. We further examine the generalization of both the global and local representations produced by our pre-trained LGD networks on four different benchmarks for video action recognition and spatio-temporal action detection tasks. Superior performances over several state-of-the-art techniques on these benchmarks are reported.
Predictors of university students` academic achievement : A prospective study

motivational and behavioural predictors of two measures of university students' academic achievement (grade point average and student efficacy status), after controlling for dispositional variables and student adjustment at the beginning of college. A representative sample of 250 undergraduate students was followed over a period of 3 years. Hierarchical regression analysis and logistic regression analysis were conducted to check the contribution of the included variables. The hypothesized significant role of cognitive, behavioural and motivational factors in students' academic achievement has been confirmed, with the automatic thoughts related to the fear of disappointing parents and time and study environment management skills as the most pronounced predictors for both criteria. Outcome expectation is a significant predictor of the students' efficacy status only, while action control beliefs and goal orientations (performance and work avoidance) are significant predictors only of the GPA. In conclusion, the results suggest that, besides personality, other more manageable variables play an even more significant role in university students' academic achievement.
Optimization of Mutation Pressure in Relation to Properties of Protein-Coding Sequences in Bacterial Genomes

Most mutations are deleterious and require energetically costly repairs. Therefore, it seems that any minimization of mutation rate is beneficial. On the other hand, mutations generate genetic diversity indispensable for evolution and adaptation of organisms to changing environmental conditions. Thus, it is expected that a spontaneous mutational pressure should be an optimal compromise between these two extremes. In order to study the optimization of the pressure, we compared mutational transition probability matrices from bacterial genomes with artificial matrices fulfilling the same general features as the real ones, e.g., the stationary distribution and the speed of convergence to the stationarity. The artificial matrices were optimized on real protein-coding sequences based on Evolutionary Strategies approach to minimize or maximize the probability of non-synonymous substitutions and costs of amino acid replacements depending on their physicochemical properties. The results show that the empirical matrices have a tendency to minimize the effects of mutations rather than maximize their costs on the amino acid level. They were also similar to the optimized artificial matrices in the nucleotide substitution pattern, especially the high transitions/transversions ratio. We observed no substantial differences between the effects of mutational matrices on protein-coding sequences in genomes under study in respect of differently replicated DNA strands, mutational cost types and properties of the referenced artificial matrices. The findings indicate that the empirical mutational matrices are rather adapted to minimize mutational costs in the studied organisms in comparison to other matrices with similar mathematical constraints.
A new definition of the intermediate group of gamma-ray bursts

Gamma-ray bursts can be divided into three groups (short, intermediate, long) with respect to their durations. This classification is somewhat imprecise, since the subgroup of intermediate duration has an admixture of both short and long bursts. In this paper a physically more reasonable definition of the intermediate group is presented, using also the hardnesses of the bursts. It is shown again that the existence of the three groups is real, no further groups are needed. The intermediate group is the softest one. From this new definition it follows that 11% of all bursts belong to this group. An anticorrelation between the hardness and the duration is found for this subclass in contrast to the short and long groups. Despite this difference it is not clear yet whether this group represents a physically different phenomenon.
Error reduction and performance improvement in the emergency department through formal teamwork training: evaluation results of the MedTeams project.

OBJECTIVE To evaluate the effectiveness of training and institutionalizing teamwork behaviors, drawn from aviation crew resource management (CRM) programs, on emergency department (ED) staff organized into caregiver teams. STUDY SETTING Nine teaching and community hospital EDs. STUDY DESIGN A prospective multicenter evaluation using a quasi-experimental, untreated control group design with one pretest and two posttests of the Emergency Team Coordination Course (ETCC). The experimental group, comprised of 684 physicians, nurses, and technicians, received the ETCC and implemented formal teamwork structures and processes. Assessments occurred prior to training, and at intervals of four and eight months after training. Three outcome constructs were evaluated: team behavior, ED performance, and attitudes and opinions. Trained observers rated ED staff team behaviors and made observations of clinical errors, a measure of ED performance. Staff and patients in the EDs completed surveys measuring attitudes and opinions. DATA COLLECTION Hospital EDs were the units of analysis for the seven outcome measures. Prior to aggregating data at the hospital level, scale properties of surveys and event-related observations were evaluated at the respondent or case level. PRINCIPAL FINDINGS A statistically significant improvement in quality of team behaviors was shown between the experimental and control groups following training (p = .012). Subjective workload was not affected by the intervention (p = .668). The clinical error rate significantly decreased from 30.9 percent to 4.4 percent in the experimental group (p = .039). In the experimental group, the ED staffs' attitudes toward teamwork increased (p = .047) and staff assessments of institutional support showed a significant increase (p = .040). CONCLUSION Our findings point to the effectiveness of formal teamwork training for improving team behaviors, reducing errors, and improving staff attitudes among the ETCC-trained hospitals.
Practical regularization of the affine projection algorithm

It is well-known that a matrix inversion is required within the affine projection algorithm (APA). Depending on the character of the input signal, this matrix can be very ill-conditioned. Consequently, it needs to be regularized before inversion, i.e., a positive constant is added to the elements of its main diagonal. Also known as the regularization parameter, this constant plays a major role in practice; if it is not chosen properly, the APA may never converge, especially under low signal-to-noise ratio conditions. The contribution of this paper is twofold. First, we provide a formula for choosing an “optimal” regularization parameter, aiming at attenuating the effects of the noise in the adaptive filter estimate. Second, two practical ways for evaluating this parameter are proposed. Simulations performed in the context of acoustic echo cancellation (in different noisy environments) prove the validity of the proposed solutions.
Efficient mutation analysis: a new approach

In previously reported research we designed and analyzed algorithms that improved upon the run time complexity of all known weak and strong mutation analysis methods at the expense of increased space complexity. Here we describe a new serial strong mutation algorithm whose running time is on the average much faster than the previous ones and that uses significantly less space than them also. Its space requirement is approximately the same as that of Mothra, a well-known and readily available implemented system. Moreover, while this algorithm can serve as basis for a new mutation system, it is designed to be consistent with the Mothra architecture, in the sense that, by replacing certain modules of that system with new ones, a much faster system will result. Such a Mothra-based implementation of the new work is in progress. Like the previous algorithms, this one, which we call Lazy Mutant Analysis or LMA, tries to determine whether a mutant is strongly killed by a given test only if it is already known that it is weakly killed by that test. Unlike those algorithms, LMA avoids executing many mutants by dynamically discovering classes of mutants that have the “same” behavior, and executing representatives of those classes. The overhead it incurs is small in proportion to the time saved, and the algorithm has a very natural parallel implementation. In comparison to the fastest known algorithms for strong mutation analysis, in the best case, LMA can improve the speed by a factor proportional to the average number of mutants per program statement. In the worst case, there is no improvement in the running time, but such a case is hard to construct. This work enables us to apply mutation analysis to significantly larger programs than is currently possible.
Full Spatiotemporal Control of Laser-Excited Periodic Surface Deformations

We demonstrate full control of acoustic and thermal periodic deformations at solid surfaces down to sub-nanosecond time scales and few-micrometer length scales via independent variation of the temporal and spatial phase of two optical transient grating (TG) excitations. For this purpose, we introduce an experimental setup that exerts control of the spatial phase of subsequent time-delayed TG excitations depending on their polarization state. Specific exemplary coherent control cases are discussed theoretically and corresponding experimental data are presented in which time-resolved x-ray reflectivity measures the spatiotemporal surface distortion of nanolayered heterostructures. Finally, we discuss examples where the application of our method may enable the control of functional material properties via tailored spatiotemporal strain fields.
A common spatial pattern approach for scalp EEG seizure detection

This paper presents patient-specific epileptic seizure detection approach based on Common Spatial Pattern (CSP) and its variants; Diagonal Loading Common Spatial Pattern (DLCSP), and Tikhonov Regularization Common Spatial Pattern (TRCSP). In this proposed approach, multi-channel scalp Electroencephalogram (sEEG) signals are traced and segmented into overlapping segments for both normal and epileptic seizure intervals. Features are extracted from each signal segment through projection on a CSP projection matrix. The extracted features are used for training a Support Vector Machine (SVM) classifier, which is then employed in the testing phase. A leave-one-out cross validation strategy is adopted in the experiments. The proposed approach was evaluated using 443.55 hours of sEEG including 39 seizures. The experimental results reveal that a patient-specific CSP-based algorithm is capable of detecting epileptic seizures with high accuracy. In particular, the CSP approach has achieved 100% an average sensitivity, 1.17 an average false alarm, and 7.02 s an average detection latency time.
Minimizing Cultural Differences Using Ontology-Based Information Retrieval System

Effective global information access is more critical now than ever before. The digital world where users have diverse languages and diverse cultural backgrounds is increasing more rapidly than at any other time in history. This chapter addresses the cause of ineffective international information access from the standpoint of the user as well as from an information and system perspectives. The chapter also describes the traditional and emerging approaches to enhancing global information access and proposes a system that shows how emerging approaches can minimize cultural differences. DOI: 10.4018/978-1-60960-833-0.ch014
Using a P300 BrainComputer Interface in an Immersive Virtual Environment

Braincomputer interfaces (BCIs) provide a novel form of humancomputer interaction. The purpose of these systems is to aid disabled people by affording them the possibility of communication and environment control. In this study, we present experiments using a P300 based BCI in a fully immersive virtual environment (IVE). P300 BCIs depend on presenting several stimuli to the user. We propose two ways of embedding the stimuli in the virtual environment: one that uses 3D objects as targets, and a second that uses a virtual overlay. Both ways have been shown to work effectively with no significant difference in selection accuracy. The results suggest that P300 BCIs can be used successfully in a 3D environment, and this suggests some novel ways of using BCIs in real world environments.
Debtors, creditors, and sovereign money: A case for institutional blending and the amalgamated mind

Abstract Much social cognition and action is dialogical in nature and profitably understood from a second-person perspective. The elemental social roles of “debtor” and “creditor” are of great importance in explaining the structure and history of a wide range of social facts and institutions. Yet these person-level experiences of indebtedness and the mental spaces they engender are not sufficient to account for complex social facts. Sovereign money systems are a leading example where our person-level experiences of exchange lead us astray by actively hindering our ability to grasp money’s macroeconomic functions. This article provides a comprehensive account of money as a distributed cognitive phenomenon. It summarizes and critiques a prior analysis of money as a conceptual blend enabling exchange and subsequently advances an alternative “institutional” blending analysis of money as primarily a store-of-value and unit-of-account. This alternative analysis tracks findings of anthropologists and legal historians of money and banking as well as heterodox economists who make money the centerpiece of their macroeconomic models. The account of money also emphasizes that the underlying logic of sovereign money systems is stubbornly difficult for users of the currency to grasp or accept, as evidenced in a brief televised debate. If money is an instance of institutional blending wherein social structures and their material manifestations have cognitive status, then it recommends a broader argument that human minds themselves are an amalgam of neural assemblies, bodily structures and functions, and environmental structures and arrangements.
Factors in Dry Period Associated with Intramammary Infection and Subsequent Clinical Mastitis in Early Postpartum Cows

The objective of this study was to determine cow characteristics and farm management factors during the dry period associated with early postparturient intramammary infection (IMI) and subsequent clinical mastitis (CM). Data were collected three times: before drying off (P1), during the dry period (P2), and 5 to 14 days after calving (P3), using questionnaires and farm investigation. Milk samples were aseptically collected for bacterial identification at P1 and P3. Factors associated with IMI and CM were analyzed using multiple logistic regression models. The final model showed that IMI in early postpartum was significantly associated with full insertion of dry cow antibiotic, dry cows in barns with a combination of tie and free stalls, body condition score (BCS) in dry period and after calving, and milk yield before drying off. For IMI cows, factors significantly associated with clinical expression of mastitis were having daily barn cleaning, teat disinfected with alcohol before administration of dry cow therapy, BCS before drying off, milk yield before drying off, and days in milk at drying off. In conclusion, both cow and farm management factors are associated with the IMI rate and subsequent expression of clinical signs of mastitis in early postpartum cows.
Using GODIVA for data flow analysis

GODIVA (GODdard Instrumentation Visualizer and Analyzer) is a software tool for probing the performance characteristics of large science simulation codes running on large-scale parallel scalable computers. Godiva has been in use for almost two years in the study of Fortran and C codes written to address “Grand Challenge” problems in Earth and space science. Godiva is portable and currently runs on several scalable parallel systems, including the lOWprocessor SGVCray T3E at Goddard and two Beowulf-class cluster computers. This paper provides a brief overview of the Godiva system design and then focuses on the unique aspects of Godiva that allow the user to probe the performance characteristics of large arrays and the flow of data into and out of arrays in large science codes. Three examples of data flow analysis are discussed in depth: off-node data flow, cache use, and live-dead analysis.
Gender Interventions in the Dutch Police Force: Resistance as a Tool for Change?

ABSTRACT Previous studies have documented strong resistance towards gender interventions. By contributing to theories on gender change, this paper increases our understanding of the reasons for this resistance and looks at how this resistance can be made productive in gendered interventions. Drawing on a case study in the Dutch Police force, the authors use case reconstructions of appointments to examine the introduction and reception of gender quota. They show how radical interventions such as quota can help to uncover the subtlety of gender inequality practices and make persistent and implicit stereotypes visible and discussable; they also show how radical interventions can trigger the reflexivity of actors. They conclude that the resistance against the interventions can be made productive because it allows for alternative construction of values. An analysis of resistance to gender interventions can contribute to diagnosing problems in the strategising and implementation of these interventions.