Miracle-dz/UniFAR-FTUs huggingface dataset:
Opening the black box of developmental experiments: behavioural mechanisms underlying long-term effects of early social experience

Typically, animals spend a considerable portion of their time with social interactions involving mates, offspring, competitors and group members. The social performance during these interactions can strongly depend on the social environment individuals have experienced early in life. Despite a considerable number of experiments investigating long-term effects of the early social environment, our understanding of the behavioural mechanisms mediating these effects is still limited, mainly for two reasons. (1) Only in few experimental studies have researchers actually observed and quantified the behaviour of their study animals during the social treatment. (2) Even if differences in social interactions between social rearing treatments are reported, these differences might not be causally linked to any observed long-term effects later in life. The aim of this review was to investigate whether behavioural records of animals during the experimental manipulation of their social environment can help (1) identifying behavioural mechanisms involved in a long-term effect and (2) obtaining a better understanding of the long-term consequences of early manipulations. First, I review studies that manipulated the social environment at an early stage of the ontogeny, observed the social interactions and behaviour during the social experience phase and subsequently tested the performance in social and non-social behavioural tasks at a later life stage. In all reviewed studies, treatment differences were reported both in social interactions during the social experience phase and in social and/or non-social behaviours later in life. Second, I discuss four classes of behavioural mechanisms that can cause the reported long-term effects of social experience, namely learning by experience, social learning, sensory stimulation and social cueing. I conclude that social interactions during the social experience phase should always be recorded for at least two reasons. Knowledge about how the social interactions differ between rearing treatments (1) permits researchers to formulate hypotheses about candidate mechanisms causing long-term effects on behaviour and (2) can help to interpret unexpected outcomes of developmental experiments. Finally, I propose that as a crucial ultimate step towards understanding effects of the early social environment, we should develop targeted experiments testing for the causality of identified candidate mechanism.
Neuromuscular electrical stimulation therapy after knee surgery: a systematic review

무릎수술 후 일반적으로 대퇴근육의 약화, 통증 및 기능감 소가 나타나고, 수술로 인한 하지 근력의 불균형은 무릎관절 의 불안정성을 초래하며, 이에 따른 체중분포의 불균형은 보 행이나 앉았다 일어서기와 같은 일상활동 수행을 어렵게 하 는 원인이 된다[1]. 그러므로 대퇴부 근력의 안정화를 위한 재활치료는 무릎의 기능과 안정성 회복에 필수요소이다[2]. 전방십자인대재건술(anterior cruciate ligament reconstruction, ACLR) 후 재활치료의 주된 목적은 염증 감소, 신 경근 근력 및 기능 회복, 관절 가동범위 회복과 환자의 일상 생활로의 복귀에 있으며, 대퇴부 근육과 넙다리뒤근육뿐 아 니라 고유감각의 운동패턴이 전방십자인대 수술 후 재활치료 에 중요한 역할을 한다[3]. ACLR 후 치료원칙은 수술 후 첫 날부터 적극적인 재활을 시행하는 것이다. 냉치료와 압박, 거 상의 조기 사용이 술 후 부종을 감소시키며, 술 후 수일 내에 완전 수동신전과 능동굴곡을 시행하는 것이 매우 중요하다. 재활의 빠른 진행은 수술과 관련된 관절경직, 근약화와 위축, 대퇴슬개 통증 등의 합병증을 감소시키며, 초기 재활에서 가 장 중요한 것은 재건된 인대에 부하를 주는 슬관절 신전 마지 막 30° 이내에서 열린 사슬의 저항 대퇴사두근 신전운동을 피 하는 것이다. 부력을 줄 수 있는 수중운동은 봉합사가 제거되 자마자 가능한 빨리 시행하여 점진적인 체중부하를 유도한다. 개인에 따라 정상적인 관절가동범위나 근력, 고유수용감각, 무릎수술 후 신경근전기자극치료: 체계적 문헌고찰
Performance of Dynamically Simulated Reference Patterns for Cross Correlation EBSD

High-resolution (or “cross-correlation”) electron backscatter diffraction analysis (HR-EBSD) utilizes cross-correlation techniques to determine relative orientation and distortion of an experimental electron backscatter diffraction pattern (EBSP) with respect to a reference pattern. The integrity of absolute strain and tetragonality measurements of a standard Si/SiGe material have previously been analyzed using reference patterns produced by kinematical simulation. While the results were promising, the noise levels were significantly higher for kinematically produced patterns, compared to real patterns taken from the Si region of the sample. This paper applies HR-EBSD techniques to analyze lattice distortion in a Si/SiGe sample, using recently developed dynamically simulated patterns. The results are compared with those from experimental and kinematically simulated patterns. Dynamical patterns provide significantly more precision than kinematical patterns. Dynamical patterns also provide better estimates of tetragonality at low levels of distortion relative to the reference pattern; kinematical patterns can perform better at large values of relative tetragonality due to the ability to rapidly generate patterns relating to a distorted lattice. A library of dynamically generated patterns with different lattice parameters might be used to achieve a similar advantage. The convergence of the cross correlation approach is also assessed for the different reference pattern types.
Imaging Striatal Dopamine Release Using a Non-Genetically Encoded Near-Infrared Fluorescent Catecholamine Nanosensor

Neuromodulation plays a critical role in brain function in both health and disease. New optical tools, and their validation in biological tissues, are needed that can image neuromodulation with high spatial and temporal resolution, which will add an important new dimension of information to neuroscience research. Here, we demonstrate the use of a catecholamine nanosensor with fluorescent emission in the 1000-1300 nm near-infrared window to measure dopamine transmission in ex vivo brain slices. These near-infrared catecholamine nanosensors (nIRCats) represent a broader class of nanosensors that can be synthesized from non-covalent conjugation of single wall carbon nanotubes (SWNT) with single strand oligonucleotides. We show that nIRCats can be used to detect catecholamine efflux in brain tissue driven by both electrical stimulation or optogenetic stimulation. Spatial analysis of electrically-evoked signals revealed dynamic regions of interest approximately 2 microns in size in which transients scaled with simulation intensity. Optogenetic stimulation of dopaminergic terminals produced similar transients, whereas optogenetic stimulation of glutamatergic terminals showed no effect on nIRCat signal. Bath application of nomifensine prolonged nIRCat fluorescence signal, consistent with reuptake blockade of dopamine. We further show that the chemically synthetic molecular recognition elements of nIRCats permit measurement of dopamine dynamics in the presence of dopamine receptor agonists and antagonists. These nIRCat nanosensors may be advantageous for future use because i) they do not require virus delivery, gene delivery, or protein expression, ii) their near-infrared fluorescence facilitates imaging in optically scattering brain tissue and is compatible for use in conjunction with other optical neuroscience tool sets, iii) the broad availability of unique near-infrared colors have the potential for simultaneous detection of multiple neurochemical signals, and iv) they are compatible with pharmacology. Together, these data suggest nIRCats and other nanosensors of this class can serve as versatile new optical tools to report dynamics of extracellular neuromodulation in the brain.
Lossless Image Segmentation using Adaptive Threshold Technique

The process of image segmentation is defined as the technique via which a given photograph is segmented into several parts in order to further analyze every of these components present in the photo. In segmentation, without a doubt image is represented into greater understandable form. Segmentation essentially used to hit upon the gadgets, obstacles and other applicable facts in the digital snap shots. There are exceptional tactics to enforce segmentation like threshold, clustering and remodel strategies etc. The reason for the popularity of image segmentation is because of its importance in the area of image processing. The prime task of the researchers working in the field is to develop a method for efficient and better image segmentation. There are certain factors that affect the process of image segmentation like the intensity of image to be segmented, color, type and the noise present in the image. No algorithm has been developed till date that could keep a look at all the above listed factors and then segment the image effectively so that all the problems that can come in the way of image segmentation can be avoided. The algorithm development for effective image segmentation is still a big research that will take place in the area of image processing. Researchers still have to go a long way to develop efficient algorithm for image segmentation. This paper presents a review of some of the algorithms developed for image segmentation.
Utility-based Allocation of Industrial IoT Applications in Mobile Edge Clouds

Mobile Edge Clouds (MECs) create new opportunities and challenges in terms of scheduling and running applications that have a wide range of latency requirements, such as intelligent transportation systems, process automation, and smart grids. We propose a two-tier scheduler for allocating runtime resources to Industrial Internet of Things (IIoT) applications in MECs. The scheduler at the higher level runs periodically - monitors system state and the performance of applications - and decides whether to admit new applications and migrate existing applications. In contrast, the lower-level scheduler decides which application will get the runtime resource next. We use performance based metrics that tells the extent to which the runtimes are meeting the Service Level Objectives (SLOs) of the hosted applications. The Application Happiness metric is based on a single application's performance and SLOs. The Runtime Happiness metric is based on the Application Happiness of the applications the runtime is hosting. These metrics may be used for decision-making by the scheduler, rather than runtime utilization, for example. We evaluate four scheduling policies for the high-level scheduler and five for the low-level scheduler. The objective for the schedulers is to minimize cost while meeting the SLO of each application. The policies are evaluated with respect to the number of runtimes, the impact on the performance of applications and utilization of the runtimes. The results of our evaluation show that the high-level policy based on Runtime Happiness combined with the low-level policy based on Application Happiness outperforms other policies for the schedulers, including the bin packing and random strategies. In particular, our combined policy requires up to 30% fewer runtimes than the simple bin packing strategy and increases the runtime utilization up to 40% for the Edge Data Center (DC) in the scenarios we evaluated.
The significance of nitrous oxide emission due to cropping of grain for biofuel production: a Swedish perspective

Abstract. The current regulations governing production of biofuels in the European Union require that they have to mitigate climate change, by producing >35% less greenhouse gases (GHG) than fossil fuels. There is a risk that this may not be achievable, since land use for crop production inevitably emits the potent GHG nitrous oxide (N2O), due to nitrogen fertilisation and cycling in the environment. We analyse first-generation biofuel production on agricultural land and conclude that efficient agricultural crop production resulting in a good harvest and low N2O emission can fulfil the EU standard, and is possible under certain conditions for the Swedish agricultural and bioethanol production systems. However, in years having low crop yields, and where cropping is on organic soils, total GHG emissions per unit of fuel produced can be even higher than those released by burning of fossil fuels. In general, the N2O emission size in Sweden and elsewhere in northern Europe is such that there is a >50% chance that the 35% saving requirement will not be met. Thus ecosystem N2O emissions have to be convincingly assessed. Here we compare Swedish emission data with values estimated by means of statistical models and by a global, top-down, approach; the measurements and the predictions often show higher values that would fail to meet the EU standard and thus prevent biofuel production development.
Genetic Variability in Rhizoctonia solani Isolated from Vitis vinifera Based on Amplified Fragment Length Polymorphism

Problem statement: Rhizoctonia solani is a potential grapevine pathogen. In order to dev elop effective methods of control, it is necessary to do cument its genetic diversity. Approach: The objective of this study was to analyze the genetic variabilit y of R. solani isolated from the rhizosphere of ungrafted V. vinifera var. perlette seedless planted in Sonora, Mexico using Amplified Fragment Length Polymorphism (AFLP). Results: In the selective amplification using eight primer combinations we obtained a total of 446 AFLP markers with a 100% polymorphism. Out of 41 isolates, 36 different AFLP patterns were observed and five were replicates of the same pattern. The dendrogram shows inter- and intrapopulation similarity indexes of 0.26, 0.98 an d 0.31, 0.98, respectively. Six groups emerged from the principal components analysis, five of which were c learly defined, while the other one was spread out. Conclusion: We conclude that R. solani growing in Sonoran vineyards shows a high degree of genetic variability, even under similar environmental condi tions.
Identification of concealed materials, including explosives, by terahertz reflection spectroscopy

Abstract. We report on a method for extracting the characteristic features of covered materials, including hexogen, in the range from 0.5 to 1.8 THz. This time-domain spectroscopy-based technique takes into account only part of the signal reflected from a covered sample and analyzes it by using the Fourier transform. The obtained power spectrum has distinctive peaks that correspond to peaks measured in the transmission configuration and can be used for further identification of the materials.
Genome Mapping and Molecular Breeding in Lolium and Festuca

Of the 8 species in the genus Lolium, Italian ryegrass (Lolium multiflorum Lam.), is one of the most important cool-season forage grasses and is the most widely cultivated annual forage grass in Japan. Another important Lolium species is perennial ryegrass (L. perenne L.); it is cultivated mainly for forage and turf in the British Isles, Europe, USA, Australia, and New Zealand. Both Italian ryegrass and perennial ryegrass are outcrossing species, and each has a relatively large genome (1 C � 2000 Mb). In recent years, many molecular marke rs, including amplified fragment length polymorphism (AFLP) markers, restriction fragment length polymorphism (RFLP) markers, simple sequence repeat (SSR) markers, and expressed sequence tag (EST) markers, have been developed for Italian ryegrass and perennial ryegrass, and several types of molecular linkage map have been constructed. In addition, markers closely linked to genes for resistance to diseases such as crown rust, gray leaf spot, and bacterial wilt have been identified. Quantitative trait loci (QTLs) for flowering time, winter hardiness, forage quality, and other important traits have also been analyzed. In this paper, we review recent progress in genome mapping and QTL analysis in Lolium spp. We also include studies on tall fescue (Festuca arundinacea Schreb.) and meadow fescue (F. pratensis Huds.), two species closely related to Lolium.
TREATMENT EFFECTS 101

TREATMENT EFFECTS 101 Thelma J. Mielenz *1, Janet K. Freburger 2, Timothy S. Carey 2,3. *1 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York 10032. 2 Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, North Carolina 27599. 3 Departments of Medicine and Social Medicine, University of North Carolina, Chapel Hill, North Carolina 27599.
MAKING A CHOICE BETWEEN FDDS AND BDDS

Much work has recently focused on DD variants; in particular those that use alternate decompositions such as FDDs and KDDs. The problem is that with addition of these decompositions the complexity of the structure is increased, with the possibility of additional computation time being required for determining variable ordering or decomposition choices. We propose to reduce this by adding a preprocessing step that selects, in general, whether a BDD or FDD structure is a better choice for a given function or class of functions. This decision is based on the analysis of a function’s autocorrelation coefficients. Preliminary work beginning an investigation into how best to make this decision is presented.
Anthropometric indicators as predictors of the risk of metabolic syndrome in adult working men

Abstract Measurement of body weight, height, waist and hip circumference is a standard procedure that allows to better define the risk of metabolic syndrome. The aim of the study is to determine the usefulness of anthropometric indicators such as BMI, WC (waist circumference), WHR, WHtR and percentage of body fat to predict the metabolic cardiovascular risk in the adult male population of Krakow, as well as an attempt to determine the metabolic cardiovascular risk with the original anthropometric risk index. The study included 405 men from the population working in the T. Sendzimir Steelworks in Kraków at the age of 30-69 years. Anthropometric measurements: body height measured to the nearest mm, circuits (waist, hips) measured to the nearest centimetre, the percentage of fat (the type of electronic scales Tanita BF 300) measured according to the standard protocol by the same technician and biomedical indicators assessing the functional status of organism, total cholesterol, HDL, LDL, triglycerides, glucose and blood pressure measured with a mercury manometer. As a measure of goodness of fit for the indices of risk (and their components), the AUC method was used for the ROC curves to evaluate the sensitivity and specificity of the diagnostic test. The results show that significant in predicting the risk of metabolic syndrome are not only standard anthropometric measurements specified in the standards of WHO, EGIR, NCEP and IDF. In addition, it is important to take into account the amount of fat and calculate the cumulative risk index based on all relevant measurements and indicators.
Sun Sensitivity and Sunburns as Related to Cutaneous Melanoma among Populations of Spanish Descent: A Meta-Analysis

Few studies have examined sun sensitivity risk factors for cutaneous melanoma specifically in populations of Spanish descent. Previous searches were conducted in PUBMED for articles on melanoma and sun exposure through 2008. Over 300 articles were reviewed and relevant data was abstracted. These abstract forms were subsequently reviewed for studies in populations of Spanish descent. PUBMED was then examined for more recent studies of melanoma in populations of Spanish descent. Eight appropriate articles were found, which comprised 7 discrete studies. We conducted a meta-analysis of these seven studies analyzing Fitzpatrick skin type, skin color and history of sunburns. The risk of melanoma was increased for fair vs. dark skin color (OR = 2.9, 95% CI 2.0-4.1) and for skin type I & II vs. II I& IV (OR = 3.5, 95% CI of 2.0-6.1). However, when skin type was examined as an ordered categorical factor in a linear dose-response analysis, a 12-fold difference was seen between skin type I and IV. Any history of sunburn in childhood and lifetime were also associated with melanoma with ORs of 5.6 and 4.0, respectively. The magnitudes of associations seen in this population were much higher than seen in previous meta-analyses of all studies of melanoma. These results provide some evidence of discrepancies of reporting skin color in heterogeneous populations including those of Spanish descent. Future studies should provide a more accurate measure of self-reported skin color in these populations.
Can Oil Prices Help Predict US Stock Market Returns: An Evidence Using a DMA Approach

Crude oil price behaviour has fluctuated wildly since 1973 which has a major impact on key macroeconomic variables. Although the relationship between stock market returns and oil price changes has been scrutinized excessively in the literature, the possibility of predicting future stock market returns using oil prices has attracted less attention. This paper investigates the ability of oil prices to predict S&P 500 price index returns with the use of other macroeconomic and financial variables. Including all the potential variables in a forecasting model may result in an over-fitted model. So instead, dynamic model averaging and dynamic model selection are applied to utilize their ability of allowing the best forecasting model to change over time while parameters are also allowed to change. The empirical evidence shows that applying the DMA/DMS approach leads to significant improvements in forecasting performance in comparison to other forecasting methodologies and the performance of these models are better when oil prices are included within predictors.
Does a Digital Assistant Need a Body? The Influence of Visual Embodiment and Social Behavior on the Perception of Intelligent Virtual Agents in AR

Intelligent Virtual Agents (IVAs) are becoming part of our everyday life, thanks to artificial intelligence technology and Internet of Things devices. For example, users can control their connected home appliances through natural voice commands to the IVA. However, most current-state commercial IVAs, such as Amazon Alexa, mainly focus on voice commands and voice feedback, and lack the ability to provide non-verbal cues which are an important part of social interaction. Augmented Reality (AR) has the potential to overcome this challenge by providing a visual embodiment of the IVA. In this paper we investigate how visual embodiment and social behaviors influence the perception of the IVA. We hypothesize that a user's confidence in an IVA's ability to perform tasks is improved when imbuing the agent with a human body and social behaviors compared to the agent solely depending on voice feedback. In other words, an agent's embodied gesture and locomotion behavior exhibiting awareness of the surrounding real world or exerting influence over the environment can improve the perceived social presence with and confidence in the agent. We present a human-subject study, in which we evaluated the hypothesis and compared different forms of IVAs with speech, gesturing, and locomotion behaviors in an interactive AR scenario. The results show support for the hypothesis with measures of confidence, trust, and social presence. We discuss implications for future developments in the field of IVAs.
An Exploratory Study of CaesarJ Based on Implementations of the Gang-of-Four patterns

This report presents the results of an exploratory study of the aspect-oriented programming language CaesarJ. The study is based on implementations of seven Gang-of-Four design patterns. The report describes the implementations and then provides a short analysis. A preliminary assessment of CaesarJ is made in which AspectJ is used as a basis for comparison.