Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Genomic Regions of Trichoderma harzianum IOC-3844 Related to Biomass Degradation

Trichoderma harzianum IOC-3844 secretes high levels of cellulolytic-active enzymes and is therefore a promising strain for use in biotechnological applications in second-generation bioethanol production. However, the T. harzianum biomass degradation mechanism has not been well explored at the genetic level. The present work investigates six genomic regions (~150 kbp each) in this fungus that are enriched with genes related to biomass conversion. A BAC library consisting of 5,760 clones was constructed, with an average insert length of 90 kbp. The assembled BAC sequences revealed 232 predicted genes, 31.5% of which were related to catabolic pathways, including those involved in biomass degradation. An expression profile analysis based on RNA-Seq data demonstrated that putative regulatory elements, such as membrane transport proteins and transcription factors, are located in the same genomic regions as genes related to carbohydrate metabolism and exhibit similar expression profiles. Thus, we demonstrate a rapid and efficient tool that focuses on specific genomic regions by combining a BAC library with transcriptomic data. This is the first BAC-based structural genomic study of the cellulolytic fungus T. harzianum, and its findings provide new perspectives regarding the use of this species in biomass degradation processes.
Chemical Affinity between Tannin Size and Salivary Protein Binding Abilities: Implications for Wine Astringency

Astringency perception, as an essential parameter for high-quality red wine, is principally elicited by condensed tannins in diversified chemical structures. Condensed tannins, which are also known as proanthocyanidins (PAs), belong to the flavonoid class of polyphenols and are incorporated by multiple flavan-3-ols units according to their degree of polymerization (DP). However, the influence of DP size of PAs on astringency perception remains unclear for decades. This controversy was mainly attributed to the lack of efficient strategies to isolate the PAs in non-galloylated forms and with individual degree size from grape/wine. In the present study, the astringency intensity of purified and identified grape oligomeric tannins (DP ranged from 1 to 5) was firstly explored. A novel non-solid phase strategy was used to rapidly exclude the galloylated PAs from the non-galloylated PAs and fractionate the latter according to their DP size. Then, a series of PAs with individual DP size and galloylation were purified by an approach of preparative hydrophilic interaction chromatography. Furthermore, purified compounds were identified by both normal phase HPLC-FLD and reverse phase UHPLC-ESI-Q-TOF. Finally, the contribution of the astringency perception of the individual purified tannins was examined with a salivary protein binding ability test. The results were observed by HPLC-FLD and quantified by changes in PA concentration remaining in the filtrate. In summary, a new approach without a solid stationary phase was developed to isolate PAs according to their DP size. And a positive relationship between the DP of PAs and salivary protein affinity was revealed.
Symptomatology and Coping Resources Predict Self-Care Behaviors in Middle to Older Age Patients with Heart Failure

Background. Symptoms of heart failure (HF) and coping resources, such as social support and social problem-solving, may influence self-care behaviors. Research regarding the influence of HF symptomatology characteristics and components of social support and social problem-solving on self-care is limited. Objective. To identify predictors of HF self-care behaviors using characteristics of HF symptomatology, components of social support and social problem-solving, and demographic and clinical factors. Methods. Using a cross-sectional, correlational predictive design, a convenience sample (N = 201) of outpatients with HF answered self-report surveys. Multiple linear regression with stepwise variable selection was conducted. Results. Six predictors of HF self-care were identified: race, symptom frequency, symptom-related interference with enjoyment of life, New York Heart Association Class HF, rational problem-solving style, and social network (β = 34.265, R 2 = 0.19, P = 0.001). Conclusions. Assessing the influence of race on self-care behaviors in middle to older age patients with HF is important. Clinical assessment that focuses on symptom frequency, symptom-related interference with enjoyment of life, and HF Class might also impact self-care behaviors in this population. Rational problem-solving skills used and evaluation of the size of and satisfaction with one's social network may be appropriate when assessing self-care.
Improving Prospective Memory in Persons With Parkinson Disease: A Randomized Controlled Trial

Background. Prospective memory (PM) is essential for productive and independent living and necessary for compliance with prescribed health behaviors. Parkinson disease (PD) can cause PM deficits that are associated with activity limitations and reduced quality of life. Forming implementation intentions (IIs) is an encoding strategy that may improve PM in this population. Objective. To determine the effect of IIs on PM performance in PD. Methods. This was a laboratory-based randomized controlled trial. Participants with mild to moderate PD without dementia (n = 62) performed a computerized PM test (Virtual Week) under standard instructions. One week later they were randomly allocated to perform it again while using either IIs or a rehearsal (RR) encoding strategy. Results. PM performance was better with the use of both strategies relative to standard instructions. This effect was larger for tasks with event-based compared with time-based cues. In addition, IIs resulted in a larger effect than RR for the nonrepeated tasks. Conclusions. Strategies that support full encoding of PM cues and actions can improve PM performance among people with PD, particularly for tasks with cues that are readily available in the environment. IIs may be more effective than RR for nonrepeated tasks, but this finding warrants verification. Future work should address transfer of strategy use from the laboratory to everyday life. Targeted strategies to manage PM impairment could improve function and quality of life and significantly affect clinical care for people with PD.
Impact of human mobility on the emergence of dengue epidemics in Pakistan

Significance Dengue virus has rapidly spread into new human populations due to human travel and changing suitability for the mosquito vector, causing severe febrile illness and significant mortality. Accurate predictive models identifying changing vulnerability to dengue outbreaks are necessary for epidemic preparedness and containment of the virus. Here we show that an epidemiological model of dengue transmission in travelers, based on mobility data from ∼40 million mobile phone subscribers and climatic information, predicts the geographic spread and timing of epidemics throughout the country. We generate fine-scale dynamic risk maps with direct application to dengue containment and epidemic preparedness. The recent emergence of dengue viruses into new susceptible human populations throughout Asia and the Middle East, driven in part by human travel on both local and global scales, represents a significant global health risk, particularly in areas with changing climatic suitability for the mosquito vector. In Pakistan, dengue has been endemic for decades in the southern port city of Karachi, but large epidemics in the northeast have emerged only since 2011. Pakistan is therefore representative of many countries on the verge of countrywide endemic dengue transmission, where prevention, surveillance, and preparedness are key priorities in previously dengue-free regions. We analyze spatially explicit dengue case data from a large outbreak in Pakistan in 2013 and compare the dynamics of the epidemic to an epidemiological model of dengue virus transmission based on climate and mobility data from ∼40 million mobile phone subscribers. We find that mobile phone-based mobility estimates predict the geographic spread and timing of epidemics in both recently epidemic and emerging locations. We combine transmission suitability maps with estimates of seasonal dengue virus importation to generate fine-scale dynamic risk maps with direct application to dengue containment and epidemic preparedness.
A comparative study of image compression techniques for digital particle image velocimetry

A comparative, quantitative study of image compression techniques for use with digital particle image velocimetry has been performed. Several candidate compression algorithms were selected for the study including a lossless technique and a series of mathematical transform-based methods. Each of the compression algorithms was implemented using commercial off-the-shelf software packages. Three image sequences were selected to exercise the various compression methods. These sequences included a set of industry standard images and two sets of images obtained from experimental work conducted at NASA Langley. Evaluation of the various methods was accomplished using quantitative perceptual and metrological performance measures. The results of the study indicate that several methods of compression are applicable to digital particle image velocimetry images. For selected applications where thresholding of the image gray levels can be performed, lossless algorithms yield excellent performance in terms of compression rates and introduction of negligible errors to the images. For applications where thresholding is not appropriate, JPEG and wavelet-based algorithms yield high compression rates with minor spatial errors introduced to the images. Fractal-based algorithms were shown to be capable of providing high compression rates, but the technique must be guided via the use of a suitably selected template image if spatial errors are to be minimized in the compressed images.
Synaptic plasticity caused by interaction of inputs on hippocampal CA 1

Synaptic plasticity of some dendritic spine is induced by the pairing stimuli of back propagating action potential (BPAP) and excitatory post-synaptic potential (EPSP). Physiological experiments show that, in addition to these stimuli, generating EPSP on another dendritic spine causes further synaptic plasticity. We examined these synaptic plasticity by numerical simulation.
Active machine learning framework for teaching object recognition skills to children with autism

Active machine learning (AML) techniques enable a machine learning model to perform better with less labeled training data. In this study, we proposed an AML approach for teaching object recognition skills to children with autism spectrum disorders (ASD) and compared its effects with passive learning (PL). A web and touch-based application was developed for teaching object recognition where objects were grouped according to their categories and difficulty levels. The teaching procedure was based on Applied Behavioral Analysis principles. Five children with mild to moderate levels of ASD participated in the study. An alternating treatments design of single-subject research methods was used. The results indicated that AML was more effective than PL for four out of the five participants. Consequently, they can learn faster with fewer teaching trials that are required to reach a learning criterion.
A fuzzy logic based approach to enhance low contrast images

Images act as major source for the assessment of data in many major fields relating to astronomical, medical, and geographical. While capturing the image or because of background lighting, some features may be lost or may be given less priority. In order to accentuate the important features present in the image we prefer enhancement process. In this paper, a new type of algorithm is used to enhance the images that are of poor quality which is based on fuzzy logic. Based on the parameter which is used for stretching of contrast, the fuzzy rules are applied to the range of pixels.
GSML: A Unified Framework for Sparse Metric Learning

There has been significant recent interest in sparse metric learning (SML) in which we simultaneously learn both a good distance metric and a low-dimensional representation. Unfortunately, the performance of existing sparse metric learning approaches is usually limited because the authors assumed certain problem relaxations or they target the SML objective indirectly. In this paper, we propose a Generalized Sparse Metric Learning method (GSML). This novel framework offers a unified view for understanding many of the popular sparse metric learning algorithms including the Sparse Metric Learning framework proposed, the Large Margin Nearest Neighbor (LMNN), and the D-ranking Vector Machine (D-ranking VM). Moreover, GSML also establishes a close relationship with the Pairwise Support Vector Machine. Furthermore, the proposed framework is capable of extending many current non-sparse metric learning models such as Relevant Vector Machine (RCA) and a state-of-the-art method proposed into their sparse versions. We present the detailed framework, provide theoretical justifications, build various connections with other models, and propose a practical iterative optimization method, making the framework both theoretically important and practically scalable for medium or large datasets. A series of experiments show that the proposed approach can outperform previous methods in terms of both test accuracy and dimension reduction, on six real-world benchmark datasets.
On-line semi-supervised multiple-instance boosting

A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deliver excellent results and run in real-time they also tend to drift in case of wrong updates during the self-learning process. Recent approaches tackled this problem by formulating tracking-by-detection as either one-shot semi-supervised learning or multiple instance learning. Semi-supervised learning allows for incorporating priors and is more robust in case of occlusions while multiple-instance learning resolves the uncertainties where to take positive updates during tracking. In this work, we propose an on-line semi-supervised learning algorithm which is able to combine both of these approaches into a coherent framework. This leads to more robust results than applying both approaches separately. Additionally, we introduce a combined loss that simultaneously uses labeled and unlabeled samples, which makes our tracker more adaptive compared to previous on-line semi-supervised methods. Experimentally, we demonstrate that by using our semi-supervised multiple-instance approach and utilizing robust learning methods, we are able to outperform state-of-the-art methods on various benchmark tracking videos.
Generic Model of Activity-Level in Workplace Communication

A single question concerning human communication represents serious issues in many social fields: "What makes communication active? " Even though this question has been partially researched, no law has yet been revealed. In this paper, a unified Hamiltonian model of activity level in face-to-face communications between people is introduced. This model consists of two basic findings drawn from real-world mass communication data of 412 people measured over three months by means of wearable social badges. The first basic finding was that the activity level of a participant in the communication increases when the situation of the communication is informal, rather than formal (p
Endocrine disorders in women with complex regional pain syndrome type I

The question of hormonal dysregulation in patients with CRPS I in whole was investigated very scantily. There are only a few studies concerning catecholamines, oestrogens and endorphins independently. Other hormones were studied in patients with different other chronic pain conditions. Considering the accumulation of sufficient knowledge about the role of disadaptation processes in CRPS I pathogenesis and the role of the hypothalamic‐pituitary‐adrenal and hypothalamic‐pituitary‐ovarian systems in the process of adaptation it was logical and consistent to define the role of hormonal dysregulation of these systems in patients with CRPS I.
New World Hantaviruses Activate IFNλ Production in Type I IFN-Deficient Vero E6 Cells

Background Hantaviruses indigenous to the New World are the etiologic agents of hantavirus cardiopulmonary syndrome (HCPS). These viruses induce a strong interferon-stimulated gene (ISG) response in human endothelial cells. African green monkey-derived Vero E6 cells are used to propagate hantaviruses as well as many other viruses. The utility of the Vero E6 cell line for virus production is thought to owe to their lack of genes encoding type I interferons (IFN), rendering them unable to mount an efficient innate immune response to virus infection. Interferon λ, a more recently characterized type III IFN, is transcriptionally controlled much like the type I IFNs, and activates the innate immune system in a similar manner. Methodology/Principal Findings We show that Vero E6 cells respond to hantavirus infection by secreting abundant IFNλ. Three New World hantaviruses were similarly able to induce IFNλ expression in this cell line. The IFNλ contained within virus preparations generated with Vero E6 cells independently activates ISGs when used to infect several non-endothelial cell lines, whereas innate immune responses by endothelial cells are specifically due to viral infection. We show further that Sin Nombre virus replicates to high titer in human hepatoma cells (Huh7) without inducing ISGs. Conclusions/Significance Herein we report that Vero E6 cells respond to viral infection with a highly active antiviral response, including secretion of abundant IFNλ. This cytokine is biologically active, and when contained within viral preparations and presented to human epithelioid cell lines, results in the robust activation of innate immune responses. We also show that both Huh7 and A549 cell lines do not respond to hantavirus infection, confirming that the cytoplasmic RNA helicase pathways possessed by these cells are not involved in hantavirus recognition. We demonstrate that Vero E6 actively respond to virus infection and inhibiting IFNλ production in these cells might increase their utility for virus propagation.
Intention Awareness: A Vision Declaration and Illustrating Scenarios

This is a position paper that introduces intention awareness (IA) as a counterfactual approach for designing IA technologies. The approach distinguishes human intention as a premise for developing intention-aware systems. Intention-aware technologies utilise information about the user's intentions to improve the services that are provided by the environment. The paper will initially define intention awareness as a concept, and subsequently introduce four scenarios that illustrate the approach.
Neural Representations of Location Composed of Spatially Periodic Bands

How to Make a Place Cell Hippocampal place cells have been studied for more than 40 years, yet the mechanisms underlying their remarkable spatial tuning are still not established. Using whole-cell patch-clamp recordings in freely moving rats, Lee et al. (p. 849) changed the baseline membrane potential in hippocampal pyramidal neurons. Place fields arose spontaneously in otherwise silent pyramidal cells after depolarization when the membrane voltage reached a threshold. Place cells in the hippocampus and grid cells in the entorhinal cortex are thought to provide the framework for spatial navigation in an animal. However, it is still unclear how the hexagonal symmetry that is so prominent in grid cells emerges. While recording from the entorhinal cortex and in the pre- and parasubiculum during spatial behavior in the rat, Krupic et al. (p. 853) discovered that many forms of spatial periodicity exist in neurons in these structures. The grid cells seem to reflect a subset of this larger set, generated by self-organized dynamics. The brain performs a Fourier analysis of space to produce the representations of space in and around the hippocampus. The mammalian hippocampal formation provides neuronal representations of environmental location, but the underlying mechanisms are poorly understood. Here, we report a class of cells whose spatially periodic firing patterns are composed of plane waves (or bands) drawn from a discrete set of orientations and wavelengths. The majority of cells recorded in parasubicular and medial entorhinal cortices of freely moving rats belonged to this class and included grid cells, an important subset that corresponds to three bands at 60° orientations and has the most stable firing pattern. Occasional changes between hexagonal and nonhexagonal patterns imply a common underlying mechanism. Our results indicate a Fourier-like spatial analysis underlying neuronal representations of location, and suggest that path integration is performed by integrating displacement along a restricted set of directions.
Imitation of emotion. How meaning affects the link between imitation and liking

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Delay Profile Using Scale Model Method for Microcells in Urban Areas

This paper proposes a scale model approach to clarify radio wave propagation for wireless link design of mobile radio communication systems. The objective of this study is channel impulse response for micro cells in urban areas. To verify reproducibility using the scale model, a one-hundredth scale model of an urban area using concrete blocks is constructed and power delay profiles are measured using the model in the 2 GHz and 10 GHz bands. The measured r.m.s. delay spreads are small values and are approximately 1/5 compared to those for a real environment. The lack of reproducibility of the power delay profile in the scale mode is examined, and the cause is found not to be the scale model itself, but rather the measurement frequency.
“Who Doesn’t?”—The Impact of Descriptive Norms on Corruption

Corruption poses one of the major societal challenges of our time. Considerable advances have been made in understanding corruption on a macro level, yet the psychological antecedents of corrupt behavior remain largely unknown. In order to explain why some people engage in corruption while others do not, we explored the impact of descriptive social norms on corrupt behavior by using a novel behavioral measure of corruption. We conducted three studies to test whether perceived descriptive norms of corruption (i.e. the belief about the prevalence of corruption in a specific context) influence corrupt behavior. The results indicated that descriptive norms highly correlate with corrupt behavior—both when measured before (Study 1) or after (Study 2) the behavioral measure of corruption. Finally, we adopted an experimental design to investigate the causal effect of descriptive norms on corruption (Study 3). Corrupt behavior in the corruption game significantly drops when participants receive short anti-corruption descriptive norm primes prior to the game. These findings indicate that perceived descriptive norms can impact corrupt behavior and, possibly, could offer an explanation for inter-personal and inter-cultural variation in corrupt behavior in the real world. We discuss implications of these findings and draw avenues for future research.