Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitochondrial uncoupling prodrug improves tissue sparing, cognitive outcome, and mitochondrial bioenergetics after traumatic brain injury in male mice

Traumatic brain injury (TBI) results in cognitive impairment, which can be long‐lasting after moderate to severe TBI. Currently, there are no FDA‐approved therapeutics to treat the devastating consequences of TBI and improve recovery. This study utilizes a prodrug of 2,4‐dinitrophenol, MP201, a mitochondrial uncoupler with extended elimination time, that was administered after TBI to target mitochondrial dysfunction, a hallmark of TBI. Using a model of cortical impact in male C57/BL6 mice, MP201 (80 mg/kg) was provided via oral gavage 2‐hr post‐injury and daily afterwards. At 25‐hr post‐injury, mice were euthanized and the acute rescue of mitochondrial bioenergetics was assessed demonstrating a significant improvement in both the ipsilateral cortex and ipsilateral hippocampus after treatment with MP201. Additionally, oxidative markers, 4‐hydroxyneneal and protein carbonyls, were reduced compared to vehicle animals after MP201 administration. At 2‐weeks post‐injury, mice treated with MP201 post‐injury (80 mg/kg; q.d.) displayed significantly increased cortical sparing (p = .0059; 38% lesion spared) and improved cognitive outcome (p = .0133) compared to vehicle‐treated mice. Additionally, vehicle‐treated mice had significantly lower (p = .0019) CA3 neuron count compared to sham while MP201‐treated mice were not significantly different from sham levels. These results suggest that acute mitochondrial dysfunction can be targeted to impart neuroprotection from reactive oxygen species, but chronic administration may have an added benefit in recovery. This study highlights the potential for safe, effective therapy by MP201 to alleviate negative outcomes of TBI.
Content of phenolic compounds and vitamin C and antioxidant activity in wasted parts of Sudanese citrus fruits

Abstract Phenolic compounds, vitamin C, and the antioxidant activity of wasted parts of citrus (orange, lemon, and grapefruit) fruits were investigated. Ethanolic extracts from whole fruit, peel, and pulp containing seeds of each type of citruses were prepared. Within each type of citrus, results revealed that peels contained a higher amount of phenolic compound, flavonoids, vitamin C, and antioxidant activity than those of their inner wasted parts (pulp and seeds). Peels of grapefruit had the highest total phenolic content followed by lemon and orange, which was found to be 77.3, 49.8, and 35.6 mg of gallic acid equivalent/g of peels, respectively. In contrast, orange peels contain the highest amount of flavonoids (83.3 mg of catechin equivalent/g) and vitamin C (110.4 mg/100 g) compared to the peels of the other citrus fruit used in this study. In general, the high content of antioxidant capacity and activity of citrus waste, particularly the peels, indicated that they may impart health and nutritional benefit when involving in the food industry as a natural antioxidant.
No Differential Effects of Neural and Psychological Explanations of Psychopathy on Moral Behavior

Research in neurocriminology has explored the link between neural functions and structures and the psychopathic disposition. This online experiment aimed to assess the effect of communicating the neuroscience of psychopathy on the degree to which lay people exhibited attitudes characteristic of psychopathy in particular in terms of moral behavior. If psychopathy is blamed on the brain, people may feel less morally responsible for their own psychopathic tendencies. In the study, participants read false feedback about their own psychopathic traits supposedly inferred from their Facebook likes, described either in neurobiological or cognitive terms. Participants were randomly allocated to read that they either had above-average or below-average psychopathic traits. We found no support for the hypothesis that the neuroscientific explanation of psychopathy influences moral behavior. This casts doubt on the fear that communicating the neuroscience of psychopathy will promote psychopathic attitudes.
The layer laboratory

We present a versatile computational framework for modeling the reflective and transmissive properties of arbitrarily layered anisotropic material structures. Given a set of input layers, our model synthesizes an effective BSDF of the entire structure, which accounts for all orders of internal scattering and is efficient to sample and evaluate in modern rendering systems. Our technique builds on the insight that reflectance data is sparse when expanded into a suitable frequency-space representation, and that this property extends to the class of anisotropic materials. This sparsity enables an efficient matrix calculus that admits the entire space of BSDFs and considerably expands the scope of prior work on layered material modeling. We show how both measured data and the popular class of microfacet models can be expressed in our representation, and how the presence of anisotropy leads to a weak coupling between Fourier orders in frequency space. In addition to additive composition, our models supports subtractive composition, a fascinating new operation that reconstructs the BSDF of a material that can only be observed indirectly through another layer with known reflectance properties. The operation produces a new BSDF of the desired layer as if measured in isolation. Subtractive composition can be interpreted as a type of deconvolution that removes both internal scattering and blurring due to transmission through the known layer. We experimentally demonstrate the accuracy and scope of our model and validate both additive and subtractive composition using measurements of real-world layered materials. Both implementation and data will be released to ensure full reproducibility of all of our results.1
Preoperative Anxiety in Greek Children and Their Parents When Presenting for Routine Surgery

Background A surgical operation in pediatric patients is a rather stressful experience for both children and their parents. The aim of this study was to assess the effect of specific demographic characteristics in parent's and children's preoperative anxiety. Methods The sample was composed of 128 Greek-speaking children (1–14 years of age) who had to undergo minor surgery in a University General Hospital. Before surgical operation, the Spielberger State-Trait Anxiety Inventory (STAI) questionnaire and a questionnaire for the social-demographic characteristics were completed by the parents. Children's preoperative anxiety was evaluated using the Modified Yale Preoperative Anxiety Scale (m-YPAS). Results The independent predictors of increased anxiety levels in parents are child's age (p=0.024) and gender (girls: p=0.008), living in rural areas (parents: p < 0.001; children: p=0.009), being a mother (p=0.046), high or low education level (p=0.031), a no premedicated child (p=0.007), and high baseline parental anxiety (p=0.003). Previous hospitalization (p=0.019), high situational parental anxiety (p < 0.001), no premedication (p=0.014), and being the only child in the family (p=0.045) are found to be the main determinants of preoperative anxiety control in children. Conclusions This study identifies possible risk factors of preoperative anxiety in parents and their children, which are high parental anxiety, child's age, no premedication, being the only child in the family, living in rural areas, education level, and previous hospitalization.
Central role of autophagic UVRAG in melanogenesis and the suntan response

Significance Skin pigmentation provides first-line protection against UV radiation (UVR) that increases the risk of skin cancers. However, mechanisms underlying this process remain poorly understood. Here, we identified the autophagic tumor suppressor UVRAG as a bona fide player in melanosome biogenesis by targeting biogenesis of lysosome-related organelles complex 1 (BLOC-1) independently of autophagy. UVRAG maintains the localization and stability of BLOC-1 to facilitate the sorting/delivery of melanogenic cargoes. Reduced levels of UVRAG rendered cells unresponsive to UVR–α-MSH–MITF signaling and defective melanocyte development in vivo. Moreover, UVRAG-mediated melanogenesis and tanning response were impaired in oncogene-driven melanoma. This study represents a description of a noncanonical role of autophagy factor in melanogenic remodeling and also provides mechanistic insights into UVRAG in pigmentation disorder and UV-associated cancer. UV-induced cell pigmentation represents an important mechanism against skin cancers. Sun-exposed skin secretes α-MSH, which induces the lineage-specific transcriptional factor MITF and activates melanogenesis in melanocytes. Here, we show that the autophagic tumor suppressor UVRAG plays an integral role in melanogenesis by interaction with the biogenesis of lysosome-related organelles complex 1 (BLOC-1). This interaction is required for BLOC-1 stability and for BLOC-1–mediated cargo sorting and delivery to melanosomes. Absence of UVRAG dispersed BLOC-1 distribution and activity, resulting in impaired melanogenesis in vitro and defective melanocyte development in zebrafish in vivo. Furthermore, our results establish UVRAG as an important effector for melanocytes’ response to α-MSH signaling as a direct target of MITF and reveal the molecular basis underlying the association between oncogenic BRAF and compromised UV protection in melanoma.
A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition

Attention-based recurrent neural encoder-decoder models present an elegant solution to the automatic speech recognition problem. This approach folds the acoustic model, pronunciation model, and language model into a single network and requires only a parallel corpus of speech and text for training. However, unlike in conventional approaches that combine separate acoustic and language models, it is not clear how to use additional (unpaired) text. While there has been previous work on methods addressing this problem, a thorough comparison among methods is still lacking. In this paper, we compare a suite of past methods and some of our own proposed methods for using unpaired text data to improve encoder-decoder models. For evaluation, we use the medium-sized Switchboard data set and the large-scale Google voice search and dictation data sets. Our results confirm the benefits of using unpaired text across a range of methods and data sets. Surprisingly, for first-pass decoding, the rather simple approach of shallow fusion performs best across data sets. However, for Google data sets we find that cold fusion has a lower oracle error rate and outperforms other approaches after second-pass rescoring on the Google voice search data set.
Characterization of Mutations Conferring Resistance to Rifampin in Mycobacterium tuberculosis Clinical Strains

Resistance of Mycobacterium tuberculosis to rifampin (RMP), mediated by mutations in the rpoB gene coding for the beta-subunit of RNA polymerase, poses a serious threat to the efficacy of clinical management and, thus, control programs for tuberculosis (TB). The contribution of many individual rpoB mutations to the development and level of RMP resistance remains elusive. ABSTRACT Resistance of Mycobacterium tuberculosis to rifampin (RMP), mediated by mutations in the rpoB gene coding for the beta-subunit of RNA polymerase, poses a serious threat to the efficacy of clinical management and, thus, control programs for tuberculosis (TB). The contribution of many individual rpoB mutations to the development and level of RMP resistance remains elusive. In this study, the incidence of mutations throughout the rpoB gene among 115 Mycobacterium tuberculosis clinical isolates, both resistant and susceptible to RMP, was determined. Of the newly discovered rpoB mutations, the role of three substitutions in the causation of RMP resistance was empirically tested. The results from in vitro mutagenesis experiments were combined with the assessment of the prevalence of rpoB mutations, and their reciprocal co-occurrences, across global M. tuberculosis populations. Twenty-two different types of mutations in the rpoB gene were identified and distributed among 58 (89.2%) RMP-resistant strains. The MICs of RMP were within the range of 40 to 800 mg/liter, with MIC50 and MIC90 values of 400 and 800 mg/liter, respectively. None of the mutations (Gln429His, Met434Ile, and Arg827Cys) inspected for their role in the development of RMP resistance produced an RMP-resistant phenotype in isogenic M. tuberculosis H37Rv strain-derived mutants. These mutations are supposed to compensate for fitness impairment incurred by other mutations directly associated with drug resistance.
A Label-Free Fluorescent Assay for the Rapid and Sensitive Detection of Adenosine Deaminase Activity and Inhibition

Adenosine deaminase (ADA), able to catalyze the irreversible deamination of adenosine into inosine, can be found in almost all tissues and plays an important role in several diseases. In this work, we developed a label-free fluorescence method for the detection of adenosine deaminase activity and inhibition. In the presence of ADA, ATP has been shown to be hydrolyzed. The ATP aptamer was shown to form a G-quadruplex/thioflavin T (ThT) complex with ThT and exhibited an obvious fluorescence signal. However, the ATP aptamer could bind with ATP and exhibited a low fluorescence signal because of the absence of ADA. This assay showed high sensitivity to ADA with a detection limit of 1 U/L based on an SNR of 3 and got a good linear relationship within the range of 1–100 U/L with R2 = 0.9909. The LOD is lower than ADA cutoff value (4 U/L) in the clinical requirement and more sensitive than most of the reported methods. This technique exhibited high selectivity for ADA against hoGG I, UDG, RNase H and λexo. Moreover, this strategy was successfully applied for assaying the inhibition of ADA using erythro-9-(2-hydroxy-3-nonyl) adenine (EHNA) and, as such, demonstrated great potential for the future use in the diagnosis of ADA-relevant diseases, particularly in advanced drug development.
A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling

We propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling. In this new architecture, we combine various transfer models using two layers of parameter sharing. On the first layer, we construct the basis of the architecture to provide universal word representation and feature extraction capability for all models. On the second level, we adopt different parameter sharing strategies for different transfer schemes. This architecture proves to be particularly effective for low-resource settings, when there are less than 200 training sentences for the target task. Using Name Tagging as a target task, our approach achieved 4.3%-50.5% absolute F-score gains compared to the mono-lingual single-task baseline model.
A Phonemic-Based Tactile Display for Speech Communication

Despite a long history of research, the development of synthetic tactual aids to support the communication of speech has proven to be a difficult task. The current paper describes a new tactile speech device based on the presentation of phonemic-based tactile codes. The device consists of 24 tactors under independent control for stimulation at the forearm. Using properties that include frequency and waveform of stimulation, amplitude, spatial location, and movement characteristics, unique tactile codes were designed for 39 consonant and vowel phonemes of the English language. The strategy for mapping the phonemes to tactile symbols is described, and properties of the individual phonemic codes are provided. Results are reported for an exploratory study of the ability of 10 young adults to identify the tactile symbols. The participants were trained to identify sets of consonants and vowels, before being tested on the full set of 39 tactile codes. The results indicate a mean recognition rate of 86 percent correct within one to four hours of training across participants. Thus, these results support the viability of a phonemic-based approach for conveying speech information through the tactile sense.
A nursing clinical information system for the assessment of the complexity of care.

BACKGROUND The complexity of care can be described through a clinical nursing information system, in particular through the Professional Assessment Instrument -PAI-, encoding each health care activity in time units and analysing the relationship of observed time to patient characteristics in relation to the functional models of care needs. DESIGNS Observational study. METHODS Data were collected for 11 months in 2016-17 in four inpatient units of an Italian hospital using the Professional Assessment Instrument, and a survey grid to measure the time of the nursing activities delivered. All activities with a frequency of 20 or more have been included. The Work Sampling technique was used for time-tracking. RESULTS The sample included 2765 nursing activities. The mean times for each care activity were compared showing significant differences. A statistically significant correlation (Sperman's correlation coefficient) was observed both between the observed time and the level of illness severity and between time and functional models. CONCLUSIONS Patient complexity, both in terms of illness severity and level of dependence, can be coded through a clinical nursing information system. This facilitates the classification and measurement of nursing care delivered, which includes the entire care process.
Learning to Explore via Meta-Policy Gradient

The performance of off-policy learning, including deep Q-learning and deep deterministic policy gradient (DDPG), critically depends on the choice of the exploration strategy. Existing exploration methods are mostly based on adding noises to the on-going actor policy and therefore only explore locally close to what the actor policy dictates. In this work, we develop a simple meta-policy gradient algorithm that allows us to adaptively learn the exploration policy in DDPG. Our algorithm allows us to train flexible exploration behaviors that are independent of the actor policy, yielding a more global exploration that significantly accelerates Q-learning. With an extensive study, we show that our method significantly improves the sample-efficiency of DDPG on a variety of reinforcement learning continuous control tasks.
State of the Art on Stylized Fabrication

Digital fabrication devices are powerful tools for creating tangible reproductions of 3D digital models. Most available printing technologies aim at producing an accurate copy of a tridimensional shape. However, fabrication technologies can also be used to create a stylistic representation of a digital shape. We refer to this class of methods as ‘stylized fabrication methods’. These methods abstract geometric and physical features of a given shape to create an unconventional representation, to produce an optical illusion or to devise a particular interaction with the fabricated model. In this state‐of‐the‐art report, we classify and overview this broad and emerging class of approaches and also propose possible directions for future research.
Survey of Authentication and Authorization for the Internet of Things

The Internet of Things is currently getting significant interest from the scientific community. Academia and industry are both focused on moving ahead in attempts to enhance usability, maintainability, and security through standardization and development of best practices. We focus on security because of its impact as one of the most limiting factors to wider Internet of Things adoption. Numerous research areas exist in the security domain, ranging from cryptography to network security to identity management. This paper provides a survey of existing research applicable to the Internet of Things environment at the application layer in the areas of identity management, authentication, and authorization. We survey and analyze more than 200 articles, categorize them, and present current trends in the Internet of Things security domain.
Game-based career learning support for youth: effects of playing the Youth@Work game on career adaptability

ABSTRACT Choosing a career is one of the most important decisions that youth has to take but many young people find this a hard issue to engage with. Current career counselling practice does not appear very compelling or motivating to young people. Professional games could provide a more engaging and motivating way of acquiring professional awareness and competence for career decision making and learning. We present the design and effects of playing a game that aims to increase career awareness and adaptabilities in youth (13–19 years). In a Randomized Controlled Trial, 93 high school students from Iceland and Romania were asked to carry out career-oriented activities, with half playing an interactive game and the other half performing a paper-and-pencil version of the same activities. The students were compared on career adaptability, career learning and career awareness scores before and after these interventions. Main results show that engaging players in these career-oriented activities has short term effects on outcome scores for career adaptabilities and for perceptions of career learning competences. Students who played the game report significantly faster growth on career adaptabilities that deal with “concern”, “control” and “confidence”. It can therefore be concluded that introducing game-based learning in career decision support for youth is a promising endeavour.
Expanding the Technology Acceptance Model with the Inclusion of Trust, Social Influence, and Health Valuation to Determine the Predictors of German Users’ Willingness to Continue using a Fitness App: A Structural Equation Modeling Approach

ABSTRACT According to one market research, fitness or running apps are hugely popular in Germany. Such a trend prompts the question concerning the factors influencing German users’ intention to continue using a specific fitness app. To address the research question, the expanded Technology Acceptance Model (with the addition of trust, social influence, and health valuation) was tested with 476 German users of fitness apps. Structural equation modeling results reveal that respondents’ intention to continue using a specific fitness app is predicated on three factors, namely perceived ease of use, perceived usefulness, and injunctive social norm. Trust in the app developer and descriptive social norm do not have statistically significant effects on repeat usage intention, but they (alongside perceived ease of use and descriptive social norm) both influence users’ perception of a fitness app’s usefulness. Furthermore, ease of use and both injunctive and descriptive social norms significantly contribute to users’ trust in a fitness app developer.