Miracle-dz/UniFAR-FTUs huggingface dataset:
A Semiempirical Approach to the Determination of Daily Erythemal Doses

The maintenance of ground‐based instruments to measure the incidence of ultraviolet radiation (UVR) from the Sun demands strict and well‐developed procedures. A piece of equipment can be out of service for a couple of weeks or months for calibration, repair or even the improvement of the facilities where it has been set up. However, the replacement of an instrument in such circumstances can be logistically and financially prohibitive. On the other hand, the lack of data can jeopardize a long‐term experiment. In this study, we introduce a semiempirical approach to the determination of the theoretical daily erythemal dose (DEDt) for periods of instrumental absence in a tropical site. The approach is based on 5 years of ground‐based measurements of daily erythemal dose (DED) linearly correlated with parameters of total ozone column (TOC) and reflectivity (RPC) from the Ozone Monitoring Instrument (OMI) and the cosine of solar zenith angle at noon (SZAn). Seventeen months of missing ground‐based data were replaced with DEDt, leading to a complete 5‐year series of data. The lowest and the highest values of typical DED were 2411 ± 322 J m−2 (1σ) (winter) and 5263 ± 997 J m−2 (summer). The monthly integrated erythemal dose (mED) varied from 59 kJ m−2 (winter) to 162 kJ m−2 (summer). Both of them depended mainly on cos(SZAn) and RPC. The 12‐month integrated erythemal dose (12‐ED) ranged from 1350 kJ m−2 to 1546 kJ m−2, but it can depend significantly on other atmospheric parameter (maybe aerosols) not explicitly considered here.
Filling the glass: Effects of a positive psychology intervention on executive task performance in chronic pain patients

Chronic pain is associated with emotional problems as well as difficulties in cognitive functioning. Prior experimental studies have shown that optimism, the tendency to expect that good things happen in the future, and positive emotions can counteract pain‐induced task performance deficits in healthy participants. More specifically, induced optimism was found to buffer against the negative effects of experimental pain on executive functioning. This clinical experiment examined whether this beneficial effect can be extended to a chronic pain population.
Adipose tissue inflammation by intermittent hypoxia: mechanistic link between obstructive sleep apnoea and metabolic dysfunction

Obstructive sleep apnoea (OSA) is a highly prevalent condition and recognized as a major public health burden conveying a significant risk of cardiometabolic diseases and mortality. Type 2 diabetes (T2D), insulin resistance (IR) and glucose tolerance are common in subjects with OSA and this association is at least in part independent of the effects of obesity. Continuous positive airway pressure (CPAP) is the treatment of choice for the majority of patients with OSA but the benefit of CPAP on glycaemic health is uncertain. Thus, a greater understanding of the mechanisms by which OSA leads to metabolic dysfunction might identify novel therapeutic approaches. Intermittent hypoxia (IH), a hallmark feature of OSA, likely plays a key role in the pathogenesis and experimental studies using animal and in vitro models suggest that IH leads to pancreatic β‐cell dysfunction and to insulin resistance in the insulin target organs liver, skeletal muscle and adipose tissue. In particular, IH induces a pro‐inflammatory phenotype of the visceral adipose tissue with polarization of adipose tissue macrophages towards a M1–pro‐inflammatory subtype, upregulation and secretion of numerous pro‐inflammatory adipokines and subsequent impairment of the insulin‐signalling pathway, changes which bear a striking similarity to adipose tissue dysfunction seen in obesity. In this review, the available evidence linking IH with metabolic dysfunction is explored with a special emphasis on the adipose tissue in this process.
Notice of RetractionShort-term forecasting of stock price based on genetic-neural network

For short-term forecasting of stock price, this paper proposes a method of analyzing and forecasting the closing price of a stock based on the genetic-BP algorithm. Results show that, within the range of allowable error, the obtained data from the present model can well forecast the short-term trend of a stock price. Though stock market is a very complex nonlinear system, it has the better foreground to predict stock price using neural network. The studies in this paper also prove that genetic-neural algorithm can improve the speed and reliability of the foresting method.
Automatische Duplikateliminierung in Aktivitätsdiagrammen von Fahrzeugfunktionen

Die Spezi®kation von Fahrzeugfunktionen ist eine komplexe Aufgabe. Zum Umgang mit dieser Komplexität werden zur Beschreibung der Funktionen gra®sche Modellierungssprachen wie UML verwendet. Bei der Modellierung können Duplikate entstehen, welche Ausgangspunkt für Fehler und Inkonsistenzen in der weiteren Entwicklung sind. Dieser Beitrag widmet sich der Eliminierung von Duplikaten, welche bei der Spezi®kation von Fahrzeugfunktionen mittels UML Aktivitätsdiagrammen auftreten. Es wird dargestellt, wie in UML Aktivitätsdiagrammen identi®zierte Duplikate automatisiert eliminiert werden, ohne die ursprüngliche Funktionalität zu verändern. Mehrfach auftretende Elemente werden zusammengefasst und durch das Einfügen von weiteren Elementen und Verbindungen zusammengesetzt.
A Submerged Filter Paper Sandwich for Long-term Ex Ovo Time-lapse Imaging of Early Chick Embryos

Due to its availability, low cost, flat geometry, and transparency, the ex ovo chick embryo has become a major vertebrate animal model for the study of morphogenetic events, such as gastrulation2, neurulation3-5, somitogenesis6, heart bending7,8, and brain formation9-13, during early embryogenesis. Key to understanding morphogenetic processes is to follow them dynamically by time-lapse imaging. The acquisition of time-lapse movies of chick embryogenesis ex ovo has been limited either to short time windows or to the need for an incubator to control temperature and humidity around the embryo14. Here, we present a new technique to culture chick embryos ex ovo for high-resolution time-lapse imaging using transmitted light microscopy. The submerged filter paper sandwich is a variant of the well-established filter paper carrier technique (EC-culture)1 and allows for the culturing of chick embryos without the need for a climate chamber. The embryo is sandwiched between two identical filter paper carriers and is kept fully submerged in a simple, temperature-controlled medium covered by a layer of light mineral oil. Starting from the primitive streak stage (Hamburger-Hamilton stage 5, HH5)15 up to at least the 28-somite stage (HH16)15, embryos can be cultured with either their ventral or dorsal side up. This allows the acquisition of time-lapse movies covering about 30 hr of embryonic development. Representative time-lapse frames and movies are shown. Embryos are compared morphologically to an embryo cultured in the standard EC-culture. The submerged filter paper sandwich provides a stable environment to study early dorsal and ventral morphogenetic processes. It also allows for live fluorescence imaging and micromanipulations, such as microsurgery, bead implantation, microinjection, gene silencing, and electroporation, and has a strong potential to be combined with immersion objectives for laser-based imaging (including light-sheet microscopy).
Notice of RetractionFinancial time series prediction based on Echo State Network

Neural networks have been popular in time series prediction in financial area for their advantages in handling nonlinear systems. However, conventional neural networks are confronted with the problem to include time property and the risk of local convergence. This paper presents a study of using a novel recurrent neural network-Echo State Network (ESN) to predict next value in a financial time series. In order to prove the predictability of financial time series, we attain the Hurst exponent through rescaled range (R/S) analysis first. In feature selection, technical analysis is utilized to extract underlying information in the time series and principal component analysis (PCA) helps to filter noise and obtain faithful representation of principle components. The data of six major stock indices in the world, DJIA, S&P 500, NASDAQ, HSI, FTSE 100 and NIKKEI 225, are employed to test our model. For comparison purpose, other neural networks such as back-propagation network and Elman network are also considered. Experiment results demonstrate that ESN performs much better than other neural networks in forecasting the next closing price and the combination of PCA with technical analysis improves the prediction accuracy a little comparing with a model using only raw price data. Our preliminary study also suggests that ESN is effective in short-term financial time series prediction and worth further investigation.
Killing of Mycolic Acid-Containing Bacteria Aborted Induction of Antibiotic Production by Streptomyces in Combined-Culture

Co-culture of Streptomyces with mycolic acid-containing bacteria (MACB), which we termed “combined-culture,” alters the secondary metabolism pattern in Streptomyces and has been a useful method for the discovery of bioactive natural products. In the course of our investigation to identify the inducing factor(s) of MACB, we previously observed that production of pigments in Streptomyces lividans was not induced by factors such as culture extracts or mycolic acids. Although dynamic changes occurred in culture conditions because of MACB, the activation of pigment production by S. lividans was observed in a limited area where both colonies were in direct contact. This suggested that direct attachment of cells is a requirement and that components on the MACB cell membrane may play an important role in the response by S. lividans. Here we examined whether this response was influenced by dead MACB that possess intact mycolic acids assembled on the outer cell membrane. Formaldehyde fixation and γ-irradiation were used to prepare dead cells that retain their shape and mycolic acids of three MACB species: Tsukamurella pulmonis, Rhodococcus erythropolis, and Rhodococcus opacus. Culturing tests verified that S. lividans does not respond to the intact dead cells of three MACB. Observation of combined-culture by scanning electron microscopy (SEM) indicated that adhesion of live MACB to S. lividans mycelia were a significant interaction that resulted in formation of co-aggregation. In contrast, in the SEM analysis, dead cells were not observed to adhere. Therefore, direct attachment by live MACB cells is proposed as one of the possible factors that causes Streptomyces to alter its specialized metabolism in combined-culture.
Effect of Carbohydrate Mouth Rinse on Performance after Prolonged Submaximal Cycling

Previous studies have shown improved shorter duration (∼1 h) performance with carbohydrate (CHO) mouth rinsing (WASH), especially in overnight fasted/non-fuelled subjects. Purpose To determine the effect of WASH on cycling time trial (TT) performance and muscle activity (EMG) after 2 h of submaximal cycling while receiving CHO (FED). Methods In a double-blind, placebo-controlled crossover design, 10 well-trained males cyclists (V˙O2max: 65 mL·kg−1·min−1) completed two experimental trials. Each trial consisted of a standardized pretrial snack (2 h prior) followed by 120 min of steady-state (SS) cycling (∼60% V˙O2max) followed by an approximately 30-min TT, randomized as follows: 1) 30 g CHO·h−1 during SS + WASH during TT (every 20% of TT) (FEDWASH); 2) 30 g CHO·h−1 during SS + placebo (PLA) wash during TT (FEDPLA). Results Although FEDWASH was not significantly different than FEDPLA (P = 0.51), there was a 1.7% (90% confidence interval, +6.4% to −3.2%; ES, 0.21) decrease in TT time (35 s) for FEDWASH compared with FEDPLA, with qualitative probabilities of a 60% positive and 23% trivial outcome. For EMG, soleus showed significant increase, whereas medial gastrocnemius showed significant decrease in muscle recruitment from the beginning 20% TT segment to the last 20% only in the FEDPLA condition, which coincided with a slower (P = 0.01) last 20% of the TT in FEDPLA versus FEDWASH. Conclusions Contrary to previous studies, this investigation utilized conditions of high ecological validity including a pretrial snack and CHO during SS. Significant changes in muscle recruitment and time over the last 20% of the TT, along with an average 1.7% improvement in TT time, suggest CHO mouth rinse helps maintain power output late in TT compared with placebo. Although marginal gains were achieved with a CHO mouth rinse (35 s), small performance effects can have significant outcomes in real-world competitions.
The reliability and validity of the standardized Mensendieck test in relation to disability in patients with chronic pain

ABSTRACT The standardized Mensendieck test (SMT) was developed to quantify posture, movement, gait, and respiration. In the hands of an experienced therapist, the SMT is proven to be a reliable tool. It is unclear whether posture, movement, gait, and respiration are related to the degree of functional disability in patients with chronic pain. The objective of this study was to assess the reliability and convergent validity of the SMT in a heterogeneous sample of 50 patients with chronic pain. Methods: Internal consistency was determined by Cronbach’s α and interrater reliability by the intraclass correlation coefficient (ICC). Convergent validity was assessed by determining the Spearman rank correlation coefficient between the movement quality measured in the SMT and functional limitation measured on the disability rating index (DRI). Results: The internal consistency was Cronbach’s α 0.91. Substantial reliability was found for the items: movement (ICC = 0.68), gait (ICC = 0.69), sitting posture (ICC = 0.63), and respiration (ICC = 0.64). Insufficient reliability was found for standing posture (ICC = 0.23). A moderate correlation was found between average test score SMT and the DRI (r = −0.37) and respiration and DRI (r = −0.45). Discussion: The SMT is a reasonably reliable tool to assess movement, gait, sitting posture, and respiration. None of the items in the domain standing posture has sufficient reliability. A thorough study of this domain should be considered. The results show little evidence for convergent validity. Several items of the SMT correlated moderately with functional limitation with the DRI. These items were global movement, hip flexion, pelvis rotation, and all respiration items.
Mapping hardness using automated depth sensing process.

Measuring the hardness of materials is a way of quickly assessing their mechanical properties. However, using traditional machines for measuring macro- and microhardness of materials and components with graded compositions, complex components, or inhomogenous properties is often time consuming and not cost effective. A new test machine developed by the National Physical Laboratory (NPL) and manufactured under licence by Plint and Partners Ltd offers a fully automated indentation process, which allows large areas to be mapped at high speed. Using the principles of depth sensing to aid automation, the scanning indentation mechanical microprobe (SIMM) has been successfully used to map the hardness of heat treated, coated, and welded components.
Belief Detection and Temporal Analysis of Experts in Question Answering Communities: case study Stack Overflow. (Détection et Analyse Temporelle des Experts dans les Réseaux Communautaires de Questions Réponses : étude de cas Stack Overflow)

During the last decade, people have changed the way they seek information online. Between question answering communities, specialized websites, social networks, the Web has become one of the most widespread platforms for information exchange and retrieval. Question answering communities provide an easy and quick way to search for information needed in any topic. The user has to only ask a question and wait for the other members of the community to respond. Any person posting a question intends to have accurate and helpful answers. Within these platforms, we want to find experts. They are key users that share their knowledge with the other members of the community. Expert detection in question answering communities has become important for several reasons such as providing high quality content, getting valuable answers, etc. In this thesis, we are interested in proposing a general measure of expertise based on the theory of belief functions. Also called the mathematical theory of evidence, it is one of the most well known approaches for reasoning under uncertainty. In order to identify experts among other users in the community, we have focused on finding the most important features that describe every individual. Next, we have developed a model founded on the theory of belief functions to estimate the general expertise of the contributors. This measure will allow us to classify users and detect the most knowledgeable persons. Therefore, once this metric defined, we look at the temporal evolution of users’ behavior over time. We propose an analysis of users activity for several months in community. For this temporal investigation, we will describe how do users evolve during their time spent within the platform. Besides, we are also interested on detecting potential experts during the beginning of their activity. The effectiveness of these approaches is evaluated on real data provided from Stack Overflow.
Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit

Early diagnosis and patient stratification may improve sepsis outcome by a timely start of the proper specific treatment. We aimed to identify metabolomic biomarkers of sepsis in urine by 1H-NMR spectroscopy to assess the severity and to predict outcomes. Urine samples were collected from 64 patients with severe sepsis or septic shock in the ICU for a 1H NMR spectra acquisition. A supervised analysis was performed on the processed spectra, and a predictive model for prognosis (30-days mortality/survival) of sepsis was constructed using partial least-squares discriminant analysis (PLS-DA). In addition, we compared the prediction power of metabolomics data respect the Sequential Organ Failure Assessment (SOFA) score. Supervised multivariate analysis afforded a good predictive model to distinguish the patient groups and detect specific metabolic patterns. Negative prognosis patients presented higher values of ethanol, glucose and hippurate, and on the contrary, lower levels of methionine, glutamine, arginine and phenylalanine. These metabolites could be part of a composite biopattern of the human metabolic response to sepsis shock and its mortality in ICU patients. The internal cross-validation showed robustness of the metabolic predictive model obtained and a better predictive ability in comparison with SOFA values. Our results indicate that NMR metabolic profiling might be helpful for determining the metabolomic phenotype of worst-prognosis septic patients in an early stage. A predictive model for the evolution of septic patients using these metabolites was able to classify cases with more sensitivity and specificity than the well-established organ dysfunction score SOFA.
EEG data compression to monitor DoA in telemedicine

Data compression techniques have been widely used to process and transmit huge amount of EEG data in real-time and remote EEG signal processing systems. In this paper we propose a lossy compression technique, F-shift, to compress EEG signals for remote depth of Anaesthesia (DoA) monitoring. Compared with traditional wavelet compression techniques, our method not only preserves valuable clinical information with high compression ratios, but also reduces high frequency noises in EEG signals. Moreover, our method has negligible compression overheads (less than 0.1 seconds), which can greatly benefit real-time EEG signal monitoring systems. Our extensive experiments demonstrate the efficiency and effectiveness of the proposed compression method.
Coupling Algorithms for Partitioned Multi-Physics Simulations

Partitioned coupling approaches are an important tool in order to achieve a decent time-to-solution for multi-physics problems with more than two physical fields or changing combinations of fields. We study different approaches to deduce coupling schemes for partitioned multi-physics scenarios, by means of a simple, but yet challenging fluid-structure-fluid model problem. To our knowledge, this is the first time that a fully implicit black-box coupling scheme for partitioned multi-physics scenarios is described. This allows the simulation of a new range of applications in a partitioned way.
Trust Inference Using Implicit Influence for Item Recommendation

Trust plays a very important role in many existing ecommerce recommendation applications. Social or trust network among users provides an additional information along with the ratings for improving the user reliability on the recommendation. However, in real world, trust data is sparse in nature. So, many algorithms are built for inferring trust. In this paper, we propose a new trust inference method based on the implicit influence information available in the existing trust network. This approach uses the transitivity property of the trust for trust propagation and scale-free complex network property to limit the propagation length in the network. In this regard, we define a new terminology, degree of trustworthiness for a user, which adds the global influence in the inferred trust. This process improves the recommendation accuracy from the existing trust-based recommendation and neighborhood-based collaborative filtering. Due to the availability of users preference from trust network which is absent in rating data, it also alleviates the very well-known cold start users problem of a recommender system. We evaluate the proposed approach on two established real world datasets and report the obtained results.
Systems biology of embryonic development: Prospects for a complete understanding of the Caenorhabditis elegans embryo

The convergence of developmental biology and modern genomics tools brings the potential for a comprehensive understanding of developmental systems. This is especially true for the Caenorhabditis elegans embryo because its small size, invariant developmental lineage, and powerful genetic and genomic tools provide the prospect of a cellular resolution understanding of messenger RNA (mRNA) expression and regulation across the organism. We describe here how a systems biology framework might allow large‐scale determination of the embryonic regulatory relationships encoded in the C. elegans genome. This framework consists of two broad steps: (a) defining the “parts list”—all genes expressed in all cells at each time during development and (b) iterative steps of computational modeling and refinement of these models by experimental perturbation. Substantial progress has been made towards defining the parts list through imaging methods such as large‐scale green fluorescent protein (GFP) reporter analysis. Imaging results are now being augmented by high‐resolution transcriptome methods such as single‐cell RNA sequencing, and it is likely the complete expression patterns of all genes across the embryo will be known within the next few years. In contrast, the modeling and perturbation experiments performed so far have focused largely on individual cell types or genes, and improved methods will be needed to expand them to the full genome and organism. This emerging comprehensive map of embryonic expression and regulatory function will provide a powerful resource for developmental biologists, and would also allow scientists to ask questions not accessible without a comprehensive picture.