Miracle-dz/UniFAR-FTUs huggingface dataset:
Baseline Correction Based on a Search Algorithm from Artificial Intelligence

Many spectra have a polynomial-like baseline. Iterative polynomial fitting is one of the most popular methods for baseline correction of these spectra. However, the baseline estimated by iterative polynomial fitting may have a substantial error when the spectrum contains significantly strong peaks or have strong peaks located at the endpoints. First, iterative polynomial fitting uses temporary baseline estimated from the current spectrum to identify peak data points. If the current spectrum contains strong peaks, then the temporary baseline substantially deviates from the true baseline. Some good baseline data points of the spectrum might be mistakenly identified as peak data points and are artificially re-assigned with a low value. Second, if a strong peak is located at the endpoint of the spectrum, then the endpoint region of the estimated baseline might have a significant error due to overfitting. This study proposes a search algorithm-based baseline correction method (SA) that aims to compress sample the raw spectrum to a dataset with small number of data points and then convert the peak removal process into solving a search problem in artificial intelligence to minimize an objective function by deleting peak data points. First, the raw spectrum is smoothened out by the moving average method to reduce noise and then divided into dozens of unequally spaced sections on the basis of Chebyshev nodes. Finally, the minimal points of each section are collected to form a dataset for peak removal through search algorithm. SA selects the mean absolute error as the objective function because of its sensitivity to overfitting and rapid calculation. The baseline correction performance of SA is compared with those of three baseline correction methods, the Lieber and Mahadevan-Jansen method, adaptive iteratively reweighted penalized least squares method, and improved asymmetric least squares method. Simulated and real Fourier transform infrared and Raman spectra with polynomial-like baselines are employed in the experiments. Results show that for these spectra the baseline estimated by SA has fewer error than those by the three other methods.
A Study on an Organic Semiconductor-Based Indirect X-ray Detector with Cd-Free QDs for Sensitivity Improvement

In this paper, we studied the optimized conditions for adding inorganic quantum dots (QD) to the P3HT:PC70BM organic active layer to increase the sensitivity of the indirect X-ray detector. Commonly used QDs are composed of hazardous substances with environmental problems, so indium phosphide (InP) QDs were selected as the electron acceptor in this experiment. Among the three different sizes of InP QDs (4, 8, and 12 nm in diameter), the detector with 4 nm InP QDs showed the highest sensitivity, of 2.01 mA/Gy·cm2. To further improve the sensitivity, the QDs were fixed to 4 nm in diameter and then the amount of QDs added to the organic active layer was changed from 0 to 5 mg. The highest sensitivity, of 2.26 mA/Gy·cm2, was obtained from the detector with a P3HT:PC70BM:InP QDs (1 mg) active layer. In addition, the highest mobility, of 1.69 × 10−5 cm2/V·s, was obtained from the same detector. Compared to the detector with the pristine P3HT:PC70BM active layer, the detector with a P3HT:PC70BM:InP QDs (1 mg) active layer had sensitivity that was 61.87% higher. The cut-off frequency of the P3HT:PC70BM detector was 21.54 kHz, and that of the P3HT:PC70BM:InP QDs (1 mg) detector was 26.33 kHz, which was improved by 22.24%.
Succinate Activation of SUCNR1 Predisposes Severely Injured Patients to Neutrophil-mediated ARDS

Objectives: Identify the metabolites that are increased in the plasma of severely injured patients that developed ARDS versus severely injured patients that did not, and assay if these increased metabolites prime pulmonary sequestration of neutrophils (PMNs) and induce pulmonary sequestration in an animal model of ARDS. We hypothesize that metabolic derangement due to advanced shock in critically injured patients leads to the PMNs, which serves as the first event in the ARDS. Summary of Background Data: Intracellular metabolites accumulate in the plasma of severely injured patients. Methods: Untargeted metabolomics profiling of 67 critically injured patients was completed to establish a metabolic signature associated with ARDS development. Metabolites that significantly increased were assayed for PMN priming activity in vitro. The metabolites that primed PMNs were tested in a 2-event animal model of ARDS to identify a molecular link between circulating metabolites and clinical risk for ARDS. Results: After controlling for confounders, 4 metabolites significantly increased: creatine, dehydroascorbate, fumarate, and succinate in trauma patients who developed ARDS (P < 0.05). Succinate alone primed the PMN oxidase in vitro at physiologically relevant levels. Intravenous succinate-induced PMN sequestration in the lung, a first event, and followed by intravenous lipopolysaccharide, a second event, resulted in ARDS in vivo requiring PMNs. SUCNR1 inhibition abrogated PMN priming, PMN sequestration, and ARDS. Conclusion: Significant increases in plasma succinate post-injury may serve as the first event in ARDS. Targeted inhibition of the SUCNR1 may decrease ARDS development from other disease states to prevent ARDS globally.
The Neural Processes Interlinking Social Isolation, Social Support, and Problem Alcohol Use

Abstract Background Subjective feeling of social isolation, as can be measured by perceived burdensomeness (PB), is a major risk factor for alcohol misuse. Heightened PB is associated with elevated stress response and diminished cognitive control, both of which contribute to problem drinking. Here, we sought to identify the neural substrates underlying the relationship between PB and alcohol misuse. Methods We employed resting-state functional magnetic resonance imaging data collected from 61 problem drinkers to characterize the functional connectivity of the hypothalamus and ventral striatum (VS) in relation to PB. We specifically examined whether the connectivities of the hypothalamus and VS were differentially influenced by PB to produce contrasting effects on alcohol use. Finally, we evaluated how individual differences in social support modulate the inter-relationships of social isolation, neural connectivity, and the severity of problem drinking. Results Whole-brain multiple regressions show a positive relationship between PB and hypothalamic connectivity with the hippocampus and an inverse pattern for VS connectivity with the middle frontal gyrus. Difference in strength between the 2 connectivities predicted the severity of problem drinking, suggesting an imbalance involving elevated hypothalamic and diminished prefrontal cortical modulation in socially isolated problem drinkers. A path analysis further revealed that the lack of social support was associated with a bias toward low prefrontal connectivity, which in turn increased PB and facilitated problem drinking. Conclusions Altered hypothalamus and VS connectivity may underlie problem drinking induced by social isolation. The current findings also highlight the important role of social support as a potential protective factor against alcohol misuse.
Effectiveness of community health workers involvement in smoking cessation programme: A systematic review

Background Sustainable Development Goals (SDG) has set the target to reduce premature mortalities from non-communicable diseases (NCDs) by one-third. One of the ways to achieve this is through strengthening the countries’ implementation of the World Health Organization Framework Convention on Tobacco Control (WHO FCTC). Community health workers (CHWs) involvement has shown promising results in the prevention of NCDs. This systematic review is aimed at critically evaluating the available evidence on the effectiveness of involving CHWs in smoking cessation. Materials and methods We systemically searched PubMed and CENTRAL up to September 2019. We searched for published interventional studies on smoking cessation interventions using the usual care that complemented with CHWs as compared to the usual or standard care alone. Our primary outcome was abstinence of smoking. Two reviewers independently extracted data and assessed study risks of bias. Result We identified 2794 articles, of which only five studies were included. A total of 3513 smokers with 41 CHWs were included in the studies. The intervention duration range from 6 weeks to 30 months. The studies used behavioral intervention or a combination of behavioral intervention and pharmacological treatment. Overall, the smoking cessation intervention that incorporated involvement of CHWs had higher smoking cessation rates [OR 1.95, 95% CI (1.35, 2.83)]. Significant smoking cessation rates were seen in two studies. Conclusion Higher smoking cessation rates were seen in the interventions that combined the usual care with interventions by CHWs as compared to the usual care alone. However, there were insufficient studies to prove the effectiveness. In addition, there was high heterogeneity in terms of interventions and participants in the current studies.
Long-Term Ambient Air Pollution Exposures and Brain Imaging Markers in Korean Adults: The Environmental Pollution-Induced Neurological EFfects (EPINEF) Study

Background: Only a limited number of neuroimaging studies have explored the effects of ambient air pollution in adults. The prior studies have investigated only cortical volume, and they have reported mixed findings, particularly for gray matter. Furthermore, the association between nitrogen dioxide (NO2) and neuroimaging markers has been little studied in adults. Objectives: We investigated the association between long-term exposure to air pollutants (NO2, particulate matter (PM) with aerodynamic diameters of ≤10μm (PM10) and ≤2. 5μm (PM2.5), and neuroimaging markers. Methods: The study included 427 men and 530 women dwelling in four cities in the Republic of Korea. Long-term concentrations of PM10, NO2, and PM2.5 at residential addresses were estimated. Neuroimaging markers (cortical thickness and subcortical volume) were obtained from brain magnetic resonance images. A generalized linear model was used, adjusting for potential confounders. Results: A 10-μg/m3 increase in PM10 was associated with reduced thicknesses in the frontal [–0.02mm (95% CI: –0.03, –0.01)] and temporal lobes [–0.06mm (95% CI: –0.07, –0.04)]. A 10-μg/m3 increase in PM2.5 was associated with a thinner temporal cortex [–0.18mm (95% CI: –0.27, –0.08)]. A 10-ppb increase in NO2 was associated with reduced thicknesses in the global [–0.01mm (95% CI: –0.01, 0.00)], frontal [–0.02mm (95% CI: –0.03, –0.01)], parietal [–0.02mm (95% CI: –0.03, –0.01)], temporal [–0.04mm (95% CI: –0.05, –0.03)], and insular lobes [–0.01mm (95% CI: –0.02, 0.00)]. The air pollutants were also associated with increased thicknesses in the occipital and cingulate lobes. Subcortical structures associated with the air pollutants included the thalamus, caudate, pallidum, hippocampus, amygdala, and nucleus accumbens. Discussion: The findings suggest that long-term exposure to high ambient air pollution may lead to cortical thinning and reduced subcortical volume in adults. https://doi.org/10.1289/EHP7133
OPUS-Rota3: Improving Protein Side-Chain Modeling by Deep Neural Networks and Ensemble Methods

Side-chain modeling is critical for protein structure prediction since the uniqueness of the protein structure is largely determined by its side-chain packing conformation. In this paper, differing from most approaches that rely on rotamer library sampling, we first propose a novel side-chain rotamer prediction method based on deep neural networks, named OPUS-RotaNN. Then, on the basis of our previous work OPUS-Rota2, we propose an open-source side-chain modeling framework, OPUS-Rota3, which integrates the results of different methods into its rotamer library as the sampling candidates. By including OPUS-RotaNN into OPUS-Rota3, we conduct our experiments on three native backbone test sets and one non-native backbone test set. On the native backbone test set, CAMEO-Hard61 for example, OPUS-Rota3 successfully predicts 51.14% of all side-chain dihedral angles with a tolerance criterion of 20° and outperforms OSCAR-star (50.87%), SCWRL4 (50.40%), and FASPR (49.85%). On the non-native backbone test set DB379-ITASSER, the accuracy of OPUS-Rota3 is 52.49%, better than OSCAR-star (48.95%), FASPR (48.69%), and SCWRL4 (48.29%). All the source codes including the training codes and the data we used are available at https://github.com/thuxugang/opus_rota3.
Temporal dynamics of the neural representation of hue and luminance polarity

Hue and luminance contrast are basic visual features. Here we use multivariate analyses of magnetoencephalography data to investigate the timing of the neural computations that extract them, and whether they depend on common neural circuits. We show that hue and luminance-contrast polarity can be decoded from MEG data and, with lower accuracy, both features can be decoded across changes in the other feature. These results are consistent with the existence of both common and separable neural mechanisms. The decoding time course is earlier and more temporally precise for luminance polarity than hue, a result that does not depend on task, suggesting that luminance contrast is an updating signal that separates visual events. Meanwhile, cross-temporal generalization is slightly greater for representations of hue compared to luminance polarity, providing a neural correlate of the preeminence of hue in perceptual grouping and memory. Finally, decoding of luminance polarity varies depending on the hues used to obtain training and testing data. The pattern of results is consistent with observations that luminance contrast is mediated by both L-M and S cone sub-cortical mechanisms. Hue (e.g. red, blue) and luminance polarity (light/dark) are basic visual features. This paper shows that the brain has both joint and separable representations of these features, and extracts hue approximately 20 milliseconds later, with a more sustained representation.
UV-visible spectroscopic study of CO2 laser produced tin plasma

A novel technique that is able to achieve high spatial resolution in the measurement of the spectral emission characteristics of laser produced expanding plasmas is reported. The image of expanding laser produced plasma is directly focused on the entrance slit of a stigmatic spectrometer coupled with image readout intensified charge-coupled device camera. The parallel spectra acquisition approach of this measurement has a better spatial resolution than conventional plasma emission spectroscopy measurements at different distances from the target. Based on the high spatial resolved measurements, the plasma plume UV-visible emission spectra in a vacuum of 10-4 Pa by pulsed CO2 laser ablating tin were obtained. Analysis of the spectra provides useful plasma parameters. Relative ion emission intensities provided electron temperature, and Stark broadened line widths yield electron densities. The high spatial resolution emission images give the plasma plume expansion dynamics. The understanding of the temporal and spatial distribution of laser produced plasma parameters of tin is of great interest to the control of the extreme ultraviolet emission process for lithography.
SIGIR 2020 E-Commerce Workshop Data Challenge Overview

This paper presents an overview of the SIGIR 2020 eCom Rakuten Data Challenge. For this data challenge we make available a multi-modal dataset of 99 thousand product listings from Rakuten France catalog. Each product in the dataset contains a textual title, a (possibly empty) textual description and an associated image. Two tasks are proposed, namely large-scale multi-modal classification and cross-modal retrieval. Among the data sets, around 85 thousand products and their corresponding product type category are released as training data, around 9.5 thousand products and 4.5 thousand products are released as the test sets for the multi-modal classification and cross-modal retrieval tasks respectively. The evaluation is run in two stages to measure system performance, first on 10% of the test data, and then on the rest 90% of the test data. The different systems are evaluated using macro-F1 score for the multi-modal classification task and recall@1 for the cross-modal retrieval task. Sixteen independent teams submitted system outputs in the proposed tasks. The top performance obtained at the end of the second stage is 91 . 94% macro-F1 and 50 . 23% recall@1 for the two tasks respectively.
An Overview of the effectiveness of mhealth technology in the Developing Countries (Preprint)

The healthcare system in most of the developing countries has been struggling to provide high-quality services. This limitation has negatively affected the performance and safety of healthcare personnel, as well as patients’ satisfaction. The objective of this paper is to provide an overview of the current knowledge of integrating human factors/ergonomics technologies to improve healthcare service delivery in developing nations. The authors reviewed the current literature focusing on mhealth interventions in developing countries. This review included the integration of mobile phone applications as well as short messaging service (SMS). Previous research on the integration of mhealth in developing countries focused on the use of mobile phone applications and SMS text messaging. However, the effectiveness of such programs has not been reported in most of these studies. Although little research was found in the implementation of mhealth technologies such as the SMS to improve healthcare service delivery in the developing nations, the effectiveness of such programs has not been reported. Therefore, future research should focus on evaluating the outcomes of mhealth interventions to ensure that patients in developing countries have access to high-quality healthcare services.
PEGASO Project - Teenage Perspectives of an Integrated Technology Platform for the Promotion of a Healthy Lifestyle

This paper presents the findings from a participatory technology development workshop with teenagers. Sixteen teenagers were recruited in the East Midlands region of England to participate in the evaluation and development of the PEGASO multi-technology system. This system is being developed to promote awareness and motivation for healthier behaviours in teenagers. The teenagers critically analysed early prototypes of mobile apps, a serious game and wearable sensors with regards to their usability, acceptability, intuitiveness and desirability for teenage users. The results reported here will be utilized in the next design iteration of the PEGASO platform.
What is being Reinvented? Toward a Conceptual Model of Reinvention

Reinvention is a key process in innovation diffusion, but often underexplored compared to other innovation concepts. Several theoretical issues emerge, such as the perception of a reinvention black box, or the ambiguity of reinvention processes. This theoretical paper looks into those issues, specifically focusing on the nature of the reinvented innovation, and the processes involved. Innovation is conceptualized to include three elements: ideas, objects, and practices. Furthermore, three prominent reinvention processes are suggested: translation, modification, and adaptation. A conceptual model of reinvention is proposed to outline the relationships between innovation ideas, objects and practices under reinvention processes over time. The paper contributes to prior studies on post-adoption behaviors, as well as general innovation adoption studies and their quest for breakthroughs and new paradigms.
IAEA Technical Meeting on “Recent Achievements on the Removal of Biohazardous Pollutants by Radiation”

Animal facility generates an increasing amount of solid residues that are considered hazardous waste due to their content of pathogenic and zoonotic microorganisms. A physico-chemical and biological characterization of the animal waste (AW) was carried out, the ability of ionizing radiation to decontaminate this product was evaluated, and a possible recycling of the decontaminated waste was studied. The material was composed by water and ammonium, with pH of 8. The biological load was mainly composed of aerobic bacteria (8.9 x 10 CFU/g), fecal coliforms (5.2 x 10 MPN/g) and Escherichia coli (3.4 x 10 MPN/g). Ionizing radiation slightly decreased pH and ammonia. The D10 value for aerobic bacteria was 0.62 kGy, fecal coliforms 0.58 kGy and E. coli for 0.68 kGy. The AW were composted with domestic organic residues for 30 days, and then worms of the genus Eisenia were added to catalyze the process. In 5 days, the worms started to circulate and feed on the substrate.
Appropriate uses of EICAT protocol, data and classifications

The Environmental Impact Classification for Alien Taxa (EICAT) can be used to classify alien taxa according to the magnitude and type of their environmental impacts. The EICAT protocol, classifications of alien taxa using the protocol (EICAT classification) and the data underpinning classifications (EICAT data) are increasingly used by scientists and practitioners such as governments, NGOs and civil society for a variety of purposes. However, the properties of the EICAT protocol and the data it generates are not suitable for certain uses. Therefore, we present guidelines designed to clarify and facilitate the appropriate use of EICAT to tackle a broad range of conservation issues related to biological invasions, as well as to guide research and communication more generally. Here we address common misconceptions and give a brief overview of some key issues that all EICAT users need to be aware of to take maximal advantage of this resource. Furthermore, we give examples of the wide variety of ways in which the EICAT protocol, classifications and data can be and have been utilised and outline common errors and pitfalls to avoid.
Applying an invasion and risk framework to track non-native island floras: a case study of challenges and solutions in Hawai‘i

Islands are plant invasion hotspots, with some having more non-native than native species. Many plants are recent arrivals, leading to concerns that their full spread and impacts are not yet realised. Given that islands host extraordinary numbers of endemic and threatened species, schemes are urgently needed to track the complex, species-rich but data-poor scenarios typical of islands. This study applies the unified framework by Blackburn et al. (2011) for categorising invasion stages to Hawai‘i’s non-native plant checklist and identifies potential uses and complications for species tracking and invasion management. Data deficiencies and ambiguities required lumping Blackburn et al.’s categories to align with Hawai‘i’s available data; nonetheless, this coarser categorisation describes invasion phases relevant to managers and could provide the basis for an effective tracking system. However, the unified framework does not accommodate uncertain invasion statuses, which prevents clear categorisation of species that exist outside of cultivation but are not definitely naturalised. In response to this obstacle, scores from the Hawai‘i-Pacific Weed Risk Assessment (WRA) are explored to understand their application for predicting naturalisation, including standard WRA scores as well as alternative scoring methods. We show that this predictive tool may be a promising supplement to on-the-ground monitoring for data-deficient elements of a flora. Finally, a categorisation system for tracking statuses of an entire non-native flora is proposed that requires limited investments in additional data collection while following the rationale of Blackburn et al.’s scheme. This categorisation system may be used to reveal overall invasion patterns and trends in a region, leading to valuable insights into strategies for biodiversity management and conservation.