Miracle-dz/UniFAR-FTUs huggingface dataset:
PHILM2Web: A high-throughput database of macromolecular host–pathogen interactions on the Web

Abstract During infection, the pathogen’s entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host–pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein–protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen–Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing. Database URL: http://philm2web.live
Recovery of gastric function in patients affected by chronic atrophic gastritis using L-cysteine (Acetium®): one year survey in comparison with a control group

Background and aim: Chronic Atrophic Gastritis (CAG) is a precancerous condition for gastric cancer (GC) as single risk factor, being a consequence of a previous Helicobacter pylori (Hp) infection or based on autoimmune mechanisms. Achlorhydria plays an important role towards the formation of a class I carcinogen, acetaldehyde, after food intake. L-cysteine has been claimed to be able to bind in a covalent way acetaldehyde when administered at meals. Methods: In this study we enrolled two CAG groups of patients, one treated whit 300 mg/daily of L-cysteine for one year, the other one untreated. We assessed gastric function lasting the one-year follow-up by using noninvasive surrogates, i.e. Pepsinogen I (PGI) and gastrin 17 (G17). Results: In the group of 77 CAG on therapy we found a statistically significant increase in PGI values and a decrease in G17 levels, in comparison with unchanged values in control group. Conclusions: L-cysteine seems able to provide a recovery in gastric function when administered in CAG patients and could be proposed as a possible therapy in such patients. (www.actabiomedica.it)
Effects of Drought on the Growth and Development of Potato, Leptinotarsa decemlineata Say & Arma chinensis Fallou.

BACKGROUND Global climate change, will result in an increasingly arid weather which will impose a significant impact on agriculture. The occurrence dynamics of plants, phytophagous insects and their natural enemies under drought have attracted much attention. The consequences could be useful for controlling insect herbivores. Leptinotarsa decemlineata (Say) is one of the most important potato pests, which causes serious damage to potato production. Arma chinensis (Fallou) is a parasitoid of L. decemlineata. However, how drought will affect the titrophic interactions among potato and these insects are unknown. RESULTS In this experiment, L. decemlineata and A. chinensis were subsequently released on potato plants, under water stress treatments. Thereafter, plant height, stem diameter and yield of potato, the growth and reproduction of L. decemlineata, and the longevity of A. chinensis adults were periodically recorded. The results showed that drought had crucial effects on height, stem diameter and yield of potato (P<0.05); it also exerted a significant impact on the pre-oviposition period, oviposition days, fecundity and life table parameters of L. decemlineata (P<0.05). Moreover, drought significantly reduced the longevity (P<0.05) and the survival rate of A. chinensis adults. The longevity of adult A. chinensis was only 29.00±3.00 d and the harvest rate of L. decemlineata adults was only 1.42±0.07% under drought treatment. CONCLUSION In this experiment, we evaluated the bottom-up effects of drought on tritrophic interactions involving potato, L. decemlineata and A. chinensis, and discuss the implications of the findings for the integrated pest management programs involving the pest. Future promising research directions are then proposed.
Conditional Density Forecast of Electricity Price Based on Ensemble ELM and Logistic EMOS

In recent years, probabilistic forecast of electricity price has become of particular interests to market participants as it can effectively model the uncertainties due to competitive market behaviors. Decision makers heavily rely on such forecast to formulate optimal strategies with minimal risk and maximum profits to deal with stochasticity in market and system operation. Different from the widely used volatility models with least square or maximum likelihood techniques in probabilistic forecast of prices, this paper proposes a reliable continuous ranked probability score-oriented predictive density construction strategy for day-ahead electricity prices. The proposed method effectively quantifies the uncertainty involved in extreme learning machine network by applying an ensemble structure and a logistic distribution-based ensemble model output statistics technique. Moreover, an efficient covariance structure directly determined by the empirical correlations of observed probabilistic forecast series is developed to capture the essential temporal interdependence thus to facilitate the operational scenarios’ generation. Through validating on the real day-ahead market in Sweden, the proposed hybrid approach proves to provide superior full distributional forecasting skill over the existing approaches.
Determinants of Hotel Guests’ Continued Use of Social Media

Purpose With the emergence of mobile devices, factors such as interaction and ubiquity have become increasingly important in the use of social media networks. However, regarding hotel guests’ use of social media, knowledge about how those factors contribute to guests’ continued social media use remains limited. In response, this study aimed to examine the influence of interaction and ubiquity mediated by trust, benefits, and enjoyment on hotel guests’ continued use of social media networks. To examine the hypotheses, a self-administered questionnaire was posted by 10 UK on their social media pages. A total of 258 usable data were collected and analyzed using partial least squares analysis. the gap in research regarding intended continued of social media networks by offering new empirical evidence concerning the determinants of hotel guests’ of social media.
LncRNAs as Biomarkers for Melanoma LncRNAs as Biomarkers for Melanoma

Melanoma is the most aggressive and serious type of skin cancer. Known for being highly malignant and metastatic, melanoma typically has low survival rates. Prognosis can be improved with an early diagnosis and a good monitoring of the disease. However, cur- rent melanoma biomarkers display severe limitations, making them inadequate for early detection of the malignancy. Therefore, it is of urgent matter for us to characterize and establish novel biomarkers with a direct application to daily clinics in order to accurately detect early american joint c ommittee on cancer (AJCC) stages in melanoma patients, efficiently monitor the disease progression, and reliably predict the response to therapies, survival, and likely future recurrence. Long non-coding RNAs (lncRNAs) are a promis- ing biomarker and regulator of tumor progression for many cancers. They are secreted into the bloodstream inside exosomes by a wide range of malignant cells and several of them have actually been validated as promising circulating molecular signatures of other cancer types, but not melanoma. However, in recent years there has been much research into lncRNA melanoma biomarkers, and many of them have been characterized as poten tially clinically relevant.
Using Association Rules in Antimicrobial Resistance in Stone Disease Patients

Association rule mining is a very popular unsupervised machine learning technique for discovering patterns in large datasets. Patients with stone disease commonly suffer from urinary tract infections (UTI), complicated by the emergence of antimicrobial resistance (AMR), due to the excessive use of antibiotics. In this study, we explore the use of association rule mining in the AMR profile of patients suffering from stone disease.
Panicle Ratio Network: Streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in fields.

The heading date and effective tiller percentage are important traits in rice, and they directly affect plant architecture and yield. Both traits are related to the ratio of the panicle number to the maximum tiller number, or simply called panicle ratio (PR). In this study, an automatic PR estimation model (PRNet) based on a deep convolutional neural network was developed. Ultra-high-definition unmanned aerial vehicle (UAV) images were collected from cultivated rice varieties planted in 2384 experimental plots in 2019 and 2020 and in a large field in 2021. The determination coefficient between estimated PR and ground measured PR reached 0.935, and the root mean square error (RMSE) values for the estimations of the heading date and effective tiller percentage were 0.687 days and 4.84%, respectively. Based on the result analysis, various factors affecting PR estimation and strategies for improving PR estimation accuracy were investigated. The satisfactory results obtained in this study demonstrate the feasibility of using UAV and deep learning techniques to replace ground-based manual methods to accurately extract phenotypic information of crop micro targets (such as grains per panicle, panicle flowering, etc.) for rice and potentially for other cereal crops with future research.
High-Efficiency Boost-Flyback Converter with Voltage Multiplier Cells for High Voltage Gain Application

Abstract In this paper, a Boost-Flyback converter is presented. It combines the voltage multiplier cells to the secondary winding of the coupled inductor in the Flyback part of the converter and a switched capacitor to the output capacitor of the Boost part of the converter. This converter shows attractive characteristics such as high voltage gain, low voltage stresses on semiconductor and a high efficiency at 97.4%. A detailed description of the proposed converter is presented and experimental results.
Satisfaction evaluation for ACLS training

Background and aim of the work: simulation became gradually pivotal in training of health professionals: indeed, it showed an improvement in practical skill of the trainees compared to theoretical lectures. Among others, ACLS (Advance Cardiovascular Life Support) courses are now one of the standard learning practices most spread around the world. The primary aim of this study is to evaluate both the level of satisfaction and the clinical thinking that the trainees perceived during an ACLS course. This was performed via the Satisfaction with Simulation Experience scale validated scale (SSE). The secondary aim was to evaluate if demographics affect the aforementioned perceived satisfaction. Methods: an SSE questionnaire was distributed after the ACLS section of practical scenarios, just before the end of the course and of the practical test. Results: 72 questionnaires have been collected. The sample was constituted by 68.1% of females, 44,4% by nurses without a master’s degree, and 52,8% personnel that works outside of a critical care setting. QTOT Median score was 89 (IQR=86-90), DTOT Median’s 45 (IQR= 44.25-45), RTOT Median’s 25 (IQR=22-25), LTOT Median was 20 (IQR=19-20). Conclusions: The ACLS course attains a high grade of satisfaction on all of the three aspects evaluated by the questionnaire. The perception was not influenced by the demographics.
Knee function after a mean of 19 years Post-retirement in 65 Italian semi-professional rugby veterans.

Background and aim: sport activity has been largely correlated to the development of knee osteoarthritis, but only few papers have investigated the long-term impact of a rugby career on the knee joint. The aim of this retrospective study was to evaluate the incidence and epidemiology of knee osteoarthritis and general health in a population of 65 retired semi-professional rugby players. Methods: demographic and anamnestic analysis was recorded and Oxford Knee score, SF-12 and VAS were submitted to all veterans in order to assess current knee function, general health condition and level of pain. Parametric analysis of Spearman was used to evaluate the statistical significance on these results and the Kruskal-Wallis test was used to assess the significant differences between the questionnaire results and the demographic and anamnestic records. Results: we found that players who sustained a knee injury during their career have a current reduction of the knee function compared to veterans who did not suffer any injury and who showed values comparable with those of the health population. In terms of general and mental health, athletes who retired later have now a better condition than those who retired from the sport earlier. Conclusions: we concluded that knee injury prevention should be an unequivocal priority because although rugby is a high energy sport, it does not increase the risk of knee osteoarthritis in absence of serious knee injuries. (www.actabiomedica.it)
The impact of insurance institutional investors on corporate value from selection and creation perspective

The majority of insurance investment funds are derived from policy liability debt funds. It differs from other institutional investors in a number of ways, including investment size, horizon, duration, risk, and so on. However, only a small portion of the extant literature focuses on in-depth and extensive analysis of Insurance Institutional Investors’ holdings (IIIs). This study analyses the impact of shareholding by insurance institutions on the value of Shanghai and Shenzhen A-share listed companies in China’s capital market. The paper offers three major contributions. First, we discovered that long-term equity-holding IIIs have both value selection and value creation functions. Second, the value creation function becomes more significant among long-term stock-holding IIIs with an increase in the period during which they retain the company’s shares; Third, fast-in and fast-out (FIFO) IIIs have a value-inhibiting effect on the held company and serve a value selection role, rather than a value creation function. This study provides more insight on the lack of academic interest in insurance institutions and serves as a foundation and reference for the design of regulatory policies for insurance institutions’ involvement in stock markets. It also gives empirical evidence for corporations to accurately analyze shareholding by insurance institutions. Furthermore, since this study concentrates on China’s capital market, it can serve as a benchmark for other nations, particularly, those designated as developing market economies.
Expression and clinical significance of the m6A reader YTHDF2 in peripheral blood mononuclear cells from rheumatoid arthritis patients

Abstract As an important m6A reader, the YT521-B homology domain family 2 (YTHDF2) has been shown to regulate mRNA degradation and translation, and to be involved in inflammation. However, little is known about the role of YTHDF2 in the autoimmune-based inflammatory disease rheumatoid arthritis (RA). To begin to ascertain any role for this reader, 74 RA patients and 63 healthy controls (HC) were recruited for this study. Blood was collected from each subject and peripheral blood mononuclear cells (PBMC) isolated. Thereafter, mRNA expression of YTHDF2, interleukin (IL)-1β, IL-6, IL-8, and tumor necrosis factor (TNF)-α in the cells was determined by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The harvested blood was also assessed for a variety of parameters, including levels of C-reactive protein (CRP), erythrocyte sedimentation rates (ESR), white blood cell counts (WBC), neutrophils counts (N)/neutrophils percentages (N%), and neutrophil:lymphocyte ratios (NLR) - each markers of inflammation during RA. The results showed that YTHDF2 mRNA expression in RA patient PBMC was decreased significantly vs that in healthy control subject cells. Further, YTHDF2 mRNA expression in RA patient PBMC negatively-correlated with ESR, CRP levels, WBC counts, as well as neutrophils counts, percentages, and NLR values. In addition, it was seen that YTHDF2 mRNA expression in RA patient PBMC was associated with host serum RF levels and treatment. Moreover, it was found that mRNA expression of IL-1β, IL-6, IL-8, and TNFα was increased in PBMC from RA patients relative to in control subject cells; however, only the increased IL-1β expression was seen to be negatively-correlated with decreased YTHDF2 mRNA expression. In conclusion, the present study illustrated that YTHDF2 expression might have some regulatory role in the underlying mechanisms associated with the autoimmune disease RA and that this m6A reader could at some point represent a potential target for regulating inflammatory responses that occur during RA.
Extrinsic and intrinsic factors acting as barriers or facilitators in nurses’ implementation of clinical practice guidelines: a mixed-method systematic review

Background and aim of the work: Greater evaluations are needed to identify barriers or facilitators in nurses’ guidelines adherence. The current review aims to explore extrinsic and intrinsic factors impacting nurses’ compliance. Methods: Mixed-method systematic review with a convergent approach, following the PRISMA checklist and the JBI Mixed Methods Review Methodological Guidance was conducted. MEDLINE, Embase, CINAHL were systematically searched, to find studies published between 2010 and 2021, including qualitative, quantitative or mixed-methods articles. Results: Sixty studies were included, and the major findings were analysed by aggregating them in two main themes: intrinsic and extrinsic factors. The intrinsic factors were: a) knowledge and skills; b) attitudes of health personnel; c) sense of belonging towards guidelines. The extrinsic factors were: a) organizational and environmental factors; b) workload; c) guidelines structure; d) patients and caregivers’ attitude. Conclusions: The included studies report lack of resources, among environmental factors, as the main barrier perceived. Nurses, who are at the forefront in addressing the direct application of knowledge and skills to ensure patient safety, have a higher perception of this kind of barriers than other healthcare personnel. Potential facilitators emerged in the review are positive feedback and reinforcements at the workplace, either from the members of the team or from the leaders. Moreover, the level of active participation of the patient and caregiver could have a positive impact on nurses’ guidelines adherence. Guidelines implementation remains a complex process, resulting in a strong recommendation to support health policymakers and nursing leaders in implementing continuing education programs. (www.actabiomedica.it)
Predicting the HIV/AIDS epidemic and measuring the effect of AIDS Conquering Project in Guangxi Zhuang Autonomous Region

To control the HIV/AIDS epidemics in Guangxi Zhuang Autonomous Region in China, Guangxi government launched the 5-year Guangxi AIDS Conquering Project (GACP, Phase I: 2010-2014, Phase II: 2015-2020). In the project, three measures are implemented, such as great improvements of the coverage of HIV/AIDS education, promotion of HIV voluntary counseling and testing, and enhancement of antiretroviral treatment. In this paper, we explore the effects of the three measures of GACP by construction of a Susceptible-Infected-Diagnosed-Treated population compartments model and via evaluation of the basic reproduction number derived from the model. A computational framework is developed for estimating the model parameters based on the HIV surveillance data, with application of the Markov-Chain Monte-Carlo method and Nonlinear Least Squares method. By estimating the new infections and evaluating the basic reproduction number, we find that the implementation of the three measures of GACP has a significant effect on controlling the rise of HIV/AIDS cases and the epidemic trend. Compared with HIV voluntary counseling and testing, strengthening HIV/AIDS education and expanding the coverage of antiretroviral treatment show a greater impact on HIV/AIDS epidemic control, which provides a reference project for other provinces with a similar epidemic situation in Guangxi Zhuang Autonomous Region. At the same time, our research fills the current research gap for the evaluation of large-scale AIDS prevention and control projects in developing areas.
Multiwave validation sampling for error‐prone electronic health records

Electronic health record (EHR) data are increasingly used for biomedical research, but these data have recognized data quality challenges. Data validation is necessary to use EHR data with confidence, but limited resources typically make complete data validation impossible. Using EHR data, we illustrate prospective, multiwave, two‐phase validation sampling to estimate the association between maternal weight gain during pregnancy and the risks of her child developing obesity or asthma. The optimal validation sampling design depends on the unknown efficient influence functions of regression coefficients of interest. In the first wave of our multiwave validation design, we estimate the influence function using the unvalidated (phase 1) data to determine our validation sample; then in subsequent waves, we re‐estimate the influence function using validated (phase 2) data and update our sampling. For efficiency, estimation combines obesity and asthma sampling frames while calibrating sampling weights using generalized raking. We validated 996 of 10,335 mother‐child EHR dyads in six sampling waves. Estimated associations between childhood obesity/asthma and maternal weight gain, as well as other covariates, are compared to naïve estimates that only use unvalidated data. In some cases, estimates markedly differ, underscoring the importance of efficient validation sampling to obtain accurate estimates incorporating validated data.