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Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unordered lists, novelty, and diversity. Results show each convenient matrix factorization model attending to their simplicity, the required prediction quality, the necessary recommendation quality, the desired recommendation novelty and diversity, the need to explain recommendations, the adequacy of assigning semantic interpretations to hidden factors, the advisability of recommending to groups of users, and the need to obtain reliability values. To ensure the reproducibility of the experiments, an open framework has been used, and the implementation code is provided.

Abstract Acute respiratory infections (ARIs) are a major cause of morbidity and mortality among children. The causative pathogens show geographic and seasonal variations. We retrospectively evaluated the frequency and seasonality of respiratory pathogens in children and adolescents (age: 0–19 years) with ARIs treated between January 1, 2021, and March 31, 2022, at a single center in Mexico. Out of 2400 patients, 1,603 were diagnosed with SARS-CoV-2 infection and 797 were diagnosed with other common respiratory pathogens (CRPs). Of the 797 patients, 632 were infected with one CRP and 165 with > 2 CRPs. Deaths occurred only in SARS-CoV-2-infected patients. Rhinovirus/Enterovirus, respiratory syncytial virus B, and parainfluenza virus 3 were the most prevalent in cases with single and multiple infections. CRP showed a high frequency between autumn and winter of 2021, with higher incidence of hospitalization compared to COVID-19. The main comorbidities were immunosuppression, cardiovascular disease (CD), and asthma. The frequency of CRPs showed a downward trend throughout the first half of 2021. CRPs increased in single- and co-infection cases between the fourth and fifth waves of COVID-19, probably due to decreased nonpharmaceutical interventions and changes in diagnostic tests. Age, cyanosis (symptom), and immunosuppression (comorbidity) were found to differentiate between SARS-CoV-2 infection and CRP infection.

One of the primary supports for extensive agriculture is pasture, which can suffer severe damage from insects including the Argentine stem weevil, Listronotus bonariensis. The main control method has been the infection of ryegrass with an endophyte fungus, forming a symbiotic association that produces alkaloids. In this study, we evaluated the impact of endophyte and peramine production on the weight of L. bonariensis across seven unnamed lines (LE161-LE167), and two Lolium perenne cultivars: Jumbo and Alto AR1. L. bonariensis adults fed on leaves from LE164, LE166, and ALTO AR1 showed weight losses of 13.3%, 17.1% and 18.2%, respectively. Similarly, the corresponding alkaloidal extract from LE164, LE166, and ALTO AR1 exhibited an antifeedant effect on L. bonariensis adults in laboratory assays, as observed through weight loss or low weight gain (−12.5%, 8.8% and 4.9%, respectively). Furthermore, one alkaloid, peramine, also elicited an antifeedant effect when incorporated into an artificial diet. Liquid chromatographic analysis of the alkaloid extract revealed that peramine was present in LE164, LE166 and ALTO AR1 in amounts ranging from 46.5–184.2 ng/g. Peramine was not detected in Jumbo and the remaining experimental lines. These data suggest that L. bonariensis were susceptible to peramine produced from endophyte infection in experimental lines LE164 and LE166, as well as ALTO AR1, affecting their feeding behavior.

La enfermedad SARS-Cov-2 (COVID-19) es responsable de una variedad de síntomas como fiebre, tos y fatiga, si bien es cierto que el padecimiento más frecuente se centra a nivel respiratorio y cardiovascular, existen reportes preliminares que han alertó sobre la posibilidad de que el SARS-Cov-2 pueda tener un potencial neuroinvasivo, ya que algunos pacientes han presentado síntomas neurológicos, entre ellos dolor de cabeza, mareos y en casos más severos eventos cerebrovasculares isquémicos, de igual manera ha descrito la aparición de anosmia y ageusia como síntomas tempranos de la infección por COVID-19. La literatura actual sugiere una asociación importante entre la infección por SARS-Cov-2 y el sistema nervioso central, por lo que es importante reconocer y controlar las complicaciones neurológicas potencialmente mortales secundarias a la infección por COVID-19

The relationship between phosphorus (P) availability and water restriction was explored in this study, focusing on its impact on phosphorus use efficiency (PUE) and water use efficiency (WUE) in various bromegrass (Bromus spp.) genotypes. Under controlled conditions, five bromegrass genotypes, as well as one ryegrass (Lolium perenne) cultivar, were compared by subjecting them to two P levels and two watering regimes. It was determined that combining water and phosphorus limitations led to reduced plant productivity. Initially, the ryegrass outperformed the bromegrass, but this result declined over time, while bromegrass exhibited consistent stability. Notably, under P and water stress, enhanced root development was observed in bromegrass compared to that in ryegrass. Distinct patterns of PUE and WUE allowed for the categorization of bromegrass genotypes into three groups. Genotype 3457 emerged as the most efficient, scoring 20 out of 24, while Pro 94-49 A achieved a score of only 10 out of 24. This study suggests that the drought resilience of bromegrass may be linked to increased root growth during the early vegetative stages, which potentially facilitates improved P acquisition. However, further validation through long-term field experiments is needed. The insights from this study are potentially valuable for use in shaping plant breeding programs by revealing the plant adaptation mechanisms for both P and water absorption.

Abstract Background Yearly, up to 1 million patients worldwide suffer from cutaneous leishmaniasis (CL). In Ecuador, CL affects an estimated 5000 patients annually. CL leads to reduced Health Related Quality of Life (HRQL) as a result of stigma in the Asian and Mediterranean contexts, but research is lacking for Ecuador. The objective of this study was to explore the influence of CL suspected lesions on the quality of life of patients in the Pacific and Amazon regions. Methods Patients for this study were included in the Amazonian Napo, Pastaza, and Morona Santiago provinces and the Pacific region of the Pichincha province. Participating centers offered free of charge CL treatment. All patients suspected of CL and referred for a cutaneous smear slide microscopy examination were eligible. This study applied the Skindex-29 questionnaire, a generic tool to measure HRQL in patients with skin diseases. All statistical analysis was done with SPSS Statistics version 28. Results The skindex-29 questionnaire was completed adequately by 279 patients who were included in this study. All patient groups from the Amazon scored significantly (P

Recommender Systems have become one of the most important tools for streaming and marketplace systems in recent years. Their increased use has revealed clear bias and unfairness against minorities and underrepresented groups. This paper seeks the origin of these biases and unfairness. To this end, it analyzes the demographic characteristics of a gold standard dataset and its prediction performance when used in a multitude of Recommender Systems. In addition, this paper proposes Soft Matrix Factorization (SoftMF), which tries to balance the predictions of different types of users to reduce the present inequality. The experimental results show that those biases and unfairness are not introduced by the different recommendation models and that they come from the socio-psychological and demographic characteristics of the used dataset.

BackgroundAn automated hand-hygiene monitoring system (AHHMS) was implemented in October 2019 at the Hospital Infantil de México Federico Gómez (HIMFG), a tertiary pediatric referral hospital, in four of the hospital wards with the highest rates of Healthcare Associated Infections (HAIs). The clinical and economic impact of this system had not yet been assessed prior to this study. This study aimed to evaluate if the AHHMS is a cost-effective alternative in reducing HAIs in the HIMFG.MethodologyA full cost-effectiveness economic assessment was carried out for the hospital. The alternatives assessed were AHHMS implementation vis-a-vis AHHMS non-implementation (historical tendency). The outcomes of interest were infection rate per 1,000 patient-days and cost savings as a result of prevented infections. Infection rate data per 1,000 patient-days (PD) were obtained from the hospital's Department of Epidemiology with respect to the AHHMS. As regards historical tendency, an infection-rate model was designed for the most recent 6-year period. Infection costs were obtained from a review of available literature on the subject, and the cost of the implemented AHHMS was provided by the hospital. The assessment period was 6 months. The incremental cost-effectiveness ratio was estimated. Costs are reported in US Dollars (2021). Univariate sensitivity and threshold analysis for different parameters was conducted.ResultsThe total estimated cost of the AHHMS alternative represented potential savings of $308,927–$546,795 US Dollars compared to non-implementation of the system (US$464,102 v. US$773,029–$1,010,898) for the period. AHHMS effectiveness was reflected in a diminished number of infections, 46–79 (−43.4–56.7%) compared to non-implementation (60 v. 106-139 infections).ConclusionThe AHHMS was found to be a cost-saving alternative for the HIMFG given its cost-effectiveness and lower cost vis-a-vis the alternate option. Accordingly, the recommendation was made of extending its use to other areas in the hospital.

In recent times, recommender systems (RSs) have been attracting a lot of attention from the research community because of their groundbreaking applications [...]

BackgroundAt the beginning of the current COVID-19 pandemic, it became critical to isolate all infected patients, regardless of their age. Isolating children has a negative effect on both, them and their parents/caregivers. Nevertheless isolation was mandatory because of the potential risk that visitation might have on COVID-19 dissemination mostly among health personnel.MethodsFrom the starting of the COVID-19 pandemic in our pediatric hospital visits were forbidden. This 2 months period (April–May) was called P1. In June parents were allowed to visit (P2), under a visiting protocol previously published. Hospital workers were monitored for the presence of COVID-19 symptoms and tested for the infection when clinically justified. The positivity proportion and the relative risk (RR) of COVID-19 among the health personnel between periods were calculated. The caregivers were also followed up by phone calls.ResultsSince April 2020 to November 2020, 2,884 health personnel were studied for 234 days, (318,146 workers days). Although the COVID-19/1,000 health personnel days rate decreased from one period to another (1.43 vs 1.23), no statistically significant differences were found. During P1, 16 patients with COVID-19 were treated. During the follow up none of the family members were infected/symptomatic in P1, while in P2, 6/129 (4.65%) were symptomatic or had a positive test. All of them initiated between 2 and 4 days after the patient's admission. As they also had some other infected family members it was not possible to ensure the source of infection. There were no statistically significant differences in the RR of COVID-19 in health personnel, (RR 1, 95% CI 0.69–1.06, p = 0.162).ConclusionsWhen safely implemented, allowing parents/caregivers to spend time with their hospitalized COVID-19 children does not increase the contagion risk for hospital workers or among themselves.

Health personnel (HP) have been universally recognized as especially susceptible to COVID-19. In Mexico, our home country, HP has one of the highest death rates from the disease. From the beginning of the SARS-CoV-2 pandemic, an office for initial attention for HP and a call center were established at a COVID-19 national reference pediatric hospital, aimed at early detection of COVID-19 cases and stopping local transmission. The detection and call center implementation and operation, and tracing methodology are described here. A total of 1,042 HP were evaluated, with 221 positive cases identified (7.7% of all HP currently working and 26% of the HP tested). Community contagion was most prevalent (46%), followed by other HP (27%), household (14%), and hospitalized patients (13%). Clusters and contact network analysis are discussed. This is one of the first reports that address the details of the implementation process of contact tracing in a pediatric hospital from the perspective of a hybrid hospital with COVID-19 and non-COVID-19 areas.

Komatiites represent the oldest known terrestrial rocks, and their composition has been cataloged as the closest to that of the first terrestrial crust after the cooling of the magma ocean. These rocks could have been present in multiple environments on the early Earth and served as concentrators of organic molecules. In this study, the adsorption of five amino acids (glycine, lysine, histidine, arginine, and aspartic acid) on a natural komatiite, a simulated komatiite, and the minerals olivine, pyroxene, and plagioclase were analyzed under three different pH values: acid pH (5.5), natural pH of the aqueous solution of each amino acid and alkaline pH (11). Adsorption experiments were performed in solid–liquid suspensions and organic molecules were analyzed by spectrophotometry. The main objective of this essay was to determine if the complex surfaces could have participated as concentrators of amino acids in scenarios of the primitive Earth and if the adsorption responds to the change of charge of the molecules. The results showed that komatiite is capable of adsorbing amino acids in different amounts depending on the experimental conditions. In total, 75 systems were analyzed that show different adsorptions, which implies that different interactions are involved, particularly in relation to the type of amino acid, the type of solid material and the conditions of the medium.

The Adapting Agriculture to Climate Change Project set out to improve the diversity, quantity, and accessibility of germplasm collections of crop wild relatives (CWR). Between 2013 and 2018, partners in 25 countries, heirs to the globetrotting legacy of Nikolai Vavilov, undertook seed collecting expeditions targeting CWR of 28 crops of global significance for agriculture. Here, we describe the implementation of the 25 national collecting programs and present the key results. A total of 4587 unique seed samples from at least 355 CWR taxa were collected, conserved ex situ, safety duplicated in national and international genebanks, and made available through the Multilateral System (MLS) of the International Treaty on Plant Genetic Resources for Food and Agriculture (Plant Treaty). Collections of CWR were made for all 28 targeted crops. Potato and eggplant were the most collected genepools, although the greatest number of primary genepool collections were made for rice. Overall, alfalfa, Bambara groundnut, grass pea and wheat were the genepools for which targets were best achieved. Several of the newly collected samples have already been used in pre-breeding programs to adapt crops to future challenges.

Visual representation of user and item relations is an important issue in recommender systems. This is a big data task that helps to understand the underlying structure of the information, and it can be used by company managers and technical staff. Current collaborative filtering machine learning models are designed to improve prediction accuracy, not to provide suitable visual representations of data. This paper proposes a deep learning model specifically designed to display the existing relations among users, items, and both users and items. Making use of representative datasets, we show that by setting small embedding sizes of users and items, the recommender system accuracy remains nearly unchanged; it opens the door to the use of bidimensional and three-dimensional representations of users and items. The proposed neural model incorporates variational embedding stages to “unpack” (extend) embedding representations, which facilitates identifying individual samples. It also replaces the join layers in current models with a Lambda Euclidean layer that better catches the space representation of samples. The results show numerical and visual improvements when the proposed model is used compared to the baselines. The proposed model can be used to explain recommendations and to represent demographic features (gender, age, etc.) of samples.

Traditionally, recommender systems have been approached as regression models aiming to predict the score that a user would give to a particular item. In this work, we propose a recommender system that tackles the problem as a classification task instead of as a regression. The new model, Dirichlet Matrix Factorization (DirMF), provides not only a prediction but also its reliability, hence achieving a better balance between the quality and quantity of the predictions (i.e., reducing the prediction error by limiting the model’s coverage). The experimental results conducted show that the proposed model outperforms other models due to its ability to discard unreliable predictions. Compared to our previous model, which uses the same classification approach, DirMF shows a similar efficiency, outperforming the former on some of the datasets included in the experimental setup.