Miracle-dz/UniFAR-FTUs huggingface dataset:
Malnutrition: Percentage and Association with Prognosis in Patients Hospitalized for Coronavirus Disease 2019

Previous studies have found a correlation between malnutrition and prognosis in respiratory infections. Our objectives were to determine (i) the percentage of malnutrition, and (ii) its prognosis in patients admitted for coronavirus disease 2019 (COVID-19). In this monocentric retrospective study, we consecutively included all adult patients presenting with acute COVID-19 between 9 April and 29 May 2020. Malnutrition was diagnosed on low body mass index (BMI) and weight loss ≥ 5% in the previous month and/or ≥ 10% in the previous six months. The Nutritional Risk Index (NRI) defined nutritional risk. Severe COVID-19 was defined as a need for nasal oxygen ≥ 6 L/min. We enrolled 108 patients (64 men, 62 ± 16 years, BMI 28.8 ± 6.2 kg/m2), including 34 (31.5%) with severe COVID-19. Malnutrition was found in 42 (38.9%) patients, and moderate or severe nutritional risk in 83 (84.7%) patients. Malnutrition was not associated with COVID-19 severity. Nutritional risk was associated with severe COVID-19 (p < 0.01; p < 0.01 after adjustment for C reactive protein), as were lower plasma proteins, albumin, prealbumin, and zinc levels (p < 0.01). The main cause of malnutrition was inflammation. The high percentage of malnutrition and the association between nutritional risk and COVID-19 prognosis supports international guidelines advising regular screening and nutritional support when necessary.
A Person-Centered Approach to the Job Demands–Control Model: A Multifunctioning Test of Addictive and Buffer Hypotheses to Explain Burnout

This study used a cross-sectional design and a person-centered approach in order to test the addictive and interactive strain hypotheses of Job Demands–Control Model to explain burnout. A large sample (n = 6357) of Portuguese workers (nurses, bank employees, retail traders, and contact center agents) was used. Through latent profile analysis (LPA), first latent profiles of demands and control were identified and then it was examined how these profiles differed in workplace well-being (engagement and burnout) through an ANCOVA. The four hypothesized profiles (i.e., “high-strain”, “low-Strain”, “passive”, and “active”) and one more profile denominated “moderate active”, emerged from LPA analysis. The hypotheses were supported in both addictive effects and interactive effects (buffer hypothesis), suggesting that the difficulty in finding consistent support for the buffer hypothesis might be related to the use of variable-centered approaches. Moreover, this reinforces that, in organizational practice, job control is a crucial characteristic to face job demands, as job control will buffer job demands’ harmful effects on workplace well-being.
Bacterial identification: from the agar plate to the mass spectrometer

For more than a century, bacteria and fungi have been identified by isolation in culture followed by enzymatic reactions and morphological analyses. The identification of environmental microorganisms, however, remains a challenge because biochemical and staining protocols for bacteria identification are tedious, usually stepwise, can be long (days) and are prone to errors. Molecular techniques based on DNA amplification and/or sequencing provide more secure molecular identification of specific bacteria, but identification based on mass spectrometry (MS), mainly on MALDI-MS, has been shown to be an alternative accurate and fast method able to identify unknown bacteria on the genus, species and even subspecies level based profiles of proteins and peptides derived from whole bacterial cells. Breakthroughs such as non-culture-based identification of bacteria from biological fluids and MS detection of antibiotic resistance have recently been reported. This review provides an overview of the traditional bacterial and fungal identification workflow and discusses the recent introduction of MS as a powerful tool for the identification of microorganisms. Principles and applications of MS, followed by the use of high-quality databases with dedicated algorithms, are discussed for routine microbial diagnostics, mainly in human clinical settings and in veterinary medicine.
DecisiveNets: Training Deep Associative Memories to Solve Complex Machine Learning Problems

Learning deep representations to solve complex machine learning tasks has become the prominent trend in the past few years. Indeed, Deep Neural Networks are now the golden standard in domains as various as computer vision, natural language processing or even playing combinatorial games. However, problematic limitations are hidden behind this surprising universal capability. Among other things, explainability of the decisions is a major concern, especially since deep neural networks are made up of a very large number of trainable parameters. Moreover, computational complexity can quickly become a problem, especially in contexts constrained by real time or limited resources. Therefore, understanding how information is stored and the impact this storage can have on the system remains a major and open issue. In this chapter, we introduce a method to transform deep neural network models into deep associative memories, with simpler, more explicable and less expensive operations. We show through experiments that these transformations can be done without penalty on predictive performance. The resulting deep associative memories are excellent candidates for artificial intelligence that is easier to theorize and manipulate.
Effects of Blood Pressure on Cognitive Performance in Aging: A Systematic Review

Introduction: Cognitive functions play a crucial role in daily functioning. Unfortunately, some cognitive abilities decline in the process of healthy aging. An increasing body of evidence has highlighted the role of lifestyle habits and cardiovascular diseases, such as high blood pressure, in increasing the risk of cognitive decline. Surprisingly, although hypertension is a modifiable risk factor for cerebrovascular damage, the role of hypertension on cognitive impairment development is not still clear. Several key questions remain unresolved, and there are many inconsistent results in studies considering this topic. This review is aimed to systematically analyze the results found by the studies that investigated whether high blood pressure, in both hypertensive and healthy people, is related to cognitive performance. Furthermore, it points to evaluate the role of age in this relationship. Method: The review process was conducted according to the PRISMA statement. Restrictions were made, selecting the studies in English and published in peer-review journals, including at least one cognitive measure and blood pressure measurement. Studies that included participants with medical conditions, dementia, psychiatric disorders, strokes, and brain injury were excluded. Cross-sectional and longitudinal studies were analyzed separately. Finally, blood pressure measured at young life (18–39 years), midlife (age 40–64 years), elderly (65–74 years), and old age (≥75 years) were considered. Results: The review allows 68 studies to be selected, which include 154,935 participants. The results provided evidence of an adverse effect of exposure to high blood pressure on cognitive performance. High blood pressure in midlife was linked with poorer cognitive functioning; this evidence was found in cross-sectional and longitudinal studies. However, this association declines with increasing age and tends to become inconsistent. In older people, the relationship between blood pressure and cognitive performance is non-linear, highlighting a beneficial effect of high blood pressure on cognition. Conclusions: Despite some limitations, this review showed that cardiovascular and neuro-cognitive systems do not operate in isolation, but they are related. Blood pressure can be considered an early biomarker of cognitive impairment, and the necessity of early blood pressure measurement and control was underlined.
Downregulation of Mitochondrial TSPO Inhibits Mitophagy and Reduces Enucleation During Human Terminal Erythropoiesis

Translocator protein (TSPO) and voltage dependent anion channels (VDAC) are two proteins forming a macromolecular complex in the outer mitochondrial membrane that is involved in pleiotropic functions. Specifically, these proteins were described to regulate the clearance of damaged mitochondria by selective mitophagy in non-erythroid immortalized cell lines. Although it is well established that erythroblast maturation in mammals depends on organelle clearance, less is known about mechanisms regulating this clearance throughout terminal erythropoiesis. Here, we studied the effect of TSPO1 downregulation and the action of Ro5-4864, a drug ligand known to bind to the TSPO/VDAC complex interface, in ex vivo human terminal erythropoiesis. We found that both treatments delay mitochondrial clearance, a process associated with reduced levels of the PINK1 protein, which is a key protein triggering canonical mitophagy. We also observed that TSPO1 downregulation blocks erythroblast maturation at the orthochromatic stage, decreases the enucleation rate, and increases cell death. Interestingly, TSPO1 downregulation does not modify reactive oxygen species (ROS) production nor intracellular adenosine triphosphate (ATP) levels. Ro5-4864 treatment recapitulates these phenotypes, strongly suggesting an active role of the TSPO/VDAC complex in selective mitophagy throughout human erythropoiesis. The present study links the function of the TSPO/VDAC complex to the PINK1/Parkin-dependent mitophagy induction during terminal erythropoiesis, leading to the proper completion of erythroid maturation.
Auriculotherapy and reducing chronic musculoskeletal pain: integrative review.

OBJECTIVES to describe the scientific evidence on the use of auriculotherapy to reduce chronic musculoskeletal pain in adults and the elderly. METHODS integrative literature review conducted in the Latin American and Caribbean Health Sciences, SciVerse Scopus and MEDLINE databases (via National Library of Medicine), in March 2019, with no time frame. RESULTS 14 original scientific articles were analyzed, 64.3% of which were classified with level of evidence 2. All productions show the benefit of auriculotherapy in reducing chronic musculoskeletal pain, especially in the lumbar spine (42.9%). The treatment was developed in one or more sessions, using semi-permanent needles (42.9%) and electro-auriculotherapy (21.4%). CONCLUSIONS auriculotherapy was effective in reducing chronic musculoskeletal pain, showing itself as an alternative to be used for the promotion and recovery of individuals' health.
Determination of Structure-Toxicity Relationship of Amphiprotic Compounds by Means of the Inhibition of the Dehydrogenase Activity of Pseudomonas Putida

Aliphatic and aromatic alcohols are amphiprotic compounds which have both polar and nonpolar parts in their structure. These compounds were studied with respect to the nonreactive toxic effects on the microorganism {\it Pseudomonas putida}. The toxicity of these chemicals to aerobic bacterium {\it P. putida} was measured in terms of inhibition of dehydrogenase activity. The test results, expressed as concentration of chemicals 50% effective in inhibition (IC50), were correlated with their physicochemical properties such as aqueous solubility (S) and octanol-water partition coefficient (P).
Disruption of Cationic/Anionic Viscoelastic Surfactant Micellar Networks by Hydrocarbon as a Basis of Enhanced Fracturing Fluids Clean-Up

Studies of the effects produced by the solubilization of hydrophobic substances by micellar aggregates in water medium are quite important for applications of viscoelastic surfactant solutions for enhanced oil recovery (EOR), especially in hydraulic fracturing technology. The present paper aims at the investigation of the structural transformations produced by the absorption of an aliphatic hydrocarbon (n-decane) by mixed wormlike micelles of cationic (n-octyltrimethylammonium bromide, C8TAB) and anionic (potassium oleate) surfactants enriched by C8TAB. As a result of contact with a small amount (0.5 wt%) of oil, a highly viscoelastic fluid is transformed to a water-like liquid. By small-angle neutron scattering (SANS) combined with cryo-TEM, it was shown that this is due to the transition of long wormlike micelles with elliptical cross-sections to ellipsoidal microemulsion droplets. The non-spherical shape was attributed to partial segregation of longer- and shorter-tail surfactant molecules inside the surfactant monolayer, providing an optimum curvature for both of them. As a result, the long-chain surfactant could preferably be located in the flatter part of the aggregates and the short-chain surfactant—at the ellipsoid edges with higher curvature. It is proven that the transition proceeds via a co-existence of microemulsion droplets and wormlike micelles, and upon the increase of hydrocarbon content, the size and volume fraction of ellipsoidal microemulsion droplets increase. The internal structure of the droplets was revealed by contrast variation SANS, and it was shown that, despite the excess of the cationic surfactant, the radius of surfactant shell is controlled by the anionic surfactant with longer tail. These findings open a way for optimizing the performance of viscoelastic surfactant fluids by regulating both the mechanical properties of the fluids and their clean-up from the fracture induced by contact with hydrocarbons.
Machine learning prediction of critical transition and system collapse

To predict a critical transition due to parameter drift without relying on model is an outstanding problem in nonlinear dynamics and applied fields. A closely related problem is to predict whether the system is already in or if the system will be in a transient state preceding its collapse. We develop a model free, machine learning based solution to both problems by exploiting reservoir computing to incorporate a parameter input channel. We demonstrate that, when the machine is trained in the normal functioning regime with a chaotic attractor (i.e., before the critical transition), the transition point can be predicted accurately. Remarkably, for a parameter drift through the critical point, the machine with the input parameter channel is able to predict not only that the system will be in a transient state, but also the average transient time before the final collapse.
Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation

Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by decomposing these patterns of fitness variation. We find that a small number of inferred phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular — affecting a small number of phenotypes that matter to fitness in the environment where they evolved — yet globally pleiotropic — affecting additional phenotypes that may reduce or improve fitness in new environments.
Hysteroscopy and female infertility: a fresh look to a busy corner

Abstract Hysteroscopy has evolved from the traditional art of examining the uterine cavity for diagnostic purposes to an invaluable modality to concomitantly diagnose and (see and) treat a multitude of intrauterine pathologies, especially in the field and clinics specialising in female reproduction. This article reviews the literature on the most common cervical, endometrial, uterine and tubal pathologies such as chronic endometritis, endometrial polyps, adenomyosis, endometriosis, endometrial atrophy, adhesions, endometrial hyperplasia, cancer, and uterine malformations. The aim is to determine the efficiency of hysteroscopy compared with other available techniques as a diagnostic and treatment tool and its association with the success of in vitro fertilisation procedures. Although hysteroscopy requires an experienced operator for optimal results and is still an invasive procedure, it has the unique advantage of combining great diagnostic and treatment opportunities before and after ART procedures. In conclusion, hysteroscopy should be recommended as a first-line procedure in all cases with female infertility, and a special effort should be made for its implementation in the development of new high-tech procedures for identification and treatment infertility-associated conditions.
Nursing Students’ Attitudes towards Immigrants’ Social Rights

The migrant population has increased in recent years and, as a result, so has cultural diversity. Universities are incorporating specific modules addressing cultural diversity. However, the native population has negative attitudes towards immigrants, as they believe that immigrants receive more social benefits and abuse healthcare services. Nurses may have these attitudes too, which may affect the way they treat patients. The objective of this study was to determine nursing students’ attitudes towards the rights of the migrant population. This is a descriptive ex post facto study using a cross-sectional design, with 821 nursing students in Melilla, Ceuta, and Almeria, Spain. An anonymous questionnaire was used for data collection. Students recognize the same rights for both the immigrant and native populations. More than 80% of the sample upholds the right of undocumented immigrants and their families to access publicly funded healthcare. Attitudes were more positive among students with a Berber background and first-year students. Students approved of the right of immigrants and their families to healthcare and education. The students’ negative attitudes towards the social rights of immigrants need to be addressed with intercultural training to reduce their prejudices as future professionals in a multicultural society.
CLT with explicit variance for products of random singular matrices related to Hill’s equation

We prove a central limit theorem (CLT) for the product of a class of random singular matrices related to a random Hill’s equation studied by Adams–Bloch–Lagarias. The CLT features an explicit formula for the variance in terms of the distribution of the matrix entries and this allows for exact calculation in some examples. Our proof relies on a novel connection to the theory of [Formula: see text]-dependent sequences which also leads to an interesting and precise nondegeneracy condition.
Sepsis—A Retrospective Cohort Study of Bloodstream Infections

Bloodstream infections (BSIs) are among the leading causes of morbidity and mortality worldwide, among infectious diseases. Local knowledge of the main bacteria involved in BSIs and their associated antibiotic susceptibility patterns is essential to rationalize the empiric antimicrobial therapy. The aim of this study was to define the incidence of infection and evaluate the antimicrobial resistance profile of the main pathogens involved in BSIs. This study enrolled patients of all ages and both sexes admitted to the University Hospital “San Giovanni di Dio e Ruggi d’Aragona”, Salerno, Italy between January 2015 to December 2019. Bacterial identification and antibiotic susceptibility testing were performed with Vitek 2. A number of 3.949 positive blood cultures were included out of 24,694 total blood cultures from 2015 to 2019. Coagulase-negative staphylococci (CoNS) were identified as the main bacteria that caused BSI (17.4%), followed by Staphylococcus aureus (12.3%), Escherichia coli (10.9%), and Klebsiella pneumoniae (9.4%). Gram-positive bacteria were highly resistant to Penicillin G and Oxacillin, while Gram-negative strains to Ciprofloxacin, Cefotaxime, Ceftazidime, and Amoxicillin-clavulanate. High susceptibility to Vancomycin, Linezolid, and Daptomycin was observed among Gram-positive strains. Fosfomycin showed the best performance to treatment Gram-negative BSIs. Our study found an increase in resistance to the latest generation of antibiotics over the years. This suggests an urgent need to improve antimicrobial management programs to optimize empirical therapy in BSI.
From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting

Human trajectory forecasting is an inherently multi-modal problem. Uncertainty in future trajectories stems from two sources: (a) sources that are known to the agent but unknown to the model, such as long term goals and (b) sources that are unknown to both the agent and the model, such as the intent of other agents and irreducible randomness in decisions. We propose to factorize this uncertainty into its epistemic and aleatoric sources. We model the epistemic uncertainty through multimodality in long term goals and the aleatoric uncertainty through multimodality in way-points and paths. To exemplify this dichotomy, we also propose a novel long term trajectory forecasting setting, with prediction horizons up to a minute, up to an order of magnitude longer than prior works. Finally, we present Y-net, a scene compliant trajectory forecasting network that exploits the proposed epistemic and aleatoric structure for diverse trajectory predictions across long prediction horizons. Y-net significantly improves previous state-of-the-art performance on both (a) The short prediction horizon setting on the Stanford Drone (31.7% in FDE) and ETH/UCY datasets (7.4% in FDE) and (b) The proposed long horizon setting on the re-purposed Stanford Drone and Intersection Drone datasets.
Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks

The attention mechanism is a key component of the neural revolution in Natural Language Processing (NLP). As the size of attention-based models has been scaling with the available computational resources, a number of pruning techniques have been developed to detect and to exploit sparseness in such models in order to make them more efficient. The majority of such efforts have focused on looking for attention patterns and then hard-coding them to achieve sparseness, or pruning the weights of the attention mechanisms based on statistical information from the training data. In this paper, we marry these two lines of research by proposing Attention Pruning (AP): a novel pruning framework that collects observations about the attention patterns in a fixed dataset and then induces a global sparseness mask for the model. Through attention pruning, we find that about 90% of the attention computation can be reduced for language modelling and about 50% for machine translation and %natural language inference prediction with BERT on GLUE tasks, while maintaining the quality of the results. Additionally, using our method, we discovered important distinctions between self- and cross-attention patterns, which could guide future NLP research in attention-based modelling. Our approach could help develop better models for existing or for new NLP applications, and generally for any model that relies on attention mechanisms. Our implementation and instructions to reproduce the experiments are available at https://github.com/irugina/AP.