Miracle-dz/UniFAR-FTUs huggingface dataset:
FactorizeNet: Progressive Depth Factorization for Efficient Network Architecture Exploration Under Quantization Constraints

Depth factorization and quantization have emerged as two of the principal strategies for designing efficient deep convolutional neural network (CNN) architectures tailored for low-power inference on the edge. However, there is still little detailed understanding of how different depth factorization choices affect the final, trained distributions of each layer in a CNN, particularly in the situation of quantized weights and activations. In this study, we introduce a progressive depth factorization strategy for efficient CNN architecture exploration under quantization constraints. By algorithmically increasing the granularity of depth factorization in a progressive manner, the proposed strategy enables a fine-grained, low-level analysis of layer-wise distributions. Thus enabling the gain of in-depth, layer-level insights on efficiency-accuracy tradeoffs under fixed-precision quantization. Such a progressive depth factorization strategy also enables efficient identification of the optimal depth-factorized macroarchitecture design (which we will refer to here as FactorizeNet) based on the desired efficiency-accuracy requirements.
Novel Aggregation-Induced Emission Materials/Cadmium Sulfide Composite Photocatalyst for Efficient Hydrogen Evolution in Absence of Sacrificial Reagent

This work focuses on the development of a novel organic–inorganic photoactive material composited by aggregation-induced emission luminogens (AIE) and CdS. Tetraphenylethene-based AIE (TPE-Ca) is synthesized on CdS to form CdS/TPE-Ca electrode, due to its suitable band structure and potential capability of renewable energy production. The CdS/TPE-Ca electrode presents over three-fold improved photocurrent density and dramatically reduced interfacial resistance, compared with the pure CdS electrode. In addition, the engineering of the band alignment allows the holes to accumulate on the valance band of TPE-Ca, which would partially prevent the CdS from photo-corrosion, thus improving the stability of the sacrificial-free electrolyte photoelectrochemical cell.
Inaccurate Supervision of Neural Networks with Incorrect Labels: Application to Epilepsy.

This work describes multiple weak supervision strategies for video processing with neural networks in the context of seizure detection. To study seizure onset, we have designed automated methods to detect seizures from electroencephalography (EEG), a modality used for recording electrical brain activity. However, the EEG signal alone is sometimes not enough for existing detection methods to discriminate seizure from artifacts having a similar signal on EEG. For example, such artifacts could be triggered by patting, rocking or suctioning in the case of neonates. In this article, we addressed this problem by automatically detecting an example artifact -- patting of neonates -- from continuous video recordings of neonates acquired during clinical routine. We computed frame-to-frame cross-correlation matrices to isolate patterns showing repetitive movements indicative of patting of the patient. Next, a convolutional neural network was trained to classify whether these matrices contained patting events using weak training labels -- noisy labels generated during daily clinical procedure. The labels were considered weak as they were sometimes incorrect. We investigated whether networks trained with more samples, containing more uncertain and weak labels, could achieve a higher performance. Our results showed that, in the case of patting detection, such networks could achieve a higher recall, without sacrificing precision. These networks focused on areas of the cross-correlation matrices that were more meaningful to the task. More generally, our work gives insights into building more accurate models from weakly labelled time sequences.
Effectiveness of Active Therapy-Based Training to Improve the Balance in Patients with Fibromyalgia: A Systematic Review with Meta-Analysis

Balance impairment is a frequent disorder in patients with fibromyalgia (FMS), increasing the risk of falls and decreasing physical function and quality of life. In recent years, the use of active therapy-based training (ATBT) has increased, with the aim of improving balance in women with FMS. Our study aimed to assess the effect of ATBT to improve different balance outcomes in subjects with FMS. A systematic review with meta-analysis was carried out. We searched PubMed Medline, SCOPUS, Web of Science, CINAHL, and PEDro (Physiotherapy Evidence Database) databases up to September 2020. We included randomized controlled trials (RCT) that assessed the balance in patients with FMS after ATBT and compared to other treatments or no intervention. In a random-effects model, the standardized mean difference (SMD) was used to calculate the effect size. Ten studies were included in the review providing data from 546 FMS patients with a mean age of 52.41 ± 2.90 years old (98% females). Our results showed a medium effect favors ATBT with respect to other therapies for monopedal static balance (SMD = 0.571; 95% CI = 0.305, 0.836; p < 0.001), dynamic balance (SMD = 0.618; 95% CI = 0.348, 0.888; p < 0.001), and functional balance (SMD = 0.409; 95% CI = 0.044, 0.774; p = 0.028). No statistically significant differences were found for balance on unstable support. The present meta-analysis showed moderate-quality evidence of a medium effect of ATBT to improve dynamic and functional balance and low-quality evidence of a medium effect to improve monopedal static balance with respect to other therapies or no intervention.
DROPS: Deep Retrieval of Physiological Signals via Attribute-specific Clinical Prototypes

The ongoing digitization of health records within the healthcare industry results in large-scale datasets. Manually extracting clinically-useful insight from such datasets is non-trivial. However, doing so at scale while simultaneously leveraging patient-specific attributes such as sex and age can assist with clinical-trial enrollment, medical school educational endeavours, and the evaluation of the fairness of neural networks. To facilitate the reliable extraction of clinical information, we propose to learn embeddings, known as clinical prototypes (CPs), via supervised contrastive learning. We show that CPs can be efficiently used for large-scale retrieval and clustering of physiological signals based on multiple patient attributes. We also show that CPs capture attribute-specific semantic relationships.
Association between Habitual Dietary Iron Intake and Glucose Metabolism in Individuals after Acute Pancreatitis

Dietary intake of iron is known to be associated with impaired glucose metabolism. However, its involvement in derangements of glucose metabolism after acute pancreatitis (AP) is not completely understood. The aim was to investigate the association between dietary iron intake and markers of glucose metabolism in individuals after an attack of AP. Fasting blood samples were collected to analyse markers of glucose metabolism (fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c)). The EPIC-Norfolk food frequency questionnaire was used to determine the habitual intake of dietary iron (total, haem, and non-haem). Multivariable linear regression analyses were conducted and six statistical models were built to adjust for covariates. A total of 109 individuals after AP were studied in a cross-sectional fashion. Total iron (β (95% confidence interval) = −0.19 (−0.35, −0.05); p = 0.01 in the most adjusted model) and non-haem iron (β (95% confidence interval) = −0.19 (−0.33, −0.04); p = 0.03 in the most adjusted model) were significantly associated with FPG, consistently in all adjusted model. Total iron and non-haem iron did not have consistent significant associations with HbA1c. Dietary haem iron intake was not associated with either FPG or HbA1c. Habitual intake of dietary iron is inversely associated with FPG in individuals after an attack of AP and may be involved in the pathogenesis of new-onset diabetes after pancreatitis. Prospective longitudinal studies are now warranted to unveil the specific mechanism underlying the involvement of dietary iron.
Evaluation of microhardness, sorption, solubility, and color stability of bulk fill resins: A comparative study

Background Due to the increasing popularity of bulk fill resins, there is a concern that their components can be leached; this is because these are inserted in a single 4-5 mm increment. This in vitro study evaluated the microhardness, sorption, solubility, and color stability of three restorative bulk fill resins, namely: Filtek Bulk Fill (FBF), Tetric N-Ceram Bulk Fill (TNC), and Opus Bulk Fill (OBF). Material and Methods Cylindrical samples were fabricated to be 15 mm in diameter and 1 mm thick (n = 10). For the microhardness test, three random indentations were formulated on the samples using a micro-durometer with a load of 300 gf for 15 s. Sorption and solubility were then evaluated (ISO 4049: 2009). Color stability was analyzed with a digital spectrophotometer three times (initially, after 24 h, and after 7 d) during immersion in coffee and distilled water (control). The Shapiro-Wilk test was applied to analyze normality. The Mann-Whitney and Kruskal-Wallis tests were used to compare the groups and the immersion solution, with a significance level of 5%. Results There were a significant difference in microhardness (p<0.001), with the FBF group showing a higher value compared to the other groups (56.38). The highest average of sorption scores was observed in the OBF group (16.9 µg / mm3), followed by FBF (16.8 µg / mm3) and TNC (11.3 µg / mm3). Solubility was lowest in the OBF group (-2.83 µg / mm3), with a significant difference (p = 0.031). There was also a significant difference after 24 h in the mean ∆E score of all groups (p<0.005). After one week of immersion, the group that pigmented most was OBF (p = 0.008). Conclusions The three bulk fill resins had acceptable hardness, sorption, and solubility values. However, all groups showed a high pigmentation rate after 7 d of immersion in coffee. Key words:Bulk fill, color stability, composite resins, microhardness, solubility, sorption.
Infection Prevention and Control Preparedness Level and Associated Determinants in 56 Acute Healthcare Facilities in Ghana

Background Infection prevention and control (IPC) is a globally relevant aspect of all health systems impacting the health and safety of both patients and healthcare workers. However, best practices remain a challenge in healthcare delivery especially in resource limited situations. The primary objective of this study was to assess the infection prevention and control (IPC) preparedness levels of acute healthcare facilities in Ghana and to determine the factors associated with the overall IPC preparedness levels in acute healthcare facilities. Methods A cross-sectional study adapting the IPC assessment framework (IPCAF) developed by the World Health Organization (WHO) was conducted. Five of the core components of the WHO IPCAF were used to assess the IPC level of 56 acute healthcare facilities in Ghana. Results Of the 56 facilities surveyed, only 19 had an IPC program with clearly defined objectives. Overall, 8 (14.3%) facilities scored an IPC preparedness level of “Advance”, 18 (32.1%) facilities received an “Intermediate” IPC preparedness score, 23 (41.1%) facilities received an IPC preparedness level of “basic” and 7 (12.5%) facilities scored an IPC preparedness level of “inadequate”. IPC materials like detergents, running water and PPEs were not significantly supplied. Government owned facilities performed better in terms of IPC preparedness as compared to privately owned facilities. A PLUM-ordinal regression analysis revealed that an IPC program with clearly defined objectives (OR= 76; 95% CI; 7.23, 808.19), dedicated IPC budget (OR= 13; 95% CI; 3.8–44.3) and regular mandatory training (OR= 50.9; 95% CI; 6.1–425) were associated with increased IPC preparedness. Conclusion Generally, the IPC preparedness levels in a majority of the facilities were low and required significant improvements in several areas. Facilities must make periodic reviews and adjust their objectives based on facility priorities.
Influence of Environmental and Productive Factors on the Biodiversity of Lactic Acid Bacteria Population from Sheep Milk

Simple Summary The dairy sheep sector in Spain is of great importance in the socioeconomic field. For this reason, obtaining quality milk has become a priority objective in the sector. In this context, the environment of dairy farms could affect the microbial communities present in milk, and therefore, the study of lactic acid bacteria (LAB) in this environment could be fundamental for the quality of milk and its dairy products. The objective of this study was to investigate the LAB population present in dairy sheep milk and the possible routes of contamination between the livestock environment and the milk on 12 sheep farms with different livestock practices in Castilla-La Mancha. The results showed that certain agricultural practices favour the presence of LAB in milk in addition to the fact that a significant transference between the livestock environment and bulk tank milk could exist. Abstract Milk is a typical and satisfactory medium for the growth of lactic acid bacteria (LAB). These microorganisms are of vital importance in the quality of the milk since they contribute to its preservation and give differential organoleptic properties to the final product. Furthermore, LABs can act as biocontrol agents in the dairy industry by inhibiting the growth of undesirable bacteria present in milk and by improving the quality of dairy products such as cheese. In this context, knowing the transfer routes used by LABs from the livestock environment to the milk is of great importance within the dairy industry. Therefore, the objectives of the present study were to expand the knowledge of the LAB population present in the milk of Manchego ewe by means of DNA sequencing techniques and to evaluate the possible transfers of LAB species based on the management of each dairy farm. Samples of bulk tank milk, air (from the milking parlour and from the livestock housing), animal feed and teat surface (taken from 10 sheep per farm) were collected in 12 traditional livestock farms in Castilla-La Mancha (Spain), where each farm presented differences regarding their farming practices. A mixed-effects model was used to evaluate the effects of livestock practices on the distribution of LAB species. Results showed that the vast majority of species identified in the milk had an isolate that was also found in other matrices, which could indicate a microbial transference via the livestock environment to the milk. In addition, the mixed model showed that the factors that positively influence the LAB count were the low-line milking system and the daily use of acid detergent in cleaning the milking machine.
Awareness of Human Papillomavirus and Its Oncogenic Potential in Head and Neck Cancer among Students: Still More Questions than Answers

In the past years, human papilloma virus (HPV) has been proved to be an important risk factor for head and neck squamous cell carcinomas (HNSCCs), especially in the oropharynx (OPCCS). The aim of this study was to assess the level of knowledge about HPV among students and to raise their awareness on the issue. A 22-question questionnaire was uploaded to an online service. Information about the project was sent out to students from three Universities in Lodz, Poland. All data were collected via questionnaire website tools. A total of 1710 students participated in this study. The group was divided into medical (MS) and non-medical (non-MS) students. Merely 59.38% of the non-MS had ever heard about HPV. Only 44.74% of the non-MS knew about vaccines against HPV. The oncogenic potential of HPV was evident for 81.17% of the MS and only 55.92% of the non-MS. Very similar numbers of respondents from both groups (39.21% vs. 36.47%) knew that HPV may cause cancers other than cervical. Nearly half of the respondents from both groups (46.28% vs. 48.32%) did not know about the risk of developing oral or oropharyngeal cancer. The level of knowledge about the consequences of HPV infection in head and neck cancers in young adults remains insufficient.
Prevalence and Outcomes Associated with Vitamin D Deficiency among Indexed Hospitalizations with Cardiovascular Disease and Cerebrovascular Disorder—A Nationwide Study

Background: According to past studies, recovery and survival following severe vascular events such as acute myocardial infarction and stroke are negatively impacted by vitamin D deficiency. However, the national estimate on disability-related burden is unclear. We intend to evaluate the prevalence and outcomes of vitamin D deficiency (VDD) among patients with cardiovascular disease (CVD) and cerebrovascular disorder (CeVD). Methods: We performed a cross-sectional study on the Nationwide Inpatient Sample data (2016–2017) of adult (≥18 years) hospitalizations. We identified patients with a secondary diagnosis of VDD and a primary diagnosis of CVD and CeVD using the 9th revision of the International Classification of Diseases, clinical modification code (ICD-10-CM) codes. A univariate and mixed-effect multivariable survey logistic regression analysis was performed to evaluate the prevalence, disability, and discharge disposition of patients with CVD and CeVD in the presence of VDD. Results: Among 58,259,589 USA hospitalizations, 3.44%, 2.15%, 0.06%, 1.28%, 11.49%, 1.71%, 0.38%, 0.23%, and 0.08% had primary admission of IHD, acute MI, angina, AFib, CHF, AIS, TIA, ICeH, and SAH, respectively and 1.82% had VDD. The prevalence of hospitalizations due to CHF (14.66% vs. 11.43%), AIS (1.87% vs. 1.71%), and TIA (0.4% vs. 0.38%) was higher among VDD patients as compared with non-VDD patients (p < 0.0001). In a regression analysis, as compare with non-VDD patients, the VDD patients were associated with higher odds of discharge to non-home facilities with an admission diagnosis of CHF (aOR 1.08, 95% CI 1.07–1.09), IHD (aOR 1.24, 95% CI 1.21–1.28), acute MI (aOR 1.23, 95% CI 1.19–1.28), AFib (aOR 1.21, 95% CI 1.16–1.27), and TIA (aOR 1.19, 95% CI 1.11–1.28). VDD was associated with higher odds of severe or extreme disability among patients hospitalized with AIS (aOR 1.1, 95% CI 1.06–1.14), ICeH (aOR 1.22, 95% CI 1.08–1.38), TIA (aOR 1.36, 95% CI 1.25–1.47), IHD (aOR 1.37, 95% CI 1.33–1.41), acute MI (aOR 1.44, 95% CI 1.38–1.49), AFib (aOR 1.10, 95% CI 1.06–1.15), and CHF (aOR 1.03, 95% CI 1.02–1.05) as compared with non-VDD. Conclusions: CVD and CeVD in the presence of VDD increase the disability and discharge to non-home facilities among USA hospitalizations. Future studies should be planned to evaluate the effect of VDD replacement for improving outcomes.
Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer

The expression and regulation of matrisome genes—the ensemble of extracellular matrix, ECM, ECM-associated proteins and regulators as well as cytokines, chemokines and growth factors—is of paramount importance for many biological processes and signals within the tumor microenvironment. The availability of large and diverse multi-omics data enables mapping and understanding of the regulatory circuitry governing the tumor matrisome to an unprecedented level, though such a volume of information requires robust approaches to data analysis and integration. In this study, we show that combining Pan-Cancer expression data from The Cancer Genome Atlas (TCGA) with genomics, epigenomics and microenvironmental features from TCGA and other sources enables the identification of “landmark” matrisome genes and machine learning-based reconstruction of their regulatory networks in 74 clinical and molecular subtypes of human cancers and approx. 6700 patients. These results, enriched for prognostic genes and cross-validated markers at the protein level, unravel the role of genetic and epigenetic programs in governing the tumor matrisome and allow the prioritization of tumor-specific matrisome genes (and their regulators) for the development of novel therapeutic approaches.
Investigation of the relationship between chronic montelukast treatment, asthma and depression-like behavior in mice

In 2008, the Food and Drug Administration of the US issued a warning about the neuropsychiatric side effects of montelukast. Previous clinical studies on montelukast have reported conflicting results and, to the best of our knowledge, no experimental studies concerning these side effects had been conducted. In the current study, the effect of montelukast on depression-like behavior in an ovalbumin (OVA)-induced mouse model was investigated. A total of 3 OVA challenges were applied at 2 week intervals for the persistence of asthma. Depression-like behavior was assessed using forced swim tests following each challenge and locomotor activities were evaluated using open field tests. At the end of the current study, plasma montelukast concentrations were measured and the development of asthma and effect of montelukast treatment were histopathologically examined. Inflammation scores that were increased in the OVA mice following all challenges were indicated to be reduced by montelukast treatment. The immobility time of mice increased beginning with the first challenge and this was also reduced by montelukast treatment. Montelukast administration to the control mice did not alter immobility times. Moreover, motor activity of the OVA and montelukast-treated mice were not altered. The results indicated there was no association between chronic montelukast treatment and depression. Furthermore, the chronic administration of montelukast to non-asthmatic mice did not increase immobility. However, depressive behavior increased at all time points in the OVA mice. These results indicated that chronic montelukast treatment is not associated with depression-like behavior and confirmed the association between asthma and depression. Further studies are required to provide an improved understanding of the neuropsychiatric side effects of montelukast.