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When analyzing occupational safety factors, a human factor is associated with 80–90% of incidents and accidents that occur. Controlling this factor is essential when it comes to creating healthy and safe organizations. Personality traits have shown great relevance when understanding the behavior of safety or self-protection in the workplace, affecting the motivation of subjects and, therefore, their behavior. The objective of this study is to analyze the influence of the extraversion trait on compliance with safety rules and behaviors, while evaluating the mediating effect of motivation for safety in health professionals. The sample is composed of 183 nurses and wardens of a hospital in Spain. The obtained results confirm the hypotheses raised, validating the negative influences of extraversion on motivation for security and compliance with norms, behaviors, and security, while verifying the effect of the total mediation of motivation for security in the influence of extraversion on security compliance. This confirms the effect of this personality trait on employees' safety behaviors, and the possibility of reducing this influence by controlling personal motivations with interventions from the organization. [ABSTRACT FROM AUTHOR]

Background and Objective: The dorsal osteophyte on the distal phalanx of the first toe (hallux) is a reactive bony protrusion that may be associated with pathologies such as onychocryptosis or pincer nail. This study aims to describe and analyze the correlation between three novel measurements—dorsal osteophyte height (HDO), distal phalangeal hyperextension (DPHA), and distal phalangeal curvature (DCDP)—and to evaluate the impact of minimally invasive surgery on the dorsal osteophyte using fluoroscopic data. Materials and Methods: A total of 125 fluoroscopic images were analyzed. Baseline measurements for the variables were compared between groups. The key variables included distal phalanx curvature, distal phalanx hyperextension, and dorsal osteophyte height. Results: The analysis revealed statistically significant differences in the main group effect for distal phalanx curvature (F [2, 122] = 7.54, p < 0.001), distal phalanx hyperextension (F [2, 122] = 28.90, p < 0.001), and dorsal osteophyte height (F [2, 122] = 13.64, p < 0.001). Significant correlations were found between distal phalanx curvature and distal phalanx hyperextension, as well as between distal phalanx hyperextension and dorsal osteophyte height. However, no significant correlation was observed between distal phalanx curvature and dorsal osteophyte height. Conclusions: The findings suggest that minimally invasive dorsal osteophyte surgery effectively restores the distal phalanx to normal conditions, as indicated by the variables studied. [ABSTRACT FROM AUTHOR]

Specific T cell responses against SARS-CoV-2 provided an overview of acquired immunity during the pandemic. Anti-SARS-CoV-2 immunity determines the severity of acute illness, but also might be related to the possible persistence of symptoms (long COVID). We retrospectively analyzed ex vivo longitudinal CD8+ T cell responses in 26 COVID-19 patients diagnosed with severe disease, initially (1 month) and long-term (10 months), and in a cohort of 32 vaccinated healthcare workers without previous SARS-CoV-2 infection. We used peptide-human leukocyte antigen (pHLA) dextramers recognizing 26 SARS-CoV-2-derived epitopes of viral and other non-structural proteins. Most patients responded to at least one of the peptides studied, mainly derived from non-structural ORF1ab proteins. After 10 months follow-up, CD8+ T cell responses were maintained at long term and reaction against certain epitopes (A*01:01-ORF1ab1637) was still detected and functional, showing a memory-like phenotype (CD127+ PD-1+). The total number of SARS-CoV-2-specific CD8+ T cells was significantly associated with protection against long COVID in these patients. Compared with vaccination, infected patients showed a less effective immune response to spike protein-derived peptides restricted by HLA. So, the A*01:01-S865 and A*24:02-S1208 dextramers were only recognized in vaccinated individuals. We conclude that initial SARS-CoV-2-specific CD8+ T cell response could be used as a marker to understand the evolution of severe disease and post-acute sequelae after SARS-CoV-2 infection. [ABSTRACT FROM AUTHOR]

Some evidence supports the influence of implant macro-design on primary stability. Additionally, tactile perception can be used to assess implant stability when placing the implant. This research aimed to quantify the primary and secondary stability of three implant systems with two different macro geometries (cylindrical and conical) determined based on the insertion torque and the implant stability quotient (ISQ) at the moment of implant placement as a function of implant-related factors (length, width, dental arch, and implant location in the arch), intraoperative factors (bone density determined subjectively by the clinician's tactile perception), and patient-related factors (age, gender, and bone density determined objectively based on cone beam computed tomography (CBCT). Methods: 102 implants from three implant systems with two different macro geometries (conical and cylindrical) were placed in 53 patients. The insertion torque, the ISQ at the implant placement (ISQ0), and the bone quality according to the clinician's tactile sensation were recorded on the day of the surgery. After a three-month healing period, the ISQ was re-evaluated (ISQ3). Results: The cylindrical implants exhibited significantly higher insertion torque and ISQ values at the moment of the surgery and after three months compared to the conical implants. The cylindrical implants also showed significantly lower indices of tactile evaluation of bone quality during the implant placement surgery. However, no differences were demonstrated in the bone density measured objectively using CBCT. (4) Conclusions: The cylindrical implants achieved the highest values for primary stability (Newtons × centimeter (Ncm) and ISQ) and secondary stability (ISQ after three months). The insertion torque was the variable that most influenced the ISQ on the day of the surgery. The implant location (incisors–canines, bicuspids–molars) and the implant macro geometry were the variables that most influenced the secondary stability (ISQ at three months). [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RIS) offer the potential to customize the radio propagation environment for wireless networks, and will be a key element for 6G communications. However, due to the unique constraints in these systems, the optimization problems associated to RIS configuration are challenging to solve. This paper illustrates a new approach to the RIS configuration problem, based on the use of artificial intelligence (AI) and deep learning (DL) algorithms. Concretely, a custom convolutional neural network (CNN) intended for edge computing is presented, and implementations on different representative edge devices are compared, including the use of commercial AI-oriented devices and a field-programmable gate array (FPGA) platform. This FPGA option provides the best performance, with × 20 performance increase over the closest FP32, GPU-accelerated option, and almost × 3 performance advantage when compared with the INT8-quantized, TPU-accelerated implementation. More noticeably, this is achieved even when high-level synthesis (HLS) tools are used and no custom accelerators are developed. At the same time, the inherent reconfigurability of FPGAs opens a new field for their use as enabler hardware in RIS applications. [ABSTRACT FROM AUTHOR]

Bronchiolitis is a viral respiratory infection, with respiratory syncytial virus (RSV) being the most frequent agent, requiring hospitalization in 1% of affected children. However, there continues to be a noteworthy incidence of antibiotic prescription in this setting, further exacerbating the global issue of antibiotic resistance. This study, conducted at Severo Ochoa Hospital in Madrid, Spain, focused on antibiotic usage in children under 2 years of age who were hospitalized for bronchiolitis between 2004 and 2022. In that time, 5438 children were admitted with acute respiratory infection, and 1715 infants (31.5%) with acute bronchiolitis were included. In total, 1470 (87%) had a positive viral identification (66% RSV, 32% HRV). Initially, antibiotics were prescribed to 13.4% of infants, but this percentage decreased to 7% during the COVID-19 pandemic thanks to adherence to guidelines and the implementation of rapid and precise viral diagnostic methods in the hospital. HBoV- and HAdV-infected children and those with viral coinfections were more likely to receive antibiotics in the univariate analysis. A multivariate logistic regression analysis revealed a statistically independent association between antibiotic prescription and fever > 38 °C (p < 0.001), abnormal chest-X ray (p < 0.001), ICU admission (p = 0.015), and serum CRP (p < 0.001). In conclusion, following guidelines and the availability of rapid and reliable viral diagnostic methods dramatically reduces the unnecessary use of antibiotics in infants with severe bronchiolitis. [ABSTRACT FROM AUTHOR]

Digital rock physics (DRP) has undergone significant advancements in the use of various imaging techniques to acquire three-dimensional volumes and images of rock samples for the computation of petrophysical properties. This study focuses on developing a DRP workflow using high-resolution thin section scans for computing porosity and permeability in cuttings samples. The workflow was tested on quarry sandstone plug samples and artificially generated pseudo-cuttings before applying it to real cuttings from oil and gas wells. The results show that the porosity and permeability values obtained through the DRP workflow are statistically equivalent to those obtained through conventional routine core analysis (RCAL). The workflow was also able to handle the presence of various lithologies in real cuttings samples. The study demonstrates the feasibility of obtaining porosity and permeability values in cutting samples using the DRP approach, offering a fast and cost-effective methodology that provides additional data and allows linking petrophysical properties to image data from the cuttings. [ABSTRACT FROM AUTHOR]

Objective: To describe the frequency of malnutrition in older patients with rheumatoid arthritis (RA) and investigate associated risk factors. Methods: This multicenter, cross-sectional study included participants aged ≥65 years who met the 2010 ACR/EULAR criteria for RA. Nutritional status was assessed using the Mini Nutritional Assessment Short Form (MNA-SF) and based on variables, such as albumin level, the Geriatric Nutritional Risk Index (GNRI), and vitamin D. Data were also collected on epidemiological variables, inflammatory disease activity, quality of life, physical function, and frailty. Multivariate models were used to study factors associated with nutritional status. Results: The study population comprised 76 RA patients aged ≥65 years, of whom 68.4% had a normal nutritional status, and 31.5% had an impaired nutritional status: 28.9% were at risk of malnutrition, and 2.6% were malnourished. Additionally, 10% had albumin levels <3.8 g/L. Patients with impaired nutritional status had poorer quality of life and physical function. The factors associated with compromised nutritional status (OR [95% CI]) were age (1.0 [1.0–1.1]; p = 0.035), DAS28-ESR (1.8 [1.0–3.2]; p = 0.024), and EuroQoL-5D-5L (0.9 [0.9–0.9]; p = 0.040). Furthermore, the GNRI was associated with the MNA score (0.06 [0.0–0.1]; p = 0.014). Conclusions: Approximately one-third of older patients with RA have impaired nutritional status. Older age, higher inflammatory disease activity, and decreased quality of life are associated with impaired nutritional status. The MNA and GNRI are valuable tools for assessing the nutritional status of patients with RA. [ABSTRACT FROM AUTHOR]

This paper proposes a procedure for the organization of meetings between leaders of research groups and representatives of R&D&I departments in companies that is inspired by speed dating. The participating candidates were first assessed and selected based on their potential for an effective linkage in accordance with certain relevant variables taken from the literature on efficiency in technology and knowledge transfer activities. The process and findings of the application of this procedure were analyzed through the action research methodology in a real case, where 12 research groups and 55 companies participated in a pilot project fostered by the public authorities. Although meetings represent a common formula to promote collaboration between universities and industry, very few academic papers have analyzed the appropriate procedures for the organization of such meetings, which could help organizers not only to achieve the best interaction possible but also to improve the matching between a research group and the companies that are potentially interested in forming a stable collaboration for sustainable transfer activities. The results show that this procedure allows research groups to efficiently achieve first contact with companies in order to assess the potential for more lasting and sustainable collaboration. [ABSTRACT FROM AUTHOR]

Objective: To describe the prevalence of sarcopenia in rheumatoid arthritis (RA) patients aged ≥65 years and identify the risk factors associated with sarcopenia. Methods: This is a multicenter, controlled, cross-sectional study of 76 RA patients and 76 age- and sex-matched healthy controls. Sarcopenia was defined according to the revised criteria of the European Working Group on Sarcopenia in Older People (EWGSOP2). Whole-body dual-energy X-ray absorptiometry (DXA) was performed. Binary regression was used to assess the relationship between sarcopenia and sex, age, duration of RA, Mini Nutritional Assessment (MNA) score, and Short Physical Performance Battery (SPPB) score in patients with RA. Results: Nearly 80% of participants were female, and the average age was >70 years. Patients with RA had lower muscle mass and greater adiposity (fat-to-muscle ratio mean [SD] 0.9 [0.2] vs. 0.8 [0.2]; p = 0.017) than controls, mainly in the central area (android/gynoid ratio, median [p25–p75]: 1.0 [0.9–1.2] vs. 0.9 [0.8–1.1]; p < 0.001). Twelve patients (15.8%) and three controls (3.9%) had confirmed sarcopenia (p = 0.014). Sarcopenic obesity was observed in 8/76 patients with RA (10.5%) and in 1/76 controls (1.3%) (p = 0.016). The factors associated with sarcopenia were male sex (OR [95% CI]: 9.3 [1.1–80.4]; p = 0.042), disease duration (OR [95% CI]: 1.1 [1.0–1.2]; p = 0.012), and nutritional status according to the MNA (OR [95% CI]: 0.7 [0.5–0.9]; p = 0.042). Conclusions: Our results suggest that patients with RA aged ≥65 years may be at increased risk for sarcopenia, adiposity, and malnutrition (especially male patients with long-standing disease) and have poor nutritional status. [ABSTRACT FROM AUTHOR]

Background: Remote diagnosis using collaborative tools have led to multilingual joint working sessions in various domains, including comprehensive health care, and resulting in more inclusive health care services. One of the main challenges is providing a real-time solution for shared documents and presentations on display to improve the efficacy of noninvasive, safe, and far-reaching collaborative models. Classic optical character recognition (OCR) solutions fail when there is a mixture of languages or dialects or in case of the participation of different technical levels and skills. Due to the risk of misunderstandings caused by mistranslations or lack of domain knowledge of the interpreters involved, the technological pipeline also needs artificial intelligence (AI)-supported improvements on the OCR side. This study examines the feasibility of machine learning-supported OCR in a multilingual environment. The novelty of our method is that it provides a solution not only for different speaking languages but also for a mixture of technological languages, using artificially created vocabulary and a custom training data generation approach. Methods: A novel hybrid language vocabulary creation method is utilized in the OCR training process in combination with convolutional recurrent neural networks (CRNNs) and a single visual model for scene text recognition within the patch-wise image tokenization framework (SVTR). Data: In the research, we used a dedicated Python-based data generator built on dedicated collaborative tool-based templates to cover and simulated the real-life variances of remote diagnosis and co-working collaborative sessions with high accuracy. The generated training datasets ranged from 66 k to 8.5 M in size. Twenty-one research results were analyzed. Instruments: Training was conducted by using tuned PaddleOCR with CRNN and SVTR modeling and a domain-specific, customized vocabulary. The Weight & Biases (WANDB) machine learning (ML) platform is used for experiment tracking, dataset versioning, and model evaluation. Based on the evaluations, the training dataset was adjusted by using a different language corpus or/and modifications applied to templates. Results: The machine learning models recognized the multilanguage/hybrid texts with high accuracy. The highest precision scores achieved are 90.25%, 91.35%, and 93.89%. Conclusions: machine learning models for special multilanguages, including languages with artificially made vocabulary, perform consistently with high accuracy. [ABSTRACT FROM AUTHOR]

Background: One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML). The novelty of this method is that it uses a 24 GHz Doppler radar sensor to detect vital signs such as the heartbeat and breathing without touching the person and to predict the intensity of physical activity, combined with the acceleration data from 3D accelerometers. Methods: This study is based on the data collected from professional athletes and freely available datasets created for research purposes. A combination of sensor data management was used: a medical radar sensor with no-contact remote sensing to measure the heart rate (HR) and 3D acceleration to measure the velocity of the activity. Various advanced ML methods and models were employed on the top of sensors to analyze the vital parameters and predict the health activity key performance indexes. three-axial acceleration, heart rate data, age, as well as activity level variances. Results: The ML models recognized the physical activity intensity and estimated the energy expenditure on a realistic level. Leave-one-out (LOO) cross-validation (CV), as well as out-of-sample testing (OST) methods, have been used to evaluate the level of accuracy in activity intensity prediction. The energy expenditure prediction with three-axial accelerometer sensors by using linear regression provided 97–99% accuracy on selected sports (cycling, running, and soccer). The ML-based RPE results using medical radar sensors on a time-series heart rate (HR) dataset varied between 90 and 96% accuracy. The expected level of accuracy was examined with different models. The average accuracy for all the models (RPE and METs) and setups was higher than 90%. Conclusions: The ML models that classify the rating of the perceived exertion and the metabolic equivalent of tasks perform consistently. [ABSTRACT FROM AUTHOR]

In a previous paper, the authors implemented a machine learning k-nearest neighbors (KNN) algorithm and Python libraries to create two 3D interactive models of the stratigraphic architecture of the Quaternary onshore Llobregat River Delta (NE Spain) for groundwater exploration purposes. The main limitation of this previous paper was its lack of routines for evaluating the confidence of the 3D models. Building from the previous paper, this paper refines the programming code and introduces an additional algorithm to evaluate the confidence of the KNN predictions. A variant of the Similarity Ratio method was used to quantify the KNN prediction confidence. This variant used weights that were inversely proportional to the distance between each grain-size class and the inferred point to work out a value that played the role of similarity. While the KNN algorithm and Python libraries demonstrated their efficacy for obtaining 3D models of the stratigraphic arrangement of sedimentary porous media, the KNN prediction confidence verified the certainty of the 3D models. In the Llobregat River Delta, the KNN prediction confidence at each prospecting depth was a function of the available data density at that depth. As expected, the KNN prediction confidence decreased according to the decreasing data density at lower depths. The obtained average-weighted confidence was in the 0.44−0.53 range for gravel bodies at prospecting depths in the 12.7−72.4 m b.s.l. range and was in the 0.42−0.55 range for coarse sand bodies at prospecting depths in the 4.6−83.9 m b.s.l. range. In a couple of cases, spurious average-weighted confidences of 0.29 in one gravel body and 0.30 in one coarse sand body were obtained. These figures were interpreted as the result of the quite different weights of neighbors from different grain-size classes at short distances. The KNN algorithm confidence has proven its suitability for identifying these anomalous results in the supposedly well-depurated grain-size database used in this study. The introduced KNN algorithm confidence quantifies the reliability of the 3D interactive models, which is a necessary stage to make decisions in economic and environmental geology. In the Llobregat River Delta, this quantification clearly improves groundwater exploration predictability. [ABSTRACT FROM AUTHOR]

A multi-technique approach combining petrographic, cathodoluminescence, and stable isotope analyses is commonly used in provenance studies of archaeological marbles. In the present paper, this characterization approach transcends provenance, and it is applied to the reconstruction of fragmented sculptures. The potential of this novel application of archaeometric measurements is illustrated with a case study consisting in 16 scattered marble fragments retrieved from a Roman villa (Els Munts) near Tarraco (presently Northeastern Spain). The samples were grouped taking into account their similarity in quantified parameters such as the cathodoluminescence color clusters and the stable carbon and oxygen isotopic ratios. The results permitted classification of the fragments into three groups corresponding to three different statues depicting Antinous (7 fragments), Minerva goddess (4 fragments), and an undetermined character (3 fragments). Two other fragments could not be ascribed to any particular statue. The archaeometric grouping provides arguments that can be used to confirm or refute archaeological hypotheses of statue reconstructions. [ABSTRACT FROM AUTHOR]

In recent years there has been an increase in the number of extreme weather events that lead to higher mortality, such as heat waves. This study carries out a new investigation that integrates the environmental quality parameters—the Surface Urban Heat Island (SUHI) and the Terrestrial Surface Temperature (LST)—during these periods of high temperatures and compares them with normal periods. The study of the relationship between these variables will allow improving the quality of life through new mitigation measures that will minimize the effects of climate change in urban areas. This study analyzes eight cities in the south of Spain (Andalusia) to assess environmental quality through gases SO2, NO2, CO, O3 and aerosols, obtained through Sentinel-5P satellite images, and the LST and SUHI obtained through Sentinel-3 images. Next, the results of periods of heat waves are compared with periods of normal environmental conditions during the summers of the years 2020 and 2021. The objective is to determine the possible impact of heat waves on environmental quality, as well as on the LST and SUHI of the investigated cities, which are located in an area identified as highly vulnerable to the effects of global warming. During the period of the heat wave and compared to the periods without a heat wave, a variety of environmental pollutants was found: SO2 (+165%), NO2 (+24%), CO (+8%), O3 (−4%) and aerosols (+193%). Both the LST and the SUHI suffered an average increase of 2.8 K. The results of this document can help to establish pollutant reduction mechanisms in periods prior to heat waves. This could minimize major effects on the population and provide sustainable development. [ABSTRACT FROM AUTHOR]

Foreland fold and thrust belts always represent a complex diagenetic history of carbonate succession, particularly multiphase dolomitization, due to the multi-sourcing nature of fluids affecting syn-to post-depositional successions. The present work documents a comprehensive study on the diagenetic changes, particularly dolomitization patterns in the Jurassic carbonates (Samana Suk Fm) in the Lesser Himalayan fold and thrust belt, NW Pakistan. To better understand the processes involved, integrated field/petrographic, geochemical, isotopic, and micro-thermometric studies were carried out. Field observations indicate that dolostones appear as light grey to brown stratabound and patchy units within the formation. Petrographic analysis reveals that fabric destructive matrix dolomite (RD-I) and fabric preserving replacive dolomite (RD-II) phases are present as distinct units. In addition, saddle dolomite cement (SD) and fracture-filling calcite (CC) are also observed in association with replacive dolomite cement. Geochemical analysis (EPMA) showed that Fe, Mn, and Ba concentrations in matrix dolomite are relatively less than those of replacive/saddle dolomite and fracture-filling calcite, suggesting a hydrothermal source of replacive/saddle dolomite in reducing conditions. Furthermore, stable isotope studies of RD-I showed non-depleted δ18O values, which represent coeval seawater signatures of Jurassic carbonates. RD shows depleted δ18O values and non-depleted δ13C, respectively, indicating burial or elevated temperature of dolomitization. Fracture-filled calcite represents lighter δ18O values and δ13C, indicative of relatively high temperatures. 87Sr/86Sr ratios of all diagenetic phases range from 0.707718 to 0.710747, showing more radiogenic values indicates interaction fluids with more radiogenic sources. Fluid inclusion micro-thermometry data of saddle dolomite shows TH ranging from 102.8 to 186 °C, and salinity ranging from 11.7 to 19.4 eq. wt.% NaCl, suggesting hot saline brines are responsible for the dolomitization. Fracture-filling calcite shows TH ranging from 68.0 to 98.4 °C and salinity ranging from 6.9 to 13.1 eq. wt.%. NaCl suggests moderately hot and saline solutions are responsible for their formation. In conclusion, the above-mentioned studies indicate two distinct processes of dolomitization are involved in the formation of matrix and replacive/saddle-type dolomites. RD-I has formed in the evaporative setting, whereas RD has formed due to the interaction of hydrothermal fluids during burial. Fracture-filling calcite is produced from hot subsurface solutions during uplift related to the Himalayan orogeny. [ABSTRACT FROM AUTHOR]

The evident implication of the insulin-degrading enzyme (IDE) in Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM), among its capacity to degrade insulin and amyloid-β peptide (Aβ), suggests that IDE could be an essential link in the relation between hyperinsulinemia, insulin resistance and AD. However, little is known about the cellular and molecular regulation of IDE expression, and even less has been explored regarding the post-transcriptional regulation of IDE, although it represents a great molecular target of interest for therapeutic treatments. We recently described that miR-7, a novel candidate for linking AD and T2DM at the molecular level, regulates IDE and other key genes in both pathologies, including some key genes involved in the insulin signaling pathway. Here, we explored whether other miRNAs as well as other post-transcriptional regulators, such as RNA binding proteins (RBP), could potentially participate in the regulation of IDE expression in vitro. Our data showed that in addition to miR-7, miR-125, miR-490 and miR-199 regulate IDE expression at the post-transcriptional level. Moreover, we also found that IDE contains multiple potential binding sites for several RBPs, and a narrow-down prediction analysis led us to speculate on a novel regulation of IDE by RALY and HuD. Taken together, these results demonstrate the novel players controlling IDE expression that could represent potential therapeutical targets to treat several metabolic diseases with a high impact on human health, including AD and T2DM. [ABSTRACT FROM AUTHOR]

The k-nearest neighbors (KNN) algorithm is a non-parametric supervised machine learning classifier; which uses proximity and similarity to make classifications or predictions about the grouping of an individual data point. This ability makes the KNN algorithm ideal for classifying datasets of geological variables and parameters prior to 3D visualization. This paper introduces a machine learning KNN algorithm and Python libraries for visualizing the 3D stratigraphic architecture of sedimentary porous media in the Quaternary onshore Llobregat River Delta (LRD) in northeastern Spain. A first HTML model showed a consecutive 5 m-equispaced set of horizontal sections of the granulometry classes created with the KNN algorithm from 0 to 120 m below sea level in the onshore LRD. A second HTML model showed the 3D mapping of the main Quaternary gravel and coarse sand sedimentary bodies (lithosomes) and the basement (Pliocene and older rocks) top surface created with Python libraries. These results reproduce well the complex sedimentary structure of the LRD reported in recent scientific publications and proves the suitability of the KNN algorithm and Python libraries for visualizing the 3D stratigraphic structure of sedimentary porous media, which is a crucial stage in making decisions in different environmental and economic geology disciplines. [ABSTRACT FROM AUTHOR]

This paper introduces a Python application for visualizing the 3D stratigraphic architecture of porous sedimentary media. The application uses the parameter granulometry deduced from borehole lithological records to create interactive 3D HTML models of essential stratigraphic elements. On the basis of the high density of boreholes and the subsequent geological knowledge gained during the last six decades, the Quaternary onshore Llobregat River Delta (LRD) in northeastern Spain was selected to show the application. The public granulometry dataset produced by the Water Authority of Catalonia from 433 boreholes in this strategic coastal groundwater body was clustered into the clay–silt, coarse sand, and gravel classes. Three interactive 3D HTML models were created. The first shows the location of the boreholes granulometry. The second includes the main gravel and coarse sand sedimentary bodies (lithosomes) associated with the identified three stratigraphic intervals, called lower (>50 m b.s.l.) in the distal LRD sector, middle (20–50 m b.s.l.) in the central LRD, and upper (<20 m b.s.l.) spread over the entire LRD. The third deals with the basement (Pliocene and older rocks) top surface, which shows an overall steeped shape deepening toward the marine platform and local horsts, probably due to faulting. The modeled stratigraphic elements match well with the sedimentary structures reported in recent scientific publications. This proves the good performance of this incipient Python application for visualizing the 3D stratigraphic architecture, which is a crucial stage for groundwater management and governance. [ABSTRACT FROM AUTHOR]

Real-time multilingual phrase detection from/during online video presentations—to support instant remote diagnostics—requires near real-time visual (textual) object detection and preprocessing for further analysis. Connecting remote specialists and sharing specific ideas is most effective using the native language. The main objective of this paper is to analyze and propose—through DEtection TRansformer (DETR) models, architectures, hyperparameters—recommendation, and specific procedures with simplified methods to achieve reasonable accuracy to support real-time textual object detection for further analysis. The development of real-time video conference translation based on artificial intelligence supported solutions has a relevant impact in the health sector, especially on clinical practice via better video consultation (VC) or remote diagnosis. The importance of this development was augmented by the COVID-19 pandemic. The challenge of this topic is connected to the variety of languages and dialects that the involved specialists speak and that usually needs human translator proxies which can be substituted by AI-enabled technological pipelines. The sensitivity of visual textual element localization is directly connected to complexity, quality, and the variety of collected training data sets. In this research, we investigated the DETR model with several variations. The research highlights the differences of the most prominent real-time object detectors: YOLO4, DETR, and Detectron2, and brings AI-based novelty to collaborative solutions combined with OCR. The performance of the procedures was evaluated through two research phases: a 248/512 (Phase1/Phase2) record train data set, with a 55/110 set of validated data instances for 7/10 application categories and 3/3 object categories, using the same object categories for annotation. The achieved score breaks the expected values in terms of visual text detection scope, giving high detection accuracy of textual data, the mean average precision ranging from 0.4 to 0.65. [ABSTRACT FROM AUTHOR]

The study's purpose was to assess the reliability of the LIS2DH12 in two different positions, using the commercial sensor Actigraph GT9X as a reference instrument. Five participants completed two gait tests on a treadmill. Firstly, both sensors were worn on the wrist and around the thigh. Each test consisted of a 1 min walk for participants to become accustomed to the treadmill, followed by a 2 min trial at ten pre-set speeds. Data from both sensors were collected in real-time. Intraclass correlation coefficient (ICC) was used to evaluate the equality of characteristics obtained by both sensors: maximum peaks, minimum peaks, and the mean of the complete signal (sequence of acceleration values along the time) by each axis and speed were extracted to evaluate the equality of characteristics obtained with LIS2DH12 compared to Actigraph. Intraclass correlation coefficient (ICC) was extracted, and a standard deviation of the mean was obtained from the data. Our results show that LIS2DH12 measurements present more reliability than Actigraph GT9X, ICC > 0.8 at three axes. This study concludes that LIS2DH12 is as reliable and accurate as Actigraph GT9X Link and, therefore, would be a suitable tool for future kinematic studies. [ABSTRACT FROM AUTHOR]

Glioma stem cells (GSCs) are critical targets for glioma therapy. SOX9 is a transcription factor with critical roles during neurodevelopment, particularly within neural stem cells. Previous studies showed that high levels of SOX9 are associated with poor glioma patient survival. SOX9 knockdown impairs GSCs proliferation, confirming its potential as a target for glioma therapy. In this study, we characterized the function of SOX9 directly in patient-derived glioma stem cells. Notably, transcriptome analysis of GSCs with SOX9 knockdown revealed STAT3 and PML as downstream targets. Functional studies demonstrated that SOX9, STAT3, and PML form a regulatory loop that is key for GSC activity and self-renewal. Analysis of glioma clinical biopsies confirmed a positive correlation between SOX9/STAT3/PML and poor patient survival among the cases with the highest SOX9 expression levels. Importantly, direct STAT3 or PML inhibitors reduced the expression of SOX9, STAT3, and PML proteins, which significantly reduced GSCs tumorigenicity. In summary, our study reveals a novel role for SOX9 upstream of STAT3, as a GSC pathway regulator, and presents pharmacological inhibitors of the signaling cascade. [ABSTRACT FROM AUTHOR]

Shoulder kinematics is a measure of interest in the clinical setting for diagnosis, evaluating treatment, and quantifying possible changes. The aim was to compare shoulder scaption kinematics between symptomatic and asymptomatic subjects by inertial sensors. Methods: Scaption kinematics of 27 subjects with shoulder symptomatology and 16 asymptomatic subjects were evaluated using four inertial sensors placed on the humerus, scapula, forearm, and sternum. Mobility, velocity, and acceleration were obtained from each sensor and the vector norm was calculated from the three spatial axis (x,y,Z). Shoulder function was measured by Upper Limb Functional Index and Disabilities of the Arm, Shoulder, and Hand questionnaires. One way ANOVA was calculated to test differences between the two groups. Effect size was calculated by Cohen's d with 95% coefficient Intervals. Pearson's correlation analysis was performed between the vector norms humerus and scapula kinematics against DASH and ULFI results in symptomatic subjects. Results: The asymptomatic group showed higher kinematic values, especially in the humerus and forearm. Symptomatic subjects showed significantly lower values of mobility for scapular protraction-retraction (Cohen's d 2.654 (1.819–3.489) and anteriorisation-posteriorisation (Cohen's d 1.195 (0.527–1.863). Values were also lower in symptomatic subjects for velocity in all scapular planes of motion. Negative correlation showed that subjects with higher scores in ULFI or DASH had lower kinematics values. Conclusion: Asymptomatic subjects tend to present greater kinematics in terms of mobility, velocity, and linear acceleration of the upper limb, and lower humerus and scapula kinematics in symptomatic subjects is associated with lower levels of function. [ABSTRACT FROM AUTHOR]

Through this study, we developed and validated a system for energy expenditure calculation, which only requires low-cost inertial sensors and open source R software. Five healthy subjects ran at ten different speeds while their kinematic variables were recorded on the thigh and wrist. Two ActiGraph wireless inertial sensors and a low-cost Bluetooth-based inertial sensor (Lis2DH12), assembled by SensorID, were used. Ten energy expenditure equations were automatically calculated in a developed open source R software (our own creation). A correlation analysis was used to compare the results of the energy expenditure equations. A high interclass correlation coefficient of estimated energy expenditure on the thigh and wrist was observed with an Actigraph and Sensor ID accelerometer; the corrected Freedson equation showed the highest values, and the Santos-Lozano vector magnitude equation and Sasaki equation demonstrated the lowest one. Energy expenditure was compared between the wrist and thigh and showed low correlation values. Despite the positive results obtained, it was necessary to design specific equations for the estimation of energy expenditure measured with inertial sensors on the thigh. The use of the same formula equation in two different placements did not report a positive interclass correlation coefficient. [ABSTRACT FROM AUTHOR]