Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty Measurements in Non-Contact Neonatal Heart Rate Monitoring

Continuously monitoring patient vital signs in the neonatal intensive care unit (NICU) requires wired sensors that can irritate fragile skin, motivating the development of non-contact physiologic signal estimation approaches. Such estimators typically involve a pipeline of multiple data analysis stages, including pre-processing, region of interest detection and tracking, and physiologic parameter estimation. Uncertainty in the estimated physiologic signal is often quantified strictly from the signal quality indicator (SQI) generated by the final stage of the pipeline. This manuscript proposes a framework to account for SQIs generated by each stage in a physiologic signal estimation pipeline and using the fusion of all SQIs to arrive at a refined measure of uncertainty in the final estimated physiologic parameter. This framework is demonstrated for heart rate (HR) estimation of newborns admitted in the NICU, where the pipeline includes bed occupancy detection, face detection, face tracking, and physiologic signal estimation. Different SQIs are derived at each stage and a novel fusion of all SQI metrics is shown to produce a more effective estimate of uncertainty in the final estimated HR.
Evaluation of Contralateral Limb Cross Education and High-Frequency Repetitive Transcranial Magnetic Stimulation on Functional Indices of the Affected Upper Limb in Subacute Phase of Stroke

Background Stroke is one of the causes of long-term morbidity. Despite rehabilitation strategies, most survivors live with motor deficits in the upper limbs. Objectives The aim of the study was to compare the effect of contralateral cross education (CE) and high-frequency repetitive magnetic stimulation (HF-rTMS) on the function of upper extremity in subacute phase of stroke. Methods Forty patients were randomly assigned into 4 groups. Group “A” received physical therapy (PT) for 10 sessions, 3 times per week. Group “B” received PT and HF-rTMS as follows: stimulation of 20 Hz for 5 s, intertrain interval for 50 s, 20 trains, 2000 pulses at 90% resting motor threshold, and conventional PT. Group “C” was treated with CE and PT. In group “D,” HF-rTMS, CE, and PT were administered. Results Significant differences were found in the Fugl-Meyer scale between “A” and “C” (P = 0.01), “A” and “D” (P = 0.02), and “B” and “C” groups (P = 0.01). In the box-block test, there were significant differences between “A” and “B” (P = 0.01), “A” and “C” (P < 0.001), “B” and “D” (P = 0.001), and “B” and “C” groups (P = 0.01). Statistical differences were observed in grip strength between “A” and “B” (P = 0.01) and “A” and “C” groups (P = 0.02). Conclusions It is suggested that clinicians select the therapeutic methods in line with their expected goal. When the goal is to improve upper extremity function, CE+PT could be more effective than HF-rTMS+PT. Also, CE+PT and HF-rTMS+PT were more effective than CE+HF-rTMS+PT at improving grip strength. Therefore, combining several methods would not always lead to better results.
How administrative AI applications enhance organizational innovation and quality of work life for disabled employees: a case study of a Saudi University

Objectives: This study examines the impact of artificial intelligence (AI) administrative applications on the relationship between organizational innovation and quality of life for persons with disabilities. Methods/Approach: The researchers will use a descriptive-analytical approach to achieve this goal. The study population consists of employees at the Northern Border University in Saudi Arabia
Prediction of Antimicrobial Efficacy of Direct Cold Atmospheric Plasma Treatment via Ensemble Learning-based Regression Models

Cold atmospheric plasma (CAP) has found successful applications in diverse fields like biological decontamination, altering surface properties, and medical treatments. Its notable effectiveness against microorganisms underscores its established utility. Nevertheless, challenges persist, exemplified by the difficulty in comparing results from different research laboratories due to variations in CAP treatment conditions. In this context, the employment of machine learning (ML) algorithms presents a pivotal strategy for addressing these challenges. This study’s objective is to assess antimicrobial effectiveness using distinct parameters for direct CAP treatment, coupled with a range of ML algorithms. Constructing an original dataset from existing literature, we trained and evaluated this dataset using ensemble learning (EL) based regression models. Our analysis highlights the Bagged Tree approach as optimal, displaying a root mean square error (RMSE) of 1.291 and the coefficient of determination (R2) value of 0.72. Employing this approach facilitates swifter and more resource-efficient outcomes by curbing the time and resources consumed by laboratory experiments. Furthermore, it sheds light on the paramount parameters influencing microbial inactivation, thereby enhancing the efficiency of CAP applications. Employing ML techniques to analyze CAP’s antimicrobial effects under diverse conditions may obviate the necessity for redundant laboratory experiments, ensuring the acquisition of dependable outcomes.
A Comprehensive Look at In Vitro Angiogenesis Image Analysis Software

One of the complex challenges faced presently by tissue engineering (TE) is the development of vascularized constructs that accurately mimic the extracellular matrix (ECM) of native tissue in which they are inserted to promote vessel growth and, consequently, wound healing and tissue regeneration. TE technique is characterized by several stages, starting from the choice of cell culture and the more appropriate scaffold material that can adequately support and supply them with the necessary biological cues for microvessel development. The next step is to analyze the attained microvasculature, which is reliant on the available labeling and microscopy techniques to visualize the network, as well as metrics employed to characterize it. These are usually attained with the use of software, which has been cited in several works, although no clear standard procedure has been observed to promote the reproduction of the cell response analysis. The present review analyzes not only the various steps previously described in terms of the current standards for evaluation, but also surveys some of the available metrics and software used to quantify networks, along with the detection of analysis limitations and future improvements that could lead to considerable progress for angiogenesis evaluation and application in TE research.
Upscale Synthesis of Magnetic Mesoporous Silica Nanoparticles and Application to Metal Ion Separation: Nanosafety Evaluation

The synthesis of core–shell magnetic mesoporous nanoparticles (MMSNs) through a phase transfer process is usually performed at the 100–250 mg scale. At the gram scale, nanoparticles without cores or with multicore systems are observed. Iron oxide core nanoparticles (IO) were synthesized through a thermal decomposition procedure of α-FeO(OH) in oleic acid. A phase transfer from chloroform to water was then performed in order to wrap the IO nanoparticles with a mesoporous silica shell through the sol–gel procedure. MMSNs were then functionalized with DTPA (diethylenetriaminepentacetic acid) and used for the separation of metal ions. Their toxicity was evaluated. The phase transfer procedure was crucial to obtaining MMSNs on a large scale. Three synthesis parameters were rigorously controlled: temperature, time and glassware. The homogeneous dispersion of MMSNs on the gram scale was successfully obtained. After functionalization with DTPA, the MMSN-DTPAs were shown to have a strong affinity for Ni ions. Furthermore, toxicity was evaluated in cells, zebrafish and seahorse cell metabolic assays, and the nanoparticles were found to be nontoxic. We developed a method of preparing MMSNs at the gram scale. After functionalization with DTPA, the nanoparticles were efficient in metal ion removal and separation; furthermore, no toxicity was noticed up to 125 µg mL−1 in zebrafish.
A Deep Learning-based Fine-tuned Convolutional Neural Network Model for Plant Leaf Disease Detection

The rapid proliferation of plant diseases poses a grave threat to global food security and agricultural productivity. To effectively address these challenges and ensure sustainable agricultural practices, the timely and accurate identification of diseases becomes paramount. Over recent years, deep learning techniques namely Convolutional Neural Networks (CNNs), have emerged as a pivotal tool with the potential to revolutionize plant disease identification by performing effective feature extraction. This study focuses on the development of a CNN model for automated plant disease identification. The dataset for the CNN implementation has been collected from Kaggel, which contains 32 varieties of plant leaf disease including normal leaves. The proposed model contains 13 different convolutional, 4 max pooling, 1 flattening and 1 dense layer for performance identification. This model has been implemented in four different scenarios by applying the model to the complete dataset with 5, 10, 15, and 20 epoch values. The results depict that the proposed model has shown the highest accuracy of 98.70% at epoch 20 while 97.87%, 95.92%, and 87.09% accuracies have resulted at epoch values 15, 10, and 5, respectively. The effectiveness of this model has been also compared with existing work which resulted in the proposed model achieving the highest accuracy.
Influenza virus immune imprinting dictates the clinical outcomes in ferrets challenged with highly pathogenic avian influenza virus H5N1

Zoonotic transmission of H5N1 highly pathogenic avian influenza virus (HPAIV) into the human population is an increasing global threat. The recent 2022 HPAIV outbreak significantly highlighted this possibility, increasing concern in the general population. The clinical outcomes of H5N1 influenza virus exposure can be determined by an individual’s primary influenza virus infection (imprinting) or vaccination status. Immunological imprinting with Group 1 - (H1N1, H2N2, and H2N3) increases survival rates following H5N1 viral infection compared to Group 2 - (H3N2) imprinted individuals. Vaccination against H5N1 influenza viruses can offer protection to at-risk populations; however, stockpiled inactivated H5N1 influenza vaccines are not readily available to the public. We hypothesize that the immunological response to vaccination and subsequent clinical outcome following H5N1 influenza virus infection is correlated with the immunological imprinting status of an individual. To test this hypothesis, our lab established a ferret pre-immune model of disease. Naïve ferrets were intranasally inoculated with seasonal influenza viruses and allowed to recover for 84 days prior to H5N1 virus infection. Ferrets imprinted following H1N1 and H2N3 virus infections were completely protected against lethal H5N1 influenza virus challenge (100% survival), with few to no clinical symptoms. In comparison, H3N2 influenza virus-imprinted ferrets had severe clinical symptoms, delayed disease progression, and a sublethal phenotype (40% mortality). Consecutive infections with H1N1 influenza viruses followed by an H3N2 influenza virus infection did not abrogate the immune protection induced by the original H1N1 influenza virus infection. In addition, ferrets consecutively infected with H1N1 and H2N3 viruses had no clinical symptoms or weight loss. H3N2 pre-immune ferrets were vaccinated with a broadly reactive H5 HA-based or H1 NA-based vaccine (Hu-CO 2). These ferrets were protected against H5N1 influenza virus challenge, whereas ferrets vaccinated with the H1N1 wild-type CA/09 rHA vaccine had similar phenotypes as non-vaccinated H3N2-imprinted ferrets with 40% survival. Overall, Group 2 imprinted ferrets, which were vaccinated with heterologous Group 1 HA vaccines, had redirected immune responses to Group 1 influenza viral antigens and rescued a sublethal phenotype to complete protection.
Day one postoperative MRI findings following electrode placement for deep brain stimulation: analysis of a large case series

Objective This study sought to characterize postoperative day one MRI findings in deep brain stimulation (DBS) patients. Methods DBS patients were identified by CPT and had their reviewed by a trained neuroradiologist and neurosurgeon blinded to MR sequence and patient information. The radiographic abnormalities of interest were track microhemorrhage, pneumocephalus, hematomas, and edema, and the occurrence of these findings in compare the detection of these complications between T1/T2 gradient-echo (GRE) and T1/T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) sequences was compared. The presence, size, and association of susceptibility artifact with other radiographic abnormalities was also described. Lastly, the association of multiple microelectrode cannula passes with each radiographic finding was evaluated. Ad-hoc investigation evaluated hemisphere-specific associations. Multiple logistic regression with Bonferroni correction (corrected p = 0.006) was used for all analysis. Results Out of 198 DBS patients reviewed, 115 (58%) patients showed entry microhemorrhage; 77 (39%) track microhemorrhage; 44 (22%) edema; 69 (35%) pneumocephalus; and 12 (6%) intracranial hematoma. T2 GRE was better for detecting microhemorrhage (OR = 14.82, p < 0.0001 for entry site and OR = 4.03, p < 0.0001 for track) and pneumocephalus (OR = 11.86, p < 0.0001), while T2 FLAIR was better at detecting edema (OR = 123.6, p < 0.0001). The relatively common findings of microhemorrhage and edema were best visualized by T2 GRE and T2 FLAIR sequences, respectively. More passes intraoperatively was associated with detection of ipsilateral track microhemorrhage (OR = 7.151, p < 0.0001 left; OR = 8.953, p < 0.0001 right). Susceptibility artifact surrounding electrodes possibly interfered with further detection of ipsilateral edema (OR = 4.323, p = 0.0025 left hemisphere only). Discussion Day one postoperative magnetic resonance imaging (MRI) for DBS patients can be used to detect numerous radiographic abnormalities not identifiable on a computed tomographic (CT) scan. For this cohort, multiple stimulating cannula passes intraoperatively was associated with increased microhemorrhage along the electrode track. Further studies should be performed to evaluate the clinical relevance of these observations.
Herramienta de soporte para el análisis de nitrogenado de cultivos

La agricultura actualmente esta tecnificandose con el objetivo de aumentar el rendimiento. Hoy en dia, los sistemas de deteccion remota se utilizan para conocer distintos indicadores del cultivo (vigor, estres hidrico, estimacion de cosecha) y se obtienen imagenes aereas multiespectrales que, debidamente procesadas, caracterizan los cultivos. Este proyecto se centra, en el desarrollo de una herramienta para estudiar la validez de los indices espectrales para determinar diferencias de fertilizacion en las diferentes regiones de un cultivo. A partir del estudio del estado del arte y de conversaciones con expertos en el area, se ha disenado e implementado una herramienta para evaluar regiones de un cultivo a traves de indices espectrales. La herramienta permite, de manera sencilla y autonoma, detectar diferencias entre regiones de un cultivo a traves de la aplicacion de distintos indices espectrales.
Geochemistry of Proterozoic Delhi Quartzites: Implications for the Provenance and Source Area Weathering

Mineralogy and geochemistry, including REE, of the Proterozoic Delhi quartzites have been studied to understand the processes and provenance involved in the origin of supermature quartzites. Post Archaean Australian Shale (PAAS) like chondrite normalised REE patterns, including negative Eu anomalies but with low abundance, show that the source for the quartzites was dominanted by evolved granitic rocks. High weathering indices (CIA) and strong negative Eu anomaly suggest that the source area had gone through intensive chemical weathering. We infer that the PAAS like pattern for the quartzites was inherited from the granitic source rocks through quantitative transfer of REE bearing heavy minerals. These heavy minerals (-0.7%) supplied nearly 60% of the total REE. The quantitative transfer of heavy minerals and conservation of REE patterns with respect to the source are likely due to the lack of any selective sorting of heavy minerals indicating a very low degree of recycling. One possible explanation for this type of transfer is the formation of mature weathering profiles. Mature weathering profiles on the stable craton could give rise to mature upper silica rich horizons and lower horizons rich in fine-grained clays due to illuviation and downward transport. The upper horizons could have acted as source for the quartzose sands and resistant REE bearing heavy minerals for the quartzites. The lower horizons could become a likely source for the argillaceous material overlying the quartzites during subsequent sedimentation.
Introduction of the Comet assay for the evaluation of oxidative stress, antioxidant effects and environmental pollutants effects on animal cells

DNA damage within single mammalian cells can be one of the earliest signs of a whole range of health problems, including disease, diet and exposure to occupational or environmental toxins. The Comet assay is a relatively simple and inexpensive method for measuring this damage. It works by determining the number of breaks in the strands of DNA within the cell. Cells are embedded in agarose gel on a microscopic slide and washed to remove the cell membranes, soluble cell contents and histones from the nucleus. An electric field is then activated across and the loops of damaged DNA are pulled towards the anode. Epifluorescence microscopy of stained gels shows the image which gives the techique its name: a clump of undamaged DNA (the head) with the loops pulled away, forming a tail of a comet. We report herein the checking of the Comet assay for the evaluation of oxidative stress on chicken, mouse and pig blood cells, pig sperm cells and chicken hepatocytes. We intend to use the Comet assay as a quantitative method in some nutritional experiments with domestic animals including oxidative food components and antioxidants and for the evaluation of some environmental pollutants effects on animal and human cells.
Effect of Compost and Bovine Dry Manure on Soil Chemical Characteristics and Production of iceberg Lettuce ( Lactuca Sativa L.) Alfonso De Luna-Jiménez a* , José Luis Arredondo-Figueroa, Jorge Ramón Rocha-Ruiz a and José de Jesús Luna-Ruiz a

Lettuce is one of the most important vegetables consumed worldwide. In recent years, lettuce production has greatly increased, and new interest has surged in their cultivation. The use of organic fertilizers in lettuce culture has been demonstrated to augment growth rate, increase yields and improve plant quality. The main goal of this research was to evaluate the effect of application of compost amended with E. densa and dry bovine manure in different combinations on the soil chemistry and the productivity of Iceberg lettuce (L. sativa). An experimental randomized block design with five treatments and four repetitions was carried out. The treatments were T1S=100% soil, T2SC=50% soil+ 50% compost, T3SCM=50% soil + 25% compost + 25% dry bovine manure, T4CM=50% compost + 50% manure and T5M =100% manure. The plants were grown inside an experimental greenhouse in 20 plastic pots, each with 2 kg of capacity. The experiment lasted 53 days from the transplant of planters to final harvest. At the end of the experimental period, a 1 kg sample of each experimental unit was removed for soil chemistry analysis. Additionally, production was evaluated throughout the experiment. The results indicated that the soil chemical parameters were significantly different (P<0.05), especially in the treatments that included organic fertilizers, which enhanced soil quality by increasing CIC, raising pH, and increasing the EC, OM, inorganic N, P, K, Ca, Mg, Zn, Fe, Mn, Cu, B and Na contents. Treatments T1S, T3SCM and T5M resulted in the highest Iceberg lettuce production and reached an acceptable production of fresh weight, thereby confirming the importance of incorporating these fertilizers to increase yields. Moreover, a direct relation between the number of leaves (NL) and foliar area (FA) was observed with respect to lettuce fresh weight (FW). Consequently, the use of both dry bovine manure mixed with soil and a combination of soil, compost and manure can be recommended for the culture of Iceberg lettuce.
Primary Incidence of Hepatitis C Virus Infection Among HIV-Infected Men Who Have Sex With Men in San Diego, 2000–2015

Abstract Background Little is known about the hepatitis C virus (HCV) epidemic among HIV-infected men who have sex with men (HIV+ MSM) in the United States. In this study, we aimed to determine the incidence of primary HCV infection among HIV+ MSM in San Diego, California. Methods We performed a retrospective cohort analysis of HCV infection among HIV+ MSM attending 2 of the largest HIV clinics in San Diego. Incident HCV infection was assessed among HIV+ MSM with a negative anti-HCV test and subsequent HCV test between 2000 and 2017, with data censored to 2015. HCV reinfection was assessed among HIV+ MSM successfully treated for HCV between 2008 and 2015. Infection/reinfection rates were calculated using person-time methods. Results Among 3068 initially HCV-seronegative HIV+ MSM, 178 new infections occurred over 15 796 person-years, giving an incidence of 1.13 per 100 person-years (/100py; 95% confidence interval [CI], 0.97–1.31). Incidence was stable from 2000 to 2014 (0.83/100py; 95% CI, 0.41–1.48), with an increase to 3.01/100py (95% CI, 1.97–4.42) in 2015 (P = .02). Among 43 successfully treated patients, 3 were reinfected. Conclusions HCV incidence is high among HIV+ MSM in San Diego, with evidence suggesting a recent increase in 2015. Strong HCV testing guidelines and active prevention efforts among HIV+ MSM are urgently needed that include rapid diagnosis, treatment, and risk reduction.
Practical Identifiability of Plant Growth Models: A Unifying Framework and Its Specification for Three Local Indices

Amid the rise of machine learning models, a substantial portion of plant growth models remains mechanistic, seeking to capture an in-depth understanding of the underlying phenomena governing the system’s dynamics. The development of these models typically involves parameter estimation from experimental data. Ensuring that the estimated parameters align closely with their respective “true” values is crucial since they hold biological interpretation, leading to the challenge of uniqueness in the solutions. Structural identifiability analysis addresses this issue under the assumption of perfect observations of system dynamics, whereas practical identifiability considers limited measurements and the accompanying noise. In the literature, definitions for structural identifiability vary only slightly among authors, whereas the concept and quantification of practical identifiability lack consensus, with several indices coexisting. In this work, we provide a unified framework for studying identifiability, accommodating different definitions that need to be instantiated depending on each application case. In a more applicative second step, we focus on three widely used methods for quantifying practical identifiability: collinearity indices, profile likelihood, and average relative error. We show the limitations of their local versions, and we propose a new risk index built on the profile likelihood-based confidence intervals. We illustrate the usefulness of these concepts for plant growth modeling using a discrete-time individual plant growth model, LNAS, and a continuous-time plant population epidemics model. Through this work, we aim to underline the significance of identifiability analysis as a complement to any parameter estimation study and offer guidance to the modeler.