Quantitative Information Research Articles

Characterization of Proton Exchange in Protonic Ceramic Electrochemical Cells Using Operando Drifts

The protonic ceramic electrochemical cells (PCECs) have attracted tremendous attention for their fast hydrogen production at intermediate temperatures. This work applied in situ Diffuse reflectance infrared Fourier transform infrared spectroscopy (DRIFTS) together with electrochemical impedance spectroscopy (EIS), to characterizes transport and hydration properties of the state-of-the-art electrolyte materials. The adsorption of water molecules and formation of surface hydroxyl groups on the electrolyte surface as a function of temperature, steam concentration, and electrical potential were systematically investigated by DRIFTS under operando conditions. The quantitative information on the concentration of hydroxyl species on the electrolyte materials before and after exposure to steam were obtained. The determination of OH intensity leads to the direct determination of the proton exchange coefficient k, at different steam concentrations and biases. The results are unique and should be able to provide valuable insights into the mechanisms of proton formation, migration, and evolution under various operating conditions, which is critical to design better electrolyte compositions and optimize the operation conditions for next-generation PCECs.

(Invited) Quantitative Photonic Characterization and Modelling of Light Emission, Confinement, and Enhancement in Nanostructured Materials

Optical spectroscopy and imaging provide non-destructive, contactless, and fast strategies to study the nature and technological aspects of thin films and nanostructured materials for photonic and optoelectronic applications. Furthermore, the design, engineering, and optimization of such devices requires quantitative information with high-precision standards in measuring and modelling as is the case, for example, in the determination of the optical critical dimension (OCD) in the semiconductor industry. Our application of these strategies to the study of tunable self-assembled nanolasers, hybrid photonic-plasmonic sensors, and photovoltaic devices, among many other materials and devices has led us to the development of i) new modelling strategies based on the Finite-Difference Time-Domain (FDTD) method, and ii) home-made customizable characterization strategies based on spectroscopic ellipsometry (SE). The advantages of the FDTD method include its ability to i) obtain a wide spectral response from one time-domain simulation, ii) model single non-periodic structures, and iii) flexibility and generality to include, for example, non-linear and thermal effects. On its part, SE stands out from other optical techniques because, as a self-referenced and phase sensitive strategy, it can deliver extreme precision and sensitivity and has demonstrated flexibility that has been implemented in multiple spectral ranges and operation conditions. With this in mind, our group has systematically demonstrated the synergic use of FDTD-SE strategy, with complementary strengths and close-matched precision, in a range of materials and devices. We will report the fundamentals, status of our on-going efforts, and a critical review of the perceived limitations and expected outcomes of the FDTD-SE strategy as a quantitative tool for photonic and plasmonic subwavelength structures of interest in optoelectronic, sensing, and energy applications.We gratefully acknowledge the financial support from the RGC (Projects CityU - 11210218, 11219919, 11215121 and 11310122) and ITC (Project ITS/461/18) of HKSAR, China.

Quantitative Three-Dimensional Imaging Analysis of HfO2 Nanoparticles in Single Cells via Deep Learning Aided X-ray Nano-Computed Tomography.

It is crucial for understanding mechanisms of drug action to quantify the three-dimensional (3D) drug distribution within a single cell at nanoscale resolution. Yet it remains a great challenge due to limited lateral resolution, detection sensitivities, and reconstruction problems. The preferable method is using X-ray nano-computed tomography (Nano-CT) to observe and analyze drug distribution within cells, but it is time-consuming, requiring specialized expertise, and often subjective, particularly with ultrasmall metal nanoparticles (NPs). Furthermore, the accuracy of batch data analysis through conventional processing methods remains uncertain. In this study, we used radioenhancer ultrasmall HfO2 nanoparticles as a model to develop a modular and automated deep learning aided Nano-CT method for the localization quantitative analysis of ultrasmall metal NPs uptake in cancer cells. We have established an ultrasmall objects segmentation method for 3D Nano-CT images in single cells, which can highly sensitively analyze minute NPs and even ultrasmall NPs in single cells. We also constructed a localization quantitative analysis method, which may accurately segment the intracellularly bioavailable particles from those of the extracellular space and intracellular components and NPs. The high bioavailability of HfO2 NPs in tumor cells from deeper penetration in tumor tissue and higher tumor intracellular uptake provide mechanistic insight into HfO2 NPs as advanced radioenhancers in the combination of quantitative subcellular image analysis with the therapeutic effects of NPs on 3D tumor spheroids and breast cancer. Our findings unveil the substantial uptake rate and subcellular quantification of HfO2 NPs by the human breast cancer cell line (MCF-7). This revelation explicates the notable efficacy and safety profile of HfO2 NPs in tumor treatment. These findings demonstrate that this 3D imaging technique promoted by the deep learning algorithm has the potential to provide localization quantitative information about the 3D distributions of specific molecules at the nanoscale level. This study provides an approach for exploring the subcellular quantitative analysis of NPs in single cells, offering a valuable quantitative imaging tool for minute amounts or ultrasmall NPs.

Recent Trends in the Study of Springs in Nepal: A Review

Springs, a component of groundwater systems, are a vital source of fresh water for fulfilling people's demand for drinking water, household uses, and irrigation, especially in the Middle Hill region of Nepal. Springs provide water for base flows and lifelines for many rivers originating from the Middle Hill regions. The present study reviews a recent trend of spring studies and investigations in Nepal through a systematic search of published and unpublished works related to springs, which are freely available. The results show 47 publications, out of 30 are published, and 17 are unpublished. The origin of published work is mainly related to project-related works, whereas unpublished works come from the academic sector for fulfilling academic criteria for thesis research. According to the physiographical division of Nepal, the study area falls in the Middle Hills of Nepal, with the maximum area located in Bagmati Province. Most of the studies that qualitative rather than quantitative information of springs. Studies are not linked with spring source and their seasonal dynamics. However, clearly available data, attributes and information about springs from 47 reviewed documents are noted. Systematic data generation and a standard framework for data collection are also missing. Nevertheless, out of 47 studies, including 11 published and 4 unpublished, the total number of springs per sq. km. in the Middle Hill region of Nepal is estimated as 2.57, which can be integrated after more research on future springs–related work.

Anisotropic Third Harmonic Generation in 2D Tin Sulfide

AbstractThe in‐plane anisotropic properties of 2D group IV monochalcogenides offer an additional degree of freedom which can be advantageous in optoelectronic applications. Here, it is demonstrated that the third harmonic generation (THG) produced by ultrathin tin (II) sulfide (SnS) is in‐plane anisotropic with respect to the direction of the excitation linear polarization. The polarization‐resolved THG (P‐THG) measurements are described with a nonlinear optics model, which accounts for the orthorhombic crystal structure of the 2D SnS. The relative magnitudes of the χ(3) tensor components are calculated by recording and simultaneously fitting two orthogonal polarization components of the P‐THG signals. Furthermore, the “THG anisotropy ratio” is introduced, which compares the THG intensity produced when the excitation linear polarization is along the armchair crystallographic direction, with the case when it is along the zigzag direction. The experimental findings provide quantitative information on the anisotropic nature of the THG process in various SnS flakes. Nonlinear anisotropic 2D materials can be used in future optoelectronic devices, where anisotropic polarization sensitivity is required. Having an optical tool that quantitatively probes the effect of the in‐plane anisotropy among different 2D materials, could be of considerable importance for evaluating the performance of such materials and devices.

Usefulness of combined pseudo-continuous arterial spin labelling and spectroscopic analysis in schizophrenic Egyptian population sample

BackgroundSchizophrenia is a prevalent psychiatric disorder that affects 1% of the global population. Schizophrenia frequently begins in late adolescence or early adulthood, causing significant educational, social, and economic costs for people and society. Functional neuroimaging research on schizophrenia physiopathology has been beneficial. Arterial spin labelling (ASL) is one of functional magnetic resonance imaging (fMRI) technologies that assess brain function without radiation. ASL uses magnetic resonance (MR) imaging to quantify tissue-level brain perfusion non-invasively. Arterial spin labelling (ASL) is one of the functional magnetic resonance imaging (fMRI) technologies that assess the brain function without radiation hazards. ASL uses magnetic resonance (MR) imaging to quantify tissue-level brain perfusion non-invasively. Many publications were performed on role of different advanced MRI techniques in schizophrenia, but our study insisted on the added value of combined ASL and MRS over the conventional MRI in schizophrenic Egyptian population sample.Aim of the workThe purpose of this work was to evaluate the added value of combined ASL-perfusion MRI and MRS in schizophrenic patients.MethodsThis prospective case–control study was carried out on two groups: First group was 30 patients who were diagnosed clinically as schizophrenic patients, and second group was 20 healthy volunteers as a control group for comparison in the period from August 2021 till July 2022.ResultsThe majority of newly diagnosed cases had significant higher positive symptoms than chronic cases. According to arterial spin labelling (ASL) data, rCBF was noticed to be reduced in anterior cingulate, frontal lobe, and parietal lobe of both patients’ subgroups but more significant in chronically ill patients. Convergent results of decreased rCBF were also found in the parietal lobe and occipital lobe. Magnetic resonance spectroscopic analysis showed that NAA was decreased in the anterior cingulate cortex, thalami and basal ganglia of the newly diagnosed cases more than chronic cases. The ASL-MRI perfusion accurately detected the hypo-perfusion of different brain regions with sensitivity 100%, specificity 66.67%, positive predictive value 96.43%, negative predictive value 100%, and accuracy 96.67%, while MR spectroscopy showed sensitivity 100%, specificity 33.33%, positive predictive value 93.10%, negative predictive value 100%, and accuracy 93.33% in evaluation of changes of brain metabolites.ConclusionASL is a promising functional MRI technique that can produce together with MRS quantitative information about the metabolites of different brain regions. The ASL-MRI appears as a reliable non-invasive technique to measure cerebral blood flow and identify decreased rCBF without any contrast administration, and it could be repeatable which helps in early diagnosis as well as follow-up of the progression of the disease.

Quantifying qualitative survey data with panel data

We develop a novel methodology to quantify forecasts based on qualitative survey data. The methodology is generally applicable when quantitative information is available on the realization of the forecasted variable, for example from firm balance sheets. The method can be applied to a wide range of panel datasets, including qualitative surveys on firm-level forecasts or household expectations. As an application, we employ a panel of Greek manufacturing firms and quantify firms' forecast errors of own sales growth. In this context, we conduct a variety of exercises to demonstrate the methodology's validity and accuracy.

Mucin incorporated electrospun fibrous matrix of zein and PVP: Towards transmucosal propranolol hydrochloride delivery

Mucoadhesive drug delivery systems are being widely explored as a substitute for conventional drug delivery devices. Several polymers and their functional modifications are under investigations. In that scenario, the cohesive influence of mucin in the polymer system itself to the biologically available mucin in the mucosa is a novel approach. The added advantage of mucin in augmenting mucoadhesion was explored. A two-fold increase in the force of adhesion and an eight-fold increase in the work of adhesion were intriguing observations to be noted. Mucin incorporation in the matrix was done via EDC crosslinker. The results of the TNBS assay confirmed 39 % of mucin binding. A three-fold decrease of elongation at break (%) after mucin binding is a piece of quantitative information to corroborate the same. This work also investigates the efficiency of the mucoadhesive matrix towards the release of Propranolol Hydrochloride (PL). Low oral bioavailability of PL demands its administration via mucosal route to avoid hepatic first-pass metabolism. The release study of PL in PBS and permeation through RPMI 2650 cells revealed a burst release followed by a sustained release profile and best fitted with Korsmeyer Peppas model. PL permeation through the cell monolayer without a cell junction opening pursued a transcellular transport mechanism. Drug permeation through porcine buccal mucosa also assured adequate permeation in ex-vivo conditions. The sustained release of PL from MUZPVP and prolonged residence time of the matrix over mucosa due to increased mucoadhesion guarantees an optimistic drug delivery device to the biomedical discipline.

Area normalization of HERFD-XANES spectra.

The normalization of X-ray absorption near-edge structure (XANES) spectra is required for comparing spectral features and extracting quantitative information in analytical techniques such as linear combination analysis, principal component analysis and multivariate curve resolution. Most published data are normalized to the edge-jump, but normalization to the spectral area has also been applied. The latter is particularly attractive if only a small energy range around the absorption can be recorded reliably. Here, the two normalization methods are compared at the L3-edge of Pt, Pd and Rh, and at the Ni K-edge using experimental and calculated spectra. Normalization to the spectral area is found to be a viable approach if the range for the area normalization is sufficiently large.

Reduced mesophyll conductance under chronic O3 exposure in poplar reflects thicker cell walls and increased subcellular diffusion pathway lengths according to the anatomical model.

Ozone (O3) is one of the most harmful and widespread air pollutants, affecting crop yield and plant health worldwide. There is evidence that O3 reduces the major limiting factor of photosynthesis, namely CO2 mesophyll conductance (gm), but there is little quantitative information of O3-caused changes in key leaf anatomical traits and their impact on gm. We exposed two O3-responsive clones of the economically important tree species Populus × canadensis Moench to 120 ppb O3 for 21 days. An anatomical diffusion model within the leaf was used to analyse the entire CO2 diffusion pathway from substomatal cavities to carboxylation sites and determine the importance of each structural and subcellular component as a limiting factor. gm decreased substantially under O3 and was found to be the most important limitation of photosynthesis. This decrease was mostly driven by an increased cell wall thickness and length of subcellular diffusion pathway caused by altered interchloroplast spacing and chloroplast positioning. By contrast, the prominent leaf integrative trait leaf dry mass per area was neither affected nor related to gm under O3. The observed relationship between gm and anatomy, however, was clone-dependent, suggesting that mechanisms regulating gm may differ considerably between closely related plant lines. Our results confirm the need for further studies on factors constraining gm under stress conditions.

On-field Head Acceleration Exposure Measurements Using Instrumented Mouthguards: Multi-stage Screening to Optimize Data Quality.

Instrumented mouthguards (iMGs) are widely applied to measure head acceleration event (HAE) exposure in sports. Despite laboratory validation, on-field factors including potential sensor skull-decoupling and spurious recordings limit data accuracy. Video analysis can provide complementary information to verify sensor data but lacks quantitative kinematics reference information and suffers from subjectivity. The purpose of this study was to develop a rigorous multi-stage screening procedure, combining iMG and video as independent measurements, aimed at improving the quality of on-field HAE exposure measurements. We deployed iMGs and gathered video recordings in a complete university men's ice hockey varsity season. We developed a four-stage process that involves independent video and sensor data collection (Stage I), general screening (Stage II), cross verification (Stage III), and coupling verification (Stage IV). Stage I yielded 24,596 iMG acceleration events (AEs) and 17,098 potential video HAEs from all games. Approximately 2.5% of iMG AEs were categorized as cross-verified and coupled iMG HAEs after Stage IV, and less than 1/5 of confirmed or probable video HAEs were cross-verified with iMG data during stage III. From Stage I to IV, we observed lower peak kinematics (median peak linear acceleration from 36.0 to 10.9g; median peak angular acceleration from 3922 to 942rad/s2) and reduced high-frequency signals, indicative of potential reduction in kinematic noise. Our study proposes a rigorous process for on-field data screening and provides quantitative evidence of data quality improvements using this process. Ensuring data quality is critical in further investigation of potential brain injury risk using HAE exposure data.

Physiologically Based Pharmacokinetic Modeling of Vancomycin in Critically Ill Neonates: Assessing the Impact of Pathophysiological Changes.

Dosing vancomycin for critically ill neonates is challenging owing to substantial alterations in pharmacokinetics (PKs) caused by variability in physiology, disease, and clinical interventions. Therefore, an adequate PK model is needed to characterize these pathophysiological changes. The intent of this study was to develop a physiologically based pharmacokinetic (PBPK) model that reflects vancomycin PK and pathophysiological changes in neonates under intensive care. PK-sim software was used for PBPK modeling. An adult model (model 0) was established and verified using PK profiles from previous studies. A neonatal model (model 1) was then extrapolated from model 0 by scaling age-dependent parameters. Another neonatal model (model 2) was developed based not only on scaled age-dependent parameters but also on quantitative information on pathophysiological changes obtained via a comprehensive literature search. The predictive performances of models 1 and 2 were evaluated using a retrospectively collected dataset from neonates under intensive care (chictr.org.cn, ChiCTR1900027919), comprising 65 neonates and 92 vancomycin serum concentrations. Integrating literature-based parameter changes related to hypoalbuminemia, small-for-gestational-age, and co-medication, model 2 offered more optimized precision than model 1, as shown by a decrease in the overall mean absolute percentage error (50.6% for model 1; 37.8% for model 2). In conclusion, incorporating literature-based pathophysiological changes effectively improved PBPK modeling for critically ill neonates. Furthermore, this model allows for dosing optimization before serum concentration measurements can be obtained in clinical practice.

Bioactivity-driven fungal metabologenomics identifies antiproliferative stemphone analogs and their biosynthetic gene cluster

IntroductionFungi biosynthesize chemically diverse secondary metabolites with a wide range of biological activities. Natural product scientists have increasingly turned towards bioinformatics approaches, combining metabolomics and genomics to target secondary metabolites and their biosynthetic machinery. We recently applied an integrated metabologenomics workflow to 110 fungi and identified more than 230 high-confidence linkages between metabolites and their biosynthetic pathways.ObjectivesTo prioritize the discovery of bioactive natural products and their biosynthetic pathways from these hundreds of high-confidence linkages, we developed a bioactivity-driven metabologenomics workflow combining quantitative chemical information, antiproliferative bioactivity data, and genome sequences.MethodsThe 110 fungi from our metabologenomics study were tested against multiple cancer cell lines to identify which strains produced antiproliferative natural products. Three strains were selected for further study, fractionated using flash chromatography, and subjected to an additional round of bioactivity testing and mass spectral analysis. Data were overlaid using biochemometrics analysis to predict active constituents early in the fractionation process following which their biosynthetic pathways were identified using metabologenomics.ResultsWe isolated three new-to-nature stemphone analogs, 19-acetylstemphones G (1), B (2) and E (3), that demonstrated antiproliferative activity ranging from 3 to 5 µM against human melanoma (MDA-MB-435) and ovarian cancer (OVACR3) cells. We proposed a rational biosynthetic pathway for these compounds, highlighting the potential of using bioactivity as a filter for the analysis of integrated—Omics datasets.ConclusionsThis work demonstrates how the incorporation of biochemometrics as a third dimension into the metabologenomics workflow can identify bioactive metabolites and link them to their biosynthetic machinery.

Efficacy of anti-seizure medications and alternative therapies (ketogenic diet, CBD, and quinidine) in KCNT1-related epilepsy: A systematic review.

KCNT1-related epilepsies encompass three main phenotypes: (i) epilepsy of infancy with migrating focal seizures (EIMFS), (ii) autosomal dominant or sporadic sleep-related hypermotor epilepsy [(AD)SHE], and (iii) different types of developmental and epileptic encephalopathies (DEE). Many patients present with drug-resistant seizures and global developmental delays. In addition to conventional anti-seizure medications (ASM), multiple alternative therapies have been tested including the ketogenic diet (KD), cannabidiol (CBD-including Epidyolex © and other CBD derivatives) and quinidine (QUIN). We aimed to clarify the current state of the art concerning the benefits of those therapies administered to the three groups of patients. We performed a literature review on PubMed and EMBase with the keyword "KCNT1" and selected articles reporting qualitative and/or quantitative information on responses to these treatments. A treatment was considered beneficial if it improved seizure frequency and/or intensity and/or quality of life. Patients were grouped by phenotype. A total of 43 studies including 197 patients were reviewed. For EIMFS patients (32 studies, 135 patients), KD resulted in benefit in 62.5% (25/40), all types of CBD resulted in benefit in 50% (6/12), and QUIN resulted in benefit in 44.6% (25/56). For (AD)SHE patients (10 studies, 32 patients), we found only one report of treatment with KD, with no benefit noted. QUIN was trialed in 8 patients with no reported benefit. For DEE patients (10 studies, 30 patients), KD resulted in benefit for 4/7, CBD for 1/2, and QUIN for 6/9. In all groups, conventional ASM are rarely reported as beneficial (in 5%-25% of patients). Ketogenic diet, CBD, and QUIN treatments appear to be beneficial in a subset of patient with drug-resistant epilepsy. The KD and CBD are reasonable to trial in patients with KCNT1-related epilepsy. Further studies are needed to identify optimal treatment strategies and to establish predictive response factors. We performed an extensive review of scientific articles providing information about the therapeutic management of epilepsy in patients with epilepsy linked to a mutation in the KCNT1 gene. Conventional anti-seizure treatments were rarely reported to be beneficial. The ketogenic diet (a medical diet with very high fat, adequate protein and very low carbohydrate intake) and cannabidiol appeared to be useful, but larger studies are needed to reach a conclusion.

Variability of energetic proton flux and pitch angle distributions in the Earth's radiation belt modulated by geomagnetic storms

Energetic protons trapped in the radiation belt, as a vital component of the ring current system, are observationally and theoretically modulated by geomagnetic disturbances. Utilizing Van Allen Probe observations, we statistically analyzed their temporal variations at 55–489 keV as well as their pitch angle distributions (PADs) index n (fitted by sinn∂, where ∂ is the pitch angle) in response to geomagnetic storms. It shows that protons at low energies are more easily accelerated during storms. The threshold of accelerations becomes greater for high-energy protons, while a large value of n can persist for a few days to months. Further investigations suggest that one-quarter of the storms increase the proton flux at all energy channels (55–489 keV) both inside and outside the plasmapause location (Lpp). Specifically, more than half of the storms enhance the flux for protons at Ek > 400 keV inside and close to the Lpp as well as protons at Ek < 100 keV deep inside the Lpp. Comparably, protons at larger pitch angles (near 90°) are more easily lost outside the Lpp, which results in more pronounced pancake PADs with larger n. The index n preferentially decreases at L > 5 during 75% of the storms on the dayside, while it decreases at L = ∼4 during 50% of the storms on the nightside, showing significant day–night asymmetry. Further detailed investigations revealed that source and loss processes, including radial diffusion, magnetopause shadowing, and wave–particle interactions, account for the statistical results. The present study provides quantitative information on the overall characteristics of energetic proton fluxes, which can enhance the comprehension of the radiation belts.

Analysis of drilling response under ultra-high-speed diamond drilling: Theory and experiment

The ultra-high-speed diamond drilling (UHSDD) technology represents a groundbreaking and effective approach to deep hard rock drilling. This innovative method addresses the critical need for reduced weight on bit (WOB) while simultaneously achieving remarkable enhancements in drilling velocities. However, the absence of a comprehensive fundamental theory poses challenges, such as drill wear and inconsistent rock crushing efficiency. In this paper, the experimental data obtained from UHSDD was processed and analyzed by leveraging the drilling response model under standard rotational speeds (D-D model). The interface laws of diamond bit based on rate-relatedness were proposed. Notably, the depth of cut per revolution d of the bit is influenced by a complex interplay of rotational speed and weight on bit variables. A new variable Γ was introduced to measure the force required per revolution for d at ultra-high speeds. Within the proposed model framework, the quantitative information from drilling data was extracted, encompassing the wear state of the bit, drilling parameters, and rock properties. To validate the accuracy of the new model, a series of extensive laboratory experiments on the UHSDD test experimental platform was conducted. The reliability of the model was preliminary verified. The results of this study enhanced our understanding of the drilling response of UHSDD in practical drilling applications.

Interpretable Causal System Optimization Framework for the Advancement of Biological Effect Prediction and Redesign of Nanoparticles.

Nanomedicine has promising applications in disease treatment, given the remarkable safety concerns (e.g., nanotoxicity and inflammation) of nanomaterials, and realizing the trade-off between the immune response and organ burden of NPs and deeply understanding the interactions of the organism-nano systems are crucial to facilitate the biological applications of NPs. Here, we propose an interpretable causal system optimization (ICSO) framework and construct the upstream and downstream tasks of accurate prediction and intelligent NP optimization. ICSO framework screens the key drivers (recovery duration, specific surface area, and nanomaterial size) and potential causal information for immune responses and organ burden, revealing the hidden priming/constraint effects in bionano interactions. ICSO can be used to quantify the thresholds of biological responses to multiple properties (e.g., the specific surface area, diameter, and zeta potential). ICSO provides quantitative information and constraint conditions for the design of highly biocompatible and targeted organ delivery nanomaterials. For example, negative inflammation is reduced by 36.19%, and positive lung accumulation is promoted by 40.14% by optimizing the specific surface areas and shape and increasing the diameter-to-length ratio. ICSO overcomes the limitations of experience-dependent approaches and provides powerful and automated solutions for decision-makers during nanomaterial design.

Anthropometric, body composition and physical performance of elite young Italian football players and differences between selected and unselected talents

Football is a team sport played worldwide and specific competition demands are needed since young categories. Several physiological and psychological aspects could influence the selection process, and adolescent investigations still be necessary. This retrospective study aims to compare anthropometric and performance features between selected and unselected adolescent footballers. The sample size was 78 players from U10 to U12 categories. Maturation, RAE, anthropometry, and physical performance (repeated sprint ability (RSA), 15-m sprint, countermovement jump (CMJ) and Harre's test) were evaluated (mean ± SD). 33.3 % of players were selected (height = 144.06 ± 6.74 cm, weight = 35.38 ± 4.56 kg) and 66.7 % were unselected (height = 143.06 ± 8.34 cm, weight = 35.94 ± 6.24 kg). Selected U10 were leaner and got the peak of height velocity (APHV) earlier (p < 0.05) than unselected U10 players, while U11 selected were faster than unselected (p < 0.05). Also, the RSA test, APHV and the humeral width well discriminated among the selection (χ2(3) = 12; p < 0.01). Football field technicians involved in scouting need quantitative and qualitative information that could help to predict talented players. Although physical performance test results and body height led to decisions, further anthropometric features and maturation could provide relevant support.

A developmental neurotoxicity adverse outcome pathway (DNT-AOP) with voltage gate sodium channel (VGSC) inhibition as a molecular initiating event (MiE).

The adverse outcome pathway (AOP) framework serves as a practical tool for organising scientific knowledge that can be used to infer cause-effect relationships between stressor events and toxicity outcomes in intact organisms. However, a major challenge in the broader application of the AOP concept within regulatory toxicology is the development of a robust AOPs that can withstand peer review and acceptance. This is mainly due to the considerable amount of work required to substantiate the modular units of a complete AOP, which can take years from inception to completion. The methodology used here consisted of an initial assessment of a single chemical hazard using the Integrated Approach to Testing and Assessment (IATA) framework. An evidence-based approach was then used to gather empirical evidence combining systematic literature review methods with expert knowledge to ensure the effectiveness of the AOP development methodology. The structured framework used assured transparency, objectivity and comprehensiveness, and included expert knowledge elicitation for the evaluation of key event relationships (KERs). This stepwise approach led to the development of an AOP that begins with binding of chemicals to Voltage Gate Sodium Channels (VGSC/Nav) during mammalian development leading to adverse consequences in neurodevelopment evidenced as deficits in cognitive functions. Disruption of the formation of precise neural circuits by alterations in VGSC kinetics during the perinatal stages of brain development may also underlie neurodevelopmental disorders. Gaps in our understanding include the specific critical developmental windows and the quantitative relationship of binding to VGSC and subsequent disruption and cognitive function. Despite the limited quantitative information at all KER levels, regulatory applications of this AOP for DNT assessment have been identified.

COMPARISON OF K-NEAREST NEIGHBOR AND NEURAL NETWORK FOR PREDICTION INTERNATIONAL VISITOR IN EAST JAVA

Tourism is one of the government's priority sectors for economic growth. East Java is one of Indonesia's provinces and is attractive to international visitors. International visitors will appreciate the natural beauty and multiculturalism offered by East Java. In this study, predictions of international visitor visits in East Java from the entrance of Juanda International Airport were carried out using k-NN (k-Nearest Neighbor) and a neural network. The dataset used is based on BPS statistics of Jawa Timur Province in the form of the number of international visitor arrivals from January 2000 to February 2024. The datasets were distributed by dividing the data into 70% for training data and 30% for testing data. The creation of the k-NN model is carried out using k-values 2 to 7. The creation of a modern neural network using hidden layers 1 to 3. The prediction results that were made using k-NN obtained optimal RMSE at k-values 2, resulting in an RMSE of 1594,674 or an error of 3,98%. Meanwhile, the prediction results that have been made using neural networks obtained optimal RMSE at two hidden layers, which resulted in an RMSE of 1873, 355 or an error of 4,68%. So, it is recommended that the k-NN algorithm be used to predict the number of international visitors in East Java. The results of this study can be used to provide quantitative information for the government and stakeholders in adjusting the program to the development of international visitors visiting East Java.