Optical Properties Prediction for Red and Near-Infrared Emitting Carbon Dots Using Machine Learning.

Functional nanostructures build up a basis for the future materials and devices, providing a wide variety of functionalities, a possibility of designing bio-compatible nanoprobes, etc. However, development of new nanostructured materials via trial-and-error approach is obviously limited by laborious efforts on their syntheses, and the cost of materials and manpower. This is one of the reasons for an increasing interest in design and development of novel materials with required properties assisted by machine learning approaches. Here, the dataset on synthetic parameters and optical properties of one important class of light-emitting nanomaterials - carbon dots are collected, processed, and analyzed with optical transitions in the red and near-infrared spectral ranges. A model for prediction of spectral characteristics of these carbon dots based on multiple linear regression is established and verified by comparison of the predicted and experimentally observed optical properties of carbon dots synthesized in three different laboratories. Based on the analysis, the open-source code is provided to be used by researchers for the prediction of optical properties of carbon dots and their synthetic procedures.

Hybrid 2D/3D-quantitative structure-activity relationship studies on the bioactivities and molecular mechanism of antibacterial peptides.

Antimicrobial peptide (AMP) is the polypeptide, which protects the organism avoiding attack from pathogenic bacteria. Studies have shown that there were some antimicrobial peptides with molecular action mechanism involved in crossing the cell membrane without inducing severe membrane collapse, then interacting with cytoplasmic target-nucleic acid, and exerting antibacterial activity by interfacing the transmission of genetic information of pathogenic microorganisms. However, the relationship between the antibacterial activities and peptide structures was still unclear. Therefore, in the present work, a series of AMPs with a sequence of 20 amino acids was extracted from DBAASP database, then, quantitative structure-activity relationship (QSAR) methods were conducted on these peptides. In addition, novel antimicrobial peptides with  stronger antimicrobial activities were designed according to the information originated from the constructed models. Hence, the outcome of this study would lay a solid foundation for the in-silico design and exploration of novel antibacterial peptides with improved activity activities.

Occurrence, source apportionment, and ecological risk of legacy and emerging per- and poly-fluoroalkyl substances (PFASs) in the Dahei river basin of a typical arid region in China.

Per- and poly-fluoroalkyl substances (PFASs) are artificial chemicals with broad commercial and industrial applications. Many studies about PFASs have been conducted in densely industrial and populated regions. However, fewer studies have focused on the PFASs' status in a typical arid region. Here, we investigated 30 legacy and emerging PFASs in surface water from the mainstream and tributaries of the Dahei River. Our results revealed that total PFASs concentrations (∑30PFASs) in water ranged from 3.13 to 289.1 ng/L (mean: 25.40 ng/L). Perfluorooctanoic acid (PFOA) had the highest mean concentration of 2.44 ng/L with a 100% detection frequency (DF), followed by perfluorohexanoic acid (PFHxA) (mean concentration: 1.34 ng/L, DF: 59.26%). Also, perfluorohexane sulfonate (DF: 44.44%), perfluorobutane sulfonate (DF: 88.89%), and perfluorooctane sulfonate (PFOS) (DF: 92.59%) had mean concentrations of 12.94, 2.00, and 1.05 ng/L, respectively. Source apportionment through ratio analysis and principal component analysis-multiple linear regression analysis showed that treated or untreated sewage, aqueous film-forming foam, degradation of precursors, and fluoropolymer production were the primary sources. The PFOS alternatives were more prevalent than those of PFOA. Conductivity, total phosphorus, and chlorophyll a positively correlated with Σ30PFASs and total perfluoroalkane sulfonates concentrations. Furthermore, ecological risk assessment showed that more attention should be paid to perfluorooctadecanoic acid, perfluorohexadecanoic acid, perfluorooctane sulfonate, perfluorohexane sulfonate, and (6:2 and 6:2/8:2) polyfluoroalkyl phosphate mono- and di-esters. The mass load of PFASs to the Yellow River was 1.28 kg/year due to the low annual runoff in the Dahei River in the arid region. This study provides baseline data for PFASs in the Dahei River that can aid in the development of effective management strategies for controlling PFASs pollution in typical arid regions in China.

Multiscale spatial relationship-based model for predicting bladder wall dose in pelvic radiotherapy.

PURPOSE: This research aimed to develop a prediction model to assess bladder wall dosimetry during radiotherapy for patients with pelvic tumors, thereby facilitating the refinement and evaluation of radiotherapy treatment plans to mitigate bladder toxicity. METHODS: Radiotherapy treatment plans of 49 rectal cancer patients and 45 gynecologic cancer patients were collected, and multiple linear regression analyses were used to generate prediction models for bladder wall dose parameters ( V 10 - 45 G y ( c m 3 ) ${V_{10 - 45Gy\ }}( {{\mathrm{c}}{{\mathrm{m}}^3}} )$ , D m e a n ( Gy ) ${D_{mean}}( {{\mathrm{Gy}}} )$ ). These models were based on the multiscale spatial relationship between the planning target volume (PTV) and the bladder or bladder wall. The proportion of bladder or bladder wall volume overlapped by the different distance expansions of the PTV was used as an indicator of the multiscale spatial relationship. The accuracy of these models was verified in a cohort of 12 new patients, with further refinement of radiotherapy treatment plans using the predicted values as optimization parameters. Model accuracy was assessed using root mean square error (RMSE) and mean percentage error (MPE). RESULTS: Models derived from individual disease data outperformed those derived from combined datasets. Predicted bladder wall dose parameters were accurate, with the majority of initial calculated values for new patients falling within the 95% confidence interval of the model predictions. There was a robust correlation between the predicted and actual dose metrics, with a correlation coefficient of 0.943. Using the predicted values to optimize treatment plans significantly reduced bladder wall dose (p < $\ < \ $ 0.001), with bladder wall D mean ( G y ) ${D_{{\mathrm{mean}}}}( {Gy} )$ and V 10 - 45 G y ( c m 3 ) ${V_{10 - 45Gy\ }}( {{\mathrm{c}}{{\mathrm{m}}^3}} )$ decreasing by 2.27±0.80 Gy (5.8%±1.8%) and 2.96±2.05 cm3 (7.9%±5.4%), respectively. CONCLUSION: The formulated prediction model provides a valuable tool for predicting and minimizing bladder wall dose and for optimizing and evaluating radiotherapy treatment plans for pelvic tumor patients. This approach holds promise for reducing bladder toxicity and potentially improving patient outcomes.

Hyperspectral Characteristics and SPAD Estimation of Wheat Leaves under CO2 Microleakage Stress.

To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO2 microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model's accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO2 microleakage stress showed a "red shift" of the green peak and a "blue shift" of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO2 at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO2 trace-leakage stress and to dynamically monitor CO2 trace-leakage stress in crops.

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments.

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

The use of the self-organizing map (SOM) methodology to study the dissolved organic matter (DOM) in different water body types.

This study investigated the characteristics of dissolved organic matter (DOM) in two distinct water bodies, through the utilization of three-dimensional fluorescence spectroscopy coupled with self-organizing map (SOM) methodology. Specifically, this analysis concentrated on neurons 3, 14, and 17 within the SOM model, identifying notable differences in the DOM compositions of a coal subsidence water body (TX) and the MaChang Reservoir (MC). The humic substance content of DOM TX exceeded that of MC. The origin of DOM in TX was primarily linked to agricultural inputs and rainfall runoff, whereas the DOM in MC was associated with human activities, displaying distinctive autochthonous features and heightened biological activity. Principal component analysis revealed that humic substances dominated the DOM in TX, while the natural DOM in MC was primarily autochthonous. Furthermore, a multiple linear regression model (MLR) determined that external pollution was responsible for 99.11% of variation in the humification index (HIX) of water bodies.

The skin tears knowledge among geriatric ward nurses and associated factors: A cross-sectional study.

AIM: To determine the level of knowledge about skin tears among geriatric ward nurses and identify associated factors. METHODS: In this cross-sectional study in Southwest China, 1172 geriatric ward nurses from 10 hospitals participated. Data were collected using Sojump, a Chinese web-based platform, and the Skin Tear Knowledge Assessment Instrument was used to assess their knowledge. The analysis involved descriptive statistics, correlation analysis, and multiple linear regression. RESULTS: The study involved participants with an average age of 36.73 (SD = 6.54) years. More than half of the participants had less than 10 years of experience in geriatric wards. 27 % specialized in wound care, and 68.1 % lacked specific training in skin tear (ST) knowledge. Additionally, 82.7 % of geriatric nurses had never been exposed to guidelines on ST prevention and management. In the geriatric ward, 36.6 % of nurses received training in ST prevention. The average knowledge score about Skin Tears (STs) was 9.52 (SD = 2.39) out of 18. 'Treatment' had the lowest mean score, while 'Specific patient groups' had the highest. The multiple linear regression analysis found that nurses' knowledge of STs was influenced by sex(ß = 0.096, P < 0.001), educational level(ß = 0.062, P < 0.001), participation in ST (ß = -0.193, P < 0.001 and wound care training(ß = -0.120, P = 0.004), and specialization as a wound care nurse(ß = -0.350, P = 0.001). These factors explained 61.3 % of the variance in knowledge about STs among the participants. CONCLUSION: The geriatric ward had limited knowledge of STs. To improve their skills in dealing with STs, managers should provide tailored training to nurses and establish a standardized, evidence-based nursing process.

Predictive modeling of methylmercury in rice (Oryza sativa L.) and species-sensitivity-distribution-based derivation of the threshold of soil mercury in karst mountain areas.

The bioavailable mercury (Hg) in the soil is highly active and can affect the formulation of methyl-Hg (MeHg) in soil and its accumulation in rice. Herein, we predicted the concentration of MeHg in rice using bioavailable Hg extracted from soils; additionally, we determined the threshold value of soil Hg in karst mountain areas based on species sensitivity distribution. The bioavailable Hg was extracted using calcium chloride, hydrochloric acid (HCl), diethylenetriaminepentaacetic acid mixture, ammonium acetate, and thioglycolic acid. Results showed that HCl is the best extractant, and the prediction model demonstrated good predictability of the MeHg concentration in rice based on the HCl-extractable Hg, pH, and soil organic matter (SOM) data. Compared with the actual MeHg concentration in rice, approximately 99% of the predicted values (n = 103) were within the 95% prediction range, indicating the good performance of the rice MeHg prediction model based on soil pH, SOM, and bioavailable Hg in karst mountain areas. Based on this MeHg prediction model, the safety threshold of soil Hg was calculated to be 0.0936 mg/kg, which is much lower than the soil pollution risk screening value of agricultural land (0.5 mg/kg), suggesting that a stricter standard should be applied regarding soil Hg in karst mountain areas. This study presents the threshold of soil Hg pollution for rice safety in karst mountain areas, and future studies should target this threshold range.

A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia.

Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients' brain function.

Pollution characteristics and quantitative source apportionment of heavy metals within a zinc smelting site by GIS-based PMF and APCS-MLR models.

The abandoned smelters present a substantial pollution threat to the nearby soil and groundwater. In this study, 63 surface soil samples were collected from a zinc smelter to quantitatively describe the pollution characteristics, ecological risks, and source apportionment of heavy metal(loid)s (HMs). The results revealed that the average contents of Zn, Cd, Pb, As, and Hg were 0.4, 12.2, 3.3, 5.3, and 12.7 times higher than the risk screening values of the construction sites, respectively. Notably, the smelter was accumulated heavily with Cd and Hg, and the contribution of Cd (0.38) and Hg (0.53) to ecological risk was 91.58%. ZZ3 and ZZ7 were the most polluted workshops, accounting for 25.7% and 35.0% of the pollution load and ecological risk, respectively. The influence of soil parent materials on pollution was minor compared to various workshops within the smelter. Combined with PMF, APCS-MLR and GIS analysis, four sources of HMs were identified: P1(25.5%) and A3(18.4%) were atmospheric deposition from the electric defogging workshop and surface runoff from the smelter; P2(32.7%) and A2(20.9%) were surface runoff of As-Pb foul acid; P3(14.5%) and A4(49.8%) were atmospheric deposition from the leach slag drying workshop; P4(27.3%) and A1(10.8%) were the smelting process of zinc products. This paper described the distribution characteristics and specific sources of HMs in different process workshops, providing a new perspective for the precise remediation of the smelter by determining the priority control factors.

Physicochemical properties as a function of lomefloxacin biological activity.

OBJECTIVE: Aim: The goal is to discover QSAR of Lomefloxacin as antibacterial activity. PATIENTS AND METHODS: Materials and Methods: A number of lomefloxacins analogs activities were studied by program Windows Chem SW. The analogues were obtained and energy minimization was carried out through Molecular Modeling Program, the calculations were performed using General Atomic and Molecular Electronic Structure System (GAMESS) software. RESULTS: Results: There were six descriptions (N-quinoline more (-) ev charge, Kinetic Energy, Potential Energy, Log p, Log S, F6 charge) results have highly compatible of physicochemical properties with lomefloxacin analogs activities. It can be used to estimate the activities depending on QSAR equation of lomefloxacin analogs. CONCLUSION: Conclusions: The parameters used for calculation were depending on the quantum chemical was employed in deriving from computational study of properties and can used to predict the activities of certain analogs of Lomefloxacins as antibacterial compounds.

Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings.

Instantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.

Hydrogen bonds of OCNH motif in rings in drugs: A molecular electrostatic potential analysis.

The OCNH unit is one of the most frequently encountered structural motifs in rings in drugs which serves dual role as the proton donor through NH bond and proton acceptor through the CO bond. Here, we predicted the HB strength (Eint ) of OCNH motif with H2 O for commonly observed 37 rings in drugs with DFT method M06L/6-311++G(d,p). The HB strength is rationalized in terms of molecular electrostatic potential (MESP) topology parameters ΔVn(NH) and ΔVn(CO) which describe the relative electron deficient/rich nature of NH and CO, respectively, with respect to the reference formamide. The Eint of formamide is -10.0 kcal/mol whereas the Eint of ring systems is in the range -8.6 to -12.7 kcal/mol-a minor increase/decrease compared to the formamide. The variations in Eint are addressed using the MESP parameters ΔVn(NH) and ΔVn(CO) and proposed the hypothesis that a positive ΔVn(NH) enhances NH…Ow interaction while a negative ΔVn(CO) enhances the CO…Hw interaction. The hypothesis is proved by expressing Eint jointly as ΔVn(NH) and ΔVn(CO) and also verified for twenty FDA approved drugs. The predicted Eint for the drugs using ΔVn(NH) and ΔVn(CO) agreed well with the calculated Eint . The study confirms that even delicate variations in the electronic feature of a molecule can be quantified in terms of MESP parameters and they provide a priori prediction of the HB strength. The MESP topology analysis is recommended to understand the tunability of HB strength in drug motifs.

Supervised machine learning in microfluidic impedance flow cytometry for improved particle size determination.

The assessment of particle and cell size in electrical microfluidic flow cytometers has become common practice. Nevertheless, in flow cytometers with coplanar electrodes accurate determination of particle size is difficult, owing to the inhomogeneous electric field. Pre-defined signal templates and compensation methods have been introduced to correct for this positional dependence, but are cumbersome when dealing with irregular signal shapes. We introduce a simple and accurate post-processing method without the use of pre-defined signal templates and compensation functions using supervised machine learning. We implemented a multiple linear regression model and show an average reduction of the particle diameter variation by 37% with respect to an earlier processing method based on a feature extraction algorithm and compensation function. Furthermore, we demonstrate its application in flow cytometry by determining the size distribution of a population of small (4.6 ± 0.9 µm) and large (5.9 ± 0.8 µm) yeast cells. The improved performance of this coplanar, two electrode chip enables precise cell size determination in easy to fabricate impedance flow cytometers.

Association of lifestyle behaviors with health-related quality of life among patients with hematologic diseases.

PURPOSE: Health-related quality of life (HRQoL) is a multi-dimensional construct used to assess the impact of health status on quality of life, and it is known to be affected by lifestyle behaviors. This study focused on multiple lifestyle behaviors among patients with hematologic diseases, including physical activity, dietary intake, sleep quality, occupational exposure, alcohol consumption and smoking. The main objective was to investigate the association of both individual and clustering of health behaviors with HRQoL among the population with hematologic diseases based on a comprehensive lifestyle survey. METHODS: A total of 539 patients with hematologic diseases aged over 18 years were enrolled in this cross-sectional study. Latent class analysis was used to identify homogeneous, mutually exclusive lifestyle classes, and multinomial logistic regression was then performed to explore the association of lifestyle classes membership with HRQoL. Meanwhile, multiple linear regression and quantile regression were used to identify the relationship between individual lifestyle behaviors and HRQoL. RESULTS: A three-class model was selected based on conceptual interpretation and model fit. We found no association between multiple lifestyle behaviors and HRQoL in the 3-class model, either in the whole patients or in subgroups stratified by hematological malignancies. Further research on each lifestyle found that physical activity, dietary intake, occupational exposure, alcohol consumption or smoking were independent of HRQoL. Sleep quality was positively associated with HRQoL. CONCLUSION: Our findings suggested that clustering of lifestyle behaviors may not be an indicator to reflect the health quality of patients with hematologic diseases. Sleep represents a viable intervention target that can confer health benefits on the hematologic patients.

Social factors of health-related quality of life in older adults: a multivariable analysis.

PURPOSE: The objective of the analysis was to examine the relationships between sociodemographic, socioeconomic, psychosocial, and behavioural factors and both physical and mental health-related quality of life (HRQOL) in older adults. METHODS: The analysis was based on recent cross-sectional data of 1687 community residents from a whole population postal survey of German adults aged 65 years and older (33% response rate, 52% female, mean age 76 years). HRQOL was assessed using the 36-Item Short Form Survey (SF-36v2). For a differentiated analysis, hierarchical multiple linear regressions were performed. RESULTS: An internal health locus of control, physical activity, social support, and income were positively associated with physical HRQOL (Adj. R2 = 0.34; p < 0.001) and mental HRQOL (Adj. R2 = 0.18; p < 0.001), whereas an external health locus of control and age were negatively associated with both. Alcohol use and educational level were positively associated only with physical HRQOL, whilst female gender was negatively associated only with mental HRQOL. CONCLUSION: Sociodemographic, socioeconomic, psychosocial, and behavioural factors were associated with physical and mental HRQOL. These results highlight the importance of social factors in HRQOL and provide approaches for policy and practice to develop and implement tailored health interventions for older adults. Our findings may be transferable to municipalities in metropolitan areas of high-income European countries. CLINICAL TRIAL REGISTRATION: Not applicable.

Development of birth weight estimation model for Ethiopian population from sonographic evaluation.

BACKGROUND: Fetal birth weight (FBW) estimation involves predicting the weight of a fetus prior to delivery. This prediction serves as a crucial input for ensuring effective, accurate, and appropriate obstetric planning, management, and decision-making. Typically, there are two methods used to estimate FBW: the clinical method (which involves measuring fundal height and performing abdominal palpation) or sonographic evaluation. The accuracy of clinical method estimation relies heavily on the experience of the clinician. Sonographic evaluation involves utilizing various mathematical models to estimate FBW, primarily relying on fetal biometry. However, these models often demonstrate estimation errors that exceed acceptable levels, which can result in inadequate labor and delivery management planning. One source of this estimation error is sociodemographic variations between population groups in different countries. Additionally, inter- and intra-observer variability during fetal biometry measurement also contributes to errors in FBW estimation. METHODS: In this research, a novel mathematical model was proposed through multiple regression analysis to predict FBW with an accepted level of estimation error. To develop the model, population data consisting of fetal biometry, fetal ultrasound images, obstetric variables, and maternal sociodemographic factors (age, marital status, ethnicity, educational status, occupational status, income, etc.) of the mother were collected. Two approaches were used to develop the mathematical model. The first method was based on fetal biometry data measured by a physician and the second used fetal biometry data measured using an image processing algorithm. The image processing algorithm comprises preprocessing, segmentation, feature extraction, and fetal biometry measurement. RESULTS: The model developed using the two approaches were tested to assess their performance in estimating FBW, and they achieved mean percentage errors of 7.53% and 5.89%, respectively. Based on these results, the second model was chosen as the final model. CONCLUSION: The findings indicate that the developed model can estimate FBW with an acceptable level of error for the Ethiopian population. Furthermore, this model outperforms existing models for FBW estimation. The proposed approach has the potential to reduce infant and maternal mortality rates by providing accurate fetal birth weight estimates for informed obstetric planning.

Size-segregated source identification of water-soluble and water-insoluble metals and trace elements of coarse and fine PM in central Los Angeles.

In this study, the water-solubility and sources of metals and trace elements in both fine and coarse particulate matter (PM) were investigated in Central Los Angeles. Sampling was performed in the winter, spring, and summer of 2022 at the Particle Instrumentation Unit (PIU) of the University of Southern California located in the proximity of I-110 freeway. Both fine and coarse PM samples were collected using Personal Cascade Impactors (PCIS) and chemically analyzed to determine their water-soluble and water-insoluble metal content. Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) were used to determine the sources of soluble and insoluble metals and obtain their contributions to total metal concentration. Our results indicate that the water-solubility of most of the metals is higher in the fine size fraction compared to the coarse fraction. Seasonal variations in the water solubility of selected metals for both coarse and fine fractions were observed, with higher water-soluble metal concentrations in summer for several species (e.g., Fe, S, Pb, Cu, La, Ni, and Al), possibly due to higher photochemical processing, while in winter, almost all species exhibited higher insoluble fraction concentrations. The PCA and MLR analyses results showed that tire and brake wear was the most significant contributor to the total metals for both fine soluble and insoluble portions, accounting for 35% and 75% of the total metals, respectively. Combustion sources also contributed substantially to water-soluble metals for fine and coarse size ranges, representing 40% and 32% of the total metal mass, respectively. In addition, mineral dust and soil and re-suspended dust were identified as the highest contributors to coarse metals. The MLR analysis also revealed that secondary aerosols contributed 11% to the fine water-soluble metals. Our results suggest that non-tailpipe emissions significantly contribute to both coarse and fine PM metals in the Central Los Angeles region.

A regression analysis using simple descriptors for multiple dermal datasets: Going from individual membranes to the full skin.

In silico methods to estimate and/or quantify skin absorption of chemicals as a function of chemistry are needed to realistically predict pharmacological, occupational, and environmental exposures. The Potts-Guy equation is a well-established approach, using multi-linear regression analysis describing skin permeability (Kp) in terms of the octanol/water partition coefficient (logP) and molecular weight (MW). In this work, we obtained regression equations for different human datasets relevant to environmental and cosmetic chemicals. Since the Potts-Guy equation was published in 1992, we explored recent datasets that include different skin layers, such as dermatomed (including dermis to a defined thickness) and full skin. Our work was consistent with others who have observed that fits to the Potts-Guy equation are stronger for experiments focused on the epidermis. Permeability estimates for dermatomed skin and full skin resulted in low regression coefficients when compared to epidermis datasets. An updated regression equation uses a combination of fitted permeability values obtained with a published 2D compartmental model previously evaluated. The resulting regression equation was: logKp = -2.55 + 0.65logP - 0.0085MW, R2 = 0.91 (applicability domain for all datasets: MW ranges from 18 to >584 g/mol and -4 to >5 for logP). This approach demonstrates the advantage of combining mechanistic with structural activity relationships in a single modeling approach. This combination approach results in an improved regression fit when compared to permeability estimates obtained using the Potts-Guy approach alone. The analysis presented in this work assumes a one-compartment skin absorption route; future modeling work will consider adding multiple compartments.