Biophysical Parameters Research Articles

A Computational Modeling Approach to Investigate the Influence of Hyperthermia on the Tumor Microenvironment.

The biophysical properties of the tumor microenvironment differ substantially from normal tissues. A constellation of features, including decreased vascularity, lack of lymphatic drainage, and elevated interstitial pressure, diminishes the penetration of therapeutics into tumors. Localhyperthermia within the tumor can alter microenvironmental properties, such as interstitial fluid pressure, potentially leading to improvements in drug penetration. In this context, multi-physics computational models can provide insight into the interplay between the biophysical parameters within the tumor microenvironment and can guide the design and interpretation of experiments that test the bioeffects of local hyperthermia. This paper describes a step-by-step workflow for a computational model coupling partial differential equations describing electrical current distribution, bioheat transfer, and fluid dynamics. The main objective is to study the effects of hyperthermia delivered by a bipolar radiofrequency device on the interstitial fluid pressure within the tumor.The system of mathematical expressions linking electrical current distribution, bioheat transfer, and interstitial fluid pressure is presented, emphasizing the changes in the distribution of the interstitial fluid pressure that could be induced by the thermal intervention.

Seasonal evaluation and mapping of aboveground biomass in natural rangelands using Sentinel-1 and Sentinel-2 data

Rangelands play a vital role in developing countries’ biodiversity conservation and economic growth, since most people depend on rangelands for their livelihood. Aboveground-biomass (AGB) is an ecological indicator of the health and productivity of rangeland and provides an estimate of the amount of carbon stored in the vegetation. Thus, monitoring seasonal AGB is important for understanding and managing rangelands’ status and resilience. This study assesses the impact of seasonal dynamics and fire on biophysical parameters using Sentinel-1 (S1) and Sentinel-2 (S2) image data in the mesic rangeland of Limpopo, South Africa. Six sites were selected (3/area), with homogenous vegetation (10 plots/site of 30m2). The seasonal measurements of LAI and biomass were undertaken in the early summer (December 2020), winter (July–August 2021), and late summer (March 2022). Two regression approaches, random forest (RF) and stepwise multiple linear regression (SMLR), were used to estimate seasonal AGB. The results show a significant difference (p < 0.05) in AGB seasonal distribution and occurrence between the fire (ranging from 0.26 to 0.39 kg/m2) and non-fire areas (0.24–0.35 kg/m2). In addition, the seasonal predictive models derived from random forest regression (RF) are fit to predict disturbance and seasonal variations in mesic tropical rangelands. The S1 variables were excluded from all models due to high moisture content. Hence, this study analyzed the time series to evaluate the correlation between seasonal estimated and field AGB in mesic tropical rangelands. A significant correlation between backscattering, AGB and ecological parameters was observed. Therefore, using S1 and S2 data provides sufficient data to obtain the seasonal changes of biophysical parameters in mesic tropical rangelands after disturbance (fire) and enhanced assessments of critical phenology stages.

Hi-BDiSCO: folding 3D mesoscale genome structures from Hi-C data using brownian dynamics.

The structure and dynamics of the eukaryotic genome are intimately linked to gene regulation and transcriptional activity. Many chromosome conformation capture experiments like Hi-C have been developed to detect genome-wide contact frequencies and quantify loop/compartment structures for different cellular contexts and time-dependent processes. However, a full understanding of these events requires explicit descriptions of representative chromatin and chromosome configurations. With the exponentially growing amount of data from Hi-C experiments, many methods for deriving 3D structures from contact frequency data have been developed. Yet, most reconstruction methods use polymer models with low resolution to predict overall genome structure. Here we present a Brownian Dynamics (BD) approach termed Hi-BDiSCO for producing 3D genome structures from Hi-C and Micro-C data using our mesoscale-resolution chromatin model based on the Discrete Surface Charge Optimization (DiSCO) model. Our approach integrates reconstruction with chromatin simulations at nucleosome resolution with appropriate biophysical parameters. Following a description of our protocol, we present applications to the NXN, HOXC, HOXAand Fbn2 mouse genes ranging in size from 50 to 100kb. Such nucleosome-resolution genome structures pave the way for pursuing many biomedical applications related to the epigenomic regulation of chromatin and control of human disease.

Seasonality of Biophysical Parameters in Extreme Years of Precipitation in Pernambuco: Relations, Regionalities, and Variability

This study analyzed the seasonality of biophysical parameters in the extreme years of precipitation and the relationship with the monthly precipitation of the state of Pernambuco at the regional level (Pernambuco) and homogeneous precipitation zones: zone 1—semiarid, zone 2—transition and zone 3—coastal. For this, the biophysical parameters at the monthly level in the extreme years, 2004 (wet) and 2012 (dry) were related to precipitation data of 45 rainfall stations. Using the Google Earth Engine platform, we calculate the biophysical parameters with MODIS products: Albedo, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI) and surface temperature (ST). Considering the most critical period, between September and December, of a wet year (2004) with a dry year (2012), there is an average reduction of 14% of vegetation indices (NDVI, EVI and SAVI), a 60% reduction in NDWI, an increase of 4% in albedo and 3% in surface temperature. For monitoring the water conditions of the state of Pernambuco, the most appropriate biophysical parameter is the NDWI index and surface temperature. In addition to NDWI, it is recommended to use EVI for semiarid areas (zone 1) and ST for coastal areas (Zones 2 and 3).

Topically Applied Magnetized Saline Water Improves Skin Biophysical Parameters Through Autophagy Activation: A Pilot Study.

Background Water exposed to a magnetic field exhibits several changes in its properties, such as increased electrical conductivity, reduced density, and low surface tension. Additionally, it has reduced dissolved oxygen levels and becomes more alkaline. Previous experimental studies have demonstrated that exposure to saline alkaline water leads to a dose-dependent increase in the expression of autophagy-related genes. Here, we hypothesize that the topical application of magnetized alkaline water to the skin can activate autophagy and improve cutaneous biophysical parameters, making it a promising strategy for enhancing skin aesthetics. Methods Two distinct substudies were undertaken. Firstly, a 12-week, uncontrolled, open-label investigation was conducted with 20 females who desired to enhance the appearance of their facial and neck skin. Secondly, a molecular study was carried out on a subset of 10 females to investigate the serum's impact on two autophagy markers (Beclin-1 and mammalian/mechanistic target of rapamycin {mTOR}) in skin biopsies taken from the posterior neck area below the hair attachment line. Results After a period of 12 weeks, the application of the serum resulted in significant improvements in skin hydration within the stratum corneum (56 ± 14 arbitrary units {a.u.}) compared to the baseline measurement (47 ± 12 a.u.; p < 0.001). Moreover, the transepidermal water loss (TEWL) decreased from 14 ± 2 g/m2/hour to 11 ± 3 g/m2/hour (p < 0.001). The results also revealed a notable reduction in sebum content from 38 ± 7 µg/cm2 to 30 ± 4 µg/cm2 after the 12-week period of serum application (<0.001). Additionally, the melanin index (p < 0.01) and erythema index (p < 0.001) were both significantly lower at 12 weeks compared to baseline. The molecular study showed a 38% increase in Beclin-1 levels after 12 weeks of serum application on the posterior neck area, as measured from skin biopsies. In contrast, mTOR levels decreased by 24% from baseline to 12 weeks. Conclusion The application of magnetized saline water topically, within a serum formulation, shows potential in improving skin biophysical parameters for females seeking to enhance the appearance of their facial and neck skin. These beneficial effects are achieved through the activation of cutaneous autophagy, as evidenced by an increase in Beclin-1 expression and a decrease in mTOR content in the skin.

Potentiality of ultraspectral sensor in biophysical and biochemical vegetation parameter inversion

ABSTRACT Biophysical and biochemical vegetation parameters are important indicators for quantitatively monitoring vegetation growth status and ecosystem. Compared to hyperspectral data, the emergence of ultraspectral remote sensing technology supports the acquisition of thousands of more narrow bands in near-infrared spectrum. It is helpful to capture more detailed vegetation spectral features, thereby providing more accurate quantitative inversion. However, there are still many unknowns in utilizing ultraspectral sensors for vegetation biochemical and biophysical parameters inversion, particularly in extracting meaningful inversion information from thousands of spectral bands. Here, we discuss the potentiality, efficiency and accuracy of the ultraspectral sensor AisaIBIS in this field. Firstly, we conducted sensitivity analysis based on simulated sensor dataset and the PROSAIL model to determine the vegetation parameters suitable for inversion. Secondly, in order to enhance the inversion efficiency and reduce the effects of multicollinearity in ultraspectral data, we proposed the stepped asymptotic optimization strategy that combined random forest (RF) and Gaussian process regression (GPR-BAT) methods for dimensionality reduction. Lastly, using experimental data, we compared the inversion accuracy between traditional band selection methods and our proposed method, thereby validating the application potential of ultraspectral sensors. The experimental results indicate that leaf area index (LAI), chlorophyll content, and brown pigment are the most sensitive parameters to the spectral information and are suitable for inversion. Based on the stepped asymptotic optimization strategy, we effectively avoid incomplete information extraction caused by high correlation and spectral redundancy, achieving optimal bands covering the entire spectrum of the sensor. The LAI inversion shows a correlation of 0.691 and an RMSE of 1.955 with the original data, outperforming the accuracy of the traditional band selection methods RF and principal component analysis (PCA). Moreover, the inversion time has decreased by approximately 66% after the dimensionality reduction. This study illustrates the application value of monitoring vegetation parameters by ultraspectral sensor.

Using Satellite Data to Characterize Land Surface Processes in Morocco

This study endeavors to produce a comprehensive land cover map for Morocco, addressing the absence of such a detailed map in the country. Our research encompasses ecological and climatic aspects specific to Morocco, while the methods used can be adapted to various regions and countries, considering their unique climatic conditions and land cover types. A combination of MODIS and Landsat datasets was employed to create a 5 km resolution Land Use and Land Cover (LULC) map for the entire nation. The process involved the aggregation and advanced processing of these datasets using surface processes algorithms. The resulting LULC map is the first of its kind for Morocco, shedding light on land cover distribution nationwide. It shows that approximately 13.5% of the country is covered by forests, predominantly in the Atlas and Rif mountains, Rabat–Sale, and the southern regions. Grasslands occupy over 16% of the study area, mainly in the north-east and west. Urban areas, including major cities like Casablanca, Rabat, and Marrakech, span nearly 3400 km². Moreover, large areas of shrublands and bare lands are evident across the country, while agricultural lands account for almost 20% of the national territory, mainly in the interior plains and north-western Atlantic coast. This study forms a crucial basis for ecological and climatic research in Morocco and serves as a valuable reference for various disciplines such as agriculture, natural resource management, and climate modeling. The mapping of biophysical parameters for each land cover class is a key feature of our research, and these parameters will be instrumental in a subsequent study examining the impact of urban development on surface climate in Morocco. Overall, our study underscores the importance of understanding biophysical parameters in addressing environmental and societal challenges.

Biophysical, thermo-physiological and perceptual determinants of cool-seeking behaviour during exercise in younger and older women.

Women continue to be under-represented in thermoregulatory research despite their undergoing unique physiological changes across the lifespan. This study investigated the biophysical, thermo-physiological, and perceptual determinants of cool-seeking behaviour during exercise in younger and older women. Eleven younger (25±5years; 1.7±0.1m; 63.1±5.2kg) and 11 older women (53±6years; 1.7±0.1m; 65.4±13.9kg) performed a 40-min incremental cycling test in a thermoneutral environment (22±1.7°C; 36±4% relative humidity). Throughout the test, participants freely adjusted the temperature of a cooling probe applied to their wrists to offset their thermal discomfort. We continuously recorded the probe-wrist interface temperature to quantify participants' cool-seeking behaviour. We also measured changes in participants' rate of metabolic heat production, core and mean skin temperatures, and skin wetness. Finally, we body-mapped participants' skin heat, cold and wetness sensitivity. Our results indicated that: (1) older and younger women exhibited similar onset and magnitude of cool-seeking behaviour, despite older women presented reduced autonomic heat-dissipation responses (i.e., whole-body sweat losses); (2) older women's thermal behaviour was less determined by changes in core temperature (this being a key driver in younger women), and more by changes in multiple thermo-physiological and biophysical parameters (i.e., physical skin wetness, temperature and heat production); (3) older women did not present lower regional skin thermal and wetness sensitivity than younger women. We conclude that predictions of female cool-seeking behaviours based on thermo-physiological variables should consider the effects of ageing. These findings are relevant for the design of wearable cooling systems and sports garments that meet the thermal needs of women across the lifespan.

Longitudinal study of the interplay between the skin barrier and facial microbiome over 1 year.

Skin is a diverse ecosystem that provides a habitat for microorganisms. The skin condition and the skin microbiome interact each other under diverse environmental conditions. This study was conducted on 10 study participants for a one-year, from September 2020 to August 2021, to investigate the variability of skin microbiome and skin biophysical parameters [TEWL, hydration, and elasticity (R5)] according to season, and to understand the interplay between skin microbiome and skin characteristics. We identified that Cutibacterium, Corynebacterium, Staphyloccocus, unclassified genus within Neisseriaceae, and Streptococcus were major skin microbial taxa at the genus level, and fluctuated with the seasons. Cutibacterium was more abundant in winter, while Corynebacterium, Staphylococcus, and Streptococcus were more abundant in summer. Notably, Cutibacterium and skin barrier parameter, TEWL, exhibited a co-decreasing pattern from winter to summer and showed a significant association between Cutibacterium and TEWL. Furthermore, functional profiling using KEGG provided clues on the impact of Cutibacterium on the host skin barrier. This study enhances our understanding of the skin microbiome and its interplay with skin characteristics and highlights the importance of seasonal dynamics in shaping skin microbial composition.

Estimation of above ground biomass, biophysical and quality parameters of spinach (Spinacia Oleracea L.) using Sentinel-2 to support the supply chain

The estimation of biophysical and biochemical parameters using Sentinel-2 satellite imagery provides crucial information to support agronomic management and logistics for the processing industry, yet this approach has been poorly explored for vegetable crops, such as spinach (Spinacia oleracea L.). For this reason, Sentinel-2 satellite images were used to estimate biophysical and biochemical parameters of open field spinach crops (Above Ground Biomass (AGB), Canopy Nitrogen Content (CNC), Leaf Area Index (LAI), Canopy Chlorophyll Content (CCC), Leaf Dry Matter Content (LDMC), Leaf Nitrogen Content (LNC), Leaf Nitrate Content (LNO3−C) and Leaf Chlorophyll Content (LCC)). Spinach samples were collected in northern Italy in two different growing seasons (2020 – 2021) to measure biophysical and biochemical parameters which were linearly regressed via linear regression k-fold to five vegetation indices retrieved from Sentinel-2 images (MCARI, NDRE, NDVI, NDWI and SR). The AGB estimation models were also validated at field scale using historical data, totally independent from those used for the model calibration, collected by the processing industry between 2018 and 2021. Overall, canopy-level parameters (AGB, LAI, CNC, CCC) were estimated more accurately than leaf-level parameters (LDMC, LNC, LNO3−C, LCC). The highest accuracy was observed for the estimation of the canopy-scale parameter AGB (nRMSE = 9.48 %, R2 = 0.87) using MCARI, while the lowest accuracy was observed for the estimation of the leaf-scale parameter LCC (nRMSE = 29.24 %, R2 = 0.01) using NDWI. At field level, the validation of the AGB estimation models showed the highest performance using SR (nRMSE = 19.69 %, R2 = 0.21). This work demonstrates that using Sentinel-2 satellite images, it is feasible to estimate biophysical and biochemical parameters useful for monitoring the health of spinach crops for both agronomic management and the industrial supply chain.

Irreversibility of heat transfer with the curvature effect on peristaltic thrusting of micropolar fluid in a resilient channel in the presence of heat generation: nonlinear analysis

In this work, we perform an investigation of the peristaltic thrusting of an incompressible viscous micropolar fluid in a symmetric resilient channel with consideration of streamline curvature effects and inertia forces. We obtained the partial differential equations for fluid flow in two dimensions for micropolar fluids in addition to the heat equation. These equations have been transformed to another domain by using a transformation between the fixed and wave frames. Asymptotic solutions of have been obtained by using the compatible boundary conditions using the perturbation technique of arbitrary Reynolds number and long-drawn wavelength. The solutions for longitudinal, normal and spin velocities, stream function, heat transfer and rate of working walls have been obtained. The several of biophysical parameters influence has been demonstrated through the graphs. Both time flow rate and pressure gradient relations are obtained. Different characteristics for fluid flow, temperature profile, normal force, microrotation (spin) velocity and Nusslet number are greatly influenced at the channel walls by the presence of micropolar fluid. Trapping and pumping phenomena are further discussed. It is found that the maximum value for the Bejan number occurs near the centre of the channel, i.e. the heat transfer irreversibility dominates around the centre of the channel.

A novel, label-free, pre-equilibrium assay to determine the association and dissociation rate constants of therapeutic antibodies on living cells.

Monoclonal antibodies (Ab) represent the fastest growing drug class. Knowledge of the biophysical parameters (kon, koff and KD) that dictate Ab:receptor interaction is critical during the drug discovery process. However, with the increasing complexity of Ab formats and their targets, it became apparent that existing technologies present limitations and are not always suitable to determine these parameters. Therefore, novel affinity determination methods represent an unmet assay need. We developed a pre-equilibrium kinetic exclusion assay using recent mathematical advances to determine the kon, koff and KD of monoclonal Ab:receptor interactions on living cells. The assay is amenable to all human IgG1 and rabbit Abs. Using our novel assay, we demonstrated for several monoclonal Ab:receptor pairs that the calculated kinetic rate constants were comparable with orthogonal methods that were lower throughput or more resource consuming. We ran simulations to predict the critical conditions to improve the performance of the assays. We further showed that this method could successfully be applied to both suspension and adherent cells. Finally, we demonstrated that kon and koff, but not KD, correlate with in vitro potency for a panel of monoclonal Abs. Our novel assay has the potential to systematically probe binding kinetics of monoclonal Abs to cells and can be incorporated in a screening cascade to identify new therapeutic candidates. Wide-spread adoption of pre-equilibrium assays using physiologically relevant systems will lead to a more holistic understanding of how Ab binding kinetics influence their potency.

A deep transfer learning framework for mapping high spatiotemporal resolution LAI

Leaf area index (LAI) is an important variable for characterizing vegetation structure. Contemporary satellite-based LAI products with moderate spatial resolution, such as those derived from the MODIS observations, offer unique opportunities for large-scale monitoring but are insufficient for resolving heterogeneous landscapes. Although high-resolution satellite observations can derive detailed LAI maps, the low revisit frequency and the presence of cloud cover disrupt the temporal continuity of these high-resolution data, thereby leading to the fact that most current LAI inversion models for high spatial resolution data are based on single pixel and date without fully utilizing temporal information. Moreover, LAI estimation models trained solely using satellite products or simulations are also impeded by the inconsistencies between field and satellite LAI exist owing to local atmospheric, soil, and canopy conditions, while in-situ LAI measurements are sparse and insufficient for large-scale missions. To address these challenges, this study proposed a new framework based on deep transfer learning, which includes three key features that contribute to its high performance. Firstly, a Bi-directional Long Short-Term Memory (Bi-LSTM) model is pre-trained using MODIS reflectance and MODIS LAI products to capture the general non-linear relationship between reflectance and LAI and incorporate temporal dependencies as prior information to reduce uncertainties associated with ill-posed inversion problems and noises. Secondly, this pre-trained Bi-LSTM is transferred from satellite to field by fine-tuning with sparse in-situ LAI measurements to overcome issues arising from local inconsistencies. Thirdly, reconstructed Landsat time-series images via fusing MODIS and Landsat reflectance images are used as inputs of the Bi-LSTM to generate high-quality Landsat LAI products at both high spatial and temporal resolutions. To validate the proposed approach, field LAI measurements were collected at nine locations across the contiguous U.S. from 2000 to 2018, including three land cover types: croplands, grasslands, and forest. Quantitative assessments demonstrate that the Bi-LSTM outperforms three benchmarks, including a PROSAIL-based Look-Up Table (LUT) method, a random forest-based LAI retrieval and MODIS LAI (MCD15A3H), exhibiting lower RMSE and higher R2 in most cases. Additionally, the Bi-LSTM predictions yield lower random fluctuations than estimations from the LUT·, random forest and MODIS LAI, indicating the higher robustness of the proposed framework. The findings of this study highlight the value of transfer learning in estimation of vegetation biophysical parameters, which involves pre-training using sufficient existing satellite products to produce a generalized model and transferring knowledge from in-situ measurements to bridge gaps between satellite and field. By leveraging advanced transfer learning techniques and multi-source and multi-scale data, the proposed framework enables the production of long-term LAI maps at fine resolutions, facilitating downstream applications in regions characterized by high spatial heterogeneity.

Combining biophysical parameters with thermal and RGB indices using machine learning models for predicting yield in yellow rust affected wheat crop.

Evaluating crop health and forecasting yields in the early stages are crucial for effective crop and market management during periods of biotic stress for both farmers and policymakers. Field experiments were conducted during 2017-18 and 2018-19 with objective to evaluate the effect of yellow rust on various biophysical parameters of 24 wheat cultivars, with varying levels of resistance to yellow rust and to develop machine learning(ML) models with improved accuracy for predicting yield by integrating thermal and RGB indices with crucial plant biophysical parameters. Results revealed that as the level of rust increased, so did the canopy temperature and there was a significant decrease in crop photosynthesis, transpiration, stomatal conductance, leaf area index, membrane stability index, relative leaf water content, and normalized difference vegetation index due to rust, and the reductions were directly correlated with levels of rust severity. The yield reduction in moderate resistant, low resistant and susceptible cultivars as compared to resistant cultivars, varied from 15.9-16.9%, 28.6-34.4% and 59-61.1%, respectively. The ML models were able to provide relatively accurate early yield estimates, with the accuracy increasing as the harvest approached. The yield prediction performance of the different ML models varied with the stage of the crop growth. Based on the validation output of different ML models, Cubist, PLS, and SpikeSlab models were found to be effective in predicting the wheat yield at an early stage(55-60 days after sowing) of crop growth. The KNN, Cubist, SLR, RF, SpikeSlab, XGB, GPR and PLS models were proved to be more useful in predicting the crop yield at the middlestage(70days after sowing) of the crop, while RF, SpikeSlab, KNN, Cubist, ELNET, GPR, SLR, XGB and MARS models were found good to predict the crop yield at late stage (80days after sowing). The study quantified the impact of different levels of rust severity on crop biophysical parameters and demonstrated the usefulness of remote sensing and biophysical parameters data integration using machine-learning models for early yield prediction under biotically stressed conditions.

Role of green synthesized nano iron oxide in alleviating the cadmium toxicity in Brassica oleracea var. italica seedlings

This work accounts for the green synthesis of iron oxide nanoparticles using Moringa oleifera leaf extract as a stabilizing and reducing agent. The preliminary confirmation of synthesized Fe3O4NPs was done by UV–visible spectroscopy in the wavelength range of 190–900 nm. The crystalline nature of Fe3O4 NPs was established via X-ray diffraction analysis. FT-IR studies reveal the bioactive phytochemicals present in the Moringa leaf extract based Fe3O4 NPs. A DLS particle size analyser was used to determine the size of green synthesized Fe3O4 NPs and its spherical morphology was determined by using SEM and TEM techniques. The growth of Brassica oleracea var. italica seedlings was measured by analysing biophysical and biochemical parameters in two concentrations of Fe3O4 NPs i.e. 15 mg/L and 30 mg/L alone and in combination with cadmium. Treatments were designated as control C, NP1, NP2, Cd+NP1, Cd+NP2, and Cd. Fe3O4 NPs treated broccoli seedlings showed noteworthy improvement in shoot length, root length, plant biomass, total chlorophyll content, carotenoid content, total soluble sugars, protein content, and nitrate reductase activity, while decline in electrolyte leakage, lipid peroxidation, proline content, superoxide dismutase activity and catalase activity was reported as compared to control. Exposure of iron oxide nano-treatments was found efficacious in reducing cadmium accumulation at a concentration of 30 mg/L. The seedlings exposed to NP2 (30 mg/L) concentration of green synthesized nano iron oxide increased nutrient uptake and thus stimulating plant growth, and also improved morphological and physiological characteristics of Brassica oleracea seedlings. Therefore, the present study reported that bio-based iron oxide nanoparticles were significantly useful for plant growth, by enhancing the plant defense mechanism in response to cadmium induced toxicity.

The Effects of Incontinence Pad Application on Loaded Skin With Reference to Biophysical and Biochemical Parameters: An Exploratory Cohort Study Using a Repeated-Measures Design.

The purpose of this study was to evaluate temporal changes in skin responses following exposure to moisture alone or moisture in combination with mechanical loading. Comparison cohort with a repeated-measures design. The sample comprised 12 healthy volunteers. Participants were purposely sampled from 2 different age groups; half were 32 to 39 years old and half were 50 to 62 years old. Participants identified as White, Black, or mixed; 83% (n = 10) identified as White; 8 (67%) were female. Four sites at the sacrum were challenged with the application of specimens taken from 2 absorbent products; the pad specimens were applied dry or saturated with synthetic urine (SU; pH = 8); a further site from the sacral skin was also selected and used as a control. Skin assessments were performed at different points in time: (1) 60 minutes after exposure to dry or SU-saturated pad specimens; (2) 60 minutes after exposure to pads and mechanical loading (application of pressure in the form of 45°C high sitting); and (3) 30 minutes after removal of all pads (recovery period). Outcome measures were transepidermal water loss (TEWL), stratum corneum (SC) hydration, erythema, pH, and skin inflammatory biomarkers measured at each of the time points described earlier. The control site and those exposed to dry pads showed minimal time-dependent changes irrespective of the parameter investigated. In contrast, significant increases in TEWL (P = .0000007) and SC hydration responses (P = .0000007) were detected at the sites under absorbent pad specimens after saturation with SU (exposure to moisture). In some participants, TEWL and SC hydration parameters were significantly higher during pressure application. Skin pH remained in the mildly acidic range throughout the test session, and no consistent trends were observed with erythema. Skin inflammatory biomarkers also exhibited considerable variability across participants; none changed significantly over time. Significant differences (P = .02) were also detected following the exposure of moisture in combination with pressure. We evaluated an array of parameters to identify changes following skin exposure to 2 absorbent pads in the presence and absence of SU and mechanical loading. Analysis revealed changes in skin barrier properties in the presence of moisture and/or pressure. This observation suggests a need for frequent pad changing as well as periods of skin off-loading to protect the skin health of individuals with incontinence.

Above ground tree biomass modeling using machine learning algorithms in western Terai Sal Forest of Nepal

The monitoring of forest biomass is a crucial biophysical parameter in forest ecosystems, as it provides valuable information for managing forests sustainably and tracking carbon circulation statistics. To achieve sustainable forest management, it is essential to monitor and study forest resources, particularly biomass. This study aimed to model above ground tree biomass (AGTB) using Machine Learning Algorithms (MLAs) in the western terai Sal forest of Nepal. AGTB was calculated using a systematic inventory sample plot, while spectral and textural variables were processed and masked for the study area using Sentinel-2A satellite imagery. Three MLAs namely support vector machine (SVM), random forest (RF), and stochastic gradient boosting (SGB), were employed for modeling with eight categorized variable datasets. Among the MLAs, the RF algorithm with a combination of gray-level co-occurrence matrix (GLCM) and raw bands (RB) dataset variable demonstrated the best performance, with a low RMSE value of 78.81 t ha−1 in the test data. However, the AGTB range from this model ranged from 118.34 to 425.97 t ha−1. The study found that traditional indices, raw bands, and GLCM texture from near-infrared were important variables for AGTB. Nevertheless, the RF algorithm and the dataset combination of GLCM plus raw bands (RB) exhibited excellent performance in all model runs. Thus, this pioneering study on comparative MLAs-based AGTB assessment with multiple datasets variables can provide valuable insights for new researchers and the development of novel approaches for biomass/carbon estimation techniques in Nepal.

Tuning the potency and selectivity of ImmTAC molecules by affinity modulation.

T-cell-engaging bispecifics have great clinical potential for the treatment of cancer and infectious diseases. The binding affinity and kinetics of a bispecific molecule for both target and T-cell CD3 have substantial effects on potency and specificity, but the rules governing these relationships are not fully understood. Using immune mobilizing monoclonal TCRs against cancer (ImmTAC) molecules as a model, we explored the impact of altering affinity for target and CD3 on the potency and specificity of the redirected T-cell response. This class of bispecifics binds specific target peptides presented by human leukocyte antigen on the cell surface via an affinity-enhanced T-cell receptor and can redirect T-cell activation with an anti-CD3 effector moiety. The data reveal that combining a strong affinity TCR with an intermediate affinity anti-CD3 results in optimal T-cell activation, while strong affinity of both targeting and effector domains significantly reduces maximum cytokine release. Moreover, by optimizing the affinity of both parts of the molecule, it is possible to improve the selectivity. These results could be effectively modelled based on kinetic proofreading with limited signalling. This model explained the experimental observation that strong binding at both ends of the molecules leads to reduced activity, through very stable target-bispecific-effector complexes leading to CD3 entering a non-signalling dark state. These findings have important implications for the design of anti-CD3-based bispecifics with optimal biophysical parameters for both activity and specificity.

Dry electrode geometry optimization for wearable ECG devices

Wearable electronic devices, particularly for health monitoring, have seen rapid advancements in recent times. Among the various biophysical parameters that are of interest in a wearable device, an electrocardiogram (ECG) is critical as it enables detection of cardiovascular-related ailments and assessment of overall cardiac health. In a wearable ECG device, the choice of electrode design and material plays a key role in the performance of the sensor. In this work, we have explored various dry electrode-based sensor design geometries to realize a compact, lightweight, portable, gel-free wearable ECG patch that would aid in point-of-care (PoC) diagnostics. Furthermore, we have studied the influence of the region of the body at which the measurements were made under different body positions across varying external stimuli. We have studied the influence of surface area, perimeter and resistance offered by the electrodes on the ECG signal acquisition, its effects on device performance and found the hexagonal labyrinth configuration to be the most suitable candidate. A prototype of a wearable ECG patch was made by combining this electrode configuration and interfacing with wireless communication capabilities, and the results were compared with a commercially available portable ECG monitor. Such a device could find potential application in remote healthcare and ambulatory care settings, and as a PoC and a preventive medical device.

Two separate mechanisms are involved in membrane permeabilization during lipid oxidation

Lipid oxidation is a universal degradative process of cell membrane lipids that is induced by oxidative stress and reactive oxygen and nitrogen species (RONS) in multiple pathophysiological situations. It has been shown that certain oxidized lipids alter membrane properties, leading to a loss of membrane function. Alteration of membrane properties is thought to depend on the initial membrane lipid composition, such as the number of acyl chain unsaturations. However, it is unclear how oxidative damage is related to biophysical properties of membranes. We therefore set out to quantify lipid oxidation through various analytical methods and determine key biophysical membrane parameters using model membranes containing lipids with different degrees of lipid unsaturation. As source for RONS, we used cold plasma, which is currently developed as treatment for infections and cancer. Our data revealed complex lipid oxidation that can lead to two main permeabilization mechanisms. The first one appears upon direct contact of membranes with RONS and depends on the formation of truncated oxidized phospholipids. These lipids seem to be partly released from the bilayer, implying that they are likely to interact with other membranes and potentially act as signaling molecules. This mechanism is independent of lipid unsaturation, does not rely on large variations in lipid packing, and is most probably mediated via short-living RONS. The second mechanism takes over after longer incubation periods and probably depends on the continued formation of lipid oxygen adducts such as lipid hydroperoxides or ketones. This mechanism depends on lipid unsaturation and involves large variations in lipid packing. This study indicates that polyunsaturated lipids, which are present in mammalian membranes rather than in bacteria, do not sensitize membranes to instant permeabilization by RONS but could promote long-term damage.