Size Descriptors Research Articles

Deciphering the mass transfer and diffusion behavior in the oxidation of fatty alcohols to fatty acids over Pt/MCM-41

Selective oxidation of long-chain fatty alcohols into acids is an important value-added reaction. However, exploring the basic catalytic steps over Pt-based catalysts throughout the entire oxidation process is still ambiguous. In this work, we systematically investigated the synergistic mechanisms of adsorption, reaction, and diffusion over Pt/MCM-41 for normal/isomeric alcohols oxidation into acids via molecular dynamics, in-situ characterization, and experiments. Specifically, diffusion coefficients decrease with the increase of the molecular weight of normal molecules due to the increased van der Waals forces, while isomeric alcohols exhibit more complex patterns originated from the steric hindrance between Pt particles and mesopores. To quantitatively describe this pattern, a cluster size descriptor of dPt0.75×dPore0.25 was defined. Notably, 2-ethylhexanol exhibits the best self-diffusion coefficients at the descriptor value of 3.14. Correspondingly, the oxidation of 2-ethylhexanol to 2-ethylhexanoic acid displays highest reaction conversion (68.67 %) and selectivity (65.59 %).

Evaluation of NCI-Designated Cancer Center and Comprehensive Cancer Center Survivorship-Focused Websites: Information Provided and Accessibility.

Individuals with a history of cancer increasingly seek health information from online resources, including NCI-designated Cancer Center websites. Centers receive NCI designation because they provide excellent care and engage in cutting-edge research. However, the information presented on these webpages and their accessibility is unknown. An evaluation of the survivorship-focused webpages from NCI-designated Cancer Centers is needed to assess survivorship information and accessibility of these webpages. We conducted an evaluation of the survivorship-focused webpages from 64 NCI-designated Cancer Centers. We evaluated where survivorship-focused webpages were housed, if there was a survivorship clinic or program, target audience of the webpage, how cancer survivor was defined, contact methods, and available resources. Accessibility outcomes included readability, font type, font size, color scheme, and alternative text (alt text) descriptors. An artificial intelligence (AI) audit was conducted to assess if the webpage was compliant with national accessibility guidelines. Most cancer centers had a survivorship-focused webpage, with 72% located on the cancer center's website and 28% on a health system website. Survivorship information available varied considerably and was often lacking in detail. Although three-quarters of webpages targeted patients only, variable definitions of cancer survivor were observed. Accessibility issues identified included inconsistent use of alt text descriptors, font size smaller than 15 points, and color schemes without adequate contrast. The average reading-level of information presented was above 12th grade. Only 9% of webpages were compliant with online accessibility guidelines; 72% semicompliant and 21% were noncompliant. Information presented on NCI-designated Cancer Center survivorship-focused webpages was inconsistent, often lacking, and inaccessible. NCI-designated Cancer Centers are role models for cancer research in the United States and have an obligation to provide survivorship information. Changes to content and website design are needed to provide better information for individuals seeking resources and health information relative to their cancer and care.

Low evolutionary potential for blue-green egg colouration in a wild bird population.

Exploring the evolutionary architecture of female sexual traits and their potential evolvability is important to understand their possible role as post-mating sexual signals. Egg colouration has been proposed to be one of these post-mating sexual signals, honestly advertising female quality in birds, especially in blue-green laying species. In this study, we used an animal model in a Bayesian framework to estimate the evolvability of multiple descriptors of blue-green egg colouration and egg size in a wild long-term monitored population of spotless starlings (Sturnus unicolor). Our results show low to moderate heritability (h2 = 0.31 - 0.44) for three egg colour descriptors (blue-green chroma, chroma and lightness) and egg size. Using the coefficient of additive genetic variance (CVA) and the evolvability (IA) as proxies of evolutionary potential of all components of this trait, we found low values of CVA for all these variables, suggesting a small evolutionary potential of these phenotypic traits, contrasting to previous results reported in another blue-green egg laying species. Our results indicate a modest raw genetic material of this trait on which sexual selection can act upon and, therefore, a small probability for these traits to respond easily to selection.

Identifying the key structural features of Ni-based catalysts for the CO2 methanation reaction

Identifying the parameters influencing the selectivity of products is prominently significant for fabricating efficient catalysts. However, little progress has been made in describing the regulation rule of CH4 selectivity toward the CO2 methanation reaction, which is considered a vital process for CO2 emission and conversion. Herein, we disclosed the integral role of the electronegativity of M atoms in Ni-MOx supported catalysts to producing CH4 molecules, which the satisfactory catalytic performance stemmed from the regulated ability to capture CO2 molecules and CO intermediates. More importantly, alongside the extensively studied descriptor of particle size, we uncovered a strong correlation between the electronegativity of M atom and CH4 selectivity, in which the CO/CO2 adsorption capacity upon the Ni/NiOx/MOx interfaces exhibited a volcanic trend based on the electronegativity of M atoms in MOx supports. The screened Ni-Y2O3 composite catalysts demonstrated excellent CO2 methanation performance, suggesting the powerful practicability of the electronegativity of M atoms in MOx supports as a descriptor. These findings not only shed fundamental insight into the reaction pathway but also paved the way for the rational design of Ni-based catalysts based on the simple descriptor of electronegativity.

Assessment of body mass-related covariates for rifampicin pharmacokinetics in healthy Caucasian volunteers

PurposeCurrently, body weight-based dosing of rifampicin is recommended. But lately, fat-free mass (FFM) was reported to be superior to body weight (BW). The present evaluation aimed to assess the influence of body mass-related covariates on rifampicin’s pharmacokinetics (PK) parameters in more detail using non-linear mixed effects modeling (NLMEM).MethodsTwenty-four healthy Caucasian volunteers were enrolled in a bioequivalence study, each receiving a test and a reference tablet of 600 mg of rifampicin separated by a wash-out period of at least 9 days. Monolix version 2023R1 was used for NLMEM. Monte Carlo simulations (MCS) were performed to visualize the relationship of body size descriptors to the exposure to rifampicin.ResultsA one-compartment model with nonlinear (Michaelis–Menten) elimination and zero-order absorption kinetics with a lag time best described the data. The covariate model including fat-free mass (FFM) on volume of distribution (V/F) and on maximum elimination rate (Vmax/F) lowered the objective function value (OFV) by 56.4. The second-best covariate model of sex on V/F and Vmax/F and BW on V/F reduced the OFV by 51.2. The decrease in unexplained inter-individual variability on Vmax/F in both covariate models was similar. For a given dose, MCS showed lower exposure to rifampicin with higher FFM and accordingly in males compared to females with the same BW and body height.ConclusionOur results indicate that beyond BW, body composition as reflected by FFM could also be relevant for optimized dosing of rifampicin. This assumption needs to be studied further in patients treated with rifampicin.

Predicting Bulk Density for Agglomerated Raspberry Ketone via Integrating Morphological and Size Metrics Using Artificial Neural Networks

The bulk density of the particles, which is directly related to transportation and storage costs, is an important basic characteristic of products as well as an important parameter in many processing systems. This work quantified the relationship between the tapped bulk density of raspberry ketone with different degrees of agglomeration and morphological metrics (particle shape descriptors and roughness descriptors) and size metrics (size descriptors) and developed an artificial neural network (ANN) prediction model for the tapped bulk density of raspberry ketone. Samples prepared under different conditions were sieved and remixed, the tapped bulk density of the particles was then measured, and the descriptor features of the particles were obtained by combining them with image processing. The dimensions of the variables were decreased by principal component analysis and variance processing. To overcome the hyperparameter estimation of the heuristic-based artificial neural networks, the network model architectures were optimized by a neural architecture search strategy combining two-objective optimization. The results demonstrated that the tapped bulk density of raspberry ketone products is not only related to the descriptors of particle size and shape but also has a non-negligible relationship with particle roughness descriptors. The performance of the optimal ANN model demonstrated that the model can well predict the tapped bulk density of raspberry ketone with different degrees of agglomeration. The ANN model obtained by extracting morphology and size metrics through online image analysis can be used to measure the tapped bulk density in real-time and has the potential to be used for developing model-based online process monitoring.

Assessment of Dosing Strategies for Pediatric Drug Products.

Pediatric drug dosing is challenged by the heterogeneity of developing physiology and ethical considerations surrounding a vulnerable population. Often, pediatric drug dosing leverages findings from the adult population; however, recent regulatory efforts have motivated drug sponsors to pursue pediatric-specific programs to meet an unmet medical need and improve pediatric drug labeling. This paradigm is further complicated by the pathophysiological implications of obesity on drug distribution and metabolism and the roles that body composition and body size play in drug dosing. Therefore, we sought to understand the landscape of pediatric drug dosing by characterizing the dosing strategies from drug products recently approved for pediatric indications identified using FDA Drug Databases and analyze the impact of body size descriptors (age, body surface area, weight) on drug pharmacokinetics for several selected antipsychotics approved in pediatric patients. Our review of these pediatric databases revealed a dependence on body size-guided dosing, with 68% of dosing in pediatric drug labelings being dependent on knowing either the age, body surface area, or weight of the patient to guide dosing for pediatric patients. This dependence on body size-guided dosing drives the need for special consideration when dosing a drug in overweight and obese patients. Exploratory pharmacokinetic analyses in antipsychotics illustrate possible effects of drug exposure when applying different dosing strategies for this class of drugs. Future efforts should aim to further understand the pediatric drug dosing and obesity paradigm across pediatric age ranges and drug classes to optimize drug development and clinical care for this patient population.

Auxetic System Based on Rotating Hexagons and Triangles

An auxetic metamaterial that exhibits isotropic in‐plane deformation is introduced herein in the form of rotating rigid units, consisting of regular hexagons and equilateral triangles, that are connected at their vertices. By inspection, the in‐plane Poisson's ratio is at a constant value of ν = −1. The size descriptor of this metamaterial can be expressed as a function of rotating unit sizes and their internal angle by means of geometrical construction. By energy method, the effective in‐plane Young's modulus of this metamaterial has been established and shown to be governed by the rotational stiffness at the connecting hinges in addition to the relative sizes of the rotating units and their internal angle. Results indicate that the in‐plane Young's modulus is linearly proportional to the rotational stiffness at the connecting hinges and inversely proportional to the rigid unit size. In addition, the Young's modulus is governed by the ratio of the hexagon‐to‐triangle size for small internal angle, and rapidly approaches extreme value as the internal angle approaches right angle. The characteristics of the proposed metamaterial would allow its use as a sieve that not only controls the filtering of particles by size but also by the aspect ratio of rod‐like particles.

Characterization of Kinetics-Controlled Morphologies in the Growth of Silver Crystals from a Primary Lead Melt

Silver, a precious metal, can be recovered as a by-product of the processing of non-ferrous metals such as lead. In this work, silver crystals grown from the controlled cooling of a 10% silver–90% lead melt have been examined to quantify crystal morphologies developed under industrial conditions. X-ray tomography (XCT) is adapted to quantify the size and morphology of silver crystal structures grown from the Ag-Pb melt. The examination utilized high X-ray energies and small sample sizes to mitigate attenuation and enhance image quality. Examination of single crystal dendrites under high magnification demonstrates that silver crystals, even those grown under commercial conditions, yield a Face-Centered Cubic (FCC) crystalline lattice, which could be important for the practical extension of this work to the commercial production of Ag nano-crystals and crystalline supra-molecular structures. The crystals observed are composed of multiple twinned euhedral grains in a variety of dendritic to acicular arrangements, yielding a substantial heterogeneity of crystalline forms. XCT data were used to generate size and shape descriptors for the individual crystals. The results were compared to an equivalent set of descriptors generated from laser sizing examination of a sample of unconsolidated crystals from the same experimental run. The correspondence to within 9% of the crystal equivalent diameters determined independently by the XCT and laser sizing demonstrates a favorable outcome in particle sizing as achieved by visual inspection of XCT results. XCT examination of crystal assemblages identifies small octahedral crystals and larger triangular platelets. The structures expected for FCC crystals grown at thermodynamically controlled conditions are not observed in our systems, suggesting the possibility of the first crystal nuclei form at such conditions, but their growth transition to kinetically controlled mechanisms occurs as their size increases above a threshold cutoff. Based on literature observations, this size threshold is much smaller than the resolution of the XCT instrumentation employed herein. Our characterization data are in fact consistent with thermodynamics/kinetics—and then kinetics-controlled mechanisms—as the crystal size increases. This observation is important because the systems considered here are representative of commercial processes. As such, this work extends prior crystal growth concepts, which were explored in aqueous systems often probed by electrodeposition.

Machine learning-guided exploration and experimental assessment of unreported compositions in the quaternary Ti-Zr-Cu-Pd biocompatible metallic glass system

Due to their outstanding elastic limit, biocompatible Ti-based bulk metallic glasses (BMGs) are candidate materials to decrease the size of medical implants and therefore reduce their invasiveness. However, the practical use of classical Ti-BMGs in medical applications is in part hindered by their high copper content: more effort is thus required to design low-copper Ti-BMGs. In this work, in line with current rise in AI-driven tools, machine learning (ML) approaches, a neural-network ML model is used to explore the glass-forming ability (GFA) of unreported low-copper compositions within the biocompatible Ti-Zr-Cu-Pd system. Two types of models are trained and compared: one based on the alloy composition only, and a second based on various features derived from the alloying elements. Contrary to expectation, the predictive power of both models in evaluating GFA is similar. The compositional space identified by ML as promising is experimentally assessed, finding unfortunately low GFA. These results indicate that the ML approach may be premature for specific composition tuning of amorphous metallic materials. We emphasise that the development of ML tools in GFA prediction requires an improvement of the dataset, in terms of homogeneity, size and GFA descriptors, which must be supported by increased reporting of high-quality experimental GFA measurements, both positive and negative. Statement of significanceBiocompatible Ti-based bulk metallic glasses (BMGs) are candidate materials for use in the next generation of minimally invasive dental implants where improved mechanical properties, such as high strength are required. Despite promising in vitro/vivo evaluations, implementation of alloys for practical applications is partly hindered by the presence of copper as the main alloying element. Recent studies have presented AI-guided and machine learning strategies as appealing approaches to understand and describe the glass forming ability (GFA) of BMG-forming compositions. In this work, we employ and evaluate the capacity of a machine-learning model to explore low-copper compositional spaces in the biocompatible Ti-Zr-Cu-Pd system. Our results highlight the limits of such a computational approach and suggest improvements for future designing routes.

Population Pharmacokinetics of Single Bolus Dose Fentanyl in Obese Children.

Childhood obesity is a significant problem. Obesity may alter the pharmacokinetics (PKs) of medications. Fentanyl is commonly used for procedural sedation, but there is a paucity of bolus dose fentanyl PK data in obese children. Better understanding of fentanyl PK in obese children would facilitate dosing recommendations. We conducted a study involving children with and without obesity to assess the potential differences in bolus dose fentanyl PK between the 2 groups. We enrolled children 2 to 12 years of age with and without obesity, defined as >95th percentile body mass index (BMI) for age and sex, undergoing elective tonsillectomy ± adenoidectomy. After induction, subjects had 2 intravenous (IV) lines placed in 2 different extremities: 1 for medications and IV fluids and 1 for obtaining blood aliquots for fentanyl concentration analysis. After administration of 1 mcg/kg of fentanyl based on total body weight (TBW), blood sample collections for fentanyl concentration analysis were attempted at 5, 15, 30, 60, 90, and 120 minutes. Five-minute fentanyl concentrations were compared between obese and nonobese cohorts. Population PK analysis to examine the differences between obese and nonobese children was performed and included various body size descriptors, such as TBW, BMI, and fat-free mass (FFM), to examine their influence on model parameters. Half of the 30 subjects were obese. Mean fentanyl concentrations at 5 minutes were 0.53 ng/mL for the nonobese group and 0.88 ng/mL for the obese group, difference 0.35 ng/mL (95% CI, 0.08-0.61 ng/mL; P = .01). Population PK analysis showed that FFM was a significant covariate for the central volume of distribution. The potential clinical effect of an IV bolus dose of fentanyl based on TBW versus FFM in an obese child was assessed in a simulation using our model. 1 mcg/kg fentanyl dose based on TBW resulted in an approximately 60% higher peak fentanyl effect site concentration than dosing based on FFM. Our data demonstrated higher peak plasma fentanyl concentrations in obese compared to nonobese subjects. Population PK analysis found that FFM was a significant covariate for the central volume of distribution. Model simulation showed dosing of fentanyl in obese children based on TBW resulted in significantly higher peak concentrations than dosing based on FFM. Based on this modeling and the known concentration-effect relationship between fentanyl and adverse effects, our results suggest that bolus dosing of fentanyl in obese children should be based on FFM rather than TBW, particularly for procedures of short duration.

Fully spherical 3D datasets on sedimentary particles: Fast measurement and evaluation

AbstractRecently it became increasingly evident that the statistical distributions of size and shape descriptors of sedimentary particles reveal crucial information on their evolution and may even carry the fingerprints of their provenance as fragments. However, to unlock this trove of information, measurement of traditional geophysical shape descriptors (mostly detectable on 2D projections) is not sufficient; fully spherical 3D imaging and mathematical algorithms suitable to extract new types of inherently 3D shape descriptors are necessary. Available 3D imaging technologies force users to choose either speed or full sphericity. Only partial morphological information can be extracted in the absence of the latter (e.g., LIDAR imaging). In the case of fully spherical imaging, speed was proved to be prohibitive for obtaining meaningful statistical samples, and inherently 3D shape descriptors were not extracted. Here we present a new method by complementing a commercial, portable 3D scanner with simple hardware to quickly obtain fully spherical 3D datasets from large collections of sedimentary particles. We also present software for the automated extraction of 3D shapes and automated measurement of inherently 3D-shape properties. This technique allows for examining large samples without the need for transportation or storage of the samples, and it may also facilitate the collaboration of geographically distant research groups. We validated our software on a large sample of pebbles by comparing previously hand-measured parameters with the results of automated shape analysis. We also tested our hardware and software tools on a large pebble sample in Kawakawa Bay, New Zealand.

Development and internal validation of a clinical prediction model for individualized dosing of BAY 81-8973, A full-length recombinant factor VIII, in pediatric patients with haemophilia A

BackgroundAs the most commonly used coagulation factor VIII (FVIII) concentrate in China, the individualized dosing prediction model of Kovaltry (BAY81–8973) is not fully investigated in pediatric patients. The prophylaxis tailored by population pharmacokinetic (PopPK) model can optimize dosing regimens. ObjectivesThis study aimed to develop PopPK models of BAY 81–8973 in pediatric patients, identify quantitative relationships of blood type (as a substitution for von Willebrand factor) on FVIII clearance and provide model-informed precision dosing (MIPD) procedures. MethodsPediatric patients with severe hemophilia A were enrolled and PK tests were conducted. The blood samples were collected at six time point. One-stage-based activated partial thromboplastin time was used for FVIII activity. Basic demographics and key covariates (blood type and von Willebrand factor antigen) were collected. A nonlinear mixed-effect modeling (NONMEM) approach was employed to establish PopPK model. Simulations were performed to evaluate current dosing regimens and present MIPD strategies. ResultsA total of 30 pediatric patients were included in the analysis. In the final model, Fat-free mass calculated from weight, age, and height was included as a size descriptor which affect FVIII apparent volume of distribution and clearance. Both von Willebrand factor antigen (VWF:Ag) and blood type accounted for the interindividual variability of FVIII clearance, but only one can retain in the final model. Therefore, two PopPK models based on VWF:Ag or blood type were developed. When the VWF:Ag value is doubled, the FVIII clearance is reduced by 35 %. Compared with blood type non-O, the clearance in pediatric patients with blood type O increased by 25.9 %. Weight-based dosing without regard to age and blood type resulted in large differences in FVIII trough activity. Patient demographics, dosing information, sparse blood samples and PopPK model together with Bayesian estimate constituted the MIPD workflow. Using it, the individual parameters and optimized dosing regimen could be achieved. ConclusionThis is the first predictive model designed to predict individualized dosing of BAY 81–8973 in pediatric patients with Hemophilia A. These results are useful in the PK-guided prophylaxis among pediatric patients and hold great potential to improve their long-term clinical outcomes.

Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree species

Invasive species threaten crops and ecosystems worldwide. Therefore, we sought to understand the relationship between the geographic distribution of species populations and the characteristics of seeds using new techniques such as seed image analysis, multivariate analysis, and machine learning. This study aimed to characterize Leucaena leucocephala (Lam.) de Wit. seeds from spatially dispersed populations using digital images and analyzed their implications for genetic studies. Seed size and shape descriptors were obtained using image analysis of the five populations. Several analyses were performed including descriptive statistics, principal components, Euclidean distance, Mantel correlation test, and supervised machine learning. This image analysis technique proved to be efficient in detecting biometric differences in L. leucocephala seeds from spatially dispersed populations. This method revealed that spatially dispersed L. leucocephala populations had different biometric seed patterns that can be used in studies of population genetic divergence. We observed that it is possible to identify the origin of the seeds from the biometric characters with 80.4% accuracy (Kappa statistic 0.755) when we applied the decision tree algorithm. Digital imaging analysis associated with machine learning is promising for discriminating forest tree populations, supporting management activities, and studying population genetic divergence. This technique contributes to the understanding of genotype-environment interactions and consequently identifies the ability of an invasive species to spread in a new area, making it possible to track and monitor the flow of seeds between populations and other sites.

Completeness of Resection and Long-Term Survival of Patients Undergoing Resection for Pathologic T3 NSCLC: An International Association for the Study of Lung Cancer Analysis

IntroductionCurrently, tumors with different histopathologic characteristics and oncologic outcomes comprise the T3 category of the eight edition TNM classification for lung cancers. To better understand the T3 category, we evaluated completeness of resection and long-term survival in patients undergoing resection for T3 NSCLC. MethodsThe International Association for the Study of Lung Cancer 1999 to 2010 database was queried for patients with pathologic T3N0M0 NSCLC who underwent lobectomy or pneumonectomy. The primary outcome evaluated was overall survival (OS) stratified by T3 descriptors and completeness of resection. ResultsOf 1448 patients with T3N0M0 tumors, 1187 (82.0%) had a single descriptor defining them as T3. T3 tumors with chest wall infiltration (CWI) or parietal pleura infiltration (PL3) had the highest rates of incomplete resection (9.8% and 8.4%, respectively), and those classified as T3 by size only had the lowest rate of incomplete resection (2.9%). Individual T3 descriptors were associated with significant differences in OS (p = 0.005). When tumors with similar survival and complete resection rates were grouped, patients with T3 tumors characterized by size or the presence of a separate nodule (SN) in the same lobe had better 5-year OS than patients with tumors characterized by PL3 or CWI (size/SN 60% versus CWI/PL3 53%, p = 0.017) independent of completeness of resection. ConclusionsSignificant differences in 5-year OS were associated with size, SN, PL3, or CWI T3 descriptors. Subdividing pathologic T3N0M0 tumors according to the presence or absence of CWI or PL3 may increase the prognostic accuracy of tumor staging.

Hydrothermal carbonization of size-controlled carbon microspheres from waste disposable towel with correlation analyze

The utilization of one-step hydrothermal carbonization (HTC) process on disposable waste towels resulted in the production of carbon microspheres characterized by uniform particle sizes. By adjusting the concentration of reactants, temperature, and residence time during hydrothermal carbonization, precise control over the size and surface morphology of carbon microspheres can be achieved. Additionally, an in-depth analysis of the chemical structure of carbon spheres was conducted, which enabled the formulation of a comprehensive mechanism for their formation and growth. The calculation of various morphological parameters was carried out based on the examination of carbon microspheres using a Scanning Electron Microscope (SEM) of the carbon microspheres under different hydrothermal carbonization conditions. The correlation of two experimental parameters, four size descriptors, four morphology descriptors, and the adsorption properties were also investigated for carbon microspheres, which simplified various characterization parameters of the carbon microspheres. The analysis of the correlation between various parameters holds significant guidance in simplifying the parameters and optimizing the process.

MRI-Based Radiomics Analysis of Levator Ani Muscle for Predicting Urine Incontinence after Robot-Assisted Radical Prostatectomy.

The exact role of the levator ani (LA) muscle in male continence remains unclear, and so this study aims to shed light on the topic by characterizing MRI-derived radiomic features of LA muscle and their association with postoperative incontinence in men undergoing prostatectomy. In this retrospective study, 140 patients who underwent robot-assisted radical prostatectomy (RARP) for prostate cancer using preoperative MRI were identified. A biomarker discovery approach based on the optimal biomarker (OBM) method was used to extract features from MRI images, including morphological, intensity-based, and texture-based features of the LA muscle, along with clinical variables. Mathematical models were created using subsets of features and were evaluated based on their ability to predict continence outcomes. Univariate analysis showed that the best discriminators between continent and incontinent patients were patients age and features related to LA muscle texture. The proposed feature selection approach found that the best classifier used six features: age, LA muscle texture properties, and the ratio between LA size descriptors. This configuration produced a classification accuracy of 0.84 with a sensitivity of 0.90, specificity of 0.75, and an area under the ROC curve of 0.89. This study found that certain patient factors, such as increased age and specific texture properties of the LA muscle, can increase the odds of incontinence after RARP. The results showed that the proposed approach was highly effective and could distinguish and predict continents from incontinent patients with high accuracy.

Microplastic pollution in Kolavai Lake, Tamil Nadu, India: quantification of plankton-sized microplastics in the surface water of lake.

The prevalence of microplastics (MP) (< 5 mm) in aquatic habitats has recently raised concerns owing to their influence on humans and aquatic organisms, as they absorb organic pollutants and pathogens from the surrounding media because of their higher surface-to-volume ratio. Freshwater systems are severely affected by the increased intake of discarded waste from diverse sources. This study focused on the microplastic-to-zooplankton ratio and its potential impact on the environment's food chain. The sampling sites of Kolavai Lake were divided into three zones (18 stations) to investigate the spatial distribution of microplastics and zooplankton biota. The average microplastic abundance was 6.1 ± 2.5 particles/L. Fourier transform infrared spectroscopy (FTIR) and SEM analysis were performed to understand the chemical composition and surface morphology of microplastics. Water samples collected along the Central and Southern Zones revealed a high abundance of microplastics, which might be due to anthropogenic activities. A negative correlation was observed between the abundance of microplastics and zooplankton. The microplastic-to-zooplankton ratio was found to range from 0.05 to 0.74. Furthermore, the impact of microplastics in the lake ecosystem was analysed using the size and shape descriptors for both zooplankton and microplastics. These findings suggest that microplastics built up in aquatic environments, particularly those with rich biota, could be a severe concern because of their capacity to infiltrate the food web.

ICARUS: flexible protein structural alignment based on Protein Units.

Alignment of protein structures is a major problem in structural biology. The first approach commonly used is to consider proteins as rigid bodies. However, alignment of protein structures can be very complex due to conformational variability, or complex evolutionary relationships between proteins such as insertions, circular permutations or repetitions. In such cases, introducing flexibility becomes useful for two reasons: (i) it can help compare two protein chains which adopted two different conformational states, such as due to proteins/ligands interaction or post-translational modifications, and (ii) it aids in the identification of conserved regions in proteins that may have distant evolutionary relationships. We propose ICARUS, a new approach for flexible structural alignment based on identification of Protein Units, evolutionarily preserved structural descriptors of intermediate size, between secondary structures and domains. ICARUS significantly outperforms reference methods on a dataset of very difficult structural alignments. Code is freely available online at https://github.com/DSIMB/ICARUS.

Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection.

According to the World Health Organisation, falling is a major health problem with potentially fatal implications. Each year, thousands of people die as a result of falls, with seniors making up 80% of these fatalities. The automatic detection of falls may reduce the severity of the consequences. Our study focuses on developing a vision-based fall detection system. Our work proposes a new feature descriptor that results in a new fall detection framework. The body geometry of the subject is analyzed and patterns that help to distinguish falls from non-fall activities are identified in our proposed method. An AlphaPose network is employed to identify 17 keypoints on the human skeleton. Thirteen keypoints are used in our study, and we compute two additional keypoints. These 15 keypoints are divided into five segments, each of which consists of a group of three non-collinear points. These five segments represent the left hand, right hand, left leg, right leg and craniocaudal section. A novel feature descriptor is generated by extracting the distances from the segmented parts, angles within the segmented parts and the angle of inclination for every segmented part. As a result, we may extract three features from each segment, giving us 15 features per frame that preserve spatial information. To capture temporal dynamics, the extracted spatial features are arranged in the temporal sequence. As a result, the feature descriptor in the proposed approach preserves the spatio-temporal dynamics. Thus, a feature descriptor of size [m×15] is formed where m is the number of frames. To recognize fall patterns, machine learning approaches such as decision trees, random forests, and gradient boost are applied to the feature descriptor. Our system was evaluated on the UPfall dataset, which is a benchmark dataset. It has shown very good performance compared to the state-of-the-art approaches.