Enantioselective Hydroaminations Catalyzed by Yttrium(III)-Bis(sulfoximine) Complexes.

Bis(sulfoximine) yttrium complexes as catalysts for enantioselective intramolecular hydroaminations were prepared for the first time and characterized by a multinuclear NMR study including the nuclei 1H, 13C, 15N and 89Y. The stoichiometries of the complexes were confirmed by a Job plot. In addition to the experimental results, the 89Y NMR shifts and the complex structures were elucidated by DFT calculations.

A novel multi-feature fusion attention neural network for the recognition of epileptic EEG signals.

Epilepsy is a common chronic brain disorder. Detecting epilepsy by observing electroencephalography (EEG) is the main method neurologists use, but this method is time-consuming. EEG signals are non-stationary, nonlinear, and often highly noisy, so it remains challenging to recognize epileptic EEG signals more accurately and automatically. This paper proposes a novel classification system of epileptic EEG signals for single-channel EEG based on the attention network that integrates time-frequency and nonlinear dynamic features. The proposed system has three novel modules. The first module constructs the Hilbert spectrum (HS) with high time-frequency resolution into a two-channel parallel convolutional network. The time-frequency features are fully extracted by complementing the high-dimensional features of the two branches. The second module constructs a grayscale recurrence plot (GRP) that contains more nonlinear dynamic features than traditional RP, fed into the residual-connected convolution module for effective learning of nonlinear dynamic features. The third module is the feature fusion module based on a self-attention mechanism to assign optimal weights to different types of features and further enhance the information extraction capability of the system. Therefore, the system is named HG-SANet. The results of several classification tasks on the Bonn EEG database and the Bern-Barcelona EEG database show that the HG-SANet can effectively capture the contribution degree of the extracted features from different domains, significantly enhance the expression ability of the model, and improve the accuracy of the recognition of epileptic EEG signals. The HG-SANet can improve the diagnosis and treatment efficiency of epilepsy and has broad application prospects in the fields of brain disease diagnosis.

Optimizing Hospital Performance Evaluation in Total Weight Loss Outcomes After Bariatric Surgery: A Retrospective Analysis to Guide Further Improvement in Dutch Hospitals.

INTRODUCTION: Bariatric surgery aims for optimal patient outcomes, often evaluated through the percentage total weight loss (%TWL). Quality registries employ funnel plots for outcome comparisons between hospitals. However, funnel plots are traditionally used for dichotomous outcomes, requiring %TWL to be dichotomized, potentially limiting feedback quality. This study evaluates whether a funnel plot around the median %TWL has better discriminatory performance than binary funnel plots for achieving at least 20% and 25% TWL. METHODS: All hospitals performing bariatric surgery were included from the Dutch Audit for Treatment of Obesity. A funnel plot around the median was constructed using 5-year %TWL data. Hospitals positioned above the 95% control limit were colored green and those below red. The same hospitals were plotted in the binary funnel plots for 20% and 25% TWL and colored according to their performance in the funnel plot around the median. We explored the hospital's procedural mix in relation to %TWL performance as possible explanatory factors. RESULTS: The median-based funnel plot identified four underperforming and four outperforming hospitals, while only one underperforming and no outperforming hospitals were found with the binary funnel plot for 20% TWL. The 25% TWL binary funnel plot identified two underperforming and three outperforming hospitals. The proportion of sleeve gastrectomies performed per hospital may explain part of these results as it was negatively associated with median %TWL (ß = - 0.09, 95% confidence interval [- 0.13 to - 0.04]). CONCLUSION: The funnel plot around the median discriminated better between hospitals with significantly worse and better performance than funnel plots for dichotomized %TWL outcomes.

Experimental Study of Needle Insertion into Gerbil Tympanic Membrane.

The perforation characteristics and fracture-related mechanical properties of the tympanic membrane (TM) greatly affect surgical procedures like myringotomy and tympanostomy performed on the middle ear. We analyzed the most important features of the gerbil TM perforation using an experimental approach that was based on force measurement during a 2-cycle needle insertion/extraction process. Fracture energy, friction energy, strain energy, and hysteresis loss were taken into consideration for the analysis of the different stages of needle insertion and extraction. The results demonstrated that (1) although the TM shows viscoelastic behavior, the contribution of hysteresis loss was negligible compared to other irreversible dissipated energy components (i.e., fracture energy and friction energy). (2) The TM puncture force did not substantially change during the first hours after animal death, but interestingly, it increased after 1 week due to the drying effects of soft tissue. (3) The needle geometry affected the crack length and the most important features of the force-displacement plot for the needle insertion process (puncture force, puncture displacement, and jump-in force) increased with increasing needle diameter, whereas the insertion velocity only changed the puncture and jump-in forces (both increased with increasing insertion velocity) and did not have a noticeable effect on the puncture displacement. (4) The fracture toughness of the gerbil TM was almost independent of the needle geometry and was found to be around 0.33 ± 0.10 kJ/m2.

Global biomass maps can increase the precision of (sub)national aboveground biomass estimates: A comparison across tropical countries.

Countries within the tropics face ongoing challenges in completing or updating their national forest inventories (NFIs), critical for estimating aboveground biomass (AGB) and for forest-related greenhouse gas (GHG) accounting. While previous studies have explored the integration of map information with local reference data to fill in data gaps, limited attention has been given to the specific challenges presented by the clustered plot designs frequently employed by NFIs when combined with remote sensing-based biomass map units. This research addresses these complexities by conducting four country case-studies, encompassing a variety of NFI characteristics within a range of AGB densities. Examining four country case-studies (Peru, Guyana, Tanzania, Mozambique), we assess the potential of European Space Agency's Climate Change Initiative (CCI) global biomass maps to increase precision in (sub)national AGB estimates. We compare a baseline approach using NFI field-based data with a model-assisted scenario incorporating a locally calibrated CCI biomass map as auxiliary information. The original CCI biomass maps systematically underestimate AGB in three of the four countries at both the country and stratum level, with particularly weak agreement at finer map resolution. However, after calibration with country-specific NFI data, stratum and country-level AGB estimates from the model-assisted scenario align well with those obtained solely from field-based data and official country reports. Introducing maps as a source of auxiliary information fairly increased the precision of stratum and country-wise AGB estimates, offering greater confidence in estimating AGB for GHG reporting purposes. Considering the challenges tropical countries face with implementing their NFIs, it is sensible to explore the potential benefits of biomass maps for climate change reporting mechanisms across biomes. While country-specific NFI design assumptions guided our model-assisted inference strategies, this study also uncovers transferable insights from the application of global biomass maps with NFI data, providing valuable lessons for climate research and policy communities.

A novel relative-equilibrium graphical plot for rapid reversible tracer studies in dynamic PET imaging.

OBJECTIVE: This study aims to address the issue of long scan durations required by traditional graphical analysis methods, such as the Logan plot and its variant, the reversible equilibrium (RE) Logan plot, for dynamic PET imaging of tracer kinetics. Approach: We propose a relative RE Logan model that builds on the principles of the Logan plot and its variant to significantly reduce scan time without compromising the accuracy of tracer kinetics analysis. The model is supported by theoretical evidence and experimental validations, including two computer simulations and one clinical data analysis. Main results: The proposed model demonstrates a significant linear relationship between the variable x and the slope DV_T of the RE Logan plot, and the variable x' and the slope DV_T' of the relative RE Logan plot. The Pearson correlation coefficients (r) of the linear fitting of the x' to the x equal 0.9849 in the simulated data and 0.9912 in the clinical data. Similarly, the r value for the linear fitting of DV_T' to DV_T equal 0.9989 and 0.9988 in the simulated data, and 0.9954 in the clinical data. Significance: These results demonstrate the model's capability to maintain strong linear relationships and produce parametric images comparable to those of the traditional RE Logan plot, but with the considerable advantage of shorter scan durations. This innovation holds significant potential for enhancing the efficiency and feasibility of PET imaging in clinical settings.

Evaluation of Electrical Cardiometry to Assess Fluid Responsiveness in Patients with Acute Circulatory Failure: A Comparative Study with Transthoracic Echocardiography.

Aim: Acute circulatory failure is commonly encountered in critically ill patients, that requires fluid administration as the first line of treatment. However, only 50% of patients are fluid-responsive. Identification of fluid responders is essential to avoid the harmful effects of overzealous fluid therapy. Electrical cardiometry (EC) is a non-invasive bedside tool and has proven to be as good as transthoracic echocardiography (TTE) to track changes in cardiac output. We aimed to look for an agreement between EC and TTE for tracking changes in cardiac output in adult patients with acute circulatory failure before and after the passive leg-raising maneuver. Materials and methods: Prospective comparative study, conducted at a Tertiary Care Teaching Hospital. Results: We recruited 125 patients with acute circulatory failure and found 42.4% (53 out of 125) to be fluid-responsive. The Bland-Altman plot analysis showed a mean difference of 2.08 L/min between EC and TTE, with a precision of 3.8 L/min. The limits of agreement (defined as bias ± 1.96SD), were -1.7 L/min and 5.8 L/min, respectively. The percentage of error between EC and TTE was 56% with acceptable limits of 30%. Conclusion: The percentage error beyond the acceptable limit suggests the non-interchangeability of the two techniques. More studies with larger sample sizes are required to establish the interchangeability of EC with TTE for tracking changes in cardiac output in critically ill patients with acute circulatory failure. How to cite this article: Sharma S, Ramachandran R, Rewari V, Trikha A. Evaluation of Electrical Cardiometry to Assess Fluid Responsiveness in Patients with Acute Circulatory Failure: A Comparative Study with Transthoracic Echocardiography. Indian J Crit Care Med 2024;28(7):650-656.

Investigating the carcinogenic and non-carcinogenic health hazards of heavy metal ions in Spinacia oleracea grown in agricultural soil treated with biochar and humic acid.

This study addressed the bioaccumulation and human health risk among the consumption of Spinacia oleracea grown in agricultural soil treated with humic acid (189-2310 ppm) and biochars (0.00-5.10%.wt). The biochars came from two local feedstocks of rice-husk (RH) and sugar-beet-pulp (SBP) pyrolyzed at temperatures 300 and 600 °C. Total concentrations of Cu, Cd, and Ni found in both the soil and biomass/biochar exceeded global safety thresholds. The bioaccumulation levels of HMs in spinach leaves varied, with Fe reaching the highest concentration at 765.27 mg kg-1 and Cd having the lowest concentration at 3.31 mg kg-1. Overall, the concentrations of Zn, Cd, Pb, and Ni in spinach leaves exceeded the safety threshold limits, so that its consumption is not recommended. The assessment of hazard quotient (HI) for the HMs indicated potential health hazards for humans (HI > 1) from consuming the edible parts of spinach. The biochar application rates of 4.35%wt and 0.00%.wt resulted in the highest (3.69) and lowest (3.15) HI values, respectively. The cumulative carcinogenic risk (TCR) ranged from 0.0085 to 0.0119, exceeding the cancer risk threshold. Introducing 5.10%wt biomass/biochar resulted in a 36% rise in TCR compared to the control. The utilization of humic acid alongside HMs-polluted biochars results in elevated levels of HMs bioaccumulation exceeding the allowable thresholds in crops (with a maximum increase of 49% at 2000 ppm humic acid in comparison to 189 ppm). Consequently, this raised the HI by 46% and the TCR by 22%. This study demonstrated that the utilization of HMs-polluted biochars could potentially pose supplementary health hazards. Moreover, it is evident that the utilization of HMs-polluted biochars in treating metal-contaminated soil does not effectively stabilize or reduce pollution.

A Split-Plot Experimentation Strategy for Making Causal Inferences in Advanced Materials: Auxetic Polyurethane Foam Manufacturing and Processing Analysis.

Development of advanced materials is often time consuming and expensive because of the large number of variables involved and experiments needed. An effective experimentation strategy would accelerate development by reducing the required amount of experiments without sacrificing the obtainable information. In this paper, the development of auxetic polyurethane (PU) foams was discussed as a case study. Auxetic materials are materials with a negative Poisson's ratio and have potential in many structural and functional applications. Auxetic PU foams are the most studied auxetic materials, and their manufacturing and properties are affected by many processing and environmental factors. This paper introduces a sophisticated design of experimental methodology to help reduce the experimental effort while effectively screening these factors. This methodology is then applied in an experiment to illustrate its utility and distinct advantages that greatly facilitate material development.

Magnetic properties of ferromagnetic nanoparticles of FexGeTe2(x= 3, 5) directly exfoliated and dispersed in pure water.

FexGeTe2(x= 3, 5) are two-dimensional ferromagnetic (FM) materials that have gained significant attention from researchers due to their relatively high Curie temperature and tunability. However, the methods for preparing FM nanoparticles (FNPs) and large-area FexGeTe2films are still in the early stages. Here, we studied the magnetic properties of FexGeTe2FNPs exfoliated via wet exfoliation in pure water. The coercive field of Fe3GeTe2FNPs increases significantly, up to 60 times, while that of Fe5GeTe2only slightly increases from that of bulk crystals. Further investigation related to the dimension of nanoparticles and the Henkel plot analysis reveals that the variation in their coercive field stems from the material's thickness-dependent coercive field and the type of term that governs the interaction between single-domain nanoparticles. Our work demonstrates a facile method for preparing FNPs using van der Waals FM materials and tuning their magnetic properties.

Ants may buffer the Janzen-Connell effect in a tropical forest in Southwest China.

Mutualistic symbioses between ants and plants are widespread in nature. Ants can deter unwanted pests and provide protection for plants in return for food or housing rewards. Using a long-term demographic dataset in a tropical seasonal rain forest in Southwest China, we found that associations with ants positively influenced seedling survival and adult growth, and also, species with extrafloral nectaries experienced weaker conspecific negative density dependence compared with species without extrafloral nectaries. Furthermore, we found strong evidence suggesting that species in our forest experienced conspecific density dependence, which we interpreted as heavy pest pressure that may drive the development of anti-pest symbioses such as the plant-ant relationship. Our findings suggest that ants and conspecific neighbors play important but inverse roles on plant survival and growth and that ants can buffer tree neighborhood interactions in this tropical forest.

Predicting anion diffusion in bentonite using hybrid machine learning model and correlation of physical quantities.

Radionuclide diffusion will be influenced by numerous factors. Establishing a model that can elucidate the internal correlation between mesoscopic diffusion and the microscopic structure of bentonite can enhance the comprehension of radionuclide diffusion mechanisms. In this study, a light gradient boosting machine (LightGBM) was employed to predict the effective diffusion coefficients of HCrO4-, I-, and CoEDTA2- in bentonite. The model's hyperparameters were optimized using the particle swarm optimization (PSO) algorithm. Several correlated physical quantities, such as mesoscopic parameters (total porosity, rock capacity factor, and ion molar conductivity) and microscopic parameters (ionic radius and montmorillonite stacking number) were incorporated to develop a machine learning model that incorporated micro- and meso-scale features. The predictive performance of PSO-LightGBM was verified using diffusion experiments, which investigated the diffusion of HCrO4-, I-, and CoEDTA2- at compacted dry densities of 1200-1800 kg/m3 using a through-diffusion method. Spearman correlation and Shapley additive explanation analyses revealed that the compacted dry density, ionic diffusion coefficient in water, ionic radius, and total porosity were the top-four influencing factors among the 16 input features. Partial dependence plot analysis elucidated the relationship between the effective diffusion coefficient and each input feature. The analysis results were consistent with the experimental findings, demonstrating the reliability of machine learning. Due to the incorporation of multi-scale features, the PSO-LightGBM model demonstrated enhanced predictive accuracy, linking the microstructure of bentonite to radionuclide diffusion, and providing a comprehensive interpretation of the diffusion mechanism.

Introducing qrlabelr: Fast user-friendly software for machine- and human-readable labels in agricultural research and development.

The advent of modern tools in agricultural experiments, digital data collection, and high-throughput phenotyping have necessitated field plot labels that are both machine- and human-readable. Such labels are usually made with commercial software, which are often inaccessible to under-funded research programs in developing countries. The availability of free fit-for-purpose label design software to under-funded research programs in developing countries would address one of the main roadblocks to modernizing agricultural research. The goal was to develop a new open-source software with design features well-suited for field trials and other agricultural experiments. We report here qrlabelr, a new software for creating print-ready plot labels that builds on the foundation of an existing open-source program. The qrlabelr software offers more flexibility in the label design steps, guarantees true string fidelity after QR encoding, and provides faster label generation to users. The new software is available as an R package and offers customizable functions for generating plot labels. For non-R users or beginners in R programming, the package provides an interactive Shiny app version that can be launched from R locally or accessed online at https://bit.ly/3Sud4xy. The design philosophy of this new program emphasizes the adoption of best practices in plot label design to enhance reproducibility, tracking, and accurate data curation in agricultural research and development studies.

An investigation on the prevalence and patterns of multi-morbidity among a group of slum-dwelling older women of Kolkata, India.

BACKGROUND: Multi-morbidity is a pervasive and growing issue worldwide. The prevalence of multi-morbidity varies across different populations and settings, but it is particularly common among older adults. It poses substantial physical, psychological, and socio-economic burdens on individuals, caregivers and healthcare systems. In this context, the present study aims to provide an insight on the prevalence and degree of multi-morbidity; and also, on the relationship between level of multi-morbidity and morbid conditions among a group of slum-dwelling older women. METHODS: This community based cross-sectional study was conducted in the slum areas of urban Kolkata, West Bengal, India. It includes total 500 older women, aged 60 years or above. Pre-tested schedules on so-demographic and morbidity profile have canvassed to obtain the information by door-to-door survey. To determine the relationship between the level of multi-morbidity and morbid conditions, correspondence analysis has performed. RESULTS: The study revealed three most prevalent morbid conditions- back and/or joint pain, dental caries/cavity and hypertension. The overall prevalence of multi-morbidity was 95.8% in this group of older women. It was highly over-represented by the oldest-old age group (80 years and above). Majority were found to suffer from five simultaneous morbid conditions that accounted for 15.2% of the total respondents. All of the oldest-old women of this study reported to suffer from more than two medical conditions simultaneously. Three distinct groups were formed based on the inter-relationship between level of multi-morbidity and morbid conditions. The group 1 and 2 represents only 27.8% and 18% of the total sample. Whereas, group 3 comprises the highest level of morbidities (≥ 6) and 52.8% of total sample, and strongly related with general debilities, cardiac problems, asthma/COPD, gastrointestinal, musculoskeletal problems, neurological disorders, hypothyroidism and oral health issues. CONCLUSION: The findings confirmed the assertion that multi-morbidity in slum living older adults is a problem with high prevalence and complexity. This study proposes an easily replicable approach of understanding complex interaction of morbidities that can help further in identifying the healthcare needs of older adults to provide them with healthy and more productive life expectancy.

Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual.

The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach.

Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system's robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis.

Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer's Disease and Frontotemporal Dementia.

This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research.

The unexpected influence of legacy conspecific density dependence.

When plants die, neighbours escape competition. Living conspecifics could disproportionately benefit because they are freed from negative intraspecific processes; however, if the negative effects of past conspecific neighbours persist, other species might be advantaged, and diversity might be maintained through legacy effects. We examined legacy effects in a mapped forest by modelling the survival of 37,212 trees of 23 species using four neighbourhood properties: living conspecific, living heterospecific, legacy conspecific (dead conspecifics) and legacy heterospecific densities. Legacy conspecific effects proved nearly four times stronger than living conspecific effects; changes in annual survival associated with legacy conspecific density were 1.5% greater than living conspecific effects. Over 90% of species were negatively impacted by legacy conspecific density, compared to 47% by living conspecific density. Our results emphasize that legacies of trees alter community dynamics, revealing that prior research may have underestimated the strength of density dependent interactions by not considering legacy effects.