Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study.

In this study, neural networks and support vector regression (SVR) were employed to predict the degradation over three pharmaceutically active compounds (PhACs): Ibuprofen (IBP), diclofenac (DCF), and caffeine (CAF) within a stirred reactor featuring a flotation cell with two non-concentric ultraviolet lamps. A total of 438 datapoints were collected from published works and distributed into 70% training and 30% test datasets while cross-validation was utilized to assess the training reliability. The models incorporated 15 input variables concerning reaction kinetics, molecular properties, hydrodynamic information, presence of radiation, and catalytic properties. It was observed that the Support Vector Regression (SVR) presented a poor performance as the ε hyperparameter ignored large error over low concentration levels. Meanwhile, the Artificial Neural Networks (ANN) model was able to provide rough estimations on the expected degradation of the pollutants without requiring information regarding reaction rate constants. The multi-objective optimization analysis suggested a leading role due to ozone kinetic for a rapid degradation of the contaminants and most of the results required intensification with hydrogen peroxide and Fenton process. Although both models were affected by accuracy limitations, this work provided a lightweight model to evaluate different Advanced Oxidation Processes (AOPs) by providing general information regarding the process operational conditions as well as know molecular and catalytic properties.

Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data.

BACKGROUND: Numerous transcriptomic-based models have been developed to predict or understand the fundamental mechanisms driving biological phenotypes. However, few models have successfully transitioned into clinical practice due to challenges associated with generalizability and interpretability. To address these issues, researchers have turned to dimensionality reduction methods and have begun implementing transfer learning approaches. METHODS: In this study, we aimed to determine the optimal combination of dimensionality reduction and regularization methods for predictive modeling. We applied seven dimensionality reduction methods to various datasets, including two supervised methods (linear optimal low-rank projection and low-rank canonical correlation analysis), two unsupervised methods [principal component analysis and consensus independent component analysis (c-ICA)], and three methods [autoencoder (AE), adversarial variational autoencoder, and c-ICA] within a transfer learning framework, trained on > 140,000 transcriptomic profiles. To assess the performance of the different combinations, we used a cross-validation setup encapsulated within a permutation testing framework, analyzing 30 different transcriptomic datasets with binary phenotypes. Furthermore, we included datasets with small sample sizes and phenotypes of varying degrees of predictability, and we employed independent datasets for validation. RESULTS: Our findings revealed that regularized models without dimensionality reduction achieved the highest predictive performance, challenging the necessity of dimensionality reduction when the primary goal is to achieve optimal predictive performance. However, models using AE and c-ICA with transfer learning for dimensionality reduction showed comparable performance, with enhanced interpretability and robustness of predictors, compared to models using non-dimensionality-reduced data. CONCLUSION: These findings offer valuable insights into the optimal combination of strategies for enhancing the predictive performance, interpretability, and generalizability of transcriptomic-based models.

Bacillus tequilensis influences metabolite production in tomato and restores soil microbial diversity during Fusarium oxysporum infection.

This study evaluates cellular damage, metabolite profiling, and defence-related gene expression in tomato plants and soil microflora during Fusarium wilt disease after treatment with B. tequilensis PBE-1. Histochemical analysis showed that PBE-1 was the primary line of defence through lignin deposition and reduced cell damage. GC-MS revealed that PBE-1 treatment ameliorated stress caused by F. oxysporum infection. PBE-1 also improved transpiration, photosynthesis, and stomatal conductance in tomato. qRT-PCR suggested that the defence-related genes FLS2, SERK, NOS, WRKYT, NHO, SAUR, and MYC2, which spread infection, were highly upregulated during F. oxysporum infection, but either downregulated or expressed normally in PBE-1 + P treated plants. This indicates that the plant not only perceives the bio-control agent as a non-pathogen entity but its presence in normal metabolism and gene expression within the host plant is maintained. The study further corroborated findings that application of PBE-1 does not cause ecological disturbances in the rhizosphere. Activity of soil microflora across four treatments, measured by Average Well Colour Development (AWCD), showed continuous increases from weeks 1 to 4 post-pathogen infection, with distinct substrate usage patterns like tannic and fumaric acids impacting microbial energy source utilization and diversity. Principal Component Analysis (PCA) and diversity indices like McIntosh, Shannon, and Simpson further illustrated significant microbial community shifts over the study period. In conclusion, our findings demonstrate that B. tequilensis PBE-1 is an ideal bio-agent for field application during Fusarium wilt disease management in tomato.

Estimating hidden relationships in dynamical systems: Discovering drivers of infection rates of COVID-19.

Discovering causal influences among internal variables is a fundamental goal of complex systems research. This paper presents a framework for uncovering hidden relationships from limited time-series data by combining methods from nonlinear estimation and information theory. The approach is based on two sequential steps: first, we reconstruct a more complete state of the underlying dynamical system, and second, we calculate mutual information between pairs of internal state variables to detail causal dependencies. Equipped with time-series data related to the spread of COVID-19 from the past three years, we apply this approach to identify the drivers of falling and rising infections during the three main waves of infection in the Chicago metropolitan region. The unscented Kalman filter nonlinear estimation algorithm is implemented on an established epidemiological model of COVID-19, which we refine to include isolation, masking, loss of immunity, and stochastic transition rates. Through the systematic study of mutual information between infection rate and various stochastic parameters, we find that increased mobility, decreased mask use, and loss of immunity post sickness played a key role in rising infections, while falling infections were controlled by masking and isolation.

Identification of a methylation panel as an alternative triage to detect CIN3+ in hrHPV-positive self-samples from the population-based cervical cancer screening programme.

BACKGROUND: The Dutch population-based cervical cancer screening programme (PBS) consists of primary high-risk human papilloma virus (hrHPV) testing with cytology as triage test. In addition to cervical scraping by a general practitioner (GP), women are offered self-sampling to increase participation. Because cytological examination on self-sampled material is not feasible, collection of cervical samples from hrHPV-positive women by a GP is required. This study aims to design a methylation marker panel to detect CIN3 or worse (CIN3+) in hrHPV-positive self-samples from the Dutch PBS as an alternative triage test for cytology. METHODS: Fifteen individual host DNA methylation markers with high sensitivity and specificity for CIN3+ were selected from literature and analysed using quantitative methylation-specific PCR (QMSP) on DNA from hrHPV-positive self-samples from 208 women with CIN2 or less (< CIN2) and 96 women with CIN3+. Diagnostic performance was determined by area under the curve (AUC) of receiver operating characteristic (ROC) analysis. Self-samples were divided into a train and test set. Hierarchical clustering analysis to identify input methylation markers, followed by model-based recursive partitioning and robustness analysis to construct a predictive model, was applied to design the best marker panel. RESULTS: QMSP analysis of the 15 individual methylation markers showed discriminative DNA methylation levels between < CIN2 and CIN3+ for all markers (p < 0.05). The diagnostic performance analysis for CIN3+ showed an AUC of ≥ 0.7 (p < 0.001) for nine markers. Hierarchical clustering analysis resulted in seven clusters with methylation markers with similar methylation patterns (Spearman correlation> 0.5). Decision tree modeling revealed the best and most robust panel to contain ANKRD18CP, LHX8 and EPB41L3 with an AUC of 0.83 in the training set and 0.84 in the test set. Sensitivity to detect CIN3+ was 82% in the training set and 84% in the test set, with a specificity of 74% and 71%, respectively. Furthermore, all cancer cases (n = 5) were identified. CONCLUSION: The combination of ANKRD18CP, LHX8 and EPB41L3 revealed good diagnostic performance in real-life self-sampled material. This panel shows clinical applicability to replace cytology in women using self-sampling in the Dutch PBS programme and avoids the extra GP visit after a hrHPV-positive self-sampling test.

Updated assessment of occupational safety and health hazards of climate change.

Workers, particularly outdoor workers, are among the populations most disproportionately affected by climate-related hazards. However, scientific research and control actions to comprehensively address these hazards are notably absent. To assess this absence, a seven-category framework was developed in 2009 to characterize the scientific literature published from 1988-2008. Using this framework, a second assessment examined the literature published through 2014, and the current one examines literature from 2014-2021. The objectives were to present literature that updates the framework and related topics and increases awareness of the role of climate change in occupational safety and health. In general, there is substantial literature on worker hazards related to ambient temperatures, biological hazards, and extreme weather but less on air pollution, ultraviolet radiation, industrial transitions, and the built environment. There is growing literature on mental health and health equity issues related to climate change, but much more research is needed. The socioeconomic impacts of climate change also require more research. This study illustrates that workers are experiencing increased morbidity and mortality related to climate change. In all areas of climate-related worker risk, including geoengineering, research is needed on the causality and prevalence of hazards, along with surveillance to identify, and interventions for hazard prevention and control.

A Jovian analogue orbiting a white dwarf star.

Studies1,2 have shown that the remnants of destroyed planets and debris-disk planetesimals can survive the volatile evolution of their host stars into white dwarfs3,4, but few intact planetary bodies around white dwarfs have been detected5-8. Simulations predict9-11 that planets in Jupiter-like orbits around stars of ≲8 M☉ (solar mass) avoid being destroyed by the strong tidal forces of their stellar host, but as yet, there has been no observational confirmation of such a survivor. Here we report the non-detection of a main-sequence lens star in the microlensing event MOA-2010-BLG-477Lb12 using near-infrared observations from the Keck Observatory. We determine that this system contains a 0.53 ± 0.11 M☉ white-dwarf host orbited by a 1.4 ± 0.3 Jupiter-mass planet with a separation on the plane of the sky of 2.8 ± 0.5 astronomical units, which implies a semi-major axis larger than this. This system is evidence that planets around white dwarfs can survive the giant and asymptotic giant phases of their host's evolution, and supports the prediction that more than half of white dwarfs have Jovian planetary companions13. Located at approximately 2.0 kiloparsecs towards the centre of our Galaxy, it is likely to represent an analogue to the end stages of the Sun and Jupiter in our own Solar System.

Robust metabolic transcriptional components in 34,494 patient-derived cancer-related samples and cell lines.

BACKGROUND: Patient-derived bulk expression profiles of cancers can provide insight into the transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Hence, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. However, consensus independent component analyses (c-ICA) can capture statistically independent transcriptional footprints of both subtle and more pronounced metabolic processes. METHODS: We performed c-ICA with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. Gene set enrichment analysis with 608 gene sets that describe metabolic processes was performed to identify the transcriptional components enriched for metabolic processes (mTCs). The activity of these mTCs was determined in all samples to create a metabolic transcriptional landscape. RESULTS: A set of 555 mTCs was identified of which many were robust across different datasets, platforms, and patient-derived tissues and cell lines. We demonstrate how the metabolic transcriptional landscape defined by the activity of these mTCs in samples can be used to explore the associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. CONCLUSIONS: To facilitate the use of our transcriptional metabolic landscape, we have provided access to all data via a web portal ( www.themetaboliclandscapeofcancer.com ). We believe this resource will contribute to the formulation of new hypotheses on how to metabolically engage the tumor or its (immune) microenvironment.

The Histopathology of Oral Cancer Pain in a Mouse Model and a Human Cohort.

Oral cancer patients often have severe, chronic, and mechanically induced pain at the site of the primary cancer. Oral cancer pain is initiated and maintained in the cancer microenvironment and attributed to release of mediators that sensitize primary sensory nerves. This study was designed to investigate the histopathology associated with painful oral cancers in a preclinical model. The relationship of pain scores with pathologic variables was also investigated in a cohort of 72 oral cancer patients. Wild-type mice were exposed to the carcinogen, 4-nitroquinoline 1-oxide (4NQO). Nociceptive (pain) behavior was measured with the dolognawmeter, an operant device and assay for measuring functional and mechanical allodynia. Lesions developed on the tongues and esophagi of the 4NQO-treated animals and included hyperkeratoses, papillomas, dysplasias, and cancers. Papillomas included lesions with benign and dysplastic pathological features. Two histologic subtypes of squamous cell carcinomas (SCCs) were identified-SCCs with exophytic and invasive components associated with papillary lesions (pSCCs) and invasive SCCs without exophytic histology (iSCCs). Only the pSCC subtype of tongue cancer was associated with nociceptive behavior. Increased tumor size was associated with greater nociceptive behavior in the mouse model and more pain experienced by oral cancer patients. In addition, depth of invasion was associated with patient-reported pain. The pSCC histology identifies 4NQO-induced tongue cancers that are expected to be enriched for expression and release of nociceptive mediators.

A Non-Fourier Bioheat Transfer Model for Cryosurgery of Tumor Tissue with Minimum Collateral Damage.

BACKGROUND AND OBJECTIVES: Incorporation of non-Fourier heat conduction while studying heat transfer phenomena in biological materials has emerged has an important approach as it predicts better and more realistic results than Fourier based models. In this article we have proposed a non-Fourier computational model and applied the same to simulate cryosurgery of lung tumor and attempted minimization of freezing damage of healthy lung tissue using pulsed laser irradiation. METHODS: A non-Fourier bioheat transfer model for phase change in biological tissues is solved via a Fourier heat conduction based solution approach. A unified model is proposed combining all variants of bioheat models: Fourier's heat conduction based Pennes' bioheat model, hyperbolic heat conduction model and dual phase lag model. The proposed model takes into account the different thermophysical properties of frozen and unfrozen regions. In order to mimic the actual biotransport process, the blood perfusion and metabolic heat generation are switched off in the frozen region. Implicit source based enthalpy method is used to model phase change process. A new iterative enthalpy update equation is developed for capturing evolution of freezing front implicitly. Finite Volume based numerical discretization technique is used to discretize the governing PDE. The resulting discrete algebraic equation set is solved implicitly by Tri-diagonal Matrix Algorithm. The proposed model is verified with existing results from the literature. RESULTS: For Fourier heat conduction, freezing time of 99.99% of tumor is 1247s, which increases to 1267s for τq= 5s (τT= 0s) and again reduces to 1255s for τq= 5s and τT= 3s. τq and τT are phase lag parameters for non-Fourier heat conduction. For τq= 5s and τT= 0.05s, the freezing damage of healthy tissue decreases by 23.76% when pulsed laser irradiation (Io = 106 W/m2) is used to warm the neighboring healthy tissue. CONCLUSIONS: So non-Fourier bioheat transport models are better and more accurate in predicting temperature history, freezing time and freezing front propagation as compared to Fourier based models. Pulsed laser irradiation can prove to be a very efficient technique in minimizing collateral damage during cryosurgery.

Carbon sequestration in the bio-edaphic ecosystem of National Highway-27 in Guwahati, Assam, India.

Vehicular pollution in cities is engendering the need to enhance the sequestration of CO2 through bio-edaphic factors, such as trees and soil. Hence, this pioneering study aimed to analyze the interdependencies of the bio-edaphic ecosystem during carbon sequestration on a national highway in Guwahati, Assam, India. To quantify the tree carbon stock, soil physicochemical properties, soil nutrients, bulk density, organic carbon, and soil carbon stock, trees with diameters at breast height (dbh) ≥ 2 cm and soil samples from different depths (0-15 cm, 15-30 cm, and 30-45 cm) were taken from six areas spread over 36 plots of equal size (2 × 10 m) arranged in a zig-zag manner. The studied parameters were compared among the different areas. It was found that the tree and soil carbon stock, along with soil nutrients, were greater in the Garchuk-Lokhra area (sample area-4). A significantly strong correlation was observed between the soil carbon stock and the above and below-ground biomass of trees (AGB, r = 0.865; BGB, r = 0.847), which indicated the co-dependencies of the bio-edaphic ecosystem in accumulating carbon. Peltophorum ferrugineum (Decne.) Benth is recommended for planting because it has emerged as a tolerant species and has the greatest carbon storage potential. The bio-edaphic ecosystem of the national highway is now on a carbon-friendly trajectory that follows the UNFCCC guidelines and the REDD+ (reducing emissions from deforestation and forest degradation) strategy. However further research on carbon budgeting is required.

Evaluation of antibiofilm efficacy of essential oil components ß-caryophyllene, cinnamaldehyde and eugenol alone and in combination against biofilm formation and preformed biofilms of Listeria monocytogenes and Salmonella typhimurium.

The aim of this study was to examine whether the process of initial colonization and the formation of mature biofilm structure of foodborne bacterial pathogens Listeria monocytogenes and Salmonella typhimurium can be impeded by active essential oil components ß-caryophyllene, cinnamaldehyde and eugenol at their individual and combined effects. Among the essential oil components tested, cinnamaldehyde and eugenol at their individual effect showed >50% degradation in biofilm biomass against preformed (matured) biofilms of both the studied bacteria, whereas ß-caryophyllene failed to do so. In combination, cinnamaldehyde/eugenol blend showed synergistic antibiofilm efficacy against preformed biofilms of both the studied bacteria L. monocytogenes (FICI: 0·24) and S. typhimurium (FICI: 0·40), whereas other tested combinations showed additive antibiofilm efficacy with FICI ranged from 2·02 to 2·35. Essential oil components alone and in combination also showed much higher inhibition effect on biofilm formation at the initial stage compared to their inhibition effect on preformed biofilms. The results provide evidence that cinnamaldehyde/eugenol combination may help in designing a more potent novel, natural antibiofilm blend at sufficiently low concentrations in the food and pharmaceutical industries. SIGNIFICANCE AND IMPACT OF THE STUDY: In the present work, synergistic antibiofilm efficacy of cinnamaldehyde/eugenol combination against established biofilms of foodborne bacterial pathogens Listeria monocytogenes and Salmonella typhimurium has been reported. These synergistic interactions may help in designing a more potent, safe and effective novel natural antibiofilm agent in food and pharmaceutical industries. Besides, this combination will also be helpful in reducing concentration of individual components, thereby minimizing the undesirable impact on sensory properties of food. To our knowledge, this is the first time, synergistic antibiofilm efficacy of cinnamaldehyde/eugenol blend against established biofilms of foodborne bacterial pathogens has been reported.

Synergistic antibacterial, antifungal and antioxidant efficacy of cinnamon and clove essential oils in combination.

The present investigation aimed to evaluate antibacterial, antifungal and antioxidant efficacy of essential oils of three commonly used spices (black pepper, cinnamon and clove) in combination along with chemical characterization and toxicity evaluation. Among the possible combinations tested, cinnamon/clove oil combination showed synergistic antibacterial activity against foodborne bacteria Staphylococcus aureus, Listeria monocytogenes, Salmonella typhimurium and Pseudomonas aeruginosa and synergistic antifungal activity against Aspergillus niger as well as synergistic antioxidant potential in DPPH radical scavenging model system. GC-HRMS analysis revealed that out of thirteen identified components from clove oil, eugenol was found to be the main constituent of the oil; whereas out of twenty one identified constituents from cinnamon oil, the main component was cinnamaldehyde. Cinnamon/clove oil combination did not show any cytotoxic potential at recommended dosage level (IC50 > 2000 µg/ml). The results provide evidence that cinnamon/clove oil combination might indeed be used as a potential source of safe and effective novel natural antibacterial, antifungal and antioxidant blend in the food and pharmaceutical industries. To the best of our knowledge, this is the first time a combination of essential oils has been tested as natural preservatives to prevent both microbial proliferation and oxidative deterioration at sufficiently low concentrations.

Symphytum officinale augments osteogenesis in human bone marrow-derived mesenchymal stem cells in vitro as they differentiate into osteoblasts.

ETHNOPHARMACOLOGICAL RELEVANCE: Mesenchymal stem cells (MSCs) are multipotent stem cells possessing regenerative potential. Symphytum officinale (SO) is a medicinal plant and in homoeopathic literature, believed to accelerate bone healing. AIM OF THE STUDY: This study aimed to determine if homoeopathic doses of SO could augment osteogenesis in MSCs as they differentiate into osteoblasts in vitro. MATERIALS AND METHODS: Bone marrow samples were obtained from patients who underwent bone grafting procedures (n = 15). MSCs were isolated, expanded and characterized by flow cytometry (CD90, CD105). Cytotoxicity of SO was evaluated by MTT assay. Osteogenic differentiation was induced in MSCs with ß-glycerophosphate, ascorbic acid and dexamethasone over 2 weeks. Different homoeopathic doses of SO (MT, 3C, 6C, 12C and 30C) were added to the basic differentiation medium (BDM) and efficiency of MSCs differentiating into osteoblasts were measured by evaluating expression of Osteocalcin using flow cytometry, and alkaline phosphatase activity using ELISA. Gene expression analyses for osteoblast markers (Runx-2, Osteopontin and Osteocalcin) were evaluated in differentiated osteoblasts using qPCR. RESULTS: Flow cytometry (CD90, CD105) detected MSCs isolated from bone marrow (93-98%). MTT assay showed that the selected doses of SO did not induce any cytotoxicity in MSCs (24 hours). The efficiency of osteogenic differentiation (2 weeks) for different doses of Symphytum officinale was determined by flow cytometry (n = 10) for osteoblast marker, Osteocalcin, and most doses of Symphytum officinale enhanced osteogenesis. Interestingly, gene expression analysis for Runx-2 (n = 10), Osteopontin (n = 10), Osteocalcin (n = 10) and alkaline phosphatase activity (n = 8) also showed increased osteogenesis with the addition of Symphytum officinale to BDM, specially mother tincture. CONCLUSIONS: Our findings suggest that homoeopathic dose (specially mother tincture) of Symphytum officinale has the potential to enhance osteogenesis.

Positron emission tomography/computed tomography guided percutaneous biopsies of Ga-68 avid lesions using an automated robotic arm.

PURPOSE: The purpose of this prospective study was to evaluate the feasibility of positron emission tomography/computed tomography (PET/CT)-guided biopsy of Ga-68 avid lesions using an automated robotic arm and determine the diagnostic yield of this technique. MATERIAL AND METHODS: Patients who underwent Ga-68 labelled tracers imaging followed by PET/CT-guided biopsies of tracer-avid lesions were prospectively included. Biopsies were performed using a dedicated automated-robotic-arm assisted PET/CT-guided biopsy device on the same-day of diagnostic PET/CT-imaging. The tissue samples were retrieved after confirming the position of needle-tip in the target lesion. Procedure-related complications and radiation exposure of the interventionist were recorded. Histopathological reports were reviewed for diagnostic yield. RESULTS: A total of 25 patients (19 men, six women) with a mean age of 50.8±17.3 (SD) years (range: 17-83 years) were included. The biopsies were performed after PET/CT using Ga-68 DOTANOC (n=16) or Ga-68 PSMA (n=8) and Ga-68 chemokine-analogue (n=1). The biopsy samples were obtained from the liver (n=9), bone (n=8), lymph-nodes (n=3), lung (n=1), pancreas (n=1), anterior mediastinal lesion (n=1), peritoneal-deposit (n=1) and thigh-lesion (n=1). No immediate or delayed procedure-related complications were documented in any patient. PET/CT-guided molecular sampling was technically successful in all the patients. Histopathology revealed malignancies in all the biopsied specimens without the need for repeat sampling or further invasive-diagnostic workup, with a diagnostic yield of 100%. The estimated absorbed-radiation dose was 566.7µSv/year for the interventionist. CONCLUSION: PET/CT-guided molecular biopsy using Ga-68 labelled radiotracers is feasible and can be performed safely and accurately with a high-diagnostic yield. It is helpful in accurately staging the disease when tracer-avid isolated distant lesion evident on imaging and highly practical in patients with previous inconclusive sampling.

Effects of Heat Exposure from Live-Burn Fire Training on Postural Stability of Firefighters.

Firefighters perform physically intensive jobs in suboptimal environments, making it even more important for them to maintain good functional postural balance or stability. As part of their training, firefighters are required to perform physically demanding tasks under high stress and high heat environments. These demanding tasks lead to increased physical fatigue which can then result in poor performance and/or postural instability. The objectives of this study were to 1) investigate the effect of live-firefighting training-induced heat stress on static postural balance, and 2) investigate the association between commonly monitored physiological responses (core body temperature, heart rate, oxygen saturation and blood pressure) and measures of static postural balance. Twenty-six firefighters (mean ± SD: age 36.0 years ±5.2, weight 216 lbs. ± 34, BMI 29.7 ± 4.2) participated in live firefighting training while performing following tasks: search and rescue, hose advancement, and backup. Prior to heat exposure (PRE) and following each scenario (POST1, POST2, POST3), firefighters' postural balance was assessed with a wearable 3-D inertial sensor system quantifying time dependent changes in linear acceleration (LIN ACC) and angular velocity (AV) about three orthogonal axes [Anterior-Posterior (AP), Medial-Lateral (ML), and vertical (V)] during one foot balance tests for 30 seconds under eyes open and eyes closed conditions. The outcome variables from 3-D wearable sensors were used to create 3-D Phase-Plane based postural stability metrics. Physiological measurement of core body temperature (CBT) (measured with a radio pill) as well as perception of heat increased significantly during the live fire-training exercise. In addition, firefighters also perceived an increase in physical fatigue and respiratory distress. Angular combined stability parameters (ACSP), RMS angular velocity around ML axis were significantly correlated with CBT. In the multivariate analysis adjusted for the scenarios, height and weight of the firefighters, these measures of static postural balance were significantly associated with CBT. As per the model results, static postural balance, as indicated by ACSP, worsened with an increase in CBT. Future studies should place sensors at body extremities along with close to center of mass to capture the kinematic movements more comprehensively influencing postural balance.

Poly(piperizinamide) with copper ion composite membranes: Application for mitigation of Hexaconazole from water and combat microbial contamination.

Thin film Poly(piperazine-amide) composite membranes using sequential interfacial polymerization with tuning by Cu2+ have brought significant findings in it. The hydrophobicity is relatively enhanced for the copper containing membranes. The membrane in which copper solution is applied prior to piperizine (Memb-III) exhibits higher hydrophobicity where as membrane (Memb-II) in which copper solution is applied following piperizine, possesses higher roughness compared to other two. Filtration experiments in terms of salts, mono/disaccharides and hexaconazole indicate that modified membranes are of different behaviours according to their sequence of preparative methods. Memb-III has shown lower SO4=/Cl- selectivity compared to Memb-II (i.e. 3.92), though they are in different range. The unmodified membrane (Memb-I) exhibits SO4=/Cl- selectivity 3.23 is in the same scale of Memb-III (2.27). Memb-III exhibits higher hexaconazole separation (91.5%) compared to Memb-II (i.e. 53.9%). The flux decline follows the order: field water > tap water > deionized water. The copper incorporated membrane (Memb-II) has shown a low flux decline compared to Memb-III as well as Memb-I. The antibacterial properties towards E. Coli and Bacillus subtilis are well reflected. The copper containing membranes have promising antibacterial properties and follows the order Memb-II > Memb-III > Memb-I.