A methodology to simulate peak water demand for water supply systems using semi-direct methods: A case study for a residential building in Mexico.

Historically, approaches for determining peak water demand in buildings have been based on probabilistic methods. Extensive research has shown that these methods lack accuracy because of the human factor in the probability of use. Inaccuracy in the calculation of peak water demand is the main cause of oversized water supply systems in buildings. This has led to unfavorable effects such as: 1) increasing the building carbon footprint due to the use of more construction materials, and 2) engendering health hazards due to the stagnation of water causing microbial growing. This paper presents a step-by-step methodology that serves to calculate the peak water demand by simulating the use of plumbing fixtures based on data obtained from standardized flowrate. With the implementation of the methodology, the peak water demand estimated was 2.6 times lower in comparison to traditional methods. The main conclusion drawn from the research is the potential of the methodology to easily simulated peak water demand in residential buildings in the short term. Thus, it reveals a hotspot for peak water demand calculation and can serve as routes for future research.

Risk factors and effect of dyspnea inappropriate treatment in adults' emergency department: a retrospective cohort study.

Dyspnea is a frequent symptom in adults' emergency departments (EDs). Misdiagnosis at initial clinical examination is common, leading to early inappropriate treatment and increased in-hospital mortality. Risk factors of inappropriate treatment assessable at early examination remain undescribed herein. The objective of this study was to identify clinical risk factors of dyspnea and inappropriate treatment in patients admitted to ED. This is an observational retrospective cohort study. Patients over the age of 15 who were admitted to adult EDs of the University Hospital of Toulouse (France) with dyspnea were included from 1 July to 31 December 2019. The primary end-point was dyspnea and inappropriate treatment was initiated at ED. Inappropriate treatment was defined by looking at the final diagnosis of dyspnea at hospital discharge and early treatment provided. Afterward, this early treatment at ED was compared to the recommended treatment defined by the International Guidelines for Acute Heart Failure, bacterial pneumonia, chronic obstructive pulmonary disease, asthma or pulmonary embolism. A total of 2123 patients were analyzed. Of these, 809 (38%) had inappropriate treatment in ED. Independent risk factors of inappropriate treatment were: age over 75 years (OR, 1.46; 95% CI, 1.18-1.81), history of heart disease (OR, 1.32; 95% CI, 1.07-1.62) and lung disease (OR, 1.47; 95% CI, 1.21-1.78), SpO2 <90% (OR, 1.64; 95% CI, 1.37-2.02), bilateral rale (OR, 1.25; 95% CI, 1.01-1.66), focal cracklings (OR, 1.32; 95% CI, 1.05-1.66) and wheezing (OR, 1.62; 95% CI, 1.31-2.03). In multivariate analysis, under-treatment significantly increased in-hospital mortality (OR, 2.13; 95% CI, 1.29-3.52) compared to appropriate treatment. Over-treatment nonsignificantly increased in-hospital mortality (OR, 1.43; 95% CI, 0.99-2.06). Inappropriate treatment is frequent in patients admitted to ED for dyspnea. Patients older than 75 years, with comorbidities (heart or lung disease), hypoxemia (SpO2 <90%) or abnormal pulmonary auscultation (especially wheezing) are at risk of inappropriate treatment.

Tongue electrical impedance myography correlates with functional, neurophysiologic, and clinical outcome measures in long-term oropharyngeal cancer survivors with and without hypoglossal neuropathy: An exploratory study.

BACKGROUND: This pilot study analyzed correlations between tongue electrical impedance myography (EIM), standard tongue electromyography (EMG), and tongue functional measures in N = 4 long-term oropharyngeal cancer (OPC) survivors. METHODS: Patients were screened for a supportive care trial (NCT04151082). Hypoglossal nerve function was evaluated with genioglossus needle EMG, functional measures with the Iowa oral performance instrument (IOPI), and multi-frequency tissue composition with tongue EIM. RESULTS: Tongue EIM conductivity was higher for patients with EMG-confirmed cranial nerve XII neuropathy than those without (p = 0.005) and in patients with mild versus normal EMG reinnervation ratings (16 kHz EIM: p = 0.051). Tongue EIM correlated with IOPI strength measurements (e.g., anterior maximum isometric lingual strength: r2 = 0.62, p = 0.020). CONCLUSIONS: Tongue EIM measures related to tongue strength and the presence of XII neuropathy. Noninvasive tongue EIM may be a convenient adjunctive biomarker to assess tongue health in OPC survivors.

Evaluation of the Insecticidal Potential of Heterotheca inuloides Acetonic and Methanolic Extracts against Spodoptera frugiperda and Their Ecotoxicological Effect on Poecilia reticulata.

For the management of Spodoptera frugiperda, botanical extracts have been used to reduce the environmental impacts of synthetic chemical pesticides. In the present investigation, the insecticidal activity of the acetonic and methanolic extracts of Heterotheca inuloides (Asteraceae) and of the main compound 7-hydroxy-3,4-dihydrocadalene on this pest as well as its ecotoxicological effect on Poecilia reticulata were evaluated. A greater insecticidal response was obtained from the acetonic extracts than from the methanolic extracts, with LC50 values of 730.4 ppm and 711.7 ppm for samples 1 and 2, respectively. Similarly, there was a lethal effect on 50% of the P. reticulata population at low concentrations in the acetonic extract compared to the methanolic extract. The sesquiterpene 7-hydroxy-3,4-dihydrocadalene has greater insecticidal activity by presenting an LC50 of 44.36 ppm; however, it is classified as moderately toxic for guppy fish.

Skin Cell and Tissue Responses to Cross-Linked Hyaluronic Acid in Low-Grade Inflammatory Conditions.

Hyaluronic acid (HA), used in a variety of medical applications, is associated in rare instances to long-term adverse effects. Although the aetiology of these events is unknown, a number of hypotheses have been proposed, including low molecular weight of HA (LMW-HA) in the filler products. We hypothesized that cross-linked HA and its degradation products, in a low-grade inflammatory microenvironment, could impact immune responses that could affect cell behaviours in the dermis. Using two different cross-linking technologies VYC-15L and HYC-24L+, and their hyaluronidase-induced degradation products, we observed for nondegraded HA, VYC-15L and HYC-24L+, a moderate and transient increase in IL-1ß, TNF-α in M1 macrophages under low-grade inflammatory conditions. Endothelial cells and fibroblasts were preconditioned using inflammatory medium produced by M1 macrophages. 24 h after LMW-HA fragments and HA stimulation, no cytokine was released in these preconditioned cells. To further characterize HA responses, we used a novel in vivo murine model exhibiting a systemic low-grade inflammatory phenotype. The intradermal injection of VYC-15L and its degradation products induced an inflammation and cell infiltration into the skin that was more pronounced than those by HYC-24L+. This acute cutaneous inflammation was likely due to mechanical effects due to filler injection and tissue integration rather than its biological effects on inflammation. VYC-15L and its degradation product potentiated microvascular response to acetylcholine in the presence of a low-grade inflammation. The different responses with 2D cell models and mouse model using the two tested cross-linking HA technologies showed the importance to use integrative complex model to better understand the effects of HA products according to inflammatory state.

Combining electromyographic and electrical impedance data sets through machine learning: A study in D2-mdx and wild-type mice.

INTRODUCTION/AIMS: Needle impedance-electromyography (iEMG) assesses the active and passive electrical properties of muscles concurrently by using a novel needle with six electrodes, two for EMG and four for electrical impedance myography (EIM). Here, we assessed an approach for combining multifrequency EMG and EIM data via machine learning (ML) to discriminate D2-mdx muscular dystrophy and wild-type (WT) mouse skeletal muscle. METHODS: iEMG data were obtained from quadriceps of D2-mdx mice, a muscular dystrophy model, and WT animals. EIM data were collected with the animals under deep anesthesia and EMG data collected under light anesthesia, allowing for limited spontaneous movement. Fourier transformation was performed on the EMG data to provide power spectra that were sampled across the frequency range using three different approaches. Random forest-based, nested ML was applied to the EIM and EMG data sets separately and then together to assess healthy versus disease category classification using a nested cross-validation procedure. RESULTS: Data from 20 D2-mdx and 20 WT limbs were analyzed. EIM data fared better than EMG data in differentiating healthy from disease mice with 93.1% versus 75.6% accuracy, respectively. Combining EIM and EMG data sets yielded similar performance as EIM data alone with 92.2% accuracy. DISCUSSION: We have demonstrated an ML-based approach for combining EIM and EMG data obtained with an iEMG needle. While EIM-EMG in combination fared no better than EIM alone with this data set, the approach used here demonstrates a novel method of combining the two techniques to characterize the full electrical properties of skeletal muscle.

Multi-omic analyses reveal the unique properties of chia (Salvia hispanica) seed metabolism.

Chia (Salvia hispanica) is an emerging crop considered a functional food containing important substances with multiple potential applications. However, the molecular basis of some relevant chia traits, such as seed mucilage and polyphenol content, remains to be discovered. This study generates an improved chromosome-level reference of the chia genome, resolving some highly repetitive regions, describing methylation patterns, and refining genome annotation. Transcriptomic analysis shows that seeds exhibit a unique expression pattern compared to other organs and tissues. Thus, a metabolic and proteomic approach is implemented to study seed composition and seed-produced mucilage. The chia genome exhibits a significant expansion in mucilage synthesis genes (compared to Arabidopsis), and gene network analysis reveals potential regulators controlling seed mucilage production. Rosmarinic acid, a compound with enormous therapeutic potential, was classified as the most abundant polyphenol in seeds, and candidate genes for its complex pathway are described. Overall, this study provides important insights into the molecular basis for the unique characteristics of chia seeds.

CRI-SPA: a high-throughput method for systematic genetic editing of yeast libraries.

Biological functions are orchestrated by intricate networks of interacting genetic elements. Predicting the interaction landscape remains a challenge for systems biology and new research tools allowing simple and rapid mapping of sequence to function are desirable. Here, we describe CRI-SPA, a method allowing the transfer of chromosomal genetic features from a CRI-SPA Donor strain to arrayed strains in large libraries of Saccharomyces cerevisiae. CRI-SPA is based on mating, CRISPR-Cas9-induced gene conversion, and Selective Ploidy Ablation. CRI-SPA can be massively parallelized with automation and can be executed within a week. We demonstrate the power of CRI-SPA by transferring four genes that enable betaxanthin production into each strain of the yeast knockout collection (≈4800 strains). Using this setup, we show that CRI-SPA is highly efficient and reproducible, and even allows marker-free transfer of genetic features. Moreover, we validate a set of CRI-SPA hits by showing that their phenotypes correlate strongly with the phenotypes of the corresponding mutant strains recreated by reverse genetic engineering. Hence, our results provide a genome-wide overview of the genetic requirements for betaxanthin production. We envision that the simplicity, speed, and reliability offered by CRI-SPA will make it a versatile tool to forward systems-level understanding of biological processes.

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

A scoping review of current and emerging techniques for evaluation of peripheral nerve health, degeneration, and regeneration: part 1, neurophysiology.

Peripheral neuroregeneration research and therapeutic options are expanding exponentially. With this expansion comes an increasing need to reliably evaluate and quantify nerve health. Valid and responsive measures that can serve as biomarkers of the nerve status are essential for both clinical and research purposes for diagnosis, longitudinal follow-up, and monitoring the impact of any intervention. Furthermore, such biomarkers can elucidate regeneration mechanisms and open new avenues for research. Without these measures, clinical decision-making falls short, and research becomes more costly, time-consuming, and sometimes infeasible. As a companion to Part 2, which is focused on non-invasive imaging, Part 1 of this two-part scoping review systematically identifies and critically examines many current and emerging neurophysiological techniques that have the potential to evaluate peripheral nerve health, particularly from the perspective of regenerative therapies and research.

A scoping review of current and emerging techniques for evaluation of peripheral nerve health, degeneration and regeneration: part 2, non-invasive imaging.

Peripheral neuroregenerative research and therapeutic options are expanding exponentially. With this expansion comes an increasing need to reliably evaluate and quantify nerve health. Valid and responsive measures of the nerve status are essential for both clinical and research purposes for diagnosis, longitudinal follow-up, and monitoring the impact of any intervention. Furthermore, novel biomarkers can elucidate regenerative mechanisms and open new avenues for research. Without such measures, clinical decision-making is impaired, and research becomes more costly, time-consuming, and sometimes infeasible. Part 1 of this two-part scoping review focused on neurophysiology. In part 2, we identify and critically examine many current and emerging non-invasive imaging techniques that have the potential to evaluate peripheral nerve health, particularly from the perspective of regenerative therapies and research.

Electrical impedance myography method of measuring anisotropic tongue tissue.

Objective.To date, measurement of the conductivity and relative permittivity properties of anisotropic biological tissues using electrical impedance myography (EIM) has only been possible through an invasiveex vivobiopsy procedure. Here, we present a novel forward and inverse theoretical modeling framework to estimate these properties combining surface and needle EIM measurements.Methods. The framework here presented models the electrical potential distribution within a monodomain, homogeneous, and three-dimensional anisotropic tissue. Finite-element method (FEM) simulations and tongue experimental results verify the validity of our method to reverse-engineer three-dimensional conductivity and relative permittivity properties from EIM measurements.Results. FEM-based simulations confirm the validity of our analytical framework, with relative errors between analytical predictions and simulations smaller than 0.12% and 2.6% in a cuboid and tongue model, respectively. Experimental results confirm qualitative differences in the conductivity and the relative permittivity properties in thex,y, andzdirections.Conclusion. Our methodology enables EIM technology to reverse-engineer the anisotropic tongue tissue conductivity and relative permittivity properties, thus unfolding full forward and inverse EIM predictability capabilities.Significance. This new method of evaluating anisotropic tongue tissue will lead to a deeper understanding of the role of biology necessary for the development of new EIM tools and approaches for tongue health measurement and monitoring.

Electrical Impedance Dermography Differentiates Squamous Cell Carcinoma In Situ from Inflamed Seborrheic Keratoses.

There are no currently available low-cost, noninvasive methods for discerning the depth of squamous cell carcinoma (SCC) invasion or distinguishing SCC from its benign mimics, such as inflamed seborrheic keratosis (SK). We studied 35 subjects with subsequently confirmed SCC or SK. Subjects underwent electrical impedance dermography measurements at six frequencies to assess the electrical properties of the lesion. Averaged greatest intrasession reproducibility values were 0.630 for invasive SCC at 128 kHz, 0.444 for SCC in situ at 16 kHz, and 0.460 for SK at 128 kHz. Electrical impedance dermography modeling revealed significant differences between SCC and inflamed SK in normal skin (P < 0.001) and also between invasive SCC and SCC in situ (P < 0.001), invasive SCC and inflamed SK (P < 0.001), and SCC in situ and inflamed SK (P < 0.001). A diagnostic algorithm classified SCC in situ from inflamed SK with an accuracy of 0.958, a sensitivity of 94.6%, and a specificity of 96.9%; it also classified SCC in situ from normal skin with an accuracy of 0.796, a sensitivity of 90.2%, and a specificity of 51.2%. This study provides preliminary data and a methodology that can be used in future studies to further advance the value of electrical impedance dermography and inform biopsy decision making in patients with lesions suspicious of SCC.

Safety evaluation of smart scales, smart watches, and smart rings with bioimpedance technology shows evidence of potential interference in cardiac implantable electronic devices.

BACKGROUND: Smart scales, smart watches, and smart rings with bioimpedance technology may create interference in patients with cardiac implantable electronic devices (CIEDs). OBJECTIVES: The purpose of this study was to determine interference at CIEDs with simulations and benchtop testing, and to compare the results with maximum values defined in the ISO 14117 electromagnetic interference standard for these devices. METHODS: The interference at pacing electrodes was determined by simulations on a male and a female computable model. A benchtop evaluation of representative CIEDs from 3 different manufacturers as specified in the ISO 14117 standard also was performed. RESULTS: Simulations showed evidence of interference with voltage values exceeding threshold values defined in the ISO 14117 standard. The level of interference varied with the frequency and amplitude of the bioimpedance signal, and between male and female models. The level of interference generated with smart scale and smart rings simulations was lower than with smart watches. Across device manufacturers, generators demonstrated susceptibility to oversensing and pacing inhibition at different signal amplitudes and frequencies. CONCLUSIONS: This study evaluated the safety of smart scales, smart watches, and smart rings with bioimpedance technology via simulation and testing. Our results indicate that these consumer electronic devices could interfere in patients with CIEDs. The present findings do not recommend the use of these devices in this population due to potential interference.

A universal, high-performance ECG signal processing engine to reduce clinical burden.

BACKGROUND: Electrocardiogram (ECG) signal conditioning is a vital step in the ECG signal processing chain that ensures effective noise removal and accurate feature extraction. OBJECTIVE: This study evaluates the performance of the FDA 510 (k) cleared HeartKey Signal Conditioning and QRS peak detection algorithms on a range of annotated public and proprietary ECG databases (HeartKey is a UK Registered Trademark of B-Secur Ltd). METHODS: Seven hundred fifty-one raw ECG files from a broad range of use cases were individually passed through the HeartKey signal processing engine. The algorithms include several advanced filtering steps to enable significant noise removal and accurate identification of the QRS complex. QRS detection statistics were generated against the annotated ECG files. RESULTS: HeartKey displayed robust performance across 14 ECG databases (seven public, seven proprietary), covering a range of healthy and unhealthy patient data, wet and dry electrode types, various lead configurations, hardware sources, and stationary/ambulatory recordings from clinical and non-clinical settings. Over the NSR, MIT-BIH, AHA, and MIT-AF public databases, average QRS Se and PPV values of 98.90% and 99.08% were achieved. Adaptable performance (Se 93.26%, PPV 90.53%) was similarly observed on the challenging NST database. Crucially, HeartKey's performance effectively translated to the dry electrode space, with an average QRS Se of 99.22% and PPV of 99.00% observed over eight dry electrode databases representing various use cases, including two challenging motion-based collection protocols. CONCLUSION: HeartKey demonstrated robust signal conditioning and QRS detection performance across the broad range of tested ECG signals. It should be emphasized that in no way have the algorithms been altered or trained to optimize performance on a given database, meaning that HeartKey is potentially a universal solution capable of maintaining a high level of performance across a broad range of clinical and everyday use cases.

Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae.

Heme is an oxygen carrier and a cofactor of both industrial enzymes and food additives. The intracellular level of free heme is low, which limits the synthesis of heme proteins. Therefore, increasing heme synthesis allows an increased production of heme proteins. Using the genome-scale metabolic model (GEM) Yeast8 for the yeast Saccharomyces cerevisiae, we identified fluxes potentially important to heme synthesis. With this model, in silico simulations highlighted 84 gene targets for balancing biomass and increasing heme production. Of those identified, 76 genes were individually deleted or overexpressed in experiments. Empirically, 40 genes individually increased heme production (up to threefold). Heme was increased by modifying target genes, which not only included the genes involved in heme biosynthesis, but also those involved in glycolysis, pyruvate, Fe-S clusters, glycine, and succinyl-coenzyme A (CoA) metabolism. Next, we developed an algorithmic method for predicting an optimal combination of these genes by using the enzyme-constrained extension of the Yeast8 model, ecYeast8. The computationally identified combination for enhanced heme production was evaluated using the heme ligand-binding biosensor (Heme-LBB). The positive targets were combined using CRISPR-Cas9 in the yeast strain (IMX581-HEM15-HEM14-HEM3-Δshm1-HEM2-Δhmx1-FET4-Δgcv2-HEM1-Δgcv1-HEM13), which produces 70-fold-higher levels of intracellular heme.

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0.

Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.

Using machine learning algorithms to enhance the diagnostic performance of electrical impedance myography.

INTRODUCTION/AIMS: We assessed the classification performance of machine learning (ML) using multifrequency electrical impedance myography (EIM) values to improve upon diagnostic outcomes as compared to those based on a single EIM value. METHODS: EIM data was obtained from unilateral excised gastrocnemius in eighty diseased mice (26 D2-mdx, Duchenne muscular dystrophy model, 39 SOD1G93A ALS model, and 15 db/db, a model of obesity-induced muscle atrophy) and 33 wild-type (WT) animals. We assessed the classification performance of a ML random forest algorithm incorporating all the data (multifrequency resistance, reactance and phase values) comparing it to the 50 kHz phase value alone. RESULTS: ML outperformed the 50 kHz analysis as based on receiver-operating characteristic curves and measurement of the area under the curve (AUC). For example, comparing all diseases together versus WT from the test set outputs, the AUC was 0.52 for 50 kHz phase, but was 0.94 for the ML model. Similarly, when comparing ALS versus WT, the AUCs were 0.79 for 50 kHz phase and 0.99 for ML. DISCUSSION: Multifrequency EIM using ML improves upon classification compared to that achieved with a single-frequency value. ML approaches should be considered in all future basic and clinical diagnostic applications of EIM.

On the measurement of skeletal muscle anisotropic permittivity property with a single cross-shaped needle insertion.

Application of minimally invasive methods to enable the measurement of tissue permittivity in the neuromuscular clinic remain elusive. This paper provides a theoretical and modeling study on the measurement of the permittivity of two-dimensional anisotropic tissues such as skeletal muscle with a multi-electrode cross-shaped needle. For this, we design a novel cross-shaped needle with multiple-electrodes and analyse apparent impedance corresponding to the measured impedance. In addition, we propose three methods of estimate anisotropic muscle permittivity. Compared to existing electrical impedance-based needle methods that we have developed, the new needle design and numerical methods associated enable estimating in vivo muscle permittivity values with only a single needle insertion. Being able to measure muscle permittivity directly with a single needle insertion could open up an entirely new area of research with direct clinical application, including using these values to assist in neuromuscular diagnosis and to assess subtle effects of therapeutic intervention on muscle health.