Logarithmic Transformation Research Articles

PPPCT: Privacy-Preserving framework for Parallel Clustering Transcriptomics data

Single-cell transcriptomics data provides crucial insights into patients’ health, yet poses significant privacy concerns. Genomic data privacy attacks can have deep implications, encompassing not only the patients’ health information but also extending widely to compromise their families’. Moreover, the permanence of leaked data exacerbates the challenges, making retraction an impossibility. While extensive efforts have been directed towards clustering single-cell transcriptomics data, addressing critical challenges, especially in the realm of privacy, remains pivotal. This paper introduces an efficient, fast, privacy-preserving approach for clustering single-cell RNA-sequencing (scRNA-seq) datasets. The key contributions include ensuring data privacy, achieving high-quality clustering, accommodating the high dimensionality inherent in the datasets, and maintaining reasonable computation time for big-scale datasets. Our proposed approach utilizes the map-reduce scheme to parallelize clustering, addressing intensive calculation challenges. Intel Software Guard eXtension (SGX) processors are used to ensure the security of sensitive code and data during processing. Additionally, the approach incorporates a logarithm transformation as a preprocessing step, employs non-negative matrix factorization for dimensionality reduction, and utilizes parallel k-means for clustering. The approach fully leverages the computing capabilities of all processing resources within a secure private cloud environment. Experimental results demonstrate the efficacy of our approach in preserving patient privacy while surpassing state-of-the-art methods in both clustering quality and computation time. Our method consistently achieves a minimum of 7% higher Adjusted Rand Index (ARI) than existing approaches, contingent on dataset size. Additionally, due to parallel computations and dimensionality reduction, our approach exhibits efficiency, converging to very good results in less than 10 seconds for a scRNA-seq dataset with 5000 genes and 6000 cells when prioritizing privacy and under two seconds without privacy considerations.Availability and implementationCode and datasets availability: https://github.com/University-of-Windsor/PPPCT.

Mixed Traffic Behaviour in Heavy Flow: Two-Lane Road Insights

The current paper outlines a comprehensive methodology for modeling speed data on two-lane roads during periods of heavy traffic characterized by heterogeneous vehicle types. In such scenarios, the presence of diverse vehicle types causes a significant departure from the normal speed distribution model. This deviation becomes particularly pronounced during heavy traffic flows due to the frequent interactions among vehicles within the traffic stream. Consequently, there arises a necessity to develop a modeling approach specifically tailored to such flow conditions. Drawing upon empirical data collected from a major intercity road in India, this study uncovers a notable skewness in speed data under heavy traffic conditions. This skewness primarily stems from the formation of vehicle platoons within the traffic stream, exerting a substantial influence on their speed characteristics. By scrutinizing the distribution of this data, the study concludes that a logarithmic transformation effectively aligns with the assumption of normality. This assertion is supported by various goodness-of-fit metrics, affirming the suitability of the proposed modeling approach for capturing the intricacies of speed behavior in heterogeneous traffic environments.

EVALUATION OF THE RELIABILITY AND CONCORDANCE OF VISUAL WEIGHT ASSESSMENT IN WATER BUFFALO FEMALES

<p><strong>Background.</strong> For the development of reproductive, nutritional and health programmes, estimation of animal body weight (BW) is a fundamental tool in herd management. <strong>Objective. </strong>To evaluate of the reliability and concordance<strong> </strong>of visual weight assessment in water buffalo females. <strong>Methodology. </strong>Data on visually estimated body weight (VM) and actual body weight (BW) were recorded for 229 female water buffaloes. BW was recorded using a digital scale and visual estimation BW was taken as the average of three observations made by three observers. The measurements obtained by each of the different estimation methods were also compared with the observed weights by inspecting the paired Bland-Altman plots prior to logarithmic transformation. <strong>Results. </strong>Correlations between observed BW and mean predicted BW for visual methods showed a positive and significant relationship (P<0.001), with an r value of 0.95. According to the intraclass correlation coefficient (ICC), there was a high degree of reliability in the visual estimates of buffalo weight, due to the similarity in the mean and dispersion of each observer's estimates. Both the Bland-Altman plot and the ICC show that there is a high level of concordance between the buffalo weights obtained by the visual and real methods. <strong>Implications.</strong> The result of the present study showed that visual assessment highlights the ability of livestock handlers to have a very accurate estimation of BW in female water buffaloes. <strong>Conclusion.</strong> The results of this study show that visual assessment highlights the ability of livestock handlers to visually estimate BW in female water buffaloes with high reliability and concordance with the animal weighbridge method.</p>

In situ detection of moisture content and gelatinization degree during rice processing using hyperspectral imaging

During the industrial production of cooked rice, soaking and cooking are two critical processes that directly determine the quality of cooked rice. In this study, hyperspectral imaging technology was used to analyze and model the moisture content and the gelatinization degree of japonica rice (Wuchang rice and Long grain rice) and indica rice (Thai fragrant rice) during processing. The results showed that by analyzing the hyperspectral images, 35 characteristic wavelengths were obtained, and predictive models were established with the reflectance values as independent variables, moisture content and gelatinization degree as dependent variables. The best predictive models were partial least squares regression models, and the correlation coefficients and root mean square errors for the two models were 0.9673 and 0.5300, 0.9534 and 4.5046, respectively. Visualization of the moisture content and the gelatinization degree during processing were obtained using the models and enhanced by pseudo color and logarithmic transformation. The research results can provide theoretical and data basis for the development of intelligent rice processing production lines and reference methods for relevant research on hyperspectral quantitative modeling.

GDF-15: a novel biomarker of heart failure predicts short-term and long-term heart-failure rehospitalization and short-term mortality in patients with acute heart failure syndrome

BackgroundAcute heart failure (AHF) is a potentially life-threatening clinical syndrome, usually requiring hospital admission. Growth Differentiation Factor-15 (GDF-15) is a distant member of the transforming growth factor-β. The increased expression of GDF-15 has been observed during heart failure (HF) and is associated with worse outcomes. However, the relationship between GDF-15 and AHF is not well understood with limited evidence among Thai patients.PurposeInvestigate the correlation between biomarker levels (measured upon admission and discharge) and short- and long-term adverse outcomes, encompassing all-cause mortality and heart-failure (HF) rehospitalization (at 30, 90, and 180 days, as well as throughout the entire follow-up duration) in individuals experiencing acute HF.MethodsThis is a prospective single-center investigation involving patients admitted for AHF. Biomarkers, including GDF-15, high-sensitivity troponin T (hsTnT), and N-terminal pro-B-type natriuretic peptide (NT-proBNP), were assessed upon admission and discharge. Outcomes, including all-cause mortality and HF rehospitalization, were examined. Logarithmic transformations were applied to the biomarker variables for subsequent analysis. Univariate and multivariate analyses of cause-specific hazards were conducted using the Cox proportional hazards regression model, while subdistribution hazards were assessed using the Fine-Gray regression model to evaluate outcomes.ResultsA total of 84 patients were enrolled (mean age of 69 years, 52% females). The GDF-15 level significantly decreased during admission (median at the time of admission 6,346 pg/mL, median at the time of discharge 5,711 pg/mL; p < 0.01). All-cause mortality at 30 days and 180 days were 6.0% and 16.7%, respectively. HF rehospitalization at 30 days and 180 days were 15.5% and 28.6%, respectively. Univariate analysis showed that total orthoedema congestion score (p = 0.02) and admission GDF-15 level (p = 0.01) were associated with 30-day all-cause mortality, whereas hsTnT or NT-proBNP levels did not show significant associations. However, higher levels of NT-proBNP upon admission were associated with all-cause mortality when considering the entire follow-up period (p < 0.01). Both univariate and multivariate analyses demonstrated that lower discharge GDF-15 levels and a greater reduction in GDF-15 levels from admission to discharge were associated with a lower risk of 30-day rehospitalization. Similarly, univariate analysis revealed that a greater reduction in NT-proBNP levels from admission to discharge was associated with lower 30-day rehospitalization rates. At 180 days, a greater reduction in GDF-15 levels remained associated with lower hazards and incidence of rehospitalization.ConclusionThe significant decrease in Growth Differentiation Factor-15 (GDF-15) levels during hospitalization suggests its potential as a dynamic marker reflecting the course of AHF. Importantly, higher GDF-15 levels at admission were associated with an increased risk of 30-day all-cause mortality, highlighting its prognostic value in this patient population. Moreover, lower discharge GDF-15 levels, reductions in GDF-15 from admission to discharge, and decreases in NT-proBNP from admission to discharge were associated with a reduced risk of 30-day rehospitalization.

Reverse engineering and analysis of microstructure polymer fiber via artificial neural networks: simplifying the design approach

Abstract Microstructure polymer fibers have been extensively researched for their applications in various fields. The design and simulation of these fibers have utilized time-consuming techniques like the finite-difference time-domain and finite element method to facilitate the process. This study proposes an optimal artificial neural network (ANN) for predicting the structural design required to achieve desired optical properties. The ANN model takes various optical properties, including confinement loss, effective index, effective mode area, and wavelengths, as inputs to predict fiber design parameters such as diameter and pitch. To address the challenge of skewed distributions, a data set with a Gaussian-like distribution for confinement loss was generated using a logarithmic transformation method, enabling effective training of machine learning models. Furthermore, the ANN model demonstrates its capability to rapidly predict unknown geometric parameters using only the core mode properties of a polymer fiber, achieving results in a significantly shorter time (3 ms) compared to the trial-and-error approach of finite element method simulation (15 s). The reverse engineering model achieves a mean square error of 3.4877 × 10−06 with five hidden layers. The ANN model not only offers ultrafast calculation speed but also delivers high prediction accuracy, thereby accelerating the design process of optical devices. The differentiation among the prediction result, target, and calculation result provides compelling evidence that the proposed approach is an effective methodology for designing microstructure polymer fibers.

Dynamical study and diverse soliton collisions for the Kraenkel–Manna–Merle system in ferrites

We investigate the rational solitons of the Kraenkel–Manna–Merle (KMM) system in ferrites via symbolic computation with the ansatz functions technique and logarithmic transformation. The KMM system describes the propagations of the ferromagnetic particles in ferrite materials. Multiwave solitons are reported by using the three-wave technique. Homocentric breathers (HBs) approach is also applied to build new forms of exact solutions. We construct M-shaped solitons and their dynamics are shown via the appropriate values of parameters. Two types of interactions for rational soliton with kink wave are investigated. Additionally, kink cross-rational solitons (KCRSs) and periodic cross-rational solitons (PCRSs) are examined via suitable transformation.

Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method

Reliable streamflow forecasting is a determining factor for water resource planning and flood control. To better understand the strengths and weaknesses of newly proposed methods in streamflow forecasting and facilitate comparisons of different research results, we test a simple, universal, and efficient benchmark method, namely, the naïve method, for short-term streamflow prediction. Using the naïve method, we assess the streamflow forecasting performance of the long short-term memory models trained with different objective functions, including mean squared error (MSE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and mean absolute error (MAE). The experiments over 273 watersheds show that the naïve method attains good forecasting performance (NSE &gt; 0.5) in 88%, 65%, and 52% of watersheds at lead times of 1 day, 2 days, and 3 days, respectively. Through benchmarking by the naïve method, we find that the LSTM models trained with squared-error-based objective functions, i.e., MSE, RMSE, NSE, and KGE, perform poorly in low flow forecasting. This is because they are more influenced by training samples with high flows than by those with low flows during the model training process. For comprehensive short-term streamflow modeling without special demand orientation, we recommend the application of MAE instead of a squared-error-based metric as the objective function. In addition, it is also feasible to perform logarithmic transformation on the streamflow data. This work underscores the critical importance of appropriately selecting the objective functions for model training/calibration, shedding light on how to effectively evaluate the performance of streamflow forecast models.

Correlation between the triglyceride-glucose index and arterial stiffness in Japanese individuals with normoglycaemia: a cross-sectional study

BackgroundThe association between the triglyceride-glucose (TyG) index and arterial stiffness in individuals with normoglycaemia remains unclear. We aimed to evaluate the relationship between the TyG index and arterial stiffness in Japanese individuals with normoglycaemia, providing additional evidence for predicting early arterial stiffness.MethodsThis study included 15,453 adults who participated in the NAGALA Physical Examination Project of the Murakami Memorial Hospital in Gifu, Japan, from 2004 to 2015. Data on clinical demographic characteristics and serum biomarker levels were collected. The TyG index was calculated from the logarithmic transformation of fasting triglycerides multiplied by fasting glucose, and arterial stiffness was measured using the estimated pulse wave velocity calculated based on age and mean blood pressure. The association between the TyG index and arterial stiffness was analysed using a logistic regression model.ResultsThe prevalence of arterial stiffness was 3.2% (500/15,453). After adjusting for all covariates, the TyG index was positively associated with arterial stiffness as a continuous variable (adjusted odds ratio (OR) = 1.86; 95% Confidence Interval = 1.45–2.39; P<0.001). Using the quartile as the cutoff point, a regression analysis was performed for arterial stiffness when the TyG index was converted into a categorical variable. After adjusting for all covariates, the OR showed an upward trend; the trend test was P<0.001. Subgroup analysis revealed a positive association between the TyG index and arterial stiffness in Japanese individuals with normoglycaemia and different characteristics.ConclusionThe TyG index in Japanese individuals with normoglycaemia is significantly correlated with arterial stiffness, and the TyG index may be a predictor of early arterial stiffness.

Video-based beat-by-beat blood pressure monitoring via transfer deep-learning

Currently, learning physiological vital signs such as blood pressure (BP), hemoglobin levels, and oxygen saturation, from Photoplethysmography (PPG) signal, is receiving more attention. Despite successive progress that has been made so far, continuously revealing new aspects characterizes that field as a rich research topic. It includes a diverse number of critical points represented in signal denoising, data cleaning, employed features, feature format, feature selection, feature domain, model structure, problem formulation (regression or classification), and model combinations. It is worth noting that extensive research efforts are devoted to utilizing different variants of machine learning and deep learning models while transfer learning is not fully explored yet. So, in this paper, we are introducing a per-beat rPPG-to-BP mapping scheme based on transfer learning. An interesting representation of a 1-D PPG signal as a 2-D image is proposed for enabling powerful off-the-shelf image-based models through transfer learning. It resolves limitations about training data size due to strict data cleaning. Also, it enhances model generalization by exploiting underlying excellent feature extraction. Moreover, non-uniform data distribution (data skewness) is partially resolved by introducing logarithmic transformation. Furthermore, double cleaning is applied for training contact PPG data and testing rPPG beats as well. The quality of the segmented beats is tested by checking some of the related quality metrics. Hence, the prediction reliability is enhanced by excluding deformed beats. Varying rPPG quality is relaxed by selecting beats during intervals of the highest signal strength. Based on the experimental results, the proposed system outperforms the state-of-the-art systems in the sense of mean absolute error (MAE) and standard deviation (STD). STD for the test data is decreased to 5.4782 and 3.8539 for SBP and DBP, respectively. Also, MAE decreased to 2.3453 and 1.6854 for SBP and DBP, respectively. Moreover, the results for BP estimation from real video reveal that the STD reaches 8.027882 and 6.013052 for SBP and DBP, respectively. Also, MAE for the estimated BP from real videos reaches 7.052803 and 5.616028 for SBP and DBP, respectively.Graphical abstractProposed camera-based blood pressure monitoring system

Discontinuous, biphasic, ontogenetic shifts in the metabolic allometry of aquatic animals?

Several investigations in recent years have reported patterns of discontinuous, biphasic, loglinear variation in the metabolic allometry of aquatic animals. These putative shifts in pattern of allometry have been attributed to changes in the primary site for gas exchange from cutaneous to branchial as animals undergo ontogenetic changes in size, shape, and surface area. Because of the important implications of the earlier research with regard to both physiology and evolution, I re-examined data that purportedly support claims of discontinuous, biphasic allometry in oxygen consumption versus body size of American eels (Anguilla rostrata) and spiny lobsters (Sagmariasus verreauxi). I used ANCOVA to fit three different statistical models to each set of logarithmic transformations and then assessed the fits by Akaike's Information Criterion. The observations for both species were described better by a single straight line fitted to the full distribution than by a biphasic model. Eels, lobsters, and other aquatic animals undergo changes in shape and surface area as they grow, but such changes are not necessarily accompanied by changes in the pattern of metabolic allometry.

Efficiency of labor allocation: An observation in Indonesian Standard Industrial Classification (ISIC)

This study aims to analyze the efficiency and elasticity of the large and medium manufacturing industry sector in Indonesia from 2012 to 2020. The analysis focuses on the 24 subsectors within the 2-digit Indonesian Standard Industrial Classification (ISIC) principal groups 10 to 33. The efficiency level of the manufacturing industry sector was analyzed using the Cobb-Douglas production function model with linear logarithmic transformation. During the period 2012–2020, the production efficiency of the 24 ISIC 2-digit subsectors increased by an average of 445%, or 4 times higher than before. The ISIC 2-digit subsector (24 subsectors) had output elasticity values smaller than one (EL &lt; 1) or inelastic 45 times. This indicates that the marginal value added of labor was lower than the average value added of labor. Therefore, the additional allocation of labor in large and medium industries tends to reduce the average value added of labor. The computer industry, electronic and optical goods computer, electronic and optical goods had the highest efficiency index in order, according to the data. This can be achieved by focusing on industries such as ISIC 17 (paper and paper goods industry), ISIC 31 (furniture industry), and ISIC 29 (motor vehicle, trailer and semi-trailer industry). To improve the efficiency of labor allocation, it is necessary to optimize the use of labor resources, increase productivity, and remain competitive in the market. To improve efficiency and elasticity, it is recommended to implement a robust workforce planning strategy that aligns labor resources with production needs. Additionally, matching the skills and abilities of the workforce with job roles is crucial. Finally, identifying tasks that can be automated using technology can also increase efficiency.

Prognostic significance of lymphocytic foci composition in minor salivary gland biopsies for severe disease flare and severity in Sjögren's syndrome: a 3-year follow-up cohort study.

This was an ambispective cohort study evaluating the prognostic significance of lymphocytic foci and its lymphoid composition in minor salivary gland biopsy (MSGB) for short-term disease flare and severity in Sjögren's syndrome (SS). The inclusion criteria comprised individuals meeting the ACR/EULAR 2016 criteria who underwent MSGB with an infiltration of more than 50 lymphocytes and received clinical diagnosis between September 2017 and December 2018. Patients with inadequate biopsy samples were excluded. The number of lymphocytic foci and their lymphoid composition in MSGB were assessed using immunofluorescence staining. Major organ damage and improvements in the EULAR Sjögren's Syndrome Disease Activity Index (ESSDAI) were measured. Statistical analyses, including Cox and linear regressions, were conducted. A total of 78 patients with at least one lymphocytic focus were included in the study. The presence of higher T-cell counts in lymphocytic foci in MSGB was associated with severe disease flare, and a logarithmic transformation of T-cell count indicated increased risk (HR 1.96, 95% CI 0.91-4.21). Improvements in the ESSDAI were associated with higher total lymphocyte count and T- and B-cell numbers in the lymphoid composition of the lymphocytic foci. Seropositive patients exhibited higher T CD4+ cell numbers. Correlation analysis showed negative associations between age and lymphocytic foci and the T-cell count. Positive correlations were observed between antinuclear antibody (ANA) titers and total lymphocyte numbers. Patients with a higher number of T cells in the lymphocytic infiltrates of lymphocytic foci may have a two-fold risk of severe disease flare. The number of B cells and T CD4+ cells in the lymphocytic infiltrates of lymphocytic foci showed a weak but positive relation with the ESSDAI improvement during follow-up. Age and seropositivity appeared to influence the lymphoid composition of the lymphocytic foci.

Primitive reflexes and dementia in older adults: a meta-analysis of observational and cohort studies.

Primitive reflexes (PRs) are clinical signs that indicate diffuse cerebral dysfunction and frontal lesions. We aimed to present a comprehensive analysis of the prevalence and risk of PRs in patients with dementia. English-language articles published from January 1990 to April 2021 were searched in PubMed, ScienceDirect, Cochrane, and Web of Science with keywords. The titles and abstracts of the identified articles were screened to identify potentially relevant papers. Odds ratios and risk ratios were extracted with 95% confidence intervals and combined using the random-effects model after logarithmic transformation. The prevalence in dementia patients was also combined using the random-effects model. This meta-analysis involved 29 studies. The snout reflex (48% of cases) was the most prevalent. It was found that the risk of PRs in individuals with dementia was significantly elevated, ranging from 13.94 to 16.38 times higher than in healthy controls. The grasp reflex exhibited the highest risk for dementia. This meta-analysis showed that the prevalence and the risk of PRs is high in older patients with dementia. Therefore, PRs, especially the grasp reflex, should be carefully assessed as a part of routine physical examination in the diagnostic process for dementia.

Comparing different versions of the Yoon-Nelson model in describing organic micropollutant adsorption within fixed bed adsorbers.

The Yoon-Nelson model serves as a widely used tool for describing the breakthrough behavior of organic micropollutants within fixed bed adsorbers. This study aims to augment its modeling efficacy through two proposed refinements found in the literature: a logarithmic transformation and the incorporation of steric hindrance effects. We systematically evaluated the original Yoon-Nelson model alongside the modified versions, using breakthrough data associated with micropollutant adsorption on solid materials. Three distinct cases were scrutinized: (1) caffeine adsorption on activated carbon; (2) tetracycline adsorption on hierarchical porous carbon; and (3) diclofenac adsorption on organoclay. While all three models demonstrated comparable performance with highly symmetric breakthrough data in case 1, their efficacy diverged significantly when confronted with strongly asymmetric breakthrough data in cases 2 and 3. The original Yoon-Nelson model and the logarithmically modified version fell short in accurately representing these intricate breakthrough curves. In contrast, the version incorporating steric hindrance effects showcased substantial accuracy, outperforming other models in capturing the complexities of asymmetric breakthrough data. This advancement markedly enhances the modeling accuracy and versatility of the Yoon-Nelson model, particularly in assessing the dynamic behavior of organic micropollutants within fixed bed adsorbers.

Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region

BackgroundMore than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005.MethodsBased on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model.ResultsDuring the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI (ADID) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) ADID and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1.ConclusionsThe present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system.Graphical

Comparing the Effects of Two Culture Methods to Determine the Total Heterotrophic Bacterial Colony Count in Hospital Purified Water

BackgroundAccurately detecting the quantity of microorganisms in hospital purified water is of significant importance for early identification of microbial contamination and reducing the occurrence of water-borne hospital infections. The choice of detection method is a prerequisite for ensuring accurate results. Traditional Plate Count Agar (PCA) belongs to a high-nutrient medium, and there may be limitations in terms of accuracy or sensitivity in detecting microorganisms in hospital purified water. On the other hand, Reasoner’s 2A agar (R2A) has characteristics, such as low-nutrient levels, low cultivation temperature, and extended incubation time, providing advantages in promoting the growth of aquatic microorganisms. This study, through comparing the differences in total colony counts between two detection methods, aims to select the method more suitable for the growth of aquatic microorganisms, offering new practical insights for accurately detecting the total count of heterotrophic bacteria in hospital purified water.MethodsThe most commonly used plate count agar (PCA) method, and the R2A agar culture were adopted to detect microorganisms and determine the total number of bacterial colonies in the water for oral diagnosis and treatment water and terminal rinse water for endoscopes in medical institutions. The two water samples were inoculated by pour plate and membrane filtration methods, respectively. Using statistical methods including Spearman and Pearson correlation, Wilcoxon signed-rank sum test, paired-Chi-square test, and linear regression, we analyze the differences and associations in the bacterial counts cultivated through two different methods.ResultsIn 142 specimens of the water, the median and interquartile range of the heterotrophic bacterial colony number under the R2A culture method and under the PCA culture method were 200 (Q1–Q3: 25–18,000) and 6 (Q1–Q3: 0–3700). The total number of heterotrophic bacteria colonies cultured in R2A medium for 7 days was more than that cultured in PCA medium for 2 days (P < 0.05). The linear regression results showed a relatively strong linear correlation between the number of colonies cultured by the R2A method and that cultured by the PCA method (R2 = 0.7264). The number of bacterial species detected on R2A agar medium is greater than that on PCA agar medium.ConclusionThe R2A culture method can better reflect the actual number of heterotrophic bacterial colonies in hospital purified water. After logarithmic transformation, the number of colonies cultured by the two methods showed a linear correlation.

An Automatic Parameter Setting Variational Mode Decomposition Method for Vibration Signals

Variational mode decomposition (VMD) provides a feasible approach to decompose vibration signals obtained from complex machinery for further applications. The mode frequency bandwidth control parameter and the total number of modes are critical parameters for VMD. Thus, optimally and automatically setting the two parameters is an essential issue for VMD for various practical vibration signal sources. To this end, this work proposes an automatic parameter setting VMD for vibration signals. First, we use the bandwidth evaluation criterion and the mean mode-location distance to evaluate the sparsity of modes; we use the energy loss to evaluate the reconstruction from modes. Then, we synthesize the three criteria into a novel optimization model using logarithmic transformation. Accordingly, a genetic algorithm-based solver is developed for the optimization model. Finally, the artificial multi-component signal and the practical rolling bearing vibration signal from CWRU Laboratory are used to verify the performance of the proposed optimization method.

The relationship between money supply and inflation: analysis with PANELVAR approach

The relationship between money supply and inflation: analysis with PANELVAR approach

Abstract TMP74: Association Between Hematoma Volume After Risk of Subsequent Ischemic Stroke: A MISTIE III and ATACH-2 Analysis

Background: Non-traumatic intracerebral hemorrhage (ICH) is independently associated with long-term increased risk of major arterial ischemic events. Whether hematoma volume influences this risk is poorly understood. Methods: We pooled individual patient data from the MISTIE III (Minimally Invasive Surgery Plus Alteplase for Intracerebral Hemorrhage Evacuation phase 3) and the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage) trials. The exposure was hematoma volume. In the primary analysis, hematoma volume was treated as a continuous measure after natural logarithmic transformation to approximate linearity, and in the secondary analysis, it was dichotomized by the median. The outcome was an ischemic stroke, adjudicated centrally within each trial. We evaluated the association between hematoma volume and the risk of an ischemic stroke using Cox regression analyses after adjustment for demographics, vascular comorbidities, and ICH characteristics. Results: Of 1,499 ICH patients, the mean age was 61.7 (SD, 12.8) years and 574 (38.3%) were female. The median hematoma volume was 17.3 mL (IQR, 7.2-35.7). During a median follow up of 107 days (IQR, 91-140), 30 ischemic strokes occurred of which 22 were in patients with median ICH volume &gt; 17.3 mL with a cumulative incidence of 4.6% (95% CI, 3.1-7.1). Among patients with median ICH volume &lt; 17.3 mL, there were 8 ischemic strokes with a cumulative incidence of 3.1% (95% CI, 1.7-6.0). Using adjusted Cox regression models, log-transformed ICH volume (continuous measure) was associated with an increased risk of ischemic stroke (HR, 1.5 per log-unit increase; 95% CI, 1.1-2.8). Similarly, ICH volume &gt; 17.3 mL was associated with an increased risk of ischemic stroke (HR, 2.5; 95% CI, 1.1-7.2), compared to those with an ICH volume &lt; 17.3 mL. Conclusions: In a heterogeneous cohort of ICH patients, initial hematoma volume was associated with a heightened risk of subsequent ischemic stroke.