Assessing bladder function is pivotal in urological health, with bladder volume a critical indicator. Traditional devices, hindered by high costs and cumbersome sizes, are being increasingly supplemented by portable alternatives; however, these alternatives often fall short in measurement accuracy. Addressing this gap, this study introduces a novel A-mode ultrasound-based bladder volume estimation algorithm optimized for portable devices, combining efficient, precise volume estimation with enhanced usability. Through the innovative application of a wavelet energy ratio adaptive denoising method, the algorithm significantly improves the signal-to-noise ratio, preserving critical signal details amidst device and environmental noise. Ultrasonic echoes were employed to acquire positional information on the anterior and posterior walls of the bladder at several points, with an ellipsoid fitted to these points using the least squares method for bladder volume estimation. Ultimately, a simulation experiment was conducted on an underwater porcine bladder. The experimental results indicate that the bladder volume estimation error of the algorithm is approximately 8.3%. This study offers a viable solution to enhance the accuracy and usability of portable devices for urological health monitoring, demonstrating significant potential for clinical application.

Leptospirosis is a zoonosis that has been linked to hydrometeorological variability. Hydrometeorological averages and extremes have been used before as drivers in the statistical prediction of disease. However, their importance and predictive capacity are still little known. In this study, the use of a random forest classifier was explored to analyze the relative importance of hydrometeorological indices in developing the leptospirosis model and to evaluate the performance of models based on the type of indices used, using case data from three districts in Kelantan, Malaysia, that experience annual monsoonal rainfall and flooding. First, hydrometeorological data including rainfall, streamflow, water level, relative humidity, and temperature were transformed into 164 weekly average and extreme indices in accordance with the Expert Team on Climate Change Detection and Indices (ETCCDI). Then, weekly case occurrences were classified into binary classes \"high\" and \"low\" based on an average threshold. Seventeen models based on \"average,\" \"extreme,\" and \"mixed\" indices were trained by optimizing the feature subsets based on the model computed mean decrease Gini (MDG) scores. The variable importance was assessed through cross-correlation analysis and the MDG score. The average and extreme models showed similar prediction accuracy ranges (61.5-76.1% and 72.3-77.0%) while the mixed models showed an improvement (71.7-82.6% prediction accuracy). An extreme model was the most sensitive while an average model was the most specific. The time lag associated with the driving indices agreed with the seasonality of the monsoon. The rainfall variable (extreme) was the most important in classifying the leptospirosis occurrence while streamflow was the least important despite showing higher correlations with leptospirosis.

Coordination between legs is necessary to complete bipedal balance task goals. Assessing bilateral homonymous muscle coordination may provide insight into the inherent neuromuscular control of the two legs in achieving equilibrium. This work introduced a method based on a normalized cross-correlation analysis to analyze bilateral homonymous muscle coordination during bipedal balancing on different support surfaces, revealing the temporal similarity in shape (i.e., form) between two electromyographic (EMG) signals (i.e., EMG-EMG correlation). Two levels of EMG-EMG correlation were considered: individual homonymous muscles and groups (patterns) of homonymous muscles relevant to the current task. In order to analyze the patterns of homonymous muscles, a principal component analysis (PCA) was applied to the cross-correlation coefficients to provide insights into functionally specialized groups of homonymous muscles constrained by the nervous system to work cooperatively. This proposed method has advantages that can be applied to several purposes. For example,•Analyzing the EMG-EMG correlation provides essential information about the inherent neuromuscular function in postural control.•At the level of individual homonymous muscles, this method can be applied to assess the neuromuscular performance after injury to the specific muscles.•At the level of multiple homonymous muscles, this method can be used to monitor the cooperative work of several pairs of homonymous muscles in achieving equilibrium.

BACKGROUND: Dengue fever is a tropical disease and a major public health concern, and almost half of the world\'s population lives in areas at risk of contracting this disease. Climate change is identified by WHO and other international health authorities as one of the primary factors that contribute to the rapid spread of dengue fever.
METHODS: We evaluated the effect of sanitation on the cross-correlation between rainfall and the first symptoms of dengue in the city of Mato Grosso do Sul, which is in a state in the Midwest region of Brazil, and employed the time-lagged detrended cross-correlation analysis (DCCAC) method.
RESULTS: Co-movements were obtained through the time-phased DCCAC to analyze the effects of climatic variables on arboviruses. The use of a time-lag analysis was more robust than DCCAC without lag to present the behavior of dengue cases in relation to accumulated precipitation. Our results show that the cross-correlation between rain and dengue increased as the city implemented actions to improve basic sanitation in the city.
CONCLUSIONS: With climate change and the increase in the global average temperature, mosquitoes are advancing beyond the tropics, and our results show that cities with improved sanitation have a high correlation between dengue and annual precipitation. Public prevention and control policies can be targeted according to the period of time and the degree of correlation calculated to structure vector control and prevention work in places where sanitation conditions are adequate.

Understanding selectivity-dependent molecular mechanism of inhibitors towards CDK2 over CDK6 is prominent for improving drug design towards the CDK family. Multiple short molecular dynamics (MD) simulations combined with MM-GBSA approach are adopted to investigate molecular mechanism on binding selectivity of inhibitors X64, X3A, and 4 AU to CDK2 and CDK6. The RMSF analysis and calculations of molecular surface areas indicate that local structural and global flexibility of CDK6 are stronger than that of CDK2. Based on dynamics cross-correlation maps (DCCMs), motion modes of CDK2 and CDK6 produce difference due to associations of X64, X3A, and 4 AU. The calculated binding free energies (BFEs) demonstrate that the compensation between binding enthalpy and entropy of X64, X34, and 4 AU is a key force driving selectivity of inhibitors towards CDK2 over CDK6. This work provides valuable information for designing highly selective inhibitors towards CDK2 and CDK6 and further promotes identification of efficient anticancer drugs in the future.

Fascin is a filamentous actin (F-actin) bundling protein, which cross-links F-actin into bundles and becomes an important component of filopodia on the cell surface. Fascin is overexpressed in many types of cancers. The mutation of fascin affects its ability to bind to F-actin and the progress of cancer. In this paper, we have studied the effects of residues of K22, K41, K43, K241, K358, K399, and K471 using molecular dynamics (MD) simulation. For the strong-effect residues, that is, K22, K41, K43, K358, and K471, our results show that the mutation of K to A leads to large values of root mean square fluctuation (RMSF) around the mutated residues, indicating those residues are important for the flexibility and thermal stability. On the other hand, based on residue cross-correlation analysis, alanine mutations of these residues reinforce the correlation between residues. Together with the RMSF data, the local flexibility is extended to the entire protein by the strong correlations to influence the dynamics and function of fascin. By contrast, for the mutants of K241A and K399A those do not affect the function of fascin, the RMSF data do not show significant differences compared with wild-type fascin. These findings are in a good agreement with experimental studies.

BACKGROUND: There is a correlation between the severe fever with thrombocytopenia syndrome (SFTS) viral load and disease severity; however, measurement of viral load is difficult in general laboratory and it takes time to obtain a viral load value. Here, the laboratory parameters for predicting the dynamic changes in SFTS viral load were identified. In addition, we tried to evaluate a specific time point for the early determination of clinical deterioration using dynamic change of laboratory parameters.
METHODS: This observational study included SFTS patients in Korea (2013 - 2020). Cross-correlation analysis at lagged values was used to determine the temporal correlation between the SFTS viral loads and time-series variables. Fifty-eight SFTS patients were included in the non-severe group (NSG) and 11 in the severe group (SG).
RESULTS: In the cross-sectional analyses, 10 parameters -white blood cell, absolute neutrophil cell, lymphocyte, platelet, activated partial thromboplastin time (aPTT), C-reactive protein, aspartate aminotransferase (AST), alanine transaminase (ALT), lactate dehydrogenase (LDH), and creatine phosphokinase (CPK)- were assessed within 30 days from the onset of symptoms; they exhibited three different correlation patterns: (1) positive, (2) positive with a time lag, and (3) negative. A prediction score system was developed for predicting SFTS fatality based on age and six laboratory variables -platelet, aPTT, AST, ALT, LDH, and CPK- in 5 days after the onset of symptoms; this scoring system had 87.5% sensitivity and 86.0% specificity (95% confidence interval: 0.831 - 1.00, P <0.001).
CONCLUSIONS: Three types of correlation patterns between the dynamic changes in SFTS viral load and laboratory parameters were identified. The dynamic changes in the viral load could be predicted using the dynamic changes in these variables, which can be particularly helpful in clinical settings where viral load tests cannot be performed. Also, the proposed scoring system could provide timely treatment to critical patients by rapidly assessing their clinical course.

In this paper, we analyzed the role of carbon pricing as a vital tool for achieving the sustainable energy transition and discussed policy implications for reaching this goal in the era of the COVID-19 pandemic. To investigate the connection and cross-correlation between COVID-19 cases and carbon price returns in EU ETS, we applied two novel approaches: the multifractal detrending moving average cross-correlation (MF-X-DMA) and the wavelet coherence techniques. The coverage of data is from 2 March 2020 to 19 March 2021. The results of the MF-X-DMA method show that the cross-correlation is substantial and negatively correlated. Moreover, the results imply the presence of multifractal cross-correlations and that a significant change in the number of COVID-19 cases further impacts carbon price fluctuations. Concerning the wavelet coherence method results, we can also find a strong (negative) relationship between selected variables, and this strong correlation is observed throughout the whole study period. In addition, we observe that in the first wave of the COVID-19 outbreak, the changes in the COVID-19 cases are leading the CO2 price returns (particularly in the 16-32 time scale). The results of this study can have significant implications for policymakers and contributions for environmental experts and investors.

Cellular Src (c-Src) belongs to a non-receptor membrane-associated tyrosine kinase family that plays essential roles in cellular processes. Growing evidence suggests that R175L and W118A mutations in SH2/SH3 domains of c-Src functionally inactivate these domains leading to constitutive activation of kinase domain (KD). Here we modeled c-SrcR175L, c-SrcW118A and c-SrcW118A+R175L structures by inducing phosphorylation at Y416 or Y527, respectively to characterize the comparative dynamics in the active versus inactive states through molecular dynamics simulation assay. We observed more conformational readjustments in c-Srcopen than its close variants. In particular, C-terminal tail residues of c-SrcW118A-open and c-SrcW118A+R175L-open demonstrate significantly higher transitions. The cross-correlation analysis revealed an anticorrelation behavior in the motion of KD with respect to SH2, SH3 and the linker region of SrcW118A+R175L-open, while in c-SrcWT-open, SH2 and SH3 domains were anticorrelated, while KD and C-terminal tail motions were correlated. Due to these conformational differences, c-Src open forms exhibited lower interaction between pY527 and SH2 domain. Through detailed structural analysis, we observed a uniform myristate binding cavity in c-SrcWT-open, while the myristoyl pockets of mutant forms were deformed. We propose that constitutive activation of mutant Src forms may presumably be achieved by the prolonged membrane binding due to unusual conformations of C-terminal and myristoyl switch residues that may result in a higher dephosphorylation rate at pY527 in the myristoylated c-Src. Thus, our study establishes novel clues to decipher the constitutive activation status of c-Src in response to known mutations that may help in devising novel therapeutic strategies for cancer metastasis treatment.Communicated by Ramaswamy H. Sarma.

The COVID-19 pandemic affected the world through its ability to cause widespread infection. The Middle East including the Kingdom of Saudi Arabia (KSA) has also been hit by the COVID-19 pandemic like the rest of the world. This study aims to examine the relationships between meteorological factors and COVID-19 case counts in three cities of the KSA. The distribution of the COVID-19 case counts was observed for all three cities followed by cross-correlation analysis which was carried out to estimate the lag effects of meteorological factors on COVID-19 case counts. Moreover, the Poisson model and negative binomial (NB) model with their zero-inflated versions (i.e., ZIP and ZINB) were fitted to estimate city-specific impacts of weather variables on confirmed case counts, and the best model is evaluated by comparative analysis for each city. We found significant associations between meteorological factors and COVID-19 case counts in three cities of KSA. We also perceived that the ZINB model was the best fitted for COVID-19 case counts. In this case study, temperature, humidity, and wind speed were the factors that affected COVID-19 case counts. The results can be used to make policies to overcome this pandemic situation in the future such as deploying more resources through testing and tracking in such areas where we observe significantly higher wind speed or higher humidity. Moreover, the selected models can be used for predicting the probability of COVID-19 incidence across various regions.