Method Estimates Research Articles

ESTIMATION OF PATIENT RADIATION DOSE WITH SIZE SPECIFIC DOSE ESTIMATE (SSDE) METHOD IN ABDOMEN CT-SCAN EXAMINATION AT SEMEN GRESIK HOSPITAL

CT-Scan abdomen is an examination to see the anatomy and pathology of the abdominal organs, and the scanning image results are in the form of three-dimensional axial images of the body. The radiation dose released from the CT-Scan examination is more significant than other radiology modalities. Therefore, it is very important to estimate the patient's dose accurately. Computed Tomography Dose Index (CTDIvol) and Dose Length Product (DLP) are dose parameters used in CT-Scan to describe the dose value received by the patient. However, these parameters are the output of the CT-Scan, not as the dose received by the patient. The Size Specific Dose Estimate (SSDE) method based on AAPM Report No. 204 is used to determine the estimated dose received by the CT-Scan Abdomen patient. Calculations are done manually and with IndoseCT software. The data used are secondary data of 60 patients, namely 21 females and 39 males, who were on the CT-Scan Abdomen examination without contrast from the Instalasi Radiologi Rumah Sakit Semen Gresik. The SSDE value is obtained from the multiplication between the conversion factor and CTDIvol, where the conversion factor is an effective diameter function. The analysis examined the error value in manual calculations and IndoseCT software in men and women, the relationship between effective diameter and scanning radiation output (CTDIvol), and dose estimation estimates (SSDE). The difference in SSDE values between manual calculations and IndoseCT software was insignificant. The highest error value in women was 3% and 3.2% in men. Meanwhile, the relationship between effective diameter (Deff) and scanning radiation output (CTDIvol) and dose estimation estimates (SSDE) showed that the correlation between effective diameter (Deff) and CTDIvol showed a stronger correlation, namely with R2 around 0.7 in women and men, compared to the correlation between effective diameter (Deff) and SSDE with R2 around 0.03 in women and 0.2 in men. Keywords: CT-Scan, Abdomen, Size Specific Dose Estimate (SSDE).

Thrust estimate method of an on-orbit Hall thruster using Hall drift current

In order to realize the thrust estimation of the Hall thruster during its flight mission, this study establishes an estimation method based on measurement of the Hall drift current. In this method, the Hall drift current is calculated from an inverse magnetostatic problem, which is formulated according to its induced magnetic flux density detected by sensors, and then the thrust is estimated by multiplying the Hall drift current with the characteristic magnetic flux density of the thruster itself. In addition, a three-wire torsion pendulum micro-thrust measurement system is utilized to verify the estimate values obtained from the proposed method. The errors were found to be less than 8% when the discharge voltage ranged from 250 V to 350 V and the anode flow rate ranged from 30 sccm to 50 sccm, indicating the possibility that the proposed thrust estimate method could be practically applied. Moreover, the measurement accuracy of the magnetic flux density is suggested to be lower than 0.015 mT and improvement on the inverse problem solution is required in the future.

Optimal planning of electric vehicle charging stations and distributed generators with network reconfiguration in smart distribution networks considering uncertainties

This paper proposes an optimal planning technique for placing the multiple renewable energy (RE) based distributed generators (DGs), Distribution Static Compensators (DSTATCOMs), and electric vehicle charging stations (EVCSs) in the radial distribution network (RDN) considering the related uncertainties. This approach gives optimal placement and sizes for DGs and DSTATCOMs as well as a number of electric vehicles (EVs) that can be charged at the EVCSs by considering the network reconfiguration (NR). The optimal allocation of EVCSs fulfills the power demand from EVs at various locations and minimizes the negative impact on the power network. The RE-based DGs considered for this work are solar photovoltaic (PV) and wind. The uncertainties related to RE-based DGs and EVCSs have been modeled by using the probabilistic-based two-point estimate method (2PEM). The best locations and sizes are identified by optimizing the individual objectives that is active power losses and voltage stability index (VSI) using the teaching learning based optimization (TLBO) algorithm. Then both objectives are optimized by using the non-dominated sorting-based TLBO algorithm. Furthermore, the optimal planning approach is implemented on IEEE 33 and 69 bus test systems to demonstrate the suitability, practicality, and efficiency of the proposed optimal planning strategy. The obtained results reveal that the proposed technique is beneficial for determining the optimal locations for DGs, DSTATCOMs, and EVCSs without affecting the grid stability. The proposed planning approach can search better network structure with reduced power losses and voltage deviation, enhanced voltage profile, and improved voltage stability.

Integration of transcriptomics and machine learning for insights into breast cancer: exploring lipid metabolism and immune interactions

BackgroundBreast cancer (BRCA) represents a substantial global health challenge marked by inadequate early detection rates. The complex interplay between the tumor immune microenvironment and fatty acid metabolism in BRCA requires further investigation to elucidate the specific role of lipid metabolism in this disease.MethodsWe systematically integrated nine machine learning algorithms into 184 unique combinations to develop a consensus model for lipid metabolism-related prognostic genes (LMPGS). Additionally, transcriptomics analysis provided a comprehensive understanding of this prognostic signature. Using the ESTIMATE method, we evaluated immune infiltration among different risk subgroups and assessed their responsiveness to immunotherapy. Tailored treatments were screened for specific risk subgroups. Finally, we verified the expression of key genes through in vitro experiments.ResultsWe identified 259 differentially expressed genes (DEGs) related to lipid metabolism through analysis of the cancer genome atlas program (TCGA) database. Subsequently, via univariate Cox regression analysis and C-index analysis, we developed an optimal machine learning algorithm to construct a 21-gene LMPGS model. We used optimal cutoff values to divide the lipid metabolism prognostic gene scores into two groups according to high and low scores. Our study revealed distinct biological functions and mutation landscapes between high-scoring and low-scoring patients. The low-scoring group presented a greater immune score, whereas the high-scoring group presented enhanced responses to both immunotherapy and chemotherapy drugs. Single-cell analysis highlighted significant upregulation of CPNE3 in epithelial cells. Moreover, by employing molecular docking, we identified niclosamide as a potential targeted therapeutic drug. Finally, our experiments demonstrated high expression of MTMR9 and CPNE3 in BRCA and their significant correlation with prognosis.ConclusionBy employing bioinformatics and diverse machine learning algorithms, we successfully identified genes associated with lipid metabolism in BRCA and uncovered potential therapeutic agents, thereby offering novel insights into the mechanisms and treatment strategies for BRCA.

Integrated machine learning and Mendelian randomization reveal PALMD as a prognostic biomarker for nonspecific orbital inflammation

Background: Nonspecific Orbital Inflammation (NSOI) remains a perplexing enigma among proliferative inflammatory disorders. Its etiology is idiopathic, characterized by distinctive and polymorphous lymphoid infiltration within the orbital region. Preliminary investigations suggest that PALMD localizes within the cytosol, potentially playing a crucial role in cellular processes, including plasma membrane dynamics and myogenic differentiation. The potential of PALMD as a biomarker for NSOI warrants meticulous exploration. Methods: PALMD was identified through the intersection analysis of common DEGs from datasets GSE58331 and GSE105149 from the GEO database, alongside immune-related gene lists from the ImmPort database, using Lasso regression and SVM-RFE analysis. GSEA and GSVA were conducted with gene sets co-expressed with PALMD. To further investigate the correlation between PALMD and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. The expression levels of PALMD were subsequently validated using GSE105149. Results: Among the 314 DEGs identified, several showed significant differences. Lasso and SVM-RFE algorithms pinpointed 15 hub genes. Functional analysis of PALMD emphasized its involvement in cell-cell adhesion, leukocyte migration, and leukocyte-mediated immunity. Enrichment analysis revealed that gene sets positively correlated with PALMD were enriched in immune-related pathways. Immune infiltration analysis indicated that resting dendritic cells, resting mast cells, activated NK cells, and plasma cells positively associate with PALMD expression. Conversely, naive B cells, activated dendritic cells, M0 and M1 macrophages, activated mast cells, activated CD4 memory T cells, and naive CD4 T cells showed a negative correlation with PALMD expression. PALMD demonstrated significant diagnostic potential in differentiating NSOI. Conclusions: This study identifies PALMD as a potential biomarker linked to NSOI, providing insights into its pathogenesis and offering new avenues for tracking disease progression.

On The Modeling of Biomedical Data Sets with a New Generalized Exponentiated Exponential Distribution

Introduction: Many experts in the field of distribution theory have focused on extending probability distributions utilizing extended families of continuous distributions to improve the modeling adaptability of the conventional probability distributions. Methods: This research employed a continuous family of probability distributions as introduced in the literature using the T-X methodology. Through this, some of statistical and mathematical properties of the model were derived and studied Results: This study had introduced a brand-new, five-parameter generalized exponentiated exponential distribution, which is a continuous probability distribution. With the aid of the quantile function, moments, moment generating function, survival function, hazard function, mean, and median, among other mathematical and statistical aspects, the new distribution's shape was deduced and researched. It was also possible to derive the probability density function for the minimum and maximum order statistics for this distribution. The method of maximum likelihood estimate was used to produce a conventional estimation of the unknown parameters. A simulation study was carried out to assess the efficiency and consistency of the estimation method used. To evaluate the fit and adaptability of the new model, it was applied to four real-world datasets in the field of medicine. Conclusion: The analysis's findings demonstrated that the new model performs better than its counterparts and offers a better fit than the Topp-Leone exponentiated exponential, Topp-Leone Kumaraswamy exponential, exponentiated exponential, and exponential distributions.

Weighted fast-trimmed likelihood estimator for mixture regression models

The fast-trimmed likelihood estimate is a robust method to estimate the parameters of a mixture regression model. However, this method is vulnerable to the presence of bad leverage points, which are outliers in the direction of independent variables. To address this issue, we propose the weighted fast-trimmed likelihood estimate to mitigate the impact of leverage points. The proposed method applies the weights of the minimum covariance determinant to the rows suspected of containing leverage points. Notably, both real data and simulation studies were considered to determine the efficiency of the proposed method compared to the previous methods. The results reveal that the weighted fast-trimmed estimate method is more robust and reliable than the fast-trimmed likelihood estimate and the expectation–maximization (EM) methods, particularly in cases with small sample sizes.

The effect of immigration policy regime change on state-level participation rates of the special supplemental nutrition program for women, infants, and children in the United States

AbstractThe change in immigration policy in state-level participation rates of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) in the United States by citizenship and ethnicity was estimated over the period 2013-2018 using the Triple Difference estimate method. The principal finding was that the state-level WIC participation rate for Hispanic non-citizens was lower by 8.6% relative to all other groups (Hispanic citizens, non-Hispanic citizens, and non-Hispanic non-citizens). This study then not only provides quantitative evidence concerning the ongoing debate about the impact of the immigration policy changes under the Trump administration but also extends the extant literature by estimating the causal effects of immigration policy regime change on WIC participation of non-citizens.

The impact of hepatitis B surface antigen seroconversion on the durability of functional cure induced by pegylated interferon alpha treatment

BackgroundHepatitis B surface antigen (HBsAg) loss is regarded as a pivotal criterion for assessing functional cure in patients diagnosed chronic hepatitis B (CHB). We conducted the research to investigate the real-world performance of HBsAg seroconversion in sustaining HBsAg loss.MethodsThis retrospective analysis confirmed 295 patients who attained HBsAg loss through combination therapy involving nucleos(t)ide analogues (NAs) and pegylated interferon alpha (peg-IFNα). Employing Kaplan–Meier estimates method to conduct survival analysis. The forest plot was used to visualize the results of multivariate Cox regression, and selected variables were included in the nomogram.ResultsHBsAg seroreversion was observed in 45 patients during follow-up periods, with a lower recurrence risk in patients with HBsAg seroconversion at the end of peg-IFNα therapy (EOT) (10.3% vs 37.3% at 96-week, P < 0.0001). Moreover, the sustainability of hepatitis B surface antibody (anti-HBs) in participants continuing therapy after HBsAg seroconversion was superior to those discontinued prematurely (72.5% vs 54.5% at 96 weeks, P = 0.012). Additionally, the former group was also relatively less likely to experience HBsAg reversion during long-term observation (8.4% vs 14.3% at 96 weeks, P = 0.280). Hepatitis B envelope antigen (HBeAg) status, anti-HBs status and consolidation treatment screened by multivariable analysis were utilized to construct a predictive model for HBsAg reversion. The concordance index(C-index = 0.77) and calibration plots indicated satisfactory discrimination and consistency of nomogram.ConclusionsHBsAg seroconversion was beneficial for sustaining functional cure in patients treated with peg-IFNα. Continuing consolidation therapy after HBsAg seroconversion also contributed to maintain HBsAg seroconversion and improve the durability of HBsAg loss. The nomogram illustrated its efficacy as a valuable instrument in showcasing survival probability of functional cure.

Drive sustainable business performance with humane entrepreneurship, knowledge management: the mediation of sustainable business advantage and sustainable business model innovation

PurposeThis study evaluates the important role of humane entrepreneurship (HE) on sustainable business advantage (SBA) and sustainable business performance (SBP) by exploring the mediating role of sustainable business model innovation (SBMI) under the influence of knowledge management (KM) in small and medium-sized enterprises (SMEs). Encouraging businesses to implement a combination of KM and HE facilitates the implementation of SBMI that brings value to businesses, communities and the environment in emerging economies.Design/methodology/approachThis study uses an empirical quantitative research method using primary information based on data collected by questionnaires. The study sample resulted in 424 valid responses (66.77% response rate). The target audience of the study is middle and senior managers in SMEs. This study aims to perform structural modeling analysis with the maximum likelihood estimates method to examine structural relationships.FindingsThis study found a positive relationship between KM and HE to SBMI. At the same time, SBMI plays an important role for businesses to achieve SBA and SBP. Similarly, a positive relationship was also found between HE to SBA and SBP. And SBA strongly promotes SMEs to achieve SBP. In addition, the mediating role of SBMI and SBA is clarified to have a positive impact between HE–SBP and SBMI–SBP. HE plays an important role in motivating businesses to innovate sustainable business models, making it easier to gain sustainable competitive advantages, thereby bringing sustainable business results.Practical implicationsThis research helps SMEs realize the importance of KM and HE in SBMI. At that time, SMEs take advantage of internal and external information sources and innovate their business models based on HE mechanisms. Applying HE mechanisms will allow businesses to innovate their business models by combining concerns between stakeholders and the social environment for sustainable development. Therefore, businesses will achieve their SBA toward SBP.Originality/valueThe present literature shows that there are few empirical studies on the mechanism mediating SBMI in the relationship between HE and SBP; and SBA in the link between SBMI and SBP. In addition, the impact of KM and HE on SBMI is also very important in the current context. The study's findings could have huge implications for entrepreneurs and senior managers in SMEs in emerging economies. Help them realize the importance of KM and HE for SBMI, thereby building strategic perspectives that help businesses achieve sustainable competitive advantages and increase SBP for businesses.

Visual-Inertial RGB-D SLAM with Encoder Integration of ORB Triangulation and Depth Measurement Uncertainties.

In recent years, the accuracy of visual SLAM (Simultaneous Localization and Mapping) technology has seen significant improvements, making it a prominent area of research. However, within the current RGB-D SLAM systems, the estimation of 3D positions of feature points primarily relies on direct measurements from RGB-D depth cameras, which inherently contain measurement errors. Moreover, the potential of triangulation-based estimation for ORB (Oriented FAST and Rotated BRIEF) feature points remains underutilized. To address the singularity of measurement data, this paper proposes the integration of the ORB features, triangulation uncertainty estimation and depth measurements uncertainty estimation, for 3D positions of feature points. This integration is achieved using a CI (Covariance Intersection) filter, referred to as the CI-TEDM (Triangulation Estimates and Depth Measurements) method. Vision-based SLAM systems face significant challenges, particularly in environments, such as long straight corridors, weakly textured scenes, or during rapid motion, where tracking failures are common. To enhance the stability of visual SLAM, this paper introduces an improved CI-TEDM method by incorporating wheel encoder data. The mathematical model of the encoder is proposed, and detailed derivations of the encoder pre-integration model and error model are provided. Building on these improvements, we propose a novel tightly coupled visual-inertial RGB-D SLAM with encoder integration of ORB triangulation and depth measurement uncertainties. Validation on open-source datasets and real-world environments demonstrates that the proposed improvements significantly enhance the robustness of real-time state estimation and localization accuracy for intelligent vehicles in challenging environments.

Global well-posedness of strong solutions to the two-dimensional inhomogeneous biaxial nematic liquid crystal flow with vacuum

This paper considers the inhomogeneous biaxial nematic liquid crystal flow in a smooth bounded domain Ω⊂R2, where the velocity u and the orthogonal unit vector fields (m,n) admit the Dirichlet and Neumann boundary condition, respectively. By applying piecewise estimate and continuity method, we get the global existence of strong solutions, provided that the basic energy is suitably small. Our result may be regarded as an extension and improvement of Gong-Lin (2022) and Li-Liu-Zhong (2017) to the Neumann boundary condition, where the initial vacuum is allowed. Some new techniques are developed in order to deal with integral estimates caused by the boundary condition, and more complicated model.

A new multi-objective-stochastic framework for reconfiguration and wind energy resource allocation in distribution network incorporating improved dandelion optimizer and uncertainty

Improving the reliability and power quality of unbalanced distribution networks is crucial for ensuring consistent and reliable electricity supply. In this research, multi-objective optimization of unbalanced distribution networks reconfiguration integrated with wind turbine allocation (MORWTA) is implemented considering uncertainties of networks load, and also wind power incorporating a stochastic framework. The multi-objective function is defined by the minimization of power loss, voltage sag (VS), total harmonic distortion (THD), voltage unbalance (VU), energy not-supplied (ENS), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and momentary average interruption frequency (MAIFI). A new improved dandelion optimizer (IDO) with adaptive inertia weight is recommended to counteract premature convergence to identify decision variables, including the optimal network configuration through opened switches and the best location and size of wind turbines in the networks. The stochastic problem is modeled using the 2m + 1 point estimate method (PEM) combined with K-means clustering, taking into account the mentioned uncertainties. The proposed stochastic methodology is implemented on three modified 33-bus, and unbalanced 25-, and 37-bus distribution networks. The results demonstrated that the MORWTA enhanced all study objectives in comparison to the base networks. The results also demonstrated that the IDO had superior capability to solve the deterministic- and stochastic-MORWTA in comparison to the conventional DO, grey wolf optimizer (GWO), particle swarm optimization (PSO), and arithmetic optimization algorithm (AOA) in terms of achieving greater objective value. Moreover, the results demonstrated that when the stochastic-MORWTA model is considered, the power loss, VS, THD, VU, ENS, SAIFI, SAIDI, and MAIFI are increased by 18.35%, 9.07%, 10.43%, 12.46%, 11.90%, 9.28%, 12.16% and 14.36%, respectively for 25-bus network, and also these objectives are increased by 12.21%, 10.64%, 12.37%, 9.82%, 14.30%, 12.65%, 12.63% and 13.89%, respectively for 37-bus network compared to the deterministic-MORWTA model, which is related to the defined uncertainty patterns.

Optimal probabilistic design of multi-band power system stabilizers using the two-point estimate method

Optimal probabilistic design of multi-band power system stabilizers using the two-point estimate method

Predicting a Risk of Transition to a Higher Level of Care for Home Support Service Recipients.

This longitudinal, non-randomized, retrospective study uses the Kaplan-Meier estimates method and the Cox Proportional Hazard model to assess the risk of home support recipients' transitioning to a higher level of care after being hospitalized. The Kaplan-Meier survival analysis revealed that 50% of home support recipients were expected to move on to a higher level of care by day 1,374. The Cox Proportional Hazard model indicated that the risk of transitioning to a higher level of care increases by about 2% as a client ages by one year, and by about 10% if there were no emergency room visits in the last 12 months. Also, the risk will decrease by about 13% if an individual is getting more than one hour of home support service per visit on average, compared to those who are receiving less than one hour of home support services per visit. These results will help project long term home support demand and resource planning for home support and the health care system.

Selection of sectionalising switch to address any switching failure in a droop controlled islanded microgrid

This paper presents a heuristic approach to address the restoration of droop-controlled islanded microgrids (DCIMGs) in the event of a sectionalising switch (SS) failure. The proposed approach consists of two-step procedures to ensure the smooth operation of the network. First, a faulty SS is isolated by replacing it with a tie switch (TS) that acts as an SS. Then, the switching configuration is adjusted to improve the performance of DCIMGs. To assess the efficacy of the proposed approach, the 33-bus and 118-bus DCIMG test networks are considered. For fulfilling the load demand in each DCIMG, droop-controlled DGs (DDGs) and renewable DGs (RDGs) are employed. Along with satisfying load demand, DGs decrease the power loss and voltage drop. Additionally, shunt capacitor banks (SCBs) are also incorporated into the DCIMG to improve the voltage profile. In conjunction with the load flow analysis, the power injection of the DDGs are determined using the modified Newton–Raphson method. For determining the power injection of RDGs and the size of SCBs, particle swarm optimisation method is implemented. The paper investigates the effectiveness of the proposed method for resolving SS faults in DCIMGs over a 24 hour period amidst hourly demand and renewable power generation variation. By employing Hong’s 2m+1 point estimate method, the uncertainties associated with load demands and renewable power generation are also considered. The effectiveness of the issue formulation is verified using MatLabR2023b. With the proposed method, the voltage and frequency can be effectively maintained after the network is restored following a fault.

Stochastic Techno-Economic Optimization of Hybrid Energy System with Photovoltaic, Wind, and Hydrokinetic Resources Integrated with Electric and Thermal Storage Using Improved Fire Hawk Optimization

In this paper, a stochastic techno-economic optimization framework is proposed for three different hybrid energy systems that encompass photovoltaic (PV), wind turbine (WT), and hydrokinetic (HKT) energy sources, battery storage, combined heat and power generation, and thermal energy storage (Case I: PV–BA–CHP–TES, Case II: WT–BA–CHP–TES, and Case III: HKT–BA–CHP–TES), with the inclusion of electric and thermal storage using the 2m + 1 point estimate method (2m + 1 PEM) utilizing real data obtained from the city of Espoo, Finland. The objective function is defined as planning cost minimization. A new meta-heuristic optimization algorithm named improved fire hawk optimization (IFHO) based on the golden sine strategy is applied to find the optimal decision variables. The framework aims to determine the best configuration of the hybrid system, focusing on achieving the optimal size for resources and storage units to ensure efficient electricity and heat supply simultaneously with the lowest planning cost in different cases. Also, the impacts of the stochastic model incorporating the generation and load uncertainties using the 2m + 1 PEM are evaluated for different case results compared with the deterministic model without uncertainty. The results demonstrated that Case III obtained the best system configuration with the lowest planning cost in deterministic and stochastic models and. This case is capable of simply meeting the electrical and thermal load with the contribution of the energy resources, as well as the CHP and TESs. Also, the IFHO superiority is proved compared with the conventional FHO, and particle swarm optimization (PSO) achieves the lowest planning cost in all cases. Moreover, incorporating the stochastic optimization model, the planning costs of cases I–III are increased by 4.28%, 3.75%, and 3.57%, respectively, compared with the deterministic model. Therefore, the stochastic model is a reliable model due to its incorporating the existence of uncertainties in comparison with the deterministic model, which is based on uncertain data.

71 Exploring DNA-methylation and DNA-hydroxymethylation in clear cell renal cell carcinoma using Dirichlet multinomial regression

Abstract Background DNA methylation alterations have been found related to clear cell renal cell carcinoma (ccRCC). In addition, DNA methylation has been used to identify cell heterogeneity at the tumor level using cell deconvolution methods. Other cytosine modifications are less studied in the field. In this study, we aim to explore the use of compositional method, Dirichlet multinomial regression1, to simultaneously explore DNA methylation and DNA hydroxymethylation in ccRCC. Methods A total of 243 clear cell renal cell carcinoma (ccRCC) samples were collected from the Dartmouth Renal Tumor Biobank. DNA methylation analysis was conducted using the Infinium MethylationEPIC BeadChip, using tandem bisulfite (BS) and oxidative bisulfite (oxBS) treatments to differentiate between 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) states. Beta values were calculated for all the arrayed samples. Quality control was performed using the ENmix pipeline to ensure the inclusion of high-quality samples using a stringent pOOBHA &amp;lt;0.05. The oxBS maximum likelihood estimate (oxBS-MLE) method was applied to assess 5mC and 5hmC levels based on paired BS and oxBS datasets. To determine the inclusion threshold for 5hmC beta values in the Dirichlet analysis, the R package oxBSCut was used to mask values close to zero2. Missing 5hmC values and those values masked below the detection limit were imputed using a compositional approach and a Tobit regression model to retain the model properties. The Dirichlet model was applied to analyze three methylation states: unmethylated, methylated, and hydroxymethylated, providing comprehensive insights into the methylation landscape of ccRCC samples. Results Here, we analyzed the top 1% most variable methylated sites (n=3020). When comparing tumor vs normal adjacent samples we tested the cytosine modifications in the same model adjusting for sex, age at diagnosis, tumor stage, and grade. For this exploratory analysis, we selected those results that passed an FDR&amp;lt;0.01 and an absolute difference &amp;gt;0.2 for further exploration. Four sites were hyperhydroxymethylated related to MYO5A, DLG2, ANKRD33B, and one open sea site. A total of 23 sites were significantly hypermethylated. Using eForge-TF, these sites tracked to transcription factors targeting estrogen receptors, retinoic acid receptors alpha and gamma, and FOXC1 among others3. Conclusions In this preliminary analysis, using a new modeling approach to interrogate cytosine modifications simultaneously, we found some intriguing results consistent with previous literature. We will expand this analysis to evaluate the relation between extreme changes in hydroxymethylation in particular in relation to alternative splicing alterations. Reference 1. Tsagris, M., Stewart, C. A Dirichlet Regression Model for Compositional Data with Zeros. Lobachevskii J Math39, 398–412 (2018). https://doi.org/10.1134/S1995080218030198 2. Zhang Z, Lee MK, Perreard L, Kelsey KT, Christensen BC, Salas LA. Navigating the hydroxymethylome: experimental biases and quality control tools for the tandem bisulfite and oxidative bisulfite Illumina microarrays. Epigenomics. 2022;14(3):139-152. doi:10.2217/epi-2021-0490 3. Charles E Breeze, Alex P Reynolds, Jenny van Dongen, Ian Dunham, John Lazar, Shane Neph, Jeff Vierstra, Guillaume Bourque, Andrew E Teschendorff, John A Stamatoyannopoulos, Stephan Beck, eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data, Bioinformatics, Volume 35, Issue 22, November 2019, Pages 4767–4769, https://doi.org/10.1093/bioinformatics/btz456

Solvability of a boundary value problem with integrable conditions for partial differential equations of fractional order

Differential equations have a remarkable ability to predict the world around us. They are used in a wide variety of disciplines, from biology, economics, physics, chemistry and engineering. The study of existence and uniqueness, periodicity, asymptotic behavior, stability, and methods of analytic and numerical solutions of fractional differential equations have been studied extensively in a large cycle works; Especially, The study of existence and uniqueness of solution of fractional partial differential equations are then proved by mani methods as: the well-known Lax–Milgram theorem, Energy estimate and fixed point theorem... Among them, we only mention here the apriori estimate method (energy inequality method). The current piece of work concentrates on exploring the existence and uniqueness of a solution for a non-linear boundary value problem that has integrable conditions in the case of fractional partial differential equations. For this we split the proof into two sections: linear and non-linear problem; for the associated linear problem, we derive the a priori bound and demonstrate the density of the operator generated by the problem posed; we solve the non-linear problem by introducing a iterative process. The results show the efficiency of energy inequality method in the case of time fractional order differential equations with integrable conditions our results illustrate the existence and uniqueness of the continuous dependence of solution on fractional order. The work of the authors could be considered as a contribution to the development of the functional analysis method. And to conclude we argue that the research carried out in this article could serve as a contribution for possible studies in the field of applied mathematics.

Diagnostic Accuracy of an Integrated AI Tool to Estimate Gestational Age From Blind Ultrasound Sweeps

Accurate assessment of gestational age (GA) is essential to good pregnancy care but often requires ultrasonography, which may not be available in low-resource settings. This study developed a deep learning artificial intelligence (AI) model to estimate GA from blind ultrasonography sweeps and incorporated it into the software of a low-cost, battery-powered device. To evaluate GA estimation accuracy of an AI-enabled ultrasonography tool when used by novice users with no prior training in sonography. This prospective diagnostic accuracy study enrolled 400 individuals with viable, single, nonanomalous, first-trimester pregnancies in Lusaka, Zambia, and Chapel Hill, North Carolina. Credentialed sonographers established the "ground truth" GA via transvaginal crown-rump length measurement. At random follow-up visits throughout gestation, including a primary evaluation window from 14 0/7 weeks' to 27 6/7 weeks' gestation, novice users obtained blind sweeps of the maternal abdomen using the AI-enabled device (index test) and credentialed sonographers performed fetal biometry with a high-specification machine (study standard). The primary outcome was the mean absolute error (MAE) of the index test and study standard, which was calculated by comparing each method's estimate to the previously established GA and considered equivalent if the difference fell within a prespecified margin of ±2 days. In the primary evaluation window, the AI-enabled device met criteria for equivalence to the study standard, with an MAE (SE) of 3.2 (0.1) days vs 3.0 (0.1) days (difference, 0.2 days [95% CI, -0.1 to 0.5]). Additionally, the percentage of assessments within 7 days of the ground truth GA was comparable (90.7% for the index test vs 92.5% for the study standard). Performance was consistent in prespecified subgroups, including the Zambia and North Carolina cohorts and those with high body mass index. Between 14 and 27 weeks' gestation, novice users with no prior training in ultrasonography estimated GA as accurately with the low-cost, point-of-care AI tool as credentialed sonographers performing standard biometry on high-specification machines. These findings have immediate implications for obstetrical care in low-resource settings, advancing the World Health Organization goal of ultrasonography estimation of GA for all pregnant people. ClinicalTrials.gov Identifier: NCT05433519.