Cumulative Distribution Function Research Articles

Agricultural activities reduce the risk of requiring long-term care among older Japanese

Abstract Introduction Controlling the risks that lead to requiring long-term care for the older adults is a crucial medical and socio-economic issue. This study investigated the impact of agricultural activities (AAs) on the risk of requiring long-term care. Methods A 5-year follow-up was conducted on 1,009 healthy individuals aged 65-80 years (mean age 72 years; 44% men) living in a rural area (Wakayama, Japan). Requiring long-term care was assessed as requiring support or nursing care (RSNC) according to the Japanese criteria. Health status including frailty and metabolic syndrome were also assessed using Japanese criteria. Work status and lifestyle habits were assessed using self-administered questionnaires. The main outcome was the probability of RSNC. COX proportional hazards regressions were performed, stratified by occupation (agricultural worker or other) or frequency of AAs (none, less than or >150 days/year), adjusted for sex, age, frailty, metabolic syndrome and household as possible confounding factors. Results Approximately 35% of the participants were in agriculture, forestry, and fisheries as occupation, and 78% engaged in AAs. A total of 85 participants RSNC and 39 deaths were observed during the follow-up. The cumulative survival probability did not differ according to occupation or frequency of AAs, whereas the probability of RSNC was significantly lower in participants engaged in AAs (hazard ratio 0.37 [95% CI 0.22, 0.62]; <150 days/year, 0.29 [95% CI 0.17, 0.50]; >150 days/year). Participants engaged in AAs were significantly more likely to engage in low- and vigorous-intensity physical activity, and to participate in industry and community organizations and relating events. Conclusions These findings suggest that AAs may promote a physically and socially active lifestyle, independent of frailty or metabolic syndrome, and reduce the risk of RSNC. Key messages • Prevention of long-term care is important in the older population. • A lifestyle associated with agricultural activities may contribute to healthy aging.

Spatial pilot reassignment algorithm for channel estimation stage of cell-free multi-ARS communication systems

In this paper, we investigate the user throughputs of a Cell-Free (CF) system with multiple aerial relay stations (ARSs), where each ARS is defined as an unmanned aerial vehicle (UAV)-mounted relay station. The system operates under a decode-and-forward (DF) protocol and facilitates connectivity between a terrestrial base station (TBS) and terrestrial users. ARSs are equipped with multiple antennas and simultaneously serve users that are outfitted with single antennas and distributed in a specific area. Additionally, a small-cell (SC) system based on the CF structure, where each ARS serves one user with the best channel conditions, is also considered. We analyze system communication in two stages, including user-ARS links and ARS-TBS links, and then we derive expressions for the data rate of users and ARSs. Moreover, we propose the spatial pilot reassignment (SPR) algorithm to optimize pilot assignment, enhancing channel estimation over random pilot assignment methods. The user throughput is evaluated by altering several system parameters, including the with/without data power control, the number of users, the number of ARSs, and the time interval allocated for channel estimation. The results show that the SPR algorithm improves throughput by about 10% compared to the random pilot assignment method at a 90%-likely user throughput, which is equal to a cumulative distribution function value of 0.1.

Clinical implications of atrial fibrillation recurrence vs. burden following catheter ablation: outcome data from 3 contemporary clinical trials using an insertable cardiac monitor

Abstract Background Recurrence of atrial fibrillation (AF) following catheter ablation is typically defined in clinical trials in a binary fashion, e.g. > 30 seconds or none. However, the clinical implications of this endpoint, adopted from formal guidelines, have been questioned. Purpose We hypothesized that assessment of AF burden can provide incremental clinical and management implications to the dichotomous definition of AF recurrence following ablation. Methods The study population comprised 147 patients enrolled in 3 ongoing multicenter randomized clinical trials (ICM REDUCE-AF [N=67]); HYBRID-AF (N=55] and DAPA-AF [N=25]), each employing an insertable cardiac monitor (ICM) to assess post-ablation AF recurrence and burden. The rate of cardiovascular (CV)-related healthcare utilization (CV-HCU, defined as the total number CV admissions, ED visits, cardioversions, procedures, and unplanned office visits) was compared based on AF recurrence (defined as >2 minutes of ICM-detected AF) vs. based on AF burden post-ablation (defined as time in AF divided by total monitoring time). Results Among the 147 study patients, mean age was 65 years; 53 (36%) were women; and 78 (53%) had persistent AF. At 12-months post-ablation (excluding blanking), the cumulative probability of AF recurrence was 60% (Figure 1A). However, 27% of AF recurrences were associated with a very low AF burden (≤0.1%) and low CV-HCU (10 events per 100 patient-months), similar to patients without AF recurrence (11 events per 100 patient-months). In contrast, recurrences with a higher AF burden (>0.1%; mean 17% ± 25%) were associated with a 3-fold higher rate of CV-HCU (31 events per 100 patient-months, p<0.001; Figure 1B). Findings were consistent regardless of AF-type, comorbidities or LVEF. Conclusions A substantial proportion of AF recurrences following contemporary AF ablation are associated with a very low AF burden without apparent effect on CV outcomes, whereas higher AF burden is associated with increased CV-HCU. These findings stress the need to move beyond a dichotomous definition of AF recurrence and to monitor AF burden post-ablation to guide management decisions in contemporary clinical practice.Figure 1

Risk Assessment of Urban Water and Energy Supply Using Copula Function: A Water–Energy Nexus Approach in an Arid City

Planning for the future of water and energy supply systems in urban areas requires a thorough assessment of associated risks. In this study, monthly water and energy demand data from 2011 to 2022 in an arid city was used to predict the corresponding demands from 2023 to 2032 using the seasonal auto-regressive integrated moving average (SARIMA) method. The aim is to estimate future water and energy supply risks both individually and jointly, using cumulative distribution functions (CDFs) derived from historical data. The main focus is to calculate the combined risk of water and energy, referred to as the water–energy nexus (WEN) risk. Based on the interdependent relationship between water and energy, the Copula function was utilized to model the bivariate distribution between these two variables. Pearson correlation analysis indicated a strong correlation between water and energy supplies. Among the distributions fitted to the data, the log-normal and gamma distributions were the best fit for water supply and energy supply systems, respectively, with the lowest Akaike information criterion (AIC) values. The Gumbel Copula, with a parameter of 1.66, was identified as the most suitable for modeling the joint distribution, yielding the lowest AIC value. The results indicate that the risks associated with energy supply, water supply, and their joint dependency could exceed 0.8% in the future, highlighting a potentially critical situation for the city. The trend analysis revealed that forecasted water and energy demands and their corresponding risks and the WEN risk are expected to have a significant upward trend in the future. Finally, local authorities need to explore alternative sources to supply water and energy in the future to address the ever-growing water and energy demands.

Predictors for spontaneous remission in childhood chronic immune thrombocytopenia.

This study examined the factors associated with spontaneous remission in children with chronic immune thrombocytopenia (ITP). We retrospectively analyzed the medical records of patients diagnosed with ITP from January 1988 to December 2019 at our institute. A total of 104 children with chronic ITP were identified. The median follow-up time from diagnosis of chronic ITP was 3.6 years (IQR 1.2-8.3, range 0.1-31.4). Fifteen (14.4%) patients with severe symptoms received specific platelet-elevating therapies, including splenectomy, rituximab, and thrombopoietin receptor agonists. Seven of them achieved remission. Among the patients with a platelet count < 30 × 109/L at the time of diagnosis of chronic ITP, those who received specific platelet-elevating therapies had a higher remission rate compared to those who did not (HR: 4.66, 95% CI: 1.36-16.0). Sixteen patients (15.4%) developed systemic lupus erythematosus, 46 (44.2%) still had thrombocytopenia after a median follow-up of 6.8 years, and 42 (40.4%) achieved remission with a median time to remission of 2.0 years (IQR 0.6-4.1, range 0.1-15.7). The two independent predictive factors for spontaneous remission in childhood chronic ITP were platelet counts > 30 × 109/L at the time of diagnosis of chronic ITP (HR: 3.16, 95% CI: 1.51-6.62) and persistently negative ANA at follow-up (HR: 6.12, 95% CI: 1.46-25.7). The cumulative probabilities of spontaneous remission at 10 years post-diagnosis of chronic ITP were 72.2% for patients without risk factor compared to 0% for patients with two risk factors.

Addressing the antibody germline bias and its effect on language models for improved antibody design.

The versatile binding properties of antibodies have made them an extremely important class of biotherapeutics. However, therapeutic antibody development is a complex, expensive and time-consuming task, with the final antibody needing to not only have strong and specific binding, but also be minimally impacted by developability issues. The success of transformer-based language models in protein sequence space and the availability of vast amounts of antibody sequences, has led to the development of many antibody-specific language models to help guide antibody design. Antibody diversity primarily arises from V(D)J recombination, mutations within the CDRs, and/or from a few non-germline mutations outside the CDRs. Consequently, a significant portion of the variable domain of all natural antibody sequences remains germline. This affects the pre-training of antibody-specific language models, where this facet of the sequence data introduces a prevailing bias towards germline residues. This poses a challenge, as mutations away from the germline are often vital for generating specific and potent binding to a target, meaning that language models need be able to suggest key mutations away from germline. In this study, we explore the implications of the germline bias, examining its impact on both general-protein and antibody-specific language models. We develop and train a series of new antibody-specific language models optimised for predicting non-germline residues. We then compare our final model, AbLang-2, with current models and show how it suggests a diverse set of valid mutations with high cumulative probability. AbLang-2 is trained on both unpaired and paired data, and is freely available at https://github.com/oxpig/AbLang2.git. Supplementary data are available at Journal Name online.

Impact of Aflibercept vs Dexamethasone Treatment on Epiretinal Membrane Formation in Eyes with Diabetic Macular Edema.

This study aimed to investigate the impact of aflibercept and dexamethasone (DEX) on the formation of epiretinal membrane (ERM) and their treatment outcomes in eyes with diabetic macular edema (DME). In this retrospective cohort study, medical records of 124 eyes from 429 patients diagnosed with DME were reviewed between June 2017 and June 2019. Patients were categorized into two groups: the aflibercept group (67 eyes) and the DEX group (57 eyes). The primary endpoint was the secondary ERM incidence following intravitreal treatments and its correlation across different medications. Secondary endpoints included longitudinal changes in best-corrected visual acuity (BCVA) and central macular thickness (CMT). Over a 24-month follow-up, eyes treated with DEX had approximately a fourfold higher incidence of ERM development compared to aflibercept [hazard ratio (HR) = 3.97, p = 0.02]. These eyes also showed worse BCVA (p = 0.059) and increased CMT (p = 0.004), despite requiring fewer total injections (p = 0.000) in the survival analysis model. The cumulative probability of ERM formation was 13.7%. Additionally, DME eyes exhibited poor functional and anatomical outcomes after developing ERM, while age, A1c level, DR severity, initial BCVA and CMT, lens status, and previous laser treatment were not associated with an elevated incidence of ERM formation. Intravitreal DEX implantation in DME eyes resulted in a higher incidence of secondary ERM formation compared to aflibercept over a 2-year period. The therapeutic efficacy for DME was diminished following ERM development, leading to worse anatomical outcomes. New therapeutic approaches should be explored to prevent ERM formation while maintaining both anatomical and functional outcomes in DME treatment.

Performance Analysis of Free Space Optical System Over Inverse Gaussian Gamma Turbulence Channel

ABSTRACTFree space optical (FSO) communication is a proficient technology with potential applications in the next generation of wireless communication. This article evaluates the performance of the FSO system under inverse Gaussian gamma (IGG) fading. A novel closed‐form equation for the cumulative distribution function (CDF) of the received signal‐to‐noise ratio (SNR) is presented by employing an intensity modulation/direct detection (IM/DD) scheme. The analytical expressions for various performance metrics such as outage probability (OP), amount of fading (AOF), and average bit error probability (ABEP) are derived for various digital modulation schemes based on the system's probability density function (PDF) and CDF. Additionally, this article presents an upper bound on OP and asymptotic analysis for the performance metrics to gain deeper insights. It is revealed that the small‐scale eddy turbulence parameter can affect the diversity order by a factor of β/2. The results demonstrate that atmospheric turbulence considerably reduces the system's performance. Finally, Monte‐Carlo simulation is performed to ascertain the correctness of the derived mathematical expressions.

About Air Quality Assessment using Time Series Analysis Techniques on Air Particles Data

This study investigates the dynamics of air pollution caused by particulate matter (PM10 and PM2.5) in Omsk, Russia. Data collected from the Sensor.Community network between 2021 and 2023 were analyzed using time series analysis methods. The findings reveal a subtle weekly seasonality in the data, potentially attributed to fluctuations in industrial activity and traffic patterns throughout the week. Additionally, a concerning trend of deteriorating air quality was identified, characterized by increasing annual average concentrations of PM10 and PM2.5. The study employed various time series analysis techniques, including Empirical Cumulative Distribution Function (ECDF), Seasonal and Trend decomposition using Loess (STL), and autocorrelation function (ACF) analysis. These methods allowed for the visualization and examination of data distribution, trend identification, and seasonality detection. Furthermore, the Mann-Kendall test confirmed a statistically significant trend of increasing pollution levels over the three-year period. To enhance the accuracy of spatial analysis, data interpolation was performed using kriging with a Gaussian model. This method, compared to simple interpolation, provides a more precise assessment of the spatial distribution of pollutants. The study also implemented Long Short-Term Memory (LSTM) for forecasting hourly pollution values. LSTM exhibited promising results, demonstrating its potential for developing early warning systems, particularly when combined with meteorological data such as wind forecasts. The results highlight the need for effective measures to mitigate air pollution in Omsk and emphasize the importance of utilizing data from diverse sources, including official air quality monitoring stations, to ensure accuracy and reliability in air quality assessments.

ON IMPROVED GAUSSIAN CORRELATION INEQUALITIES FOR SYMMETRICAL -RECTANGLES EXTENDED TO CERTAIN MULTIVARIATE GAMMA DISTRIBUTIONS AND SOME FURTHER PROBABILITY INEQUALITIES

The Gaussian correlation inequality (GCI) for symmetrical n-rectangles is improved if the absolute components have a joint cumulative distribution function (cdf), which is MTP2 (multivariate totally positive of order 2). Inequalities of the given type hold at least for all MTP2-cdfs on or with everywhere positive smooth densities. In particular, at least some infinitely divisible multivariate chi-square distributions (gamma distributions in the sense of Krishnamoorthy and Parthasarathy) with any positive real “degree of freedom” are shown to be MTP2. Moreover, further numerically calculable probability inequalities for a broader class of multivariate gamma distributions are derived. A different improvement for inequalities of the GCI-type, and of a similar type with three instead of two groups of components with more special correlation structures is also obtained. The main idea behind these inequalities is to find for a given correlation matrix with positive correlations, a further correlation matrix with smaller correlations whose inverse is an M-matrix and where the corresponding multivariate gamma distribution function is numerically available.

A synthesis of machine learning and internet of things in developing autonomous fleets of heterogeneous unmanned aerial vehicles for enhancing the regenerative farming cycle

AbstractThe use of Unmanned Aerial Vehicles (UAVs) for agricultural monitoring and management offers additional advantages over traditional methods, ranging from cost reduction to environmental protection, especially when they utilize Machine Learning (ML) methods, and Internet of Things (IoT). This article presents an autonomous fleet of heterogeneous UAVs for use in regenerative farming the result of a synthesis of Deep Reinforcement Learning (DRL), Ant Colony Optimization (ACO) and IoT. The resulting aerial framework uses DRL for fleet autonomy and ACO for fleet synchronization and task scheduling inflight. A 5G Multiple Input Multiple Output-Long Range (MIMO-LoRa) antenna enhances data rate transmission and link reliability. The aerial framework, which has been originally prototyped as a simulation to test the concept, is now developed into a functional proof-of-concept of autonomous fleets of heterogeneous UAVs. For assessing performance, the paper uses Normalized Difference Vegetation Index (NDVI), Mean Squared Error (MSE) and Received Signal Strength Index (RSSI). The 5G MIMO-LoRa antenna produces improved results with four key performance indicators: Reflection Coefficient (S11), Cumulative Distribution Functions (CDF), Power Spectral Density Ratio (Eb/No), and Bit Error Rate (BER).

Paradox-Free Data Analysis in Numerical Comparisons of Optimization Algorithms

Numerical comparison is essential for evaluating an optimization algorithm. Unfortunately, recent research has shown that two paradoxes may occur, namely the cycle ranking paradox and survival of the nonfittest paradox. Further exploitation reveals that these paradoxes stem from the method of data analysis, especially its comparison strategy. Therefore, the design and widespread use of paradox-free data analysis methods has become urgent. This paper is dedicated to reviewing the recent progress in paradox in numerical comparisons of optimization algorithms, especially the reasons for paradoxes and the ways to eliminate them. Specifically, significant progress has been made in eliminating paradoxes from two popular data analysis methods, including hypothesis testing and methods based on the cumulative distribution function. Furthermore, in this paper the authors provide case studies, aiming to show that paradoxes are common and how to eliminate them with paradox-free data analysis methods.

Reliability analysis based on doubly-truncated and interval-censored data

Field data provide important information on product reliability. Interval sampling is widely used for collection of field data, which typically report incident cases during a certain time period. Such sampling scheme induces doubly truncated (DT) data if the exact failure time is known. In many situations, the exact failure date is known only to fall within an interval, leading to doubly truncated and interval censored (DTIC) data. This article considers analysis of DTIC data under parametric failure time models. We consider a conditional likelihood approach and propose interval estimation for parameters and the cumulative distribution functions. Simulation studies show that the proposed method performs well for finite sample size.

A Study in Pituitary Neuroendocrine Tumors (PitNETs): Real-Life Data Amid Baseline and Serial CT Scans.

We aimed to evaluate CT-related PIs diameters following 3 aspects: a cross-sectional analysis based on the age' groups at first PI diagnosis and on the gender distribution and a longitudinal analysis in PIs with <0.5 cm versus ≥0.5 cm as the largest diameter at baseline. A retrospective, real-life, multi-centric study in adults with NF micro-PIs was performed. 208 subjects (92.79% females) were included (average age 43.18 ± 12.58 y). The mean largest diameter (between transversal and longitudinal diameters) was 0.55 ± 0.16cm, and 44.71% of the PIs were located on the right part of the pituitary gland. The patients were divided into 10 age-groups (21 to 70 y), and no difference was observed between the mean largest diameters of these groups (p = 0.693). Females and males had a similar age at PIs diagnosis (p = 0.353), transverse diameter (p = 0.910), longitudinal diameter (p = 0.229), and PI location (p = 0.368). Serial CT scans (2 to 4 per patient) included a median (Q1-Q3) of 20 (12-36) months for the second CT scan, 58 (36-84) for the third CT scan, 78 (53-118) for the fourth CT scan, and a total follow-up between baseline and last CT scan of 40 (13-72) months. The initial largest diameter correlated with the diameter change between the baseline and the last CT (r = -0.575, p = 0.000). The largest PI diameter was similar between each serial CT scan (p = 0.840). According to the cut-off value of 0.5 cm diameter (for initial largest tumor diameter), group A (N = 78, 37.50%, <0.5 cm) and group B (N = 130, 62. 50%; ≥0.5 cm) had similar age at baseline (43.83 ± 12.72 versus 42.79 ± 12.54 y, p = 0.565) and PIs locations (right, left, median). The largest PI diameter remained smaller in group A versus B amid the second CT scan (p = 0.000) and the last CT scan (p = 0.009). The largest diameter change from the first to the last CT scan showed an increase in group A (median of +0.10 cm, p = 0.000) and a decrease in group B (median of -0.01 cm, p = 0.002); this diameter change was different in group A versus B (p = 0.000). The cumulative probability of tumor-growth-free survival showed different survival functions (log rank p = 0.000): group A exhibited a more gradual decrease versus B; at 60 months, the cumulative probability was 0.32 ± 0.08 for group A versus 0.75 ± 0.07 for B. During follow-up, all PIs remained NF, and no hypopituitarism was noted (as limits, we mention that dynamic hypopituitarism testing was selectively performed). NF micro-PIs in adults showed a similar age and sex distribution. During follow-up, PIs with a largest diameter < 0.5 cm increased after a median of 40 months but remained <1 cm, while in PIs with ≥0.5 cm, the largest diameter decreased. This highlights a lower predictability in tumor behavior than expected, particularly in larger micro-PIs that, overall, remained without relevant clinical implications after surveillance.

A New Lifetime Distribution and its Application to Cancer Data

Introduction: Recently, researchers have introduced new generated families of univariate lifetime distributions. These new generators are obtained by adding one or more extra shape parameters to the underlying distribution or compounding two distributions to get more flexibility in fitting data in different areas such as medical sciences, environmental sciences, and engineering. The addition of parameter(s) has been proven useful in exploring tail properties and for improving the goodness-of-fit of the family of the proposed distributions. Methods:A new Three-Parameter Weibull-Generalized Gamma distribution which provides more flexibility in modeling lifetime data is developed using a two-component mixture of Weibull distribution (with parameters θ and λ) and Generalised Gamma distribution (with parameters α=4,θ and λ). Some of its mathematical properties such as the density function, cumulative distribution function, survival function, hazard rate function, moment generating function, Renyi entropy and order statistics are obtained. The maximum likelihood estimation method was used in estimating the parameters of the proposed distribution and a simulation study is performed to examine the performance of the maximum likelihood estimators of the parameters. Results: Real life applications of the proposed distribution to two cancer datasets are presented and its fit was compared with the fit attained by some existing lifetime distributions to show how the Three-Parameter Weibull-Generalized Gamma distribution works in practice Conclusion: The results suggest that the proposed model performed better than its competitors and it’s a useful alternative to the existing models.

A UUV Cluster Route-Planning Method for Dynamic Target Search

Aiming to address the problem of regional dynamic target search under weak communication conditions, this paper proposes a UUV cluster search method based on cumulative probability optimization. First, by estimating the probability distribution of the initial target location, an initial probability map is established. Then, based on the Bayesian model and Markov decision model, the target probability distribution is periodically updated, and based on the cumulative detection probability optimal principle of the UUV cluster, the UUV cluster is guided to search the region with high detection probability preferentially. Finally, we implement the simulation experiment and compare with the random search method. The results verify that the proposed method has higher search efficiency in the cases of without prior information and with prior information.

Performance analysis of mean level CFAR detectors in homogeneous gamma-distributed radar clutter

ABSTRACT In this paper, a novel and exact mathematical formulation of three mean level constant false alarm rate (CFAR) detectors has been developed considering a homogeneous gamma-distributed (GM) clutter. These detectors include the cell averaging (CA), the greatest-of (GO), and the smallest-of (SO)-CFAR detection schemes. To do so, we derived closed-form expressions for the probability density function (pdf), and the cumulative distribution function (cdf) of the sum of an exponential fluctuating target submerged in gamma-distributed sea clutter. Then, we developed exact and explicit expressions for the probability of false alarm (P fa ) and probability of detection (P d ) of the optimal fixed threshold detector and the three considered CFAR detectors. The correctness of our expressions is then examined and validated via numerical simulations. Assuming a homogeneous GM background, the detection performance of these three detectors is carried out, discussed, and compared with that of the optimal detector. Experimental results with Sentinel-1 synthetic aperture radar (SAR) data demonstrate the effectiveness of the proposed closed-form expressions and show that the considered CFAR schemes are efficient for ship detection in sea clutter backgrounds.

Bivariate Pareto–Feller Distribution Based on Appell Hypergeometric Function

The Pareto–Feller distribution has been widely used across various disciplines to model “heavy-tailed” phenomena, where extreme events such as high incomes or large losses are of interest. In this paper, we present a new bivariate distribution based on the Appell hypergeometric function with marginal Pareto–Feller distributions obtained from two independent gamma random variables. The proposed distribution has the beta prime marginal distributions as special case, which were obtained using a Kibble-type bivariate gamma distribution, and the stochastic representation was obtained by the quotient of a scale mixture of two gamma random variables. This result can be viewed as a generalization of the standard bivariate beta I (or inverted bivariate beta distribution). Moreover, the obtained bivariate density is based on two confluent hypergeometric functions. Then, we derive the probability distribution function, the cumulative distribution function, the moment-generating function, the characteristic function, the approximated differential entropy, and the approximated mutual information index. Based on numerical examples, the exact and approximated expressions are shown.

Data-driven Approximation of Cumulative Distribution Function Using Particle Swarm Optimization based Finite Mixtures of Logistic Distribution

This paper proposes a data-driven approximation of the Cumulative Distribution Function using the Finite Mixtures of the Cumulative Distribution Function of Logistic distribution. Since it is not possible to solve the logistic mixture model using the Maximum likelihood method, the mixture model is modeled to approximate the empirical cumulative distribution function using the computational intelligence algorithms. The Probability Density Function is obtained by differentiating the estimate of the Cumulative Distribution Function. The proposed technique estimates the Cumulative Distribution Function of different benchmark distributions. Also, the performance of the proposed technique is compared with the state-of-the-art kernel density estimator and the Gaussian Mixture Model. Experimental results on κ−μ distribution show that the proposed technique performs equally well in estimating the probability density function. In contrast, the proposed technique outperforms in estimating the cumulative distribution function. Also, it is evident from the experimental results that the proposed technique outperforms the state-of-the-art Gaussian Mixture model and kernel density estimation techniques with less training data.

Development and evaluation of a panel of newly screened Y chromosome InDels for inferring paternal ancestry information in Southwest China.

Y-InDels (insertions/deletions) are genetic markers which are extremely understudied. It is unknown whether this type of markers can be utilized for genetic ancestry inference. We have developed an innovative Y chromosome ancestry inference system tailored for forensic applications. This panel amplifies 21 Y chromosome loci, encompassing Y-InDels and Y-SNPs (Single Nucleotide Polymorphism), utilizing the capillary electrophoresis (CE) platform. The system performed well at DNA concentrations greater than 0.125 ng/ul and produced accurate results at a 1:100 mixing ratio of male and female DNA. The Cumulative probability of matching (CPM) was between 0.95 and 0.97 in the experimental population. The system's efficacy in inferring ancestral origins was demonstrated through intercontinental population discrimination, revealing high discrimination power between African and East Asian populations. Population genetic analyses conducted on Han, Qiang and Hui populations in Southwest China, where the smallest FST value was 0.0002 between Han Chinese in Beijing (from 1000 Genomes Project) and Qiang Chinese from Sichuan (CQSC). Phylogenetic tree construction further illuminated distinct haplotypes among populations, with ethnically unique haplotypes observed in 34.6% of Hui and 7.1% of Qiang populations. K-fold cross-validation show the system's inference abilities at the intercontinental level. In addition, our investigations identified potential associations between the Y-InDel locus Y: 15,385,547 (GRCh37) and haplogroup R1a1a1b2a2- Z2124, as well as locus Y: 13,990,180 (GRCh37) and haplogroup F-M89. In conclusion, we have established a Y-chromosome inference system tailored for grassroots-level application, underscoring the value of incorporating Y-InDel markers in forensic analyses.