Statistical Model Research Articles

Climate-based prediction of carbon fluxes from deadwood in Australia

Abstract. Deadwood is an important yet understudied carbon pool in tropical ecosystems. Deadwood degradation to CO2 through decomposer (microbial, termite) activities is driven by wood moisture and temperature, which are in turn strongly influenced by local climate. Thus, climate data could be used to predict CO2 fluxes from decaying wood. Given the increasing availability of gridded climate data, this link would allow for the rapid estimation of deadwood-related CO2 fluxes from tropical ecosystems worldwide. In this study, we adapted a mechanistic fuel moisture model that uses weather variables (e.g., air temperature, precipitation, solar radiation) to simulate wood moisture and temperature along a rainfall gradient in Queensland, Australia. We then developed a Bayesian statistical relationship between wood moisture and temperature and CO2 flux from pine (Pinus radiata) blocks and combined this relationship with our simulations to predict CO2 fluxes from deadwood at 1 h temporal resolution. We compared our pine-based simulations to the moisture–CO2 relationships from stems of native tree species deployed at the wettest and driest sites. Finally, we integrated fluxes over time to estimate the amount of carbon entering the atmosphere and compared these estimates to measured mass loss in pines and native stems. Our statistical model showed a positive relationship between CO2 fluxes and wood moisture and temperature. Comparing cumulative CO2 with wood mass loss, we observed that carbon from deadwood decomposition is mainly released as CO2 regardless of the precipitation regime. At the dry savanna, only about 20 % of the wood mass loss was decomposed within 48 months, compared to almost 100 % at the wet rainforest, suggesting longer residence times of deadwood compared to wetter sites. However, the amount of carbon released in situ as CO2 is lower when wood blocks are attacked by termites, especially at drier sites. These results highlight the important but understudied role of termites in the breakdown of deadwood in dry climates. Additionally, mass loss–flux relationships of decaying native stems deviated from those of pine blocks. Our results indicate that wood moisture and temperature are necessary but not sufficient for predicting CO2 fluxes from deadwood degradation. Other factors, such as wood traits (wood quality, chemical composition, and stoichiometry) and biotic processes, should be considered in future modeling efforts.

Implications of Variability and Trends in Coastal Extreme Water Levels

AbstractProbabilities of coastal extreme water levels (EWLs) are increasing as sea levels rise. Using a time‐dependent statistical model on tide gauge data along U.S. and Pacific Basin coastlines, we show that EWL probability distributions also shift on an annual basis from climate forcing and long‐period tidal cycles. In some regions, combined variability (>15 cm) can be as large or larger than the amount of sea level rise (SLR) experienced over the past 30 years and projected over the next 30 years. Considering SLR and variability by 2050 at a location like La Jolla, California suggests a moderate‐level (damaging) flood today with a 50‐year return level (2% annual chance) would occur about 3–4 times a year during an El Nino nearing the peak of the nodal tide cycle. If interannual variability is overlooked, SLR related impacts could be more severe than anticipated based solely upon decadal‐scale projections.

New pharmacodynamic parameters linked with ibrutinib responses in chronic lymphocytic leukemia: Prospective study in real-world patients and mathematical modeling.

One of the first clinical observations of ibrutinib activity in the treatment of chronic lymphocytic leukemia (CLL) is a rapid decline in lymph nodes size. This phenomenon is accompanied by an hyperlymphocytosis, either transient or prolonged, which is associated with distinct clinical responses and thus has an impact on long-term outcomes. Understanding which factors determine distinct disease courses upon ibrutinib treatment remains a scientific challenge. From 2016 to 2021, we conducted a longitudinal and observational study in 2 cohorts of patients with chronic lymphocytic leukemia (CLL) (cohort 1, n = 41; cohort 2, n = 81). These cohorts reflect the well-known clinical features of CLL patients, such as Male/Female sex ratio of 2/1, a median age of 70 years at diagnosis, and include patients in first-line therapy (27%) or relapsed/refractory patients (73%). Blood cell counts were followed for each patient during 2 years of ibrutinib treatment. In addition, immunophenotyping and whole-body magnetic resonance imaging (MRI) were assessed in patients from cohort 1. These data were integrated in a newly built mathematical model, inspired by previous mathematical works on CLL treatment and combining dynamical and statistical models, leading to the identification of biological mechanisms associated with the 2 types of clinical responses. This multidisciplinary approach allowed to identify baseline parameters that dictated lymphocytes kinetics upon ibrutinib treatment. Indeed, ibrutinib-induced lymphocytosis defined 2 CLL patient subgroups, transient hyperlymphocytosis (tHL) or prolonged hyperlymphocytosis (pHL), that can be discriminated, before the treatment, by absolute counts of CD4+ T lymphocytes (p = 0.026) and regulatory CD4 T cells (p = 0.007), programmed cell death protein 1 PD1 (p = 0.022) and CD69 (p = 0.03) expression on B leukemic cells, CD19/CD5high/CXCR4low level (p = 0.04), and lymph node cellularity. We also pinpointed that the group of patients identified by the transient hyperlymphocytosis has lower duration response and a poor clinical outcome. The mathematical approach led to the reproduction of patient-specific dynamics and the estimation of associated patient-specific biological parameters, and highlighted that the differences between the 2 groups were mainly due to the production of leukemic B cells in lymph node compartments, and to a lesser extent to T lymphocytes and leukemic B cell egress into bloodstream. Access to additional data, especially longitudinal MRI data, could strengthen the conclusions regarding leukemic B cell dynamics in lymph nodes and the relevance of 2 distinct groups of patients. Altogether, our multidisciplinary study provides a better understanding of ibrutinib response and highlights new pharmacodynamic parameters before and along ibrutinib treatment. Since our results highlight a reduced duration response and outcome in patients with transient hyperlymphocytosis, our approach provides support for managing ibrutinib therapy after 3 months of treatment. ClinicalTrials.gov NCT02824159.

Association of Autoimmune Diseases with the Risk of Parkinson's Disease.

PD is a progressive neurodegeneration disease characterized by cardinal motor symptoms such as bradykinesia and tremor. The pathogenesis of PD remains unclear. It is hypothesized that immune system dysfunction may contribute to PD. Thus, autoimmune diseases may influence the risk of incident PD. We included 398,329 participants without PD at the baseline from UK Biobank. The association between 20 autoimmune diseases with PD was examined using cox hazards regression analyses, adjusting covariates like age, sex, and smoking status in the statistical models. Sensitivity analyses were conducted, adjusting for polygenic risk score and the reported source of PD, to check the robustness. After an average follow-up of 13.1 ± 0.816 years, 2,245 participants were diagnosed with incident PD. After multiple comparison correction, only multiple sclerosis (MS) reached statistical significance and showed an increased risk for incident PD. Compared with non-MS patients, the risk of incident PD in MS patients was 2.57-fold with age and sex being adjusted (95% CI, 1.59-4.14; adjust p value = 0.002). After adjusting lifestyle and other factors, the hazard ratio of incident PD in MS patients was 2.49 (95% CI, 1.55-4.02; adjust p value = 0.004). Excluding the self-reported PD cases in the sensitivity analysis, MS was a detrimental factor for incident PD (HR, 2.06; 95% CI, 1.56-4.05; adjust p value = 0.004). The link between MS and PD did not reach the statistical significance in the sensitivity analysis adjusting the PRS (adjust p value = 0.95). Our study provided evidence from observational analyses that MS was associated with an increased risk of PD. Further investigations should be performed to determine the causal association and potential pathophysiology between MS and PD.

Network resonance and the auditory steady state response

The auditory steady state response (ASSR) arises when periodic sounds evoke stable responses in auditory networks that reflect the acoustic characteristics of the stimuli, such as the amplitude of the sound envelope. Larger for some stimulus rates than others, the ASSR in the human electroencephalogram (EEG) is notably maximal for sounds modulated in amplitude at 40 Hz. To investigate the local circuit underpinnings of the large ASSR to 40 Hz amplitude-modulated (AM) sounds, we acquired skull EEG and local field potential (LFP) recordings from primary auditory cortex (A1) in the rat during the presentation of 20, 30, 40, 50, and 80 Hz AM tones. 40 Hz AM tones elicited the largest ASSR from the EEG acquired above auditory cortex and the LFP acquired from each cortical layer in A1. The large ASSR in the EEG to 40 Hz AM tones was not due to larger instantaneous amplitude of the signals or to greater phase alignment of the LFP across the cortical layers. Instead, it resulted from decreased latency variability (or enhanced temporal consistency) of the 40 Hz response. Statistical models indicate the EEG signal was best predicted by LFPs in either the most superficial or deep cortical layers, suggesting deep layer coordinators of the ASSR. Overall, our results indicate that the recruitment of non-uniform but more temporally consistent responses across A1 layers underlie the larger ASSR to amplitude-modulated tones at 40 Hz.

Measuring The Harms Caused by Illicit Drugs. A New Methodology for Estimating Drug Harm Index

Background: Substance use carries a wide range of negative consequences, impacting both the individual using the substances and others. In recent years, there have been multiple efforts to assess the harm caused by drugs and to rank them, with each taking a distinctive approach to the matter. Objectives: This study seeks to introduce a new model for assessing the harm index and ranking of drugs. Methods: This prospective study involved the evaluation of 277 male drug users, assessing substance use harm on four separate occasions throughout the span of 1 year. Various aspects of harm were quantified through the utilization of the Duke Health Profile (DUKE) and the Addiction Severity Index (ASI) questionnaire. The pharmaceutical properties of each drug were incorporated into the study. The relationship between the combined variables in a mixed statistical model was determined at a significance level of .05 using the Rsoftware. This procedure facilitated the establishment of models and the definition of harm index ranges for each substance. Result: The results indicated that heroin had the highest harm index at 71.2 (95% CI69.6–72.8), while pure methadone scored the lowest at 36.5 (95% CI31.7–41.7), along with methadone combined with methamphetamine, which scored 35 (95% CI33–37.1). Conclusion: The variables utilized in this study can help estimate the approximate harm index range for both traditional and novel substances. Furthermore, the harm model designed in this study has the capability to predict the extent of harm to a drug user.

Improvement of decadal predictions of monthly extreme Mei-yu rainfall via a causality guided approach

Abstract While the improved performance of climate prediction systems has allowed better predictions of the East Asian Summer Monsoon rainfall to be made, the ability to predict extreme Mei-yu rainfall (MYR) remains a challenge. Given that large scale climate modes (LSCMs) tend to be better predicted by climate prediction systems than local extremes, one useful approach is to employ causality-guided statistical models (CGSMs), which link known LSCMs to improve MYR prediction. However, previous work suggests that CGSMs trained with data from 1979-2018 might struggle to model MYR in the pre-1978 period. One hypothesis is that this is due to potential changes in causal processes, which modulate MYR in different phases of the multidecadal variability, such as the Pacific Decadal Oscillation (PDO). In this study, we explore this hypothesis by constructing CGSMs for different PDO phases, which reflect the different phases of specific causal process, and examine the difference in quality as well as with respect to difference drivers and thus causal links between CGSMs of different PDO phases as well as the non-PDO phase specific CGSMs. 
Our results show that the set of predictors of CGSMs is PDO phase specific. Furthermore, the performance of PDO phase specific CGSMs are better than the non-PDO phase specific CGSMs. To demonstrate the added value of CGSMs, the PDO phase specific versions are applied to the latest UK Met Office decadal prediction system, DePreSys4, and it is shown that the root-mean squared errors of MYR prediction based on PDO phase specific CGSMs is consistently smaller than the MYR predicted based on the direct DePreSys4 extreme rainfall simulations. We conclude that the use of a causality approach improves the prediction of extreme precipitation based solely on known LSCMs because of the change in the main drivers of extreme rainfall during different PDO-phases.

TECHNICAL-ECONOMIC IMPACT OF RISK MANAGEMENT ON THE SEMI-TRANSHUMANT LIVESTOCK SYSTEM, CASE OF AIN EL BELL REGION IN ALGERIA

Objective: This study aims to investigate the impact of risk management on the semi-transhumant livestock system, in order to suggest a better model for managing the sheep production cycle. Theoretical Framework: The semi-transhumant livestock system is facing an unprecedented set of risks occurring simultaneously. Therefore, studying the organization and management techniques of the semi-transhumant livestock system through a techno-economic approach, compares two situations (SWOR and SWR) based on using indisputable measures. Method: A field survey is conducted with 300 breeders during the period from 2019 to 2021. The collected data are processed using the statistical model SPSS. Results and Discussion: It is concluded that despite different situations, the same simplified production organization and marketing channels (social networks, informal contracts, etc.) are maintained. The SWR shows a decrease in herd size, adoption of 3 reproductive cycles, herd mobility and average profit, etc. However, there is an increase in production costs and acquisition of food products, etc. Ultimately, the results show that while unique risk situations impact semi-transhumant farming systems differently, their technical organizational and management measures render this impact insignificant based on non-parametric chi-square testing. Research Implications: This study provides insight into risk management offering implications for technically feasible practices that are reliable and bio-economically viable for sustaining semi-transhumant livestock system. Originality/Value: The value of this research is demonstrated by appropriate technical organizational and management measures impacting techno-economic farming results which will be useful to consider when other risks arise in future.

Short-Term Energy Forecasting to Improve the Estimation of Demand Response Baselines in Residential Neighborhoods: Deep Learning vs. Machine Learning

Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts.

A petrophysically driven seismic inversion method for TOC content of hydrocarbon source rocks

The organic carbon content is the main index for evaluating organic matter abundance of source rocks, and it is still difficult to quantitatively predict TOC in source rocks based on seismic data. The study of seismic fluid identification driven by petrophysics can help to understand the fluid characteristics and distribution patterns of subsurface oil and gas reservoirs. This paper, first of all, clarified the sweet spot parameters (parameters characterizing hydrocarbon enrichment) and sensitive elastic parameters (a parameter characterizing the nature of an ideal elastic body) of source rocks through theoretical petrophysical modeling, and established the relationship between sensitive elastic parameters and sweet spot parameters TOC, and construct a statistical petrophysical model that can characterize the relationship between the two on this basis. And then construct the joint distribution of TOC and elastic impedance through the Bayesian theoretical framework to obtain the maximum posterior probability estimate as the final TOC inversion results of source rocks. Our method successfully predicts the spreading of high-quality source rocks in the Wen4 Section of Lufeng 13 Subsag, and the inversion results are within an uncertainty range of ±14 m for well data, which proves the reliability of the method. The prediction results show that the organic matter abundance of source rocks in the Wen4 Section is high and the organic carbon content is generally higher than 2%, which provides a reliable basis for the further implementation of the resource scale of the depression and the clarification of the hydrocarbon rich area, which provides technical support for the evaluation of the source rocks of the new depression in the new area.

Integrated predictive analytics in IT audit planning

This review explores the integration of predictive analytics in IT audit planning, emphasizing its transformative potential in enhancing the efficiency and effectiveness of audit processes. Predictive analytics, which involves the use of machine learning, data mining, and statistical modeling to forecast future events based on historical data, has seen widespread adoption across various industries. In the context of IT audit planning, its application can significantly improve risk assessment, resource allocation, and overall audit execution. The study begins by defining predictive analytics and outlining its key techniques. It then provides an overview of the traditional IT audit planning process, highlighting its critical steps and inherent challenges, such as limited risk visibility and inefficient resource use. By integrating predictive analytics, organizations can address these challenges by leveraging data-driven insights to identify emerging risks, prioritize audit areas, and optimize audit schedules. The integration process involves several stages, including data collection and preparation, model development, and implementation. The review discusses the importance of selecting appropriate data sources, cleaning and preprocessing data, and choosing the right predictive models. It also covers the deployment of these models within existing IT audit frameworks, emphasizing the role of advanced tools and technologies. The benefits of integrated predictive analytics are manifold. Enhanced risk assessment allows auditors to proactively identify and mitigate potential issues, while improved resource allocation ensures that audit efforts are focused on the most critical areas. Additionally, predictive analytics can detect anomalies and patterns that might go unnoticed in traditional audits, thereby increasing audit effectiveness. Case studies of successful implementations in various organizations are presented to illustrate the practical benefits and outcomes of integrating predictive analytics into IT audit planning. The review also addresses potential challenges, such as data quality issues, model accuracy, and organizational resistance, offering strategies to overcome these hurdles. The integration of predictive analytics in IT audit planning represents a significant advancement in audit practices. By adopting these techniques, organizations can enhance their audit capabilities, leading to more proactive and effective risk management. The review provides recommendations for implementation and highlights future trends in predictive analytics and IT auditing. Keywords: Integrated, Predictive Analytics, IT, Aduit Planning.

Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia.

Transcriptome-wide association study (TWAS) tools have been applied to conduct proteome-wide association studies (PWASs) by integrating proteomics data with genome-wide association study (GWAS) summary data. The genetic effects of PWAS-identified significant genes are potentially mediated through genetically regulated protein abundance, thus informing the underlying disease mechanisms better than GWAS loci. However, existing TWAS/PWAS tools are limited by considering only one statistical model. We propose an omnibus PWAS pipeline to account for multiple statistical models and demonstrate improved performance by simulation and application studies of Alzheimer disease (AD) dementia. We employ the Aggregated Cauchy Association Test to derive omnibus PWAS (PWAS-O) p values from PWAS p values obtained by three existing tools assuming complementary statistical models-TIGAR, PrediXcan, and FUSION. Our simulation studies demonstrated improved power, with well-calibrated type I error, for PWAS-O over all three individual tools. We applied PWAS-O to studying AD dementia with reference proteomic data profiled from dorsolateral prefrontal cortex of postmortem brains from individuals of European ancestry. We identified 43 risk genes, including 5 not identified by previous studies, which are interconnected through a protein-protein interaction network that includes the well-known AD risk genes TOMM40, APOC1, and APOC2. We also validated causal genetic effects mediated through the proteome for 27 (63%) PWAS-O risk genes, providing insights into the underlying biological mechanisms of AD dementia and highlighting promising targets for therapeutic development. PWAS-O can be easily applied to studying other complex diseases.

Multilevel Hierarchical Bayesian Modeling of Cross-National Factors in Vehicle Sales

SUVs (sport utility vehicles), as a car segment, have become a foundation within the automotive industry due to their versatility, which is used by a wide range of customers. Recognising the complex interplay between geographical and economic conditions across countries, we delve into cross-national factors that significantly influence SUV sales. This article presents an analysis of the global sales of SUVs (sport utility vehicles) using multilevel hierarchical Bayesian modelling. We identify key predictors of SUV sales, including the effects of fuel prices, income levels and geographical aspects. We prepared four statistical models that differ in their probability distribution or hierarchical internal structure. The last presented model, with Student’s t-distribution and separate distribution for unique alpha parameter values, turned out to be the best one. Our analysis contributes to a deeper understanding of the automotive market dynamics, and it can also assist manufacturers and policymakers in designing effective sales strategies.

Effects of MgO on the fibre spinnability, mechanical properties and acid resistance of simulated Martian soil

The ultimate goal of Mars exploration is to construct a Mars base. In particular, it is necessary to prepare fibres by using Martian soil as a raw material. High-strength "Martian glass fibres" can be used to reinforce composite materials to meet the requirements of high-strength functional materials in base construction. Owing to the wide variation in MgO content in Martian soil, in this study, the effects of MgO on the structure, strength and acid resistance of "Martian glass and glass fibres" were investigated. The preparation conditions and mechanical properties of simulated Martian soil fibre (SMSF) were studied via DSC, XRD, Raman, NMR, FT-IR and high-temperature rotational viscometry. The corrosion behaviour of MgO-SMSF in H2SO4 solution was subsequently studied via SEM/EDS. The results showed that MgO reduces the spinnability window and prevents the fibres from stretching continuously, and a threshold appears to exist at 10.89 % MgO. The viscosity of the melt decreased significantly, and the crystallization trend increased with MgO above the threshold. The fibre tensile strength showed a nonlinear relationship with a 22.85 % increase in the fibre tensile strength at 9.24 % MgO. SEM/EDS revealed that the surface of the SMSF formed a gel layer, and the mass retention of the MgO-SMSF in the H2SO4 solution reached 90.17 %. The corrosion of SMSF under acidic conditions was controlled by ion diffusion, with Mg+ and Ca+ diffusing to the fibre surface, resulting in nonuniform corrosion. Raman-based statistical structureproperty modelling further explains the impact of MgO-induced structural changes on the tensile strength and elastic modulus, with good agreement between the model predictions and measured values.

The Tumor Risk Score (TRS) - next level risk prediction in head and neck tumor surgery.

Head and neck cancer surgery often requires postoperative monitoring in an intensive care unit (ICU) or intermediate care unit (IMC). With a variety of different risk scores, it is incumbent upon the investigator to plan a risk-adapted allocation of resources. Tumor surgery in the head and neck region itself offers a wide range of procedures in terms of resection extent and reconstruction methods, which can be stratified only vaguely by a cross-disciplinary score. Facing a variety of different risk scores we aimed to develop a new Tumor Risk Score (TRS) enabling anterograde preoperative risk evaluation, resource allocation and optimization of cost and outcome measurements in tumor surgery of the head and neck. A collective of 547 patients (2010-2021) with intraoral tumors was studied to develop the TRS by grading the preoperative tumor size and location as well as the invasiveness of the planned surgery by means of statistical modeling. Two postoperative complications were defined: (1) prolonged postoperative stay in IMC/ICU and (2) prolonged total length of stay (LOS). Each parameter was analyzed using TRS and all preoperative patient parameters (age, sex, preoperative hemoglobin, body-mass-index, preexisting medical conditions) using predictive modeling design. Established risk scores (Charlson Comorbidity Index (CCI), American Society of Anesthesiologists risk classification (ASA), Functional Comorbidity Index (FCI)) and Patient Clinical Complexity Level (PCCL) were used as benchmarks for model performance of the TRS. The TRS is significantly correlated with surgery duration (p < 0.001) and LOS (p = 0.001). With every increase in TRS, LOS rises by 9.3% (95%CI 4.7-13.9; p < 0.001) or 1.9 days (95%CI 1.0-2.8; p < 0.001), respectively. For each increase in TRS, the LOS in IMC/ICU wards increases by 0.33 days (95%CI 0.12-0.54; p = 0.002), and the probability of an overall prolonged IMC/ICU stay increased by 32.3% per TRS class (p < 0.001). Exceeding the planned IMC/ICU LOS, overall LOS increased by 7.7 days (95%CI 5.35-10.08; p < 0.001) and increases the likelihood of also exceeding the upper limit LOS by 70.1% (95%CI 1.02-2.85; p = 0.041). In terms of predictive power of a prolonged IMC/ICU stay, the TRS performs better than previously established risk scores such as ASA or CCI (p = 0.031). The lack of a standardized needs assessment can lead to both under- and overutilization of the IMC/ICU and therefore increased costs and losses in total revenue. Our index helps to stratify the risk of a prolonged IMC/ICU stay preoperatively and to adjust resource allocation in major head and neck tumor surgery.

Clinical-epidemiological profile and factors associated with viral non-suppression in patients living with HIV/AIDS assisted at the Integrated Treatment Center at the Maputo Military Hospital (CITRA/MMH), 2019 to 2020

BackgroundHIV remains a critical global public health challenge. In 2022, it was estimated that approximately 39.0 million people worldwide were living with HIV, and of these, around 29.8 million were receiving antiretroviral therapy (ART). The objective was to evaluate the clinical and epidemiological profile and factors associated with viral load (VL) non-suppression in people living with HIV/AIDS at the Maputo Military Hospital (CITRA/MMH).MethodsA retrospective cross-sectional analytical study was conducted on 9105 people aged 15 years and over. We use secondary data from participants on ART for at least 2 years being followed up between the years 2019–2020 at CITRA/MMH. Those recently enrolled (on ART < 1 year) were excluded and data analysis was performed using STATA version 16. Pearson's chi-square test and logistic regression were used for statistical modeling of viral non-suppression with a 95%/CI confidence interval and p < 0.05.ResultsAmong a total of 9105 HIV participants included, 52.8% (n = 4808) were female and 13.6% (n = 1235) were military personnel. The average age was 47.9 years (standard deviation ± 12.1), with the most prevalent age group being individuals aged between 25 and 59, totalizing 7,297 (80.2%) participants. Only 5395 (100%) participants had VL results. Among these, 23.1% (n = 1247) had a result VL non-suppressed. Single marital status (Adjusted Odds Ratio [AOR] = 4.8, 95%CI: 3.93–5.76, p < 0.001), with active tuberculosis (AOR = 4.6, 95%CI: 3.15–6.63, p < 0.001) and current ART regimen in categories TDF + 3TC + EFV (AOR = 12.7, 95%CI: 9.74–16.63, p < 0.001), AZT + 3TC + NVP (AOR = 21.8, 95% CI: 14.13–33.59, p < 0.001) and “other” regimens (AOR = 25.8, 95%CI: 18.58–35.80, p < 0.001), when compared to the TDF + 3TC + DTG regime, were statistically significant for viral non- suppression.ConclusionThe study highlights the crucial role of ART adherence and ongoing monitoring to achieve viral suppression, particularly among adults aged 25 to 59. It underscores the need for transitioning eligible individuals to DTG-based regimens and addressing the implications of single marital status and comorbid conditions like active tuberculosis. The study emphasizes the importance of ARV adherence and continuous monitoring to meet the UNAIDS 95–95-95 targets and improve clinical outcomes for people living with HIV/AIDS.

Metabolite profiling of different Iranian traditional yogurts using an untargeted metabolomics approach

We used gas chromatography-mass spectrometry (GC-MS) with an untargeted metabolomics approach to look at the metabolite profiles of traditional Iranian yogurts made from cow, goat, buffalo, and sheep milk. Results showed that different animal milks significantly influenced physicochemical properties and fatty acid (FA) composition, resulting in diverse metabolites. Over 80 % of all the fatty acids in the yogurt samples were saturated. The main fatty acids found were myristic acid (C14:0), palmitic acid (C16:0), and oleic acid + petroselenic acid (cis-9 C18:1 + cis-6 C18:1). In total, 36 metabolites, including esters, aldehydes, alcohols, and acids, were detected. Some important metabolites that changed yogurt profiles were 2-heptanone, methyl acetate, 2-propanone, butyl formate, and 4-methyl benzal. Associations between metabolite profiles and milk compositional traits were also observed, with statistical models showing a strong correlation between metabolite profiles and FA content. This study is the first to explore the impact of different animal sources and regions in Iran on the metabolome profiles of traditional yogurts. These results give us useful information about how metabolites differ between species and can be used to make new dairy products based on milk compositions and metabolites, which will help with future formulations of autochthonous starters.

Landslide susceptibility mapping using GIS-based statistical models and Remote Sensing in the Kathmandu valley, Nepal

Landslide susceptibility mapping using GIS-based statistical models and Remote Sensing in the Kathmandu valley, Nepal

Independent isomeric yield ratios of fission products in the epi-cadmium neutron-induced fission of 245Cm

Abstract Independent isomeric yield ratios (IR) of 128,130,132Sb, 131,133Te, 132,134I, 135Xe and 138Cs in the epi-cadmium neutron-induced fission of 245Cm have been measured for the first time by using an off-line gamma-ray spectrometric technique. The average energy of the epi-cadmium neutron spectrum (&lt;E n &gt;) is 1.9 MeV. From the IR values, root mean square fragment angular momenta (J RMS) were deduced by using the spin dependent statistical model analysis. The IR and JRMS values of the fission products from the present work were compared with the literature data in the thermal neutron-induced fission of 245Cm to examine the role of excitation energy. Effect of nuclear structure on J RMS value was also examined.

Validation of DOE Factorial/Taguchi/Surface Response Models of Mechanical Properties of Synthetic and Natural Fiber Reinforced Epoxy Matrix Hybrid Material.

A validation of the factorial, Taguchi and response surface methodology (RSM) statistical models is developed for the analysis of mechanical tests of hybrid materials, with an epoxy matrix reinforced with natural Chambira fiber and synthetic fibers of glass, carbon and Kevlar. These materials present variability in their properties, so for the validation of the models a research methodology with a quantitative approach based on the statistical process of the design of experiments (DOE) was adopted; for which the sampling is in relation to the design matrix using 90 treatments with three replicates for each of the study variables. The analysis of the models reveals that the greatest pressure is obtained by considering only the source elements that are significant; this is reflected in the increase in the coefficient of determination and in the predictive capacity. The modified factorial model is best suited for the research, since it has an R2 higher than 90% in almost all the evaluated mechanical properties of the material; with respect to the combined optimization of the variables, the model showed an overall contribution of 99.73% and global desirability of 0.7537. These results highlight the effectiveness of the modified factorial model in the analysis of hybrid materials.