Future Updates Research Articles

NIMG-54. AUTOMATED IMAGING RESPONSE EVALUATION SYSTEM (AIRES) FOR NEURO-ONCOLOGY: PROTOTYPE SOFTWARE TOOL FOR DETERMINING STANDARDIZED RADIOGRAPHIC RESPONSE ASSESSMENT FOR GLIOBLASTOMA CLINICAL TRIALS

Abstract Standardized radiographic response assessment criteria like Modified Response Assessment in Neuro-Oncology (mRANO) and now the RANO 2.0 are utilized for evaluating progression-free survival and objective response rate during glioblastoma clinical trials. After measuring tumor size with either bidimensional diameters or volumetric segmentations, the crucial and final time-consuming step of categorizing progression, stable disease, and response based on multiple criteria and numerical thresholds (e.g. ≥40% volumetric growth = progressive disease) is currently done manually. We developed an application prototype, Automated Imaging Response Evaluation System (AIRES) for Neuro-Oncology, that uses the mRANO criteria to label each timepoint as treatment response, stable disease, or disease progression based on either bidimensional or volumetric measurements as well as optional inputs of clinical status and corticosteroid dosage. These inputs may be uploaded via CSV files, which can integrate with automated tumor segmentation volumetry workflows, or entered in the AIRES graphical user interface (GUI). The AIRES GUI displays a patient report consisting of a table and graph that contain the lesion size, percent change from baseline, percent change from nadir, and the mRANO response category for each timepoint. To test AIRES in a real-world application, tumor size measurements from 367 scans across 41 patients from NUTMEG, a phase II multicenter clinical trial, were used to perform mRANO reads and calculate progression-free survival and time to response. AIRES-assisted reads performed by reader 1 were significantly quicker than fully manual reads performed by reader 2 (37.1±8.6 vs 107.7±58.6 seconds, P<0.0001). These results show that the AIRES app can make the reads quicker and potentially feasible for non-trained personnel, while reducing the amount of error-prone manual steps. Additionally, the underlying logic of AIRES is easily adaptable to integrate other response criteria including but not limited to RANO 2.0 and other future response assessment updates.

Are Neutrality Targets Alone Sufficient for Protecting Nature? Learning From Land Cover Change and Land Degradation Neutrality Targets in Mauritius

ABSTRACTLand degradation contributes to loss of land productivity, climate change, and biodiversity loss worldwide, making the monitoring of land cover change a fundamental component of sustainable land use planning. To facilitate this task, international research organizations publish increasingly high‐quality global land cover data, yet those are still proving too imprecise at the scale of Small Island Developing States (SIDS) such as Mauritius. Though rapidly developing, Mauritius does not have a locally produced land cover map which is consistently updated and made available to the public in the scope of participatory planning processes. This study sought to bridge this gap by revising a 2010 land cover map of Mauritius island and producing a new map for 2020, which can serve as a basis for future updates through a consistent methodology that combines photointerpretation and semi‐automated classification from various datasets. These maps enabled the observation of key changes in the island's landscape, which are consistent with socio‐economic events, namely the decline of agricultural coverage (12.7%) due to the phasing out of preferential trade agreements for sugar, and the expansion of real estate (14.3% increase in built area) following new investment promotion policies. Considering the small size and high population density of the island, as is the case for SIDS, these developments and the Land Degradation Neutrality Targets of Mauritius bring the feasibility of area‐based land degradation neutrality into question and call for the analysis of land and ecosystem conditions through additional indicators.

A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products

Abstract. Current global reanalyses show marked discrepancies in snow mass and snow cover extent for the Northern Hemisphere. Here, benchmark snow datasets are produced by driving a simple offline snow model, the Brown Temperature Index Model (B-TIM), with temperature and precipitation from three reanalyses. The B-TIM offline snow performs comparably to or better than online (coupled land–atmosphere) reanalysis snow when evaluated against in situ snow measurements. Sources of discrepancy in snow climatologies, which are difficult to isolate when comparing online reanalysis snow products amongst themselves, are partially elucidated by separately bias-adjusting temperature and precipitation in the B-TIM. Interannual variability in snow mass and snow spatial patterns is far more self-consistent amongst offline B-TIM snow products than amongst online reanalysis snow products, and the self-consistent products are more similar to in situ observations, as evaluated in a validation study. Specific artifacts related to temporal inhomogeneity in snow data assimilation are revealed in the analysis. The B-TIM, released here as an open-source, self-contained Python package, provides a simple benchmarking tool for future updates to more sophisticated online and offline snow datasets.

Cohort profile: the Nanjing Diabetes Cohort database – a population-based surveillance cohort

PurposeTo study epidemiology, complications, risk factors, clinical course and treatment patterns of diabetes, the Nanjing Diabetes Cohort (NDC) was established using anonymised electronic health records from 650 hospitals and primary...

Artificial intelligence for children: UNICEF's policy guidance and beyond

AbstractThis policy review introduces the Policy Guidance on Artificial Intelligence (AI) for Children, produced by the United Nations Children's Fund (UNICEF). This Policy Guidance is the first international‐level output to boost the development of child‐centred AI and relevant policies. A main contribution of this Policy Guidance is that it outlines the foundations, requirements and specific recommendations for developing child‐centred AI and surrounding policies. The shortcomings of this Policy Guidance are also introduced, especially the insufficient gender responsiveness and age sensitivity, plus relatively low representation of the developing world. Possible suggestions for future updates of the Policy Guidance and improving policies on child‐centred AI are provided in this review, such as the inclusion of a broader age range of children during the consultation process. The coexistence of contributions and limitations of this Policy Guidance reflects the situation of development of child‐centred AI and relevant policies, which is currently immature but promising.

Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability.

Interventions for preventing the progression of autosomal dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is the leading inherited cause of kidney disease. Clinical management has historically focused on symptom control and reducing associated complications. Improved understanding of the molecular and cellular mechanisms involved in kidney cyst growth and disease progression has resulted in new pharmaceutical agents targeting disease pathogenesis and preventing disease progression. However, the role of disease-modifying agents for all people with ADPKD is unclear. This is an update of a review first published in 2015. We aimed to evaluate the benefits and harms of interventions to prevent the progression of ADPKD and the safety based on patient-important endpoints, defined by the Standardised Outcomes in NephroloGy-Polycystic Kidney Disease (SONG-PKD) core outcome set, and general and specific adverse effects. We searched the Cochrane Kidney and Transplants Register of Studies up to 13 August 2024 through contact with the Information Specialist using search terms relevant to this review. Studies in the Register are identified through searches of CENTRAL, MEDLINE, and EMBASE, conference proceedings, the International Clinical Trials Registry Platform (ICTRP) Search Portal, and ClinicalTrials.gov. Randomised controlled trials (RCTs) comparing any interventions for preventing the progression of ADPKD with other interventions, placebo, or standard care were considered for inclusion. Two authors independently assessed study risks of bias and extracted data. Summary estimates of effects were obtained using a random-effects model, and results were expressed as risk ratios (RR) and their 95% confidence intervals (CI) for dichotomous outcomes and mean difference (MD) or standardised mean difference (SMD) and 95% CI for continuous outcomes. Confidence in the evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. We included 57 studies (8016 participants) that investigated 18 pharmacological interventions (vasopressin 2 receptor (V2R) antagonists, antihypertensive therapy, mammalian target of rapamycin (mTOR) inhibitors, somatostatin analogues, antiplatelet agents, eicosapentaenoic acids, statins, kinase inhibitors, diuretics, anti-diabetic agents, water intake, dietary intervention, and supplements) in this review. Compared to placebo, the V2R antagonist tolvaptan probably preserves eGFR (3 studies, 2758 participants: MD 1.26 mL/min/1.73 m2, 95% CI 0.73 to 1.78; I2 = 0%) and probably slows total kidney volume (TKV) growth in adults (1 study, 1307 participants: MD -2.70 mL/cm, 95% CI -3.24 to -2.16) (moderate certainty evidence). However, there was insufficient evidence to determine tolvaptan's impact on kidney failure and death. There may be no difference in serious adverse events; however, treatment probably increases nocturia, fatigue and liver enzymes, may increase dry mouth and thirst, and may decrease hypertension and urinary and upper respiratory tract infections. Data on the impact of other therapeutic interventions were largely inconclusive. Compared to placebo, somatostatin analogues probably decrease TKV (6 studies, 500 participants: SMD -0.33, 95% CI -0.51 to -0.16; I2 = 11%), probably have little or no effect on eGFR (4 studies, 180 participants: MD 4.11 mL/min/1.73 m3, 95% CI -3.19 to 11.41; I2 = 0%) (moderate certainty evidence), and may have little or no effect on kidney failure (2 studies, 405 participants: RR 0.64, 95% CI 0.16 to 2.49; I2 = 39%; low certainty evidence). Serious adverse events may increase (2 studies, 405 participants: RR 1.81, 95% CI 1.01 to 3.25; low certainty evidence). Somatostatin analogues probably increase alopecia, diarrhoea or abnormal faeces, dizziness and fatigue but may have little or no effect on anaemia or infection. The effect on death is unclear. Targeted low blood pressure probably results in a smaller per cent annual increase in TKV (1 study, 558 participants: MD -1.00, 95% CI -1.67 to -0.33; moderate certainty evidence) compared to standard blood pressure targets, had uncertain effects on death, but probably do not impact other outcomes such as change in eGFR or adverse events. Kidney failure was not reported. Data comparing antihypertensive agents, mTOR inhibitors, eicosapentaenoic acids, statins, vitamin D compounds, metformin, trichlormethiazide, spironolactone, bosutinib, curcumin, niacinamide, prescribed water intake and antiplatelet agents were sparse and inconclusive. An additional 23 ongoing studies were also identified, including larger phase III RCTs, which will be assessed in a future update of this review. Although many interventions have been investigated in patients with ADPKD, at present, there is little evidence that they improve patient outcomes. Tolvaptan is the only therapeutic intervention that has demonstrated the ability to slow disease progression, as assessed by eGFR and TKV change. However, it has not demonstrated benefits for death or kidney failure. In order to confirm the role of other therapeutic interventions in ADPKD management, large RCTs focused on patient-centred outcomes are needed. The search identified 23 ongoing studies, which may provide more insight into the role of specific interventions.

Predicting COVID-19 booster immunogenicity against future SARS-CoV-2 variants and the benefits of vaccine updates

The ongoing evolution of the SARS-CoV-2 virus has led to a move to update vaccine antigens in 2022 and 2023. These updated antigens were chosen and approved based largely on in vitro neutralisation titres against recent SARS-CoV-2 variants. However, unavoidable delays in vaccine manufacture and distribution meant that the updated booster vaccine was no longer well-matched to the circulating SARS-CoV-2 variant by the time of its deployment. Understanding whether the updating of booster vaccine antigens improves immune responses to subsequent SARS-CoV-2 circulating variants is a major priority in justifying future vaccine updates. Here we analyse all available data on the immunogenicity of variants containing SARS-CoV-2 vaccines and their ability to neutralise later circulating SARS-CoV-2 variants. We find that updated booster antigens give a 1.4-fold [95% CI: 1.07–1.82] greater increase in neutralising antibody levels when compared with a historical vaccine immunogen. We then use this to predict the relative protection that can be expected from an updated vaccine even when the circulating variant has evolved away from the updated vaccine immunogen. These findings help inform the rollout of future booster vaccination programmes.

Bridging the gap between college and career: Development of a graduate attribute framework for Saudi students

The objective of this quantitative study was to examine the efforts of one large university in the Eastern Province of Saudi Arabia to develop and approve a graduate attributes (GA) framework. The large-scale initiative drew on faculty members and students’ perceptions of graduate attributes that can bridge the gap between college and career readiness at Imam Abdulrahman bin Faisal University (IAU). The study’s methodology employed a web-based questionnaire administered to the institutional community of faculty members and students. Descriptive and inferential statistics were conducted and analyzed to refine the final attributes. The results indicated the following five core GAs based on 1,826 participant responses: 1) knowledge application and lifelong learning; 2) problem-solving and decision-making abilities; 3) information technology skills; 4) an active personality and 5) a commitment to values, ethics and responsibility. This inaugural study’s results can contribute to the growing interest in creating GA frameworks for Saudi universities and their graduates. The absence of GA frameworks in the Saudi context should be considered in future updates. Recommendations further suggest secondary analyses to provide more meaningful insights into the IAU’s GA framework and its relevance to Saudi Arabia’s higher education sector in general.

Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models

Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and forum posts can provide valuable insights into user satisfaction and areas for improvement. This can guide the development of future updates and features. However, accurately identifying sentiments in software engineering datasets remains challenging. This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software engineering tasks. We conduct a comprehensive empirical study using five established datasets to assess three open-source bLLMs in zero-shot and few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs, using sLLMs to learn contextual embeddings of text from software platforms. Our experimental findings demonstrate that bLLMs exhibit state-of-the-art performance on datasets marked by limited training data and imbalanced distributions. bLLMs can also achieve excellent performance under a zero-shot setting. However, when ample training data is available or the dataset exhibits a more balanced distribution, fine-tuned sLLMs can still achieve superior results.

Improved species assignments across the entire Anopheles genus using targeted sequencing.

Accurate species identification of the mosquitoes in the genus Anopheles is of crucial importance to implement malaria control measures and monitor their effectiveness. We use a previously developed amplicon panel (ANOSPP) that retrieves sequence data from multiple short nuclear loci for any species in the genus. Species assignment is based on comparison of samples to a reference index using k-mer distance. Here, we provide a protocol to generate version controlled updates of the reference index and present its latest release, NNv2, which contains 91 species, compared to 56 species represented in its predecessor NNv1. With the updated reference index, we are able to assign samples to species level that previously could not be assigned. We discuss what happens if a species is not represented in the reference index and how this can be addressed in a future update. To demonstrate the increased power of NNv2, we showcase the assignments of 1789 wild-caught mosquitoes from Madagascar and demonstrate that we can detect within species population structure from the amplicon sequencing data.

Evaluation of the reliability and usability of CARE-Radiology: A descriptive-analytic study.

The study aimed to evaluate the reliability and usability of the CARE-Radiology checklist in assessing radiological case reports and provide a basis for its broader adoption and optimization. Ten randomly selected radiological case reports published in scientific journals in 2020 were evaluated using the CARE-Radiology checklist. Twenty-six experts from 10 countries were invited to independently assess all ten reports. The reliability of the checklist was measured using Fleiss' Kappa, and Cronbach's alpha coefficient. Usability was evaluated by recording the time taken to complete the assessments and requesting the evaluators to rate each item on a Likert scale for its easiness of use. The median time for evaluating one radiological case report was 15 min. The overall agreement among evaluators showed moderate reliability with a Kappa value of 0.47 and a Cronbach's alpha of 0.51. The mean compliance rate for the items of CARE-Radiology was 61.8%, with some items exceeding 90% compliance. Items related to abstracts and keywords had the lowest compliance rates. The evaluators found most items easy to understand, with a few exceptions. The CARE-Radiology checklist is relatively easy for researchers to use and understand. Continuous feedback is necessary for future revisions and updates, to enhance the effectiveness of the checklist, and to improve user experience.

Bayesian identification and estimation of radon-related increased hazard rates of cancer death in the updated French cohort of uranium miners (1946-2014).

A recent update of the French cohort of uranium miners added seven years of follow-up data. We use these new data to look for new possible radon-related increased risks and refine the estimation of the potential association between cumulative radon exposure and four cancer sites: lung cancer, kidney cancer, brain and central nervous system (CNS) cancer and leukemia (excluding chronic lymphocytic leukemia, which is not radiation-induced). Several parametric survival models are proposed, fitted and compared under the Bayesian paradigm, to perform new and original exposure-risk analyses. In line with recent UNSCEAR recommendations, we consider time-related effect modifiers and exposure rate as potential effect modifying factors. We use Bayesian model selection criteria to identify radon-related increased hazard rates. Under the assumption of a linear exposure-risk relationship, we found a substantial evidence for a strictly positive effect of cumulative radon exposure on the hazard rate of death by lung cancer among French uranium miners. Given the current available data under the assumptions of a linear or log-linear exposure-risk relationship, it is not possible to conclude in favour of the absence or the existence of a strictly positive effect of chronic exposure to radon on the hazard rate of death by kidney cancer. Regarding death by brain and CNS cancer, there is a substantial evidence for the absence of radon-related effect. Finally, under the assumption of a log-linear exposure-risk relationship, a small positive radon-related effect appears when looking at the risk of death by leukemia (excluding CLL). This study investigates the existence of radon-related increased risk of death by lung cancer, kidney cancer, brain and CNS cancer and leukemia under a Bayesian framework and assumptions of linear and log-linear exposure-risk relationships. If there is no doubt in the interpretation of the results for lung cancer and brain and CNS cancer, the conclusion is less clear-cut in the case of kidney cancer and leukemia (excluding CLL). A future update of the French cohort, increasing the follow-up time for miners, may help to reach a clearer conclusion for these two cancer sites.

Nanofabrication of Biochar from Cellulosic Waste for Bio-Sensing Application of Waste Water Treatment: Process, Challenges and Future Update

Nanofabrication of Biochar from Cellulosic Waste for Bio-Sensing Application of Waste Water Treatment: Process, Challenges and Future Update

A proposal for the development of a feed consumption database using a standardised feed classification system

Abstract There is currently no comprehensive feed consumption database using a harmonised feed classification system. This means there is a lack of accurate information on the individual amount of feed consumed daily by farmed and companion animals. Such a database would support exposure assessments of feed. This report provides a complete overview of the main feed databases, their structure, and how feed materials are classified. It highlights their limits and potential and reports any differences from FoodEx2. A proposal for future updates of FoodEx2 and the development of a comprehensive feed consumption model database is provided. The proposed model database is based on three information areas represented by three Excel sheets. These represent Animal, Feed, and Consumption, allowing the determination of animal dietary exposure. A proof of concept of the developed model database was carried out by performing two case studies focused on genetically modified feed and feed contaminants. For genetically modified feed, a reduced animal dietary exposure was obtained compared to estimations reported in the scientific opinions and obtained using Excel calculators proposed by EFSA. For the contaminants in feed, differences were limited, resulting in slightly higher or lower exposure values. Weaknesses and possible mitigations are also addressed, and recommendations are made for a comprehensive feed consumption database. These include recommendations establishing an EU classification system in which the main features and items reported in the European Catalogue of Feed Materials are harmonised with FoodEx2. It is also recommended that real‐life animal consumption data are collected by the feed industry, animal nutritionists, breeders, and farmers, along with the collaboration of stakeholders.

API integration in FinTech: Challenges and best practices

API integration is a cornerstone of innovation in the FinTech sector, enabling seamless connectivity between disparate financial systems, enhancing service delivery, and fostering the development of new financial products. This review explores the challenges and best practices associated with API integration in FinTech, emphasizing the importance of effective strategies for overcoming integration obstacles and achieving successful outcomes. APIs (Application Programming Interfaces) facilitate the exchange of data and functionality between financial applications, such as payment systems, banking platforms, and financial data aggregators. However, integrating these APIs presents several challenges, including compatibility issues, security concerns, and regulatory compliance. Ensuring that APIs are compatible with various systems and standards is essential for smooth integration. Security is another critical concern, as financial data is highly sensitive and requires robust protection against breaches and unauthorized access. Compliance with financial regulations, such as GDPR and PSD2, adds an additional layer of complexity to API integration. To address these challenges, best practices in API integration include adopting standardized protocols and frameworks to enhance compatibility and interoperability. Implementing comprehensive security measures, such as encryption, authentication, and authorization, is crucial to safeguarding data and maintaining trust. Additionally, establishing clear documentation and version control practices helps ensure that APIs are easy to use and integrate while accommodating future updates and changes. Effective API management also involves continuous monitoring and testing to identify and resolve issues promptly. Leveraging API management platforms can streamline the integration process by providing tools for monitoring performance, managing security, and facilitating communication between systems. Collaboration between development teams, IT departments, and business stakeholders is essential for aligning technical and business requirements and ensuring that integration goals are met. In conclusion, while API integration in FinTech presents various challenges, adhering to best practices and leveraging appropriate tools can facilitate successful integration. By addressing compatibility, security, and compliance issues, FinTech organizations can enhance their service offerings, drive innovation, and improve operational efficiency. Keywords: API Integration, Fintech, Compatibility, Security, Regulatory Compliance, Standardized Protocols, Encryption, Authentication, API Management, Performance Monitoring.

Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows

Abstract. VERT (Vehicular Emissions from Road Traffic) is an R package developed to estimate traffic emissions of a wide range of pollutants and greenhouse gases based on traffic estimates and vehicle fleet composition data, following the EMEP/EEA methodology. Compared to other tools available in the literature, VERT is characterised by its ease of use and rapid configuration, while it maintains great flexibility in user input. It is capable of estimating exhaust, non-exhaust, resuspension, and evaporative emissions and is designed to accommodate future updates of available emission factors. In this paper, case studies conducted at both urban and regional scales demonstrate VERT's ability to accurately assess transport emissions. In an urban setting, VERT is integrated with the Lagrangian dispersion model GRAMM–GRAL and provides NOx concentrations in line with observed trends at monitoring stations, especially near traffic hotspots. On a regional scale, VERT simulations provide emission estimates that are highly consistent with the reference inventories for the Emilia-Romagna region (Italy). These findings make VERT a valuable tool for air quality management and traffic emission scenario assessment.

Architectural Approaches to Migrating Key Features in Android Apps

The rapid evolution of Android platforms and user expectations necessitates continual enhancement and migration of key features within Android applications. This paper explores various architectural approaches to effectively migrate essential functionalities in Android apps, ensuring scalability, maintainability, and optimal user experience. Initially, the study delineates the challenges inherent in feature migration, including compatibility issues, performance constraints, and the preservation of existing user interfaces. It then examines prominent architectural paradigms such as Model-View-View Model (MVVM), Clean Architecture, and modular architectures, evaluating their suitability for facilitating seamless feature transitions. The research further investigates the role of microservices and component-based designs in enabling incremental migration, allowing developers to update or replace specific modules without overhauling the entire application. Additionally, the paper discusses best practices for managing dependencies, ensuring backward compatibility, and employing automated testing to mitigate risks during the migration process. Case studies of successful Android app migrations are presented to illustrate the practical application of these architectural strategies. The findings highlight that adopting a modular and flexible architectural framework significantly enhances the ability to migrate key features efficiently while minimizing disruptions. Moreover, the integration of robust architectural patterns fosters better code maintainability and accelerates the development cycle for future updates. In conclusion, this study provides a comprehensive guide for Android developers aiming to navigate the complexities of feature migration, advocating for architectural approaches that balance innovation with stability. The insights garnered offer valuable implications for the design and evolution of resilient Android applications in a dynamic technological landscape.

MtDNA “nomenclutter” and its consequences on the interpretation of genetic data

Population-based studies of human mitochondrial genetic diversity often require the classification of mitochondrial DNA (mtDNA) haplotypes into more than 5400 described haplogroups, and further grouping those into hierarchically higher haplogroups. Such secondary haplogroup groupings (e.g., “macro-haplogroups”) vary across studies, as they depend on the sample quality, technical factors of haplogroup calling, the aims of the study, and the researchers' understanding of the mtDNA haplogroup nomenclature. Retention of historical nomenclature coupled with a growing number of newly described mtDNA lineages results in increasingly complex and inconsistent nomenclature that does not reflect phylogeny well. This “clutter” leaves room for grouping errors and inconsistencies across scientific publications, especially when the haplogroup names are used as a proxy for secondary groupings, and represents a source for scientific misinterpretation. Here we explore the effects of phylogenetically insensitive secondary mtDNA haplogroup groupings, and the lack of standardized secondary haplogroup groupings on downstream analyses and interpretation of genetic data. We demonstrate that frequency-based analyses produce inconsistent results when different secondary mtDNA groupings are applied, and thus allow for vastly different interpretations of the same genetic data. The lack of guidelines and recommendations on how to choose appropriate secondary haplogroup groupings presents an issue for the interpretation of results, as well as their comparison and reproducibility across studies. To reduce biases originating from arbitrarily defined secondary nomenclature-based groupings, we suggest that future updates of mtDNA phylogenies aimed for the use in mtDNA haplogroup nomenclature should also provide well-defined and standardized sets of phylogenetically meaningful algorithm-based secondary haplogroup groupings such as “macro-haplogroups”, “meso-haplogroups”, and “micro-haplogroups”. Ideally, each of the secondary haplogroup grouping levels should be informative about different human population history events. Those phylogenetically informative levels of haplogroup groupings can be easily defined using TreeCluster, and then implemented into haplogroup callers such as HaploGrep3. This would foster reproducibility across studies, provide a grouping standard for population-based studies, and reduce errors associated with haplogroup nomenclatures in future studies.

Hybrid Radix-4 SESA/SEDA Adders for Medical Image Processing

Adders play a critical role as primary modules in circuit design, supporting error-tolerant applications such as machine learning, digital image processing, and signal processing. Therefore, the development of adders with low power consumption and energy efficiency is of utmost importance. Approximate adders outperform exact adders in VLSI chips. Among approximate adders, the hybridization of Single Exact and Single Approximation (SESA) and Single Exact and Dual Approximation (SEDA) techniques has emerged as a promising solution. These hybrid adders offer superior power and energy savings relative to other accurate and approximate adder designs. In this scheme, we propose the integration of hybrid radix-4 adders with approximation adders to achieve enhanced performance and pave the way for future hybrid approximate adder circuits. Extensive simulations validated the efficacy of the proposed approach. Furthermore, practical applications demonstrated the versatility and potential of hybrid adders in real-world scenarios. The proposed methodology ensures cost-effectiveness and facilitates seamless future updates and extensions. Overall, this research contributes to the advancement of efficient and low-power adder designs, addressing the evolving requirements of modern digital systems.