Previous Models Research Articles

Creativity Through Embodied Movement in Expressive Activities: Conceptualising a New Pedagogical Model

ABSTRACT This work aimed to outline the first stages for the development of a pedagogical model for teaching-learning of expressive content within physical education (creative body movement). Drawing on several suggestions from previous models’ conceptualizations, we explore and present the theoretical foundations, practical considerations, and assumptions that align and support their core features. The major theme of the model is that students explore new expressive movements and develop capabilities through expressive games or tasks that nurture their creativity and ease their embodied movements. This implies that teachers should provide students with several resources and stimuli to nurture their creativity. Also, teachers should promote students’ embodiment through the exploration of new movements that allow knowing themselves in different movement situations. The affective and cognitive domains will be prominent in this model. The teachers’ and students’ assumptions, implementation needs, and enactment about how to apply this model are also proposed.

A framework for the facilitation of accelerated leadership and management capability development in the workplace

A holistic framework for the design of leadership and management programs to accelerate leadership and management capability development in the workplace is presented. Previous models and frameworks have been primarily based on program design inputs and outcomes and have not adequately considered the mediating role of learning processes and learner preferences in the relationship between program design inputs and outcomes. Additionally, most existing models and frameworks do not offer holistic approaches. Four theoretical implications and five practical applications of the framework are presented. The three defining features of the leadership and management learning framework are the focus on the development of competencies that underpin effective leadership, the central role of learning facilitation in this development, and the workplace learning processes that enable leadership and management development, and specifically meta-learning processes. Contributions to theory involve the integration of meta-learning and facilitation in the framework and the six propositions posited. A series of practical implications for advancing leadership program design, delivery and evaluation is also presented.

The relationship between digital development and economic growth in the European Union

AbstractNumerous studies have proven that digital development positively affects economic growth. This study aims to confirm or refute the positive impact of digital evolution on economic growth by applying the dimensions of the International Digital Economy and Society Index (I-DESI). The analysis refers to the period 2015–2020 of the European Union member states. The study's novelty is that the I-DESI index has yet to be used in research to investigate the relationship between the digital transition and GDP production. The present study, therefore, goes one step further than the previous typical DESI-GDP models. The research uses Pearson correlation and F-statistic analysis to show the relationship between the variables. The study confirms that digital development has positively impacted the economic growth of EU member states. This result was confirmed by both Pearson and Spearman correlation. However, the results are ambivalent. The empirical results indicate that the more digitally developed member countries had a higher GDP per capita. However, the positive effect is different. The results confirm that the development of digitalization and GDP increased more dynamically in the more digitally developed EU member states than in the less developed member states. Therefore, an increase in the backwardness of the less developed member countries and not a catch-up can be observed in the period under review.

Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking

Roads degrade over time due to various factors such as traffic loads, environmental conditions, and the quality of materials used. Significant investments have been poured into road construction globally, necessitating regular evaluations and the implementation of maintenance and rehabilitation (M&R) strategies to keep the infrastructure performing at a satisfactory level. The development and refinement of performance prediction models are essential for forecasting the condition of pavements, especially to address longitudinal cracking distress, a major issue in thick asphalt pavements. This research leverages multiple machine learning methods to create models predicting non-wheel path (NWP) and wheel path (WP) longitudinal cracking using data from the Long-Term Pavement Performance (LTPP) program. This study highlights the marked differences in distress conditions between WP and NWP, underscoring the importance of precise models that cater to their unique features. Aging trends for both types of cracking were identified through correlation analysis, showing an increase in WP cracking with age and a higher initial International Roughness Index (IRI) linked to NWP cracking. Factors such as material characteristics, kinematic viscosity, pavement thickness, air voids, particle size distribution, temperature, KESAL, and asphalt properties were found to significantly influence both WP and NWP cracking. The Exponential Gaussian Process Regression (GPR) emerged as the best model for NWP cracking, showcasing exceptional accuracy with the lowest RMSE of 89.11, MSE of 7940.72, and an impressive R-Squared of 0.63. For WP cracking, the Squared Exponential GPR model was most effective, with the lowest RMSE of 12.00, MSE of 143.93, and a high R-Squared of 0.62. The GPR models, with specific kernels for each cracking type, proved their adaptability and efficiency in various pavement scenarios. A comparative analysis highlighted the superiority of our new machine learning model, which achieved an R2 of 0.767, outperforming previous empirical models, demonstrating the strength and precision of our machine learning approach in predicting longitudinal cracking.

Development and Application of a New Exponential Model for Hydraulic Conductivity with Depth of Rock Mass

Hydraulic conductivity generally decreases with depth in the Earth’s crust. The hydraulic conductivity–depth relationship has been assessed through mathematical models, enabling predictions of hydraulic conductivity in depths beyond the reach of direct measurements. However, it is observed that beyond a certain depth, hydraulic conductivity tends to stabilize; this phenomenon cannot be effectively characterized by the previous models. Thus, these models may make inaccurate predictions at deeper depths. In this work, we introduce an innovative exponential model to effectively assess the conductivity–depth relationship, particularly addressing the stabilization at greater depths. This model, in comparison with an earlier power-like model, has been applied to a globally sourced dataset encompassing a range of lithologies and geological structures. Results reveal that the proposed exponential model outperforms the power-like model in correctly representing the stabilized conductivity, and it well captures the fast stabilization effect of multiple datasets. Further, the proposed model has been utilized to analyze three distinct groups of datasets, revealing how lithology, geological stabilization, and faults impact the conductivity–depth relationship. The hydraulic conductivity decays to the residual hydraulic conductivity in the order (fast to slow): metamorphic rocks, sandstones, igneous rock, mudstones. The mean hydraulic conductivity in stable regions is roughly an order of magnitude lower than unstable regions. The faults showcase a dual role in both promoting and inhibiting hydraulic conductivity. The new exponential model has been successfully applied to a dataset from a specific engineering site to make predictions, demonstrating its practical usage. In the future, this model may serve as a potential tool for groundwater management, geothermal energy collection, pollutant transport, and other engineering projects.

A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images

AbstractSerious consequences due to brain tumors necessitate a timely and accurate diagnosis. However, obstacles such as suboptimal imaging quality, issues with data integrity, varying tumor types and stages, and potential errors in interpretation hinder the achievement of precise and prompt diagnoses. The rapid identification of brain tumors plays a pivotal role in ensuring patient safety. Deep learning-based systems hold promise in aiding radiologists to make diagnoses swiftly and accurately. In this study, we present an advanced deep learning approach based on the Swin Transformer. The proposed method introduces a novel Hybrid Shifted Windows Multi-Head Self-Attention module (HSW-MSA) along with a rescaled model. This enhancement aims to improve classification accuracy, reduce memory usage, and simplify training complexity. The Residual-based MLP (ResMLP) replaces the traditional MLP in the Swin Transformer, thereby improving accuracy, training speed, and parameter efficiency. We evaluate the Proposed-Swin model on a publicly available brain MRI dataset with four classes, using only test data. Model performance is enhanced through the application of transfer learning and data augmentation techniques for efficient and robust training. The Proposed-Swin model achieves a remarkable accuracy of 99.92%, surpassing previous research and deep learning models. This underscores the effectiveness of the Swin Transformer with HSW-MSA and ResMLP improvements in brain tumor diagnosis. This method introduces an innovative diagnostic approach using HSW-MSA and ResMLP in the Swin Transformer, offering potential support to radiologists in timely and accurate brain tumor diagnosis, ultimately improving patient outcomes and reducing risks.

Energetics‐based connectivity mapping reveals new conservation opportunities for the endangered tiger in Nepal

AbstractEnhancing habitat connectivity is a key strategy for conserving endangered species in anthropogenic landscapes. However, connectivity planning often overlooks the crucial energetic costs to animals of traversing complex terrains. We applied a novel approach for estimating energy costs of movement for tigers – a globally endangered species. We used those estimates to calculate landscape connectivity for these animals across the extreme altitudinal gradient of Nepal, where recent sightings of tigers at higher elevations (~3200 m) suggest an upward range expansion from the tiger‐rich lowlands. To evaluate our estimates, we simulated tiger routes to higher‐elevation locations and compared modeled energy costs of those ascents to those derived from a previous model calibrated with data from GPS‐collared tigers in Russia. In areas below 3200 m, we found about 7.5 times greater land areas with high connectivity outside protected areas (~51 000 km2) than inside (~6800 km2). However, most of the highly connected areas below 3200 m consist of croplands (56%). Importantly, community‐managed forests, which spanned the altitudinal gradient, tended to include areas with moderate levels of connectivity. Our estimates of energy costs and those from Russia showed a strong consensus (ρ = 0.70, P < 0.05), with ours better capturing the higher energy costs of traversing mountains and of very large total ascents. Our results show that while barriers to tiger movement across Nepal are ubiquitous, other effective area‐based conservation measures (OECMs), like community‐managed forests, can play prominent roles in promoting tiger habitat connectivity while minimizing human–tiger conflict across anthropogenic landscapes. Our results also underscore the utility of integrating first principles of energy efficiency into connectivity analyses and planning.

Abstract A015: Predicting efficacious drugs and drug-biomarker relationships for targeted glioblastoma treatment

Abstract Glioblastoma multiforme (GBM) is the deadliest, most heterogeneous, and common brain cancer in adults. Despite major advancements in neurosurgery as well as chemotherapy and radiotherapy techniques, overall prognosis has changed little over the decades. Not only is there an urgent need to identify efficacious therapeutics but there is also a great need to pair these therapeutics with biomarkers that can help tailor treatment to the right patient populations given the heterogeneous nature of the disease. Here, we implement several machine learning and statistical based pipelines for drug and biomarker discovery, leveraging previous work building patient drug response models using high-throughput cell line drug screening data as well as Bayesian network structure learning. Through these discovery pipelines, we identified multiple agents of interest for GBM, including MEK inhibitors (MEKis). MEKis were consistently predicted to be effective against GBM in multiple cross platform (microarray and RNA-sequencing) patient and patient derived xenograft (PDX) datasets. Additionally, they were experimentally shown to be more efficacious in tumor samples than standard of care agents (Temozolomide and Carmustine). We also predicted PHGDH gene expression levels to be causally associated with MEKi efficacy, which increased tumor sensitivity to a MEKi (Trametinib) during experimental testing in GBM cell lines with knockdown. These findings support our drug and biomarker discovery pipelines as an effective strategy to unite multiple cross platform and cross species datasets to help achieve GBM precision medicine. They also demonstrate that patient PHGDH levels can help inform Trametinib response in the fight against GBM. Citation Format: Danielle Maeser, Robert Gruener, Robert Galvin, Stephanie Huang, Tomo Koga, Florina Grigore. Predicting efficacious drugs and drug-biomarker relationships for targeted glioblastoma treatment [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr A015.

Abstract PR-010: Asc1lb progenitor-specific RB conditional inactivation in zebrafish models rare CNS primitive neuroectodermal tumors

Abstract PR-010: Asc1lb progenitor-specific RB conditional inactivation in zebrafish models rare CNS primitive neuroectodermal tumors

AI based Identification of Students Dress Code in Schools and Universities

Automatic dress code verification (ADCV) uses advanced technology to quickly and fairly check if people are following the dress code. It is more accurate than humans and does not have personal biases. This can be helpful in places like schools, universities, and offices where dress codes are important for safety. One of the great things about it is that it saves time and money. It does not require people to manually check the dress codes, which can be slow and not always consistent. This technology can be adjusted to fit different dress code rules, whether it's a formal office dress or a school uniform. The technology can be customized for different dress code rules, from formal office attire to school uniforms. One notable advantage of ADCV is its capacity to save both time and financial resources. The system gets rid of the requirement for people to manually check dress codes, which can be slow and inconsistent. The technology's flexibility extends to customization for different dress code rules, addressing the unique needs of diverse settings. It is important to highlight the evolution of ADCV from previous models, where limitations existed in terms of singular application focus. In earlier iterations, functionalities such as face recognition or object detection could only be implemented individually, creating a gap in the comprehensive identification of individuals that encompassed both facial features and attire. The proposed system aims to rectify these shortcomings by integrating capabilities, ensuring a more holistic approach to dress code verification. This enhancement signifies a significant stride toward a more versatile and effective solution for monitoring and enforcing dress codes in various environments. In the previous models the only one application could be designed. For instance, the face recognition or the object detection is only possible individually. It cannot do both identification of person’s face and attire of the person. Considering these disadvantages, the rectification of these problems will be achieved through the implementation of the proposed system. ADCV's impact extends beyond its immediate application in dress code enforcement. As a pioneering example of the intersection between computer vision and artificial intelligence.

Characterization and mitigation of artifacts derived from NGS library preparation due to structure-specific sequences in the human genome

BackgroundHybridization capture-based targeted next generation sequencing (NGS) is gaining importance in routine cancer clinical practice. DNA library preparation is a fundamental step to produce high-quality sequencing data. Numerous unexpected, low variant allele frequency calls were observed in libraries using sonication fragmentation and enzymatic fragmentation. In this study, we investigated the characteristics of the artifact reads induced by sonication and enzymatic fragmentation. We also developed a bioinformatic algorithm to filter these sequencing errors.ResultsWe used pairwise comparisons of somatic single nucleotide variants (SNVs) and insertions and deletions (indels) of the same tumor DNA samples prepared using both ultrasonic and enzymatic fragmentation protocols. Our analysis revealed that the number of artifact variants was significantly greater in the samples generated using enzymatic fragmentation than using sonication. Most of the artifacts derived from the sonication-treated libraries were chimeric artifact reads containing both cis- and trans-inverted repeat sequences of the genomic DNA. In contrast, chimeric artifact reads of endonuclease-treated libraries contained palindromic sequences with mismatched bases. Based on these distinctive features, we proposed a mechanistic hypothesis model, PDSM (pairing of partial single strands derived from a similar molecule), by which these sequencing errors derive from ultrasonication and enzymatic fragmentation library preparation. We developed a bioinformatic algorithm to generate a custom mutation “blacklist” in the BED region to reduce errors in downstream analyses.ConclusionsWe first proposed a mechanistic hypothesis model (PDSM) of sequencing errors caused by specific structures of inverted repeat sequences and palindromic sequences in the natural genome. This new hypothesis predicts the existence of chimeric reads that could not be explained by previous models, and provides a new direction for further improving NGS analysis accuracy. A bioinformatic algorithm, ArtifactsFinder, was developed and used to reduce the sequencing errors in libraries produced using sonication and enzymatic fragmentation.

Improvement of Albedo and Snow-Cover Simulation during Snow Events over the Tibetan Plateau

Abstract The snow albedo is a vital component of land–atmosphere coupling models. It plays a critical role in regulating land surface energy exchange by controlling incoming solar radiation absorbed by the land surface and influencing the timing and rate of snowmelt. Accurate snow albedo simulation is essential to obtain surface energy balance and snow-cover estimates. Here, the simulation of albedo and snow cover using the Weather Research and Forecasting Model and an improved snow albedo scheme is verified against satellite-retrieved products during and immediately following eight snowfall events over the Tibetan Plateau. The improved model successfully characterizes the spatial pattern and inverted U-shaped temporal pattern of albedo over the entire Tibetan Plateau. This is attributed to the local optimization of snow-age parameters and explicit consideration of snow depth in the improved scheme. Compared with the previous model, the model proposed herein greatly decreases the overestimated albedo (by 0.13–0.27), yielding a bias range of ±0.08, mean relative bias decrease of 70%, and significant increase in the spatial correlation coefficient of 0.03–0.39 (mean: 0.13). The significant improvements of albedo estimates appear in deep snow-covered regions, largely attributed to parameter optimization related to snow albedo decay, while less improvements appear over the shallow snow-covered regions. Accurate reproduction of the spatiotemporal variation in albedo alleviated snow-cover overestimation by small amounts. For snow-cover estimates, the improved model consistently decreases the false-alarm rate by 0.03, and increases the overall accuracy and equitable threat score by 0.04 and 0.03, respectively. Moreover, the improved scheme shows an equivalent improvement of albedo estimates at both 1- and 5-km grid spacing over the eastern Tibetan Plateau; this is also true for snow-cover estimates. Significance Statement Snow albedo schemes in widely used numerical weather prediction models show notable shortcomings in complex mountainous regions, hindering accurate surface energy balance and snow-cover prediction. The purpose of this study is to better understand the role of snow albedo on snow-cover estimates and reveal the application potential of an improved snow albedo scheme across the Tibetan Plateau. This is important because snow albedo influences the timing and rate of snowmelt, and in turn snow-cover estimates, through regulating the surface energy budget. Our results highlight the strong application potential of our improved scheme in reducing snow simulation errors, confirm the importance of snow depth on snow albedo, and provide a new perspective for improving the accuracy of snow forecast over the topographically high Tibetan Plateau.

Induced Polarization of Clayey Rocks and Soils: Non‐Linear Complex Conductivity Models

AbstractThe past decades have witnessed the increased applications of induced polarization (IP) method in the critical zone studies with ubiquitous clay minerals. Although IP outperforms traditional electrical and electromagnetic methods through its unique ability to measure quadrature conductivity, the nonlinearity that quadrature conductivity behaves with salinities and frequencies greatly tortures IP practitioners, as (a) salinity‐dependency makes the quadrature conductivity a varyingly unstable parameter to quantitatively estimate hydraulic properties and clay content; (b) frequency‐dependent Cole‐Cole and Debye/Warburg decomposition models, although mathematically sound, physically mingle the properties of pore water and clay minerals and are empirical in nature. From basic principles, we demonstrate that quadrature conductivity remains a hybrid property involving both clay and water, and develop relevant models to distinguish them. Our models are validated by theories, experiments, simulations, and comparisons, all of which proclaim considerable advantages over previous models and offer the prospect of quantitative applications.

Patterns in bird and pollinator occupancy and richness in a mosaic of urban office parks across scales and seasons.

Urbanization is a leading cause of global biodiversity loss, yet cities can provide resources required by many species throughout the year. In recognition of this, cities around the world are adopting strategies to increase biodiversity. These efforts would benefit from a robust understanding of how natural and enhanced features in urbanized areas influence various taxa. We explored seasonal and spatial patterns in occupancy and taxonomic richness of birds and pollinators among office parks in Santa Clara County, California, USA, where natural features and commercial landscaping have generated variation in conditions across scales. We surveyed birds and insect pollinators, estimated multi-species occupancy and species richness, and found that spatial scale (local, neighborhood, and landscape scale), season, and urban sensitivity were all important for understanding how communities occupied sites. Features at the landscape (distance to streams or baylands) and local scale (tree canopy, shrub, or impervious cover) were the strongest predictors of avian occupancy in all seasons. Pollinator richness was influenced by local tree canopy and impervious cover in spring, and distance to baylands in early and late summer. We then predicted the relative contributions of different spatial scales to annual bird species richness by simulating "good" and "poor" quality sites based on influential covariates returned by the previous models. Shifting from poor to good quality conditions locally increased annual avian richness by up to 6.8 species with no predicted effect on the quality of the neighborhood. Conversely, sites of poor local and neighborhood scale quality in good-quality landscapes were predicted to harbor 11.5 more species than sites of good local- and neighborhood-scale quality in poor-quality landscapes. Finally, more urban-sensitive bird species were gained at good quality sites relative to urban tolerant species, suggesting that urban natural features at the local and landscape scales disproportionately benefited them.

Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine.

The advancements in next-generation sequencing have made it possible to effectively detect somatic mutations, which has led to the development of personalized neoantigen cancer vaccines that are tailored to the unique variants found in a patient's cancer. These vaccines can provide significant clinical benefit by leveraging the patient's immune response to eliminate malignant cells. However, determining the optimal vaccine dose for each patient is a challenge due to the heterogeneity of tumors. To address this challenge, we formulate a mathematical dose optimization problem based on a previous mathematical model that encompasses the immune response cascade produced by the vaccine in a patient. We propose an optimization approach to identify the optimal personalized vaccine doses, considering a fixed vaccination schedule, while simultaneously minimizing the overall number of tumor and activated T cells. To validate our approach, we perform in silico experiments on six real-world clinical trial patients with advanced melanoma. We compare the results of applying an optimal vaccine dose to those of a suboptimal dose (the dose used in the clinical trial and its deviations). Our simulations reveal that an optimal vaccine regimen of higher initial doses and lower final doses may lead to a reduction in tumor size for certain patients. Our mathematical dose optimization offers a promising approach to determining an optimal vaccine dose for each patient and improving clinical outcomes.

Emerging Trends in Play-to-Earn (P2E) Games

This research aims to establish the primary drivers influencing the development and consumers’ decision-making process in web3 games—decentralized games that function according to the play-to-earn paradigm. We observe several types of micro-economies developed within five play-to-earn games and highlight four roles consumers play at any given time. Our study offers a different perspective on rational consumer behaviour in cryptocurrency-based games and paves the way to better understanding their dynamics and evolution. Results shed light on the construction of in-game economies and how individuals of a given type engage in different playing activities. Furthermore, we compare the key features of web3 games with those similar to classic online games and assess if the play-and-earn implementations represent an evolution from previous revenue models. Using our proposed methodology, researchers can compare and classify any P2E games. We conclude by establishing a set of actions that enable consumers to benefit from this new phenomenon.

Intra-colony spatial variance of oxyregulation and hypoxic thresholds for key Acropora coral species.

Oxygen (O2) availability is essential for healthy coral reef functioning, yet how continued loss of dissolved O2 via ocean deoxygenation impacts performance of reef building corals remains unclear. Here, we examine how intra-colony spatial geometry of important Great Barrier Reef (GBR) coral species Acropora may influence variation in hypoxic thresholds for upregulation, to better understand capacity to tolerate future reductions in O2 availability. We first evaluate the application of more streamlined models used to parameterise Hypoxia Response Curve data, models that have been used historically to identify variable oxyregulatory capacity. Using closed-system respirometry to analyse O2 drawdown rate, we show that a two-parameter model returns similar outputs as previous 12th-order models for descriptive statistics such as the average oxyregulation capacity (Tpos) and the ambient O2 level at which the coral exerts maximum regulation effort (Pcmax), for diverse Acropora species. Following an experiment to evaluate whether stress induced by coral fragmentation for respirometry affected O2 drawdown rate, we subsequently identify differences in hypoxic response for the interior and exterior colony locations for the species Acropora abrotanoides, Acropora cf. microphthalma and Acropora elseyi. Average regulation capacity across species was greater (0.78-1.03 ± SE 0.08) at the colony interior compared with exterior (0.60-0.85 ± SE 0.08). Moreover, Pcmax occurred at relatively low pO2 of <30% (±1.24; SE) air saturation for all species, across the colony. When compared against ambient O2 availability, these factors corresponded to differences in mean intra-colony oxyregulation, suggesting that lower variation in dissolved O2 corresponds with higher capacity for oxyregulation. Collectively, our data show that intra-colony spatial variation affects coral oxyregulation hypoxic thresholds, potentially driving differences in Acropora oxyregulatory capacity.

Prognostic Value of the Modeled Prostate-Specific Antigen KELIM Confirmation in Metastatic Castration-Resistant Prostate Cancer Treated With Taxanes in FIRSTANA.

In a previous exploratory study, modeled early longitudinal prostate-specific antigen (PSA) kinetics observed within the 100-first treatment days with androgen deprivation therapy with or without docetaxel was associated with progression-free survival (PFS) and overall survival (OS) in patients with prostate cancer with rising PSA levels after primary local therapy. This prognostic value had to be confirmed in different settings. The objectives were to assess PSA kinetics modeling in patients with metastatic castration-resistant prostate cancer (mCRPC) treated with chemotherapy in FIRSTANA trial and to investigate modeled PSA kinetic parameters prognostic/predictive value. FIRSTANA phase III trial (ClinicalTrials.gov identifier: NCT01308567) assessed whether cabazitaxel is superior to docetaxel in terms of PFS/OS in patients with chemotherapy-naïve mCRPC. PSA longitudinal kinetics was assessed using the previous kinetic-pharmacodynamics model. Patient modeled ELIMination rate constant K (PSA.KELIM) was used to categorize favorable/unfavorable PSA declines (standardized PSA.KELIM < or ≥ 1.0 days-1) and further correlated with PFS/OS. In total, 1,050 of 1,168 enrolled patients were assessable for PSA.KELIM estimation. The median PSA.KELIM was 0.02 days-1. In univariate analyses, PSA.KELIM exhibited a significant prognostic value regarding survival: unfavorable versus favorable PSA.KELIM; median PFS, 3.6 months (95% CI, 3.0 to 4.2) versus 4.7 months (95% CI, 3.9 to 5.2), P = .002; median OS, 17.4 months (95% CI, 14.8 to 19.3) versus 28.4 months (95% CI, 26.7 to 31.6), P < .001. In multivariate analyses, PSA.KELIM was significant for PFS (hazard ratio [HR], 0.79 [95% CI, 0.67 to 0.93], P = .005) and OS (HR, 0.51 [95% CI, 0.44 to 0.60], P < .001), together with baseline radiological tumor progression and PSA doubling time. PSA.KELIM predictive value was not significant across treatment arms. This external validation study confirmed previous results about modeled PSA longitudinal kinetics prognostic value regarding PFS/OS in patients with mCRPC treated with taxanes. PSA.KELIM could be used to identify a subpopulation with poor prognosis, who may benefit from treatment intensification.

Radial Evolution of the Near-Sun Solar Wind: Parker Solar Probe Observations

Abstract A statistical study of the radial evolution of the solar wind within 0.3 au is shown in this Letter based on Parker Solar Probe observations. We show the radial distribution of the main solar wind parameters, including the solar wind speed V sw, magnetic field ∣B∣, the number density of electrons N e , protons N p , and α particles N α , and the temperature of protons T p and α particles T α . The power-law fitting results of these parameters in the near-Sun solar wind are compared with previous radial models. We also show the radial distribution of the angle between the magnetic field B , solar wind V sw, and radial vector R . In the solar wind within 0.3 au, θ BV sw , and θ BR mainly concentrate around 135°, and θ RV sw almost concentrates in the region less than 20°. Furthermore, we also present the radial distribution of the relative values between the solar wind parameters, including the electric neutrality estimation ((2 × N α + N p )/N e ≃ 1 within 0.2 au), relative number density ratio (N α /N p ≃ 0.02 in slow solar wind and N α /N p ≃ 0.04 in faster solar wind), relative temperature ratio (T α /T p decreases with the increase of heliocentric distance and its decay rate is larger in faster solar wind), and differential speed (both V α − V p and (V α − V p )/V A are larger in the faster solar wind and decrease as heliocentric distance increases).

Performance of machine-learning approach for prediction ofpre-eclampsia in a middle-income country.

Pre-eclampsia (PE) is a serious complication of pregnancy associated with maternal and fetal morbidity and mortality. As current prediction models have limitations and may not be applicable in resource-limited settings, we aimed to develop a machine-learning (ML) algorithm that offers a potential solution for developing accurate and efficient first-trimester prediction of PE. We conducted a prospective cohort study in Mexico City, Mexico to develop a first-trimester prediction model for preterm PE (pPE) using ML. Maternal characteristics and locally derived multiples of the median (MoM) values for mean arterial pressure, uterine artery pulsatility index and serum placental growth factor were used for variable selection. The dataset was split into training, validation and test sets. An elastic-net method was employed for predictor selection, and model performance was evaluated using area under the receiver-operating-characteristics curve (AUC) and detection rates (DR) at 10% false-positive rates (FPR). The final analysis included 3050 pregnant women, of whom 124 (4.07%) developed PE. The ML model showed good performance, with AUCs of 0.897, 0.963 and 0.778 for pPE, early-onset PE (ePE) and any type of PE (all-PE), respectively. The DRs at 10% FPR were 76.5%, 88.2% and 50.1% for pPE, ePE and all-PE, respectively. Our ML model demonstrated high accuracy in predicting pPE and ePE using first-trimester maternal characteristics and locally derived MoM. The model may provide an efficient and accessible tool for early prediction of PE, facilitating timely intervention and improved maternal and fetal outcome. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.