Independent Data Research Articles

How does running technique change with running speed? A quantitative analysis based on practice-informed principal components

Introduction & Purpose “Running technique is an important component of running economy and performance” (Folland et al., 2017). While economy and performance can directly be measured, no common measuring method exists for running technique. The aims of this study were twofold: (I) develop practice-informed running technique measures based on technique characterizations used by coaches (i.e. distinct variations of body segment movements instead of discrete kinematics); (II) investigate, how running technique changes with running speed. Methods Two independent kinematic data sets were recorded on a treadmill using a retro-reflective marker setup (“Plugin-Gait”) and a 10-camera system (Vicon Motion Systems Ltd., Oxford, UK). First, 20 experienced runners (age 25 ± 2 y; 10 females, 10 males) were tasked to vary their running according to 14 technique elements (vertical, horizontal, hip and upper body movement, foot strike, track width, ankle rotation, leg swing, back posture, elbow flexion/extension, arm swing amplitude and direction, gaze direction, cadence) into two opposing extreme forms per element. Measures for the 14 running technique elements were developed using principal component analyses (one PCA per two opposing technique element variations) and selecting principal components (PCs; Federolf, 2016) that aligned with the represented technique element. Second, an additional 19 experienced runners (age 30 ± 9 y, 3 females, 16 males) ran at different speeds (10-17 km/h with 1 km/h incremental steps) without any technique instructions. Their data were projected onto the selected PCs, resulting in averaged PC score waveforms of the different speeds along each technique element. Waveforms were analysed using statistical parametric mapping (SPM) with a repeated measures ANOVA design (Pataky et al., 2013). Results For all 14 technique elements, sections of significant differences between at least two different speeds were found within the cycle-normalized PC score waveforms. Figure 1 exemplifies the results for the foot track width, where the lateral range of motion of the feet decreased with speed. Post-hoc analyses revealed further differences, such as less vertical and greater horizontal movement, as well as smaller leg swing and greater arm swing amplitude at 17 km/h compared to 10 km/h. Discussion Firstly, practice-informed and data-driven running technique measures could be developed (I). Secondly, the measures could be applied on a group of runners and plausible changes in their technique due to running speed were identified (II). So far, the speed-dependent technique adaptations only reflect the specific techniques on a treadmill for a predominately male reference group, but the presented concept can be transferred to other runners and settings. Conclusion Running technique varies with speed, and the developed technique measures facilitate comparisons of these differences using practically relevant technique elements. Since the movement adaptations associated with running speed are directly quantifiable and visualizable, technique models for different speed ranges might be developed by researchers and then utilized by runners to train and assess their technique in alignment with these models.

Herbage biomass predictions from UAV data using a derived digital terrain model and machine learning

AbstractMore than 70% of Switzerland's agricultural area is covered by grasslands that often exhibit highly diverse species compositions and heterogeneous growth patterns. An essential requirement for efficient and effective pasture management is the regular estimation of herbage biomass. While various methods exist for estimating herbage biomass, they are often time‐consuming and may not accurately capture the variability within pastures. This highlights the need for more efficient, accurate estimation techniques. To help improve herbage biomass estimation, we present estiGrass3D+, a Random Forest model. This model predicts pasture biomass using a digital terrain model (DTM) derived from a digital surface model (DSM) for sward height modelling, along with vegetation indices and agronomic variables from UAV images only. The model was successfully evaluated with independent test data from different sites on the Swiss central plateau, including both grazed and mown areas. Model performance on an independent validation dataset achieved a NRMSE of 20.3%, while the training dataset had an NRMSE of 21.5%. These consistent results confirm that estiGrass3D+ is both transferable and applicable to unseen data while maintaining accuracy and reliability across different datasets. The wide applicability of our method demonstrates its practicality for predicting herbage biomass under different pasture management scenarios. Additionally, our method of deriving a DTM directly from a DSM simplifies the measurement of grass sward height by UAVs, eliminating the need for prior ground control point (GCP) marking and subsequent aligning, enhancing the efficiency of herbage biomass estimation.

Algorithm for constructing unmeasured logging curves (pseudo-logging) in wells of developed fields

In many cases, the diagrams of some logs in an old well of producing fields are not recorded for some reason or another. Currently, the importance of reassessing the efficiency of such fields is obvious, while the absence of some log curves causes great difficulties. In particular very few studies with acoustic logging (AL) have been carried out in old wells. On the other hand, in 1960-80, among the studies carried out in wells, preference was given to various types of electrical logging (EL), neutron-gamma logging (NGL), and gamma ray logging (GRL). To overcome such difficulties, it is necessary to create diagrams of unmeasured well logs or pseudo-logs. Nowadays, there are methods for constructing pseudolog curves. For example, the method of statistical analysis is widely used. However, in most cases, their implementation is very complex and requires a lot of work. We have developed a new method to construct pseudo-log curves. At first, the propagation velocity of elastic waves in rocks is studied based on seismic survey (SS) and AL data, which are performed presently on the investigation field. Based on this information, objects of interest to the oil industry are selected, their geometric dimensions are determined, the contour of oil and gas saturation is assessed, areas with abnormally high reservoir pressure are studied, the process of accumulation deposition is re-examined, and the preparation of an optimal well network diagram for the effective exploitation of the field is determined. Further, based on the long-ago measured curves of electrical loggings and neutron-gamma logging, we obtain the pseudo-log AL curve in old boreholes and the data SS. The proposed method was tested in 48 wells in the Bahar field (Azerbaijan). The method has shown high efficiency, its reliability is confirmed by independent data. The results of the method help in solving several geological problems: monitoring changes in petrophysical quantities of rocks between wells during development; study of well formations located below the design depth; recalculation of hydrocarbon reserves; determination of the location for drilling new wells, etc

Penerapan Media Pembelajaran Berbasis Web “Genially” Terhadap Hasil Belajar Siswa Pada Materi Mufradāt Bahasa Arab

This research is motivated by Arabic language learning on mufrodat material with the teaching process in class still using conventional methods, lecture methods, and sometimes assisted with powepoint media. However, the media is considered less diverse and innovative so that it still does not attract the attention of students. So that students still have difficulty in learning this mufrodat, because it feels monotonous. Therefore, researchers try to implement Genially web-based learning media on Arabic mufrodat material which has different characteristics from other media because of its interesting gamification features and can give a pleasant impression during the learning process. The purpose of this study is to see the effectiveness and comparison of student learning outcomes before and after the application of genially web-based learning media on mufordat material. This research uses quantitative research with experimental methods with Independent Sample T-Test data analysis, the samples taken in this study were class VIII A as an experimental class with a total of 24 students and class VIII B as a control class with a total of 21 students. The results of this study indicate that the value of a Sig. (2-tailed) 0.003 is smaller than 0.05, which means that there is an influence and significant average difference between learning outcomes with the application of Genially web learning media and those that do not use Genially web application at SMP Muhammadiyah 5 Tulangan.

Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis.

People with psychosis exhibit thalamo-cortical hyperconnectivity and cortico-cortical hypoconnectivity with sensory networks, however, it remains unclear if this applies to all sensory networks, whether it arises from other illness factors, or whether such differences could form the basis of a viable biomarker. To address the foregoing, we harnessed data from the Human Connectome Early Psychosis Project and computed resting-state functional connectivity (RSFC) matrices for 54 healthy controls and 105 psychosis patients. Primary visual, secondary visual ("visual2"), auditory, and somatomotor networks were defined via a recent brain network partition. RSFC was determined for 718 regions via regularized partial correlation. Psychosis patients- both affective and non-affective-exhibited cortico-cortical hypoconnectivity and thalamo-cortical hyperconnectivity in somatomotor and visual2 networks but not in auditory or primary visual networks. When we averaged and normalized the visual2 and somatomotor network connections, and subtracted the thalamo-cortical and cortico-cortical connectivity values, a robust psychosis biomarker emerged (p=2e-10, Hedges' g=1.05). This "somato-visual" biomarker was present in antipsychotic-naive patients and did not depend on confounds such as psychiatric comorbidities, substance/nicotine use, stress, anxiety, or demographics. It had moderate test-retest reliability (ICC=.61) and could be recovered in five-minute scans. The marker could discriminate groups in leave-one-site-out cross-validation (AUC=.79) and improve group classification upon being added to a well-known neurocognition task. Finally, it could differentiate later-stage psychosis patients from healthy or ADHD controls in two independent data sets. These results introduce a simple and robust RSFC biomarker that can distinguish psychosis patients from controls by the early illness stages.

Causal Relationships of Circulating Inflammatory Proteins and Portal Vein Thrombosis: A Mendelian Randomization Study.

Portal vein thrombosis (PVT) is commonly encountered in patients with cirrhosis, challenging our understanding of its development, particularly the ambiguous contribution of inflammation. This study utilized Mendelian randomization (MR) to explore the causal impact of circulating inflammatory markers on PVT.Employing a two-sample MR framework, we merged genome-wide association study (GWAS) meta-analysis findings of 91 inflammation-associated proteins with independent PVT data from the FinnGen consortium's R10 release. A replication analysis was performed using a distinct GWAS dataset from the UK Biobank. Inverse variance weighting, MR-Egger regression, weighted median estimator, and Mendelian Randomization Pleiotropy RESidual Sum and Outlier were used for analysis, supplemented by multivariable MR (MVMR) to adjust for cirrhosis effects.Findings indicate a significant inverse association between the genetically inferred concentration of eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1) and PVT risk, evidenced by an odds ratio (OR) of 0.37 (95% confidence interval [CI]: 0.21-0.67; p = 9.2 × 10-4; adjusted for multiple testing p = 0.084). This association was corroborated in the replication phase (OR = 0.39, 95% CI: 0.17-0.93; p = 0.03) and through MVMR analysis (OR = 0.34, 95% CI: 0.15-0.79; p = 0.012). Sensitivity analyses disclosed no evidence of heterogeneity or pleiotropy.Our investigation emphasizes the 4E-BP1 as a protective factor against PVT, underscoring its potential relevance in understanding PVT pathogenesis and its implications for diagnosis and therapy.

Bayesian approaches to assigning the source of an odour detected by an electronic nose

After a brief review of electronic nose technology, the use of an Australian electronic nose to identify an unknown odour out of a set of known odours is described. Multivariate supervised learning is accomplished by applying Bayes’ theorem to data from metal oxide semiconductor sensors responding to each of a number of target odours. An odour from an unknown source is then assigned a probability of membership of each of the training sets by applying either a Naïve Bayes algorithm to the deemed independent data from each sensor, or to a multinormal distribution of the data. A flat prior (equal probabilities of each outcome) is usually adopted, but for particular situations where one odour is known to predominate, then suitably weighted priors can be used. A source ‘none of the above’, which has a small likelihood covering the space of the possible sensor responses, is included for completeness. This also avoids the assignment to a source that has an extremely small probability but which is greater than that of any other source. Examples are given of a single source (detecting diabetes from a patient’s breath), and three sources of unpleasant odours in a meat processing plant.

Unveiling metabolite network dynamics during Pu-erh tea storage via non-targeted metabolomics

Pu-erh tea's quality and price are inextricably intertwined with age, but false claims about production year can harm both consumers and producers. While tea quality is currently assessed subjectively by experts, developing a scientific method to determine storage time is crucial for the tea industry's growth and integrity. In the present study, analysis of Pu-erh tea (PT) with different storage times showed that the storage process enhanced the complexity of the metabolite networks of extracts and flocs, but the metabolic network characteristics of tea flocs did not exhibit significant changes with the progression of storage time. It is noteworthy that the 41 storage-related core metabolites are important to the storage process; the storage score based on these metabolites was related to the biochemical indicators of the extract and the storage time. Our findings were validated in another independent metabolic data set of green tea (GT), oolong tea (OT), Assam black tea (ABT) and PT. Finally, our regression model based on the storage score could accurately predict the storage time of different teas (R2 = 95.70%). Overall, the findings of our have important guiding significance for expanding the methods for predicting tea storage duration.

Using a Triple Sensor Collocation Approach to Evaluate Small-Holder Irrigation Scheme Performances in Northern Ethiopia

This study uses a triple-sensor collocation approach to evaluate the performance of small-holder irrigation schemes in the Zamra catchment of Northern Ethiopia. Crop water productivity (CWP), as an integrator of biomass production and water use, was used to compare the overall efficiencies of three types of irrigation systems: traditional and modern diversions, and dam-based irrigation water supply. Farmer-reported data often rely on observations, which can introduce human estimation and measurement errors. As a result, the evaluation of irrigation scheme performance has frequently been insufficient to fully explain crop water productivity. To overcome the challenges of using one single estimation method, we used a triple-sensor collocation approach to evaluate the efficiency of three small-scale irrigation schemes, using water productivity as an indicator. It employed three independent methods: remotely sensed data, a model-based approach, and farmer in-situ estimates to assess crop yields and water consumption. To implement the triple collocation appraisal, we first applied three independent evaluation methods, i.e., remotely sensed, model-based, and farmer in-situ estimates of crop yields and water consumption, to assess the crop water productivities of the systems. Triple-sensor collocation allows for the appraisal and comparison of estimation errors of measurement sensor systems, and enables the ranking of the estimators by their quality to represent the de-facto unknown true value, in our case: crop yields, water use, and its ratio CWP, in small-holder irrigated agriculture. The study entailed four main components: (1) collecting in-situ information and data from small-holder farmers on crop yields and water use; (2) derivation of remote sensing-based CWP from the FAO WaPOR open database and time series; (3) evaluation of biomass, crop yields and water use (evapotranspiration) using the AquaCrop model, integrating climate, soil data, and irrigation management practices; (4) performing and analysis of a categorical triple collocation analysis of the independent estimator data and performance ranking of the three sensing and small-holder irrigation systems. Maize and vegetables were used as main crops during three consecutive irrigation seasons (2017/18, 2018/19, 2019/20). Civil war prevented further field surveying, in-situ research, and data collection. The results indicate that remote sensing products are performed best in the modern and dam irrigation schemes for maize. For vegetables, AquaCrop performed best in the dam irrigation scheme.

Towards robust corridors: a validation framework to improve corridor modeling

ContextEcological corridors are one of the most recommended ways to mitigate biodiversity loss. With growing recognition of corridor importance, corridor modeling lags others in the development of robust, quantitative validation methods.ObjectiveWe propose a post-hoc corridor validation framework, considering the range of methods across data needs and statistical intensity. We demonstrate the importance of post-hoc corridor validation by testing several validation methods on different corridor model outputs.MethodsWe used three different transformations on a Florida black bear (Ursus americanus floidanus) habitat suitability model to create different resistance grids, independent GPS collar data from a case study population, and Circuitscape to create corridor models. We used several validation methods, including a novel method, to compare resulting corridors.ResultsTransformed resistance grids were all correlated, yet differing validation and resistance grids resulted in different recommended corridors. The use of one resistance surface and one validation type can result in the selection of inefficient or ineffective corridors. At a minimum, modelers should determine what proportion of an independent population falls within resulting corridors and should move towards more robust, documented methods as resources allow. The use of multiple validation methods can ensure greater confidence of modeling results.ConclusionsWe encourage the use and further development of the framework presented here to drive the corridor modeling field towards more effective corridor creation and improved conservation outcomes. If validation methods are not improved, the ecological and economic cost of poor corridor science will continue to increase with increasing biodiversity loss.

Abstract A074: Integrated clinical and single-cell profiling analysis reveals cellular states associated with metastatic organotropism and survival in patients with pancreatic ductal adenocarcinoma

Abstract Some patients with localized pancreatic ductal adenocarcinoma (PDAC) undergo resection with curative intent, but most experience disease recurrence and succumb to their disease. Through examination of 743 PDAC patients in a single center we found that the site of first recurrence is a major predictor for survival. Patients with liver-metastatic recurrence (LIV-MR) had significantly shorter disease free and overall survival compared to those with recurrence to the lung (LUN-MR). We performed single-nucleus RNA-seq (snRNA-seq) and whole-genome sequencing (WGS) of primary PDAC with either LIV-MR or LUN-MR and found that malignant cells adopt transcriptional programs that mimicked that of their ultimate metastatic target organ. We validated this observation in independent data sets of human adult and fetal liver and lung tissues, and two additional patient cohorts who underwent molecular profiling of their actual PDAC liver and lung metastatic lesions. We reconstructed two different trajectories of malignant transformation in PDAC and found that the LIV-MR and LUN-MR are associated with these, suggesting that subsequent metastatic organotropism may be imprinted early in tumorigenesis. Furthermore, primary PDAC tumors recurring with LIV-MR compared to LUN-MR were depleted of infiltrating T cells and enriched with M2-like macrophages, highlighting potential contributions to subsequent outcomes. Taken together, this work provides evidence for pre-existing cellular adaptations that may determine metastatic organotropism in the context of cancer recurrence. These insights may be useful for prognostication and development of therapeutic strategies at the time of diagnosis of resectable PDAC. Citation Format: Morteza Chalabi Hajkarim, Michael May, Amit Dipak Amin, Jacob Jamison, Somnath Tagore, Lindsay Caprio, Zachary Walsh, Parin Shah, Sinan Abuzaid, Alissa Michel, Sun Dajiang, Winston Wong, Neha Shaikh, Priyanka Ramaradj, Basil Bakir, Michael D Kluger, John Chabot, Hanina Hibshoosh, Anil K Rustgi, Alexander Raufi, Gulam A Manji, Benjamin Izar. Integrated clinical and single-cell profiling analysis reveals cellular states associated with metastatic organotropism and survival in patients with pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A074.

Constitutive Model for Thermal-Oxygen-Aged EPDM Rubber Based on the Arrhenius Law.

Ethylene-propylene-diene monomer (EPDM) is a key engineering material; its mechanical characterization is important for the safe use of the material. In this paper, the coupled effects of thermal degradation temperature and time on the tensile mechanical behavior of EPDM rubber were investigated. The tensile stress-strain curves of the aged and unaged EPDM rubber show strong nonlinearity, demonstrating especially rapid stiffening as the strain increases under small deformation. The popular Mooney-Rivlin and Ogden (N = 3) models were chosen to fit the test data, and the results indicate that neither of the classical models can accurately describe the tensile mechanical behavior of this rubber. Six hyperelastic constitutive models, which are excellent for rubber with highly nonlinearity, were employed, and their abilities to reproduce the stress-strain curve of the unaged EPDM were assessed. Finally, the Davis-De-Thomas model was found to be an appropriate hyperelastic model for EPDM rubber. A Dakin-type kinetic relationship was employed to describe the relationships between the model parameters and aging temperature and time, and, combined with the Arrhenius law, a thermal aging constitutive model for EPDM rubber was established. The ability of the proposed model was checked by independent testing data. In the moderate strain range of 200%, the errors remained below 10%. The maximum errors of the prediction results at 85 °C for 4 days and 100 °C for 2 and 4 days were computed to be 17.06%, 17.51% and 19.77%, respectively. This work develops a theoretical approach to predicting the mechanical behavior of rubber material that has suffered thermal aging; this approach is helpful in determining the safe long-term use of the material.

Axial compression behaviour of circular concrete-filled stainless-clad bimetallic steel tubular stub columns

The axial compression behaviour of circular concrete-filled stainless-clad bimetallic steel tubular (CFSCBST) stub columns is addressed in this paper by experimental and numerical investigations. A total of five specimens were fabricated and subjected to axial compression loading. The varying parameters in the experimental study included the clad ratio of steel tube, the diameter-to-thickness ratio and the strength of the concrete. A three-dimensional finite element (FE) model was developed and validated against both dependent and independent test data to further study the axial compressive behaviour of circular CFSCBST stub columns in this paper. The parametric studies considering five major parameters, including the strength of the concrete, the substrate steel grade, the clad ratio, the diameter-to-thickness ratio and different bonding conditions of the stainless-clad (SC) bimetallic steel, were carried out by using the verified FE model. The applicability of existing design codes to the circular CFSCBST stub columns was further analysed through comparisons with the numerical results. New design methods based on unified and superposition theory have been proposed herein, which have improved accuracy for predicting the ultimate compression capacity of such circular CFSCBST stub columns.

B - 145 CVLT-3 Forced Choice Recognition Failure Rate in a Mixed Clinical Population

Abstract Objective The CVLT-3 is a measure of verbal learning and memory used extensively in clinical and research settings that contains an embedded performance validity indicator, a forced choice recognition trial (CVLT-3-FCR). The CVLT-2-FCR was modified for the CVLT-3-FCR, replacing abstract with concrete distractor words. To date, no one has reported independent data on failure rates in clinical populations on the CVLT-3-FCR. Method Participants were 373 patients referred for clinical neuropsychological evaluation who completed the CVLT-3 (Standard form) as part of their evaluation. Mean age was 61.20 (SD = 14.30, range = 20–86), mean education was 15.00 years (SD = 2.87, range = 6–21), 53.1% were male, and 97.6% White. Failure was defined as CVLT-3-FCR score ≤ 14. Results 85.5% of our mixed clinical sample had CVLT-3-FCR score of 16/16, and 7.5% had 15/16. Only 7% (N = 26) scored ≤14, most common primary diagnoses being mild neurocognitive disorder (N = 9) and psychiatric (N = 7), while only four had dementia of any etiology. Those who failed tended to be a little older [pass = 60.82 years (SD = 14.44), fail = 66.38 years (SD = 11.28); p = 0.055) and scored lower for CVLT-3 Total Trials 1–5, short- and long-delay free recall, recognition hits, false positives, and discriminability (all p < 0.001). Conclusions Findings indicate CVLT-3-FCR ≤ 14 is extremely rare in a large mixed clinical sample of adults and older adults, even in subsamples for whom lower scores on PVTs have been suggested (e.g., dementia). While passing the CVLT-3-FCR does not in itself confirm adequate test engagement, failure should raise concern and the need for additional PVTs in one’s assessment.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals ubiquitin promotes pulmonary fibrosis in chronic pulmonary diseases

It is estimated that there are 544.9 million people suffering from chronic respiratory diseases in the world, which is the third largest chronic disease. Although there are various clinical treatment methods, there is no specific drug for chronic pulmonary diseases, including chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD) and idiopathic pulmonary fibrosis (IPF). Therefore, it is urgent to clarify the pathological mechanism and medication development. Single-cell transcriptome data of human and mouse from GEO database were integrated by “Harmony” algorithm. The data was standardized and normalized by using “Seurat” package, and “SingleR” algorithm was used for cell grouping annotation. The “Findmarker” function is used to find differentially expressed genes (DEGs), which were enriched and analyzed by using "clusterProfiler", and a protein interaction network was constructed for DEGs, and four algorithms are used to find the hub genes. The expression of hub genes were analyzed in independent human and mouse single-cell transcriptome data. Bulk RNA data were used to integrate by the "SVA" function, verify the expression levels of hub genes and build a diagnostic model. The L1000FWD platform was used to screen potential drugs. Through exploring the similarities and differences by integrated single-cell atlas, we found that the lung parenchymal cells showed abnormal oxidative stress, cell matrix adhesion and ubiquitination in COPD, corona virus disease 2019 (COVID-19), ILD and IPF. Meanwhile, the lung resident immune cells showed abnormal Toll-like receptor signals, interferon signals and ubiquitination. However, unlike acute pneumonia (COVID-19), chronic pulmonary disease shows enhanced ubiquitination. This phenomenon was confirmed in independent external human single-cell atlas, but unfortunately, it was not confirmed in mouse single-cell atlas of bleomycin-induced pulmonary fibrosis model and influenza virus-infected mouse model, which means that the model needs to be optimized. In addition, the bulk RNA-Seq data of COVID-19, ILD and IPF was integrated, and we found that the immune infiltration of lung tissue was enhanced, consistent with the single-cell level, UBA52, UBB and UBC were low expressed in COVID-19 and high expressed in ILD, and had a strong correlation with the expression of cell matrix adhesion genes. UBA52 and UBB have good diagnostic efficacy, and salermide and SSR-69071 can be used as their candidate drugs. Our study found that the disorder of protein ubiquitination in chronic pulmonary diseases is an important cause of pathological phenotype of pulmonary fibrosis by integrating scRNA-Seq and bulk RNA-Seq, which provides a new horizons for clinicopathology, diagnosis and treatment.

Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods

Apoptotic proteins play a crucial role in the apoptosis process, ensuring a balance between cell proliferation and death. Thus, further elucidating the regulatory mechanisms of apoptosis will enhance our understanding of their functions. However, the development of computational methods to accurately identify positive and negative regulation of apoptosis remains a significant challenge. This work proposes a machine learning model based on multi-feature fusion to effectively identify the roles of positive and negative regulation of apoptosis. Initially, we constructed a reliable benchmark dataset containing 200 positive regulation of apoptosis and 241 negative regulation of apoptosis proteins. Subsequently, we developed a classifier that combines the support vector machine (SVM) with pseudo composition of k-spaced amino acid pairs (PseCKSAAP), composition transition distribution (CTD), dipeptide deviation from expected mean (DDE), and PSSM-composition to identify these proteins. Analysis of variance (ANOVA) was employed to select optimized features that could yield the maximum prediction performance. Evaluating the proposed model on independent data revealed and achieved an accuracy of 0.781 with an AUROC of 0.837, demonstrating our model's potent capabilities.

Combining High-Throughput Experiments and Active Learning to Characterize Deep Eutectic Solvents.

The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large-scale applications, the reduction of DES viscosity through the addition of a low-molecular-weight solvent was explored as a case study. A high-throughput experimental screening was initially performed on nine different ternary DESs. Then, GPs were successfully trained to predict DES viscosity from its composition and temperature, showcasing the ability of these stochastic, nonparametric models to accurately describe the physicochemical properties of complex mixtures. Finally, the ability of GPs to provide estimates of their own uncertainty was leveraged through an AL framework to minimize the number of data points necessary to obtain accurate viscosity modes. This led to a significant reduction in data requirements, with many systems requiring only five independent viscosity data points to be properly described.

Algorithmic Identification of Multiple Sclerosis in the SAIL databank

BackgroundMultiple Sclerosis (MS) is a challenging disease to identify within large repositories of healthcare data. Diagnosis can be protracted requiring many modalities that may not form part of the repository data collection. Accurate case finding would have multiple applications for studying epidemiology. AimDevelop a case finding algorithm within the Secure Anonymised Information Linkage (SAIL) Databank that can reliably elicit Multiple Sclerosis. MethodUtilising a cross sectional cohort study we used multiple datasets within SAIL; General Practice, Inpatient, Outpatient and Office of National Statistics to develop our case finding algorithm based on the coding nomenclatures and timing within these datasets. The results of this algorithm were then tested against two patient data sets: The UK MS Register (n=836), consisting of mostly self confirmed disease and a clinical cohort from South West Wales (n=713). ResultsFrom 4,757,428 records, the algorithm identified 6,194 cases of MS within Wales on 31st December 2020 (prevalence 221.65 [95%CI 216.17–227.24] per 100,000). Case finding sensitivity and specificity was 96.8% and 99.9% for the clinically validated population-based cohort and sensitivity was 96.7% from the self-declared registry population. ConclusionWe successfully identified MS cases within Wales and verified this within two independent data sets.

3-D parallel anisotropic inversion of controlled-source electromagnetic data using nested tetrahedral grids

SUMMARY Geophysicists today face the challenge of quickly and reliably interpreting extensive controlled-source electromagnetic (CSEM) data sets to map subsurface conductivity structures within realistic geological environments. An ideal 3-D CSEM inversion algorithm using tetrahedral grids should be capable of distinguishing different resolution requirements between forward modelling and inversion grids, have an optimal parallel strategy that fully exploits the inherent independence of CSEM data sets while also possessing the capability to handle large-scale geo-electrical models, and incorporate conductivity anisotropy which should be a common characteristic in realistic subsurface environments. However, existing tools in the geo-electromagnetic community often fall short of these three demands. Addressing this gap, our study introduces a scalable and parallel anisotropic inversion technique for CSEM data, capitalizing on the potential of unstructured tetrahedral grids. We first apply the tetrahedral longest-edge bisection method to create a refined dense, heterogeneous forward modelling grid from a coarse inversion grid. This refinement, focused on areas around transmitters and receivers, is seamlessly integrated within the coarser inversion grid’s topology, enabling precise conductivity mapping and preserving electromagnetic response accuracy during model updates. We further innovate with a source-mesh double-level parallel strategy, utilizing the message passing interface technique for parallel handling of independent CSEM data sets and large-scale geo-electrical models. Externally, we dedicate a processor for inversion model updates employing the Limited-memory Broyden–Fletcher–Goldfarb–Shanno optimization algorithm and divide other processors into groups, each associated with specific transmitting sources and frequencies. Internally, in each group, we employ a domain-decomposition-based scalable and robust iterative solvers using the Auxiliary-Space Maxwell pre-conditioner to parallel quickly calculate the electromagnetic responses from its assigned source-frequency set. Additionally, recognizing the potential for electrical conductivity anisotropy in field data, we incorporate the case of vertical transverse isotropy. We validate the effectiveness of our method through examples, including an isotropic land model with undulating topography, an anisotropic marine model and a real-field data case. Results from both synthetic and field data inversions underscore our method’s significant advancements in efficiency and practicality, particularly in addressing large-scale 3-D CSEM data sets inversion challenges in realistic geological environments.

Metroticket approach in a retrospective cohort study to predict overall survival after surgical resection for intermediate stage hepatocellular carcinoma.

Surgical resection is a curative therapy for early-stage hepatocellular carcinoma (HCC) patients meeting the Milan criteria as well as a widely used therapy in intermediate-stage HCC. However, intermediate-stage HCC encompasses a wide spectrum of disease and there is a lack of good predictive models for the long-term clinical outcome of HCC patients currently. Here, we adopt Mazzaferro's Metroticket 2.0 to create a robust survival prediction model for intermediate-stage HCC patients undergoing surgical resection. Our algorithm considers age, AFP levels, ALBI score, and nodule size/number to generate survival estimates in an accessible graph format. Importantly, our model surpasses the American Joint Committee on Cancer staging model and was validated with independent US patient data. We conducted a retrospective analysis of OS and RFS in early- and intermediate-stage HCC patients treated with liver resection, including a training cohort in Singapore and a validation cohort in North Carolina, USA. We recorded 278 deaths (35.0%) and 428 patients (53.9%) in the first 5-years after surgical resection; higher ALBI score, higher lnAFP, more advanced age and higher tumour burden index were identified as significant parameters. The overall predictive capability of our model, with the inclusion of AFP, is reflected with a UNO's C-statistic of 0.655, which is 1.11 times better than the 0.5895 C-statistic of the 8th AJCC TNM Staging model. Our modified Metroticket model allows for more granular and better-informed prognostication. This will help surgeons and patients make accurate comparisons between the clinical outcomes of surgical resection and other non-surgical treatments.