Combined Analysis Research Articles

Fragmented perspective of self-organized criticality and disorder in log gravity

We use a statistical model to discuss nonequilibrium fragmentation phenomena taking place in the stochastic dynamics of the log sector in log gravity. From the canonical Gibbs model, a combinatorial analysis reveals an important aspect of the n-particle evolution previously shown to generate a collection of random partitions according to the Ewens distribution realized in a disconnected double Hurwitz number in genus zero. By treating each possible partition as a member of an ensemble of fragmentations, and ensemble averaging over all partitions with the Hurwitz number as a special case of the Gibbs distribution, a resulting distribution of cluster sizes appears to fall as a power of the size of the cluster. Dynamical systems that exhibit a distribution of sizes giving rise to a scale-invariant power-law behavior at a critical point possess an important property called self-organized criticality. As a corollary, the log sector of log gravity is a self-organized critical system at the critical point μl = 1. A similarity between self-organized critical systems, spin glass models and the dynamics of the log sector which exhibits aging behavior reminiscent of glassy systems is pointed out by means of the Pòlya distribution, also known to classify various models of (randomly fragmented) disordered systems, and by presenting the cluster distribution in the log sector of log gravity as a distinguished member of this probability distribution. We bring arguments from a probabilistic perspective to discuss the disorder in log gravity, largely anticipated through the conjectured AdS3/LCFT2 correspondence.

GENERALIZED PASCAL’S PYRAMIDS AND COMBINATORIAL INFORMATION RETRIEVAL PROBLEMS

The work studies combinatorial objects of pyramidal structure. It proposes fundamental relations for generalizations of Pascal’s triangle and pyramid. It considers problems of finding new ways of constructing information relevance indices and offers a way of constructing such an index with the help of Pascal’s generalized pyramids. Using structures of a type of Pascal’s generalized pyramids with weight factors allow construction of an index that reflects a degree of relevance of keywords, as well as makes it possible to allocate subsets of keywords with specific properties. The offered methods of the combinatorial analysis of hierarchical structures in information retrieval problems can be used in creating and analyzing knowledge databases.

Supersolvable posets and fiber-type abelian arrangements

We present a combinatorial analysis of fiber bundles of generalized configuration spaces on connected abelian Lie groups. These bundles are akin to those of Fadell–Neuwirth for configuration spaces, and their existence is detected by a combinatorial property of an associated finite partially ordered set. This is consistent with Terao’s fibration theorem connecting bundles of hyperplane arrangements to Stanley’s lattice supersolvability. We obtain a combinatorially determined class of K(π,1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K(\\pi ,1)$$\\end{document} toric and elliptic arrangements. Under a stronger combinatorial condition, we prove a factorization of the Poincaré polynomial when the Lie group is noncompact. In the case of toric arrangements, this provides an analogue of Falk–Randell’s formula relating the Poincaré polynomial to the lower central series of the fundamental group.

Valved Microwell Array Platforms for Stepwise Liquid Dispensing.

The fabrication of microarray chips and the precise dispensing of nanoliter to microliter liquids are fundamental for high-throughput parallel biochemical testing. Conventional microwells, typically featuring a uniform cross section, fill completely in a single operation, complicating the introduction of multiple reagents for stepwise and combinatorial analyses. To overcome this limitation, we developed an innovative valved microwell array. Using ultraviolet (UV)-curing resin three-dimensional (3D) printing, these multilayer configurations can be rapidly fabricated through direct template printing and polydimethylsiloxane (PDMS) casting. Each microwell incorporates a microvalve structure, truncating fluids within the upper metering well and allowing transfer to the bottom reservoir well under centrifugal force. Sequential operations enable the introduction of multiple reagents, facilitating orthogonal combinations for complex assays. We explored four types of valving methods: DeepWell, Expansion, Bottleneck, and Membrane valve, each offering varying degrees of design complexity, operational efficiency, robustness, and precision. These methods constitute a versatile toolkit to accommodate a broad spectrum of analytical requirements. Our innovative approach redefines microwell architecture, direct manufacturing techniques, and stepwise fluid dispensation in microarrays.

Sexual somatic dimorphism in connection with ecological factors

Introduction. Analysis of correlations of sexual somatic dimorphism (SD) with the degree of urbanization (quantity of population) and geographic latitude of the residence place is carried out using the material of monitoring of Russian children and adolescents. Material and methods. The study embraces the wide specter of ethno-territorial samples of children and adolescents from Russia and neighboring countries (literary data), examined through the historic interval 1930-2010 years. To estimate the direction and degree of associations of anthropometric traits (height, weight, chest girth) and ecological factors the classic correlation analysis for pairwise combinations of variables was implemented for age groups of 9-year-old children and 13-year-old adolescents. The quantitative estimation of SD was carried out using Kullback divergence, the analogue of Makhalanobis distance. Results. Significant correlations of SD of height with both ecological factors for 13-year-old adolescents and SD of weight for 9-year-old children with the quantity of population were fixed on base of the whole data massive. The more homogeneous data base (only Slavonic groups examined in 1960s-1970s) shows significant correlations of latitude with SD of height for 13-year-old adolescents only in combination with the quantity of population of the residence place up to 500 thousands of people; and latitude with SD of weight for 9-year-old children only for million-plus cities. Significant correlations of quantity of population were fixed for SD of height of 13-year-old adolescents in more southern ranges of latitude (37-50degrees), for SD of weight for 9-year-old children in more northern ranges of latitude (50 degrees and more). Conclusion. The study shows complex superposition of ecological factors, which influence somatic variability of children through growth process. High degree of urbanization conceal anthropoecological correlations SD-latitude for ecosensitive age 13 years, but reveal them for “neutral” age, 9 years, with the opposite vector. The most southern range of latitudes (about 40 degrees) reveal positive associations of SD with the degree of urbanization for 13-year-olds, which are not fixed in more northern latitudes. More frequent anthropoecological correlations of SD of height, as genetic marker, of 13-year-olds, and SD of weight as the marker of nutrition status and life style of 9-year-olds reflect the physiological essence of age periods - increase of intersex differences for adolescents and decrease of intersex differences for second childhood children

Predicting genome-wide tissue-specific enhancers via combinatorial transcription factor genomic occupancy analysis.

Enhancers are non-coding cis-regulatory elements crucial for transcriptional regulation. Mutations in enhancers can disrupt gene regulation, leading to disease phenotypes. Identifying enhancers and their tissue-specific activity is challenging due to their lack of stereotyped sequences. This study presents a sequence-based computational model that uses combinatorial transcription factor (TF) genomic occupancy to predict tissue-specific enhancers. Trained on diverse datasets, including ENCODE and Vista enhancer browser data, the model predicted 25 000 forebrain-specific cis-regulatory modules (CRMs) in the human genome. Validation using biochemical features, disease-associated SNPs, and in vivo zebrafish analysis confirmed its effectiveness. This model aids in predicting enhancers lacking well-characterized chromatin features, complementing experimental approaches in tissue-specific enhancer discovery.

B-122 Improving the Diagnostic Utility of Multiplexed Assays in Laboratory Medicine with Machine Learning

Abstract Background The application of machine learning (ML) and artificial intelligence (AI) in precision medicine is of great interest and continues to grow. ML approaches offer a flexible and data-centric methodology for modeling diagnostic and prognostic trends, capable of identifying complex non-linear patterns and relationships between predictor variables and outcomes within large data sets. Although ML holds great promise, there are several common pitfalls that must be avoided for ML models to provide clinical utility. In the current study, we provide a rigorous and systematic evaluation of the utility of ML algorithms to predict disease in several representative data sets generated via multiplexed biomarker assays. We compare five major classes of ML algorithms compared to more traditional combinatorial analysis (logistic regression) to offer perspective against the current gold standard. Methods We obtained five data sets from published literature that include measurements of multiplexed biomarkers in patients with obstructive sleep apnea, sepsis, and breast and colon cancers in comparison to healthy controls. These datasets were generated by bead-based and planar multiplexed immunoassays. We chose representative ML algorithms from the classes of support vector machines (SVM), random forests (RF), neural networks (NNet), and extreme gradient boosting (xgBoost) in comparison to logistic regression (LR). Datasets were randomly split into internal validation and external validation sets via 10 iterations of permutation analysis. We tuned and validated the models using 5-fold cross validation, then tested those on external subsets. We report the average of several model classification metrics and associated variances across all resampled data sets. Results For the classification of sepsis using inflammatory cytokines measured on the Luminex bead-based immunoassay, RF outperformed LR in external validation sets by a 9.8% increase in AUROC. For classification of breast cancer patients, xgBoost and RF performed best with 20.0% and 16.9% increases in AUROC, respectively, whereas for classification of colon cancer patients, NNet outperformed LR by 11.5%. Across all datasets, on average, all ML algorithms outperformed LR in the internal validation sets by a 7.9% (6.3% - 9.9%) increase in AUROC. However, in external validation sets, only xgBoost and SVM outperformed LR, where RF and NNet showed signs of overfitting. Conclusions Overall, certain ML algorithms show improvement over LR for diagnostic applications using multiplexed assays, by up to 20% increase in AUROC. Interpretation of ML results must be performed rigorously to prevent overly optimistic and unrealistic conclusions. In the context of laboratory medicine, our results showcase the utility of ML in clinical applications, while highlighting potential disadvantages of ML. This analysis underscores the need for establishing rigorous guidelines to support data analytics during the development of novel multiplexed assays, which is needed to advance precision medicine.

Simultaneous Determination of Levo-tetrahydropalmatine and Naltrexone in Rat Plasma by LC-MS/MS and its Application in a Pharmacokinetic Study.

Levo-tetrahydropalmatine and low-dose naltrexone are used in association with reducing cocaine-related cravings, but there are no analytical methods for the quantitative simultaneous analysis of this drug combination. A highly selective and sensitive LC-MS/MS assay was developed and validated to simultaneously quantify l-THP and naltrexone. The analytical method for l-THP offers improved sensitivity compared to previously published methods. The product ion transitions of l-THP and naltrexone were 357.0→193.0 and 342.2→324.1, respectively. Chromatographic separations were performed using a BEH-C18 column by an isocratic elution mode with acetonitrile and 0.1% formic acid in water containing 3 mM ammonium acetate. L-THP and naltrexone were extracted from rat plasma using a liquidliquid extraction method. For l-THP and naltrexone, the assay displayed good linear response over a concentration range of 0.5-1000 ng/mL and 0.25-500 ng/mL, respectively. The intra-day accuracy of the method for l-THP and naltrexone was 93.8-101% with a precision (%CV) of 2.43-8.15% and 93.4-108% with a precision of 3.47-8.22%. The inter-day accuracy for l-THP and naltrexone was 91.2-102% with a CV of 2.46-8.06% and 91.5-97.8% with a CV of 3.29-8.92%, respectively. The assay has been used for pharmacokinetic studies of l-THP and naltrexone in the rat.

Combinatorial Reliability Analysis of Multicore Processors

Combinatorial Reliability Analysis of Multicore Processors

Assessment of rainfall trend over Periyar Vaigai Command area of Tamil Nadu

Rainfall trend analysis is critical for studying the effects of climate change. The most crucial aspect of understanding climate change at the basin level is, how it will affect water usage, planning, and development. The present study has been attempted to analyze the trend of annual and seasonal rainfall data over Periyar Vaigai Command area from 1982 – 2021 (40 years) employing different trend analysis methods i.e., Mann-Kendall test for trend significance, Sequential Mann-Kendall test for assessment of start and end of trend, Sen’s slope test for trend magnitude, Innovative Trend Analysis for combination of trends and linear regression analysis for trend detection. Statistically positive trend was detected in summer season using all methods whereas, annual and SWM rainfall showed a statistically significant decreasing trend. Winter and NEM rainfall resulted with no significant trend. The results from this study helps to adopt various water management practices and useful for policy makers to prepare appropriate mitigation strategies.

Galectin-3 Predicts Long-Term Risk of Cerebral Disability and Mortality in Out-of-Hospital Cardiac Arrest Survivors.

Out-of-hospital cardiac arrest (OHCA) is associated with high mortality and cerebral disability in survivors. Current models of risk prediction and survival are mainly based on resuscitation duration. We examined the prognostic value of circulating biomarkers in predicting mortality and severe cerebral disability for OHCA survivors, alongside traditional clinical risk indicators. Biomarkers including BNP, troponin I, and galectin-3 were measured at hospital admission in resuscitated OHCA patients. Prognostic significance for mortality and cerebral disability involving circulating biomarkers, resuscitation duration, demographics, and laboratory and clinical characteristics was examined via univariate and multivariate Cox proportional hazards regression models. The incremental prognostic value of the index covariates was examined through model diagnostics, focusing on the Akaike information criterion (AIC) and Harrell's concordance statistic (c-statistic). In a combinatorial analysis of 144 OHCA survivors (median follow-up 5.7 years (IQR 2.9-6.6)), BNP, galectin-3, arterial pH, and resuscitation time were significant predictors of all-cause death and severe cerebral disability, whereas troponin I levels were not. Multivariate regression, adjusting for BNP, arterial pH, and resuscitation time, identified galectin-3 as an independent predictor of long-term mortality. Multiple linear regression models also confirmed galectin-3 as the strongest predictor of cerebral disability. The incorporation of galectin-3 into models for predicting mortality and cerebral disability enhanced fit and discrimination, demonstrating the incremental value of galectin-3 beyond traditional risk predictors. Galectin-3 is a significant, independent long-term risk predictor of cerebral disability and mortality in OHCA survivors. Incorporating galectin-3 into current risk stratification models may enhance early prognostication and guide targeted clinical interventions.

CMAA–AHP: combinatorial multicriteria acceptability analysis with the analytic hierarchy process

AbstractCombinatorial multi-criteria acceptability analysis (CMAA) is a framework for supporting multicriteria group decisions that provides both a detailed analysis of the effects of individual decision-maker inputs as well as interactive guidance for a consensus-building process. The analytical hierarchy process (AHP) is a widely-used model of decision-maker evaluations that is based on pairwise comparisons. The goal of this work is to show how CMAA can be integrated with AHP in order to make its benefits available to AHP users. We use a minimal input format for AHP which avoids a problem with inconsistency and also reduces the cognitive load on the decision-makers. We extend the CMAA method by introducing new judgement and preference sensitivity variables, which provide helpful insights for the facilitator of the group decision. An example illustrates the combined CMAA–AHP method and its ability to deliver consensus in a very small number of iterations. Monte Carlo simulation is used to study the convergence behavior of the method for a range of problem dimensions. It was found that the mean number of steps to reach consensus grows linearly with the number of alternatives and criteria. We consider two previously published group decisions that use the standard AHP approach of averaging decision-maker judgements and preferences. In both cases, CMAA–AHP delivers the same rankings based on the original input. However, the new method also provides insight into each decision and would have been able to guide each group to consensus within a small number of resolution steps.

Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.

Purpose To determine whether time-dependent deep learning models can outperform single time point models in predicting preoperative upgrade of ductal carcinoma in situ (DCIS) to invasive malignancy at dynamic contrast-enhanced (DCE) breast MRI without a lesion segmentation prerequisite. Materials and Methods In this exploratory study, 154 cases of biopsy-proven DCIS (25 upgraded at surgery and 129 not upgraded) were selected consecutively from a retrospective cohort of preoperative DCE MRI in women with a mean age of 59 years at time of diagnosis from 2012 to 2022. Binary classification was implemented with convolutional neural network (CNN)-long short-term memory (LSTM) architectures benchmarked against traditional CNNs without manual segmentation of the lesions. Combinatorial performance analysis of ResNet50 versus VGG16-based models was performed with each contrast phase. Binary classification area under the receiver operating characteristic curve (AUC) was reported. Results VGG16-based models consistently provided better holdout test AUCs than did ResNet50 in CNN and CNN-LSTM studies (multiphase test AUC, 0.67 vs 0.59, respectively, for CNN models [P = .04] and 0.73 vs 0.62 for CNN-LSTM models [P = .008]). The time-dependent model (CNN-LSTM) provided a better multiphase test AUC over single time point (CNN) models (0.73 vs 0.67; P = .04). Conclusion Compared with single time point architectures, sequential deep learning algorithms using preoperative DCE MRI improved prediction of DCIS lesions upgraded to invasive malignancy without the need for lesion segmentation. Keywords: MRI, Dynamic Contrast-enhanced, Breast, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.

Decomposer faecal food web and C sequestration in soil. Can near infrared spectroscopy describe transfers and transformations from fresh organic inputs to protected forms in soil aggregates?

This study sought to characterize the transfer of agricultural organic inputs by macrofauna to the biogenic aggregate compartments of the soil and test the existence of a faecal food web process based on coprophagic feeding behaviours. In an experimental plantain crop field of Colombia, we applied the agroecological FBO (Fertilisation Bio Organique ®) technology, a nucleation technique which consists in planting perennial plants in 1.0 x 0.4 × 0.4 m deep trenches where low- and high-quality organic materials are added in a specific design, and endogeic earthworms are inoculated. We evaluated the effectiveness of Near Infrared Spectroscopy (NIRS) to track this flow of organic matter through different natural stages of decomposition represented by soil macroaggregates produced by soil macroinvertebrates as faecal pellets and casts. After one year, great part of the organic inputs had been either mineralized or incorporated into macroaggregates of different sizes and origins. Using NIRS and physicochemical laboratory analyses, we assessed the quality and quantity of organic matter in the different types of aggregates separated manually according to their origin: large biogenic aggregates (LBA), medium sized biogenic aggregates (MBA) and small biogenic aggregates (SBA); physical aggregates (PA), root aggregates (RA) and residual soil (RS).Earlier studies had shown that these structures were presumably formed by the activity of macroinvertebrates of three different functional groups, Diplopoda and Isopoda litter transformers for SBA, epidendogeic earthworms for MBA and mesohumic endogeic earthworms for LBA, organized in a feeding succession.A Coinertia analysis between showed significant covariation among the two sets of physico chemical (22 variables) and NIR spectral (100 different wavelength absorbances) characterization of 135 samples representative of the three classes of biogenic macroaggregates, physical aggregates and residual non macroaggregated soil, (RV = 0.55; p < 0.001). This analysis clearly separated biogenic structures and ranked them according to their size, from small SBA to medium sized MBA and large LBA. Physical PA aggregates and RS residual soil were projected close to the LBAs in the coinertia factorial plan. Multiple combinatorial data analysis CDA, associated 5 specific wavelength absorbance ranges with aggregate types and residual soil. Along the sequence from small to large biogenic aggregates, residual soil and physical aggregates, wavelengths associated to easily decomposed substrates (in ranges 1708–1716 nm; 1796–1948 and 2164–2316 nm) had progressively decreasing absorbances. Substrates associated to slowly decomposing aromatic, alkane and phenolic substrates either increased (1420–1436) or decreased (1284–1380) along this sequence.These results are compatible with the hypothesis of a progressive transformation and transfer of organic residues first into small biogenic aggregates that are presumably faecal pellets of Isopoda and Diplopoda, then to medium sized biogenic aggregates identified as casts of epiendogeic earthworms and finally to large biogenic aggregates expected to be the casts of the large endogeic earthworms present in these soils. A definitive verification of the hypothesis and the involvement of specific macroinvertebrates will require direct experimentation in controlled laboratory conditions.

Proteome and metabolomic profile of Mongolian horse follicular fluid during follicle development

During follicular development, changes in the composition of the follicular fluid are synchronized with the development of oocytes. Our aim was to screen the key factors affecting oocyte maturation and optimize the in vitro culture protocol by understanding the changes of proteins and metabolites in follicular fluid. Follicles are divided into three groups according to their diameter (small follicle fluid (SFF): 10 mm < d < 20 mm; medium follicle fluid (MFF): 20 mm < d < 30 mm; large follicle fluid (LFF): 30 mm < d). Proteins and metabolites from the follicular fluid were analyzed by mass spectrometry. The results showed that: in LFF vs MFF, 20 differential abundant protein (DAP) and 88 differential abundant metabolites (DAM) were screened out; In SFF vs MFF, 3 DAPs and 65 DAMs were screened out; In MFF vs SFF, 24 DAPs and 35 DAMs were screened out. The analysis of differential proteins and metabolites showed that glycerophosphate hydrolysis decreased during follicular development, and proteins played a major role in metabolism and binding. In addition, DAMs and DAPs are co-enriched in the “linoleic acid metabolism” pathway. Combinatorial analysis reveals the dynamic profile of follicular fluid during follicular development and provides fundation for further exploring the function of follicular fluid in Mongolian horse.

Somatic Mutation Profile as a Predictor of Treatment Response and Survival in Unresectable Pancreatic Ductal Adenocarcinoma Treated with FOLFIRINOX and Gemcitabine Nab-Paclitaxel.

(1) Background: Pancreatic ductal adenocarcinoma (PDAC) has low survival rates despite treatment advancements. Aim: This study aims to show how molecular profiling could possibly guide personalized treatment strategies, which may help improve survival outcomes in patients with PDAC. (2) Materials and Methods: A retrospective analysis of 142 PDAC patients from a single academic center was conducted. Patients underwent chemotherapy and next-generation sequencing for molecular profiling. Key oncogenic pathways were identified using the Reactome pathway database. Survival analysis was performed using Kaplan-Meier curves and Cox Proportional Hazards Regression. (3) Results: Patients mainly received FOLFIRINOX (n = 62) or gemcitabine nab-paclitaxel (n = 62) as initial chemotherapy. The median OS was 13.6 months. Longer median OS was noted in patients with NOTCH (15 vs. 12.3 months, p = 0.007) and KIT pathway mutations (21.3 vs. 12.12 months, p = 0.04). Combinatorial pathway analysis indicated potential synergistic effects on survival. In the PFS, PI3K pathway (6.6 vs. 5.7 months, p = 0.03) and KIT pathway (10.3 vs. 6.2 months, p = 0.03) mutations correlated with improved PFS within the gemcitabine nab-paclitaxel subgroup. (4) Conclusions: Molecular profiling could play a role in PDAC for predicting outcomes and responses to therapies like FOLFIRINOX and gemcitabine nab-paclitaxel. Integrating genomic data into clinical decision-making can benefit PDAC treatment, though further validation is needed to fully utilize precision oncology in PDAC management.

The Biological Function of POLA2 in Hepatocellular Carcinoma.

The role and prognostic value of POLA2 in liver cancer were comprehensively analyzed through TCGA, GEO, and ICGC databases, and the role of POLA2 in liver cancer cells and the regulatory mechanism involved were further verified through cell experiments. Hepatocellular carcinoma (HCC) is the most prevalent malignancy with high morbidity and mortality. Consequently, it is critical to identify robust and reliable predictive biomarkers and therapeutic targets for HCC patients. POLA2 is involved in the regulation of various tumors, but the specific role of POLA2 in HCC has not been reported. The regulatory role and prognostic value of POLA2 in HCC were determined by bioinformatics techniques and cell experiments. The specific role and prognostic value of POLA2 in HCC were comprehensively analyzed by combining the expression data of POLA2 in TCGA, GEO, and ICGC databases and clinical data. In clinical samples, the expression of POLA2 in liver cancer was verified by QPCR. Further, the regulatory role of POLA2 in HCC was explored through cell experiments such as CCK-8, clonal formation experiment, EDU cell proliferation experiment, and flow cytometry. In terms of mechanism exploration, western blot was used to verify the specific regulatory mechanism that POLA2 participated in. Finally, the relationship between POLA2 and immune invasion of HCC was analyzed by using the TIMER database. A POLA2 expression and prognosis analysis of HCC patients was conducted using the TCGA, GEO, and ICGC databases. We hypothesized that POLA2 might be one of the key factors contributing to the HCC progression. According to a combined analysis of TCGA, ICGC, and GEO databases, POLA2 was highly expressed in HCC. This was further confirmed in clinical samples using the qPCR. POLA2 knockdown was also performed in vitro on HCC cell lines to study the changes in their biological behavior. We confirmed that POLA2 was associated with HCC proliferation by CCK-8, Colony Formation, and EDU assay. We verified the POLA2's involvement in cell cycle regulation using flow techniques. The relationship between POLA2 and PI3K/AKT/mTOR pathway was explored using Western Blotting experiments regarding its mechanism. Further analysis revealed that the POLA2 expression was significantly associated with HCC immune infiltration. Our study demonstrated POLA2's importance in HCC development and progression and its potential role as a biomarker for disease progression on multiple levels. POLA2 has an important role in regulating the cell cycle and cell proliferation. By interfering with the cell cycle and proliferation, HCC cell growth is inhibited. Furthermore, POLA2 expression was significantly associated with immune infiltration. POLA2 may play a role in HCC immunotherapy based on its correlation with several immune cell types' genetic markers. The findings of this study are expected to lead to new anticancer strategies for HCC.

On the Combinatorial Acceptability Entropy Consensus Metric for Multi-Criteria Group Decisions

AbstractIn group decisions, achieving consensus is important, because it increases commitment to the result. For cooperative groups, Combinatorial Multicriteria Acceptability Analysis (CMAA) is a group decision framework that can achieve consensus efficiently. It is based on a novel Combinatorial Acceptability Entropy (CAE) consensus metric. As an output measure, the CAE metric is unique in its ability to identify the evaluations that have the greatest impact on consensus and to prevent premature consensus. This paper is intended to complement the original CMAA publication by providing additional insights into the CAE consensus metric. The design requirements for the CAE algorithm are presented, and it is shown how these requirements follow from the properties of cooperative decisions. The CAE-based consensus-building algorithm is contrasted both qualitatively and quantitatively with a representative example of the conventional input distance and input averaging approach to multi-criteria consensus-building. A simulation experiment illustrates the ability of the CAE-based algorithm to converge quickly to the correct decision as defined for cooperative decisions. The metric is able to meet a new, more stringent definition of hard consensus. The CAE approach highlights the need to distinguish between competitive and cooperative group decisions. Attention in the literature has been paid almost exclusively to the former type; the CAE approach demonstrates the greater efficiency and effectiveness that can be achieved with an approach that is designed specifically for the latter.

Insights into reduction of hysteresis in (Mn, Fe)2(P, Si) compounds by experimental approach and Landau theory

The giant magnetocaloric effect in (Mn, Fe)2(P, Si) based alloys arises from a magnetoelastic ferromagnetic-paramagnetic (FM-PM) phase transition accompanied by a large thermal hysteresis. The thermal hysteresis leads to an undesirable irreversibility of the magnetocaloric effect (MCE) during cyclic operation, impeding the practical application in solid-state magnetic refrigeration. Here, we present pre-existing PM nuclei play a role in reducing hysteresis. A combinatorial analysis, using in situ X-ray diffraction (XRD) and magneto-optical Kerr effect (MOKE) microscopy, reveals that the residual PM phases at the ferromagnetic state of the compound acts as the nuclei for the growth of the PM phases. This is kinetically favorable for the FM-PM phase transition and contributes to a smaller hysteresis from an extrinsic perspective. In addition, the smaller changes in the lattice constants during the phase transition indicates a weakened first-order phase transition. Through a combined effect of intrinsic and extrinsic contributions, a giant MCE in the magnetic entropy change of 16 J kg–1 K−1 under 2 T and a low thermal hysteresis of 3.0 K were achieved in a basic Mn–Fe–Si–P quaternary system for room temperature applications. Furthermore, based on Landau theory and experimentally obtained data, we established an H-T phase diagram and unveiled the crucial role of the magnetoelastic coupling in manipulating thermal hysteresis. Overall, the findings in this work offer a strategy to mitigate hysteresis while retaining a large MCE through extrinsic control.

Injury incidence according to athlete impairment type during the 2012 and 2016 Summer Paralympic Games: a combined analysis of 101 108 athlete days

ObjectivesThe relationship between sport-related injuries and Para athlete impairment type has not yet been comprehensively studied. This study aimed to describe injury incidence according to athlete impairment type during the...