Clumppling: cluster matching and permutation program with integer linear programming.

MOTIVATION: In the mixed-membership unsupervised clustering analyses commonly used in population genetics, multiple replicate data analyses can differ in their clustering solutions. Combinatorial algorithms assist in aligning clustering outputs from multiple replicates so that clustering solutions can be interpreted and combined across replicates. Although several algorithms have been introduced, challenges exist in achieving optimal alignments and performing alignments in reasonable computation time. RESULTS: We present Clumppling, a method for aligning replicate solutions in mixed-membership unsupervised clustering. The method uses integer linear programming for finding optimal alignments, embedding the cluster alignment problem in standard combinatorial optimization frameworks. In example analyses, we find that it achieves solutions with preferred values of a desired objective function relative to those achieved by Pong and that it proceeds with less computation time than Clumpak. It is also the first method to permit alignments across replicates with multiple arbitrary values of the number of clusters K. AVAILABILITY AND IMPLEMENTATION: Clumppling is available at https://github.com/PopGenClustering/Clumppling.

When is the allele-sharing dissimilarity between two populations exceeded by the allele-sharing dissimilarity of a population with itself?

Allele-sharing statistics for a genetic locus measure the dissimilarity between two populations as a mean of the dissimilarity between random pairs of individuals, one from each population. Owing to within-population variation in genotype, allele-sharing dissimilarities can have the property that they have a nonzero value when computed between a population and itself. We consider the mathematical properties of allele-sharing dissimilarities in a pair of populations, treating the allele frequencies in the two populations parametrically. Examining two formulations of allele-sharing dissimilarity, we obtain the distributions of within-population and between-population dissimilarities for pairs of individuals. We then mathematically explore the scenarios in which, for certain allele-frequency distributions, the within-population dissimilarity - the mean dissimilarity between randomly chosen members of a population - can exceed the dissimilarity between two populations. Such scenarios assist in explaining observations in population-genetic data that members of a population can be empirically more genetically dissimilar from each other on average than they are from members of another population. For a population pair, however, the mathematical analysis finds that at least one of the two populations always possesses smaller within-population dissimilarity than the value of the between-population dissimilarity. We illustrate the mathematical results with an application to human population-genetic data.

Hypoxia-Preconditioned Bone Marrow Mesenchymal Stem Cells Improved Cerebral Collateral Circulation and Stroke Outcome in Mice.

BACKGROUND: Adequate collateral circulation can remarkably improve patient prognoses for patients experiencing ischemic stroke. Hypoxic preconditioning enhances the regenerative properties of bone marrow mesenchymal stem cells (BMSCs). Rabep2 (RAB GTPase binding effector protein 2) is a key protein in collateral remodeling. We investigated whether BMSCs and hypoxia-preconditioned BMSCs (H-BMSCs) augment collateral circulation poststroke, particularly through Rabep2 regulation. METHODS: BMSCs or H-BMSCs (1×106) were delivered intranasally in ischemic mice with distal middle cerebral artery occlusion at 6 hours poststroke. Two-photon microscopic imaging and vessel painting methods were used to analyze collateral remodeling. Blood flow, vascular density, infarct volume, and gait analysis were assessed to evaluate poststroke outcomes. Expressions of proangiogenic marker VEGF (vascular endothelial growth factor) and Rabep2 were determined by Western blotting. Western blot, EdU (5-ethynyl-2'-deoxyuridine) incorporation, and tube formation assays were conducted on cultured endothelial cells treated with BMSCs. RESULTS: BMSCs were more effectively transplanted in the ischemic brain after hypoxic preconditioning. The ipsilateral collateral diameter was increased by BMSCs and strengthened by H-BMSCs (P<0.05). BMSCs increased peri-infarct blood flow and vascular density and reduced infarct volume, gait deficits (P<0.05), and furthermore by H-BMSCs (P<0.05). VEGF and Rabep2 protein expression was increased by BMSCs (P<0.05), which was enhanced by preconditioning (P<0.01). Additionally, BMSCs increased Rabep2 expression, proliferation, and tube formation of endothelial cells in vitro (P<0.05). H-BMSCs enhanced these effects (P<0.05), which were annulled by Rabep2 knockdown. CONCLUSIONS: BMSCs increased collateral circulation and improved poststroke outcomes, through the upregulation of Rabep2. These effects were enhanced by hypoxic preconditioning.

Effects of cultural transmission of surnaming decisions on the sex ratio at birth.

The patriarchal tradition of surnaming a child after its father in Han Chinese families may contribute to their preference for sons, a major cause of the abnormally high SRB (sex ratio at birth) in China. This high SRB can subsequently contribute to the marriage squeeze on males of marriageable age. Encouraging matrilineal surnaming has been proposed as a strategy that could potentially reduce son preference and help to adjust the imbalance in SRB. Here, we model factors that are likely to influence surnaming decisions, including cultural transmission of parents' surnaming decisions, the cultural value of a daughter, reward given to matrilineal surnaming, and awareness of current imbalance in SRB. Mathematical and computational analyses suggest that offering a significant reward and raising public awareness of the problems inherent in an excess of marriage-age males may overcome the son preference and reduce the male-biased SRB.

Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model.

BACKGROUND: Identification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid machine learning method for predicting futile recanalisation. METHODS: Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management workflow. These models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following EVT. The optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation prediction. RESULTS: Using a hybrid feature selection approach, we trained and tested multiple classifiers on two independent patient cohorts (n=1122) to develop a hybrid machine learning-based prediction model. The model demonstrated superior discriminative ability compared with other models and scoring systems (area under the curve=0.80, 95% CI 0.73 to 0.87) and was transformed into a web application (RESCUE-FR Index) that provides a risk stratification system for individual prediction (accessible online at fr-index.biomind.cn/RESCUE-FR/). CONCLUSIONS: The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

A Dirichlet model of alignment cost in mixed-membership unsupervised clustering.

Mixed-membership unsupervised clustering is widely used to extract informative patterns from data in many application areas. For a shared data set, the stochasticity and unsupervised nature of clustering algorithms can cause difficulties in comparing clustering results produced by different algorithms, or even multiple runs of the same algorithm, as outcomes can differ owing to permutation of the cluster labels or genuine differences in clustering results. Here, with a focus on inference of individual genetic ancestry in population-genetic studies, we study the cost of misalignment of mixed-membership unsupervised clustering replicates under a theoretical model of cluster memberships. Using Dirichlet distributions to model membership coefficient vectors, we provide theoretical results quantifying the alignment cost as a function of the Dirichlet parameters and the Hamming permutation difference between replicates. For fixed Dirichlet parameters, the alignment cost is seen to increase with the Hamming distance between permutations. Data sets with low variance across individuals of membership coefficients for specific clusters generally produce high misalignment costs-so that a single optimal permutation has far lower cost than suboptimal permutations. Higher variability in data, as represented by greater variance of membership coefficients, generally results in alignment costs that are similar between the optimal permutation and suboptimal permutations. We demonstrate the application of the theoretical results to data simulated under the Dirichlet model, as well as to membership estimates from inference of human-genetic ancestry. The results can contribute to improving cluster alignment algorithms that seek to find optimal permutations of replicates.

Effect of left ventricular ejection fraction Spectrum on 1-Year mortality in patients with acute ischemic stroke or transient ischemic attack.

AIMS: We aimed to investigate the association of the left ventricular ejection fraction (LVEF) spectrum with 1-year clinical outcomes in patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA). METHODS: In a prospective registry for the Third China National Stroke Registry (CNSR-III), AIS or TIA patients with echocardiography records during hospitalization were recruited. All LVEFs were categorized into intervals of 5% in width. The lowest and highest intervals are ≤40% and >70%, respectively. The primary outcome was all-cause death at 1 year. Cox proportional hazards regression analysis was performed to investigate the association between baseline LVEF and clinical outcomes. RESULTS: This analysis included a total of 14,053 patients. In total, 418 patients died during 1-year follow-up. Overall, LVEF ≤60% was associated with a higher risk of all-cause death compared to LVEF >60%, independent of demographic and clinical characteristics (aHR 1.29 [95% CI 1.06-1.58]; p = 0.01). The cumulative incidence of all-cause death was significantly different among the eight LVEF groups that survival declined successively with the decrease of LVEF (log-rank p ≤ 0.0001). CONCLUSIONS: Patients with AIS or TIA with decreased LVEF (≤60%) had a lower 1-year survival rate after onset. LVEF 50%-60% even within the normal range, may still contribute to poor outcomes in AIS or TIA. Comprehensive evaluation of cardiac function after acute ischemic cerebrovascular disease should be strengthened.

Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity.

Malignant pleural mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Through a large series of whole-genome sequencing data, integrated with transcriptomic and epigenomic data using multiomics factor analysis, we demonstrate that the current World Health Organization classification only accounts for up to 10% of interpatient molecular differences. Instead, the MESOMICS project paves the way for a morphomolecular classification of MPM based on four dimensions: ploidy, tumor cell morphology, adaptive immune response and CpG island methylator profile. We show that these four dimensions are complementary, capture major interpatient molecular differences and are delimited by extreme phenotypes that-in the case of the interdependent tumor cell morphology and adapted immune response-reflect tumor specialization. These findings unearth the interplay between MPM functional biology and its genomic history, and provide insights into the variations observed in the clinical behavior of patients with MPM.

Extracting hierarchical features of cultural variation using network-based clustering.

High-dimensional datasets on cultural characters contribute to uncovering insights about factors that influence cultural evolution. Because cultural variation in part reflects descent processes with a hierarchical structure - including the descent of populations and vertical transmission of cultural traits - methods designed for hierarchically structured data have potential to find applications in the analysis of cultural variation. We adapt a network-based hierarchical clustering method for use in analysing cultural variation. Given a set of entities, the method constructs a similarity network, hierarchically depicting community structure among them. We illustrate the approach using four datasets: pronunciation variation in the US mid-Atlantic region, folklore variation in worldwide cultures, phonemic variation across worldwide languages and temporal variation in first names in the US. In these examples, the method provides insights into processes that affect cultural variation, uncovering geographic and other influences on observed patterns and cultural characters that make important contributions to them.

Impact of Early Rehabilitation on Outcomes in Patients With Acute Ischemic Stroke After Endovascular Treatment.

Background: This study aims to examine the effects of early rehabilitation on functional outcomes in patients with acute ischemic stroke treated with endovascular treatment (EVT). Methods: Eligible patients with large vessel occlusion stroke treated with EVT, who received early rehabilitation or standard care treatment during hospitalization, were enrolled in a multicenter registration, prospective observational study, a registration study for Critical Care of Acute Ischemic Stroke After Recanalization. Early rehabilitation was defined as rehabilitation interventions initiated within 1 week after acute stroke. The primary outcome was the favorable functional outcome (defined as modified Rankin Scale scores of 0 to 2) at 90 days. Independent association between early rehabilitation and the primary outcome was investigated using multivariable logistic regression in the entire sample and in subgroups. Results: A total of 1,126 patients (enrolled from July 2018 to May 2019) were included in the analyses, 273 (24.2%) in the early rehabilitation group and 853 (75.8%) in the standard care group. There was no significant difference in favorable functional outcomes at 90 days between the two groups (45.4 vs. 42.6%, p = 0.41). Patients in the early rehabilitation group had a lower death rate within 90 days compared with the standard care group (6.2 vs. 20.5%, p < 0.01). The multivariable logistic regression analyses showed that the early rehabilitation was not significantly associated with the favorable functional outcome at 90 days (adjusted odds ratio, 1.01 [95% CI, 0.70-1.47]; p = 0.95). There was no significant difference between subgroups in the favorable functional outcome at 90 days. No significant interaction was found between subgroups. Conclusions: Patients with stroke receiving early rehabilitation had a lower death rate. However, these clinically meaningful effects of early rehabilitation did not show on functional outcome at 90 days in patients with large vessel occlusion stroke treated with EVT. Registration: URL: http://www.chictr.org.cn; Unique identifier: ChiCTR1900022154.

Increased serum uric acid level is associated with better outcome after endovascular treatment for acute ischemic stroke-a prospective cohort study.

Background: The role of serum uric acid (SUA) in affecting outcomes after endovascular treatment (EVT) in patients with ischemic stroke remains unclear. This study investigated the association of SUA with outcomes of patients with acute large vessel occlusion (LVO) who had received EVT. Methods: Patients with acute LVO stroke who underwent EVT within 24 hours were enrolled from a prospective, nationwide registry study. Baseline characteristics and SUA level within 24 hours of EVT were collected. The primary outcome was an excellent 90-day functional outcome [modified Rankin Scale (mRS) score 0-1]. Secondary outcomes included a favorable 90-day outcome (mRS score 0-2), symptomatic intracranial hemorrhage (sICH), and 90-day mortality. The SUA level was analyzed in quartiles and as a continuous variable. We investigated the independent association of SUA with the primary outcome using multivariable logistic regression. Results: Among 780 patients (mean age 64 years; 66.28% males), 230 (29.49%) had an excellent 90-day outcome. A higher SUA level was significantly associated with an excellent outcome in univariate logistic regression (P=0.045) and after adjusting for confounders in multivariate analysis [adjusted odds ratio (aOR), 0.998; 95% confidence interval (CI), 0.996-1.000; P=0.018]. Multivariate logistic regression analysis showed patients with SUA level in the fourth quartile had an excellent 90-day outcome (aOR, 0.367; 95% CI, 0.154-0.876; P=0.024). There was no significant association for SUA level with favorable 90-day outcome, sICH, or 90-day mortality (P>0.05). Conclusions: Among patients with acute LVO type of stroke who received EVT, baseline high SUA level may predict a better 90-day functional outcome.

Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy.

The SARS-CoV-2 pandemic has differentially impacted populations across race and ethnicity. A multi-omic approach represents a powerful tool to examine risk across multi-ancestry genomes. We leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from nasopharyngeal swabs of 1049 individuals (736 SARS-CoV-2 positive and 313 SARS-CoV-2 negative) and integrate them with digital phenotypes from electronic health records from a diverse catchment area in Northern California. Genome-wide association disaggregated by admixture mapping reveals novel COVID-19-severity-associated regions containing previously reported markers of neurologic, pulmonary and viral disease susceptibility. Phylodynamic tracking of consensus viral genomes reveals no association with disease severity or inferred ancestry. Summary data from multiomic investigation reveals metagenomic and HLA associations with severe COVID-19. The wealth of data available from residual nasopharyngeal swabs in combination with clinical data abstracted automatically at scale highlights a powerful strategy for pandemic tracking, and reveals distinct epidemiologic, genetic, and biological associations for those at the highest risk.

[Application of three-pedicle reduction mammaplasty in breast cancer patients with moderate or greater breast hypertrophy and/or moderate-to-severe breast ptosis].

OBJECTIVE: To explore the application and effectiveness of three-pedicle reduction mammoplasty in breast cancer patients with moderate or greater breast hypertrophy and/or moderate-to-severe breast ptosis. METHODS: The clinical data of 15 breast cancer female patients with hypertrophy and/or moderate-to-severe breast ptosis treated by three-pedicle reduction mammaplasty with inverted T incision between January 2019 and March 2021 were retrospectively analysed. The patients were aged 31-58 years, with a median age of 39 years. The disease duration ranged from 10 days to 9 months (median, 3.4 months). All patients had unifocal tumor, with a maximum diameter of primary tumor of 0.5-3.9 cm (mean, 2.0 cm), of which 12 were diagnosed with invasive carcinoma and 3 carcinoma in situ. Tumor stage: T isN 0M 0 in 3 cases, T 1N 0M 0 in 4 cases, T 1N 2M 0 in 2 cases, T 2N 0M 0 in 4 cases, and T 2N 1M 0 in 2 cases. The preoperative cup sizes of patients were D cup in 3 cases, DD cup in 1 case, E cup in 2 cases, EE cup in 2 cases, F cup in 2 cases, FF cup in 1 case, and ≥G cup in 4 cases. The distance from nipple to inframammary fold was 8-18 cm (mean, 12.2 cm) before operation. The patients were followed up regularly after operation to evaluate the breast reduction effect and complications; Breast cancer reporting outcome scale (BREAST-Q) was used to assess patients' satisfaction and quality of life; and ultrasound, chest and abdominal CT, whole-body bone scan were performed to assess local tumor recurrence or distant metastasis. RESULTS: The postoperative nipple position was slightly higher than inframammary fold in all patients. Postoperative cup sizes were A cup in 3 cases, B cup in 6, C cup in 4, D cup in 1, and DD cup in 1, which showing significant difference when compared with preoperative cup sizes ( Z=3.420, P=0.001). The median follow-up time was 9 months (range, 6-33 months). Postoperatively, 2 cases (13.3%) had wound-site cellulitis, 1 (6.7%) had mild fat liquefaction, 2 (13.3%) had nipple and areola hypoesthesia but recovered after 3 months. No complication such as fat necrosis, papillary areola complex, or flap necrosis occurred. All patients had undergone adjuvant radiotherapy, of which 1 (6.7%) showed mild skin color change after radiotherapy, but no radiotherapy-related complication occurred in all patients. No patient was readmitted, received reoperation, or delayed to adjuvant therapy due to complications. In the BREAST-Q score, breast satisfaction and quality of life scores at 3 and 6 months after operation were significantly better than those before operation and at 1 month after operation ( P<0.05); no significant difference was found between at 1 month after operation and before operation ( P>0.05). Nipple satisfaction scores at 1, 3, and 6 months after operation were 15.6±2.2, 18.5±1.4, 19.3±0.7, respectively. At discharge after operation, the patient's satisfaction with the outcome of the operation was scored 84.7±11.4. The score of adverse events of radiotherapy at 6 months after operation was 6.5±0.8. During the follow-up, patient had no local recurrence, distant metastasis, or breast cancer related death. CONCLUSION: For breast cancer patients with moderate or greater breast hypertrophy and/or moderate-to-severe breast ptosis, three-pedicle reduction mammoplasty can not only remove the lesions, but also reduce hypertrophic breasts, accomplish the mammoplasty, reduce the radiotherapy complications, and improve the satisfaction and quality of life of patients.

Mortality Prediction in Cerebral Hemorrhage Patients Using Machine Learning Algorithms in Intensive Care Units.

Objectives: This study aims to investigate whether the machine learning algorithms could provide an optimal early mortality prediction method compared with other scoring systems for patients with cerebral hemorrhage in intensive care units in clinical practice. Methods: Between 2008 and 2012, from Intensive Care III (MIMIC-III) database, all cerebral hemorrhage patients monitored with the MetaVision system and admitted to intensive care units were enrolled in this study. The calibration, discrimination, and risk classification of predicted hospital mortality based on machine learning algorithms were assessed. The primary outcome was hospital mortality. Model performance was assessed with accuracy and receiver operating characteristic curve analysis. Results: Of 760 cerebral hemorrhage patients enrolled from MIMIC database [mean age, 68.2 years (SD, ±15.5)], 383 (50.4%) patients died in hospital, and 377 (49.6%) patients survived. The area under the receiver operating characteristic curve (AUC) of six machine learning algorithms was 0.600 (nearest neighbors), 0.617 (decision tree), 0.655 (neural net), 0.671(AdaBoost), 0.819 (random forest), and 0.725 (gcForest). The AUC was 0.423 for Acute Physiology and Chronic Health Evaluation II score. The random forest had the highest specificity and accuracy, as well as the greatest AUC, showing the best ability to predict in-hospital mortality. Conclusions: Compared with conventional scoring system and the other five machine learning algorithms in this study, random forest algorithm had better performance in predicting in-hospital mortality for cerebral hemorrhage patients in intensive care units, and thus further research should be conducted on random forest algorithm.

Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology.

PURPOSE: Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted to examine GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation remains an unmet need in the clinical management of GBMs. EXPERIMENTAL DESIGN: We employ a novel diffusion histology imaging (DHI) approach, combining diffusion basis spectrum imaging (DBSI) and machine learning, to detect, differentiate, and quantify areas of high cellularity, tumor necrosis, and tumor infiltration in GBM. RESULTS: Gadolinium-enhanced T1-weighted or hyperintense fluid-attenuated inversion recovery failed to reflect the morphologic complexity underlying tumor in patients with GBM. Contrary to the conventional wisdom that apparent diffusion coefficient (ADC) negatively correlates with increased tumor cellularity, we demonstrate disagreement between ADC and histologically confirmed tumor cellularity in GBM specimens, whereas DBSI-derived restricted isotropic diffusion fraction positively correlated with tumor cellularity in the same specimens. By incorporating DBSI metrics as classifiers for a supervised machine learning algorithm, we accurately predicted high tumor cellularity, tumor necrosis, and tumor infiltration with 87.5%, 89.0%, and 93.4% accuracy, respectively. CONCLUSIONS: Our results suggest that DHI could serve as a favorable alternative to current neuroimaging techniques in guiding biopsy or surgery as well as monitoring therapeutic response in the treatment of GBM.