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Consciousness is something we experience in our everyday life, more especially between the time we wake up in the morning and go to sleep at night, but also during the rapid eye movement (REM) sleep stage. Disorders of consciousness (DoC) are states in which a person's consciousness is damaged, possibly after a traumatic brain injury. Completely locked-in syndrome (CLIS) patients, on the other hand, display covert states of consciousness. Although they appear unconscious, their cognitive functions are mostly intact. Only, they cannot externally display it due to their quadriplegia and inability to speak. Determining these patients' states constitutes a challenging task. The ultimate goal of the approach presented in this paper is to assess these CLIS patients consciousness states. EEG data from DoC patients are used here first, under the assumption that if the proposed approach is able to accurately assess their consciousness states, it will assuredly do so on CLIS patients too. This method combines different sets of features consisting of spectral, complexity and connectivity measures in order to increase the probability of correctly estimating their consciousness levels. The obtained results showed that the proposed approach was able to correctly estimate several DoC patients' consciousness levels. This estimation is intended as a step prior attempting to communicate with them, in order to maximise the efficiency of brain–computer interfaces (BCI)-based communication systems. [ABSTRACT FROM AUTHOR]

Completely locked-in (CLIS) patients are characterized by sufficiently intact cognitive functions, but a complete paralysis that prevents them to interact with their surroundings. On one hand, studies have shown that the ability to communicate plays an important part in these patients' quality of life and prognosis. On the other hand, brain-computer interfaces (BCIs) provide a means for them to communicate using their brain signals. However, one major problem for such patients is the difficulty to determine if they are conscious or not at a specific time. This work aims to combine different sets of features consisting of spectral, complexity and connectivity measures, to increase the probability of correctly estimating CLIS patients' consciousness levels. The proposed approach was tested on data from one CLIS patient, which is particular in the sense that the experimenter was able to point out one time frame Δ t during which he was undoubtedly conscious. Results showed that the method presented in this paper was able to detect increases and decreases of the patient's consciousness levels. More specifically, increases were observed during this Δ t , corroborating the assertion of the experimenter reporting that the patient was definitely conscious then. Assessing the patients' consciousness is intended as a step prior attempting to communicate with them, in order to maximize the efficiency of BCI-based communication systems. [ABSTRACT FROM AUTHOR]

Background: Single-cell RNA-Sequencing (scRNA-Seq) has provided single-cell level insights into complex biological processes. However, the high frequency of gene expression detection failures in scRNA-Seq data make it challenging to achieve reliable identification of cell-types and Differentially Expressed Genes (DEG). Moreover, with the explosive growth of single-cell data using 10x genomics protocol, existing methods will soon reach the computation limit due to scalability issues. The single-cell transcriptomics field desperately need new tools and framework to facilitate large-scale single-cell analysis. Results: In order to improve the accuracy, robustness, and speed of scRNA-Seq data processing, we propose a generalized zero-inflated negative binomial mixture model, "JOINT," that can perform probability-based cell-type discovery and DEG analysis simultaneously without the need for imputation. JOINT performs soft-clustering for cell-type identification by computing the probability of individual cells, i.e. each cell can belong to multiple cell types with different probabilities. This is drastically different from existing hard-clustering methods where each cell can only belong to one cell type. The soft-clustering component of the algorithm significantly facilitates the accuracy and robustness of single-cell analysis, especially when the scRNA-Seq datasets are noisy and contain a large number of dropout events. Moreover, JOINT is able to determine the optimal number of cell-types automatically rather than specifying it empirically. The proposed model is an unsupervised learning problem which is solved by using the Expectation and Maximization (EM) algorithm. The EM algorithm is implemented using the TensorFlow deep learning framework, dramatically accelerating the speed for data analysis through parallel GPU computing. Conclusions: Taken together, the JOINT algorithm is accurate and efficient for large-scale scRNA-Seq data analysis via parallel computing. The Python package that we have developed can be readily applied to aid future advances in parallel computing-based single-cell algorithms and research in various biological and biomedical fields. [ABSTRACT FROM AUTHOR]

Local multiple kernel learning is a promising strategy because it could learn a sample-specific composite kernel according to the characteristic of samples. However, these methods are insufficient to describe the sample diversity and correlation, which leads to the classifier less reliable. In this paper, we propose a soft-clustering-based local multiple kernel learning algorithm to tackle the issues above. In the proposed algorithm, there is a fuzzy clustering preprocessing for the training data and then the kernel weights are calculated on the groups. We use an alternative optimization method to learn the kernel weights and support vector coefficients. The final combination weights of kernels are determined by the kernel weights of clusters and the probability of samples falling into the clusters. Therefore, our method is actually a sample-based LMKL method with a soft constraint on the kernel weights. This constraint is actually the representation of the correlation of samples. The experiments on synthetic dataset indicate the kernel weights solved by our algorithm are better suitable for the characteristics of the dataset. Then a series of experiments verify the improvement on classification accuracies. [ABSTRACT FROM AUTHOR]

We propose two probability-like measures of individual cluster-membership certainty that can be applied to a hard partition of the sample such as that obtained from the partitioning around medoids (PAM) algorithm, hierarchical clustering or k-means clustering. One measure extends the individual silhouette widths and the other is obtained directly from the pairwise dissimilarities in the sample. Unlike the classic silhouette, however, the measures behave like probabilities and can be used to investigate an individual's tendency to belong to a cluster. We also suggest two possible ways to evaluate the hard partition using these measures. We evaluate the performance of both measures in individuals with ambiguous cluster membership, using simulated binary datasets that have been partitioned by the PAM algorithm or continuous datasets that have been partitioned by hierarchical clustering and k-means clustering. For comparison, we also present results from soft-clustering algorithms such as soft analysis clustering (FANNY) and two model-based clustering methods. Our proposed measures perform comparably to the posterior probability estimators from either FANNY or the model-based clustering methods. We also illustrate the proposed measures by applying them to Fisher's classic dataset on irises. [ABSTRACT FROM AUTHOR]

Highlights • A novel data-dependent kernel method based on soft-grouping is proposed. • The diversity and correlation of samples are both taken into consideration. • The learned composite kernel for each sample is more suitable for its character. Graphical abstract Abstract Multiple kernel learning strategy has emerged as a powerful tool because it can easily combine information from multiple data sources. However, learning an optimal kernel is still a challenging work and need to be further researched. In this paper, we propose a data-dependent multiple kernel learning algorithm based on soft-grouping (SC-DMKL). The core ideas of the SC-DMKL are twofold: (1) we take a soft-group process on the training samples to accommodate the correlation and the diversity of the samples; (2) alternatively optimize the kernel weights and the classifier to adaptively learn a data-dependent composite kernel. The final composite kernel is determined by the probability of samples falling to the groups and the kernel weights of these groups. Therefore, our method is actually a sample-specific MKL method with a soft restriction on the kernel weights. This restriction is actually the representation of the correlation of samples. The experiments on the synthetic dataset indicate that the kernel weights solved by our algorithm are more suitable for the characteristics of the datasets and the experiments on the real world datasets verify that the classification accuracies are improved. [ABSTRACT FROM AUTHOR]

The architecture of eukaryotic coding genes allows the production of several different protein isoforms by genes. Current gene phylogeny reconstruction methods make use of a single protein product per gene, ignoring information on alternative protein isoforms. These methods often lead to inaccurate gene tree reconstructions that require to be corrected before phylogenetic analyses. Here, we propose a new approach for the reconstruction of gene trees and protein trees accounting for alternative protein isoforms. We extend the concept of reconciliation to protein trees, and we define a new reconciliation problem called MinDRGT that consists in finding a gene tree that minimizes a double reconciliation cost with a given protein tree and a given species tree. We define a second problem called MinDRPGT that consists in finding a protein supertree and a gene tree minimizing a double reconciliation cost, given a species tree and a set of protein subtrees. We propose a shift from the traditional view of protein ortholog groups as hard-clusters to soft-clusters and we study the MinDRPGT problem under this assumption. We provide algorithmic exact and heuristic solutions for versions of the problems, and we present the results of applications on protein and gene trees from the Ensembl database. The implementations of the methods are available at and . [ABSTRACT FROM AUTHOR]

Ovulation is an integral part of women's menstrual cycle and fertility. Understanding the mechanisms of ovulation has broad implications for the treatment of anovulatory diseases and the development of novel contraceptives. Now, few studies have developed effective models that both faithfully recapitulate the hallmarks of ovulation and possess scalability. We established a three-dimensional encapsulated in vitro follicle growth (eIVFG) system that recapitulates folliculogenesis and produces follicles that undergo ovulation in a controlled manner. Here, we determined whether ex vivo ovulation preserves molecular signatures of ovulation and demonstrated its use in discovering novel ovulatory pathways and nonhormonal contraceptive candidates through a high-throughput ovulation screening. Mature murine follicles from eIVFG were induced to ovulate ex vivo using human chorionic gonadotropin and collected at 0, 1, 4, and 8 hours post-induction. Phenotypic analyses confirmed key ovulatory events, including cumulus expansion, oocyte maturation, follicle rupture, and luteinization. Single-follicle RNA-sequencing analysis revealed the preservation of ovulatory genes and dynamic transcriptomic profiles and signaling. Soft clustering identified distinct gene expression patterns and new pathways that may critically regulate ovulation. We further used this ex vivo ovulation system to screen 21 compounds targeting established and newly identified ovulatory pathways. We discovered that proprotein convertases activate gelatinases to sustain follicle rupture and do not regulate luteinization and progesterone secretion. Together, our ex vivo ovulation system preserves molecular signatures of ovulation, presenting a new powerful tool for studying ovulation and anovulatory diseases as well as for establishing a high-throughput ovulation screening to identify novel nonhormonal contraceptives for women., (© The Author(s) 2023. Published by Oxford University Press on behalf of Society for the Study of Reproduction. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Completely locked-in (CLIS) patients are characterized by sufficiently intact cognitive functions, but a complete paralysis that prevents them to interact with their surroundings. On one hand, studies have shown that the ability to communicate plays an important part in these patients' quality of life and prognosis. On the other hand, brain-computer interfaces (BCIs) provide a means for them to communicate using their brain signals. However, one major problem for such patients is the difficulty to determine if they are conscious or not at a specific time. This work aims to combine different sets of features consisting of spectral, complexity and connectivity measures, to increase the probability of correctly estimating CLIS patients' consciousness levels. The proposed approach was tested on data from one CLIS patient, which is particular in the sense that the experimenter was able to point out one time frame Δt during which he was undoubtedly conscious. Results showed that the method presented in this paper was able to detect increases and decreases of the patient's consciousness levels. More specifically, increases were observed during this Δt, corroborating the assertion of the experimenter reporting that the patient was definitely conscious then. Assessing the patients' consciousness is intended as a step prior attempting to communicate with them, in order to maximize the efficiency of BCI-based communication systems.

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments., Competing Interests: The views expressed in this article are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. M.I.C. has served on advisory panels for Pfizer, Novo Nordisk, and Zoe Global; has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly; and has received research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, M.I.C. is an employee of Genentech and a holder of Roche stock. S.B. is holder of stock in Intomics, Hoba Therapeutics, Novo Nordisk, and Lundbeck and holds managing board memberships in Proscion and Intomics., (© 2021 The Authors.)