Discriminative feature of cells characterizes cell populations of interest by a small subset of genes

Organisms are composed of various cell types with specific states. To obtain a comprehensive understanding of the functions of organs and tissues, cell types have been classified and defined by identifying specific marker genes. Statistical tests are critical for identifying marker genes, which often involve evaluating differences in the mean expression levels of genes. Differentially expressed gene (DEG)-based analysis has been the most frequently used method of this kind. However, in association with increases in sample size such as in single-cell analysis, DEG-based analysis has faced difficulties associated with the inflation of P-values. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using DEG-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for discriminating a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data and that DFC enabled characterization of the muscle satellite/progenitor cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement DEG-based methods for interpreting large data sets. DEG-based analysis uses lists of genes with differences in expression between groups, while DFC, which can be termed a discriminative approach, has potential applications in the task of cell characterization. Upon recent advances in the high-throughput analysis of single cells, methods of cell characterization such as scRNA-seq can be effectively subjected to the discriminative methods.

How children learn to communicate discriminatively

How do children learn to communicate, and what do they learn? Traditionally, most theories have taken an associative, compositional approach to these questions, supposing children acquire an inventory of form-meaning associations, and procedures for composing / decomposing them; into / from messages in production and comprehension. This paper presents an alternative account of human communication and its acquisition based on the systematic, discriminative approach embodied in psychological and computational models of learning, and formally described by communication theory. It describes how discriminative learning theory offers an alternative perspective on the way that systems of semantic cues are conditioned onto communicative codes, while information theory provides a very different view of the nature of the codes themselves. It shows how the distributional properties of languages satisfy the communicative requirements described in information theory, enabling language learners to align their expectations despite the vastly different levels of experience among language users, and to master communication systems far more abstract than linguistic intuitions traditionally assume. Topics reviewed include morphological development, the acquisition of verb argument structures, and the functions of linguistic systems that have proven to be stumbling blocks for compositional theories: grammatical gender and personal names.

Diversity Management and Organizational Socialization

In the current study, it is aimed to determine the effect of diversity management on organizational socialization in tourism enterprises. For this purpose, a questionnaire was conducted with 215 employees who work in hotel enterprises with tourism operation certificate. Regression and correlation analysis were performed using the data obtained from the survey. Analysis results reveal that the diversity management practices in hotel enterprises positively affect the dimensions of organizational socialization, and organizational socialization dimensions are significantly explained by the diversity management dimensions. In addition, according to the correlation analysis results, there is a high positive correlation between diversity management and organizational socialization. In this context, the positive management of employee differences within the organization and non-discriminative approach positively affects the relationships of the employees with other and can increase their level of organizational socialization through job adaptation.

A Two Consequent Multi-layers Deep Discriminative Approach for Classifying fMRI Images

Functional Magnetic Resonance Imaging (fMRI), for many decades acts as a potential aiding method for diagnosing medical problems. Several successful machine learning algorithms have been proposed in literature to extract valuable knowledge from fMRI. One of these algorithms is the convolutional neural network (CNN) that competent with high capabilities for learning optimal abstractions of fMRI. This is because the CNN learns features similarly to human brain where it preserves local structure and avoids distortion of the global feature space. Focusing on the achievements of using the CNN for the fMRI, and accordingly, the Deep Convolutional Auto-Encoder (DCAE) benefits from the data-driven approach with CNN’s optimal features to strengthen the fMRI classification. In this paper, a new two consequent multi-layers DCAE deep discriminative approach for classifying fMRI Images is proposed. The first DCAE is unsupervised sub-model that is composed of four CNN. It focuses on learning weights to utilize discriminative characteristics of the extracted features for robust reconstruction of fMRI with lower dimensional considering tiny details and refining by its deep multiple layers. Then the second DCAE is a supervised sub-model that focuses on training labels to reach an outperformed results. The proposed approach proved its effectiveness and improved literately reported results on a large brain disorder fMRI dataset.

Advancing diagnostics of chronic paradontitis in children

Chronic periodontitis in children and adolescents holds a lead place in morbidity pattern of dental pathology. Development of chronic periodontitis is accompanied by emergence of various complications in the maxillofacial region, leading to bite disturbance being of high relevance for pediatric patients. These and other complications are related to immune system immaturity in children and adolescents as well as virulence of microorganisms. However, the immunological changes developing in children with chronic periodontitis remain poorly studied. The aim of the work was to improve diagnostics of chronic periodontitis in children and adolescents based on informative parameters of systemic immunity and discriminative models taking into account such changes. We examined systemic immunity in 127 children and adolescents with chronic periodontitis, aged 12 to 16 years, by using flow cytometry and enzyme immunoassay. In control group, age-matched 108 patients lacking overt somatic and dental pathology were enrolled. Generation of mathematical models was carried out by using a discriminative approach, whereas informativeness was assessed in accordance with generally accepted formula. Relative and absolute count of peripheral blood CD13+ cells exert the peak informativeness holding the first and second ranking places with marked dominance of informativeness value for assessing relative amount of CD13+ cells are among immunological parameters in children with chronic periodontitis. High informativeness value evidencing about pronounced intensity of developed pathological changes and diagnostic significance for chronic periodontitis in children is intrinsic to the relative percentage of peripheral blood CD8+ cells being slightly lower than that one in absolute count of CD13+ cells. On the other hand, humoral immune-parameters were of lowest informative value among all analyzed immunological parameters in patients with chronic periodontitis serum referring to all antibody classes. Generated discriminative models for the most valuable immunological parameters ensure adequate medical diagnostics for chronic periodontitis in childhood. Diagnostic sensitivity for created mathematical models was high and reached 0.94, whereas diagnostic specificity — 0.92. Immunological examination of patients improves diagnostics of chronic periodontitis. It was found that patients with chronic periodontitis had lowered immune status peaking in decreased absolute and relative count of peripheral blood CD3+ lymphocytes. Finally, parameters of humoral immunity in children with chronic periodontitis were also reduced.

Inpainting forgery detection using hybrid generative/discriminative approach based on bounded generalized Gaussian mixture model

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

Study of the Stability Control of Rock Surrounding Longwall Recovery Roadways in Shallow Seams

The rock pressure appearance of longwall faces in shallow seams is generally violent, and roofs and supports are susceptible to damage during equipment extraction. Stability control of the rock surrounding longwall recovery roadways allows safe and rapid equipment extraction. Herein, via theoretical analysis, numerical simulations, and field observations, the stability control of the rock surrounding recovery roadways is studied to ensure the release of the accumulated rock pressure on the roof, the working resistance of the supports and the reasonableness of the recovery roadway support design. Pressure-relief technology is introduced to release the accumulated rock pressure before equipment extraction, and a discriminative approach is proposed to determine the breaking and articulated forms of key strata and broken blocks, respectively. On this basis, mechanical models of roof instability are established based on four key stratum structures in the overburden of shallow seams. Methods for calculating a reasonable working resistance for supports are discussed. Finally, Liangshuijing Coal Mine and Fengjiata Coal Mine are taken as research objects to evaluate the roof stability of recovery roadways based on observations of weighting characteristics. The support working resistances and reasonable recovery roadway widths under three key stratum structures are determined. Considering the time effect of plastic zone development, the support design of recovery roadways is optimized. FLAC2D software simulates the surrounding rock control effect of two support designs, and roof subsidence curves are obtained. The results show that the key to equipment extraction in shallow seams is to ensure that supports have reasonable working resistances and to improve the support of recovery roadways. The results provide a reference for the selection and extraction of supports in shallow seam faces.

Unsupervised deep learning on seismic data to detect volcanic unrest

<p>The significant efforts of the last years in new monitoring techniques and networks have led to large datasets and improved our capabilities to measure volcano conditions.  Thus nowadays the challenge is to retrieve information from this huge amount of data to significantly improve our capability to automatically recognize signs of potentially hazardous unrest.<br>Unrest detection from unlabeled data is a particularly challenging task, since the lack of annotations on the temporal localization of these phenomena makes it impossible to train a machine learning model in a supervised way. The proposed approach, therefore, aims at learning unsupervised low-dimensional representations of the input signal during normal volcanic activity by training a variational autoencoder (VAE) to compress, reconstruct and synthesize input signals. Thanks to the internal structure of the proposed VAE architecture, with 1-dimensional convolutional layers with residual blocks and attention mechanism, the representation learned by the model can be employed to detect deviations from normal volcanic activity. In our experiments, we test and evaluate two techniques for unrest detection: a generative approach, with a bank of synthetic signals used to assess the degree of correspondence between normal activity and an input signal; and a discriminative approach, employing unsupervised clustering in the VAE representation space to identify prototypes of normal activity for comparison with an input signal.</p>

A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding

Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical framework for inferring the posterior distribution of the current state given all measurements up to the present time. For example, the Apollo lunar module implemented a Kalman filter to infer its location from a sequence of earth-based radar measurements and land safely on the moon. To perform Bayesian filtering, we require a measurement model that describes the conditional distribution of each observation given state. The Kalman filter takes this measurement model to be linear, Gaussian. Here we show how a nonlinear, Gaussian approximation to the distribution of state given observation can be used in conjunction with Bayes’ rule to build a nonlinear, non-Gaussian measurement model. The resulting approach, called the Discriminative Kalman Filter (DKF), retains fast closed-form updates for the posterior. We argue there are many cases where the distribution of state given measurement is better-approximated as Gaussian, especially when the dimensionality of measurements far exceeds that of states and the Bernstein—von Mises theorem applies. Online neural decoding for brain-computer interfaces provides a motivating example, where filtering incorporates increasingly detailed measurements of neural activity to provide users control over external devices. Within the BrainGate2 clinical trial, the DKF successfully enabled three volunteers with quadriplegia to control an on-screen cursor in real-time using mental imagery alone. Participant “T9” used the DKF to type out messages on a tablet PC. Nonstationarities, or changes to the statistical relationship between states and measurements that occur after model training, pose a significant challenge to effective filtering. In brain-computer interfaces, one common type of nonstationarity results from wonkiness or dropout of a single neuron. We show how a robust measurement model can be used within the DKF framework to effectively ignore large changes in the behavior of a single neuron. At BrainGate2, a successful online human neural decoding experiment validated this approach against the commonly-used Kalman filter.