Recognition Pattern Research Articles

Engineering γδ T Cells: Recognizing and Activating on Their Own Way

Adoptive cell therapy (ACT) with engineered T cells has emerged as a promising strategy for the treatment of malignant tumors. Among them, there is great interest in engineered γδ T cells for ACT. With both adaptive and innate immune characteristics, γδ T cells can be activated by γδ TCRs to recognize antigens in a MHC-independent manner, or by NK receptors to recognize stress-induced molecules. The dual recognition system enables γδ T cells with unique activation and cytotoxicity profiles, which should be considered for the design of engineered γδ T cells. However, the current designs of engineered γδ T cells mostly follow the strategies that used in αβ T cells, but not making good use of the specific characteristics of γδ T cells. Therefore, it is no surprising that current engineered γδ T cells in preclinical or clinical trials have limited efficacy. In this review, we summarized the patterns of antigen recognition of γδ T cells and the features of signaling pathways for the functions of γδ T cells. This review will additionally discuss current progress in engineered γδ T cells and provide insights in the design of engineered γδ T cells based on their specific characteristics.

A Deep Learning-Based Facial Acne Classification System.

IntroductionAcne is one of the most common pathologies and affects people of all ages, genders, and ethnicities. The assessment of the type and severity status of a patient with acne should be done by a dermatologist, but the ever-increasing waiting time for an examination makes the therapy not accessible as quickly and consequently less effective. This work, born from the collaboration with CHOLLEY, a Swiss company with decades of experience in the research and production of skin care products, with the aim of developing a deep learning system that, using images produced with a mobile device, could make assessments and be as effective as a dermatologist.MethodsThere are two main challenges within this task. The first is to have enough data to train a neural model. Unlike other works in the literature, it was decided not to collect a proprietary dataset, but rather to exploit the enormity of public data available in the world of face analysis. Part of Flickr-Faces-HQ (FFHQ) was re-annotated by a CHOLLEY dermatologist, producing a dataset that is sufficiently large, but still very extendable. The second challenge was to simultaneously use high-resolution images to provide the neural network with the best data quality, but at the same time to ensure that the network learned the task correctly. To prevent the network from searching for recognition patterns in some uninteresting regions of the image, a semantic segmentation model was trained to distinguish, what is a skin region possibly affected by acne and what is background and can be discarded.ResultsFiltering the re-annotated dataset through the semantic segmentation model, the trained classification model achieved a final average f1 score of 60.84% in distinguishing between acne affected and unaffected faces, result that, if compared to other techniques proposed in the literature, can be considered as state-of-the-art.

Recognition of pareidolic objects in developmental prosopagnosic and neurotypical individuals

Developmental prosopagnosia (DP) is a neurodevelopmental disorder associated with difficulties in the perception and recognition of faces. However, the extent to which DP affects non-face object is an ongoing debate. In this study, we asked whether pareidolic objects (which give rise to the perception of a face) are also affected in DP. First, we compared performance in DPs (n = 30) and controls (n = 27) on a recognition task with faces, pareidolic objects and non-pareidolic objects (bottles). The pareidolic objects had either similar or dissimilar image statistics to faces. Consistent with our understanding of DP, we found that the pattern of recognition across items between DPs and controls was lowest for faces. Interestingly, there was also a low correlation between DPs and controls for pareidolic-similar objects that was similar to faces. In contrast, there were higher correlations between DPs and controls for pareidolic-dissimilar objects and bottles, which were both significantly different to faces. These findings suggest that the deficit in DP involves processing image properties that are common to faces. Next, using an individual differences approach across a large group of neurotypical adults (n = 94), we found that face recognition covaried with the recognition of pareidolic-similar objects, but not with pareidolic-dissimilar objects or non-pareidolic objects. Together, these findings support the idea that a representation based on image properties plays an important role in the perception and recognition of objects and faces and that the deficit in the perception of some object categories in DP could be explained by their similarity to the image properties found in faces.

SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes

Single-cell RNA-Sequencing has the potential to provide deep biological insights by revealing complex regulatory interactions across diverse cell phenotypes at single-cell resolution. However, current single-cell gene regulatory network inference methods produce a single regulatory network per input dataset, limiting their capability to uncover complex regulatory relationships across related cell phenotypes. We present SimiC, a single-cell gene regulatory inference framework that overcomes this limitation by jointly inferring distinct, but related, gene regulatory dynamics per phenotype. We show that SimiC uncovers key regulatory dynamics missed by previously proposed methods across a range of systems, both model and non-model alike. In particular, SimiC was able to uncover CAR T cell dynamics after tumor recognition and key regulatory patterns on a regenerating liver, and was able to implicate glial cells in the generation of distinct behavioral states in honeybees. SimiC hence establishes a new approach to quantitating regulatory architectures between distinct cellular phenotypes, with far-reaching implications for systems biology.

Robust local binary pattern for face recognition in different challenges

Robust local binary pattern for face recognition in different challenges

Robust pattern for face recognition using combined Weber and pentagonal-triangle graph structure pattern

Face recognition is a challenging task in computer vision and information retrieval due to the large variation in facial images in terms of pose, expression and illumination. These challenges motivate us to propose a robust pattern for face recognition using combined Weber pattern (CWP) and pentagonal-triangle graph structure pattern (PTGSP). Firstly, abrupt intensity variations in facial images are removed by multiblock operation. Further, by exploring the relationship among center, neighboring and adjacent pixels, CWP consisting of three unique Weber patterns is computed. In order to obtain the pattern that is more robust to facial variations, PTGSP has been computed on the facial images. PTGSP covers the widely used features of face images. A feature set of high dimension is produced by combining the features of CWP and PTGSP. Dimensionality of the proposed feature and also variance within class is reduced through principal component analysis (PCA) plus linear discriminant analysis (LDA) algorithm. The classification performance of the proposed method is compared with various state-of-the-art methods on variety of benchmark face datasets with variation in pose, expression, illumination and occlusion. Experiments on FEI, Georgia, ORL, Yale and Faces94 databases clearly prove the robustness of the proposed method in contrast to existing handcrafted feature extraction techniques.

Immunorecognition and Neutralization of Crotalus durissus cumanensis Venom by a Commercial Antivenom Produced in Colombia.

In Colombia, on average 2.9% of the nearly 5600 snakebite events that occur annually involve the rattlesnake Crotalus durissus cumanensis. The envenomation by this snake is mainly characterized by neurotoxicity and the main toxin is crotoxin (~64.7% of the total venom). The Instituto Nacional de Salud (INS) produces a polyvalent antivenom aimed at the treatment of bothropic, crotalid, and lachesic envenomations; nonetheless, its immune reactivity profile and neutralizing capacity over biological activities of the C. d. cumanensis venom has been poorly evaluated. In this sense, the study aims: (1) to describe an in-depth exploration of its immunoreactivity through second-generation antivenomics and HPLC fraction-specific ELISA immunoprofiles; and (2) to evaluate the neutralization pattern of the rattlesnake venom in vitro and in vivo biological activities. The results obtained showed a variable recognition of crotoxin subunits, in addition to a molecular mass-dependent immunoreactivity pattern in which the disintegrins were not recognized, and snake venom metalloproteinases and L-amino acid oxidases were the most recognized. Additionally, a high neutralization of proteolytic and coagulant activities was observed, but not over the PLA2 activity. Further, the median effective dose against C. d. cumanensis venom lethality was 962 μL of antivenom per mg of venom. In conclusion, (1) the antivenom recognition over the crotoxin and the disintegrins of the C. d. cumanensis should be improved, thus aiming upcoming efforts for the exploration of new techniques and approaches in antivenom production in Colombia, and (2) the neutralization activity of the antivenom seems to follow the molecular mass-dependent recognition pattern, although other explanations should be explored.

Face and Liveliness Detection Based Smart Bank Locker

Face is one of the easiest ways to differentiate each other's identities. Facial recognition is a personal identification system that uses the personal characteristics of someone to identify who you are. The process of recognizing a person's face is basically it consists of two stages, namely facial recognition, in which this process occurs most frequently immediately to humans, except under circumstances where the object is available in a short period of time far, next is an introduction, which recognizes faces as individuals. Stage then it is repeated and developed as a model for face image recognition (face recognition) is one of the most widely studied biometric technologies and developed by experts. There are two types of currently popular methods of advanced facial recognition pattern namely, Eigen face method and Fisher face method. We use fisher face a monitoring method to verify any system. The location of this project is facing image processing system. The software requirements for this project are an anaconda.

Sites of Indeterminacy in Lisa Robertson

AbstractIndeterminacy is a term that has been asked to do a lot of work for literary and cultural criticism. For strictly formalist critics, it might be the quality of a text or artwork that both preserves useful ambiguity and invites participation from respondents. For criticism that challenges the status quos of social and political life, it is often the interpretive condition that can unsettle existing patterns of recognition and judgment. What this essay finds in the work of Lisa Robertson is a more situated notion of indeterminacy, one that relates indeterminacies of speech, form, and affect to the destabilizing effects that postindustrial capitalism can have and has had on cities and their inhabitants. Following a brief overview of indeterminacy as a critical concept, from New Criticism to poststructuralist theory, the essay closely reads Robertson's Occasional Work and Seven Walks from the Office for Soft Architecture (2003) as a multi-genre study in Vancouver becoming “money” and the effects of this process on work characterized as temporary or feminine. Indeterminacy becomes a double bind for the speakers of her “Office,” in that freedoms of expression, feeling, or movement fold into occasions for speculation and displacement. Finally, the essay relates Robertson's indeterminacies to recent debates about the character of avant-garde writing, arguing for a more socially and historically contingent notion of aesthetic experimentation.

Discovery of 3H-pyrrolo[2,3-c]quinolines with activity against Mycobacterium tuberculosis by allosteric inhibition of the glutamate-5-kinase enzyme

The therapeutic potential of 3H-pyrrolo[2,3-c]quinolines—the main core of Marinoquinoline natural products—has been explored for the development of new anti-TB agents. The chemical modification of various positions in this scaffold has led to the discovery of two pyrroloquinolines (compounds 50 and 54) with good in vitro activity against virulent strains of Mycobacterium tuberculosis (H37Rv, MIC = 4.1 μM and 4.2 μM, respectively). Enzymatic assays showed that both derivatives are inhibitors of glutamate-5-kinase (G5K, encoded by proB gene), an essential enzyme for this pathogen involved in the first step of the proline biosynthesis pathway. G5K catalyzes the phosphoryl-transference of the γ-phosphate group of ATP to L-glutamate to provide L-glutamyl-5-phosphate and ADP, and also regulates the synthesis of L-proline. The results of various molecular dynamics simulation studies revealed that the inhibition of G5K would be caused by allosteric interaction of these compounds with the interface between enzyme domains, against different pockets and with distinct recognition patterns. The binding of compound 54 promotes long-distance conformational changes at the L-glutamate binding site that would prevent it from anchoring for catalysis, while compound 50 alters the ATP binding site architecture for recognition. Enzyme assays revealed that compound 50 caused a substancial increase in the Kmapp for ATP, while no significant effect was observed for derivative 54. This work also demonstrates the potential of the G5K enzyme as a biological target for the development of new anti-TB drugs.

Deep Learning Approaches to Image Texture Analysis in Material Processing

Texture analysis is key to better understanding of the relationships between the microstructures of the materials and their properties, as well as the use of models in process systems using raw signals or images as input. Recently, new methods based on transfer learning with deep neural networks have become established as highly competitive approaches to classical texture analysis. In this study, three traditional approaches, based on the use of grey level co-occurrence matrices, local binary patterns and textons are compared with five transfer learning approaches, based on the use of AlexNet, VGG19, ResNet50, GoogLeNet and MobileNetV2. This is done based on two simulated and one real-world case study. In the simulated case studies, material microstructures were simulated with Voronoi graphic representations and in the real-world case study, the appearance of ultrahigh carbon steel is cast as a textural pattern recognition pattern. The ability of random forest models, as well as the convolutional neural networks themselves, to discriminate between different textures with the image features as input was used as the basis for comparison. The texton algorithm performed better than the LBP and GLCM algorithms and similar to the deep learning approaches when these were used directly, without any retraining. Partial or full retraining of the convolutional neural networks yielded considerably better results, with GoogLeNet and MobileNetV2 yielding the best results.

A novel hybrid model for flow image segmentation and bubble pattern extraction

Direct imaging method has been proved to be quite convenient and importance for measuring bubble shape parameters of gas–liquid two-phase flow due to its advantages of non-contact and visualization. The accuracy of flow pattern segmentation and measurement has an importantly impact on flow characteristic. For bubble recognition (pattern segmentation and extraction) with low-quality image, the aim of this paper is to propose a novel hybrid image analysis model (U-net-QR-EMD) combining the U-net algorithm with the quantile regression method (QR) and empirical mode decomposition (EMD). For a direct-contact gas–liquid mixing system, both the synthetic and experimental images of bubble swarm are respectively captured and used to verify the feasibility of the new model. The main findings of our study are as follows. (1) Computing and experimental results show that it is effective in the recognition of bubbles with irregular shape and have a potential in recognizing other bubbles taken under different working conditions. The number of iterations with the proposed model is 90 in the training process, leading to the prediction accuracy with more than 90%. Specifically, it can accurately and reliably identify the individual small and large bubbles in a direct-contact gas–liquid mixing system, but the classical Otsu and K-means methods cannot. (2) Using QR method, it is found that with the increase of gas–liquid mixing time, the number of the identified bubbles decreases but the gas holdup increases. Meanwhile, 90% of the bubble number are between the 5% and 95% quantile regression curves. Both the time series of bubble number and gas holdup tend to be non-stationary and non-linear, but intrinsic mode function (IMF) components and residual obtained by EMD display a higher stability and stronger periodicity. (3) The six parameters (area, perimeter, deformation coefficient, equivalent diameter, aspect ratio and eccentricity) related to bubble patterns can be measured and agree with each other in the evolution process of the mixing state quality with the help of the new model. The proposed model can not only successfully identify the micro-details of small-scale bubbles in low-quality images of gas–liquid two-phase flow, but also improve the ability to obtain low-quality deep processing of other two-phase flow taking advantage of direct imaging method.

Multi-layered meaning formation and linguisticcultural peculiarity of fake messages in the massmedia

The paper investigates the issues and challenges facing the information distortion (the concept of fake news) in the media discourse of Kaznet (Kazakhstani segment of Internet). Particular attention is paid to this phenomenon of the influence and impact of modern “hot media” mechanism and technologies for the perception has been used with the information’s user. The research linguistic ways of distorting information were explored and conducted in line with critical discourse analysis. This article reviews the literature to identify the current state of linguistic methods for creating a fabricated and unreliable hot media product to describe in detail. It is proved with specific examples that fake news carries a certain threat to adequate perception of hot news and recognition patterns when person falls into information captivity and cannot free himself from it because the impact on his consciousness is carried out in a complex manner. A framework of Internet search procedure was used to test the hypothesis. Empirical material presented by posts and comments from open sources of social networks to show and clarify the reasons why a media person becomes so vulnerable, there is a transformation of thinking and traditional values are leveled, which allows communication technology and fake news to destroy his worth system. The authors raise and highlight the need for a person of social net to develop critical thinking to express his own rational and emotional attitude to the described situations and facts on the Internet. The obtained data arouse a certain important in the issue of determining the reliability and application of methods for checking information in the media field of Kaznet for its compliance with reality.

The angry versus happy recognition advantage: the role of emotional and physical properties.

Facial emotional expressions are pivotal for social communication. Their fast and accurate recognition is crucial to promote adaptive responses to social demands, for the development of functional relationships, and for well-being. However, the literature has been inconsistent in showing differentiated recognition patterns for positive vs. negative facial expressions (e.g., happy and angry expressions, respectively), likely due to affective and perceptual factors. Accordingly, the present study explored differences in recognition performance between angry and happy faces, while specifically assessing the role of emotional intensity and global/regional low-level visual features. 98 participants categorized angry and happy faces morphed between neutral and emotional across 9 levels of expression intensity (10-90%). We observed a significantly higher recognition efficiency (higher accuracy and shorter response latencies) for angry compared to happy faces in lower levels of expression intensity, suggesting that our cognitive resources are biased to prioritize the recognition of potentially harmful stimuli, especially when briefly presented at an ambiguous stage of expression. Conversely, an advantage for happy faces was observed from the midpoint of expression intensity, regarding response speed. However, when compensating for the contribution of regional low-level properties of distinct facial key regions, the effect of emotion was maintained only for response accuracy. Altogether, these results shed new light on the processing of facial emotional stimuli, emphasizing the need to consider emotional intensity and regional low-level image properties in emotion recognition analysis.

Electro-Acupuncture Improve the Early Pattern Separation in Alzheimer's Disease Mice via Basal Forebrain-Hippocampus Cholinergic Neural Circuit.

ObjectivesTo explore the effect of electro-acupuncture (EA) treatment on pattern separation and investigate the neural circuit mechanism involved in five familial mutations (5 × FAD) mice.MethodsFive familial mutations mice were treated with EA at Baihui (DU20) and Shenting (DU24) acupoints for 30 min each, lasting for 4 weeks. Cognitive-behavioral tests were performed to evaluate the effects of EA treatment on cognitive functions. 1H-MRS, Nissl staining, immunohistochemistry, and immunofluorescence were performed to examine the cholinergic system alteration. Thioflavin S staining and 6E10 immunofluorescence were performed to detect the amyloid-β (Aβ). Furthermore, hM4Di designer receptors exclusively activated by designer drugs (DREADDs) virus and long-term clozapine-N-oxide injection were used to inhibit the medial septal and vertical limb of the diagonal band and dentate gyrus (MS/VDB-DG) cholinergic neural circuit. Cognitive-behavioral tests and immunofluorescence were performed to investigate the cholinergic neural circuit mechanism of EA treatment improving cognition in 5 × FAD mice.ResultsElectro-acupuncture treatment significantly improved spatial recognition memory and pattern separation impairment, regulated cholinergic system via reduction neuron loss, upregulation of choline/creatine, choline acetyltransferase, vesicular acetylcholine transporter, and downregulation of enzyme acetylcholinesterase in 5 × FAD mice. Aβ deposition was reduced after EA treatment. Subsequently, the monosynaptic hM4Di DREADDs virus tracing and inhibiting strategy showed that EA treatment activates the MS/VDB-DG cholinergic neural circuit to improve the early pattern separation. In addition, EA treatment activates this circuit to upregulating M1 receptors positive cells and promoting hippocampal neurogenesis in the dentate gyrus (DG).ConclusionElectro-acupuncture could improve the early pattern separation impairment by activating the MS/VDB-DG cholinergic neural circuit in 5 × FAD mice, which was related to the regulation of the cholinergic system and the promotion of neurogenesis by EA treatment.

Model of Genetic Code Structure Evolution under Various Types of Codon Reading

The standard genetic code (SGC) is a set of rules according to which 64 codons are assigned to 20 canonical amino acids and stop coding signal. As a consequence, the SGC is redundant because there is a greater number of codons than the number of encoded labels. This redundancy implies the existence of codons that encode the same genetic information. The size and organization of such synonymous codon blocks are important characteristics of the SGC structure whose evolution is still unclear. Therefore, we studied possible evolutionary mechanisms of the codon block structure. We conducted computer simulations assuming that coding systems at early stages of the SGC evolution were sets of ambiguous codon assignments with high entropy. We included three types of reading systems characterized by different inaccuracy and pattern of codon recognition. In contrast to the previous study, we allowed for evolution of the reading systems and their competition. The simulations performed under minimization of translational errors and reduction of coding ambiguity produced the coding system resistant to these errors. The reading system similar to that present in the SGC dominated the others very quickly. The survived system was also characterized by low entropy and possessed properties similar to that in the SGC. Our simulation show that the unambiguous SGC could emerged from a code with a lower level of ambiguity and the number of tRNAs increased during the evolution.

ANEMIA TREATMENT PATTERNS AND OUTCOMES AMONG A NATIONAL COHORT OF PATIENTS WITH INFLAMMATORY BOWEL DISEASE

Abstract BACKGROUND Anemia is a common complication of inflammatory bowel disease (IBD) and can have a significant negative impact on quality of life by causing fatigue and reduced exercise tolerance. However, system wide patterns of anemia recognition and treatment are lacking. The aim of our study is to identify practice patterns of screening for anemia, assessment of iron stores, and administration of iron repletion therapy among a national cohort of patients with IBD. METHODS We conducted a retrospective cohort study of patients with IBD in the national Veterans Affairs (VA) dataset from 2000-2017. IBD patients were identified using a previously validated algorithm using administrative codes for Crohn’s disease (CD) and ulcerative colitis (UC). Laboratory values, patient demographics, and medical treatment data were extracted from the national VA datasets. Anemia was defined as hemoglobin < 13 g/dL in males and < 12g/dL in females. Initial observation of anemia during the study period was considered index anemia date. Analyses were descriptive of proportion of IBD patients with anemia, treatment, or outcomes. Temporal trends were presented as number patients by calendar year. RESULTS Of the 89,687 patients with IBD identified from 2000-2017, 50,781 (56%) were found to have anemia at least once during the study period. The number of IBD patients with anemia increased five-fold during the study period from 3,260 to 15,579 unique patients per calendar year. Testing for iron deficiency among patients with anemia was low- only 36% of anemic patients had iron studies completed within 6 months of anemia result. Of anemic patients who completed iron studies, the vast majority (89%) were consistent with iron deficiency anemia. Of patients with low iron stores, only 23% of patients received iron therapy within 6 months of lab result. Of patients with anemia, only 19% had anemia resolve within 1 year. Hospitalization within 5 years was observed in 30% of patients with anemia, with 60% of hospitalizations occurring within 1 year of index anemia date. IBD-associated surgery within 5 years of index anemia date was observed in only 2% of patients. CONCLUSIONS In a large national cohort of IBD patients, anemia is a common and undertreated complication, and is frequently followed by hospitalization and steroid use. Although iron deficiency is the most common cause of anemia, adequate iron testing and treatment is lacking, which may contribute to the low rates of anemia resolution. Interventions to improve anemia testing and treatment for iron deficiency anemia are needed.

ANEMIA TREATMENT PATTERNS AND OUTCOMES AMONG A NATIONAL COHORT OF PATIENTS WITH INFLAMMATORY BOWEL DISEASE

Anemia is a common complication of inflammatory bowel disease (IBD) and can have a significant negative impact on quality of life by causing fatigue and reduced exercise tolerance. However, system wide patterns of anemia recognition and treatment are lacking. The aim of our study is to identify practice patterns of screening for anemia, assessment of iron stores, and administration of iron repletion therapy among a national cohort of patients with IBD.

Beyond invisibility: Landscapes of intersubjective recognition experienced by cleaners in Australian schools

Dominant approaches to researching cleaners’ experiences of invisibility and recognition have tended to focus on either the structural determinants of invisibility, such as outsourcing or ‘dirty work’ status, or the ways in which workers seek recognition as a strategy for managing taint. This article uses Honneth's concept of intersubjective recognition as a basis for bringing together structure- and agency-oriented approaches through a focus on the ways in which cleaners’ recognition experiences arise from historically specific contexts that are both found and made by cleaners. The article illustrates the usefulness of Honneth's theory through a comparison of the intersubjective recognition experiences of Australian school cleaners working in different work paradigms in two historical periods, public service cleaners in the 1910s–1990s and outsourced cleaners in the 2000s–2020s. The case studies contribute new empirical findings concerning patterns of cleaner recognition across all three dimensions theorised by Honneth – love, rights and solidarity – and the significance of shared horizons of purpose between cleaners and other workplace actors for enabling intersubjective recognition. Theoretically, the paper advances the concept of ‘dense’ and ‘sparse’ intersubjective recognition landscapes as a lens for understanding the changing nature and sources of cleaner invisibility and recognition over time.

Hand Gesture of Recognition Pattern Analysis by Image Treatment Techniques.

The goal of this project is to write a program in the C++ language that can recognize motions made by a subject in front of a camera. To do this, in the first place, a sequence of distance images has been obtained using a depth camera. Later, these images are processed through a series of blocks into which the program has been divided; each of them will yield a numerical or logical result, which will be used later by the following blocks. The blocks into which the program has been divided are three; the first detects the subject's hands, the second detects if there has been movement (and therefore a gesture has been made), and the last detects the type of gesture that has been made accomplished. On the other hand, it intends to present to the reader three unique techniques for acquiring 3D images: stereovision, structured light, and flight time, in addition to exposing some of the most used techniques in image processing, such as morphology and segmentation.