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The nematode Caenorhabditis elegans has been a model for studying infection since the early 2000s and many major discoveries have been made regarding its innate immune responses. C. elegans has been found to utilize some key conserved aspects of immune responses and signaling, but new interesting features of innate immunity have also been discovered in the organism that might have broader implications in higher eukaryotes such as mammals. Some of the distinctive features of C. elegans innate immunity involve the mechanisms this bacterivore uses to detect infection and mount specific immune responses to different pathogens, despite lacking putative orthologs of many important innate immune components, including cellular immunity, the inflammasome, complement, or melanization. Even when orthologs of known immune factors exist, there appears to be an absence of canonical functions, most notably the lack of pattern recognition by its sole Toll-like receptor. Instead, recent research suggests that C. elegans senses infection by specific pathogens through contextual information, including unique products produced by the pathogen or infection-induced disruption of host physiology, similar to the proposed detection of patterns of pathogenesis in mammalian systems. Interestingly, C. elegans can also transfer information of past infection to their progeny, providing robust protection for their offspring in face of persisting pathogens, in part through the RNAi pathway as well as potential new mechanisms that remain to be elucidated. Altogether, some of these strategies employed by C. elegans share key conceptual features with vertebrate adaptive immunity, as the animal can differentiate specific microbial features, as well as propagate a form of immune memory to their offspring. [ABSTRACT FROM AUTHOR]

Humans readily recognize familiar rhythmic patterns, such as isochrony (equal timing between events) across a wide range of rates. This reflects a facility with perceiving the relative timing of events, not just absolute interval durations. Several lines of evidence suggest this ability is supported by precise temporal predictions arising from forebrain auditory–motor interactions. We have shown previously that male zebra finches, Taeniopygia guttata , which possess specialized auditory–motor networks and communicate with rhythmically patterned sequences, share our ability to flexibly recognize isochrony across rates. To test the hypothesis that flexible rhythm pattern perception is linked to vocal learning, we ask whether female zebra finches, which do not learn to sing, can also recognize global temporal patterns. We find that females can flexibly recognize isochrony across a wide range of rates but perform slightly worse than males on average. These findings are consistent with recent work showing that while females have reduced forebrain song regions, the overall network connectivity of vocal premotor regions is similar to males and may support predictions of upcoming events. Comparative studies of male and female songbirds thus offer an opportunity to study how individual differences in auditory–motor connectivity influence perception of relative timing, a hallmark of human music perception. • Female zebra finches can recognize temporal regularity across a wide range of rates. • Regularity detection extends to tempi far outside conspecific male song rates. • Females were slightly worse than males at flexibly detecting temporal regularity. [ABSTRACT FROM AUTHOR]

• Deficient auditory processing in Temporal Epilepsy is evidenced by psychoacoustic and electrophysiological evaluation. • Lack of hearing sensitivity assessment is documented in electrophysiological studies. Hearing efficiency is known to influence and interact with communication and mental health. Hearing impairment may be hidden when co-occurring with neurological disorders. We performed a systematic review and meta -analysis in order to address the following questions: 1) which specific tools of auditory processing show clear deficits, separating Temporal Lobe Epilepsy (TLE) patients from normal controls,2) How well is TLE evaluated in terms of hearing and auditory processing? The study inclusion criteria were: 1) patients diagnosed with temporal lobe epilepsy, 2) presence of a normal control group, 3) auditory processing assessment using auditory stimuli with behavioral tests and/or P300 or Mitch Match Negativity (MMN) latency and/or amplitude, 4) publications written in English, 5) publication date after 2000. 132 articles were retrieved and based on PRISMA & PICO criteria 23 articles were analyzed. Temporal resolution and processing as measured by the behavioral tests of Gaps-In-Noise (GIN) and Duration Pattern Test (DPT) document deficiencies in TLE patients and separate them from normal controls. Electrophysiology as measured by MMN & P300 shows statistically significant differences in TLE patients compared to controls with patients showing deficient auditory processing. A clear difference between studies with psychoacoustic assessment as opposed to electrophysiology ones may be due to lacking or incomplete evaluation of peripheral hearing by gold standard tools (76.9% in electrophysiology studies). Auditory processing is deficient in patients with TLE. There is a clear need to evaluate hearing efficiency before proceeding to auditory processing evaluation with behavioral or electrophysiological tests. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper aims to address the issue of slow testing speeds in deep graph convolutional models for skeleton-based action recognition by simplifying the network structure through knowledge distillation, resulting in a compact and lightweight student GNN. • To enhance the robustness and generalization capabilities of the model, we introduce a novel data augmentation mechanism that generates diverse action sequences while maintaining consistent behavior labels, providing a more comprehensive learning basis for the model. • Our model integrates three distinct knowledge learning paths: teacher networks, original datasets, and generated data. The combination of knowledge distillation and data augmentation enables the lightweight student network to surpass the large teacher network in terms of both performance and efficiency. • Experimental results demonstrate the effectiveness of our approach in the context of skeleton-based human action recognition, highlighting its potential to streamline state-of-the-art models while improving their performance. • This research provides a new perspective for skeleton-based action recognition, namely, combining knowledge distillation and data augmentation to achieve efficient and accurate behavior recognition, offering reference and inspiration for future related studies. Human action recognition, a pivotal topic in computer vision, is a highly complex and challenging task. It requires the analysis of not only spatial dependencies of targets but also temporal changes in these targets. In recent decades, the advancement of deep learning has led to the development of numerous action recognition methods based on deep neural networks. Given that the skeleton points of the human body can be treated as a graph structure, graph neural networks (GNNs) have emerged as an effective tool for modeling such data, garnering significant interest from researchers. This paper aims to address the issue of low test speed caused by over-complicated deep graph convolutional models. To achieve this, we compress the network structure using knowledge distillation from a teacher-student architecture, leading to a compact and lightweight student GNN. To enhance the model's robustness and generalization capabilities, we introduce a data augmentation mechanism that generates diverse action sequences while maintaining consistent behavior labels, thereby providing a more comprehensive learning basis for the model. The proposed model integrates three distinct knowledge learning paths: teacher networks, original datasets, and derived data. The fusion of knowledge distillation and data augmentation enables lightweight student networks to outperform their teacher networks in terms of both performance and efficiency. Experimental results demonstrate the efficacy of our approach in the context of skeleton-based human action recognition, highlighting its potential to simplify state-of-the-art models while enhancing their performance. [ABSTRACT FROM AUTHOR]

This paper proposed an efficient and novel end-to-end text detection and recognition framework called DiZNet. DiZNet is built upon a core representation using text detail maps and employs the classical lightweight ResNet18 as the backbone for the text detection and recognition algorithm model. The redesigned Text Attention Head (TAH) takes multiple shallow backbone features as input, effectively extracting pixel-level information of text in images and global text positional features. The extracted text features are integrated into the stackable Feature Pyramid Enhancement Fusion Module (FPEFM). Supervised with text detail map labels, which include boundary information and texture of important text, the model predicts text detail maps and fuses them into the text detection and recognition heads. Through end-to-end testing on publicly available natural scene text benchmark datasets, our approach demonstrates robust generalization capabilities and real-time detection speeds. Leveraging the advantages of text detail map representation, DiZNet achieves a good balance between precision and efficiency on challenging datasets. For example, DiZNet achieves 91.2% Precision and 85.9% F-measure at a speed of 38.4 FPS on Total-Text and 83.8% F-measure at a speed of 30.0 FPS on ICDAR2015, it attains 83.8% F-measure at 30.0 FPS. The code is publicly available at: https://github.com/DiZ-gogogo/DiZNet • Text Attention Head (TAH): To augment the model's representation capacity, we introduced TAH. This component efficiently extracts essential text features from shallow feature maps at varying resolutions derived from the ResNet18 backbone. These features are then integrated into the input for further enhancement. • Feature Pyramid Enhancement Fusion Module (FPEFM): A stackable module composed of a pyramid structure built using a feature pyramid and depth-wise separable convolutions. It integrates pixel-level text details and global positional features extracted by TAH, enhancing the model's feature representation capacity. The residual design of FPEFM and TAH strengthens the model's feature extraction, representation capabilities, and adaptive text feature extraction. • Text Detail Map: Harnessing the benefits of Text Detail Map representation, we introduced a Text Detail Head to predict Text Detail Maps. These predicted maps are integrated into both the text detection and recognition modules, markedly improving the model's robustness. • Overall Innovation Our work presents an efficient end-to-end algorithm for natural scene text detection and recognition grounded in Text Detail Maps, capturing both text texture and boundary information. The integration of TAH adeptly extracts pixel-level text details and global positional features, enhancing feature representation. The incorporation of residual connections further bolsters detection accuracy and speeds up post-processing inference. [ABSTRACT FROM AUTHOR]

• Listeners show variability learning to recognize acoustically degraded speech. • Pattern completion in a visual modality predicts recognition of degraded speech. • Domain-general prediction mechanisms may support perceptual learning of speech. Recognizing acoustically degraded speech relies on predictive processing whereby incomplete auditory cues are mapped to stored linguistic representations via pattern recognition processes. While listeners vary in their ability to recognize degraded speech, performance improves when a written transcription is presented, allowing completion of the partial sensory pattern to preexisting representations. Building on work characterizing predictive processing as pattern completion, we examined the relationship between domain-general pattern recognition and individual variation in degraded speech learning. Participants completed a visual pattern recognition task to measure individual-level tendency towards pattern completion. Participants were also trained to recognize noise-vocoded speech with written transcriptions and tested on speech recognition pre- and post-training using a retrieval-based transcription task. Listeners significantly improved in recognizing speech after training, and pattern completion on the visual task predicted improvement for novel items. The results implicate pattern completion as a domain-general learning mechanism that can facilitate speech adaptation in challenging contexts. [ABSTRACT FROM AUTHOR]

Lectins are proteins capable of recognizing and binding to glycan in a specific way. In invertebrates, lectins are a crucial group of Pattern Recognition Proteins (PRRs), activating cellular and humoral responses in the innate immune system. The shrimp Penaeus vannamei is the main crustacean cultivated worldwide, however, the productivity of cultures is strongly affected by diseases, mainly viral ones, such as Infectious Myonecrosis (IMN). Thus, we investigated the participation of five lectins (LvAV , LvCTL4 , LvCTL5 , LvCTLU , and LvLdlrCTL) in IMNV-challenged shrimp. We verified upregulation gene profiles of lectins after IMNV-challenge, especially in hepatopancreas and gills, in addition to an increase in total hemocytes count (THC) after to 12 h post-infection (hpi). The bioinformatics characterization also revealed several sites of post-translational modification (PTM), such as phosphorylation and glycosylation, which possibly influence the action and stabilization of these lectins. We conclude that LvLdlrCTL and LvCTL5 are the lectins with greater participation in the activation of the immune system against IMNV, showing the greatest potential for PTM, higher upregulation levels, and overlapping with the THC and IMNV viral load. • The lectins of P. vannamei are able to act against IMNV. • LvLdlrCTL and LvCTL5 are the most expressed lectins after IMNV-challenge. • Post-translational modifications influence the action of lectins. • Upregulation of lectins influences hemocytes recruitment. [ABSTRACT FROM AUTHOR]

Recent works on Video Anomaly Detection (VAD) have made advancements in the unsupervised setting, known as Unsupervised VAD (UVAD), which brings it closer to practical applications. Unlike the classic VAD task that requires a clean training set with only normal events, UVAD aims to identify abnormal frames without any labeled normal/abnormal training data. Many existing UVAD methods employ handcrafted surrogate tasks, such as frame reconstruction, to address this challenge. However, we argue that these surrogate tasks are sub-optimal solutions, inconsistent with the essence of anomaly detection. In this paper, we propose a novel approach for UVAD that directly detects anomalies based on similarities between events in videos. Our method generates representations for events while simultaneously capturing prototypical normality patterns, and detects anomalies based on whether an event's representation matches the captured patterns. The proposed model comprises a memory module to capture normality patterns, and a representation learning network to obtain representations matching the memory module for normal events. A pseudo-label generation module as well as an anomalous event generation module for negative learning are further designed to assist the model to work under the strictly unsupervised setting. Experimental results demonstrate that the proposed method outperforms existing UVAD methods and achieves competitive performance compared with classic VAD methods. [ABSTRACT FROM AUTHOR]

This study proposes an integrated approach by combining a pattern recognition technique and a process simulation model, to assess the impact of various climatic conditions on influent characteristics of the largest Italian wastewater treatment plant (WWTP) at Castiglione Torinese. Eight years (viz. 2009–2016) of historical influent data namely influent flow rate (Q in), chemical oxygen demand (COD), ammonium (N-NH 4) and total suspended solids (TSS), in addition to two climatic attributes, average temperature and daily mean precipitation rates (P I) from the plant catchment area, are evaluated in this study. Following the outlier removal and missing-data imputation, five influent climate-based scenarios are identified by K -means clustering approach. Statistical characteristics of clustered observations are further investigated. Finally, to demonstrate that the proposed approach could improve the process control and efficiency, a process simulation model was developed and calibrated. Steady-state simulations were conducted, and the performance of the plant was studied under five influent scenarios. Further, an optimization scenario-based method was conducted to improve the energy consumption of the plant while meeting effluent requirements. The results indicate that with the adaptation of suitable aeration strategies for each of the influent scenarios, 10–40% energy saving can be achieved while meeting effluent requirements. • An integrated approach is proposed to investigate the impact of climatic variations on the performance of a WWTP. • Data pre-processing and clustering methods are performed on eight years of historical influent and climatic data. • Five influent climate-based scenarios are identified by K-means clustering approach. • The process simulation model is developed and calibrated for steady state condition. • Aeration energy optimization measures are proposed for each of the obtained influent climate-based scenarios. [ABSTRACT FROM AUTHOR]

Toll-like receptors (TLRs) are a large family of pattern recognition receptors (PRRs) that play a critical role in innate immunity. TLRs are activated when they recognize microbial associated molecular patterns (MAMPs) of bacteria, viruses, or fungus. In the present study, two TLRs were isolated from the mantle of the hard-shelled mussel (Mytilus coruscus) and designated McTLR2 and McTLR3 based on their sequence similarity and phylogenetic clustering with Crassostrea gigas , CgiTLR2 and CgiTLR3, respectively. Quantitative RT-PCR analysis demonstrated that McTLR2 and McTLR3 were constitutively expressed in many tissues but at low abundance. • Two toll-like receptors (McTLR2 and McTLR3) were identified and characterized in Mytilus coruscus. • The two TLRs had a widespread tissue distribution. • McTLR2 was the most abundant in haemocytes. [ABSTRACT FROM AUTHOR]

• Unfounded health claims pollute scientific literature. • Discussion of human and scientific causes. • Suggestion of corrective actions to be made by scientists, referees and publishers. • Introduction of holistic biological filters to triage enigmatic compounds. The publication of unfounded health claims on small molecules in peer-reviewed scientific literature is a problem that requires attention. It undermines the evidence-based decision making processes of modern-day society, weakens the credibility of the scientific enterprise, and diverts resources to futile research efforts. In the present essay we discuss some human and scientific causes behind the issue. We propose a number of actions to be taken up by scientists, referees and publishers. One particularly important factor is the issue of enigmatic compound behavior in biological assays. We therefore also introduce the idea of biological filters, a pattern recognition method to triage enigmatic compounds into valuable hits and false positives, based on the entirety of their biological effects in cell-based systems. [ABSTRACT FROM AUTHOR]

Facial expressions are complex, progress over time, and are challenging to interpret. The research subject of automated emotion recognition associated with the facial expressions has been a mainstream topic in computer vision focused on image processing and pattern recognition. Numerous databases of facial expressions are available to the research community, and are used as fundamental tools for the evaluation of a wide range of algorithms for face expression recognition. In this paper, we assessed the existing collections of facial expression datasets as a basis for building and evaluating the largely unmapped facet of pain expressions. To accentuate this topic, the study provides the summary of different characteristics of expressions that are relevant and justifiable indicators of pain. A preliminary platform is tested with accuracy rate of 85.66% using the collected FER datasets as testing inputs. Common challenges in face recognition were discussed, as well as the different methods used to address them. Different variants of feature learning techniques were also compared, and why some methods outperforms other existing methods. [ABSTRACT FROM AUTHOR]

With the continuous development of urbanization, urban population, economy and other factors have a close impact on the geometry and distribution of urban buildings. Obtaining information of urban buildings from aerial images or satellite images quickly and accurately is not only conducive to updating geospatial data, but also of great significance for effective monitoring of new thematic information such as new buildings. Moreover, in recent years, the research and improvement of building recognition and contour extraction algorithms based on satellite images or aerial images are helpful to the recognition and classification of urban buildings. It is of great significance to the acquisition of GIS data, the understanding of images, large-scale mapping and many other applications of remote sensing data. With the development of artificial intelligence and computer technology, the image processing of intelligent building environment based on pattern recognition technology has become an important research direction in the field of intelligent building image recognition. Based on the concept, principle and technology analysis of pattern recognition technology, this paper studies the application of pattern recognition technology in the image processing of intelligent building environment. In this paper, based on image processing of intelligent building as the basic theoretical platform, with the pattern recognition technology as the basic research means, three problems of image processing, image extraction and image recognition in image processing of building intelligent environment are studied respectively, and corresponding reasonable solutions are put forward. [ABSTRACT FROM AUTHOR]

Skilled visual object and pattern recognition form the basis of many everyday behaviours. The game of chess has often been used as a model case for studying how long-term experience aides in perceiving objects and their spatio-functional interrelations. Earlier research revealed two brain regions, posterior middle temporal gyrus (pMTG) and collateral sulcus (CoS), to be linked to chess experts' superior object and pattern recognition, respectively. Here we elucidated the brain networks these two expertise-related regions are embedded in, employing resting-state functional connectivity analysis and meta-analytic connectivity modelling with the BrainMap database. pMTG was preferentially connected with dorsal visual stream areas and a parieto-prefrontal network for action planning, while CoS was preferentially connected with posterior medial cortex and hippocampus, linked to scene perception, perspective-taking and navigation. Functional profiling using BrainMap meta-data revealed that pMTG was linked to semantic processing as well as inhibition and attention, while CoS was linked to face and shape perception as well as passive viewing. Our findings suggest that pMTG subserves skilled object recognition by mediating the link between object identity and object affordances, while CoS subserves skilled pattern recognition by linking the position of individual objects with typical spatio-functional layouts of their environment stored in memory. [ABSTRACT FROM AUTHOR]

Abstract We say that an unordered rooted labeled forest avoids the pattern π ∈ S n if the sequence obtained from the labels along the path from the root to any vertex does not contain a subsequence that is in the same relative order as π. We enumerate several classes of forests that avoid certain sets of permutations, including the set of unimodal forests, via bijections with set partitions with certain properties. We also define and investigate an analog of Wilf equivalence for forests. [ABSTRACT FROM AUTHOR]

Abstract The binary Constraint Satisfaction Problem (CSP) is to decide whether there exists an assignment to a set of variables which satisfies specified constraints between pairs of variables. A binary CSP instance can be presented as a labelled graph encoding both the forms of the constraints and where they are imposed. We consider subproblems defined by restricting the allowed form of this graph. One type of restriction is to forbid certain specified substructures (patterns). This captures some tractable classes of the CSP, but does not capture classes defined by language restrictions, or the well-known structural property of acyclicity. We extend the notion of pattern and introduce the notion of a topological minor of a binary CSP instance. By forbidding a finite set of patterns from occurring as topological minors we obtain a compact mechanism for expressing novel tractable subproblems of the CSP, including new generalisations of the class of acyclic instances. [ABSTRACT FROM AUTHOR]

Some animals are thought to exhibit cognitive specialization, as they have specialized cognitive modules that solve specific social or ecological problems instead of one general-purpose mechanism that addresses diverse problems. Although there are many examples of specialized cognition, little is known about whether specialization develops through experience or is produced by innate, species-specific differences. Previous work has shown that Polistes fuscatus wasps use face recognition to individually identify other wasps and that P. fuscatus are specialized for learning conspecific faces. Here, we test how experimentally altering face experience in three Polistes species influences the development of face specialization. We show face learning is influenced by both experience and innate, species-specific differences. In P. fuscatus , experience with conspecific faces is not required for the development of face specialization. In two related Polistes species that naturally lack individual face recognition, Polistes metricus and Polistes dominula , experience has different effects on specialization. Polistes metricus , a close relative of P. fuscatus , develops face specialization with experience. However, P. dominula , a more distant relative, uses general pattern recognition to learn faces regardless of experience. Therefore, some species have innate mechanistic architecture that facilitates the development of face specialization, while other species do not. These results suggest that selection shapes animal minds in a modular manner. The capacity for specialized cognitive skills evolves in response to specific ecological or social demands, such as social benefits associated with accurate individual face recognition. Highlights • We test whether cognitive specialization is inherited or requires experience. • We experimentally altered face experience in three species of paper wasps. • Experience produces face specialization in some wasp species but not others. • Face specialization in wasps is influenced by both inheritance and experience. [ABSTRACT FROM AUTHOR]

Enumeration is one of the building blocks of arithmetic and fingers are used as a counting tool in early steps. Subitizing-fast and accurate enumeration of small quantities-has been vastly studied in the visual modality, but less in the tactile modality. We explored tactile enumeration using fingers, and gray matter (GM) changes using voxel-based morphometry (VBM), in acalculia. We examined JD, a 22-year-old female with acalculia following a stroke to the left inferior parietal cortex. JD and a neurologically healthy normal comparison (NC) group reported how many fingers were stimulated. JD was tested at several time points, including at acute and chronic phases. Using the sensory intact hand for tactile enumeration, JD showed deficit in the acute phase, compared to the NC group, and improvement in the chronic phase of (1) the RT slope of enumerating up to four stimuli, (2) enumerating neighboring fingers, and (3) arithmetic fluency performance. Moreover, VBM analysis showed a larger GM volume for JD relative to the NC group in the right middle occipital cortex, most profoundly in the chronic phase. JD's performance serves as a first glance of tactile enumeration in acalculia. Pattern-recognition-based results support the suggestion of subitizing being the enumeration process when using one hand. Moreover, the increase in GM in the occipital cortex lays the groundwork for studying the innate and primitive ability to perceive and evaluate sizes or amounts-"sense of magnitude"- as a multisensory magnitude area and as part of a recovery path for deficits in basic numerical abilities. [ABSTRACT FROM AUTHOR]

This paper considers testing the means of two p -variate normal samples in high dimensional settings. We show that under the null hypothesis, a necessary and sufficient condition for the asymptotic normality of the test statistic of Chen and Qin (2010) is that the eigenvalues of the covariance matrix are concentrated around their average. However, this condition is not satisfied when the variables are strongly correlated. To characterize the correlations between variables, we adopt a spiked covariance model. Under the spiked covariance model, we derive the asymptotic distribution of the test statistic of Chen and Qin (2010) and correct its critical value. The recently proposed random projection test procedures suggest that the power of tests may be boosted using the projected data. By maximizing an average signal to noise ratio, we find that the optimal projection subspace is the orthogonal complement of the principal subspace. We propose a new test procedure through the projection onto the estimated orthogonal complement of the principal subspace. The asymptotic normality of the new test statistic is proved and the asymptotic power function of the test is given. Theoretical and simulation results show that the new test outperforms the competing tests substantially under the spiked covariance model. [ABSTRACT FROM AUTHOR]

The traceability of food products in terms of their geographical origins is getting more and more public attention. To be certain of the origin of organically grown potato ( Solanum tuberosum L.), samples were subjected to stable isotope analysis of the major bioelements ( δ 13 C, δ 15 N, δ 18 O, δ 34 S) and to element profiling (Na, Mg, P, S, Cl, K, Ca, Mn, Fe, Ni, Cu, Zn, Mo, Br, Rb, Sr), which included the rare earth elements (Sc, Y, Nb, La, Ce, Pr, Nd, Dy, Er). The present study was performed at the scale of Slovenia, which has diverse geographical characteristics. Using supervised pattern recognition statistical analysis (i.e., multivariate discriminant analysis), the characterisation and classification of organically grown potatoes were defined in terms of four Slovenian macro-regions: the Alpine, Dinaric, Mediterranean and Pannonian regions. The proposed model showed that 100% of the samples were correctly classified. However, an important prerequisite for official monitoring of the origin of a commodity is the availability of national and/or international databanks. As no databank exists for potato to date, the present study was performed to initiate the collection of data at the national level, to build a relevant and up-to-date databank. Once established, a reliable traceability model can be created. [ABSTRACT FROM AUTHOR]

Real-time, accurate, and robust pupil detection is an essential prerequisite to enable pervasive eye-tracking and its applications – e.g., gaze-based human computer interaction, health monitoring, foveated rendering, and advanced driver assistance. However, automated pupil detection has proved to be an intricate task in real-world scenarios due to a large mixture of challenges such as quickly changing illumination and occlusions. In this paper, we introduce the Pu pil Re constructor ( PuRe ), a method for pupil detection in pervasive scenarios based on a novel edge segment selection and conditional segment combination schemes; the method also includes a confidence measure for the detected pupil. The proposed method was evaluated on over 316,000 images acquired with four distinct head-mounted eye tracking devices. Results show a pupil detection rate improvement of over 10 percentage points w.r.t. state-of-the-art algorithms in the two most challenging data sets (6.46 for all data sets), further pushing the envelope for pupil detection. Moreover, we advance the evaluation protocol of pupil detection algorithms by also considering eye images in which pupils are not present and contributing a new data set of mostly closed eyes images. In this aspect, PuRe improved precision and specificity w.r.t. state-of-the-art algorithms by 25.05 and 10.94 percentage points, respectively, demonstrating the meaningfulness of PuRe ’s confidence measure. PuRe operates in real-time for modern eye trackers (at 120 fps ) and is fully integrated into EyeRecToo – an open-source state-of-the-art software for pervasive head-mounted eye tracking. The proposed method and data set are available at http://www.ti.uni-tuebingen.de/perception . [ABSTRACT FROM AUTHOR]

The cerebral cortex is conventionally divided into a number of domains based on cytoarchitectural features. Diffusion tensor imaging (DTI) enables noninvasive parcellation of the cortex based on white matter connectivity patterns. However, the correspondence between DTI-connectivity-based and cytoarchitectural parcellation has not been systematically established. In this study, we compared histological parcellation of New World monkey neocortex to DTI- connectivity-based classification and clustering in the same brains. First, we used supervised classification to parcellate parieto-frontal cortex based on DTI tractograms and the cytoarchitectural prior (obtained using Nissl staining). We performed both within and across sample classification, showing reasonable classification performance in both conditions. Second, we used unsupervised clustering to parcellate the cortex and compared the clusters to the cytoarchitectonic standard. We then explored the similarities and differences with several post-hoc analyses, highlighting underlying principles that drive the DTI-connectivity-based parcellation. The differences in parcellation between DTI-connectivity and Nissl histology probably represent both DTI's bias toward easily-tracked bundles and true differences between cytoarchitectural and connectivity defined domains. DTI tractograms appear to cluster more according to functional networks, rather than mapping directly onto cytoarchitectonic domains. Our results show that caution should be used when DTI-tractography classification, based on data from another brain, is used as a surrogate for cytoarchitectural parcellation. [ABSTRACT FROM AUTHOR]

In bivalve mollusks, circulating hemocyte mediated phagocytosis is one of the primary ways to eliminate invading microbes. Here, we have identified one CgLRFN (leucine-rich repeat and fibronectin type-III domain-containing protein) in the Crassostrea gigas as a novel transmembrane LRR (Leucine-rich repeat) domain containing protein in C. gigas , homologous to the jawless fish VLR protein, that plays an important role in recognizing bacteria and promoting hemocytic phagocytosis. Tissue distribution analysis of Cg LRFN in Pacific oyster showed that it is widely expressed in various tissues like the gills, adductor muscles, digestive glands, gonads, heart and in the hemocytes. Furthermore, infection of Pacific oysters with two marine Vibrio strains V. alginolyticus and V. parahaemolyticus was found to significantly increase Cg LRFN expression in the hemocytes. Analysis of subcellular localization showed that Cg LRFN is primarily localized in the cell membrane. Additionally, Cg LRFN was found to be able to bind both the bacterial strains, indicating its possible role as a cell surface receptor. Flow cytometry analysis revealed that Cg LRFN coated bacteria was phagocytosed by oyster hemocytes at a significantly higher rate compared to the uncoated bacteria. Finally, RNAi mediated knockdown of Cg LRFN in vivo resulted in reduced clearance of both the bacterial strains from the oyster hemolymph. Overall, our study demonstrates that Cg LRFN acts as a pattern recognition receptor for Vibrio spp. and promotes hemocytic phagocytosis in the Pacific oyster, which is critical for understanding the mechanism of bacterial infection in lower invertebrates, and also contributes to disease management of this economically and ecologically important marine mollusk. [ABSTRACT FROM AUTHOR]

As the China's top-ranked oolong tea, there are many varieties of Wuyi rock tea (WRT) in the market. The prices of different varieties vary greatly, and the differential chemical composition is unclear. Therefore, we developed a rapid and systematic chemometrics-assisted HPLC–DAD strategy for targeted metabolites analysis and variety discrimination of WRT. The strategy involved design of experiment, targeted metabolites quantification, varieties discrimination and differential component screening. Specifically, optimal extraction conditions of WRT are obtained by response surface methodology-Box-Behnken design method. Then, 22 targeted metabolites are rapidly and accurately quantified by alternate trilinear decomposition algorithm (ATLD) with average spiked recoveries ranging from 85.4% to 108.6%. Finally, the four WRT varieties (110 samples) are discriminated by linear discriminant analysis (LDA) and partial least squares-discriminant analysis (PLS-DA) with relative concentration results as model input. The correct classification rates obtained by PLS-DA are greater than 88%, especially for high-quality "Niulankeng rougui" tea, its specificity and sensitivity are 100%. Moreover, ten differential metabolites related to identification are screened by variable importance in projection (VIP). This work scientifically and comprehensively elaborates the whole-process of WRT analysis, which can become a comprehensive food analysis strategy for the purpose of precise quantification and pattern recognition. [Display omitted] • Chemometrics guides the entire analysis process of Wuyi rock tea. • The optimal extraction conditions of Wuyi rock tea are obtained by RSM-BBD. • 22 targeted metabolites can be rapidly and accurately quantified by ATLD method. • 110 Wuyi rock tea samples can be reasonably identified by PLS-DA and LDA. • 10 differential metabolites affecting variety discrimination are screened by VIP. [ABSTRACT FROM AUTHOR]

Structural damage identification approaches can be divided into two categories, i.e. data-driven approaches via statistical pattern recognition and model-based approaches via finite element (FE) model updating. These two approaches have their own merits, and their main shortcomings can be remedied by each other's merits. Therefore, this study proposed a deep learning-based damage identification strategy involving both data-driven and model-based approaches, termed as model-informed deep learning (MIDL)-based strategy. This strategy first proposes a vision-based displacement estimation approach to extract structural displacement responses from video data. This approach reduces the displacement drift induced by conventional optical flow approaches and improves the tracking accuracy of feature points. Then, a calibrated FE model is built to construct data sets with different damage levels via FE model updating and time-history analysis. Following this, a one-dimensional convolutional neural network (1D CNN) is established to detect and localize structural damage by using direct displacement responses. Finally, FE model updating is performed again to quantify structural damage level with constrained targets. A truss structure is further used to evaluate the accuracy of the proposed strategy experimentally. Results illustrate that the proposed MIDL strategy, which uses time series to localize the structural damage, achieves a global location accuracy of 86.09% and avoids the feature extraction process. Meanwhile, with the known damage location, the efficiency and accuracy of structural damage quantification via rerunning model updating can also be significantly improved. In addition, the displacements estimated by the proposed approach have a good match with ground truth values with error standard deviations of less than 0.3 mm. [ABSTRACT FROM AUTHOR]

How do people recognize and learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is achieved by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels, and compare this approach with other structure learning approaches. Participants consistently chose compositional (over non-compositional) extrapolations and interpolations of functions. Experiments designed to elicit priors over functional patterns revealed an inductive bias for compositional structure. Compositional functions were perceived as subjectively more predictable than non-compositional functions, and exhibited other signatures of predictability, such as enhanced memorability and reduced numerosity. Taken together, these results support the view that the human intuitive theory of functions is inherently compositional. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for developing multi-site damage location systems for engineering structures that can be trained using single-site damaged state data only. The methodology involves training a sequence of binary classifiers based upon single-site damage data and combining the developed classifiers into a robust multi-class damage locator. In this way, the multi-site damage identification problem may be decomposed into a sequence of binary decisions. In this paper Support Vector Classifiers are adopted as the means of making these binary decisions. The proposed methodology represents an advancement on the state of the art in the field of multi-site damage identification which require either: (1) full damaged state data from single- and multi-site damage cases or (2) the development of a physics-based model to make multi-site model predictions. The potential benefit of the proposed methodology is that a significantly reduced number of recorded damage states may be required in order to train a multi-site damage locator without recourse to physics-based model predictions. In this paper it is first demonstrated that Support Vector Classification represents an appropriate approach to the multi-site damage location problem, with methods for combining binary classifiers discussed. Next, the proposed methodology is demonstrated and evaluated through application to a real engineering structure – a Piper Tomahawk trainer aircraft wing – with its performance compared to classifiers trained using the full damaged-state dataset. [ABSTRACT FROM AUTHOR]

In crustaceans, an innate immune system is solely required because they lack an adaptive immunity. One kind of pattern recognition receptors (PRRs) that plays a particular role in the innate immunity of aquatic shrimp is lectin. A new diverse C-type lectin (FmLC4) was cloned from the hepatopancreas of Fenneropenaeus merguiensis by using RT-PCR and 5′ and 3′ rapid amplification of cDNA ends approaches. A full-length FmLC4 cDNA comprises 706 bp with an open reading frame of 552 bp, encoding a peptide of 184 amino acids. The predicted primary sequence of FmLC4 consists of a signal peptide of 19 amino acids, a molecular mass of 20.4 kDa, an isoelectric point of 5.13, one carbohydrate recognition domain with a QPD motif and a Ca 2+ binding site as well as a double-loop characteristic supported by two conserved disulfide bonds. The FmLC4 mRNA expression was found only in the hepatopancreas of normal shrimp and significantly up-regulated upon challenge the shrimp with Vibrio harveyi or white spot syndrome virus (WSSV). Recombinant FmLC4 (rFmLC4) could agglutinate various bacterial strains with Ca 2+ -dependence. Lipopolysaccharide (LPS) could specifically inhibit the agglutinating activity and potently bind to rFmLC4, indicating that FmLC4 was LPS-specific binding C-type lectin. Moreover, rFmLC4 itself displayed the in vivo effective clearance of the pathogenic bacterium V. harveyi. Altogether, FmLC4 may serve as LPS-specific PRR to recognize opportunistic bacterial and viral pathogens, and thus to play a role in the immune defense of aquatic shrimp via the binding and agglutination. [ABSTRACT FROM AUTHOR]

High dimensional curse for hyperspectral images is one major challenge in image classification. In this work, we introduce a novel spectral band selection method by representative band mining. In the proposed method, the distance between two spectral bands is measured by using disjoint information. For band selection, all spectral bands are first grouped into clusters, and representative bands are selected from these clusters. Different from existing clustering-based band selection methods which select bands from each cluster individually, the proposed method aims to select representative bands simultaneously by exploring the relationship among all band clusters. The optimal representative band selection is based on the criteria of minimizing the distance inside each cluster and maximizing the distance among different representative bands. These selected bands can be further applied in hyperspectral image classification. Experiments are conducted on the 92AV3C Indian Pine data set. Experimental results show that the disjoint information-based spectral band distance measure is effective and the proposed representative band selection approach outperforms state-of-the-art methods for high dimensional image classification. [ABSTRACT FROM AUTHOR]

Previous research on perceptual organization operations still provides contradictory evidence on whether the integration of sparse local elements into coherently unified shapes and the construction of the illusory form are accomplished without the need of awareness. In the present study, three experiments were conducted in which participants were presented with masked (Experiment 1, SOA = 27 ms; Experiment 2; SOA = 53 ms) and unmasked (Experiment 3) primes consisting of geometric shapes (a square or a diamond) that could be congruent or incongruent with subsequent probe stimuli (square vs. diamond). Furthermore, the primes were divided into: a grouping condition (where local elements may group together into global shapes), an illusory condition (where the arrangement of local elements produced illusory shapes) and a hybrid condition (where both operations were presented simultaneously). While no priming effects were found for the shortest SOA (27 ms), both grouping and illusory primes produced significant priming effects in the longer SOA (53 ms). On the other hand, results in Experiment 3 (unmasked) showed strong priming effects for the grouping of the inducers in both the grouping and the hybrid conditions , and also a significant but weaker priming effect for the illusory condition . Overall, our results support the possibility of the integration of local visual features into a global shape in the absence of awareness and, likewise, they suggest an early –subliminal– construction of the illusory shape, implying that feedback projections from higher to lower visual areas are not crucial in the construction of the illusory form. [ABSTRACT FROM AUTHOR]

Most state-of-the-art binary image data hiding methods concentrate the embedding changes on the centers of l -shape patterns. This embedding criterion, however, introduces an unbalanced modification on boundary structures. This paper proposes a steganalytic scheme to detect recently developed content-adaptive binary image data hiding by exploiting the embedding effect associated with the l -shape pattern-based embedding criterion. We first assess how changing l -shape patterns affects the distribution of a special 4 × 3 sized pattern. Based on the assessment, 4 classes of patterns that model the distribution of two pixels oriented the direction of pattern changing are employed to define a 32-dimensional steganalytic feature set. Experimental results show that, despite of the low dimensionality, the proposed steganalytic features can effectively detect state-of-the-art binary image data hiding schemes, especially those pattern-tracing-based approaches. [ABSTRACT FROM AUTHOR]

The use of computer vision techniques in post-harvest processing of agricultural products has increased considerably in recent years due to their non-destructive and rapid monitoring abilities. Image processing, combined with pattern recognition, has been applied in fruit sorting and classification. In this study, a Bag-of-Feature (BoF) model is used for the classification of 20 sweet and bitter almond varieties. Harris, Harris–Laplace, Hessian, Hessian–Laplace and Maximally Stable Extremal Regions (MSER) keypoint detectors along with a Scale Invariant Feature Transform (SIFT) descriptor are used in the BoF model. The k-means clustering method is applied for building a codebook from keypoint descriptors. The performance of 3 classifiers, which were k-Nearest Neighbour (k-NN), linear and chi-square Support Vector Machine (L-SVM and Chi-SVM, respectively) were compared using classification results in the model. It was observed that the Chi-SVM classifier outperformed the k-NN and L-SVM classifiers. Using the BoF model, it was possible to detect and classify sweet and bitter varieties with high overall accuracy. [ABSTRACT FROM AUTHOR]

Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseases on an individual basis. The driving assumption behind the vast majority of the existing methodologies is that a single imaging pattern can distinguish between healthy and diseased populations, or between two subgroups of patients ( e.g. , progressors vs. non-progressors). This assumption effectively ignores the ample evidence for the heterogeneous nature of brain diseases. Neurodegenerative, neuropsychiatric and neurodevelopmental disorders are largely characterized by high clinical heterogeneity, which likely stems in part from underlying neuroanatomical heterogeneity of various pathologies. Detecting and characterizing heterogeneity may deepen our understanding of disease mechanisms and lead to patient-specific treatments. However, few approaches tackle disease subtype discovery in a principled machine learning framework. To address this challenge, we present a novel non-linear learning algorithm for simultaneous binary classification and subtype identification, termed HYDRA (Heterogeneity through Discriminative Analysis). Neuroanatomical subtypes are effectively captured by multiple linear hyperplanes, which form a convex polytope that separates two groups ( e.g. , healthy controls from pathologic samples); each face of this polytope effectively defines a disease subtype. We validated HYDRA on simulated and clinical data. In the latter case, we applied the proposed method independently to the imaging and genetic datasets of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) study. The imaging dataset consisted of T1-weighted volumetric magnetic resonance images of 123 AD patients and 177 controls. The genetic dataset consisted of single nucleotide polymorphism information of 103 AD patients and 139 controls. We identified 3 reproducible subtypes of atrophy in AD relative to controls: (1) diffuse and extensive atrophy, (2) precuneus and extensive temporal lobe atrophy, as well some prefrontal atrophy, (3) atrophy pattern very much confined to the hippocampus and the medial temporal lobe. The genetics dataset yielded two subtypes of AD characterized mainly by the presence/absence of the apolipoprotein E (APOE) ε 4 genotype, but also involving differential presence of risk alleles of CD2AP, SPON1 and LOC39095 SNPs that were associated with differences in the respective patterns of brain atrophy, especially in the precuneus. The results demonstrate the potential of the proposed approach to map disease heterogeneity in neuroimaging and genetic studies. [ABSTRACT FROM AUTHOR]

Introduction Pattern recognition analysis (PRA) applied to functional magnetic resonance imaging (fMRI) has been used to decode cognitive processes and identify possible biomarkers for mental illness. In the present study, we investigated whether the positive affect (PA) or negative affect (NA) personality traits could be decoded from patterns of brain activation in response to a human threat using a healthy sample. Methods fMRI data from 34 volunteers (15 women) were acquired during a simple motor task while the volunteers viewed a set of threat stimuli that were directed either toward them or away from them and matched neutral pictures. For each participant, contrast images from a General Linear Model (GLM) between the threat versus neutral stimuli defined the spatial patterns used as input to the regression model. We applied a multiple kernel learning (MKL) regression combining information from different brain regions hierarchically in a whole brain model to decode the NA and PA from patterns of brain activation in response to threat stimuli. Results The MKL model was able to decode NA but not PA from the contrast images between threat stimuli directed away versus neutral with a significance above chance. The correlation and the mean squared error (MSE) between predicted and actual NA were 0.52 (p-value = 0.01) and 24.43 (p-value = 0.01), respectively. The MKL pattern regression model identified a network with 37 regions that contributed to the predictions. Some of the regions were related to perception (e.g., occipital and temporal regions) while others were related to emotional evaluation (e.g., caudate and prefrontal regions). Conclusion These results suggest that there was an interaction between the individuals' NA and the brain response to the threat stimuli directed away, which enabled the MKL model to decode NA from the brain patterns. To our knowledge, this is the first evidence that PRA can be used to decode a personality trait from patterns of brain activation during emotional contexts. [ABSTRACT FROM AUTHOR]

C-type lectins are pattern recognition proteins that play important roles in innate immunity in invertebrates by mediating the recognition of pathogens. In this study, a novel C-type lectin gene, Pm CLec, was cloned and characterized from the black tiger shrimp Penaeus monodon . The open reading frame of Pm CLec is 657 bp in length. It encodes a predicted protein of 218 amino acids with a calculated molecular mass and an isoelectric point of 24086 Da and 4.67, respectively. Sequence analysis of Pm CLec showed similarity to members of the C-type lectin gene superfamily. The deduced protein contains a single carbohydrate recognition domain (CRD) and four conserved cysteine residues (Cys 58 , Cys 126 , Cys 141 , Cys 149 ) that are involved in the formation of disulfide bridges. Pm CLec transcripts are expressed in various tiger shrimp tissues, with the highest expression in the lymphoid organ. RNAi-mediated silencing of Pm CLec resulted in higher cumulative mortality of knockdown shrimp after Vibrio harveyi infection compared to the control groups. Recombinant Pm CLec was successfully expressed in the E. coli system. In the presence of Ca 2+ , purified r Pm CLec protein binds and agglutinates Gram-positive bacteria ( Staphylococcus aureus , S. hemolyticus ), but only slightly binds and agglutinates E. coli and could not bind to the Gram-negative bacteria Bacillus megaterium and Vibrio harveyi . These results suggest that Pm CLec functions as a pattern recognition receptor that is implicated in shrimp innate immunity. [ABSTRACT FROM AUTHOR]

A 1299 bp full length cDNA encoding Teladorsagia circumcincta enolase ( Teci ENO) was cloned, expressed in Escherichia coli and the recombinant protein purified and its kinetic properties determined. Helminth enolase sequences were used to construct a phylogenetic tree. The predicted protein consisted of 433 amino acids and was present as a single band of about 50 kDa on SDS-PAGE. Multiple alignments of the protein sequence of Teci ENO with homologues from other helminths showed 98% similarity with Haemonchus contortus enolase, 78–95% similarity to other nematode sequences and 72–75% similarity to cestode and trematode enolases. Substrate binding sites and conserved regions were identified and were completely conserved in other homologues. The optimum pH for Teci ENO activity at 25 °C was pH 7, the K m for 2-phophoglycerate 0.09 ± 0.04 mM and the V max was 604 ± 6 nmol min −1 mg −1 protein (both mean ± SD, n = 2). Teci ENO activity was inhibited by 11.5% by 1 mM citrate (p < 0.001). Antibodies in both serum and saliva from field-immune, but not nematode-naïve, sheep recognised recombinant Teci ENO in enzyme-linked immunosorbent assays. The recognition of the recombinant protein by antibodies generated by exposure of sheep to native enolase indicates similar antigenicity of the two proteins. [ABSTRACT FROM AUTHOR]

Deltas are among the most productive and economically important of global ecosystems but unfortunately they are also among the most threatened by human activities. Here we discuss deltas and human impact, several approaches to defining deltaic sustainability and present a ranking of sustainability. Delta sustainability must be considered within the context of global biophysical and socioeconomic constraints that include thermodynamic limitations, scale and embeddedness, and constraints at the level of the biosphere/geosphere. The development, functioning, and sustainability of deltas are the result of external and internal inputs of energy and materials, such as sediments and nutrients, that include delta lobe development, channel switching, crevasse formation, river floods, storms and associated waves and storm surges, and tides and other ocean currents. Modern deltas developed over the past several thousand years with relatively stable global mean sea level, predictable material inputs from drainage basins and the sea, and as extremely open systems. Human activity has changed these conditions to make deltas less sustainable, in that they are unable to persist through time structurally or functionally. Deltaic sustainability can be considered from geomorphic, ecological, and economic perspectives, with functional processes at these three levels being highly interactive. Changes in this functioning can lead to either enhanced or diminished sustainability, but most changes have been detrimental. There is a growing understanding that the trajectories of global environmental change and cost of energy will make achieving delta sustainability more challenging and limit options for management. Several delta types are identified in terms of sustainability including those in arid regions, those with high and low energy-intensive management systems, deltas below sea level, tropical deltas, and Arctic deltas. Representative deltas are ranked on a sustainability range. Success in sustainable delta management will depend on utilizing natural delta functioning and an ecological engineering approach. [ABSTRACT FROM AUTHOR]

A system was developed to identify and quantify up to seven species of helminth eggs ( Ascaris lumbricoides -fertile and unfertile eggs-, Trichuris trichiura, Toxocara canis, Taenia saginata, Hymenolepis nana, Hymenolepis diminuta, and Schistosoma mansoni) in wastewater using different image processing tools and pattern recognition algorithms. The system was developed in three stages. Version one was used to explore the viability of the concept of identifying helminth eggs through an image processing system, while versions 2 and 3 were used to improve its efficiency. The system development was based on the analysis of different properties of helminth eggs in order to discriminate them from other objects in samples processed using the conventional United States Environmental Protection Agency (US EPA) technique to quantify helminth eggs. The system was tested, in its three stages, considering two parameters: specificity (capacity to discriminate between species of helminth eggs and other objects) and sensitivity (capacity to correctly classify and identify the different species of helminth eggs). The final version showed a specificity of 99% while the sensitivity varied between 80 and 90%, depending on the total suspended solids content of the wastewater samples. To achieve such values in samples with total suspended solids (TSS) above 150 mg/L, it is recommended to dilute the concentrated sediment just before taking the images under the microscope. The system allows the helminth eggs most commonly found in wastewater to be reliably and uniformly detected and quantified. In addition, it provides the total number of eggs as well as the individual number by species, and for Ascaris lumbricoides it differentiates whether or not the egg is fertile. The system only requires basically trained technicians to prepare the samples, as for visual identification there is no need for highly trained personnel. The time required to analyze each image is less than a minute. This system could be used in central analytical laboratories providing a remote analysis service. [ABSTRACT FROM AUTHOR]

Normal aging is known to be accompanied by loss of brain substance. The present study was designed to examine whether the practice of meditation is associated with a reduced brain age. Specific focus was directed at age fifty and beyond, as mid-life is a time when aging processes are known to become more prominent. We applied a recently developed machine learning algorithm trained to identify anatomical correlates of age in the brain translating those into one single score: the BrainAGE index (in years). Using this validated approach based on high-dimensional pattern recognition, we re-analyzed a large sample of 50 long-term meditators and 50 control subjects estimating and comparing their brain ages. We observed that, at age fifty, brains of meditators were estimated to be 7.5 years younger than those of controls. In addition, we examined if the brain age estimates change with increasing age. While brain age estimates varied only little in controls, significant changes were detected in meditators: for every additional year over fifty, meditators' brains were estimated to be an additional 1 month and 22 days younger than their chronological age. Altogether, these findings seem to suggest that meditation is beneficial for brain preservation, effectively protecting against age-related atrophy with a consistently slower rate of brain aging throughout life. [ABSTRACT FROM AUTHOR]

Endothelial cells (EC) are able to actively control vascular permeability, coagulation, blood pressure and angiogenesis. Most recently, a role for endothelial cells in the immune response has been described. Therefore, the endothelium has a dual role controlling homeostasis but also being the first line for host defence and tissue damage repair thanks to its ability to mount an inflammatory response. Endothelial cells have been shown to express pattern-recognition receptors (PRR) including Toll-like receptors (TLR) that are activated in response to stimuli within the bloodstream including pathogens and damage signals. TLRs are strategic mediators of the immune response in endothelial cells but they also regulate the angiogenic process critical for tissue repair. Nevertheless, endothelial activation and angiogenesis can contribute to some pathologies. Thus, inappropriate endothelial activation, also known as endothelial dysfunction, through TLRs contributes to tissue damage during autoimmune and inflammatory diseases such as atherosclerosis, hypertension, ischemia and diabetes associated cardiovascular diseases. Also TLR induced angiogenesis is required for the growth of some tumors, atherosclerosis and rheumatoid arthritis, among others. In this review we discuss the importance of various TLRs in modulating the activation of endothelial cells and their importance in immunity to infection and vascular disease as well as their potential as therapeutic targets. [ABSTRACT FROM AUTHOR]

Phenolic compounds in sixteen Cabernet Sauvignon wines from different wine-growing sub-regions in the Balkan region were investigated using HPLC with DAD and fluorescence detector and spectroscopic analysis, as well as statistical PC/F and cluster analysis. The HPLC analysis of investigated red wines showed that the content of total hydroxybenzoic acids, detected at 280 nm, was the highest in wines from Tikveš wine-growing subregion, Macedonia (127–140 mg L −1 ). Total hydroxycinnamic acids, detected at 320 nm, were the highest in wines from Župa wine-growing subregion, Serbia (43–45 mg L −1 ). The concentration of total flavonoids (flavan-3-ols, flavonols, flavons and flavanon), detected at 280, 360 and 322/275 nm, respectively, was the highest in wine from Katarzyna Estate wine-growing subregion, Bulgaria (167 mg L −1 ). Finally, the concentration of total anthocyanins, detected at 520 nm, was the highest in wine from Šumadija wine-growing subregion, Serbia (1463 mg L −1 ). The results of PCA and cluster analysis together confirmed that the content of phenolic compounds in Cabernet Sauvignon wines depends on agro-climatic factors, oenological practice in different wineries and the growing season in the Balkan region that were investigated. The areas in the Balkan region in this study with similar agro-climatic characteristics showed shorter clustering distance, indicating similar phenol profiling in the red wines tested. [ABSTRACT FROM AUTHOR]

Human language involves combining items into meaningful, syntactically structured wholes. The evolutionary origin of syntactic abilities has been investigated by testing pattern perception capacities in nonhuman animals. New World primates can respond spontaneously to structural changes in acoustic sequences and songbirds can learn to discriminate between various patterns in operant tasks. However, there is no conclusive evidence that songbirds respond spontaneously to structural changes in patterns without reinforcement or training. In this study, we tested pattern perception capacities of common ravens, Corvus corax , in a habituation–discrimination playback experiment. To enhance stimulus salience, call recordings of male and female ravens were used as acoustic elements, combined to create artificial territorial displays as target patterns. We habituated captive territorial raven pairs to displays following a particular pattern and subsequently exposed them to several test and control playbacks. Subjects spent more time visually orienting towards the loudspeaker in the discrimination phase when they heard structurally novel call combinations, violating the pattern presented during habituation. This demonstrates that songbirds, much like primates, can be sensitive to structural changes in auditory patterns and respond to them spontaneously, without training. [ABSTRACT FROM AUTHOR]

Automatic analysis of human spontaneous behavior has attracted increasing attention in recent years from researchers in computer vision. This paper proposes an approach for multi-modal video-induced emotion recognition, based on facial expression and electroencephalogram (EEG) technologies. Spontaneous facial expression is utilized as an external channel. A new feature, formed by percentage of nine facial expressions, is proposed for analyzing the valence and arousal classes. Furthermore, EEG is used as an internal channel supplementing facial expressions for more reliable emotion recognition. Discriminative spectral power and spectral power difference features are exploited for EEG analysis. Finally, these two channels are fused on feature-level and decision-level for multi-modal emotion recognition. Experiments are conducted on MAHNOB-HCI database, including 522 spontaneous facial expression videos and EEG signals from 27 participants. Moreover, human perception in emotion recognition compared to the proposed approach is tested with 10 volunteers. The experimental results and comparisons with the average human performance show the effectiveness of the proposed multi-modal approach. [ABSTRACT FROM AUTHOR]

The innate immune system comprises both a cellular and a humoral arm. Neutrophils are key effector cells of the immune and inflammatory responses and have emerged as a major source of humoral pattern recognition molecules (PRMs). These molecules, which include collectins, ficolins, and pentraxins, are specialised in the discrimination of self versus non-self and modified-self and share basic multifunctional properties including recognition and opsonisation of pathogens and apoptotic cells, activation and regulation of the complement cascade and tuning of inflammation. Neutrophils act as a reservoir of ready-made soluble PRMs, such as the long pentraxin PTX3, the peptidoglycan recognition protein PGRP-S, properdin and M-ficolin, which are stored in neutrophil granules and are involved in neutrophil effector functions. In addition, other soluble PRMs, such as members of the collectin family, are not expressed in neutrophils but can modulate neutrophil-dependent immune responses. Therefore, soluble PRMs are an essential part of the innate immune response and retain antibody-like effector functions. Here, we will review the expression and general function of soluble PRMs, focusing our attention on molecules involved in neutrophil effector functions. [ABSTRACT FROM AUTHOR]

The mechanisms for pattern completion and pattern separation are described in the context of a theory of hippocampal function in which the hippocampal CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The factors important in the pattern completion in CA3 and also a large number of independent memories stored in CA3 include: a sparse distributed representation, representations that are independent due to the randomizing effect of the mossy fibres, heterosynaptic long-term depression as well as long-term potentiation in the recurrent collateral synapses, and diluted connectivity to minimize the number of multiple synapses between any pair of CA3 neurons which otherwise distort the basins of attraction. Recall of information from CA3 is implemented by the entorhinal cortex perforant path synapses to CA3 cells, which in acting as a pattern associator allow some pattern generalization. Pattern separation is performed in the dentate granule cells using competitive learning to convert grid-like entorhinal cortex firing to place-like fields, and in the dentate to CA3 connections that have diluted connectivity. Recall to the neocortex is achieved by a reverse hierarchical series of pattern association networks implemented by the hippocampo-cortical backprojections, each one of which performs some pattern generalization, to retrieve a complete pattern of cortical firing in higher-order cortical areas. New results on competitive networks show which factors contribute to their ability to perform pattern separation, pattern clustering, and pattern categorisation, and how these apply in different hippocampal and neocortical systems. [ABSTRACT FROM AUTHOR]

Pattern separation (PS) has been defined as a process of reducing overlap between similar input patterns to minimize interference amongst stored representations. The present article describes this putative PS process from the “representational–hierarchical” perspective (R–H), which uses a hierarchical continuum instead of a cognitive modular processing framework to describe the organization of the ventral visual perirhinal–hippocampal processing stream. Instead of trying to map psychological constructs onto anatomical modules in the brain, the R-H model suggests that the function of brain regions depends upon what representations they contain. We begin by discussing a main principle of the R–H framework, the resolution of “ambiguity” of lower level representations via the formation of unique conjunctive representations in higher level areas, and how this process is remarkably similar to definitions of PS. Work from several species and experimental approaches suggest that this principle of resolution of ambiguity via conjunctive representations has considerable explanatory power, leads to wide possibilities for experimentation, and also supports some perhaps surprising conclusions. [ABSTRACT FROM AUTHOR]

In many pattern recognition and computer vision problems, it is often necessary to compare multiple sets of elements that are completely or partially overlapping and possibly corrupted by noise. Finding a correspondence between elements from the different sets is one of the crucial tasks that several computer vision, robotics or image registration methods have to cope with. The aim of this paper is to find a consensus correspondence between two sets of points, given several initial correspondences between these two sets. We present three different methods: iterative, voting and agglomerative. If the noise randomly affects the original data, we suppose that, while using the deducted correspondence, the process obtains better results than each individual correspondence. The different correspondences between two sets of points are obtained through different feature extractors or matching algorithms. Experimental validation shows the runtime and accuracy for the three methodologies. The agglomerative method obtains the highest accuracy compared to the other consensus methods and also the individual ones, while obtaining an acceptable runtime. [ABSTRACT FROM AUTHOR]

Fluency, the ease of processing, can increase familiarity and the ability to recognize previously encountered information. Mixing or blocking the pre-experimental familiarity of test probes alters the pattern of recognition Event-Related Potentials (ERPs) for picture stimuli (Bruett & Leynes, 2015). The present study tested this idea further by mixing or blocking pre-experimental familiarity of word stimuli. A test with only pre-experimentally familiar words (Experiment 1) elicited the prototypical mid-frontal FN400 old/new ERP difference, which is a correlate of familiarity. However, tests with a mix of pre-experimentally familiar and unfamiliar words (Experiment 2) elicited posterior N400 old/new differences. Based on the Unexpected Fluency Attribution model (Mecklinger & Bader, 2020), this pattern of results suggests that pre-experimental fluency can influence the use of relative (changes from recent exposure) or absolute (baseline levels accrued from experience) familiarity to make a recognition judgment. [ABSTRACT FROM AUTHOR]