Search results for: pattern recognition receptor

Authors: Janneth González, George Barreto, Ludis Morales, Angélica Sabogal

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Sea anemone neurotoxins are peptides that interact with Na+ and K+ channels, resulting in specific alterations on their functions. Some of these neurotoxins (1ROO, 1BGK, 2K9E, 1BEI) are important for the treatment of nearly eighty autoimmune disorders due to their specificity for Kv1.3 channel. The aim of this study was to identify the common residues among these neurotoxins by computational methods, and establish whether there is a pattern useful for the future generation of a treatment for autoimmune diseases. Our results showed eight new key common residues between the studied neurotoxins interacting with a histidine ring and the selectivity filter of the receptor, thus showing a possible pattern of interaction. This knowledge may serve as an input for the design of more promising drugs for autoimmune treatments.

Keywords: neurotoxins, potassium channel, Kv1.3, computational methods, autoimmune diseases

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Authors: Bing Gu

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Abstract—Most of Conventional facial recognition systems are based on image features, such as LBP, SIFT. Recently some DBN-based 2D facial recognition systems have been proposed. However, we find there are few DBN-based 3D facial recognition system and relative researches. 3D facial images include all the individual biometric information. We can use these information to build more accurate features, So we present our DBN-based face recognition system using Light Field. We can see Light Field as another presentation of 3D image, and Light Field Camera show us a way to receive a Light Field. We use the commercially available Light Field Camera to act as the collector of our face recognition system, and the system receive a state-of-art performance as convenient as conventional 2D face recognition system.

Keywords: DBN, face recognition, light field, Lytro

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Authors: Sara Hasani

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This research focuses on natural sudden onset disasters characterised as ‘occurring with little or no warning and often cause excessive injuries far surpassing the national response capacities’. Based on the panel analysis of the historic record of 4,252 natural onset disasters between 1980 to 2015, a predictive method was developed to predict the human impact of the disaster (fatality, injured, homeless) with less than 3% of errors. The geographical dispersion of the disasters includes every country where the data were available and cross-examined from various humanitarian sources. The records were then filtered into 4252 records of the disasters where the five predictive variables (disaster type, HDI, DRI, population, and population density) were clearly stated. The procedure was designed based on a combination of pattern recognition techniques and rule-based clustering for prediction and discrimination analysis to validate the results further. The result indicates that there is a relationship between the disaster human impact and the five socio-economic characteristics of the affected country mentioned above. As a result, a framework was put forward, which could predict the disaster’s human impact based on their severity rank in the early hours of disaster strike. The predictions in this model were outlined in two worst and best-case scenarios, which respectively inform the lower range and higher range of the prediction. A necessity to develop the predictive framework can be highlighted by noticing that despite the existing research in literature, a framework for predicting the human impact and estimating the needs at the time of the disaster is yet to be developed. This can further be used to allocate the resources at the response phase of the disaster where the data is scarce.

Keywords: disaster management, natural disaster, pattern recognition, prediction

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Authors: Hamid Mustafa, Adeela Ajmal, Kim EuiSoo, Noor-ul-Ain

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World wild, buffalo production is considered as most important component of food industry. Efficient buffalo production is related with reproductive performance of this species. Lack of knowledge of reproductive efficiency and its related genes in buffalo species is a major constraint for sustainable buffalo production. In this study, we performed some bioinformatics analysis on Follicle Stimulating Hormone Receptor (FSHR) gene and explored the possible relationship of this gene among different buffalo breeds and with other farm animals. We also found the evolution pattern for this gene among these species. We investigate CDS lengths, Stop codon variation, homology search, signal peptide, isoelectic point, tertiary structure, motifs and phylogenetic tree. The results of this study indicate 4 different motif in this gene, which are Activin-recp, GS motif, STYKc Protein kinase and transmembrane. The results also indicate that this gene has very close relationship with cattle, bison, sheep and goat. Multiple alignment (MA) showed high conservation of motif which indicates constancy of this gene during evolution. The results of this study can be used and applied for better understanding of this gene for better characterization of Follicle Stimulating Hormone Receptor (FSHR) gene structure in different farm animals, which would be helpful for efficient breeding plans for animal’s production.

Keywords: buffalo, FSHR gene, bioinformatics, production

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Authors: Ziyue Zeng

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Bacteriophages are viruses which infect bacteria specifically. Over the past decades, phage λ has been extensively studied, especially its interaction with the Escherichia coli LamB (EcLamB) protein receptor. Nonetheless, despite the enormous numbers and near-ubiquity of environmental phages, aside from phage λ, there is a paucity of information on other phages which target EcLamB as a receptor. In this study, to answer the question of whether there are other EcLamB-targeting phages in the natural environment, a simple and convenient method was developed and used for isolating environmental phages which target a particular surface structure of a particular bacterium; in this case, the EcLamB outer membrane protein. From the enrichments with the engineered bacterial hosts, a collection of EcLamB-targeting phages (ΦZZ phages) were easily isolated. Intriguingly, unlike phage λ, an obligate EcLamB-dependent phage in the Siphoviridae family, the newly isolated ΦZZ phages alternatively recognised EcLamB or E. coli OmpC (EcOmpC) as a receptor when infecting E. coli. Furthermore, ΦZZ phages were suggested to represent new species in the Tequatrovirus genus in the Myoviridae family, based on phage morphology and genomic sequences. Most phages are thought to have a narrow host range due to their exquisite specificity in receptor recognition. With the ability to optionally recognise two receptors, ΦZZ phages were considered relatively promiscuous. Via the heterologous expression of EcLamB on the bacterial cell surface, the host range of ΦZZ phages was further extended to three different enterobacterial genera. Besides, an interesting selection of evolved phage mutants with a broader host range was isolated, and the key mutations involved in their evolution to adapt to new hosts were investigated by genomic analysis. Finally, and importantly, two ΦZZ phages were found to be putative generalised transducers, which could be exploited as tools for DNA manipulations.

Keywords: environmental microbiology, phage, microbe-host interactions, microbial ecology

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Authors: Jianan Sun, Ziwen Ye

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Multidimensional computerized adaptive testing (MCAT) is a popular research topic in psychometrics. It is important for practitioners to clearly know the item-trait patterns of administered items when a test like MCAT is operated. Item-trait pattern recognition refers to detecting which latent traits in a psychological test are measured by each of the specified items. If the item-trait patterns of the replenished items in MCAT item pool are well detected, the interpretability of the items can be improved, which can further promote the abilities of the examinees who attending the MCAT to be accurately estimated. This research explores to solve the item-trait pattern recognition problem of the replenished items in MCAT item pool from the perspective of statistical variable selection. The popular multidimensional item response theory model, multidimensional two-parameter logistic model, is assumed to fit the response data of MCAT. The proposed method uses the least absolute shrinkage and selection operator (LASSO) to detect item-trait patterns of replenished items based on the essential information of item responses and ability estimates of examinees collected from a designed MCAT procedure. Several advantages of the proposed method are outlined. First, the proposed method does not strictly depend on the relative order between the replenished items and the selected operational items, so it allows the replenished items to be mixed into the operational items in reasonable order such as considering content constraints or other test requirements. Second, the LASSO used in this research improves the interpretability of the multidimensional replenished items in MCAT. Third, the proposed method can exert the advantage of shrinkage method idea for variable selection, so it can help to check item quality and key dimension features of replenished items and saves more costs of time and labors in response data collection than traditional factor analysis method. Moreover, the proposed method makes sure the dimensions of replenished items are recognized to be consistent with the dimensions of operational items in MCAT item pool. Simulation studies are conducted to investigate the performance of the proposed method under different conditions for varying dimensionality of item pool, latent trait correlation, item discrimination, test lengths and item selection criteria in MCAT. Results show that the proposed method can accurately detect the item-trait patterns of the replenished items in the two-dimensional and the three-dimensional item pool. Selecting enough operational items from the item pool consisting of high discriminating items by Bayesian A-optimality in MCAT can improve the recognition accuracy of item-trait patterns of replenished items for the proposed method. The pattern recognition accuracy for the conditions with correlated traits is better than those with independent traits especially for the item pool consisting of comparatively low discriminating items. To sum up, the proposed data-driven method based on the LASSO can accurately and efficiently detect the item-trait patterns of replenished items in MCAT.

Keywords: item-trait pattern recognition, least absolute shrinkage and selection operator, multidimensional computerized adaptive testing, variable selection

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Authors: Merry Meryam Martgrita, Marselina Irasonia Tan

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One of several aggresive cancer is cancer that overexpress c-erbB2 receptor along with the expression of estrogen receptor. Components of extracellular matrix play an important role to increase cancer cells proliferation, migration and invasion. Both components can affect cancer development by regulating the signal transduction pathways in cancer cells. In recent research, SKOV-3 ovarian cancer cell line, that overexpress c-erbB2 receptor was cultured on type IV collagen and treated with estradiol-17β, to reveal the role of both components on RNA and protein level of c-erbB2 receptor. In this research we found a modulation phenomena of increasing and decreasing of c-erbB2 RNA level and a stabilisation phenomena of c-erbB2 protein expression due to estradiol-17β and type IV collagen. It seemed that estradiol-17β has an important role to increase c-erbB2 transcription and the stability of c-erbB2 protein expression. Type IV collagen has an opposite role. It blocked c-erbB2 transcription when it bound to integrin receptor in SKOV-3 cells.

Keywords: c-erbB2, estradiol-17β, SKOV-3, type IV collagen

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Authors: Yulong Wang, Yuan Yan Tang, Cuiming Zou, Lina Yang

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This paper presents a novel sparse representation method for robust pattern classification. Generalized orthogonal matching pursuit (GOMP) is a recently proposed efficient sparse representation technique. However, GOMP adopts the mean square error (MSE) criterion and assign the same weights to all measurements, including both severely and slightly corrupted ones. To reduce the limitation, we propose an information-theoretic GOMP (ITGOMP) method by exploiting the correntropy induced metric. The results show that ITGOMP can adaptively assign small weights on severely contaminated measurements and large weights on clean ones, respectively. An ITGOMP based classifier is further developed for robust pattern classification. The experiments on public real datasets demonstrate the efficacy of the proposed approach.

Keywords: correntropy induced metric, matching pursuit, pattern classification, sparse representation

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Authors: Juliana Barcelos Cordeiro, Khashayar Mahani, Farbod Farzan, Mohsen A. Jafari

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Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.  

Keywords: energy consumption forecasting, energy efficiency, load disaggregation, pattern recognition approach

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Authors: Degale Desta, Cheng Jian

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The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions.

Keywords: deep learning, face recognition, identification, fast-RCNN

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Authors: H. Yousefnia, MS. Mousavi-Daramoroudi, S. Zolghadri, F. Abbasi-Davani

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High expression of somatostatin receptors on a wide range of human tumours makes them as potential targets for peptide receptor radionuclide tomography. A series of octreotide analogues were synthesized while [DOTA-DPhe1, Tyr3]octreotide (DOTATOC) indicated advantageous properties in tumour models. In this study, 177Lu-DOTATOC was prepared with the radiochemical purity of higher than 99% in 30 min at the optimized condition. Biological behavior of the complex was studied after intravenous injection into the Syrian rats. Major difference uptake was observed compared to 177LuCl3 solution especially in somatostatin receptor-positive tissues such as pancreas and adrenal.

Keywords: Biodistribution, 177Lu, Octreotide, Syrian rats

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Authors: Tareg Belali, Mosleh Abomughaid, Muhanad Alhujaily

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HNF4a is one of the steroid hormone receptor family of transcription factors with roles in the development of the liver and the regulation of several critical metabolic pathways, such as glycolysis, drug metabolism, and apolipoproteins and blood coagulation. The transcriptional potency of HNF4a is well known due to its involvement in diabetes and other metabolic diseases. However, recently HNF4a has been discovered to be closely associated with several haematological disorders, mainly because of genetic mutations, drugs, and hepatic disorders. We review HNF4a structure and function and its role in haematological disorders. We discuss possible good therapies that are based on targeting HNF4a.

Keywords: hepatocyte nuclear factor 4 alpha, HNF4a nuclear receptor, steroid hormone receptor family of transcription factors, hematological disorders

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Authors: Norah Al Hokbany, Ibrahim Al Jammaz, Basem Al Otaibi, Yousif Al Malki, Subhani M. Okarvi

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Objective: The folate receptor is over-expressed in a wide variety of human tumors. Conjugates of folate have been shown to be selectively taken up by tumor cells via the folate receptor. In an attempt to develop new folate radiotracers with favorable biochemical properties for detecting folate receptor-positive cancers. Methods: we synthesized ⁶⁴Cu-NOTA- and ⁶⁴Cu-NOTAM-folate conjugates using a straightforward and simple one-step reaction. Radiochemical yields were greater than 95% (decay-corrected) with a total synthesis time of less than 20 min. Results: Radiochemical purities were always greater than 98% without high-performance liquid chromatography (HPLC) purification. These synthetic approaches hold considerable promise as a rapid and simple method for ⁶⁴Cu-folate conjugate preparation with high radiochemical yield in a short synthesis time. In vitro tests on the KB cell line showed that significant amounts of the radio conjugates were associated with cell fractions. Bio-distribution studies in nude mice bearing human KB xenografts demonstrated a significant tumor uptake and favorable bio-distribution profile for ⁶⁴Cu-NOTA- and ⁶⁴Cu-NOTAM-folate conjugate. The uptake in the tumors was blocked by the excess injection of folic acid, suggesting a receptor-mediated process. Conclusion: These results demonstrate that the ⁶⁴Cu-NOTAM-folate conjugate may be useful as a molecular probe for the detection and staging of folate receptor-positive cancers, such as ovarian cancer and their metastasis, as well as monitoring tumor response to treatment.

Keywords: folate, receptor, tumor imaging, ⁶⁴Cu-NOTA-folate, PET

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Authors: Apurba Kanti Chowdhury, Taslima Zahan

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The inclusion of mungbean in a cropping pattern not only increases the cropping intensity but also enriches soil health as well as ensures nutrition for the fast-growing population of Bangladesh. A study was conducted in the farmers’ field during 2013-14 and 2014-15 to observe the performance of four-crop based improve cropping pattern Potato-Mungbean-Jute -t.aman rice against the existing cropping pattern Potato-Jute -t.aman rice at Domar, Nilphamari followed by randomized complete block design with three replications. Two years study revealed that inclusion of mungbean and better management practices in improved cropping pattern provided higher economic benefit over the existing pattern by 73.1%. Moreover, the average yield of potato increased in the improved pattern by 64.3% compared to the existing pattern; however yield of jute and t.aman rice in improved pattern declined by 5.6% and 10.7% than the existing pattern, respectively. Nevertheless, the additional yield of mungbean in the improved pattern helped to increase rice equivalent yield of the whole pattern by 38.7% over the existing pattern. Thus, the addition of mungbean in the existing pattern Potato-Jute -t.aman rice seems to be profitable for the farmers and also might be sustainable if the market channel of mungbean developed.

Keywords: crop diversity, food nutrition, production efficiency, yield improvement

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Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

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Authors: Srie Rezeki Nur Endah, Richa Mardianingrum

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Cancer is one of the most feared diseases and is considered the leading cause of death worldwide. Generally, cancer drugs are synthetic drugs with relatively more expensive prices and have harmful side effects, so many people turn to traditional medicine, for example by utilizing herbal medicine. Erythrina poeppigiana is one of the plants that can be used as a medicinal plant containing 10,11-dihidroerisodin compounds that are useful anticancer etnofarmakologi. The purpose of this study was to identify the target of 10,11 dihydroerisodin receptor compound as in silico anticancer candidate. The pure isolate was tested physicochemically by MS (Mass Spectrometry), UV-Vis (Ultraviolet – Visible), IR (Infra Red), 13C-NMR (Carbon-13 Nuclear Magnetic Resonance), 1H-NMR (Hydrogen-1 Nuclear Magnetic Resonance), to obtain the structure of 10,11-dihydroerisodin alkaloid compound then identified to target receptors in silico. From the results of the study, it was found that 10,11-dihydroerisodin compound can work on the Serine / threonine-protein kinase Chk1 receptor that serves as an anti-cancer candidate.

Keywords: anti-cancer, Erythrina poeppigiana, target receptor, 10, 11- dihidroerisodin

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Authors: B. Sadek, N. Khan, Shreesh K. Ojha, Adel Sadeq, D. Lazewska, K. Kiec-Kononowicz

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The involvement of Histamine H3 receptors (H3Rs) in memory and the potential role of H3R antagonists in pharmacological control of neurodegenerative disorders, e.g., Alzheimer disease (AD) is well established. Therefore, the memory-enhancing effects of the H3R antagonist DL77 on MK801-induced cognitive deficits were evaluated in passive avoidance paradigm (PAP) and novel object recognition (NOR) tasks in adult male rats, applying donepezil (DOZ) as a reference drug. Animals pretreated with acute systemic administration of DL77 (2.5, 5, and 10 mg/kg, i.p.) were significantly ameliorated in regard to MK801-induced memory deficits in PAP. The ameliorative effect of most effective dose of DL77 (5 mg/kg, i.p.) was abrogated when animals were pretreated with a co-injection with the H3R agonist R-(α)-methylhistamine (RAMH, 10 mg/kg, i.p.). Moreover, and in the NOR paradigm, DL77 (5 mg/kg, i.p.) reversed MK801-induced deficits long-term memory (LTM), and the DL77-provided procognitive effect was comparable to that of reference drug DOZ, and was reversed when animals were co-injected with RAMH (10 mg/kg, i.p.). However, DL77(5 mg/kg, i.p.) failed to alter short-term memory (STM) impairment in NOR test. Furthermore, DL77 (5 mg/kg) failed to induce any alterations of anxiety and locomotor behaviors of animals naive to elevated-plus maze (EPM), indicating that the ameliorative effects observed in PAP or NOR tests were not associated to alterations in emotions or in natural locomotion of tested animals. These results reveal the potential contribution of H3Rs in modulating CNS neurotransmission systems associated with neurodegenerative disorders, e.g., AD.

Keywords: histamine H3 receptor, antagonist, learning and memory, Alzheimer's disease, neurodegeneration, passive avoidance paradigm, novel object recognition, behavioral research

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Authors: Woolfrey K., Woolfrey M., Bolton C. L., Warchuk D.

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Objectives: Osteoarthritis is the most common joint disease of adults worldwide. Despite total knee arthroplasty (TKA) demonstrating high levels of success, 20% of patients report dissatisfaction with their result. VOXX Wellness Stasis Socks are embedded with a proprietary pattern of neuro-receptor activation points that have been proven to activate a precise neuro-response, according to the pattern theory of haptic perception, which stimulates improvements in pain and function. The use of this technology in TKA patients may prove beneficial as an adjunct to recovery as many patients suffer from deficits to their proprioceptive system caused by ligamentous damage and alterations to mechanoreceptors during the procedure. We hypothesized that VOXX Wellness Stasis Socks are a safe, cost-effective, and easily scalable strategy to support TKA patients through their recovery. Design: Double-blinded, placebo-controlled randomized trial. Participants: Patients scheduled to receive TKA were considered eligible for inclusion in the trial. Interventions: Intervention group (I): VOXX Wellness Stasis socks containing receptor point-activation technology. Control group (C): VOXX Wellness Stasis socks without receptor point-activation technology. Sock use during the waking hours x 6 weeks. Main Outcome Measures: Western Ontario McMaster Universities Osteoarthritis Index (WOMAC) questionnaire completed at baseline, 2 weeks, and 6 weeks to assess pain, stiffness, and physical function. Results: Data analysis using SPSS software. P-values, effect sizes, and confidence intervals are reported to assess clinical relevance of the finding. Physical status classifications were compared using t-test. Within-subject and between-subject differences in the mean WOMAC were analyzed by ANOVA. Effect size was analyzed using Cramer’s V. Consistent improvement in WOMAC scores for pain and stiffness at 2 weeks post op in the I over the C group. The womac scores assessing physical function showed a consistent improvement at both 2 and 6 weeks post op in the I group compared to C group. Conclusions: VOXX proved to be a low cost, safe intervention in TKA to help patients improve with regard to pain, stiffness, and physical function. Disclosures: None

Keywords: osteoarthritis, RCT, pain management, total knee arthroplasty

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Authors: Van Nhan Nguyen, Harald Holone

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Over the past few years, a lot of research has been conducted to bring Automatic Speech Recognition (ASR) into various areas of Air Traffic Control (ATC), such as air traffic control simulation and training, monitoring live operators for with the aim of safety improvements, air traffic controller workload measurement and conducting analysis on large quantities controller-pilot speech. Due to the high accuracy requirements of the ATC context and its unique challenges, automatic speech recognition has not been widely adopted in this field. With the aim of providing a good starting point for researchers who are interested bringing automatic speech recognition into ATC, this paper gives an overview of possibilities and challenges of applying automatic speech recognition in air traffic control. To provide this overview, we present an updated literature review of speech recognition technologies in general, as well as specific approaches relevant to the ATC context. Based on this literature review, criteria for selecting speech recognition approaches for the ATC domain are presented, and remaining challenges and possible solutions are discussed.

Keywords: automatic speech recognition, asr, air traffic control, atc

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Authors: Malika Yaici, Soraya Aloui, Sara Semchaoui

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This paper deals with human activity recognition from sensor data. It is an active research area, and the main objective is to obtain a high recognition rate. In this work, a recognition system based on expert systems is proposed; the recognition is performed using the objects, object states, and gestures and taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions and the activity). The system recognizes complex activities after decomposing them into simple, easy-to-recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision.

Keywords: human activity recognition, ubiquitous computing, context-awareness, expert system

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Authors: Ainagul Yermekova, Liudmila Goncharenko, Ali Baghirzade, Sergey Sybachin

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In this article, we address the problem of Kazakhstan's transition to the Latin alphabet. The transition process started in 2017 and is scheduled to be completed in 2025. In connection with these events, the problem of recognizing the characters of the new alphabet is raised. Well-known character recognition programs such as ABBYY FineReader, FormReader, MyScript Stylus did not recognize specific Kazakh letters that were used in Cyrillic. The author tries to give an assessment of the well-known method of character recognition that could be in demand as part of the country's transition to the Latin alphabet. Three methods of character recognition: template, structured, and feature-based, are considered through the algorithms of operation. At the end of the article, a general conclusion is made about the possibility of applying a certain method to a particular recognition process: for example, in the process of population census, recognition of typographic text in Latin, or recognition of photos of car numbers, store signs, etc.

Keywords: text detection, template method, recognition algorithm, structured method, feature method

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Authors: Gheida J. Shahrour, Martin J. Russell

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The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively.

Keywords: person recognition, topic recognition, culture recognition, 3D body movement signals, variability compensation

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Authors: Arunima Verma, Padmabati Mondal

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Serotonin-receptor binding plays a key role in several neurological and biological processes, including mood, sleep, hunger, cognition, learning, and memory. In this article, we performed molecular dynamics simulation to examine the key residues that play an essential role in the binding of serotonin to the G-protein-coupled 5-HT₁ᴮ receptor (5-HT₁ᴮ R) via electrostatic interactions. An end-point free energy calculation method (MM-PBSA) determines the stability of the 5-HT1B R due to serotonin binding. The single-point mutation of the polar or charged amino acid residues (Asp129, Thr134) on the binding sites and the calculation of binding free energy validate the importance of these residues in the stability of the serotonin-receptor complex. Principal component analysis indicates the serotonin-bound 5-HT1BR is more stabilized than the apo-receptor in terms of dynamical changes. The difference dynamic cross-correlations map shows the correlation between the transmembrane and mini-Go, which indicates signal transduction happening between mini-Go and the receptor. Allosteric communication reveals the key nodes for signal transduction in 5-HT1BR. These results provide useful insights into the signal transduction pathways and mutagenesis study to regulate the functionality of the complex. The developed protocols can be applied to study local non-covalent interactions and long-range allosteric communications in any protein-ligand system for computer-aided drug design.

Keywords: allostery, CADD, MD simulations, MM-PBSA

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: retail stores, faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition

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Authors: Jihoon Yang, Jeong II Choi

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Mincle is expressed in macrophages and is members of immunoreceptors induced after exposure to various stimuli and stresses. Mincle receptor activation promotes the production of these substances by increasing the transcription of inflammatory cytokines and chemokines. Cytokines, which play an important role in the initiation and maintenance of such inflammatory pain diseases, have a significant effect on sensory neurons in addition to their enhancement and inhibitory effects on immune and inflammatory cells as mediators of cell interaction. Glial cells in the central nervous system play a critical role in development and maintenance of chronic pain states. Microglia are tissue-resident macrophages in the central nervous system, and belong to a group of mononuclear phagocytes. In the central nervous system, mincle receptor is present in neurons and glial cells of the brain.This study was performed to identify the Mincle receptor in the spinal cord and to investigate the effect of Mincle receptor activation on nociception and the changes of microglia. Materials and Methods: C-type lectins(Mincle) was identified in spinal cord of Male Sprague–Dawley rats. Then, mincle receptor ligand (TDB), via an intrathecal catheter. Mechanical allodynia was measured using von Frey test to evaluate the effect of intrathecal injection of TDB. Result: The present investigation shows that the intrathecal administration of TDB in the rat produces a reliable and quantifiable mechanical hyperalgesia. In addition, The mechanical hyperalgesia after TDB injection gradually developed over time and remained until 10 days. Mincle receptor is identified in the spinal cord, mainly expressed in neuronal cells, but not in microglia or astrocyte. These results suggest that activation of mincle receptor pathway in neurons plays an important role in inducing activation of microglia and inducing mechanical allodynia.

Keywords: mincle, spinal cord, pain, microglia

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Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton

Abstract:

Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.

Keywords: pollen recognition, logistic model tree, expectation-maximization, local binary pattern

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Authors: Yiding Qu, Svetlana V. Komarova

Abstract:

Chemokines acting through their cognate chemokine receptors guide the directional migration of the cell along the chemokine gradient. Several chemokine receptors were recently identified as non-signaling (decoy), based on their ability to bind the chemokine but produce no measurable signal in the cell. The function of these decoy receptors is not well understood. We examined the expression of a decoy receptor CCRL1 and a signaling receptor that binds to the same ligands, CCR7, in prostate cancer using publically available microarray data (www.oncomine.org). The expression of both CCRL1 and CCR7 increased in an approximately half of prostate carcinoma samples and the majority of metastatic cancer samples compared to normal prostate. Moreover, the expression of CCRL1 positively correlated with the expression of CCR7. These data suggest that CCR7 and CCRL1 can be used as clinical markers for the early detection of transformation from carcinoma to metastatic cancer. In addition, these data support our hypothesis that the non-signaling chemokine receptors actively stimulate cell migration.

Keywords: bioinformatics, cell migration, decoy receptor, meta-analysis, prostate cancer

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, Rebecca K. Rossi, Yanmin Yuan, Xianpei Li

Abstract:

The fiscal growth of any country hinges on the prudent administration of water resources. The river Yamuna and Ganga are measured as the life line of India as it affords the needs for life to endure. Earth observation over remote sensing images permits the precise description and identification of ingredients on the superficial from space and airborne platforms. Multiple and heterogeneous image sources are accessible for the same geographical section; multispectral, hyperspectral, radar, multitemporal, and multiangular images. In this paper, a taxonomical learning of the fluvial geomorphological features of river Yamuna and Ganga over doon valley using color fusion of multispectral remote sensing images was performed. Experimental results exhibited that the segmentation based colorization technique stranded on pattern recognition, and color mapping fashioned more colorful and truthful colorized images for geomorphological feature extraction.

Keywords: color fusion, geomorphology, fluvial processes, multispectral images, pattern recognition

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Authors: Wei-Ting Kuo, Feng-Huei Lin

Abstract:

Epidermal growth factor receptor (EGFR) is often constitutively stimulated in cancer owing to the binding of ligands such as epidermal growth factor (EGF), so it is necessary to investigate the interaction between EGFR and its targeting biomolecules which were over ligands binding. This study would focus on the binding affinity and adhesion force of two targeting products anti-EGFR monoclonal antibody (mAb) and peptide A to EGFR comparing with EGF. Surface plasmon resonance (SPR) was used to obtain the equilibrium dissociation constant to evaluate the binding affinity. Atomic force microscopy (AFM) was performed to detect adhesion force. The result showed that binding affinity of mAb to EGFR was higher than that of EGF to EGFR, and peptide A to EGFR was lowest. The adhesion force between EGFR and mAb that was higher than EGF and peptide A to EGFR was lowest. From the studies, we could conclude that mAb had better adhesion force and binding affinity to EGFR than that of EGF and peptide A. SPR and AFM could confirm the interaction between receptor and targeting ligand easily and carefully. It provide a platform to screen ligands for receptor targeting and drug delivery.

Keywords: adhesion force, binding affinity, epidermal growth factor receptor, target molecule

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Authors: Malika Yaici, Soraya Aloui, Sara Semchaoui

Abstract:

Recognition of human activities from sensor data is an active research area, and the main objective is to obtain a high recognition rate. In this work, we propose a recognition system based on expert systems. The proposed system makes the recognition based on the objects, object states, and gestures, taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions, and the activity). This work focuses on complex activities which are decomposed into simple easy to recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision.

Keywords: human activity recognition, ubiquitous computing, context-awareness, expert system

Procedia PDF Downloads 104