Recognition Of Components Research Articles

Patterns of Computational Thinking Skills for Elementary Prospectives Teacher in Science Learning: Gender Analysis Studies

The PISA data results show that computational thinking abilities are still lacking. Computational thinking ability is influenced by gender. This research aims to analyze patterns of computational thinking skills of prospective elementary school teachers based on gender at 8 universities in Indonesia. In this research, the components of computational thinking skills analyzed are abstraction, algorithmic, decomposition, and pattern recognition. This research is a mix method research with research subjects as many as 234 prospective elementary school teachers at 8 higher educational institutions. The instruments used were test and interviews. The data analysis technique used is a quantitative data analysis technique using SEM PLS and for qualitative data analysis using miles and Huberman. The research results show that computational thinking skills are still low on the decomposition and pattern recognition components. Based on the SEM PLS test results, it shows that computational thinking abilities are related to gender. In general, the computational thinking ability of female students is slightly higher in all sub-indicators than men and there are differences in the pattern of computational thinking ability between male and female elementary school teacher prospective. The ability of prospective female elementary school teachers to answer in more detail and more structured, while the answers of male prospective teachers are shorter and less comprehensive. Recommendations for developing computational thinking skills by developing problem-based learning, contextual project-based learning and STEAM based learning.

A supersensitive electrochemical sensor based on RCA amplification-assisted “silver chain”-linked gold interdigital electrodes and CRISPR/Cas9 for the detection of Staphylococcus aureus in food

With the rising emphasis on food safety, technology to rapidly identify Staphylococcus aureus (S. aureus) is of great significance. Herein, we developed a novel electrochemical biosensor based on the CRISPR/Cas9 system and rolling circle amplification (RCA)-assisted “silver chain”-linked gold interdigital electrodes (Au-IDE). This sensor utilizes RCA to create DNA long chains that span the Au-IDE, and CRISPR/Cas9 as a recognition component to recognize capture/target dsDNA. Additionally, we used silver staining technology to improve detection sensitivity. Then, we detected S. aureus through impedance changes that occurred when the silver chain between the Au-IDE was connected or broke, with a limit of detection (LOD) of 7 CFU/mL and a detection time of 1.5 h. Lastly, we successfully employed this sensor to detect S. aureus in real food samples, making it a promising tool for food monitoring.

Recent update on biomimetic sensor technology for cancer diagnosis

Biomimetic sensors are devices that replicate biological receptors and enzymes with extraordinary accuracy and sensitivity by taking their cues from nature's effective mechanisms. They enable quick diagnosis and better treatment options by providing non-invasive detection of cancer-specific biomarkers in physiological fluids. This review addresses numerous optical and electronic biomimetic sensor types and emphasizes the benefits of their usage in cancer diagnostics. Some of the recognition components used in biomimetic sensors are aptamers, molecularly imprinted polymers (MIPs), and immunomimetic sensors. The widespread use of biomimetic sensors for cancer diagnostics has promise despite obstacles like specificity and cost-effectiveness. The use of wearable technology and artificial intelligence improves individualized cancer management even further. The overall objective of this review is to comprehensively explore the field of biomimetics sensors in the context of cancer diagnostics, highlighting their various types and recognition components and to emphasize on the significant benefits of biomimetics sensors, such as their accuracy and sensitivity, in enabling non-invasive detection of cancer-specific biomarkers. Overall, biomimetic sensors are a revolutionary development that advances our understanding of early cancer detection, improved patient outcomes, and enhanced healthcare.

MiRNA-Responsive CRISPR-Cas System via a DNA Regulator.

Clustered regularly interspaced short palindromic repeats (CRISPR)- CRISPR-associated protein 9 (Cas9) genome editing technology is widely used for gene editing because it provides versatility in genetic manipulation. Several methods for regulating CRISPR activity already exist for accurate editing, but these require complex engineering. Thus, a simple and convenient regulatory system is required. In this study, we devised a CRISPR activation system using a DNA regulator that can be activated by miRNAs. The designed regulator was divided into two parts. The inhibition component consisted of the protospacer-adjacent motif (PAM) and seed sequence, which are important for Cas9 target recognition and bind to the ribonucleoprotein (RNP) complex for inhibition. The miRNA recognition component has a single-stranded toehold DNA for target miRNA binding and a partial double-stranded DNA complementary to the remaining miRNA sequence. In the presence of target miRNAs, the structure of the regulator is disrupted by the miRNAs, leading to its dissociation from the RNP complex and subsequent restoration of CRISPR activity. This method is easy to design and can be applied to various miRNAs via simple sequence manipulation. Therefore, this strategy provides a general platform for controlled genome editing.

Association between major histocompatibility complex haplotypes and susceptibility of unvaccinated and vaccinated cattle to paratuberculosis

Bovine Johne’s disease (BJD) or paratuberculosis is caused by Mycobacterium avium spp. paratuberculosis (MAP) and is a worldwide problem among domestic and wild ruminants. While vaccines are available, natural differences in background immunity between breeds within species and between individuals within herds suggest that genetic differences may be able to be exploited in marker-assisted selection as an aid to disease control. The major histocompatibility complex (MHC) is an important component in immune recognition with considerable genetic variability. In this study, associations between the MHC and resistance to BJD were explored in dairy cattle across two herds in which some of the cattle had been vaccinated with Silirum® (n = 540 cows). A BJD susceptible animal was exposed to MAP and became infected, while a resistant animal was exposed but did not become infected. There are different ways to define both exposure and infection, with different levels of stringency, therefore many classifications of the same set of animals are possible and were included in the analysis. The polymorphic regions of major histocompatibility complex class I (MHC I) and class II (MHC II) genes were amplified from the genomic DNA by PCR and sequenced, targeting exons 2 and 3 of the classical and non-classical MHC I genes and exon 2 from the DRB3, DQA1, DQA2 + 3 and DQB MHC II genes. The frequencies of MHC I and MHC II haplotypes and alleles were determined in susceptible and resistant populations. In unvaccinated animals, seven MHC I haplotypes and seven MHC II haplotypes were associated with susceptibility while two MHC I and six MHC II haplotypes were associated with resistance (P < 0.05). In vaccinated animals, two MHC I and three MHC II haplotypes were associated with susceptibility, while one MHC I and two MHC II haplotypes were associated with resistance (P < 0.05). The alleles in significant haplotypes were also identified. Case definitions with higher stringency resulted in fewer animals being included in the analyses, but the power to detect an association was not reduced and there was an increase in strength and consistency of associations. Consistent use of stringent case definitions is likely to improve agreement in future association studies.

Recognition-Activated Primer-Mediated Exponential Rolling Circle Amplification for Signal Probe Production and Ultrasensitive Visual Detection of Ochratoxin A with Nucleic Acid Lateral Flow Strips.

We proposed a visual strategy for rapid and ultrasensitive detection of ochratoxin A (OTA) by integration of primer-mediated exponential rolling circle amplification (P-ERCA) with a designed nucleic acid lateral flow strip (LFS). The recognition component was preimmobilized in the tube by hybridization between the immobilized functionalized aptamer and complementary ssDNA. Recognition of OTA induces the release of complementary ssDNA from the tube, which will also act as the primer of the designed P-ERCA. Three nicking sites on the template P-ERCA could contribute to the production of enormous signal probes based on the simultaneous amplification-nicking model, which can be visually measured directly with the constructed nucleic acid LFS. Importantly, the nicked signal probe can also act as the trigger of the new-round RCA, achieving exponential growth of signal probes for measurement and signal enhancement. Taking advantage of the extraordinary amplification efficiency of P-ERCA and the simplicity of LFS, this P-ERCA-LFS method demonstrates ultrasensitive detection of OTA with a visual limit of detection as low as 100 fg/mL for qualitative screening and a limit of detection of 35 fg/mL for semiquantitative analysis. This designed strategy could also be utilized as a universal method for detection of other chemical analytes with the replacement of the aptamer for recognition, and the nucleic acid LFS unit could also be a useful protocol for direct ssDNA analysis.

New computer components for proximity assessment and recognition of double objects, which are encoded with symbols of the binary alphabet

Numerous binary measures of similarity and distance make it possible to effectively solve various tasks of recognition, classification, estimation of proximity of binary sequences, etc. Since the performance of the proposed systems largely depends on the choice of appropriate measures and distances, many researchers have spent considerable effort to find more efficient binary ratios for solving the mentioned tasks. Numerous binary relations, especially binary ones, have been proposed and investigated in various fields of science. However, it was not possible to determine one or a group of distances or similar measures effective in solving any tasks. In this regard, a number of new methods were proposed, based on the use of algorithms for learning, and on the external similarity of ratios that have different names of methods and proposed by different specialists, and the introduction of pre-processing of the initial data in order to determine the weighting factors when using them. Il.: 2; Bibliogr.: 15 titles.

Peptide-antifouling interface for monitoring β-amyloid based on electrochemiluminescence resonance energy transfer

In this study, a novel antifouling electrochemiluminescence (ECL) analytical platform has been developed for the highly sensitive quantification of β-amyloid (Aβ) peptides based on the ECL resonance energy transfer (ECL-RET) mechanism. Specifically, glassy carbon electrodes (GCE) were initially coated with graphite-phase carbon nitride (g-C3N4) nanosheets, followed by the electropolymerization of polyaniline (PANI) onto the electrode surface. Subsequently, a promising peptide motif candidate (COOH-CPPPPDKDKDKDKKLVFF) was immobilized onto the PANI-modified electrode, functioning as a critical component for both antifouling and specific recognition of full-length Aβ peptides. Furthermore, this peptide motif demonstrated inhibitory effects on Aβ aggregation and dissociation. Upon immobilization of the peptide motif, Aβ aptamer-CdS QDs were bound to the electrode surface through peptide-specific interactions with Aβ, thereby facilitating the highly sensitive ECL detection of Aβ. Under the optimal conditions, the proposed biosensor exhibited an Aβ detection range from 0.1 pM to 100 nM with a detection limit of 16.1 fM. As such, this innovative platform offers a straightforward approach to antifouling, quantification, and monitoring of Aβ concentrations in the blood samples.

A comprehensive review of arsenic contamination in India with an emphasis on its detection through biosensors and bioremediation from the aqueous system

AbstractIn India, where millions of people use groundwater for drinking, arsenic pollution in groundwater is a major environmental and public health issue. The review begins by analyzing the sources and causes of groundwater arsenic pollution, including natural geological processes and anthropogenic activities like mining and industrial discharge. It emphasizes India's most arsenic‐contaminated regions and the health risks of long‐term exposure. The review's second section focuses on the use of biosensors to find arsenic in groundwater. A potential method is provided by biosensors because of their great sensitivity, selectivity, and quick response. The benefits and drawbacks of several types of biosensors, including optical, electrochemical, and microbiological biosensors, are discussed. The utilization of nanomaterials, biomolecular recognition components, and miniature devices are only a few of the most recent developments in biosensor technology for arsenic detection that are studied. The review's last section discusses methods for bio‐remediating aqueous systems that have been contaminated with arsenic. Various bioremediation techniques are covered in detail, including microbial degradation, phytoremediation, and biosorption. The promise for removing arsenic using native microbes and plant species is highlighted, along with the difficulties of widespread application and long‐term sustainability. Overall, focusing on the detection of arsenic using biosensors and investigating bioremediation techniques, this review offers a thorough grasp of the arsenic poisoning in groundwater in India. In order to combat arsenic contamination and protect the health and wellbeing of the impacted populations in India, it emphasizes the need for interdisciplinary research and collaboration among scientists, engineers, policymakers, and local communities.

Note on the finite vertex-based partitioning of supramolecular chain in Dialkyltin

ABSTRACT Complex networks are difficult to understand and deal with, yet building a graph of any complex and huge structure, whether chemical or computer-related, is quite straightforward. In the form of a graph theory view, it becomes simple to design networks. Each component's recognition is aided by the ease of its production in the form of graph theory. Finding a vertex (node or atom) in a structure is similarly difficult; in this instance, resolvability parameters come into play. This technique plays a significant role in managing or allowing access to each vertex in relation to some chosen vertices called a resolving set that shapes an entire cluster of vertices into a unique form and every single vertex is easy to recognise or locate, whether the small or large the structure. This article contributes the bounds on the parameter of partition dimension of chemical complexes in dialkyltin of supramolecular chain from N-Salicylidene-L-Valine. We supposed the complexes of chains from 2,3,4 and another chemical network, and we demonstrated that the number of vertices has a significant impact on the composition of resolving partition sets.

Benzylfentanyl as a Surrogate Template for Fentanyl-Selective Imprinted Polymers.

The illicit use of fentanyl has led to hundreds of thousands of opioid-related deaths worldwide. Therefore, the detection of fentanyl by law enforcement and recreational users is of utmost importance. However, current detection methods are expensive, time-consuming, require special storage conditions, and necessitate complex instrumentation that is generally unportable and requires skilled personnel to operate. An alternative approach would be using molecularly imprinted polymers (MIPs) as the recognition component of a handheld sensor, testing strip, or color-based assay. In this work, a molecularly imprinted polymer was constructed using the functional monomer methacrylic acid (MAA) and the cross-linking monomer ethyleneglycol dimethacrylate (EGDMA), with benzylfentanyl (Bfen) as the template. The use of benzylfentanyl is advantageous because it closely mimics fentanyl's structure but does not cause any physiological narcotic effects. Important studies herein determined the optimum ratio of the template/functional monomer, with subsequent evaluations of selectivity of the MIP for the template and fentanyl versus the commonly encountered narcotics such as methamphetamine, cocaine, and heroin. The data obtained from the HPLC analysis showed that the Bfen-MIP was successful in selectively binding the template and actual fentanyl, better than other common narcotics.

An FPGA-Based Hardware Low-Cost, Low-Consumption Target-Recognition and Sorting System

In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in industrial item-sorting systems and proposes an object-recognition and sorting system for autonomous driving. In industrial sorting lines, goods-sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot’s real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consumption. These issues mean that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array)-based item-sorting system that achieves similar performance to current mainstream sorting systems but at a lower cost and consumption. The recognition component employs a morphological-recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degrees-of-freedom robotic arm is introduced into the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26 ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.

Narcissism predicts noise perception but not signal decoding in emotion

Grandiose narcissists claim that they have better-than-average emotion recognition abilities, but many objective tests do not support this claim. We sought to clarify the relation between grandiose (both agentic and communal) narcissism and emotion recognition by taking a closer look at the components of emotion recognition. In two studies (N1 = 147, N2 = 520), using culturally distinct samples and different stimulus materials, we investigated the relation between grandiose narcissism and signal decoding (accurate view of the intended emotion displayed in an expression) as well as noise perception (inaccurate deciphering of secondary emotions that are not part of the emotional message). Narcissism was inconsistently related to signal decoding, but consistently and positively related to noise perception. High grandiose (agentic and communal) narcissists are not necessarily better at signal decoding, but are more susceptible to noise perception. We discuss implications for narcissists’ social interactions and interpersonal relationships.

Evidence for selective emotional memory enhancement in a mock witness paradigm.

Elucidating the effects of negative emotion on eyewitness memory is an important part of understanding how witnesses remember and report criminal events. Extant research in this area has been inconsistent in its methodology and conclusions, thus warranting further empirical investigation. In the current experiment, participants (N = 204) viewed either a Negative or Neutral version of a video of a staged social interaction and had their memory assessed either immediately or 1 week later. Memory assessment consisted of both recall and recognition (lineup identification) components. The Negative video group showed enhanced recall for some aspects of the video compared to the Neutral group, but no enhancement (or impairment) of lineup identification performance. Theoretical and practical implications are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

AdaML: An Adaptive Meta-Learning model based on user relevance for user cold-start recommendation

User cold-start recommendation is a critical problem due to the sparse user–item​ interactions of new users. To address this issue, an emerging popular direction employs the meta-learning paradigm, which can quickly adapt to new tasks with sparsely labeled data. Early meta-learning recommendation works use the same global prior to initialize the parameters for each user’s preference learning task, which may be insufficient to discern users’ complex behaviors and leads to poor generalization. As a result, recent works have proposed the concept of relevance-based meta-learning recommendation, which ensures that similar priors are locally shared by related users with similar preferences, and have yielded promising results. However, as an important factor influencing the performance of relevance-based meta-learning recommendation, related user information is still not sufficiently explored and exploited in existing works. To that end, in this paper, we present a novel adaptive meta-learning model based on user relevance (AdaML) to further improve the user cold-start recommendations. Specifically, AdaML firstly proposes a related user recognition component, which recognizes related users with similar preferences by integrating the meta-path-based and the clustering-based strategies. The retrieved high-quality information of related users is then effectively utilized by a well-designed user relevance enhanced recommender, on one hand, to enhance user representations and tailor relevance-specific priors from the globally shared prior, on the other. Finally, extensive experiments on several public datasets demonstrate AdaML’s superior performance over state-of-the-art approaches in the user cold-start scenario.

A Review on Identity Management Using Face Detection Techniques

Face recognition, which has applications in iris and fingerprint recognition in addition to face recognition itself, is a crucial component of computer vision and object recognition. In everyday life, face detection is critical in security and forensic investigations. Face identification, whether in fixed photos or video sequences, poses considerable hurdles due to elements such as facial expressions, position fluctuations, occlusion, aging, and resolution. The authors propose the notion of face synthesis to overcome these issues and improve accuracy. They want to enhance recognition rates when working with different face databases by using face synthesis techniques. The study provides an in-depth examination of various facial recognition algorithms, highlighting the problems involved and emphasizing the need to improve efficiency and recognition rates. for large face databases. To show the efficacy of the suggested strategies, the authors compare rates of accuracy and recognition

Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface

BackgroundMotor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized. Thus, using convolutional neural network (CNNs) to extract discriminative features from EEG signals of different frequency components is a promising method in multisubject EEG recognition.MethodsThis paper presents a novel overlapping filter bank CNN to incorporate discriminative information from multiple frequency components in multisubject motor imagery recognition. Specifically, two overlapping filter banks with fixed low-cut frequency or sliding low-cut frequency are employed to obtain multiple frequency component representations of EEG signals. Then, multiple CNN models are trained separately. Finally, the output probabilities of multiple CNN models are integrated to determine the predicted EEG label.ResultsExperiments were conducted based on four popular CNN backbone models and three public datasets. And the results showed that the overlapping filter bank CNN was efficient and universal in improving multisubject motor imagery BCI performance. Specifically, compared with the original backbone model, the proposed method can improve the average accuracy by 3.69 percentage points, F1 score by 0.04, and AUC by 0.03. In addition, the proposed method performed best among the comparison with the state-of-the-art methods.ConclusionThe proposed overlapping filter bank CNN framework with fixed low-cut frequency is an efficient and universal method to improve the performance of multisubject motor imagery BCI.

Cyclic di-peptide in situ inhibited protein-aggregation

An increasing number of neurodegenerative diseases seem to be associated with protein misfolding that often leads to misfolded protein aggregates with a β-sheet conformation and accumulation in the brain which directly contributes to or modulates the associated pathology. Protein aggregation diseases like Huntington’s disease results from the deposition of aggregated huntingtin proteins within the nucleus, transmissible prion encephalopathies occur due to extracellular deposition of pathogenic prion proteins whereas Alzheimer’s disease from the accumulation of both extracellular β-amyloid and intracellular hyperphosphorylated tau protein aggregates. In the generalized purpose, we have taken the core sequence of amyloid-β (responsible for its aggregation) as the aggregating peptide (AP). Among the various emerging therapeutic approaches against aggregation-related degenerative diseases such as diminishing the monomeric precursor protein, inhibiting aggregation, or blocking aggregation-induced cellular toxicity pathways, we focussed on the strategy based on the inhibition of protein aggregation using rationally designed peptide inhibitors comprising both the recognition and β-breaking component in the sequence. The “O → N acyl migration” concept was used to form cyclic peptide in situ for the generation of a bent unit which may act as disruption moiety for the inhibition process. The kinetics of aggregation was characterized by various biophysical tools (ThT-assay, TEM, CD, and FTIR). Results implied that the designed inhibitor peptides (IP) might be valuable to inhibit all the related aggregated peptides.

Award Recipients for Pediatric Gastroenterology: A Descriptive Analysis of Gender Distribution.

Society awards are an important component of recognition in medicine and for career advancement such as promotion. Multiple studies conducted in pediatrics and gastroenterology have shown underrepresentation of women awardees even in fields with a higher proportion of women than men. To our knowledge, no such studies have been conducted in pediatric gastroenterology. We hypothesized that among all recipients, women would be underrepresented when compared to men and that women were more likely to be given teaching awards compared to other career achievement awards. We collected data on recipients of major recognition award presented by The North American Society for Pediatric Gastroenterology, Hepatology and Nutrition from 1987 to 2022. We found that 80.90% of the awards had been given to men and that most nominators were men. This study highlights inequities in women recipients for major awards and presents an opportunity for a call for action to examine and address the factors contributing to this gender inequity.

Dual-signal electrochemical immunosensor based on the oxidation and oxygen evolution signals of CoTe2@CeO2 nanocomposites: Application for prostate-specific antigen detection

Electrochemical immunosensors are widely used in the detection of biomarkers. In order to further improve the detection sensitivity and accuracy, we constructed a dual-signal electrochemical immunosensor with CoTe2@CeO2 nanocomposites (CoTe2@CeO2 NCs) as an electrocatalytic label and successfully applied it to the detection of prostate-specific antigen (PSA). It was discovered that the CoTe2@CeO2 NCs obtained by two-step hydrothermal process with outstanding oxygen evolution reaction (OER) activity and the Te- oxidation during OER, it produces a strong OER signal and a huge oxidation peak signal in neutral solution. Based on this, we propose an ingenious and simple dual-signal strategy that requires only one label, using CoTe2@CeO2 NCs as an electrochemical immunosensor electrocatalytic label. Experimental results show that the developed dual-signal electrochemical immunosensor with a broad linear response (0.002 ∼ 200 ng mL−1) and very low detection limits (Ipeak: 8.26 fg mL−1, IOER: 448 fg mL−1) for PSA. It was successfully used for the detection of real samples with good detection and self-calibration capability. Furthermore, this constructed dual-signal immunosensor may also be used to detect other biomarkers by altering the recognition components. In conclusion, the construction strategy of this novel immunosensor is very meaningful for the early detection and treatment of diseases.