Discrimination Ratio Research Articles

The State of Hiring Discrimination: A Meta-Analysis of (Almost) All Recent Correspondence Experiments

Notwithstanding the improved integration of various minority groups in the workforce, unequal treatment in hiring still hinders many individuals' access to the labour market. To tackle this inaccessibility, it is essential to know which and to what extent minority groups face hiring discrimination. Past meta-studies have charted parts of the discrimination literature but permit only limited comparisons across minority groups. This meta-analysis synthesises a quasi-exhaustive register of correspondence experiments on hiring discrimination published between 2005 and 2020. Using a random-effects model, we computed pooled discrimination ratios concerning a total of ten discrimination grounds upon which unequal treatment in hiring is forbidden under United States federal or state law. First, we find that hiring discrimination against candidates with disabilities, older candidates, and less physically attractive candidates is at least equally severe as the unequal treatment of candidates with salient racial or ethnic characteristics. Remarkably, hiring discrimination against older applicants is even higher in Europe than in the United States. Furthermore, unequal treatment in hiring based on sexual orientation seems to be prompted mainly by signalling activism through an affiliation with an LGB+ rights organisation rather than same-sex orientation in itself. Last, hiring discrimination remains pervasive. Aside from a decrease in hiring discrimination based on race and national origin in Europe, we find no structural evidence of temporal changes in hiring discrimination based on the various other grounds within the scope of this review.

Modulation of the rat hippocampal-cortex network and episodic-like memory performance following entorhinal cortex stimulation.

AimsEntorhinal cortex (EC) deep brain stimulation (DBS) has shown a memory enhancement effect. However, its brain network modulation mechanisms remain unclear. The present study aimed to investigate the functional connectivity in the rat hippocampal‐cortex network and episodic‐like memory performance following EC‐DBS.Methods7.0 T functional MRI (fMRI) scans and episodic‐like memory tests were performed 3 days and 28 days after EC‐DBS in healthy rats. The fMRI data processing was focused on the power spectra, functional connectivity, and causality relationships in the hippocampal‐cortex network. In addition, the exploration ratio for each object and the discrimination ratio of the “when” and “where” factors were calculated in the behavioral tests.ResultsEC‐DBS increased the power spectra and the functional connectivity in the prefrontal‐ and hippocampal‐related networks 3 days after stimulation and recovered 4 weeks later. Both networks exhibited a strengthened connection with the EC after EC‐DBS. Further seed‐based functional connectivity comparisons showed increased connectivity among the prefrontal cortex, hippocampus and EC, especially on the ipsilateral side of DBS. The dentate gyrus is a hub region closely related to both the EC and the prefrontal cortex and receives information flow from both. Moreover, acute EC‐DBS also enhanced the discrimination ratio of the “where” factor in the episodic‐like memory test on Day 3.ConclusionEC‐DBS caused a reversible modulation effect on functional connectivity in the hippocampal‐cortex network and episodic‐like memory performance.

High-impedance fault modeling and classification in power distribution networks

This work presents a novel method for the classification of high-impedance faults (HIFs) in power distribution networks based on the association of higher-order statistics (HOS) and a multilayer perceptron (MLP) artificial neural network (ANN). An alternative model is developed to represent the HIF phenomenon considering five different contact surfaces with the ground. A broad analysis comprising six types of typical events that occur in distribution networks is performed in the Alternative Transient Program (ATP) software, including several conditions such as: normal operation; single-phase faults; two-phase faults; three-phase faults; energization of transformers and capacitor banks; switching of inductive loads; as well as faults involving five modeled surfaces. HOS is combined with Fisher’s discriminant ratio (FDR) to extract the best characteristics. At the end, the MLP-type ANN is used to recognize the specific patterns of each event aiming to identify each event accurately, especially HIFs. The obtained results demonstrate that the proposed technique proves to be a reliable and accurate tool, achieving classification hits above 98%.

Random subspace and random projection nearest neighbor ensembles for high dimensional data

The random subspace and the random projection methods are investigated and compared as techniques for forming ensembles of nearest neighbor classifiers in high dimensional feature spaces. The two methods have been empirically evaluated on three types of high-dimensional datasets: microarrays, chemoinformatics, and images. Experimental results on 34 datasets show that both the random subspace and the random projection method lead to improvements in predictive performance compared to using the standard nearest neighbor classifier, while the best method to use depends on the type of data considered; for the microarray and chemoinformatics datasets, random projection outperforms the random subspace method, while the opposite holds for the image datasets. An analysis using data complexity measures, such as attribute to instance ratio and Fisher’s discriminant ratio, provide some more detailed indications on what relative performance can be expected for specific datasets. The results also indicate that the resulting ensembles may be competitive with state-of-the-art ensemble classifiers; the nearest neighbor ensembles using random projection perform on par with random forests for the microarray and chemoinformatics datasets.

High sucrose diet does not impact spatial cognition in rats using advanced touchscreen technology

IntroductionWestern diets, including those consisting of saturated fats, simple sugars and processed foods, is rising at an unprecedented rate. These lead to obesity and metabolic diseases, and possibly cognitive deficits. Exploring this, recent studies demonstrate marked impairment in spatial learning in rodents exposed to high-sugar diets. We utilised advanced touchscreen technology to assess several spatial and non-spatial components of cognition in rats chronically exposed to a high sucrose diet. MethodsMale Wistar rats received 70 ml of 10% sucrose solution each day, or control tap water, persisting for the experiment duration (total n = 32). After 5 weeks of diet, rats performed Pairwise Discrimination, Location Discrimination, or Progressive Ratio tasks on automated touchscreens, and performance compared between groups. ResultsSucrose rats consumed all the sugar solution provided to them, and had significantly increased caloric intake, compared to controls (p < 0.0001). However, in all tests, we found no significant difference in cognitive performance between Sucrose and Control treated rats. This included the number of trials for acquisition, and reversal, in Pairwise Discrimination, and number of trials required to complete Location Discrimination (p > 0.05 for all outcomes). No differences were observed in perseverative behaviour, motivation levels, or processing speed. ConclusionOur study found no evidence to suggest that chronic consumption of sucrose impairs cognition, including both spatial and non-spatial learning tasks. These findings suggest that not all aspects of spatial cognition are negatively impacted by high sugar diet in rodents, and that particular use of touchscreen technology may probe different aspects of cognition than traditional tasks.

Hydrogel Droplet Microarray for Genotyping Antimicrobial Resistance Determinants in Neisseria gonorrhoeae Isolates.

A multiplex assay based on a low-density hydrogel microarray was developed to identify genomic substitutions in N. gonorrhoeae that determine resistance to the currently recommended treatment agents ceftriaxone and azithromycin and the previously used drugs penicillin, tetracycline, and ciprofloxacin. The microarray identifies 74 drug resistance determinants in the N. gonorrhoeae penA, ponA, porB, gyrA, parC, rpsJ, mtrR, blaTEM, tetM, and 23S rRNA genes. The hydrogel elements were formed by automated dispensing of nanoliter-volume droplets followed by UV-induced copolymerization of NH2-containing oligonucleotides with gel-forming monomers. Polybutylene terephthalate plates without special modifications were used as microarray substrates. Sequences and concentrations of immobilized oligonucleotides, gel composition, and hybridization conditions were carefully selected, and the median discrimination ratio ranged from 2.8 to 29.4, allowing unambiguous identification of single-nucleotide substitutions. The mutation identification results in a control sample of 180 N. gonorrhoeae isolates were completely consistent with the Sanger sequencing results. In total, 648 clinical N. gonorrhoeae isolates obtained in Russia during the last 5 years were analyzed and genotyped using these microarrays. The results allowed us to draw conclusions about the present situation with antimicrobial susceptibility of N. gonorrhoeae in Russia and demonstrated the possibility of using hydrogel microarrays to control the spread of antibiotic resistance.

Dual-labeling ratiometric electrochemical strategy initiated with ISDPR for accurate screening MecA gene

An outstanding dual-labeling ratiometric electrochemical biosensor based on isothermal strand displacement polymerization reaction (ISDPR) for highly sensitive and selective detection of mecA gene has been proposed. Concretely, in the presence of mecA gene, the addition of methylene blue (MB)-labeled primer and polymerase induced recycling amplification to change the structure of the ferrocene (Fc)-labeled hairpin probe, thereby releasing abundant target gene to realize the signal amplification and dual-signal output. Through this process, the electrochemical responses of Fc (IFc) and MB (IMB) were both substantially reduced and increased proportionally, ensuring that the value of IMB/IFc can accurately reflect the true detection level of mecA gene. Benefiting from the “signal-on/off” strategy, the fabricated biosensor exhibited outstanding sequence specificity to discriminate mismatched mecA gene, which verified to be 2.72 times that of single-label detection for perfect match/single base mismatch (PM/MM) discrimination ratio. This strategy effectively integrated the advantages of signal amplification and ratiometric modes, making the biosensor exhibit a broad working range with 10 fM - 3000 pM and a limit of detection (LOD) with 3.33 fM (S/N = 3). Moreover, the proposed biosensor has good feasibility for mecA gene determination in water samples due to acceptable recoveries (95–115%) and repeatability relative standard deviations (RSD) value of 4%. This will provide a powerful sensing platform for improving accuracy and decreasing background signal of sensor for ARGs screening in environmental monitoring.

The Impact of EUTOS Long-Term Survival (ELTS) Score on Predicting Progression and Survival Among Turkish Patients with Chronic Myeloid Leukemia

Introduction and Objectives: For patients (pts) with chronic myeloid leukemia in chronic phase (CML-CP), four baseline prognostic scores are commonly used including the Sokal score and the most recently introduced EUTOS long-term survival (ELTS) score. The ELTS score was shown to be superior to the Sokal score for predicting survival. The aim of the study is to evaluate the value of ELTS score on predicting disease progression and survival in Turkish pts with CML-CP.Material and Method: Demographic, laboratory and clinical features, disease responses to tyrosine kinase inhibitor (TKI) therapy and survival of CML-CP pts, who received upfront imatinib (IM) between 2003 and 2018 were analyzed retrospectively. Treatment responses were reevaluated according to European LeukemiaNet 2013 recommendations. Risk groups analysis, discrimination and hazard ratios (HRs) were evaluated with Cox regression and Kaplan-Meier survival analysis. Receiver operating characteristic (ROC) analysis was performed to examine the effects of scores on predicting overall survival (OS) and progression-free survival (PFS).Results: A total of 185 pts were included, of which 103 (55.7%) were male and median age was 47 years (range, 16 - 81 years) (Table 1). The percentages of pts with low-, intermediate-, and high-risk ELTS scores were 60.5%, 25.9%, and 13.5%, respectively. For the Sokal score, these percentages were 37.3%, 40.5%, and 22.2% respectively. For Sokal high-risk pts, only 46.3% were classified as high-risk according to the ELTS score. Similarly, 44% of pts with intermediate Sokal risk had low-risk ELTS score (Fig. 1). Seventy-seven pts (41.6%) had at least one comorbidity, and the most common comorbidities were hypertension (21.6%), diabetes mellitus (13%), and ischemic heart disease (12.4%) (Table 1). The median durations of IM therapy and follow-up were 2728 (range, 14 - 6320 days) and 3473 (range, 71 - 6320 days) days, respectively. Complete hematologic and early molecular (BCR-ABL1 IS <10% at 3 months) responses at 3 months were 95.6% and 75.9%, respectively. Complete cytogenetic and major molecular response rates at 6 and 12 months were 72.3% and 86.1% and 45.4% and 54%, respectively. Thirty-five pts (18.9%) switched to second-generation TKI therapy and 6 pts (3.2%) progressed to advanced-phase disease during the follow-up (Table 1). For PFS, with reference to the low-risk Sokal score, the HR of high-risk groups was 9.301 (95% CI: 1.086-79.656, p=0.042) (Fig. 2A). Similarly, with reference to the low-risk ELTS score, the HR of intermediate- and high-risk groups were 4.744 (95% CI: 0.43-52.314, p=0.204) and 14.642 (95% CI: 1.523-140.791, p=0.020) (Fig. 2B). Regarding OS, with reference to the low-risk Sokal score, the HR of the intermediate- and high-risk groups were 1.835 (95% CI: 0.564-5.964, p=0.313) and 6.412 (95% CI: 2.11-19.489, p=0.001), respectively (Fig. 2C). With reference to the low-risk ELTS score, the HR of the intermediate- and high-risk groups were, 3.263 (95% CI: 1.242-8.576, p=0.016) and 7.258 (95% CI: 2.762-19.074, p<0.001) respectively (Fig. 2D). In the ROC analysis, the ELTS score was superior than the Sokal risk score for both predicting PFS (AUC=0.820 vs. AUC=0.818) and OS (AUC=0.762 vs. AUC=0.744). During the follow-up, 27 (14.6%) pts died, of which 6 died due to CML progression and causes of death were unrelated to CML in 21.Conclusion: In our study, we showed that the ELTS score could successfully predict high-risk pts compatible with the literature. With higher hazard ratios and better risk group stratifications, the ELTS score outperformed the Sokal score. The ELTS score can help clinicians to better discriminate poor prognostic pts and can promote optimal treatment strategies for these pts with potentially worse prognosis. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Reproducibility and discrimination of different indices of insulin sensitivity and insulin secretion.

AimsInsulin sensitivity and insulin secretion can be estimated by multiple indices from fasting blood samples or blood samples obtained during oral glucose tolerance tests. The test-retest reliability of these indices in repeated measurements within the same individuals can strongly vary.MethodsWe analyzed data of persons without diabetes who underwent two repeated OGTTs. For each measurement pair, we calculated multiple commonly used indices for the assessment of insulin secretion and insulin sensitivity. We then evaluated the coefficient of variation (standard deviation/mean) and discriminant ratio for each index.Results89 persons underwent two OGTTs with a median interval of 86 days (IQR 64–249). Among indices of insulin sensitivity derived from fasting blood samples, the revised quantitative insulin sensitivity check index had the smallest coefficient of variation (2.8 ± 2.1%) whereas the C-peptide based homeostasis model assessment 2 had the highest discriminant ratio (1.97 (1.65–2.39)). As for insulin sensitivity indices that are based on OGTT, the oral glucose insulin sensitivity index had the smallest coefficient of variation (6.5 ± 5.1%). The highest discriminant ratio was found for the non-esterified fatty acids-based insulin sensitivity index (NEFA-ISI, 2.70 (2.30–3.22)). For the assessment of insulin secretion from fasting variables, the lowest mean coefficient of variation was found for C-peptide based homeostasis model assessment 2 beta with 10.8 ± 8% and the highest discriminant ratio for the C-peptide / Glucose-Ratio (2.18 (1.84–2.63)). Among indices assessing insulin secretion from an OGTT, the lowest coefficient of variation was found for the ratio of the areas under the C-peptide and glucose curves from 0 to 120 minutes with 11.3 ± 9.7%.ConclusionThe data reveal large differences in the reproducibility and the discrimination capability of different indices that assess insulin sensitivity or insulin secretion. Our findings can aid the selection of an appropriate index in clinical studies.

The reliability and validity of the Turkish version of the Quality of Life Questionnaire of the European Foundation for Osteoporosis-31 (QUALEFFO-31).

SummaryThis study reveals the Turkish version of QUALEFFO-31 has a sufficient level of reliability, validity, and psychometric properties. The questionnaire, which is potentially capable of differentiating patients with fractures, does not appear to have the differential capacity in terms of osteoporosis.PurposeThis study aims to conduct the reliability and validity study of QUALEFFO-31 in Turkish and to evaluate the capacity of the questionnaire to distinguish patients in terms of osteoporosis.MethodsThe original English version was translated into Turkish by two translators whose native language was Turkish. Subsequently, this Turkish version was translated back into English by two different bilingual translators whose native language was English. After this preliminary questionnaire was tested in 30 patients, words, terminology, information errors, and parts difficult to understand were revised, and the questionnaire was finalized. Internal consistency and test–retest analyses were used for the reliability study. For the validity study, convergent-discriminant validity, concurrent validity, factor analysis, known-group validity, and receiver operating characteristic (ROC) analyses were performed.ResultsA total of 111 patients were evaluated. Internal consistency levels were optimal except for the mental function. ICC coefficients showed good retest reliability for all domains and total tests. The convergent and discriminant validity ratios for the mental function domain were 78% for both and 100% for the other domains. There was a moderate and good negative correlation between QUALEFFO-31 and SF-36 domains which had similar names. Exploratory factor analysis revealed 3 structures. However, there was a spread to the other factors in physical function domain items. Confirmatory factor analysis (CFA) markers were not at a very good fitting level except for the relative chi-square index. When CFA was performed according to the assumed model, the fitting level increased in all analyses. There was no significant differential capacity in terms of osteoporosis or fracture for either QUALEFFO-31 or SF-36.ConclusionThe Turkish version of QUALEFFO-31 has a sufficient level of reliability, validity, and psychometric properties. Nevertheless, improvements in pain and mental function domains and some changes applying to the model may increase the psychometric capacity of the questionnaire. The questionnaire, which is potentially capable of differentiating patients with fractures, does not appear to have the differential capacity in terms of osteoporosis.ClinicalTrials.gov identifierNCT04259099 (date of registration: February 6, 2020)Supplementary InformationThe online version contains supplementary material available at 10.1007/s11657-021-00997-4.

Strain comparisons in inhibitory discrimination learning and novel object recognition procedures

Strain differences in visual abilities and exploratory tendencies can confound rats’ performance in cognitive tests of learning and memory. In the present study we compared the performance of albino Wistar and pigmented Lister Hooded rats in appetitive conditioning and recognition memory procedures, specifically within-subjects inhibitory learning (A+ /AX-) and novel object recognition (NOR) variants. The inhibition task included an excitatory training stage and summation and retardation tests. Difference scores were used to help control for individual variation in baseline nosepoke responding. NOR was tested after a 10 min delay, following 24hr delay and using a recency variant. Discrimination ratios were used to control for individual variation in exploratory activity. In the inhibitory learning procedure, Lister Hooded showed more magazine activity prior to stimulus presentations than Wistar rats but this was a transient effect restricted to day 1 of excitatory training. There was no strain difference in associative learning at the excitatory training stage. The Wistars went on to show some performance advantage at the inhibitory discrimination stage and marginally stronger retardation test performance. In the NOR tasks, there was no significant effect of strain on cognitive performance, but the Wistars showed some advantage in the 10 min delay variant, whereas in the 24hr delay and relative recency NOR variants, the Lister Hooded rats showed some advantage. Overall the results of the present study confirm the suitability of Wistar rats for use in associative learning and basic NOR procedures.

Machine Learning Classifiers for Surface Crack Detection in Fracture Experiments

Correctly determining the onset of fracture is crucial when performing mechanical experiments. Commonly carried out by visual inspection, here an image-based machine learning approach is proposed to classify cracked and un-cracked specimens. It yields the potential to objectify and automate crack detection, thereby removing sources of uncertainty and error from the post-processing of experiments. More than 30’000 speckle-pattern images obtained from 77 experiments on three specimen geometries are evaluated. They comprise uniaxial tension, notched tension as well as axisymmetric V-bending experiments. Statistical texture features are extracted from all images. They include both first-order (variance, skewness, kurtosis) and higher-order statistical texture features, i.e. Haralick features. The discriminatory power of the texture information is evaluated based on the Fisher's Discriminant Ratio and feature correlations are identified and quantified. Image texture feature subsets of high discriminatory power are used to parse neural network architectures of different complexities from simple perceptron to feed-forward and cascade neural networks. It is found that a small subset of the investigated texture features is highly significant for all experiments. Using this feature subset in conjunction with multi-layer, non-linear and low complexity feed-forward network architectures classification accuracies in the order of 99% are obtained. At the same time, it is shown that linear classifiers are not sufficient to robustly distinguish the state of the specimens, even when high discriminatory power features are used.Graphical Abstract: [Display omitted]

A Novel Method That Allows SNP Discrimination with 160:1 Ratio for Biosensors Based on DNA-DNA Hybridization.

Highly sensitive (high SBR) and highly specific (high SNP discrimination ratio) DNA hybridization is essential for a biosensor with clinical application. Herein, we propose a method that allows detecting multiple pathogens on a single platform with the SNP discrimination ratios over 160:1 in the dynamic range of 101 to 104 copies per test. The newly developed SWAT method allows achieving highly sensitive and highly specific DNA hybridizations. The detection and discrimination of the MTB and NTM strain in the clinical samples with the SBR and SNP discrimination ratios higher than 160:1 indicate the high clinical applicability of the SWAT.

Characteristics of forest road cut slopes affecting the movement of mammals in South Korea

Existing forest roads that are the base infrastructure foundation for forest management have been built with a focus on structural stability rather than ecological impact. In recent, however, the concerns on the ecological and environmental impacts on the forest road construction has been increased with the emphasis of sustainable forest management and the improvement of public awareness. Especially, the cut slopes that occurred on the forest road construction are known to affect the movement and habitat of wild animals living in the forest. This study aims to establish environmental and structural standards for the cut slopes by identifying the effect of forest road cut slopes on the movement of wild animals based on a survey of national forest roads in South Korea (30 km in total). Seven factors associated with forest road cut slopes, including cut slope length, cut slope gradient, soil type, longitudinal position, crossing position, aspect, and vegetation coverage, as well as wildlife tracks were investigated. As a result of statistical analysis using cross tabulation to determine the correlation of wildlife tracks with each cut slope factor, five factors (soil type, cut slope gradient, cut slope length, vegetation coverage, and crossing position) were significantly identified. Using these five factors, a prediction model to predict whether wildlife move or not on the cut slope was developed, and the discriminant hit ratio was 76.0%. Thus, using such models, there is a need to develop the guidelines of forest road installation considering the wildlife movement and of ecological corridor installation for more eco-friendly forest road construction and design in the future.

WITHDRAWN: Comparing between DFT and DCT techniques to classification earthquake or explosion

Seismic data is data that is recorded on the Seismographs as a result of the movement of the earth resulting from earthquakes and explosions or any source that causes the movement of the earth. The seismic tremor stations consistently gather information in any event, when there is no occasion. This data is processed data using the techniques used to process digital signals and digital image processing techniques to obtain information that helps in exploring the ground and mining oil…etc. From this information, we have to recognize seismic tremor and non-quake. AI procedures can be utilized to analyze consistent time arrangement information to recognize seismic tremors viably, In this paper we suggested processing seismic data with DFT and DCT techniques and classify it by K-means algorithms in order to distinguish whether these seismic signals are caused by earthquakes or explosions and we obtained the result that DFT technology has a discrimination ratio of its coefficients is 14 of 16 while DCT technology has an error rate of 5 of 16.

Emotion Recognition Using Facial Expressions

Emotion recognition is an important aspect of human interaction, and this ability of humans to interpret emotions based on facial expressions is a basic element for effective communication. Machine learning can help automate this complicated task with the help of feature engineering. This work proposes some pipelines trained on the JAFFE dataset using feature extraction methods, namely principal component analysis (PCA) and local binary pattern (LBP) combined with Fisher discriminant ratio (FDR) as a feature selection method. In order to build a classification scheme capable of successfully identifying face images related to the six universal emotions and neutral expression, all possible combinations have been empirically analyzed. In the final model, PCA combined with FDR has been used on the support vector machine classifier with a linear kernel. The results obtained are encouraging and this work may also prove important for disciplines other than computer science such as for management purposes.

On effective cognitive state classification using novel feature extraction strategies.

Investigating new features for human cognitive state classification is an intiguing area of research with Electroencephalography (EEG) based signal analysis. We plan to develop a cost-effective system for cognitive state classification using ambulatory EEG signals. A novel event driven environment is created using external stimuli for capturing EEG data using a 14-channel Emotiv neuro-headset. A new feature extraction method, Gammatone Cepstrum Coefficients (GTCC) is introduced for ambulatory EEG signal analysis. The efficacy of this technique is compared with other feature extraction methods such as Discrete Wavelet Transformation (DWT) and Mel-Frequency Cepstral Coefficients (MFCC) using statistical metrics such as Fisher Discriminant Ratio (FDR) and Logistic Regression (LR). We obtain higher values for GTCC features, demonstrating its discriminative power during classification. A superior performance is achieved for the EEG dataset with a novel ensemble feature space comprising of GTCC and MFCC. Furthermore, the ensemble feature sets are passed through a proposed 1D Convolution Neural Networks (CNN) model to extract novel features. Various classification models like Probabilistic neural network (P-NN), Linear Discriminant Analysis (LDA), Multi-Class Support Vector Machine (MCSVM), Decision Tree (DT), Random Forest (RF) and Deep Convolutional Generative Adversarial Network (DCGAN) are employed to observe best accuracy on extracted features. The proposed GTCC, (GTCC+MFCC) & (GTCC +MFCC +CNN) features outperform the state-of-the-art techniques for all cases in our work. With GTCC+MFCC feature space and GTCC+MFCC+CNN features, accuracies of 96.42% and 96.14% are attained with the DCGAN classifier. Higher classification accuracies of the proposed system makes it a cynosure in the field of cognitive science.

The effect of dimensional reinforcement prediction on discrimination of compound visual stimuli by pigeons.

We trained eight pigeons (Columba livia) on a stagewise go/no-go visual discrimination task. A total of 16 visual stimuli were created from all possible combinations of four binary dimensions: brightness (dark/bright), size (large/small), line orientation (vertical/horizontal), and shape (circle/square). In the first stage, we presented S + and four S- stimuli: sharing one (brightness), two (brightness and orientation), three (brightness, orientation, and size), or no dimensional values with S + . In the second stage, all 16 stimuli were presented. In the first stage, stimulus discrimination was controlled by the number of dimensional disparities between non-rewarded stimuli and a rewarded one rather than by stimulus dimensional salience, whereas at the beginning of the second stage, pigeon behaviour was controlled mainly by dimensional reinforcement expectancy learned in the first stage. At the beginning of the second stage, pigeons correctly rejected 6-8 of 11 new added S- stimuli. A significant inverse correlation between the number of S- stimuli sharing dimension values with S + in the first stage and the dimensional discrimination ratios at the beginning of the second stage was found.

Classification of Toddler Nutritional Status Based on Antrophometric Index and Feature Discrimination using Support Vector Machine Hyperparameter Tuning

Nutritional status is the study of food and is related to health. Nutritional status is a benchmark to assess the health development of toddler. The nutritional status of toddler is assessed according to three index, such as body weight to age (BW / A), body height to age (BH / A), body weight to body height (BW / BH). The issue of nutrition is still a major factor in the growth and development of toddler in Indonesia. Public Health Center (Puskesmas) and Integrated Healthcare Center (Posyandu) as public health services work together to control the growth and development of toddler in Indonesia. To help control the growth and development of toddler, we proposed a research to classify the nutritional status of toddler based on anthropometric index. The nutritional status of toddler dataset was formed into a classification model using SVM Hyperparameter Tuning. SVM is a machine learning which the classification model used a hypothesis space in the form of linear functions in a high dimensional feature space. Adjustment of the hyperparameter was involved to reach a model that can optimally solve machine learning problems. We implemented feature selection using Fisher's Discriminant Ratio as a preprocessing stage, which the most important features were body weight (BB) and height (BH). The experimental results showed the classification model using SVM on training and testing data with a ratio of 70:30 reached accuracy of 84%, while SVM Hyperparameter Tuning with parameter of Cost = 100 parameters, Gamma = 0.01, Kernel = RBF reached accuracy of 97%. They represented a significant accuracy difference of 13%.

Effects of Eugenol on Memory Performance, Neurogenesis, and Dendritic Complexity of Neurons in Mice Analyzed by Behavioral Tests and Golgi Staining of Brain Tissue.

Eugenol, as the main component in clove, has neuroprotective abilities, including its effect to learning memory of mice. However, there is no evidence showing whether eugenol can expand the growth of dendrites in the brain. The objective of this research was to examine the effects of eugenol towards dendritic complexity of neurons, neurogenesis, and memory performance in hippocampus. A total of 21 mice were divided into three groups; (i) mice were administered 30 mg/kg bw eugenol orally, (ii) mice were administered 100 mg/kg bw eugenol orally, and (iii) mice were administered distilled water as control. Mice were kept for 30 consecutive days following the standard animal housing. The memory performance was observed through the Y-arm maze alternation, Novel Object Recognition (NOR), and Morris Water Maze (MWM) test. The brain was dissected and stained with FD Rapid Golgi StainingTM kit to observe dendrites in the dentate gyrus (DG) and cornu ammonis 1 (CA1) region; and Haematoxylin-Eosin (HE) staining to assess neurogenesis in the DG. Our results showed that eugenol enhanced putative neural stem cells (NPCs) and granular cells (GC) number, and also decrease neuronal cell death in DG (p<0.0001). Eugenol also increased dendritic complexity of neurons in DG region; while in CA1, eugenol has given a positive effect only on the basal area. Eugenol increased spatial and recognition memory in mice, indicated by a higher number of correct alternations and discrimination ratio compared to the control group (p<0.05), although escape latency in MWM did not show significant effect (p>0.05). As analyzed by behavioral tests and Golgi staining of brain tissue, eugenol can increase memory performance, neurogenesis, and dendritic complexity of neurons in the DG and CA1 basal region of brain in mice.