Low Discrimination Research Articles

GC-Loc

Indoor localization techniques play a fundamental role in empowering plenty of indoor location-based services (LBS) and exhibit great social and commercial values. The widespread fingerprint-based indoor localization methods usually suffer from the low feature discriminability with discrete signal fingerprint or high time overhead for continuous signal fingerprint collection. To address this, we introduce the collaboration mechanism and propose a graph attention based collaborative indoor localization framework, termed GC-Loc, which provides another perspective for efficient indoor localization. GC-Loc utilizes multiple discrete signal fingerprints collected by several users as input for collaborative localization. Specifically, we first construct an adaptive graph representation to efficiently model the relationships among the collaborative fingerprints. Then taking state-of-the-art GAT model as basic unit, we design a deep network with the residual structure and the hierarchical attention mechanism to extract and aggregate the features from the constructed graph for collaborative localization. Finally, we further employ ensemble learning mechanism in GC-Loc and devise a location refinement strategy based on model consensus for enhancing the robustness of GC-Loc. We have conducted extensive experiments in three different trial sites, and the experimental results demonstrate the superiority of GC-Loc, outperforming the comparison schemes by a wide margin (reducing the mean localization error by more than 42%).

HIV testing uptake, enablers, and barriers among African migrants in China: A nationwide cross-sectional study

African migrants in China face social, structural, and cultural barriers to human immunodeficiency virus (HIV) testing with scarce information on their HIV testing behaviours. This study estimated the prevalence of HIV testing and its social and behavioural correlates to understand how to better provide HIV testing services for African migrants living in China. We conducted a national cross-sectional survey among adult African migrants who lived in China for more than one month between January 19 to February 7, 2021. The survey was disseminated online through six African community organizations and via participant referrals. We collected data on HIV testing behaviours and history of HIV testing, social, and cultural factors and applied univariate and multivariable logistic regression to identify testing correlates. Among a total of 1305 participants, 72.9% (n = 951/1305) tested for HIV during their stay in China and yielded a self-reported HIV prevalence of 0.4% (n = 4/951). The most common reason for HIV testing was to comply with Chinese residence policy requirements (88.5%, n = 842/951); for not testing was "no need to be tested" (79.4%, n = 281/354). We found most African migrants have experienced low acculturation stress (54.5%, n = 750/1305), low social discrimination (65.6%, n = 856/1305), have a moderate stigma towards HIV (54.3%, n = 709/1305), and low community engagement around sexual health and HIV topics. In multivariable analysis, African migrants who were students (adjusted odds ratio (aOR) = 3.36, 95% CI = 2.40-4.71), living in student dormitories (aOR = 3.86, 95% CI = 1.51-9.84), received health services in China in past year (aOR = 1.67, 95% CI = 1.25-2.23), had lifetime sexually transmitted infections (STI) testing (aOR = 1.95, 95% CI = 1.23-3.10), had HIV testing before coming to China (aOR = 13.56, 95% CI = 9.36-19.65), and those engaged in community discussions of HIV and sexual health (aOR = 2.77, 95% CI = 1.31-5.83) were more likely to test for HIV in China. Despite 73% of African migrants having tested for HIV in China, there are unmet needs and barriers identified in our study, such as language barriers. Access to HIV knowledge and testing services were the most important enablers for testing, including studentship, past STI/HIV testing, and community discussion on sexual health. Culturally appropriate and community-based outreach programs to provide information on HIV and testing venues for African migrants might be helpful to promote testing uptake.

Can time-series Sentinel images be used to properly identify wetland plant communities?

ABSTRACT Understanding the distribution of wetland plant communities is critical to biodiversity conservation and wetland habitat sustainable management, especially for migratory birds. However, limited road accessibility and low spectral discriminability make the mapping of wetland plant communities inadequate for wetland health assessment, necessitating the improvement of classification methods. In this study, we proposed a random forest classifier that combined multi-source remote sensing features for wetland plant community classification and evaluated this method for the Momoge Ramsar wetland site (MRWS) in China. The major result of this work was that the phenological and time-series features based on Sentinel-2 images were the most valuable discriminators for wetland plant community classification in the MRWS. The SAR_sum extracted from Sentinel-1 images also had high importance for classification. Moreover, the spatial pattern of different wetland plant communities was revealed, and the resultant classification map had a high overall accuracy (91.3%) and Kappa coefficient (0.90). And the six important features prompted the classification accuracy to reach 84.8%. In 2020, the total coverage area of natural wetland plant communities in MRWS reached 628.5 km2 (42.1%), of which Carex meyeriana distributed the most widely, followed by Suaeda glauca, Phragmites australis, Typha orientalis, and Scirpus triquater. The findings of this study can provide scientific decision-making support for the protection and management of migratory birds and plants in the MRWS. The proposed method employed freely available Sentinel 1/2 satellite images and fully automated programs on Google Earth Engine, and has guiding significance for large-scale and long-time-series wetland classification.

An Item-Level Evaluation of the Attentional Style Questionnaire (ASQ) Using a Sample of Experienced Cyclists

Abstract. Effective attentional control is crucial for safe urban cycling. The Attentional Style Questionnaire (ASQ) assesses orientation of attention and its underpinning processes. This study contributes to the ASQ literature by detecting intraindividual and interindividual item response patterns in a group of cyclists to derive a parsimonious context-specific version of the ASQ. We performed a multidimensional item response theory analysis of Internal and External Distraction Control scales of the ASQ using data from experienced road cyclists, fitting both constrained and unconstrained graded response models to the data; we also examined person fit. Five of the original 17 items were discarded due to low factor coefficients and item discrimination parameters. The remaining items exhibited good psychometric properties including high discrimination parameters and category utilization. Only two and five people exhibited person misfit for Internal and External Distraction Control scales, respectively. This abbreviated ASQ may be appropriate for assessing cyclists’ attentional control.

Prefrontal Cerebral Oxygenated Hemoglobin Concentration during the Category Fluency and Finger-Tapping Tasks in Adults with and without Mild Cognitive Impairment: A Near-Infrared Spectroscopy Study.

Mild cognitive impairment (MCI) is considered to be the limit between the cognitive changes of aging and early dementia; thus, discriminating between participants with and without MCI is important. In the present study, we aimed to examine the differences in the cerebral oxyhemoglobin signal between individuals with and without MCI. The cerebral oxyhemoglobin signal was measured when the participants (young and elderly controls as well as patients with MCI) performed category fluency, finger tapping, and dual tasks using head-mounted near-infrared spectroscopy; the results were compared between the groups. The cerebral oxyhemoglobin signal trended toward the highest values during the category fluency task in young participants and during the finger-tapping task in elderly participants regardless of the MCI status. The area under the curve was approximately 0.5, indicating a low discrimination ability between elderly participants with and without MCI. The measurement of the blood flow in the prefrontal cortex may not accurately quantify cognitive and motor performance to detect MCI. Finger tapping may increase cerebral blood flow in individuals with and without MCI during the task.

External validation of the Johns Hopkins Fall Risk Assessment Tool in older Dutch hospitalized patients.

Fall prevention is a safety goal in many hospitals. The performance of the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) in older inpatients is largely unknown. We aimed to assess the JHFRAT performance in a large sample of Dutch older inpatients, including its trend over time. We used an Electronic Health Records (EHR) dataset with hospitalized patients (≥ 70), admitted for ≥ 24h between 2016 and 2021. Inpatient falls were extracted from structured and free-text data. We assessed the association between JHFRAT and falls using logistic regression. For test accuracy, we calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Discrimination was measured by the AUC. For calibration, we plotted the predicted fall probability with the actual probability of falls. For time-related effects, we calculated the AUC per 6months (using data of patients admitted during the 6months' time interval) and plotted these different AUC values over time. Furthermore, we compared the model (JHFRAT and falls) with and without adjusting for seasonal influenza, COVID-19, spring, summer, fall or winter periods. Data included 17,263 admissions with at least 1 JHFRAT measurement, a median age of 76 and a percentage female of 47%. The in-hospital fall prevalence was 2.5%. JHFRAT [OR = 1.11 (1.03-1.20)] and its subcategories were significantly associated with falls. For medium/high risk of falls (JHFRAT > 5), sensitivity was 73%, specificity 51%, PPV 4% and NPV 99%. The overall AUC was 0.67, varying over time between 0.62 and 0.71 (for 6months' time intervals). Seasonal influenza did affect the association between JHFRAT and falls. COVID-19, spring, summer, fall or winter did not affect the association. Our results show an association between JHFRAT and falls, a low discrimination by JHFRAT for older inpatients and over-prediction in the calibration. Improvements in the fall-risk assessment are warranted to improve efficiency.

How Grit-S Items Work in Measuring Growth: Focusing on Item Discrimination and Sensitivity to Change

This study investigates how sensitively the current grit measure (Grit-S) is able to reflect the differences between individuals (i.e. item discrimination) and change within individuals (i.e. sensitivity to change of items) using longitudinal data. Data from the Korean Children and Youth Panel Study 2018 of elementary (fourth grade) and middle school (seventh grade) cohorts were analyzed. We compared a series of longitudinal factor models of change with an initial trait and a change in grit represented by latent traits. Results showed that the models hypothesizing two latent traits with respect to the two elements of grit (interest and effort) best fit the data. In the elementary school cohort, sensitivity to change was different from discriminating between-person differences at baseline. Meanwhile, in the middle school cohort, sensitivity to change was identical to discriminating between-person differences at baseline. The items having low discrimination and low sensitivity to change resulted in syntactical complexity and did not accurately reflect the trait that was intended to be measured. Despite the Grit-S being widely used for many years, it is suggested that researchers and educators should administer this measure at taking an individual’s change and school-level difference into account.

Abstract 15871: Associations of Everyday Discrimination and Socioeconomic Status With Insomnia and Short Sleep Duration Among Older Women

Introduction: Discrimination may contribute to sleep disparities, and subsequent adverse cardiovascular health. Despite the higher prevalence of insomnia among women, particularly historically minoritized women compared to men, limited studies examine the association between discrimination and insomnia and sleep duration. Methods: Among 26,616 women (Race/Ethnicity: Asian = 323; Black = 457; Hispanic= 258; White=25,287) in the Women’s Health Study stress follow-up cohort (Mean age =72.3 + 6.1 years), we investigated the relationship of discrimination with insomnia and sleep duration and tested education and income as effect modifiers. Insomnia was self-reported as trouble falling asleep OR waking after sleep onset 3+/week and daytime sleepiness 3+/week. Sleep duration was defined according to weekday and weekend bed and wake times, and categorized as short (<7 hours), recommended ( > 7 and <9 hours), and long ( > 9 hours). Everyday discrimination (e.g., treated with less courtesy or respect, received poorer service, etc.) frequency was divided into tertiles then dichotomized as high (upper tertile) and low (lower & middle tertile). Results: Insomnia, and short and long sleep duration were reported by 11%, 11%, and 15% of women, respectively, and high levels of discrimination were reported by 40% of women. There was a higher proportion of Asian and Black women who reported high discrimination compared to Hispanic and White women (53.2% and 57.3% vs 39.8% and 38.4%, respectively). Women reporting high vs. low discrimination were more likely to have insomnia and short sleep duration, but less likely to have long sleep [Insomnia Adjusted Prevalence Ratio (aPR): 1.33, 95% CI: 1.28-1.38; Short Sleep (aPR): 1.14, 95% CI:1.09-1.19; Long Sleep (aPR): 0.96, 95% CI: 0.95-0.97)]. Associations of discrimination with insomnia and short sleep duration were pronounced among Hispanic and White, but not Asian and Black women, and those of higher education ( > bachelors vs <bachelors) and income ( > $50,000 vs <$50,000). Conclusions: Discrimination may be a target for reducing the prevalence of insomnia and short sleep duration among women. Individuals of higher socioeconomic status may have increased vulnerability to the effects of discrimination on sleep.

Under-resourced or overloaded? Rethinking working memory deficits in developmental language disorder.

Dominant theoretical accounts of developmental language disorder (DLD) commonly invoke working memory capacity limitations. In the current report, we present an alternative view: That working memory in DLD is not under-resourced but overloaded due to operating on speech representations with low discriminability. This account is developed through computational simulations involving deep convolutional neural networks trained on spoken word spectrograms in which information is either retained to mimic typical development or degraded to mimic the auditory processing deficits identified among some children with DLD. We assess not only spoken word recognition accuracy and predictive probability and entropy (i.e., predictive distribution spread), but also use mean-field-theory based manifold analysis to assess; (a) internal speech representation dimensionality and (b) classification capacity, a measure of the networks' ability to isolate any given internal speech representation that is used as a proxy for attentional control. We show that instantiating a low-level auditory processing deficit results in the formation of internal speech representations with atypically high dimensionality, and that classification capacity is exhausted due to low representation separability. These representation and control deficits underpin not only lower performance accuracy but also greater uncertainty even when making accurate predictions in a simulated spoken word recognition task (i.e., predictive distributions with low maximum probability and high entropy), which replicates the response delays and word finding difficulties often seen in DLD. Overall, these simulations demonstrate a theoretical account of speech representation and processing deficits in DLD in which working memory capacity limitations play no causal role. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Entorhinal-Hippocampal Circuit Integrity Is Related to Mnemonic Discrimination and Amyloid-β Pathology in Older Adults.

Mnemonic discrimination, a cognitive process that relies on hippocampal pattern separation, is one of the first memory domains to decline in aging and preclinical Alzheimer's disease. We tested whether functional connectivity (FC) within the entorhinal-hippocampal circuit, measured with high-resolution resting state fMRI, is associated with mnemonic discrimination and amyloid-β (Aβ) pathology in a sample of 64 cognitively normal human older adults (mean age, 71.3 ± 6.4 years; 67% female). FC was measured between entorhinal-hippocampal circuit nodes with known anatomical connectivity, as well as within cortical memory networks. Aβ pathology was measured with 18F-florbetapir-PET, and neurodegeneration was assessed with subregional volume from structural MRI. Participants performed both object and spatial versions of a mnemonic discrimination task outside of the scanner and were classified into low-performing and high-performing groups on each task using a median split. Low object mnemonic discrimination performance was specifically associated with increased FC between anterolateral entorhinal cortex (alEC) and dentate gyrus (DG)/CA3, supporting the importance of this connection to object memory. This hyperconnectivity between alEC and DG/CA3 was related to Aβ pathology and decreased entorhinal cortex volume. In contrast, spatial mnemonic discrimination was not associated with altered FC. Aβ was further associated with dysfunction within hippocampal subfields, particularly with decreased FC between CA1 and subiculum as well as reduced volume in these regions. Our findings suggest that Aβ may indirectly lead to memory impairment through entorhinal-hippocampal circuit dysfunction and neurodegeneration and provide a mechanism for increased vulnerability of object mnemonic discrimination.SIGNIFICANCE STATEMENT Mnemonic discrimination is a critical episodic memory process that is performed in the dentate gyrus (DG) and CA3 subfield of the hippocampus, relying on input from entorhinal cortex. Mnemonic discrimination is particularly vulnerable to decline in older adults; however, the mechanisms behind this vulnerability are still unknown. We demonstrate that object mnemonic discrimination impairment is related to hyperconnectivity between the anterolateral entorhinal cortex and DG/CA3. This hyperconnectivity was associated with amyloid-β pathology and neurodegeneration in entorhinal cortex, suggesting aberrantly increased network activity is a pathological process. Our findings provide a mechanistic explanation of the vulnerability of object compared to spatial mnemonic discrimination in older adults and has translational implications for choice of outcome measures in clinical trials for Alzheimer's disease.

Memory as a scale of simulation depending on the trace distinctiveness.

It has been proposed that representations emerge from a single memory system organized along a continuum of specificity. This continuum is assumed to reflect a scale between the simulation of overlapping and specific features of the traces, which depends on trace distinctiveness. More specifically, higher trace distinctiveness facilitates the simulation of trace-specific features, which increase the discriminability of traces and lead to the emergence of a more specific representation. In two experiments, participants were asked to identify match (low task discrimination demand) or mismatch (high task discrimination demand) associations between actions and characters that were visually either highly or lowly distinctive. The results of Experiment 1show that in the high-distinctiveness context, performance was better when identifying a mismatch rather than a match, while the opposite was true in the low-distinctiveness context. The results of Experiment 2 show that using a dynamic visual noise to interfere with the participants' ability to simulate the features of the characters also reduced the benefit of the high-distinctiveness context for the mismatch trials (Experiment 2a and 2b) and increased the benefit of the low-distinctiveness context for the match trials (Experiment 2b). Taken together, these results suggest that the simulation of trace-specific features underlies the emergence of specific representations, which can be beneficial when the discrimination demand of the task is high and detrimental when this demand is low. Memory might therefore be viewed as a scale of simulation between overlapping and specific trace features.

Pest-YOLO: A model for large-scale multi-class dense and tiny pest detection and counting.

Frequent outbreaks of agricultural pests can reduce crop production severely and restrict agricultural production. Therefore, automatic monitoring and precise recognition of crop pests have a high practical value in the process of agricultural planting. In recent years, pest recognition and detection have been rapidly improved with the development of deep learning-based methods. Although certain progress has been made in the research on pest detection and identification technology based on deep learning, there are still many problems in the production application in a field environment. This work presents a pest detector for multi-category dense and tiny pests named the Pest-YOLO. First, the idea of focal loss is introduced into the loss function using weight distribution to improve the attention of hard samples. In this way, the problems of hard samples arose from the uneven distribution of pest populations in a dataset and low discrimination features of small pests are relieved. Next, a non-Intersection over Union bounding box selection and suppression algorithm, the confluence strategy, is used. The confluence strategy can eliminate the errors and omissions of pest detection caused by occlusion, adhesion and unlabeling among tiny dense pest individuals to the greatest extent. The proposed Pest-YOLO model is verified on a large-scale pest image dataset, the Pest24, which includes more than 20k images with over 190k pests labeled by agricultural experts and categorized into 24 classes. Experimental results show that the Pest-YOLO can obtain 69.59% for mAP and 77.71% for mRecall on the 24-class pest dataset, which is 5.32% and 28.12% higher than the benchmark model YOLOv4. Meanwhile, our proposed model is superior to other several state-of-the-art methods, including the SSD, RetinaNet, Faster RCNN, YOLOv3, YOLOv4, YOLOv5s, YOLOv5m, YOLOX, DETR, TOOD, YOLOv3-W, and AF-RCNN detectors. The code of the proposed algorithm is available at: https://github.com/chr-secrect/Pest-YOLO.

The Protective Effects of Nutraceutical Components in Methotrexate-Induced Toxicity Models-An Overview.

There are multiple concerns associated with methotrexate (MTX), widely recognized for anti-neoplastic and anti-inflammatory effects in life-threatening disease conditions, i.e., acute lymphoblastic leukemia, non-Hodgkin’s lymphoma, psoriasis, and rheumatoid arthritis, due to long-term side effects and associated toxicity, which limits its valuable potential. MTX acts as an inhibitor of dihydrofolate reductase, leading to suppression of purine and pyrimidine synthesis in high metabolic and turnover cells, targeting cancer and dysregulated immune cells. Due to low discrimination between neoplastic cells and naturally high turnover cells, MTX is prone to inhibiting the division of all fast-dividing cells, causing toxicity in multiple organs. Nutraceutical compounds are plant-based or food-derived compounds, used for their preventive and therapeutic role, ascertained in multiple organ dysfunctions, including cardiovascular disease, ischemic stroke, cancer, and neurodegenerative diseases. Gut microbiota and microbiota-derived metabolites take part in multiple physiological processes, their dysregulation being involved in disease pathogenesis. Modulation of gut microbiota by using nutraceutical compounds represents a promising therapeutic direction to restore intestinal dysfunction associated with MTX treatment. In this review, we address the main organ dysfunctions induced by MTX treatment, and modulations of them by using nutraceutical compounds. Moreover, we revealed the protective mechanisms of nutraceuticals in MTX-induced intestinal dysfunctions by modulation of gut microbiota.

Development of MVMD-EO-LSTM Model for a Short-Term Photovoltaic Power Prediction

The accuracy and stability of short-term photovoltaic (PV) power prediction is crucial for power planning and dispatching in a grid system. For this reason, the multi-resolution variational modal decomposition (MVMD) method is proposed to achieve multi-scale input features mining for short-term PV power prediction. Here, the MVMD combined with Spearman extracts correlation features of the weather data. An equilibrium optimizer (EO) is integrated with MVMD to achieve optimal values of the long short-term memory (LSTM) parameters. Firstly, the correlation of input features is determined and selected by Spearman. The MVMD model is used to mine the high correlation features of solar radiation and conduct cross-correlation analysis to extract input feature components. Secondly, the similar weather days of the sample set are classified to ensure a good adaptability in different weather situations. Finally, the high correlation features are introduced into the photovoltaic power prediction model of EO optimized LSTM. Performance analysis using actual output power data from a PV plant shows that the proposed MVMD feature extraction method can effectively mine correlation features to achieve an optimized dataset under different seasons. Compared with the gray wolf and particle swarm optimization algorithms, the proposed model has a better optimization performance in a low discrimination of input feature decomposition components and low correlation with output power.

Posture Calibration Based Cross-View & Hard-Sensitive Metric Learning for UAV-Based Vehicle Re-Identification

Machine vision based vehicle re-identification (ReID) plays an important role in some Intelligent Transportation Systems (ITS). Yet, most previous methods mainly focus on fixed surveillance cameras instead of Unmanned Aerial Vehicle (UAV). With high flexibility, the UAV-based vehicle ReID problem has some special challenges including complicated shooting angles, low discrimination of top-down features, and large variance in vehicle scales, etc. To overcome these challenges, we propose a novel structure for UAV-based vehicle ReID without license plates. Firstly, a triple-head segmentation net is proposed for segmenting UAV-captured vehicles under different heights and directions. Secondly, a posture calibration model is designed to uniform the vehicle postures based on the segmentation results, with the purpose of reducing the influence of different postures. Thirdly, the novel Cross-View & Hard-Sensitive Metric Learning (CHSML) method is proposed to train a ReID network with cross-view training constraint and hard sensitive principles; the mechanism of CHSML takes the cross-view samples of same ID as a training unit to learn the potential visual relationship in cross-view and builds a hard sensitive weight matrix to make learning more focus on hard samples, which improves the low ReID accuracy brought by cross-view or hard samples. Moreover, to facilitate the research of UAV-based vehicle ReID, a large-scale UAV-based vehicle ReID dataset called VeRi-UAV is released with 17 516 vehicles of 453 IDs. The experiments show that the proposed structure gains a better performance compared with the representative methods in the UAV-based vehicle ReID task.

Species and population genomic differentiation in Pocillopora corals (Cnidaria, Hexacorallia).

Correctly delimiting species and populations is a prerequisite for studies of connectivity, adaptation and conservation. Genomic data are particularly useful to test species differentiation for organisms with few informative morphological characters or low discrimination of cytoplasmic markers, as in Scleractinians. Here we applied Restriction site Associated DNA sequencing (RAD-sequencing) to the study of species differentiation and genetic structure in populations of Pocillopora spp. from Oman and French Polynesia, with the objectives to test species hypotheses, and to study the genetic structure among sampling sites within species. We focused here on coral colonies morphologically similar to P. acuta (damicornis type β). We tested the impact of different filtering strategies on the stability of the results. The main genetic differentiation was observed between samples from Oman and French Polynesia. These samples corresponded to different previously defined primary species hypotheses (PSH), i.e., PSHs 12 and 13 in Oman, and PSH 5 in French Polynesia. In Oman, we did not observe any clear differentiation between the two putative species PSH 12 and 13, nor between sampling sites. In French Polynesia, where a single species hypothesis was studied, there was no differentiation between sites. Our analyses allowed the identification of clonal lineages in Oman and French Polynesia. The impact of clonality on genetic diversity is discussed in light of individual-based simulations.

The role of discriminability in face perception: Interference processing of expression, gender, and gaze.

Eye gaze plays a fundamental role in social interaction and facial recognition. However, interference processing between gaze and other facial variants (e.g., expression) and invariant information (e.g., gender) remains controversial and unclear, especially the role of facial information discriminability in interference. A Garner paradigm was used to conduct two experiments. This paradigm allows simultaneous investigation of the mutual influence of two kinds of facial information in one experiment. In Experiment 1, we manipulated facial expression discriminability and investigated its role in interference processing of gaze and facial expression. The results show that individuals were unable to ignore expression when classifying gaze with both high and low discriminability but could ignore gaze when classifying expression with high discriminability only. In Experiment 2, we manipulated gender discriminability and investigated its function in interference processing of gaze and gender. Participants were unable to ignore gender when classifying gaze with both high and low discriminability but could ignore gaze when classifying gender with low discriminability only. The results indicate that gaze categorization is affected by facial expression and gender regardless of facial information discriminability, whereas interference of gaze on facial expression and gender depends on the degree of discriminability. The present study provides evidence that the processing of gaze and other variant and invariant information is interdependent.

Camouflage Target Recognition Based on Dimension Reduction Analysis of Hyperspectral Image Regions

Hyperspectral reconnaissance technology can realize three-dimensional reconnaissance by using target space and spectral information, which effectively improves the efficiency of battlefield reconnaissance. However, in order to obscure what is true and what is false to confuse the enemy, camouflage technology is also developing. Hiding the target in the background environment and setting false targets have become common camouflage procedures on the battlefield. The camouflaged target has very similar spatial and spectral characteristics to the real target, so the method of identifying the camouflaged target according to the similarity threshold of the original spectral data is no longer reliable. In order to solve the problem of high spectral similarity and low discrimination between a camouflaged target and a real target in a hyperspectral image, a joint processing method of spatial spectrum information is adopted in this paper. Firstly, the hyperspectral image is preprocessed, and then the target area to be measured is determined. Finally, the dimensions of the determined sensitive small area are reduced. Experiments show that this processing method can effectively reduce the spectral similarity of true and false targets, increase the spectral difference of true and false targets and improve the ability to identify true and false targets based on hyperspectral images.

Identification of a SGCD × Discrimination Interaction Effect on Systolic Blood Pressure in African American Adults in the Jackson Heart Study.

In the United States, hypertension disproportionately afflicts over half of African American adults, many of whom also experience racial discrimination. Understanding gene × discrimination effects may help explain racial disparities in hypertension. We tested for the main effects and interactive effects of 5 candidate single nucleotide polymorphisms (SNPs: rs2116737, rs11190458, rs2445762, rs2597955, and rs2416545) and experiences of discrimination on blood pressure (BP) in African Americans not taking antihypertensive medications in the Jackson Heart Study from Mississippi (n = 2,933). Multiple linear regression models assumed an additive genetic model and adjusted for ancestry, age, sex, body mass index, education, and relatedness. We additionally tested recessive and dominant genetic models. Discrimination was significantly associated with higher diastolic BP (P = 0.003). In contrast, there were no main effects of any SNP on BP. When analyzing SNPs and discrimination together, SGCD (Sarcoglycan Delta; rs2116737) demonstrated a gene × environment interaction. Specifically, an SGCD × Discrimination interaction was associated with systolic BP (β =1.95, P = 0.00028) in a recessive model. Participants carrying a T allele, regardless of discrimination experiences, and participants with a GG genotype and high experiences of discrimination had higher systolic BP than participants with a GG genotype and low experiences of discrimination. This finding suggests the SGCD GG genotype may have a protective effect on systolic BP, but only in a setting of low discrimination. The inclusion of culturally relevant stressors, like discrimination, may be important to understand the gene-environment interplay likely underlying complex diseases with racial health inequities.

Orthogonally Responsive Donor–Acceptor Stenhouse Adduct/Poly(2-ethylsulfonyl-2-oxazoline) Colorimetric Sensors with Nonvolatile Memories

Owing to their facile readout by naked eyes, wireless sensing without energy consumption and complicated optical systems, and low cost, colorimetric sensors have attracted tremendous attention and found broad applications in diverse fields. Despite the promise, most colorimetric sensors suffer from low selectivity and discriminability toward different stimuli, giving rise to cross-talk and/or false-positive signals. Furthermore, they generally return to their initial states and lose responses when the stimuli are removed, offering good reversibility, which could be problematic under the circumstances where the accumulated responses play a role, e.g., tracking the exposure history and pinning the cause of a given event. Here, a series of colorimetric sensors based on donor–acceptor Stenhouse adduct/poly(2-ethylsulfonyl-2-oxazoline) hybrids (DASAs/PEtsOx) that orthogonally respond to light, humidity, and heat have been developed, of which the responsive behaviors are exquisitely tuned by altering the acceptor units. Furthermore, attributed to the dynamic confinement of the PEtsOx matrix, the reversibility of DASA isomerization is almost hampered, endowing the sensors with excellent nonvolatile memories. By virtue of these features, the DASA/PEtsOx colorimetric sensors have broad applications in food safety, medicine preservation, and others. This work offers a general design principle and paves the path for the next-generation intelligent sensors.