Three roots of online toxicity: disembodiment, accountability, and disinhibition.

Online communication is central to modern social life, yet it is often linked to toxic manifestations and reduced well-being. How and why online communication enables these toxic social effects remains unanswered. In this opinion, we propose three roots of online toxicity: disembodiment, limited accountability, and disinhibition. We suggest that virtual disembodiment results in a chain of psychological states primed for deleterious social interaction. Drawing from differences between face-to-face and online interactions, the framework highlights and addresses the fundamental problems that result in impaired communication between individuals and explicates its effects on social toxicity online.

Unbiasing Fairness Evaluation of Radiology AI Model.

Fairness of artificial intelligence and machine learning models, often caused by imbalanced datasets, has long been a concern. While many efforts aim to minimize model bias, this study suggests that traditional fairness evaluation methods may be biased, highlighting the need for a proper evaluation scheme with multiple evaluation metrics due to varying results under different criteria. Moreover, the limited data size of minority groups introduces significant data uncertainty, which can undermine the judgement of fairness. This paper introduces an innovative evaluation approach that estimates data uncertainty in minority groups through bootstrapping from majority groups for a more objective statistical assessment. Extensive experiments reveal that traditional evaluation methods might have drawn inaccurate conclusions about model fairness. The proposed method delivers an unbiased fairness assessment by adeptly addressing the inherent complications of model evaluation on imbalanced datasets. The results show that such comprehensive evaluation can provide more confidence when adopting those models.

Contactless vital signs monitoring in macaques using a mm-wave FMCW radar.

Heart rate (HR) and respiration rate (RR) play an important role in the study of complex behaviors and their physiological correlations in non-human primates (NHPs). However, collecting HR and RR information is often challenging, involving either invasive implants or tedious behavioral training, and there are currently few established simple and non-invasive techniques for HR and RR measurement in NHPs owing to their stress response or indocility. In this study, we employed a frequency-modulated continuous wave (FMCW) radar to design a novel contactless HR and RR monitoring system. The designed system can estimate HR and RR in real time by placing the FMCW radar on the cage and facing the chest of both awake and anesthetized macaques, the NHP investigated in this study. Experimental results show that the proposed method outperforms existing methods, with averaged absolute errors between the reference monitor and radar estimates of 0.77 beats per minute (bpm) and 1.29 respirations per minute (rpm) for HR and RR, respectively. In summary, we believe that the proposed non-invasive and contactless estimation method could be generalized as a HR and RR monitoring tool for NHPs. Furthermore, after modifying the radar signal-processing algorithms, it also shows promise for applications in other experimental animals for animal welfare, behavioral, neurological, and ethological research.

Epidemiological exploration of the impact of bluetooth headset usage on thyroid nodules using Shapley additive explanations method.

With an increasing prevalence of thyroid nodules globally, this study investigates the potential correlation between the use of Bluetooth headsets and the incidence of thyroid nodules, considering the cumulative effects of non-ionizing radiation (NIR) emitted by these devices. In this study, we analyzed 600 valid questionnaires from the WenJuanXing platform using Propensity Score Matching (PSM) and the XGBOOST model, supplemented by SHAP analysis, to assess the risk of thyroid nodules. PSM was utilized to balance baseline characteristic differences, thereby reducing bias. The XGBOOST model was then employed to predict risk factors, with model efficacy measured by the area under the Receiver Operating Characteristic (ROC) curve (AUC). SHAP analysis helped quantify and explain the impact of each feature on the prediction outcomes, identifying key risk factors. Initially, 600 valid questionnaires from the WenJuanXing platform underwent PSM processing, resulting in a matched dataset of 96 cases for modeling analysis. The AUC value of the XGBOOST model reached 0.95, demonstrating high accuracy in differentiating thyroid nodule risks. SHAP analysis revealed age and daily Bluetooth headset usage duration as the two most significant factors affecting thyroid nodule risk. Specifically, longer daily usage durations of Bluetooth headsets were strongly linked to an increased risk of developing thyroid nodules, as indicated by the SHAP analysis outcomes. Our study highlighted a significant impact relationship between prolonged Bluetooth headset use and increased thyroid nodule risk, emphasizing the importance of considering health impacts in the use of modern technology, especially for devices like Bluetooth headsets that are frequently used daily. Through precise model predictions and variable importance analysis, our research provides a scientific basis for the formulation of public health policies and personal health habit choices, suggesting that attention should be paid to the duration of Bluetooth headset use in daily life to reduce the potential risk of thyroid nodules. Future research should further investigate the biological mechanisms of this relationship and consider additional potential influencing factors to offer more comprehensive health guidance and preventive measures.

Dietary resveratrol improves the flesh quality of Siberian sturgeon (Acipenser baerii) by enhancing myofiber growth, nutrient accumulation and antioxidant capacity.

BACKGROUND: In aquaculture, sturgeons are generally maintained in the confined spaces, which not only hinders sturgeon movement, but also threatens their flesh quality that seriously concerned by aquaculture industry. As a typical antioxidant, resveratrol can improve the flesh quality of livestock and poultry. However, the mechanism of resveratrol's effect on the muscle of Siberian sturgeon is still unclear. RESULTS: In this study, the dietary resveratrol increased the myofiber diameter, the content of the amino acids, antioxidant capacity markers (CAT, LDH and SOD) levels and the expression levels of mTORC1 and MYH9 in muscle of Siberian sturgeon. Further transcriptome analysis displayed that ROS production-related pathways ("Oxidative phosphorylation" and "Chemical carcinogenes-reactive oxygen species") were enriched in KEGG analysis, and the expression levels of genes related to the production of ROS (COX4, COX6A, ATPeF1A, etc.) in mitochondria were significantly down-regulated, while the expression levels of genes related to scavenging ROS (SOD1) were up-regulated. CONCLUSIONS: In summary, this study reveals that resveratrol may promote the flesh quality of Siberian sturgeon probably by enhancing myofiber growth, nutritional value and the antioxidant capacity of muscle, which has certain reference significance for the development of a new type of feed for Siberian sturgeon.

A New Denoising Method for Belt Conveyor Roller Fault Signals.

In the process of the intelligent inspection of belt conveyor systems, due to problems such as its long duration, the large number of rollers, and the complex working environment, fault diagnosis by acoustic signals is easily affected by signal coupling interference, which poses a great challenge to selecting denoising methods of signal preprocessing. This paper proposes a novel wavelet threshold denoising algorithm by integrating a new biparameter and trisegment threshold function. Firstly, we elaborate on the mutual influence and optimization process of two adjustment parameters and three wavelet coefficient processing intervals in the BT-WTD (the biparameter and trisegment of wavelet threshold denoising, BT-WTD) denoising model. Subsequently, the advantages of the proposed threshold function are theoretically demonstrated. Finally, the BT-WTD algorithm is applied to denoise the simulation signals and the vibration and acoustic signals collected from the belt conveyor experimental platform. The experimental results indicate that this method's denoising effectiveness surpasses that of traditional threshold function denoising algorithms, effectively addressing the denoising preprocessing of idler roller fault signals under strong noise backgrounds while preserving useful signal features and avoiding signal distortion problems. This research lays the theoretical foundation for the non-contact intelligent fault diagnosis of future inspection robots based on acoustic signals.

Evaluation of a Photo Captioning Cognitive Empathy Intervention for Dementia Caregivers.

OBJECTIVES: The goal of this study was to develop and evaluate an intervention aimed at increasing cognitive empathy, improving mental health, and reducing inflammation in dementia caregivers, and to examine the relevant neural and psychological mechanisms. METHODS: Twenty dementia caregivers completed an intervention that involved taking 3-5 daily photographs of their person living with dementia (PLWD) over a period of 10 days and captioning those photos with descriptive text capturing the inner voice of the PLWD. Both before and after the intervention, participants completed questionnaires, provided a blood sample for measures of inflammation, and completed a neuroimaging session to measure their neural response to viewing photographs of their PLWD and others. RESULTS: 87% of enrolled caregivers completed the intervention. Caregivers experienced pre- to post-intervention increases in cognitive empathy (i.e. Perspective-Taking) and decreases in both burden and anxiety. These changes were paralleled by an increased neural response to photographs of their PLWD within brain regions implicated in cognitive empathy. CONCLUSION: These findings warrant a larger replication study that includes a control condition and follows participants to establish the duration of the intervention effects. CLINICAL IMPLICATIONS: Cognitive empathy interventions may improve caregiver mental health and are worthy of further investigation.

Revisiting the Trustworthiness of Saliency Methods in Radiology AI.

Purpose To determine whether saliency maps in radiology artificial intelligence (AI) are vulnerable to subtle perturbations of the input, which could lead to misleading interpretations, using prediction-saliency correlation (PSC) for evaluating the sensitivity and robustness of saliency methods. Materials and Methods In this retrospective study, locally trained deep learning models and a research prototype provided by a commercial vendor were systematically evaluated on 191 229 chest radiographs from the CheXpert dataset and 7022 MR images from a human brain tumor classification dataset. Two radiologists performed a reader study on 270 chest radiograph pairs. A model-agnostic approach for computing the PSC coefficient was used to evaluate the sensitivity and robustness of seven commonly used saliency methods. Results The saliency methods had low sensitivity (maximum PSC, 0.25; 95% CI: 0.12, 0.38) and weak robustness (maximum PSC, 0.12; 95% CI: 0.0, 0.25) on the CheXpert dataset, as demonstrated by leveraging locally trained model parameters. Further evaluation showed that the saliency maps generated from a commercial prototype could be irrelevant to the model output, without knowledge of the model specifics (area under the receiver operating characteristic curve decreased by 8.6% without affecting the saliency map). The human observer studies confirmed that it is difficult for experts to identify the perturbed images; the experts had less than 44.8% correctness. Conclusion Popular saliency methods scored low PSC values on the two datasets of perturbed chest radiographs, indicating weak sensitivity and robustness. The proposed PSC metric provides a valuable quantification tool for validating the trustworthiness of medical AI explainability. Keywords: Saliency Maps, AI Trustworthiness, Dynamic Consistency, Sensitivity, Robustness Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Yanagawa and Sato in this issue.

Knockdown of circSOD2 ameliorates osteoarthritis progression via the miR-224-5p/PRDX3 axis.

BACKGROUND: Although the implications of circular RNAs (circRNAs) with the progression of diverse pathological conditions have been reported, the circRNA players in osteoarthritis (OA) are barely studied. METHODS: In this study, twenty-five OA patients who received arthroplasty were recruited for cartilage tissue collection. Public circRNA microarray data from Gene Expression Omnibus was retrieved for circRNA identification. An in vitro cell model of OA-related damages was constructed by treating human chondrocytes (CHON-001 cell line) with IL-1ß, and circSOD2 siRNA was used to silence circSOD2 expression to study its functional role in apoptosis, inflammatory responses, and extracellular matrix (ECM) degradation. Besides, we investigated the functional interactions among circSOD2, miR-224-5p, and peroxiredoxin 3 (PRDX3) by luciferase reporter assay, RNA-immunoprecipitation assay, and quantitative reverse transcription polymerase chain reaction. RESULTS: Our findings revealed the overexpression of circSOD2 in the OA cartilage and cell samples, and circSOD2 knockdown alleviated ECM degradation, inflammation, and apoptosis in CHON-001 cell model. In addition, our findings suggested the regulatory function of circSOD2 knockdown on miR-224-5p expression, while miR-224-5p was capable of downregulating PRDX3 expression. The co-transfection of miR-224-5p inhibitor or pcDNA-PRDX3 could prevent the effect of circSOD2 knockdown. CONCLUSION: Hence, our results demonstrated that knockdown of circSOD2 may serve as an intervention strategy to alleviate OA progression through modulating miR-224-5p/PRDX3 signaling axis.

Effect of Purslane (Portulaca oleracea L.) on Intestinal Morphology, Digestion Activity and Microbiome of Chinese Pond Turtle (Mauremys reevesii) during Aeromonas hydrophila Infection.

Large-scale mortality due to Aeromonas hydrophila (A. hydrophila) infection has considerably decreased the yield of the Chinese pond turtle (Mauremys reevesii). Purslane is a naturally active substance with a wide range of pharmacological functions, but its antibacterial effect on Chinese pond turtles infected by A. hydrophila infection is still unknown. In this study, we investigated the effect of purslane on intestinal morphology, digestion activity, and microbiome of Chinese pond turtles during A. hydrophila infection. The results showed that purslane promoted epidermal neogenesis of the limbs and increased the survival and feeding rates of Chinese pond turtles during A. hydrophila infection. Histopathological observation and enzyme activity assay indicated that purslane improved the intestinal morphology and digestive enzyme (α-amylase, lipase and pepsin) activities of Chinese pond turtle during A. hydrophila infection. Microbiome analysis revealed that purslane increased the diversity of intestinal microbiota with a significant decrease in the proportion of potentially pathogenic bacteria (such as Citrobacter freundii, Eimeria praecox, and Salmonella enterica) and an increase in the abundance of probiotics (such as uncultured Lactobacillus). In conclusion, our study uncovers that purslane improves intestinal health to protect Chinese pond turtles against A. hydrophila infection.

Towards Adversarial Robustness in Unlabeled Target Domains.

In the past several years, various adversarial training (AT) approaches have been invented to robustify deep learning model against adversarial attacks. However, mainstream AT methods assume the training and testing data are drawn from the same distribution and the training data are annotated. When the two assumptions are violated, existing AT methods fail because either they cannot pass knowledge learnt from a source domain to an unlabeled target domain or they are confused by the adversarial samples in that unlabeled space. In this paper, we first point out this new and challenging problem-adversarial training in unlabeled target domain. We then propose a novel framework named Unsupervised Cross-domain Adversarial Training (UCAT) to address this problem. UCAT effectively leverages the knowledge of the labeled source domain to prevent the adversarial samples from misleading the training process, under the guidance of automatically selected high quality pseudo labels of the unannotated target domain data together with the discriminative and robust anchor representations of the source domain data. The experiments on four public benchmarks show that models trained with UCAT can achieve both high accuracy and strong robustness. The effectiveness of the proposed components is demonstrated through a large set of ablation studies. The source code is publicly available at https://github.com/DIAL-RPI/UCAT.

Resveratrol Improves the Digestive Ability and the Intestinal Health of Siberian Sturgeon.

The lack of detailed information on nutritional requirement results in limited feeding in Siberian sturgeon. In this study, resveratrol, a versatile natural extract, was supplemented in the daily diet, and the digestive ability and microbiome were evaluated in the duodena and valvular intestines of Siberian sturgeon. The results showed that resveratrol increased the activity of pepsin, α-amylase, and lipase, which was positively associated with an increase in the digestive ability, but it did not influence the final body weight. Resveratrol improved the digestive ability probably by distinctly enhancing intestinal villus height. Microbiome analysis revealed that resveratrol changed the abundance and composition of the microbial community in the intestine, principally in the duodenum. Random forests analysis found that resveratrol significantly downregulated the abundance of potential pathogens (Citrobacter freundii, Vibrio rumoiensis, and Brucella melitensis), suggesting that resveratrol may also improve intestinal health. In summary, our study revealed that resveratrol improved digestive ability and intestinal health, which can contribute to the development of functional feed in Siberian sturgeon.

FAM117A Is a New Prognostic Marker of Lung Adenocarcinoma and Predicts Sensitivity to PD0332991.

Lung cancer is the second most common cancer and the leading cause for cancer mortality worldwide. Accelerated cell cycle progression is a well-characterized hallmark for cancer. The present study aims to identify biomarkers for clinical outcomes of lung cancer patients and their sensitivity to CDK inhibitors. To this end, bioinformatics analysis of transcriptome datasets from the Cancer Genome Atlas (TCGA) was first performed to identify survival-related genes; cell proliferation assay, colony formation assay, flow cell cytometry, western blot, EDU labelling, and xenograft models were then used to confirm the potential roles of the identified factors. Our results identified the decreased FAM117A expression as the most significant survival related factor for poor outcome. The cell cycle transition from G1 to S phase was suppressed upon FAM117A overexpression and was promoted upon FAM117A knockdown. Accordingly, the tumor cell growth induced by FAM117A depletion was completely blocked by treatment with PD0332991, which has been approved for cancer therapy. In summary, our work identified FAM117A as a new prognostic marker for poor outcomes of lung cancer patients, predicting sensitivity to PD0332991 treatment.

Fabrication of a novel hydrogel-based microfluidic chip and its application in pathogen analysis.

Herein, we develop a novel hydrogel-based microfluidic chip, which can serve as a multifunctional analytical platform. The chip was fabricated through a newly developed hydrogel material, which shows satisfactory properties such as fast forming speed and good hydrophilicity. The chip mainly consists of two independent functional parts: a chromogenic layer and a microfluidic layer. The specially-designed toothed structure in the microfluidic layer can promote surface interactions and realize efficient enrichment of the target. The chromogenic layer contains chromogenic media, which can achieve rapid target identification through a simple visual readout. As a proof of concept, the proposed chip is employed for pathogen analysis. It shows satisfactory performance for efficient enrichment of Escherichia coli (E. coli) O157:H7. On the other hand, the visual detection limit of the chip for E. coli O157:H7 can reach 10 cfu mL-1. It is believed that this work could provide a valuable reference for chip material exploitation and application.

Problematising formative assessment in an undeveloped region of China: voices from practitioners.

Despite the widely acknowledged pro-learning function of formative assessment and its wide adoption around the globe, the gaps between policy intention, interpretation and implementation remain a problem to be solved. While this problem is noted universally, it could be particularly serious in China, where Confucian Heritage Culture is deeply ingrained and education development is not quite balanced. This study, via interview data with English teachers and deans from eight universities in an undeveloped region of the Mid-western China, explores the overall environment for a formative assessment initiative that is currently in place. Data analysis reveals multiple issues, such as insufficient support, improper dissemination and ineffective training at the meso-level and the instructors' limited assessment ability, large class sizes and student's resistance at the micro-level. A conclusion is thus drawn that the overall environment in this region is by no means favourable for the effective implementation of formative assessment, and implications are derived for better realisation of assessment innovations in this and other undeveloped regions of China.

Fabrication of Cu-hemin metal-organic-frameworks nanoflower supported on three-dimensional reduced graphene oxide for the amperometric detection of H2O2.

A novel electrochemical sensor based on Cu-hemin metal-organic-frameworks nanoflower/three-dimensional reduced graphene oxide (Cu-hemin MOFs/3D-RGO) was constructed to detect H2O2 released from living cells. The nanocomposite was synthesized via a facile co-precipitation method using hemin as the ligand, then decorated with 3D-RGO. The prepared Cu-hemin MOFs showed a 3D hollow spherical flower-like structure with a large specific surface area and mesoporous properties, which could load more biomolecules and greatly enhance the stability by protecting the activity of hemin. In addition, the introduction of 3D-RGO effectively enhanced the conductivity of Cu-hemin MOFs. Thus, the proposed sensor (Cu-hemin MOFs/3D-RGO/GCE) showed excellent electrochemical performances towards H2O2 with a wide linear range (10-24,400 µM), high sensitivity (207.34 µA mM-1 cm-2), low LOD (0.14 µM), and rapid response time (less than 3 s). Most importantly, we prepared a Cu-hemin MOFs/3D-RGO/ITO electrode with cells growing on it. Compared with detecting H2O2 in cell suspension by GCE-based electrode, adhesion of cells on ITO could shorten the diffusion distance of H2O2 from solution to the surface of the electrode and achieve in situ and a real-time monitor of H2O2 released by living cells. This self-supported sensing electrode showed great potential applications in monitoring the pathological and physiological dynamics of cancer cells.

Association of AI quantified COVID-19 chest CT and patient outcome.

PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. METHODS: We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). RESULTS: AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, [Formula: see text]). Using AI-based scores produced significantly higher ([Formula: see text]) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. CONCLUSIONS: Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.

Integrative analysis for COVID-19 patient outcome prediction.

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia. The source code of our work is available at https://github.com/DIAL-RPI/COVID19-ICUPrediction.

Development of a multi-detector readout circuitry for ultrahigh energy resolution single-photon imaging applications.

In this paper, we present the design and preliminary performance evaluation of a novel external multi-channel readout circuitry for small-pixel room-temperature semiconductor detectors, namely CdZnTe (CZT) and CdTe, that provide an excellent intrinsic spatial (250 and 500 µm pixel size) and an ultrahigh energy resolution (~1% at 122 keV) for X-ray and gamma-ray imaging applications. An analog front-end printed circuit board (PCB) was designed and developed for data digitization, data transfer and ASIC control of pixelated CZT or CdTe detectors. Each detector unit is 2 cm × 2 cm in size and 1 or 2 mm in thickness, being bump-bonded onto a HEXITEC ASIC, and wire-bonded to a readout detector module PCB. The detectors' front-end is then connected, through flexible cables of up to 10 m in length, to a remote data acquisition system that interfaces with a PC through USB3.0 connection. We present the design and performance of a prototype multi-channel readout system that can read out up to 24 detector modules synchronously. Our experimental results demonstrated that the readout circuitry offers an ultrahigh spectral resolution (0.8 keV at 60 keV and 1.05 keV at 122 keV) with the Cd(Zn)Te/HEXITEC ASIC modules tested. This architecture was designed to allow easy expansion to accommodate a larger number of detector modules, and the flexibility of arranging the detector modules in a large and deformable detector array without degrading the excellent energy resolution.