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Journal of Experimental Psychology.... Jun 2023Spatial attention affects not only where we look, but also what we perceive and remember in attended and unattended locations. Previous work has shown that manipulating...
Spatial attention affects not only where we look, but also what we perceive and remember in attended and unattended locations. Previous work has shown that manipulating attention via top-down cues or bottom-up capture leads to characteristic patterns of feature errors. Here we investigated whether experience-driven attentional guidance-and probabilistic attentional guidance more generally-leads to similar feature errors. We conducted a series of pre-registered experiments employing a learned spatial probability or probabilistic pre-cue; all experiments involved reporting the color of one of four simultaneously presented stimuli using a continuous response modality. When the probabilistic cues guided attention to an invalid (nontarget) location, participants were less likely to report the target color, as expected. But strikingly, their errors tended to be clustered around a nontarget color the color of the invalidly-cued nontarget. This "feature avoidance" was found for both experience-driven and top-down probabilistic cues, and appears to be the product of a strategic-but possibly subconscious-behavior, occurring when information about the features and/or feature-location bindings outside the focus of attention is limited. The findings emphasize the importance of considering how different types of attentional guidance can exert different effects on feature perception and memory reports. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Topics: Humans; Reaction Time; Photic Stimulation; Cues; Mental Recall; Attention; Visual Perception
PubMed: 37141038
DOI: 10.1037/xhp0001095 -
Heliyon Apr 2023Early purchase prediction plays a vital role for an e-commerce website. It enables e-shoppers to enlist consumers for product suggestions, offer discount and for many...
Early purchase prediction plays a vital role for an e-commerce website. It enables e-shoppers to enlist consumers for product suggestions, offer discount and for many other interventions. Several work has already been done using session log for analyzing customer behavior whether he performs a purchase on the product or not. In most cases, it is difficult to find out and make a list of customers and offer them discount when their session ends. In this paper, we propose a customer's purchase intention prediction model where e-shoppers can detect customer's purpose earlier. First, we apply feature selection technique to select best features. Then the extracted features are fed to train supervised learning models. Several classifiers like support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), decision tree (DT), and XGBoost classifiers have been applied along with oversampling method for balancing the dataset. The experiments were performed on a standard benchmark dataset. Experimental results show that XGBoost classifier with feature selection techniques and oversampling method has the significantly higher area under ROC curve (auROC) score and are under precision-recall curve (auPR) score which are 0.937 and 0.754 respectively. On the other hand accuracy achieved by XGBoost and Decision tree are significantly improved and they are 90.65% and 90.54% respectively. Overall performance of the gradient boosting method is significantly improved compared to other classifiers and state-of-the-art methods. In addition to this, a method for explainable analysis on the problem was outlined.
PubMed: 37095970
DOI: 10.1016/j.heliyon.2023.e15163 -
The International Journal, Advanced... 2023Surface defects are a common issue that affects product quality in the industrial manufacturing process. Many companies put a lot of effort into developing automated...
Surface defects are a common issue that affects product quality in the industrial manufacturing process. Many companies put a lot of effort into developing automated inspection systems to handle this issue. In this work, we propose a novel deep learning-based surface defect inspection system called the forceful steel defect detector (FDD), especially for steel surface defect detection. Our model adopts the state-of-the-art cascade R-CNN as the baseline architecture and improves it with the deformable convolution and the deformable RoI pooling to adapt to the geometric shape of defects. Besides, our model adopts the guided anchoring region proposal to generate bounding boxes with higher accuracies. Moreover, to enrich the point of view of input images, we propose the random scaling and the ultimate scaling techniques in the training and inference process, respectively. The experimental studies on the Severstal steel dataset, NEU steel dataset, and DAGM dataset demonstrate that our proposed model effectively improved the detection accuracy in terms of the average recall (AR) and the mean average precision (mAP) compared to state-of-the-art defect detection methods. We expect our innovation to accelerate the automation of industrial manufacturing process by increasing the productivity and by sustaining high product qualities.
PubMed: 37073280
DOI: 10.1007/s00170-023-11087-9 -
JAMA Network Open Apr 2023High-risk medical devices approved by the US Food and Drug Administration (FDA) can undergo modifications to their original premarket approval (PMA) via 1 of 5 types of...
IMPORTANCE
High-risk medical devices approved by the US Food and Drug Administration (FDA) can undergo modifications to their original premarket approval (PMA) via 1 of 5 types of supplements. Only panel track supplements (approximately 1%) require clinical data for approval. The association between device modifications and risk to patient safety has not previously been analyzed.
OBJECTIVE
To determine the association between PMA supplements and the risk of any device recall and high-risk (class 1) recall.
DESIGN, SETTING, AND PARTICIPANTS
In this cohort study, the FDA database was queried for original devices approved via PMA from January 1, 2008, through December 31, 2019. Supplement and recall data were obtained for these devices from January 1, 2008, through December 31, 2021, giving a minimum 2-year follow-up after initial approval. Data were analyzed from July 6 to August 6, 2022. Retrospective, time-to-event analysis investigated the association between the number and type of supplements and risk of recall.
EXPOSURES
Supplements submitted by manufacturers for FDA approval to modify devices.
MAIN OUTCOMES AND MEASURES
A mixed-effects Cox proportional hazards regression model with frailty terms was used, modeling device recall as an outcome variable during the observation period. A second model was performed for class 1 (high-risk) recall. Explanatory variables are the number of supplements, number of panel track supplements, and cardiovascular devices. Multivariable analysis was performed to identify independent risk factors for recall with hazard ratios (HRs) as the main end point.
RESULTS
A total of 373 original PMA devices with 10 776 associated supplements were included in the analysis. A median 2.5 (IQR, 1.2-5.0) supplements per device were approved annually. Cardiovascular devices contributed 138 supplements (37.0%), followed by microbiology with 45 (12.1%). No other specialty contributed more than 10%. Multivariable analysis demonstrated that each increase of 1 supplement per year was associated with increased risk of recall (HR, 1.28 [95% CI, 1.15-1.44]; P < .001). For class 1 recall, increased number of supplements (HR, 1.32 [95% CI, 1.06-1.64]; P = .01) and cardiovascular vsnoncardiovascular classification of devices (HR, 3.51 [95% CI, 1.15-10.72]; P = .03) were significantly associated with an increased risk of recall.
CONCLUSIONS AND RELEVANCE
The findings of this cohort study suggest that PMA supplements are associated with an approximately 30% increased risk of any recall and class 1 recall. The FDA processes for approving modifications to high-risk medical devices should be reevaluated to optimize patient safety and public health.
Topics: United States; Humans; United States Food and Drug Administration; Retrospective Studies; Cohort Studies; Product Surveillance, Postmarketing; Risk Factors
PubMed: 37043202
DOI: 10.1001/jamanetworkopen.2023.7699 -
Nature Microbiology May 2023Vaccines play a critical role in combating the COVID-19 pandemic. Future control of the pandemic requires improved vaccines with high efficacy against newly emerging...
Vaccines play a critical role in combating the COVID-19 pandemic. Future control of the pandemic requires improved vaccines with high efficacy against newly emerging SARS-CoV-2 variants and the ability to reduce virus transmission. Here we compare immune responses and preclinical efficacy of the mRNA vaccine BNT162b2, the adenovirus-vectored spike vaccine Ad2-spike and the live-attenuated virus vaccine candidate sCPD9 in Syrian hamsters, using both homogeneous and heterologous vaccination regimens. Comparative vaccine efficacy was assessed by employing readouts from virus titrations to single-cell RNA sequencing. Our results show that sCPD9 vaccination elicited the most robust immunity, including rapid viral clearance, reduced tissue damage, fast differentiation of pre-plasmablasts, strong systemic and mucosal humoral responses, and rapid recall of memory T cells from lung tissue after challenge with heterologous SARS-CoV-2. Overall, our results demonstrate that live-attenuated vaccines offer advantages over currently available COVID-19 vaccines.
Topics: Animals; Cricetinae; Humans; Vaccines, Attenuated; SARS-CoV-2; COVID-19; COVID-19 Vaccines; BNT162 Vaccine; Pandemics; Mesocricetus
PubMed: 37012419
DOI: 10.1038/s41564-023-01352-8 -
Frontiers in Medicine 2023Manually keeping up-to-date with regulations such as directives, guidance, laws, and ordinances related to cell and gene therapy is a labor-intensive process. We used...
BACKGROUND
Manually keeping up-to-date with regulations such as directives, guidance, laws, and ordinances related to cell and gene therapy is a labor-intensive process. We used machine learning (ML) algorithms to create an augmented intelligent system to optimize systematic screening of global regulations to improve efficiency and reduce overall labor and missed regulations.
METHODS
Combining Boolean logic and artificial intelligence (i.e., augmented intelligence) for the search process, ML algorithms were used to identify and suggest relevant cell and gene therapy regulations. Suggested regulations were delivered to a landing page for further subject matter expert (SME) tagging of words/phrases to provide system relevance on functional words. Ongoing learning from the repository regulations continued to increase system reliability and performance. The automated ability to train and retrain the system allows for continued refinement and improvement of system accuracy. Automated daily searches for applicable regulations in global databases provide ongoing opportunities to update the repository.
RESULTS
Compared to manual searching, which required 3-4 SMEs to review ~115 regulations, the current system performance, with continuous system learning, requires 1 full-time equivalent to process approximately 9,000 regulations/day. Currently, system performance has 86% overall accuracy, a recommend recall of 87%, and a reject recall of 84%. A conservative search strategy is intentionally used to permit SMEs to assess low-recommended regulations in order to prevent missing any applicable regulations.
CONCLUSION
Compared to manual searches, our custom automated search system greatly improves the management of cell and gene therapy regulations and is efficient, cost effective, and accurate.
PubMed: 36950510
DOI: 10.3389/fmed.2023.1072767 -
Journal of Food Protection Apr 2023Allergens are one of the leading causes of food recalls in the US. The Food and Drug Administration (FDA) enforces requirements relating to major food allergens (MFAs)...
Allergens are one of the leading causes of food recalls in the US. The Food and Drug Administration (FDA) enforces requirements relating to major food allergens (MFAs) and gluten-free labeling to ensure food safety for allergic and celiac patients, respectively. Violative foods are subject to recalls. In this study, recall data for FDA-regulated foods were analyzed for fiscal years (FYs) 2013-2019 to identify trends and root causes associated with 1471 food allergen and gluten recalls. Of the 1471 recalls, 1415 recalls were due to MFAs, 34 recalls were due to gluten-free labeling violation and 23 recalls involved other allergens. Recalls due to MFAs overall increased during the study period with a peak incidence in FY 2017. MFA recall health hazard classifications were assessed as Class I (51.2%), Class II (45.5%), and Class III (3.3%). A majority of MFA recalls involved one allergen (78.8%). Milk was the most common MFA involved in MFA recalls (37.5%), followed by soy (22.5%) and tree nuts (21.6%). Almond, anchovy, and shrimp were the most common allergens recalled within the MFA groups of tree nuts, fish, and Crustacean shellfish, respectively. About 97% of MFA recalls involved one product category and among them, the category of 'bakery products, dough, bakery mixes and icings' ranked first (367 recalls), followed by the category of 'chocolate and cocoa products' (120 recalls). Labeling-associated errors accounted for 71.1% of MFA recalls with known root causes (914 out of 1286). It is important for the industry to develop and implement appropriate allergen controls to reduce the number of MFA recalls.
Topics: Animals; United States; Food Contamination; Glutens; United States Food and Drug Administration; Food Safety; Food Hypersensitivity; Allergens
PubMed: 36940660
DOI: 10.1016/j.jfp.2023.100069 -
BioRxiv : the Preprint Server For... Jun 2023Sleep supports memory consolidation. However, it is not completely clear how different sleep stages contribute to this process. While rapid eye movement sleep (REM) has...
Sleep supports memory consolidation. However, it is not completely clear how different sleep stages contribute to this process. While rapid eye movement sleep (REM) has traditionally been implicated in the processing of emotionally charged material, recent studies indicate a role for slow wave sleep (SWS) in strengthening emotional memories. Here, to directly examine which sleep stage is primarily involved in emotional memory consolidation, we used targeted memory reactivation (TMR) in REM and SWS during a daytime nap. Contrary to our hypothesis, reactivation of emotional stimuli during REM led to impaired memory. Consistent with this, REM% was correlated with worse recall in the group that took a nap without TMR. Meanwhile, cueing benefit in SWS was strongly correlated with the product of times spent in REM and SWS (SWS-REM product), and reactivation significantly enhanced memory in those with high SWS-REM product. Surprisingly, SWS-REM product was associated with better memory for reactivated items and poorer memory for non-reactivated items, suggesting that sleep both preserved and eliminated emotional memories, depending on whether they were reactivated. Notably, the emotional valence of cued items modulated both sleep spindles and delta/theta power. Finally, we found that emotional memories benefited from TMR more than did neutral ones. Our results suggest that emotional memories decay during REM, unless they are reactivated during prior SWS. Furthermore, we show that active forgetting complements memory consolidation, and both take place across SWS and REM. In addition, our findings expand upon recent evidence indicating a link between sleep spindles and emotional processing.
PubMed: 36909630
DOI: 10.1101/2023.03.01.530661 -
Foods (Basel, Switzerland) Mar 2023Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning...
Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and do not consider the unevenness of detection data categories. In this paper, To overcome these limitations, we propose a Contrastive Self-supervised learning-based Graph Neural Network framework (CSGNN) for contamination warning of food quality. Specifically, we structure the graph for detecting correlations between samples and then define the positive and negative instance pairs for contrastive learning based on attribute networks. Further, we use a self-supervised approach to capture the complex relationships between detection samples. Finally, we assessed each sample's contamination level based on the absolute value of the subtraction of the prediction scores from multiple rounds of positive and negative instances obtained by the CSGNN. Moreover, we conducted a sample study on a batch of dairy product detection data in a Chinese province. The experimental results show that CSGNN outperforms other baseline models in contamination assessment of food quality, with AUC and recall of unqualified samples reaching 0.9188 and 1.0000, respectively. Meanwhile, our framework provides interpretable contamination classification for food detection. This study provides an efficient early warning method with precise and hierarchical contamination classification for contamination warning of food quality work.
PubMed: 36900566
DOI: 10.3390/foods12051048