Search

Background: Depression is common in persons with dementia and is often under‐detected and under‐treated. It is critical to understand which available tools accurately detect depression in the context of dementia. Methods: We updated our systematic review completed in 2015. The search strategy of our original review was replicated in Medline, Embase, and PsycINFO. Studies describing the use of a tool to identify depression in persons with dementia, compared to a criterion standard, and reporting diagnostic accuracy outcomes were included in the review update. Pooled prevalence estimates of major depression and pooled estimates of diagnostic accuracy outcomes (i.e., sensitivity [SN], specificity [SP]) for tools were calculated. Results: Three studies were included of the 8980 returned from the database search and were added to the prior 20 articles from the 2015 review. The Cornell Scale for Depression in Dementia (CSDD), Geriatric Depression Scale (GDS)−15 item, Neuropsychiatric Inventory‐Depression items (NPI‐D), and Depression in Old Age Scale (DIA‐S) were evaluated in the three studies. Two new studies were added to the existing pooled prevalence estimate of major depression (29%, 95% confidence interval [CI] = 21.6%–36.5%, n = 17) and pooled diagnostic accuracy estimate for the CSDD at the best cut‐off (SN = 0.83, 95% CI = 0.74–0.90; SP = 0.81, 95% CI = 0.69–0.89). New pooled diagnostic accuracy estimates were completed for the CSDD (cut‐off ≥12) (SN = 0.61, 95% CI = 0.42–0.77; SP = 0.83, 95% CI = 0.76–0.88), GDS‐15 (best cut‐off) (SN = 0.65, 95% CI = 0.40–0.83; SP = 0.72, 95% CI = 0.55–0.85), and Montgomery Asberg Depression Rating Scale (MADRS) (best cut‐off) (SN = 0.77, 95% CI = 0.67–0.85; SP = 0.68, 95% CI = 0.60‐0.75). Conclusions: The CSDD continues to have the most evidence for depression case finding in persons living with dementia. The CSDD and Hamilton Depression Rating Scale have the highest sensitivities and may be recommended for use over other common tools like the GDS‐15 and MADRS. Newly identified tools like the NPI‐D and DIA‐S require further study before they can be recommended for use in practice. Key points: We updated our existing systematic review identifying tools to detect depression in persons with dementia compared to a criterion standard.The Cornell Scale for Depression in Dementia continues to have the most evidence and adequate sensitivity and specificity for use in identifying depression in the context of dementia.Two tools, the NPI‐D and DIA‐S, not previously identified in the original systematic review were identified and had adequate sensitivity in detecting depression in the context of depression.It is critical for clinicians to understand which tools accurately detect depression in the context of dementia so that depression can be diagnosed, treatment can be initiated, and care can be improved for persons canwith dementia. [ABSTRACT FROM AUTHOR]

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent advances in generative models and the availability of large-scale benchmarks have made deepfake video generation and manipulation easier. Nowadays, the number of new hyper-realistic deepfake videos used for negative purposes is dramatically increasing, thus creating the need for effective deepfake detection methods. Although many existing deepfake detection approaches, particularly CNN-based methods, show promising results, they suffer from several drawbacks. In general, poor generalization results have been obtained under unseen/new deepfake generation methods. The crucial reason for the above defect is that CNN-based methods focus on the local spatial artifacts, which are unique for every manipulation method. Therefore, it is hard to learn the general forgery traces of different manipulation methods without considering the dependencies that extend beyond the local receptive field. To address this problem, this article proposes a framework that combines Convolutional Neural Network (CNN) with Vision Transformer (ViT) to improve detection accuracy and enhance generalizability. Our method, named HCiT, exploits the advantages of CNNs to extract meaningful local features, as well as the ViT's self-attention mechanism to learn discriminative global contextual dependencies in a frame-level image explicitly. In this hybrid architecture, the high-level feature maps extracted from the CNN are fed into the ViT model that determines whether a specific video is fake or real. Experiments were performed on Faceforensics++, DeepFake Detection Challenge preview, Celeb datasets, and the results show that the proposed method significantly outperforms the state-of-the-art methods. In addition, the HCiT method shows a great capacity for generalization on datasets covering various techniques of deepfake generation. The source code is available at: https://github.com/KADDAR-Bachir/HCiT [ABSTRACT FROM AUTHOR]

Addressing the current reliance on manual sorting and grading of spray rose cut flowers, this paper proposed an improved YOLOv5s model for intelligent recognition and grading detection of rose color series and flowering index of spray rose cut flowers. By incorporating small-scale anchor boxes and small object feature output, the model enhanced the annotation accuracy and the detection precision for occluded rose flowers. Additionally, a convolutional block attention module attention mechanism was integrated into the original network structure to improve the model's feature extraction capability. The WIoU loss function was employed in place of the original CIoU loss function to increase the precision of the model's post-detection processing. Test results indicated that for two types of spray rose cut flowers, Orange Bubbles and Yellow Bubbles, the improved YOLOv5s model achieved an accuracy and recall improvement of 10.2% and 20.0%, respectively. For randomly collected images of spray rose bouquets, the model maintained a detection accuracy of 95% at a confidence threshold of 0.8. [ABSTRACT FROM AUTHOR]

Background and Hypothesis Psychosis-associated diagnostic codes are increasingly being utilized as case definitions for electronic health record (EHR)-based algorithms to predict and detect psychosis. However, data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis. Study Design Using EHRs at 3 health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into 5 higher-order groups. 1133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings. Study Results PPVs across all diagnostic groups and hospital systems exceeded 70%: Mass General Brigham 0.72 [95% CI 0.68–0.77], Boston Children's Hospital 0.80 [0.75–0.84], and Boston Medical Center 0.83 [0.79–0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80–0.92) and major depressive disorder with psychosis were the most variable (0.57–0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08–0.62). Conclusions We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the case definitions used in the development of risk prediction models designed to predict or detect undiagnosed psychosis. [ABSTRACT FROM AUTHOR]

Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions. While credit card fraud incidents are relatively rare, they can result in substantial financial losses, particularly due to the high monetary value associated with fraudulent transactions. Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investigation process is often time-consuming, limiting the number of alerts that can be thoroughly examined each day. Therefore, the primary objective of a fraud detection model is to provide accurate alerts while minimizing false alarms and missed fraud cases. In this paper, we introduce a state-of-the-art hybrid ensemble (ENS) dependable machine learning (ML) model that intelligently combines multiple algorithms with proper weighted optimization using grid search, including decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), to enhance fraud identification. To address the data imbalance issue, we employ the instant hardness threshold (IHT) technique in conjunction with logistic regression (LR), surpassing conventional approaches. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid ensemble model outperforms existing works, establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. The results highlight the effectiveness and reliability of our approach, demonstrating superior performance metrics and showcasing its exceptional potential for real-world fraud detection applications. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Inorganic Analytical Chemistry / Zhongguo Wuji Fenxi Huaxue is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Proficiency testing is an essential tool, which laboratories may use to demonstrate their competence, enabling them to evaluate their performance for specific measurements, tests or calibrations by comparison with results of other laboratories. The pressure therefore to consistently perform 'well' in proficiency tests may lead participants to consider colluding with other labs or falsifying data, in order to ensure a high standard of performance. The requirements for the accreditation of proficiency testing schemes to the ISO/IEC 17043 standard place a responsibility on the providers of proficiency testing to consider the possibility of collusion within their schemes and to take measures to discourage or prevent it. A survey of proficiency testing providers has been performed to gather a range of opinions on the 'state' of collusion within their schemes. The options available to providers for prevention of collusion and the difficulties of detection are also discussed. [ABSTRACT FROM AUTHOR]

Due to the fusion of culinary cultures, chili products have gained widespread popularity among consumers. Capsaicin, the primary compound responsible for the spiciness of chili products, serves as a pivotal parameter in assessing the quality of chili peppers. A three-dimensional (3D) cell-based electrochemical sensor was developed to detect capsaicin by monitoring the release of hydrogen peroxide (H2O2). The working electrode utilized in this study was a screen-printed carbon electrode (SPCE) that had been modified with AuPtNPs/ZIF-8-RGO. Encapsulation of HepG2 cells within a 4.0 wt% sodium alginate gel enabled their immobilization onto the working electrode, thus serving as the biometric element for signal recording via the amperometric i-t method. H2O2 kit assays showed that H2O2 release increased with increasing capsaicin concentration over a range of concentrations. With a detection limit of 0.848 μM, the developed biosensor effectively detected capsaicin at concentrations that varied from 10 μM to 120 μM. The sensor that was developed displayed exceptional sensitivity, stability, and reliability, offering a novel method for monitoring capsaicin level in food. [ABSTRACT FROM AUTHOR]

The separation of radionuclides is critical for the sustainable development of nuclear energy. It is urgent to design and prepare functionalized materials for efficient radionuclides separation. Porous materials are considered excellent candidates for the separation of radionuclides under complex conditions due to their high specific surface areas, tunable pore structures and controllable functionalities. In this review, we summarized the design, preparation and functionalization of porous materials and their application for separation of radionuclides in the past five years, discussed the separation performance and analyzed the structure-activity relationship between various radionuclides and porous materials, and systematically clarified their characterization and mechanism of different type porous materials. We also introduced the detection, irradiation and chemical toxicity of different reflective radionuclides. [ABSTRACT FROM AUTHOR]

Human activities are exacerbating environmental change globally. Turbidity in freshwater systems is increasing due to extreme weather events and intensification of activities such as deforestation, urbanisation, agriculture and altering water flow with dams and other structures. Prey species may benefit from turbid habitats as turbidity creates a visual barrier to predators; such predators thus face reduced foraging success as water clarity decreases. However, the literature on the effects of water turbidity on predator–prey interactions in fish is often contrasting. Environmentally driven changes in predator and prey behaviour, foraging and survival ultimately have the potential to affect community structure and ecosystem functioning, thus understanding the impact of changes in turbidity on predator–prey interactions should be a priority. Given the contrasting results in the primary literature, the aim of this review is to provide a broad overview of the current knowledge on the impacts of increased turbidity on predator–prey interactions in freshwater fishes, summarise the published literature and identify knowledge gaps on this topic. We collated 281 studies from the peer‐reviewed literature that tested the impact of water turbidity on the foraging behaviour of predatory fish and anti‐predator behaviour of prey fish. We recorded changes in predator and prey behaviours in response to changes in turbidity levels. Furthermore, we reported whether these behavioural changes were considered to have a positive (e.g. enhanced foraging success) or negative (e.g. higher mortality) impact on the focal species, or whether the impact was unspecified (e.g. changes in anti‐predator behaviour without quantifying actual predation risk). The literature has some strong taxonomic, geographical and environmental biases, where most research focused on commercially important, temperate species. A large proportion of studies (47%) reported a negative (versus a positive or unspecified) impact of turbidity on the focal species (either predator or prey freshwater fish). Negative impacts were reported more often for predators (58%) compared to prey (19%) and while changes in behaviour were frequently reported in prey species, if these changes are adaptive or not is unclear, as actual predation risk was not assessed in the majority of relevant studies (55%). Increases in turbidity have complex effects on predator–prey interactions, with negative impacts more often reported for adult piscivore predators, while prey species may gain a relative advantage compared to visual predators. However, whether prey behavioural responses to increased turbidity are adaptive is generally unclear. We suggest that a more holistic and ecologically relevant experimental approach is needed to disentangle the underlying mechanisms and determine the adaptive nature of prey behavioural responses to increased turbidity. Our review serves as a broad and comprehensive summary of the empirical research conducted on impacts of increasing water turbidity on predator–prey interactions in freshwater fishes. It highlights knowledge gaps and provides a reference point to guide future experimentation and research synthesis in this crucial and growing area of research. [ABSTRACT FROM AUTHOR]

A novel d‐gluconamide‐functionalized water‐soluble pillar[5]arene (GWP5) was synthesized through a simple synthetic route. GWP5 showed a remarkable aggregation‐induced emission (AIE) with increasing concentration. This feature served as the basis for the construction of an AIE‐based sensor for fluorescence detection of Fe3+ with high sensitivity and selectivity, with a linear relationship between luminescence quenching and Fe3+ concentration observed from 0 to 20 µM, and a limit of detection of 7.84 × 10−9 M. This fluorescent probe could be applied to the detection of Fe3+ ions in real environmental water samples and in serum samples. [ABSTRACT FROM AUTHOR]

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Plastics / Zhongguo Suliao is the property of Journal Office of CHINA PLASTICS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The novel coronavirus disease has caused severe threats to the daily life and health of people all over the world. Hence, early detection and timely treatment of this disease are significant to prevent the coronavirus's spread and ensure more effective patient care. This work adopted an integrated framework comprising deep learning and attention mechanism to provide a more effective and reliable diagnosis. This framework consists of two convolution neural network (CNN), a bidirectional LSTM, two fully-connected layers (FCL), and an attention mechanism. The main aim of the proposed framework is to reveal a promising approach based on deep learning for early and timely detection of coronavirus disease. For greater accuracy, the framework's hyperparameters are tuned by means of a genetic algorithm. The effectiveness of the proposed framework has been examined utilizing a public dataset including 18 different blood findings from Albert Einstein Israelita Hospital in Sao Paulo, Brazil. Additionally, within the experimental studies, the proposed framework is subjected to comparison with the state-of-the-art techniques, evaluated across various metrics. Based on the derived consequences, the proposed framework has yielded enhancements in accuracy, recall, precision, and F1-score, registering approximate improvements of 1.27%, 4.07%, 3.20%, and 2.88%, respectively, as measured against the second-best rates. [ABSTRACT FROM AUTHOR]

Developing highly sensitive and selective detection methods is crucial for environmental and healthcare monitoring. In this study, the chiral and fluorescent signals of L-glutathione-modified gold nanoclusters (L-GSH-Au NCs) were discovered to be responsive to Co2+, which displayed linear correlations with the concentration changes of Co2+. Notably, the chiral signal was more sensitive than the FL signal, whose limit of detection (LOD) was calculated to be 0.37 μM and 3.93 times lower than the LOD obtained with fluorescent signals. Moreover, the chiral signals exhibited unexpectedly high selectivity towards Co2+, effectively avoiding interference from other metal ions and biomolecules. Furthermore, the concentrations of Co2+ in various samples, such as Taihu water, tap water, bottled water, and animal serum, were accurately quantified using the chiral signals of L-GSH-Au NCs without complex pretreatment, with recoveries ranging between 95.64% and 103.22%. This study not only provides an innovative approach for Co2+ detection but also highlights the detection capabilities of chiral signals in complex environments. [ABSTRACT FROM AUTHOR]

In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious transactions, Ethereum network transaction information and phishing wallet addresses were collected, and a unique dataset was created after the data preprocessing process. During the data preprocessing process, we manually distinguished the features within the original dataset that contained potential risk indicators. The learning process of the RG-Guard engine in risk classification was achieved by developing a deep learning model based on LSTM + Softmax. In the training process of the model, RG-Guard was optimised for maximum accuracy, and optimum hyperparameters were obtained. The reliability and dataset performance of the preferred LSTM + Softmax model were verified by comparing it with algorithms used in risk classification and detection applications in the literature (Decision tree, XG boost, Random forest and light gradient boosting machine). Accordingly, among the trained models, LSTM + Softmax has the highest accuracy with an F1-score of 0.9950. When a cryptocurrency transaction occurs, RG-Guard extracts the feature vectors of the transaction and assigns a risk level between 1 and 5 to the parameter named βrisk. Since transactions with βrisk > = 3 are labelled as suspicious transactions, RG-Guard blocks this transaction. Thus, thanks to the use of the RG-Guard engine in metaverse applications, it is aimed to easily distinguish potential suspicious transactions from instant transactions. As a result, it is aimed to detect and prevent instant potential suspicious transactions with the RG-Guard engine in money transfers, which have the greatest risk in cryptocurrency transactions and are the target of fraud. The original dataset prepared in the proposed study and the hybrid LSTM + Softmax model developed specifically for the model are expected to contribute to the development of such studies. [ABSTRACT FROM AUTHOR]

Computer vision and deep learning are essential in human authentication. It provides answers to numerous issues faced in the real world. Moreover, it has a great potential in detecting and recognizing the biometrics. It plays a significant role in reducing frauds such as spoofing, identification (ID) theft, and masking types of issues, which are difficult to perform manually, and many case studies used deep learning algorithms (DLA) like viola-jones, AlexNet, and Tiny YOLO3 but the main limitations of these studies are that they are not capable of giving high accuracy and robustness in the multi-face scenarios. So, in this article, an enhanced ArcFace (Additive Angular Margin loss) referred to as Improved ArcFace (I-AF) utilizes Convolution Neural Network (CNN) as its base architecture for feature extraction and RetinaFace are combined to overcome the above limitation, whereas RetinFace is for detecting and I-AF is for recognizing and authenticating human faces. It gives robust and accurate results while dealing with multi-faces. To evaluate the performance of the human monitoring system, it is implemented on real-time student data in a classroom to track the attendance of individuals. The faces of individuals in a classroom are detected and each detected face will be recognized and finally the result will be mapped for the attendance of individuals. To improve accuracy, the data set termed as labeled data, which contains images of students, is trained using I-AF. The system is 97% accurate, which is better than other methods. [ABSTRACT FROM AUTHOR]

ABSTRACT The quarantine nematode Bursaphelenchus xylophilus is a causative agent of the pine wilt disease. In Europe, the nematode is vectored by Monochamus galloprovincialis which is also a natural vector of the native, nonpathogenic B. mucronatus. Based on the established regulations, long‐term detection surveys of pine wood and vector insects for the presence of B. xylophilus are obligatory in all EU member countries. The main objective of the presented research was to use the real‐time PCR technique for the fast and precise detection, and identification of Bursaphelenchus nematodes associated with M. galloprovincialis ssp. pistor occurring in Poland. The research conducted on beetles collected in forest stands in Poland revealed exclusive presence of the native, nonpathogenic nematode B. mucronatus. At the beginning of the beetle flight season (June–July), the use of the real‐time PCR allowed detection of B. mucronatus, in the case of its physical presence in the body of the dissected M. galloprovincialis. However, at the end of the flight season (September–October), the DNA of B. mucronatus was also found in some beetles with no physical presence of nematodes in tracheas or under elytra. The univoltine biology of the beetle, gradual decline in dispersal juveniles to zero in/on the beetle body, and positive results of real‐time PCR reaction could indicate that in some of the beetles earlier loaded with nematodes, at the end of the season, only their remains were left after the live nematodes' departure during the vector's maturation feeding and/or egg laying. The obtained results confirm extremely high sensitivity of the real‐time PCR method used in detection and identification of both nematode species which may simultaneously occur in M. galloprovincialis beetles. It allows for precise detection of the nematodes based on their remains left in/on the vector insects. [ABSTRACT FROM AUTHOR]

Stored products, such as grains and processed foods, are susceptible to infestation by various insects. The early detection of insects in the supply chain is crucial, as introducing invasive pests to new environments may cause disproportionate harm. The STAR Center at Stevens Institute of Technology developed the Acoustic Stored Product Insect Detection System (A-SPIDS) to detect pests in stored products. The system, which comprises a sound-insulated container for product samples with a built-in internal array of piezoelectric sensors and additional electret microphones to record outside noise, was used to conduct numerous measurements of the vibroacoustic signatures of various insects, including the Callosobruchus maculatus, Tribolium confusum, and Tenebrio molitor, in different materials. A normalization method was implemented using the ambient noise of the sensors as a reference, to accommodate for the proprietary, non-calibrated sensors and allowing to set relative detection thresholds for unknown sensitivities. The normalized envelope of the filtered signals was used to characterize and compare the insect signals by estimating the Normalized Signal Pulse Amplitude (NSPA) and the Normalized Spectral Energy Level (NSEL). These parameters characterize the insect detection Signal Noise Ratio (SNR) for pulse-based detection (NSPA) and averaged energy-based detection (NSEL). These metrics provided an initial step towards the design of a reliable detection algorithm. In the conducted tests NSPA was significantly larger than NSEL. The NSPA reached 70 d B for T. molitor in corn flakes. The insect signals were lower in flour where the averaged NSPA and NSEL values were around 40 dB and 11 d B to 16 d B , respectively. [ABSTRACT FROM AUTHOR]

Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially at the local scale, as maintaining and deploying networks is costly. With increasing interest in smaller-yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosive Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either "explosion", "ambient", or "other" with true positive rates (recall) greater than 96% for all three categories. [ABSTRACT FROM AUTHOR]

AbstractPathogens contamination is a pressing global public issue that has garnered significant attention worldwide, especially in light of recent outbreaks of foodborne illnesses. Programmable nucleases like CRISPR/Cas and Argonaute hold promise as tools for nucleic acid testing owning to programmability and the precise target sequence specificity, which has been utilized for the development pathogens detection. At present, fluorescence, as the main signal output method, provides a simple response mode for sensing analysis. However, the dependence of fluorescence output on large instruments and correct analysis of output data limited its use in remote areas. Lateral flow strips (LFS), emerging as a novel flexible substrate, offer a plethora of advantages, encompassing easy-to-use, rapidity, visualization, low-cost, portability, etc. The integration of CRISPR/Cas and Argonaute with LFS, lateral flow assay (LFA), rendered a new and on-site mode for pathogens detection. In the review, we introduced two programmable nucleases CRISPR/Cas and Argonaute, followed by the structure, principle and advantages of LFA. Then diversified engineering detection pattens for viruses, bacteria, parasites, and fungi based on CRISPR/Cas and Argonaute were introduced and summarized. Finally, the challenge and perspectives involved in on-site diagnostic assays were discussed. [ABSTRACT FROM AUTHOR]

Purpose: Although cystoscopy is a reliable tool for detecting bladder cancer, it poses a high burden on patients and entails high costs. This highlights the need for non-invasive and cost-effective alternatives. This study aimed to validate a previously developed urinary methylation marker panel containing GHSR and MAL. Methods: We enrolled 134 patients who underwent cystoscopy because of hematuria, including 63 individuals with primary bladder cancer and 71 with non-malignant findings. Urine samples were self-collected at home and sent via regular mail. Subsequently, DNA was extracted and the hypermethylation of GHSR and MAL was evaluated using quantitative methylation-specific polymerase chain reaction. The performance of methylation markers was assessed using area-under-the-curve (AUC) analysis and sensitivity and specificity based on pre-established cut-off values. Results: Validation of the marker panel GHSR/MAL resulted in an AUC of 0.87 at 79% sensitivity and 80% specificity. Sensitivity was comparable to the previous investigation (P > 0.9), though specificity was significantly lower (P = 0.026). Sensitivity was higher for high-grade tumors compared to low-grade tumors (94% vs. 60%, P = 0.002). Conclusion: Validation of the GHSR/MAL methylation marker panel on at home collected urine samples confirms its robust performance for bladder cancer detection in a hematuria population, and underscores the diagnostic potential for future clinical application. [ABSTRACT FROM AUTHOR]

The optical properties of quantum dots are significantly influenced by photoactivation. However, the progress in the development of photoactivation carbon dot (CDs) is still stagnant. Herein, a method for preparing photoactivated CDs/NiFe2O4 by introducing affluent defects onto the surface of CDs through modification with NiFe2O4, extremely filling the gap of photoactivated CDs is proposed. Interestingly, a fantastic fluorescence detection strategy in a unique “OFF‐ON‐OFF” mode, combined with Ag+‐assisted fluorescence enhancement, to establish a dual‐channel regulation mechanism for ultra‐sensitive detection of Hg2+ is first proposed. By increasing surface traps and non‐radiative recombination, defect‐induced manipulation on the surface of CDs creates favorable conditions for enhancing photoactivation capacity. Unexpectedly, the CDs/NiFe2O4 shows a 25‐fold enhancement in fluorescence intensity after exposure to UV‐lamp. Compared to CDs/NiFe2O4, Ag+‐assisted photoactivation CDs/NiFe2O4 exhibits superior selectivity and sensitivity for Hg2+ detection, with low detection limits of 0.7 nM. Moreover, TiO2@CDs/NiFe2O4 catalyst is successfully prepared by anchoring CDs/NiFe2O4 onto the surface of TiO2, which demonstrates efficient degradation of tetracycline within 90 min under visible light irradiation, with a degradation rate of 98.3%. This work first provides a novel defects modification strategy for developing highly active photoactivated CDs technology that is significant for future environmentally sensitive applications. [ABSTRACT FROM AUTHOR]

In food safety analysis, the detection and control of foodborne pathogens and their toxins are of great importance. Monitoring of virus transmission is equally important, especially in light of recent findings that coronaviruses have been detected in frozen foods and packages during the current global epidemic of coronavirus disease 2019. In recent years, field-effect transistor (FET) biosensors have attracted considerable scholarly attention for pathogenic microorganisms and toxins detection and sensing due to their rapid response time, high sensitivity, wide dynamic range, high specificity, label-free detection, portability, and cost-effectiveness. FET-based biosensors can be modified with specific recognition elements, thus providing real-time qualitative and semiquantitative analysis. Furthermore, with advances in nanotechnology and device design, various high-performance nanomaterials are gradually applied in the detection of FET-based biosensors. In this article, we review specific detection in different biological recognition elements are immobilized on FET biosensors for the detection of pathogenic microorganisms and toxins, and we also discuss nonspecific detection by FET biosensors. In addition, there are still unresolved challenges in the development and application of FET biosensors for achieving efficient, multiplexed, in situ detection of pathogenic microorganisms and toxins. Therefore, directions for future FET biosensor research and applications are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cell-derived extracellular vesicles (EVs), which carry diverse biomolecules such as nucleic acids, proteins, metabolites, and lipids reflecting their cell of origin, are released under both physiological and pathological conditions. EVs have been demonstrated to mediate cell-to-cell communication and serve as biomarkers. EV-associated DNA (EV-DNA) comprises genomic and mitochondrial DNA (i.e., gDNA and mtDNA) fragments. Some studies have revealed that EV-DNA can represent the full nuclear genome and mitochondrial genome of parental cells. Furthermore, DNA fragments loaded into EVs are stable and can be transferred to recipient cells to regulate their biological functions. In this review, we summarized and discussed EV-DNA research advances with an emphasis on EV-DNA detection at the population-EV and single-EV levels, gene transfer-associated biological functions, and clinical applications as biomarkers for disease liquid biopsy. We hope that this review will provide potential directions or guidance for future EV-DNA investigations. [ABSTRACT FROM AUTHOR]

This paper proposes a ship-radiated noise (S-RN) detection method based on fractional-order Duffing coupled Van der Pol oscillator (FODVP) for detecting unknown signal frequency. To begin with, aiming at the low detection accuracy of integer-order Duffing coupled Van der Pol oscillator (IODVP), FODVP is proposed and the selection of oscillator parameters is further analyzed. According to the system performance, FODVP is immune to white noise greater than about − 13 dB. This system's minimum signal-to-noise ratio threshold detectable is − 69.03 dB, which is 33.98 dB lower than that of IODVP. Then, accurately determining the critical threshold of the system is the key to chaos detection. The entropy value measurement curve is used to quantitatively ascertain the critical threshold and study the positive phase transition detection of the oscillator. Furthermore, the conditions under which fractional-order Duffing coupled Van der Pol oscillator with oscillator array (FODVP-OA) produces intermittent chaos effects are studied. The geometric sequence structure that satisfies the driving force frequency in the oscillator array is determined. Finally, the effectiveness of the proposed detection method is verified by detecting analog and measured signals. The results demonstrate that FODVP-OA presents significant sensitivity to low-frequency signals. This research is the result of the combination of chaotic system and fractional theory, which further promotes the progress of S-RN detection and provides a new idea for detecting other types of signals. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of mitigating unknown partial path overlaps in communication systems. This study demonstrates that by utilizing the front-end insight of communication systems along with the sum–product algorithm applied to factor graphs, it is possible not only to track these overlapping components accurately, but also to detect all multipath channel impairments simultaneously. The proposed methodology involves discretizing channel parameters, such as channel paths and attenuation coefficients, to ensure the most accurate computation of means of Gaussian observations. These parameters are modeled as Bernoulli random variables with priors set to 0.5 . A notable aspect of the algorithm is its integration of the received signal power into the calculation of noise variance, which is critical for its performance. To further reduce the receiver complexity, a novel implementation strategy, based on provided pre-defined look up tables (LOTs) to the reciver, is introduced. The simulation results, covering both distributed and concentrated pilot scenarios, reveal that the algorithm performs almost equally under both conditions and surpasses the established upper bound in performance. [ABSTRACT FROM AUTHOR]

In this study, a D‐shaped, extremely sensitive surface plasmon resonance (SPR) sensor with a blue shift characteristic is proposed that can be used to identify various malignant cancer cells. A sensing layer made of titanium is inserted to produce intense sensitivity. For the simulation and numerical evaluation of the proposed biosensor, we used the finite element method (FEM) and the COMSOL Multiphysics 5.6 software. To regulate how much light passes through the sensor, which incites the released electrons at the surface of the plasmonic material, air holes in different radii are used. The characteristics that make our proposed sensor superior to other D‐shaped SPR sensors are—it gives an excellent amplitude sensitivity (AS) of 200 RIU−1 also ensures extremely high wavelength sensitivity (WS) spanning from 34,350 to 42,145 nm/RIU with an outstanding spectral resolution of 2.37 × 10−6 RIU. Furthermore, the R2 (0.9966) value is also extremely close to 1 which ensures the quality of our sensor regarding its performance. The given sensor is tailored for four vital human cancers, which are blood cancer, skin cancer, cervical cancer, and adrenal gland cancer. Here, optimization of the structural geometric parameters, plasmonic materials, and material thickness are also conducted in search of the best functionality of the sensor. The offered structure, nonetheless, can be an obvious prologue in the case of the crucial human cancer detection platform due to its mild sensing performances. [ABSTRACT FROM AUTHOR]

Seafood is an important source of food and protein for humans. However, it is highly susceptible to microbial contamination, which has become a major challenge for the seafood processing industry. Bacteriophages are widely distributed in the environment and have been successfully used as biocontrol agents against pathogenic microorganisms in certain food processing applications. However, due to the influence of environmental factors and seafood matrices, using bacteriophages for commercial-scale biocontrol strategies still faces some challenges. This article briefly introduces the current processes used for the production and purification of bacteriophages, lists the latest findings on the application of phage-based biocontrol in seafood, summarizes the challenges faced at the current stage, and provides corresponding strategies for solving these issues. [ABSTRACT FROM AUTHOR]

Background and Aim Methods Results Conclusions Epidemiological research is scarce in Asian countries including Japan. We aimed to clarify the epidemiological trend of nonampullary duodenal cancer (NADC) in Japan using a national database.All patients with NADC diagnosed between 2016 and 2020 were identified from the Japanese national cancer registry. The crude and age‐adjusted incidences were determined and temporal trends including diagnostic mode, tumor stage, and treatments were analyzed.A total of 14 484 patients were included. The crude and age‐adjusted incidences were 21.8–23.5 and 10.7–12.1 per 1 000 000 person‐years. Male‐to‐female ratio was 1.7:1, and the incidence increased with age. The number of patients was highest among 70–79 year age group in both sexes. In total, 55% of patients were diagnosed during screening or surveillance for other diseases. In 54% of patients, the tumor stage was localized. The major treatment was surgery (32%), followed by endoscopic resection (27%). The trends for detection mode, tumor stage, and treatment were consistent during 2016–2020.This study clarified and confirmed the high incidence of duodenal cancer in Japan. Characteristically, many NADC cases are diagnosed asymptomatically and treated by endoscopic or surgical treatment. [ABSTRACT FROM AUTHOR]

Background: A Tonsil-Oral-Scrubbing (TOSc) method was developed to sample the sow's oropharyngeal and tonsillar area without snaring and has shown comparable porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection rates with tonsil scraping in infected sows. This study investigated the effect of specific TOSc collection factors on the PRRSV RT-rtPCR results (detection rates and Ct values). Those factors include whether the sow was snared or not snared at TOSc collection ("snared" vs. "not snared"); whether the sow was laying down or standing at collection ("laying down" vs. "standing"); and type of collectors used for TOSc collection ("TOSc prototype" vs. "Spiral-headed AI catheter (SHAC)"). Volume of fluid was compared between "snared" and "not snared" groups, and collection time was compared between "laying down" and "standing" groups as well. Results: The effect for each factor was assessed in three independent studies following the same design: TOSc was collected twice from each studied sow, once with the baseline level for a factor ("not snared", or "standing", or "TOSc prototype"), and another time followed by the other level of the paired factor ("snared", "laying down", or "SHAC", correspondingly). Results showed that "not snared" TOSc had numerically higher PRRSV RNA detection rate (60.7% vs. 52.5%, p = 0.11), significantly lower median Ct values (31.9 vs. 32.3, p < 0.01), and significantly higher volume of fluid than "snared" samples (1.8 mL vs. 1.2 mL, p < 0.01); "laying down" TOSc samples did not differ statistically (60.7% vs. 60.7%) in the PRRSV RNA detection rate, obtained numerically lower median Ct values (30.9 vs. 31.3, p = 0.19), but took 40% less collection time compared to "standing" TOSc samples; samples collected using the "TOSc prototype" had numerically higher PRRSV RNA detection rate (91.7% vs. 88.3%, p = 0.27) and significantly lower median Ct values (32.8 vs. 34.5, p < 0.01) than that from "SHAC". Conclusions: Under the conditions of this study best practices for TOSc collection aiming higher detection rate of PRRSV RNA while minimizing time for collection were suggested to be sampling TOSc without snaring, when sows are laying down, and using a prototype TOSc collector. [ABSTRACT FROM AUTHOR]

Introduction: Brain tumors are characterized by abnormal cell growth within or around the brain, posing severe health risks often associated with high mortality rates. Various imaging techniques, including magnetic resonance imaging (MRI), are commonly employed to visualize the brain and identify malignant growths. Computer-aided diagnosis tools (CAD) utilizing Convolutional Neural Networks (CNNs) have proven effective in feature extraction and predictive analysis across diverse medical imaging modalities. Methods: This study explores a CNN trained and evaluated with nine activation functions, encompassing eight established ones from the literature and a modified version of the soft sign activation function. Results: The latter demonstrates notable efficacy in discriminating between four types of brain tumors in MR images, achieving an accuracy of 97.6%. The sensitivity for glioma is 93.7%; for meningioma, it is 97.4%; for cases with no tumor, it is 98.8%; and for pituitary tumors, it reaches 100%. Discussion: In this manuscript, we propose an advanced CNN architecture that integrates a newly developed activation function. Our extensive experimentation and analysis showcase the model's remarkable ability to precisely distinguish between different types of brain tumors within a substantial and diverse dataset. The findings from our study suggest that this model could serve as an invaluable supplementary tool for healthcare practitioners, including specialized medical professionals and resident physicians, in the accurate diagnosis of brain tumors. [ABSTRACT FROM AUTHOR]

Timely detection and control of airborne disease is important to improve productivity. This study proposed a novel approach that utilizes micro polarization image features and a backpropagation neural network (BPNN) to classify and identify airborne disease spores in a greenhouse setting. Firstly, disease spores were collected in the greenhouse, and their surface morphological parameters were analyzed. Subsequently, the micropolarization imaging system for disease spores was established, and the micropolarization images of airborne disease spores from greenhouse crops were collected. Then the micropolarization images of airborne disease spores were processed, and the image features of polarization degree and polarization angle of disease spores were extracted. Finally, a disease spore classification model based on the BPNN was ultimately developed. The results showed that the texture position of the surface of the three disease spores was inconsistent, and the texture also showed an irregular shape. Texture information was present on the longitudinal and transverse axes, with the longitudinal axis exhibiting more uneven texture information. The polarization-degree images of the three disease spores exhibit variations in their representation within the entirety of the beam information. The disease spore polarization angle image exhibited the maximum levels of contrast and entropy when the Gabor filter's direction was set to n/15. The recognition accuracy of cucumber downy mildew spores, tomato gray mildew spores, and cucumber powdery mildew spores were 75.00%, 83.33%, and 96.67%, respectively. The average recognition accuracy of disease spores was 86.67% based on BPNN and micropolarization image features. This study can provide a novel method for the detection of plant disease spores in the greenhouse. [ABSTRACT FROM AUTHOR]

Tomato brown rugose fruit virus (ToBRFV) is regulated as a quarantine pest in many countries worldwide. To assess whether ToBRFV is present in cultivations, plants or seed lots, testing is required. The interpretation of test results, however, can be challenging. Reverse transcription‐quantitative (real‐time) PCR results, even though considered "positive", may not always signify plant infection or indicate the presence of infectious virus, but could be due to the presence of viral residues in the environment. Here, case studies from the Netherlands, Belgium, and the United Kingdom address questions regarding the detection of ToBRFV in various settings, and the infectiousness of ToBRFV‐positive samples. These exploratory analyses demonstrate widespread detection of ToBRFV in diverse samples and environments. ToBRFV was detected inside and around greenhouses with no prior history of ToBRFV infection, and on different materials and surfaces, including those that were untouched by individuals, plants or objects. This suggested the dispersal of viral residues through aerosols. ToBRFV or its residues were more often detected in regions with nearby tomato production, yet were also found in a wider environment extending beyond infected crops. ToBRFV originating from environmental contamination may or may not be infectious, adding complexity to decision‐making in response to positive test results. Contextual information, such as the origin of the sample and the likelihood of residues from prior cultivations and/or the broader environment, is important for interpreting test results. A nuanced approach is crucial to correctly interpret ToBRFV test results, necessitating further research to support risk assessment. [ABSTRACT FROM AUTHOR]

The introduction of generative artificial intelligence (AI) to the wider public could have a huge impact on EFL learning and teaching. Researchers have voiced concerns that learners might lean too much on technology. Previous studies have investigated the use of AI tools in L2 writing with various populations and found that it was difficult for teachers to detect use of AI and that teachers mainly relied on linguistic strategies to detect AI-generated texts. This paper reports on a qualitative study that investigated whether novice English teachers were able to detect AI-generated writing and which strategies they used to do this. The results show that some novice teachers are quite good at detecting AI-generated texts, while others proved to have more difficulties. The teachers used both linguistic and content-related strategies to detect AI-generated writing. The results point towards the value of including this topic in teaching methodology courses in (initial) teacher training programmes. [ABSTRACT FROM AUTHOR]

Background: Cocaine may be applied to decongest the nasal mucosa before nasotracheal intubation, but patients risk a criminal offence if cocaine is detected when patients drive a car shortly after surgery. We aimed to evaluate whether benzoylecgonine levels in saliva exceeded the cut‐off point 24 h after administration in patients undergoing nasotracheal intubation and whether cocaine would be detectable above the Danish legal fixed limit in blood samples 1 and 24 h after surgery. Methods: We conducted a prospective study following approval from the local research ethics committee and the national medicine agency. Written informed consent was obtained from all patients. We included patients scheduled for surgery under general anaesthesia with nasotracheal intubation. They received 80 mg cocaine as a nasal spray 5 min before induction and nasotracheal intubation. The primary outcome was a dichotomous assessment of benzoylecgonine levels in saliva samples measured 24 h after administration of nasal cocaine with a cut‐off limit of 200 ng/mL. Secondary outcomes were dichotomous assessments of cocaine in whole blood samples measured 1 and 24 h after administration of nasal cocaine with a cut‐off limit of 0.01 mg/kg. Results: Overall, 70 patients had valid saliva samples and 75 had valid blood samples 24 h after cocaine administration. Benzoylecgonine in saliva was traceable above the cut‐off in 9/70 patients (13%; CI95%: 6% to 23%), and cocaine in blood was detected above the cut‐off in 2/75 patients (3%; CI95%: 0.3% to 9%). Conclusion: We found benzoylecgonine traceable in saliva in 13% of patients and cocaine traceable in blood in 3% of patients 24 h after administration of 80 mg nasal cocaine. Patients should be informed when receiving cocaine and advised not to drive for at least 24 h. [ABSTRACT FROM AUTHOR]

Interleukin-11 (IL-11) is a multifunctional cytokine that plays a crucial role in various biological processes, including the promotion of hematopoiesis, regulation of the immune system, and facilitation of tissue repair. These functions highlight its significant importance in both medical research and clinical therapy. Consequently, the precise detection and assessment of IL-11′s biological activity are essential. The currently employed cell proliferation inhibition assay is cumbersome, time-consuming, and susceptible to interference from Interleukin-6. Here we study the JAK-STAT signaling pathway, a key mechanism of IL-11 actions. We initially establish a stable monoclonal cell line that expresses a luciferase reporter gene responsive to IL-11. We subsequently optimized critical parameters, including the pre-dilution concentration and gradient of IL-11, cell inoculum size, and IL-11 incubation duration. Comprehensive validation was undertaken to evaluate accuracy, precision, specificity, stability, and concordance with pharmacopeial methods, ultimately leading to the establishment of a novel reporter gene-based approach for detecting the biological activity of IL-11. This innovative method significantly enhances the detection accuracy and sensitivity while reducing the testing time, thereby offering promising prospects for broad applications. [ABSTRACT FROM AUTHOR]

Background: The development of artificial intelligence (AI) is rapidly expanding, showing promise in the dermatological field. Skin checks are a resource‐heavy challenge that could potentially benefit from AI‐tool assistance, particularly if provided in widely available AI solutions. A novel smartphone application(app)‐based AI system, "SCAI," was developed and trained to recognize spots in paired images of skin, pursuing identification of new skin lesions. This pilot study aimed to investigate the feasibility of the SCAI‐app to identify simulated skin changes in vivo. Materials and methods: The study was conducted in a controlled setting with healthy volunteers and standardized, simulated skin changes (test spots), consisting of customized 3‐mm adhesive spots in three colors (black, brown, and red). Each volunteer had a total of eight test spots adhered to four areas on back and legs. The SCAI‐app collected smartphone‐ and template‐guided standardized images before and after test spot application, using its backend AI algorithms to identify changes between the paired images. Results: Twenty‐four volunteers were included, amounting to a total of 192 test spots. Overall, the detection algorithms identified test spots with a sensitivity of 92.0% (CI: 88.1–95.9) and a specificity of 95.5% (CI: 95.0–96.0). The SCAI‐app's positive predictive value was 38.0% (CI: 31.0–44.9), while the negative predictive value was 99.7% (CI: 99.0–100). Conclusion: This pilot study showed that SCAI‐app could detect simulated skin changes in a controlled in vivo setting. The app's feasibility in a clinical setting with real‐life skin lesions remains to be investigated, where the challenge with false positives in particular needs to be addressed. [ABSTRACT FROM AUTHOR]

The major concern in signal processing for holographic data storage (HDS) systems is the two-dimensional (2D) inter-symbol interference (ISI), which arises from neighboring pixels in every direction on the data page of HDS systems. Recently, serial–parallel detection has been proposed for bit-patterned media recording (BPMR). We have realized that the serial–parallel detection structure is more suitable for HDS systems than BPMR systems, because, unlike BPMR systems, the interferences in HDS systems from horizontal and vertical directions are equal. Therefore, the serial–parallel structure for HDS systems can extract better bit-error-rate performance. [ABSTRACT FROM AUTHOR]

Metal‐organic frameworks (MOFs) as an efficient and convenient sensing material for environmental pollutants have attracted widespread attention in recent years. Here in, we synthesized a series of lanthanide co‐doping MOFs, EuxTb1‐x‐MOFs (x=0, 0.005, 0.00667, 0.01, 0.02 and 1) under solvothermal conditions. The luminescence colors can be adjusted by changing metal molar mass ratios and altering excitation wavelength. Fortunately, a near white‐light‐emitting material Eu0.01Tb0.99‐MOF was obtained under the excitation at 350 nm, whose CIE coordinates of (0.3444, 0.3409) are very close to standard white light coordinates (0.3333, 0.3333). We further studied the fluorescence sensing performances of Eu‐MOF, Tb‐MOF and Eu0.01Tb0.99‐MOF towards NACs. The results indicate that all MOFs can selectively detect 4‐nitroaniline, 4‐nitrophenol and 2‐nitrophenol with good sensitivity and recyclability. [ABSTRACT FROM AUTHOR]

Background: Lesion detection is one of the most important clinical tasks in positron emission tomography (PET) for oncology. An anthropomorphic model observer (MO) designed to replicate human observers (HOs) in a detection task is an important tool for assessing task‐based image quality. The channelized Hotelling observer (CHO) has been the most popular anthropomorphic MO. Recently, deep learning MOs (DLMOs), mostly based on convolutional neural networks (CNNs), have been investigated for various imaging modalities. However, there have been few studies on DLMOs for PET. Purpose: The goal of the study is to investigate whether DLMOs can predict HOs better than conventional MOs such as CHO in a two‐alternative forced‐choice (2AFC) detection task using PET images with real anatomical variability. Methods: Two types of DLMOs were implemented: (1) CNN DLMO, and (2) CNN‐SwinT DLMO that combines CNN and Swin Transformer (SwinT) encoders. Lesion‐absent PET images were reconstructed from clinical data, and lesion‐present images were reconstructed with adding simulated lesion sinogram data. Lesion‐present and lesion‐absent PET image pairs were labeled by eight HOs consisting of four radiologists and four image scientists in a 2AFC detection task. In total, 2268 pairs of lesion‐present and lesion‐absent images were used for training, 324 pairs for validation, and 324 pairs for test. CNN DLMO, CNN‐SwinT DLMO, CHO with internal noise, and non‐prewhitening matched filter (NPWMF) were compared in the same train‐test paradigm. For comparison, six quantitative metrics including prediction accuracy, mean squared errors (MSEs) and correlation coefficients, which measure how well a MO predicts HOs, were calculated in a 9‐fold cross‐validation experiment. Results: In terms of the accuracy and MSE metrics, CNN DLMO and CNN‐SwinT DLMO showed better performance than CHO and NPWMF, and CNN‐SwinT DLMO showed the best performance among the MOs evaluated. Conclusions: DLMO can predict HOs more accurately than conventional MOs such as CHO in PET lesion detection. Combining SwinT and CNN encoders can improve the DLMO prediction performance compared to using CNN only. [ABSTRACT FROM AUTHOR]

Recently, planar and broadband hot-electron photodetectors (HE PDs) were established but exhibited degraded performances due to the adoptions of the single-junction configurations and the utilizations of absorbable films with thicknesses larger than the electronic mean free path. In this work, we present a five-layer design for planar HE PDs assisted by triple junctions in which an ultrathin Pt layer dominates the broadband and displays strong optical absorption (>0.9 from 900 nm to 1700 nm). Optical studies reveal that the optical admittance matching between optical admittances of designed device and air at all interested wavelengths is responsible for broadband light-trapping that induces prominent energy depositions in Pt layers. Electrical investigations show that, benefitting from suppressed hot-electron transport losses and increased hot-electron harvesting junctions, the predicted responsivity of the designed HE PD is up to 8.51 mA/W at 900 nm. Moreover, the high average absorption (responsivity) of 0.96 (3.66 mA/W) is substantially sustained over a broad incidence angle regardless of the polarizations of incident light. The comparison studies between five-layer and three-layer devices emphasize the superiority of five-layer design in strong optical absorption in Pt layers and efficient hot-electron extraction. [ABSTRACT FROM AUTHOR]

Background and Aim: Few studies have evaluated the adenoma detection rate (ADR) of colonoscopy with texture and color enhancement imaging (TXI), a novel image‐enhancing technology. This study compares the detection of colorectal polyps using TXI to that using white light imaging (WLI). Methods: This single‐center retrospective study used propensity‐matched scoring based on the patients' baseline characteristics (age, sex, indication, bowel preparation, endoscopist, colonoscope type, and withdrawal time) to compare the results of patients who underwent chromoendoscopy using WLI or TXI at the Toyoshima Endoscopy Clinic. The differences in polyp detection rates and the mean number of detected polyps per colonoscopy were determined between the TXI and WLI groups. Results: After propensity score matching, 1970 patients were enrolled into each imaging modality group. The mean patient age was 57.2 ± 12.5 years, and 44.5% of the cohort were men. The ADR was higher in the TXI group than in the WLI group (55.0% vs 49.4%, odds ratio: 1.25). High‐risk ADR were more common in the TXI group than in the WLI group (17.6% vs 12.8%; OR: 1.45). The mean number of adenomas per colonoscopy (APC) was higher in the TXI group than in the WLI group (1.187 vs 0.943, OR: 1.12). APC with a flat morphology (1.093 vs 0.848, OR: 1.14) and APC of <6 mm (0.992 vs 0.757, OR: 1.16) were higher in the TXI group than in the WLI group. Conclusion: Compared to WLI, TXI improved the ADR in patients who underwent chromoendoscopy based on actual clinical data. [ABSTRACT FROM AUTHOR]

Hypoxia is one of the defining characteristics of the tumor microenvironment (TME) in solid cancers. It has a major impact on the growth and spread of malignant cells as well as their resistance to common treatments like radiation and chemotherapy. Here, we explore the complex functions of hypoxia in the TME and investigate its effects on angiogenesis, immunological evasion, and cancer cell metabolism. For prognostic and therapeutic reasons, hypoxia identification is critical, and recent developments in imaging and molecular methods have enhanced our capacity to precisely locate underoxygenated areas inside tumors. Furthermore, targeted therapies that take advantage of hypoxia provide a potential new direction in the treatment of cancer. Therapeutic approaches that specifically target hypoxic conditions in tumors without causing adverse effects are being led by hypoxia-targeted nanocarriers and hypoxia-activated prodrugs (HAPs). This review provides an extensive overview of this dynamic and clinically significant area of oncology research by synthesizing current knowledge about the mechanisms of hypoxia in cancer, highlighting state-of-the-art detection methodologies, and assessing the potential and efficacy of hypoxia-targeted therapies. [ABSTRACT FROM AUTHOR]

With advancements in DNA amplification research, isothermal amplification technology has emerged as an attractive method for detecting target DNA. Here, we describe primer generation-rolling circle amplification (PG-RCA) as an isothermal amplification method for detecting Escherichia coli O157:H7, Salmonella Typhimurium, Bacillus cereus, and Listeria monocytogenes. To improve PG-RCA sensitivity, the concentrations of the reaction components, dNTPs, phi29 DNA polymerase, and circular probes were optimized; the optimized conditions were applied to detect each target bacterium. A pair of forward and reverse circular probes that hybridized to the sense and anti-sense target genes was used in PG-RCA, exhibiting target selectivity. PG-RCA, which generated additional primers simultaneously with linear RCA and comprised multiple reaction cycles, resulted in higher accumulation of amplified DNA products than did linear RCA within the same reaction period. The threshold time (Tt) for each target gene concentration was determined based on the threshold value set in the amplification plot for PG-RCA, and a linear correlation between the Tt value and genomic DNA concentration was proven for each of the four bacteria. The PG-RCA-based assay could be applied to gene-based detection of various microorganisms and may be a useful isothermal amplification method for replacing traditional PCR methods. [ABSTRACT FROM AUTHOR]

There is global demand for methods to prioritize child sexual exploitation material (CSEM) investigations. Previous research comparing online CSEM offenders based on how they were detected found potentially meaningful differences in offense and individual characteristics, including factors relating to targets for prioritization, such as risk of other offending. The present study builds on this work by providing an in-depth comparison of the individual characteristics and offending behavior of a sample of 336 men convicted of CSEM offenses, divided into four detection groups: (1) those reported by others; (2) those identified during another police investigation; (3) those identified due to their online web purchases or downloads, and; (4) those detected during proactive online police investigations. As a group, the riskiest individuals were detected by reports of others and during other investigations (Cohen's f =.25). This finding suggests that it is important to search for CSEM when doing other police investigations, particularly those involving allegations of sexual offending or crimes against children. Risk relevant information may also assist prioritization, though it will depend on the information available at different points in an investigation and may require the use of professional judgement in approximating evidence of robust risk factors. [ABSTRACT FROM AUTHOR]