Detecting thyroid nodules along with surrounding tissues and tracking nodules using motion prior in ultrasound videos.

Ultrasound examination plays a crucial role in the clinical diagnosis of thyroid nodules. Although deep learning technology has been applied to thyroid nodule examinations, the existing methods all overlook the prior knowledge of nodules moving along a straight line in the video. We propose a new detection model, DiffusionVID-Line, and design a novel tracking algorithm, ByteTrack-Line, both of which fully leverage the prior knowledge of linear motion of nodules in thyroid ultrasound videos. Among them, ByteTrack-Line groups detected nodules, further reducing the workload of doctors and significantly improving their diagnostic speed and accuracy. In DiffusionVID-Line, we propose two new modules: Freq-FPN and Attn-Line. Freq-FPN module is used to extract frequency features, taking advantage of these features to reduce the impact of image blur in ultrasound videos. Based on the standard practice of segmented scanning by doctors, Attn-Line module enhances the attention on targets moving along a straight line, thus improving the accuracy of detection. In ByteTrack-Line, considering the characteristic of linear motion of nodules, we propose the Match-Line association module, which reduces the number of nodule ID switches. In the testing of the detection and tracking datasets, DiffusionVID-Line achieved a mean Average Precision (mAP50) of 74.2 for multiple tissues and 85.6 for nodules, while ByteTrack-Line achieved a Multiple Object Tracking Accuracy (MOTA) of 83.4. Both nodule detection and tracking have achieved state-of-the-art performance.

A new input-output-based framework for measuring the active and passive water use.

•Construct a dual-perspective framework for measuring water use.•Define the water use fluctuating due to macroeconomic condition changes.•Introduce a new indicator to measure the rate of passive water use.•Estimate the key water use channels from a passive perspective through SPA.•Discuss the supply chain's intermediate sector's role for the water use.

Artificial Intelligence-Related Dental Research: Bibliometric and Altmetric Analysis.

BACKGROUND: Recent years have witnessed an explosive surge in dental research related to artificial intelligence (AI). These applications have optimised dental workflows, demonstrating significant clinical importance. Understanding the current landscape and trends of this topic is crucial for both clinicians and researchers to utilise and advance this technology. However, a comprehensive scientometric study regarding this field had yet to be performed. METHODS: A literature search was conducted in the Web of Science Core Collection database to identify eligible "research articles" and "reviews." Literature screening and exclusion were performed by 2 investigators. Thereafter, VOSviewer was utilised in co-occurrence analysis and CiteSpace in co-citation analysis. R package Bibliometrix was employed to automatically calculate scientific impacts, determining the core authors and journals. Altmetric data were described narratively and supplemented with Spearman correlation analysis. RESULTS: A total of 1558 research publications were included. During the past 5 years, AI-related dental publications drastically increased in number, from 36 to 581. Diagnostics and Scientific Reports published the most articles, whereas Journal of Dental Research received the highest number of citations per article. China, the US, and South Korea emerged as the most prolific countries, whilst Germany received the highest number of citations per article (23.29). Charité Universitätsmedizin Berlin was the institution with the highest number of publications and citations per article (29.16). Altmetric Attention Score was correlated with News Mentions (P < .001), and significant associations were observed amongst Dimension Citations, Mendeley Readers, and Web of Science Citations (P < .001). CONCLUSIONS: The publication numbers regarding AI-related dental research have been rising rapidly and may continue their upwards trend. China, the US, South Korea, and Germany had promoted the progress of AI-related dental research. Disease diagnosis, orthodontic applications, and morphology segmentation were current hotspots. Attention mechanism, explainable AI, multimodal data fusion, and AI-generated text assistants necessitate future research and exploration.

Measurements of Atmospheric HO2 Radicals Using Br-CIMS with Elimination of Potential Interferences from Ambient Peroxynitric Acid.

As a promising direct measurement method of atmospheric hydroperoxyl radicals (HO2), bromide chemical ionization mass spectrometry (Br-CIMS) has been first demonstrated by Sanchez et al. (Atmos. Meas. Tech. 2016, 9, 3851-3861). However, field application of this method is currently still sparse, and there is still a gap between measured HO2 concentrations and calculated ones derived from the atmospheric equilibrium between HO2 and peroxynitric acid (HO2NO2). In this work, we constructed an improved Br-CIMS with optimizations of custom-built front-end devices, chamber pressures, and instrumental voltages to achieve a 3σ detection limit of 0.5 ppt at an integration time of 60 s and a sensitivity of 1-3 cps ppt-1 under a total reagent ion signal of 0.2 MHz for HO2 detection. HO2NO2, a product from atmospheric reactions between HO2 and NO2, can also be detected by Br-CIMS, whose interference on the HO2 measurement was found but nearly eliminated by regulating key CIMS voltages to minimize the decomposition of (BrHO2NO2)- ions in the MS. In addition, a 2 week field campaign was carried out in urban Shanghai, demonstrating that the interference of HO2 from ambient HO2NO2 was less than 10% of the true HO2 signal under our optimized CIMS voltage setting. Our study suggests that Br-CIMS is a reliable technique for atmospheric HO2 measurements.

Labor linkages and flow paths of industry in China.

As economic power increases and market patterns adapt, labor becomes an increasingly significant factor of production. However, there is a dearth of discourse regarding the structural changes that have occurred in the correlation of the labor force across industries, as well as a visual representation of the labor force's movement across industries. To quantify and analyze the correlation effect with greater precision, it is necessary to establish an input-output model as the foundation of analysis, comparing the changes in the total output of the economic system prior to and subsequent to the exclusion using the vertical integration algorithm. By decomposing the path structure, the average propagation distance of the labor force population's demand for each industry can be determined. By employing labor force population data from the corresponding years and China's input-output tables published by the National Bureau of Statistics of China (NBS) from 2005 to 2020, this study conducts a quantitative analysis of the correlation effect between labor force population and the trend of its transfer across 19 industries. The findings indicate that the correlation effect between labor force and population is most pronounced in the manufacturing sector. Furthermore, the construction sector faces an especially critical requirement for labor force personnel from other industries. The article culminates with a recommendation that the government enhance its macro-control endeavors to address labor market risk shocks and take an active stance in response to labor market fluctuations.

Catalytic pyrolysis and in situ carbonization of walnut shells: poly-generation and enhanced electrochemical performance of carbons.

In this study, walnut shell (WS) was used as feedstock, incorporating lithium carbonate (LC), sodium carbonate (SC), potassium carbonate (PC), and potassium hydroxide (PH) as pyrolysis catalysts and carbonization activators. A one-step method that allows catalytic pyrolysis and carbonization to be carried out consecutively under their respective optimal conditions is employed, enabling the concurrent production of high-quality pyrolysis oil, pyrolysis gas, and carbon materials from biomass conversion. The effects of LC, SC, PC, and PH on the yield and properties of products derived from WS pyrolysis as well as on the properties and performance of the resulting carbon materials were examined. The results indicated that the addition of LC, SC, PC, and PH enhanced the secondary cracking of tar, leading to increased solid and gas yields from WS. Additionally, it increased the production of phenolic compounds in bio-oil and H2 in syngas, concurrently yielding a walnut shell-based carbon material exhibiting excellent electrochemical performance. Specifically, when PC was used as an additive, the phenolic content in the pyrolysis oil increased by 27.64% compared to that without PC, reaching 74.9%, but the content of ketones, acids, aldehydes, and amines decreased. The hydrogen content increased from 2.5% (without the addition of PC) to 12.75%. The resulting carbon (WSC-PC) displayed a specific surface area of 598.6 m2 g-1 and achieved a specific capacitance of 245.18 F g-1 at a current density of 0.5 A g-1. Even after 5000 charge and discharge cycles at a current density of 2 A g-1, the capacitance retention rate remained at 98.16%. This method effectively enhances the quality of the biomass pyrolysis oil, gas, and char, contributing to the efficient and clean utilization of biomass in industrial applications.

The 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification.

Introduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area. The first 1010 submitted mitotic figures were used to create an image dataset, with each figure transformed into an individual tile at 40x magnification. The dataset was redistributed to all pathologists to review and determine whether each tile constituted a mitotic figure. Results. Overall pathologists had a median agreement rate of 80.2% (range 42.0%-95.7%). Individual mitotic figure tiles had a median agreement rate of 87.1% and a fair inter-rater agreement across all tiles (kappa = 0.284). Mitotic figures in prometaphase had lower percentage agreement rates compared to other phases of mitosis. Conclusion. This dataset stands as the largest international consensus study for mitotic figures to date and can be utilized as a training set for future studies. The agreement range reflects a spectrum of criteria that pathologists use to decide what constitutes a mitotic figure, which may have potential implications in tumor diagnostics and clinical management.

Distributed cyber attack detection and physical fault diagnosis for a class of interconnected large-scale systems.

In this paper we focus on the distributed cyber attack detection and physical fault diagnosis problem for a class of interconnected large-scale systems (ILSSs). In the proposed scheme, apart from node measurement, edge measurement is also used to construct distributed Kalman filter to estimate the state of each subsystem. The gain matrices of Kalman filter are determined by minimizing the covariance of estimation error in the attack-free and fault-free case, which reduces the false alarm rate of cyber attack detection and physical fault diagnosis. Based on this filter, a bank of adjacent residual generators is constructed to characterize the influence of cyber attack on the edge measurement, and the Chi-square test is used to detect whether the received edge measurements are attacked. At the same time, a local residual generator is constructed for each subsystem to characterize the influence of physical faults on it, and the residual signal is evaluated by variance and directional residual, so as to make distributed fault detection and isolation of each subsystem. It is worth noting that at each step, each subsystem first performs attack detection on the received edge measurements, and then estimates its own state using the attack-free edge measurements and node measurement, which further improves the accuracy of fault detection and isolation. In addition, a sufficient condition that ensuring the mean square exponential boundedness of the estimation error is given. Finally, the proposed scheme is verified by an illustrative example.

Bimetal doped Cu-Fe-ZIF-8/g-C3N4 nanocomposites for the adsorption of tetracycline hydrochloride from water.

Bimetal doped Cu-Fe-zeolitic imidazole framework-8 (ZIF-8)/graphitic carbon nitride (GCN) (Cu-Fe-ZIF-8/GCN) nanocomposites were prepared via one-pot and ion-exchange methods. The main influencing factors, such as adsorbent concentration, TC concentration, initial pH, and coexisting ions, were evaluated in detail. Due to the suitable pore structures and the presence of multiple interactions on the surface, the nanocomposite showed a high adsorption capacity up to 932 mg g-1 for tetracycline hydrochloride (TC), outperforming ZIF-8 by 4.8 times. The adsorption kinetics and adsorption isotherm were depicted in good detail using pseudo-second-order kinetic and Langmuir models, respectively. Thermodynamic calculation revealed that the adsorption of the nanocomposite under experimental conditions was a spontaneous heat absorption process, and was primarily driven by chemisorption. After four cycles of use, the nanocomposite retained 87.2% of its initial adsorption capacity, confirming its high reusability and broad application prospects in removing tetracycline-type pollutants from wastewater.