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ObjectivePhysical exercise may increase brain volume and cortical thickness in late adulthood. However, few studies have examined the possibility for exercise to influence brain morphology in women treated for breast cancer. We conducted a nested sub-study within a randomized clinical trial to examine whether 6 months of moderate-intensity aerobic exercise in postmenopausal women with early-stage breast cancer influences brain morphology.MethodsWe included twenty-eight postmenopausal women newly diagnosed with Stage 0-IIIa breast cancer (M age = 62.96 ± 5.40) who were randomized to either 45–60 min of supervised aerobic exercise 3 days/week (n = 16) or usual care (n = 12). Before beginning aromatase inhibitor aromatase inhibitor therapy, and the exercise intervention, and again at 6-month follow-up, volumetric and cortical thickness measures were derived from magnetic resonance imaging scans.ResultsThere were no significant intervention effects on brain volume and cortical thickness. However, greater average exercise intensity (%) during the intervention was associated with greater post-intervention cortical volume, mean cortical thickness, precentral gyrus thickness, and superior parietal thickness (all p < 0.05). Finally, total supervised exercise time was associated with higher precentral gyrus thickness after the intervention (p = 0.042, R2 = 0.263).ConclusionThe exercise intervention did not significantly affect brain volumes and cortical thickness compared to the control group. However, positive associations were found between exercise intensity and brain morphology changes after the 6-month intervention, indicating that exercise may reduce the vulnerability of the brain to the deleterious effects of breast cancer and its treatment.

Particle scattering is a key factor affecting underwater light transport. The diffusion length (zD), defined as 1/[b(1 − g)], where b is the scattering coefficient and g is the asymmetry factor, is obtained from the volume scattering function (VSF) of the particle and plays a vital role in assessing the potential for underwater optical detection, imaging and communication. Owing to the lack of VSF datasets, the variation in zD at different wavelengths in various ocean areas remains unclear. In this study, we used a dual-wavelength (488 & 532 nm) VSFLab to conduct the VSF measurement experiments in the East China Sea (ECS) and the South China Sea (SCS), obtaining VSFs from 1.5° to 178.5° at 51 stations. Seven optical properties, including absorption (a), scattering (b), attenuation (c), diffuse attenuation coefficient (Kd), backscattering (bb), g, and zD, were calculated from the measured VSFs. A comparative analysis of the results was performed, which showed that the laser transmission capability at 532 nm was better than that at 488 nm in terms of the absorption or diffuse attenuation coefficient in the ECS, whereas superior performance was observed at 488 nm in the SCS. However, from the perspective of scattering, zD at 532 nm (zD(532)) demonstrated superior performance in both the ECS and SCS. This superiority was particularly noticeable in regions with exceptionally clear water, such as the eastern side of the Luzon Strait, where zD(532) exceeded zD(488) by approximately 20%. Overall, the findings of this study provide a new perspective for assessing underwater light transmission capabilities.

Disinfection by-products (DBPs) such as chloroacetic acids (CAAs) and N-Nitrosodimethylamine (NDMA) are prevalent pollutants in surface waters, particularly with the increasing use of chlorine-based disinfectants. The entry of these DBPs into water bodies may increase accordingly, posing ecological risks to aquatic life. To assess the toxic effects of CAAs and NDMA on submerged macrophytes, Vallisneria natans was exposed to different concentrations of CAAs (1.0, 10.0, and 100.0 μg L−1) and NDMA (0.1, 1.0, and 10.0 μg L−1). A RI value of −1) showed the most significant change in SOD activity, reaching 3.57 times that of the CT group. Ultrastructural changes also revealed stress responses in leaf cells and damage to organelles. Furthermore, metabolomics provided insights into the metabolic responses induced by CAAs or NDMA in V. natans leaves, where the composition and metabolism of lipids, fatty acids, cofactors and vitamins, amino acids, nucleotides, and some antioxidants were regulated, affecting plant growth. This study provides preliminary information for the ecological risk assessment of submerged plants by complex contamination with the disinfection by-products CAA and NDMA.

This study used GOCI-II data to systematically evaluate the feasibility of Rayleigh-corrected reflectance (Rrc) to detect algal blooms in the complex optical environment of the East China Sea (ECS). Based on long-term in situ remote sensing reflectance (Rrs), Rrc spectra demonstrated the similar capability of reflecting the water condition under various atmospheric conditions, and the baseline indices (BLIs) derived from Rrc and Rrs showed good consistency (R2 > 0.98). The effectiveness of five Rrc-based BLIs (SS490, CI, DI, FLH, and MCI) for algal bloom detection was assessed, among which SS490 and MCI showed better performances. A synthetic bloom detection algorithm based on the BLIs of Rrc was then developed to avoid the impact of turbid water. The validation of the BLI algorithm was carried out based on the in situ algal abundance data from 2021 to 2023. Specifically, SS490 showed the best bloom detection result (F-measure coefficient, FM = 0.97), followed by MCI (FM = 0.88). Since the 709 nm bands used in MCI were missing in many ocean color satellites, the SS490 algorithm was more useful in application. Compared to Rrs based bloom detection algorithms, synthetical Rrc BLI proposed in this paper provides more effective observation results and even better algal bloom detection performance. In conclusion, the study confirmed the feasibility of utilizing Rrc for algal bloom detection in the coastal areas of the ECS, and recognized the satisfactory performance of synthetical SS490 by comparing with the other BLIs.

The quality of crude oil varies significantly according to its geographical origin. The efficient identification of the source region of crude oil is pivotal for petroleum trade and processing. However, current methods, such as mass spectrometry and fluorescence spectroscopy, suffer problems such as complex sample preparation and a long characterization time, which restrict their efficiency. In this work, by combining terahertz time-domain spectroscopy (THz-TDS) and a machine learning analysis of the spectra, an efficient workflow for the accurate and fast identification of crude oil was established. Based on THz-TDS of 83 crude oil samples obtained from six countries, a machine learning protocol involving the dimension reduction of spectra and classification was developed to identify the geological origins of crude oil, with an overall accuracy of 96.33%. This work demonstrates that THz spectra combined with a modern numerical scheme analysis can be readily employed to categorize crude oil products efficiently.

The recently developed Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2), furnished with the Advanced Terrain Laser Altimeter System (ATLAS), delivers considerable benefits in providing accurate bathymetric data across extensive geographical regions. By integrating active lidar-derived reference seawater depth data with passive optical remote sensing imagery, efficient bathymetry mapping is facilitated. In recent times, machine learning models are frequently used to define the nonlinear connection between remote sensing spectral data and water depths, which consequently results in the creation of bathymetric maps. A salient model among these is the convolutional neural network (CNN), which effectively integrates contextual information concerning bathymetric points. However, current CNN models and other machine learning approaches mainly concentrate on recognizing mathematical relationships within the data to determine a water depth function and remote sensing spectral data, while oftentimes disregarding the physical light propagation process in seawater before reaching the seafloor. This study presents a physics-informed CNN (PI-CNN) model which incorporates radiative transfer-based data into the CNN structure. By including the shallow water double-band radiative transfer physical term (swdrtt), this model enhances seawater spectral features and also considers the context surroundings of bathymetric pixels. The effectiveness and reliability of our proposed PI-CNN model are verified using in situ data from St. Croix and St. Thomas, validating its correctness in generating bathymetric maps with a broad experimental R2 accuracy exceeding 95% and remaining errors below 1.6 m. Preliminary results suggest that our PI-CNN model surpasses conventional methodologies.

Alzheimer’s disease is currently the leading cause of dementia and one of the most expensive, lethal and severe diseases worldwide. Age-related decline in executive function is widespread and plays a key role in subsequent dementia risk. Physical exercise has been proposed as one of the leading non-pharmaceutical approaches to improve executive function and ameliorate cognitive decline. This single-site, two-arm, single-blinded, randomized controlled trial (RCT) will include 90 cognitively normal older adults, aged 65–80 years old. Participants will be randomized to a 24-week resistance exercise program (3 sessions/week, 60 min/session, n = 45), or a wait-list control group (n = 45) which will be asked to maintain their usual lifestyle. All study outcomes will be assessed at baseline and at 24-weeks after the exercise program, with a subset of selected outcomes assessed at 12-weeks. The primary outcome will be indicated by the change in an executive function composite score assessed with a comprehensive neuropsychological battery and the National Institutes of Health Toolbox Cognition Battery. Secondary outcomes will include changes in brain structure and function and amyloid deposition, other cognitive outcomes, and changes in molecular biomarkers assessed in blood, saliva, and fecal samples, physical function, muscular strength, body composition, mental health, and psychosocial parameters. We expect that the resistance exercise program will have positive effects on executive function and related brain structure and function, and will help to understand the molecular, structural, functional, and psychosocial mechanisms involved.

Pedestrian attribute recognition (PAR) and re-identification (ReID) are important works in the area of computer vision, which are widely used in intelligent surveillance and are of great significance to the creation of smart life. The purpose of this article is to focus on organizing a review of ReID based on deep learning and analyze the associations between PAR and ReID. Firstly, we summarize the major ideas of Attribute-Assisted ReID and compare the differences in datasets and algorithmic concerns between the two areas. Secondly, we introduce a wide range of representative ReID methods. By analyzing some cutting-edge researches, we summarize their specific network structure, loss function design, and effective training tricks. Reference methods and solutions are provided for the main challenges of ReID, such as cloth-changing, domain adaptation, occlusion condition, resolution changes, etc. Finally, we conclude the performance and characteristics of the SOTA methods, obtain inspiration and prospects for future research directions, and demonstrate the effectiveness of Attribute-Assisted ReID.

Hong Kong International Airport (HKIA) is an important sea airdrome in China. The aim of this study is to evaluate the impacts of this reclamation on the water quality of the Northwestern Bay of Hong Kong (NWBHK). In all, 117 Landsat 5 TM and 44 Landsat 8 OLI images were preprocessed and matched with the marine water data of 18 in situ monitoring points, acquiring 458 and 119 sets of data, respectively. This study adopted BPNN Machine Learning methods to establish the retrieval algorithm. Based on the images, the construction of HKIA was divided into three stages: (1) the construction of the first and second runways from 1992 to 1995; (2) the construction of the Hong Kong Port from 2013 to 2016; and (3) the construction of the third runway from 2017 to 2020. The concentrations of suspended particulate matter, orthophosphate phosphorus, and dissolved inorganic nitrogen from 1986 to 2022 were retrieved. In this paper, it was found that (1) the construction activities led to an increase in SPM, PO4P, and DIN concentrations in adjacent water bodies; (2) the impact of the Tuen Mun River on the NWBHK increased, while the impact of the Tung Chung River on the NWBHK decreased; and (3) the interception impact of HKIA on the transportation of the Pearl River water became stronger.

Cyanobacteria bloom is a global aquatic ecological problem that seriously threatens human health and social development. The outbreak of cyanobacteria bloom is affected by various environmental factors, among which light dose is an essential factor. In this study, the growth changes of cyanobacteria under different amounts of natural light were studied by simulating different depths of Taihu Lake, and we used 16S rRNA and non-targeted metabolomics for sequencing to reveal the effects of light on the diversity of cyanobacteria and coexisting microorganisms, and to analyze the changes of related genes, functional structures and internal metabolism involved in nitrogen cycling. The result shows that excessive and insufficient light could limit the growth, photosynthesis, and EPS secretion of cyanobacteria, resulting in an antioxidant stress response. At the same time, the amount of natural light affects the vertical distribution of cyanobacteria, and under the condition of 1/3 natural light, cyanobacteria first appeared to float. In addition, the amount of natural light affects the diversity, abundance, and metabolites of cyanobacteria and coexisting microorganisms, and the expression of nifH, nirK, and nirS, three nitrogen-fixing genes, is significantly different in different genera. This study provides valuable information on the molecular mechanism of the effects of the amount of natural light on cyanobacteria bloom.

The ocean front has a non-negligible role in global ocean–atmosphere interactions, marine fishery production, and the marine military. Hence, obtaining the positions of the ocean front is crucial in oceanic research. At present, the positioning method of recognizing an ocean front has achieved a breakthrough in the mean dice similarity coefficient (mDSC) of above 90%, but it is difficult to use to achieve rapid extraction in emergency scenarios, including marine fisheries and search and rescue. To reduce the its dependence on machines and apply it to more requirements, according to the characteristics of an ocean front, a multi-scale model SQNet (Simple and Quick Net) dedicated to ocean front position recognition is designed, and its perception domain is expanded while obtaining current scale data. In experiments along the coast of China and the waters of the Gulf of Mexico, it was not difficult to find that SQNet exceedingly reduced running time while ensuring high-precision results (mDSC of higher than 90%). Then, after conducting intra-model self-comparison, it was determined that expanding the perceptual domain and changing the weight ratio of the loss function could improve the accuracy and operational efficiency of the model, which could be better applied in ocean front recognition.

The data quality of the remote sensing reflectance (Rrs) from the two ocean color satellites HaiYang-1C (HY-1C) and HaiYang-1D (HY-1D) and the consistency with other satellites are critical for the products. The Layer Removal Scheme for Atmospheric Correction (LRSAC) has been applied to process the data of the Chinese Ocean Color and Temperature Scanner (COCTS) on HY-1C/1D. The accuracy of the Rrs products was evaluated by the in situ dataset from the Marine Optical BuoY (MOBY) with a mean relative error (MRE) of −1.56% and a mean absolute relative error (MAE) of 17.31% for HY-1C. The MRE and MAE of HY-1D are 1.05% and 15.68%, respectively. The comparisons of the global daily Rrs imagery with the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra show an MRE of 10.94% and an MAE of 21.38%. The comparisons between HY-1D and Aqua exhibit similar results, with an MRE of 13.31% and an MAE of 21.46%. The percentages of valid pixels of the global daily images of HY-1C and HY-1D are 32.3% and 32.6%, much higher than that of Terra (11.9%) and Aqua (11.9%). The gaps in the 8-day composite images have been significantly reduced, with 83.9% of valid pixels for HY-1C and 85.4% for HY-1D, which are also much higher than that of Terra (52.9%) and Aqua (50.9%). The gaps due to the contamination of sun glint have been almost removed from the 3-day composite imagery, with valid pixels of 63.5% for HY-1C and 65.6% for HY-1D, which are higher than that of the 8-day imagery of Terra and Aqua. The patterns of HY-1C imagery exhibit a similarity with those of HY-1D, but they are different on a pixel scale, mainly due to the changes in the ocean dynamic features within 3 h. The evaluations of the COCTS indicate that the imagery of HY-1C/1D can be used as a kind of standard product.

The maximum cross-coefficient (MCC) algorithm based on the template matching technique is a typical algorithm for obtaining the sea-surface currents (SSCs) in marginal seas. However, this algorithm has mismatches between images in highly turbid water. In this study, we implemented the MCC algorithm to Geostationary Ocean Color Imager-derived total suspended matter to obtain the SSCs in the Yellow Sea and the East China Sea. We propose a novel vector optimization algorithm, which is combined with the accurate estimate of tidal ellipses from the OSU tidal model. This method considers the three greatest candidate acquisitions from multi-correlation coefficients as potential vectors. The rotation direction of the vector within the tidal oscillation is used to identify and substitute for the spurious vector. The obtained average speed of SSC reached 0.60 m/s, which was close to the buoy-measured average speed of 0.58 m/s. Compared with the existing spurious vector eliminating method, the average angular error was improved by 20%, and the average relative amplitude error was improved by 4% in our case study. On the basis of ensuring data integrity, the inversion accuracy was improved.

Visible Infrared Imaging Radiometer Suite (VIIRS) data were systematically evaluated and used to detect harmful algal bloom (HAB) and classify algal bloom types in coasts of the East China Sea covered by optically complex and sediment-rich waters. First, the accuracy and spectral characteristics of VIIRS retrieved normalized water-leaving radiance or the equivalent remote sensing reflectance from September 2019 to October 2020 that were validated by the long-term observation data acquired from an offshore platform and underway measurements from a cruise in the Changjiang Estuary and adjacent East China Sea. These data were evaluated by comparing them with data from the Moderate-Resolution Imaging Spectroradiometer. The bands of 486, 551, and 671 nm provided much higher quality than those of 410 and 443 nm and were more suitable for HAB detection. Secondly, the performance of four HAB detection algorithms were compared. The Ratio of Algal Bloom (RAB) algorithm is probably more suitable for HAB detection in the study area. Importantly, although RAB was also verified to be applicable for the detection of different kinds of HAB (Prorocentrum donghaiense, diatoms, Ceratium furca, and Akashiwo sanguinea), the capability of VIIRS in the classification of those algal species was limited by the lack of the critical band near 531 nm.

Particulate volume scattering function (VSF), especially at angles larger than 170°, is of particular importance for interpreting ocean optical remote sensing signals and underwater imagery. In this study, a laboratory-based VSF instrument (VSFlab) adopting the periscopic optical system was developed to obtain VSF measurements from 1°–178.5°. In the VSFlab, a new prism design that simply combines a single prism and a neutral density filter was proposed to more efficiently reduce the stray light in the backward direction, while a detailed calibration procedure was given. A full validation based on standard beads of various sizes and a comparison with the results from LISST-VSF and POLVSM indicated that the VSFlab can provide reliable results from 1° to 178.5°. VSFlab measurements in the East China Sea (ECS) exhibited a moderate increase (not more than 5 times) in VSF from 170° to 178.5° rather than a sharp increase of more than one order of magnitude presented in other instrument results measured in other coastal regions. The estimates of the particulate backscattering coefficient using single angle scattering measurements near 120° or 140° and suitable χp were justified. Two types of the VSFs with different size distribution and shape parameters in the ECS can be distinguished based on the variability of χp after 155°. The measured VSF could provide a basis for the parameterization of VSF in the radiative transfer model and the variability of χp in the backward direction had the potential to be used to characterize the particles in the coastal region of the ECS.

Alzheimer’s disease is currently the leading cause of dementia and one of the most expensive, lethal and severe diseases worldwide. Age-related decline in executive function is widespread and plays a key role in subsequent dementia risk. Physical exercise has been proposed as one of the leading non-pharmaceutical approaches to improve executive function and ameliorate cognitive decline. This single-site, two-arm, single-blinded, randomized controlled trial (RCT) will include 90 cognitively normal older adults, aged 65–80 years old. Participants will be randomized to a 24-week resistance exercise program (3 sessions/week, 60 min/session, n = 45), or a wait-list control group (n = 45) which will be asked to maintain their usual lifestyle. All study outcomes will be assessed at baseline and at 24-weeks after the exercise program, with a subset of selected outcomes assessed at 12-weeks. The primary outcome will be indicated by the change in an executive function composite score assessed with a comprehensive neuropsychological battery and the National Institutes of Health Toolbox Cognition Battery. Secondary outcomes will include changes in brain structure and function and amyloid deposition, other cognitive outcomes, and changes in molecular biomarkers assessed in blood, saliva, and fecal samples, physical function, muscular strength, body composition, mental health, and psychosocial parameters. We expect that the resistance exercise program will have positive effects on executive function and related brain structure and function, and will help to understand the molecular, structural, functional, and psychosocial mechanisms involved. [ABSTRACT FROM AUTHOR]