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Photothermal therapy (PTT) is a promising cancer therapy modality with significant advantages such as precise targeting, convenient drug delivery, better efficacy, and minimal adverse effects. Photothermal therapy effectively absorbs the photothermal transducers in the near-infrared region (NIR), which induces the photothermal effect to work. Although PTT has a better role in tumor therapy, it also suffers from low photothermal conversion efficiency, biosafety, and incomplete tumor elimination. Therefore, the use of nanomaterials themselves as photosensitizers, the targeted modification of nanomaterials to improve targeting efficiency, or the combined use of nanomaterials with other therapies can improve the therapeutic effects and reduce side effects. Notably, noble metal nanomaterials have attracted much attention in PTT because they have strong surface plasmon resonance and an effective absorbance light at specific near-infrared wavelengths. Therefore, they can be used as excellent photosensitizers to mediate photothermal conversion and improve its efficiency. This paper provides a comprehensive review of the key role played by noble metal nanomaterials in tumor photothermal therapy. It also describes the major challenges encountered during the implementation of photothermal therapy. [ABSTRACT FROM AUTHOR]

Sonodynamic therapy (SDT) has attracted significant attention in recent years as it is an innovative approach to tumor treatment. It involves the utilization of sound waves or ultrasound (US) to activate acoustic sensitizers, enabling targeted drug release for precise tumor treatment. This review aims to provide a comprehensive overview of SDT, encompassing its underlying principles and therapeutic mechanisms, the applications of nanomaterials, and potential synergies with combination therapies. The review begins by introducing the fundamental principle of SDT and delving into the intricate mechanisms through which it facilitates tumor treatment. A detailed analysis is presented, outlining how SDT effectively destroys tumor cells by modulating drug release mechanisms. Subsequently, this review explores the diverse range of nanomaterials utilized in SDT applications and highlights their specific contributions to enhancing treatment outcomes. Furthermore, the potential to combine SDT with other therapeutic modalities such as photothermal therapy (PTT) and chemotherapy is discussed. These combined approaches aim to synergistically improve therapeutic efficacy while mitigating side effects. In conclusion, SDT emerges as a promising frontier in tumor treatment that offers personalized and effective treatment options with the potential to revolutionize patient care. As research progresses, SDT is poised to play a pivotal role in shaping the future landscape of oncology by providing patients with a broader spectrum of efficacious and tailored treatment options. [ABSTRACT FROM AUTHOR]

Against the background of climate warming, marine heatwaves (MHWs) and terrestrial drought events have become increasingly frequent in recent decades. However, the combined effects of MHWs and terrestrial drought on CO2 uptake in marginal seas are still unclear. The East China Sea (ECS) experienced an intense and long-lasting MHW accompanied by an extreme terrestrial drought in the Changjiang basin in the summer of 2022. In this study, we employed multi-source satellite remote sensing products to reveal the patterns, magnitude, and potential drivers of CO2 flux changes in the ECS resulting from the compounding MHW and terrestrial drought extremes. The CO2 uptake of the ECS reduced by 17.0% (1.06 Tg C) in the latter half of 2022 and the Changjiang River plume region shifted from a CO2 sink to a source (releasing 0.11 Tg C) in July-September. In the majority of the ECS, the positive sea surface temperature (SST) anomaly during the MHW diminished the solubility of CO2 in seawater, thereby reducing CO2 uptake. Moreover, the reduction in nutrient input associated with terrestrial drought, which is unfavorable to phytoplankton growth, further reduced the capacity of CO2 uptake. Meanwhile, the CO2 sink doubled for the offshore waters of the ECS continental shelf in July-September 2022, indicating the complexity and heterogeneity of the impacts of extreme climatic events in marginal seas. This study is of great significance in improving the estimation results of CO2 fluxes in marginal seas and understanding sea–air CO2 exchanges against the background of global climate change. [ABSTRACT FROM AUTHOR]

Against the backdrop of global warming, extreme precipitation events have become more frequent. In complex terrain regions, due to the vulnerability of their ecosystems, extreme precipitation events can lead to significant secondary disasters. Utilizing daily rainfall data from the National Meteorological Information Center of China and statistical analysis, this study explores the spatial and temporal distribution of extreme precipitation in the Central Southwest China (CSC) region. The temporal pattern of extreme precipitation in CSC shows a consistent trend, while the spatial distribution reveals an opposite phase between the northern and southern parts of CSC. Based on this, we propose a new method for constructing extreme precipitation prediction models for complex terrain regions based on physical mechanisms, and take CSC area as a study case. Instead of anonymous feature selection, this method improves the accuracy and stability of the model by studying the impact of sea–air interactions on extreme precipitation and then introducing it into deep learning. It was found that the sea surface temperature (SST) anomaly in the South Indian Ocean affects extreme precipitation in the CSC by influencing uplift, atmospheric instability, and moisture. The SST anomaly also affects the intensity of cross-equatorial airflow, which changes the trajectory of the Pacific–Japan teleconnection wave and impacts extreme precipitation. These findings provide a comprehensive and reliable approach for forecasting extreme precipitation in CSC and are further integrated into the extreme precipitation prediction models. [ABSTRACT FROM AUTHOR]

The spatiotemporal variations in suspended sediment concentration (SSC) in the lower reaches of the Yellow River exhibit significant variability and are influenced by reservoir operations. Understanding the spatiotemporal distribution characteristics of SSC in water holds crucial implications for environmental protection and reservoir operation management. Based on daily-scale SSC monitoring data from four hydrological stations in the lower Yellow River, this study established an SSC remote sensing model applicable to Landsat series satellite data. The independent variable of the model, Rrs(NIR)/(Rrs(G) + Rrs(R) + Rrs(SWIR)), demonstrated sensitivity to water bodies with different SSC values. Distinctive spatiotemporal characteristics in sediment transport were observed across the lower Yellow River. Spatially, the SSC values in the Sanmenxia and Xiaolangdi reservoirs were notably lower than those in other river sections, averaging 1008.42 ± 602.83 mg/L and 1177.89 ± 627.95 mg/L, respectively. Over time, the majority of the river sections (96%) exhibited decreasing trends in SSC during 1984–2022, particularly in the downstream Xiaolangdi reservoir, with average SSC values of 4265.58 ± 1101.77 mg/L in the 1980s and 1840.80 ± 2255.15 mg/L in the 2020s. Seasonal variations in SSC were prominent, with higher summer concentrations, averaging 5536.43 ± 2188.77 mg/L (2020s summer) and 814.11 ± 158.27 mg/L (2020s winter). Reductions in SSC during 1984–2022 primarily occurred in summer, weakening its seasonal variability in the lower Yellow River. Water discharge emerged as a critical factor influencing suspended sediment transport, with SSC increasing in high-water-flow months. Following the construction of the Xiaolangdi reservoir, the relationship between SSC and water discharge at different stations underwent notable alterations. This study enhances our understanding of the spatiotemporal dynamics of suspended sediment transport in the lower Yellow River, providing valuable insights for utilizing long-term Landsat series data in the dynamic monitoring of river sediment transport. [ABSTRACT FROM AUTHOR]

Tumors are a major public health issue of concern to humans, seriously threatening the safety of people's lives and property. With the increasing demand for early and accurate diagnosis and efficient treatment of tumors, noninvasive optical imaging (including fluorescence imaging and photoacoustic imaging) and tumor synergistic therapies (phototherapy synergistic with chemotherapy, phototherapy synergistic with immunotherapy, etc.) have received increasing attention. In particular, light in the near-infrared second region (NIR-II) has triggered great research interest due to its penetration depth, minimal tissue autofluorescence, and reduced tissue absorption and scattering. Nanomaterials with many advantages, such as high brightness, great photostability, tunable photophysical properties, and excellent biosafety offer unlimited possibilities and are being investigated for NIR-II tumor imaging-guided synergistic oncotherapy. In recent years, many researchers have tried various approaches to investigate nanomaterials, including gold nanomaterials, two-dimensional materials, metal sulfide oxides, polymers, carbon nanomaterials, NIR-II dyes, and other nanomaterials for tumor diagnostic and therapeutic integrated nanoplatform construction. In this paper, the application of multifunctional nanomaterials in tumor NIR-II imaging and collaborative therapy in the past three years is briefly reviewed, and the current research status is summarized and prospected, with a view to contributing to future tumor therapy. [ABSTRACT FROM AUTHOR]

Multiferroic tunnel junctions (MFTJs) comprised of magnetic electrodes and a ferroelectric tunnel barrier have been emerging as promising candidates for nonvolatile memory applications. The recently discovered above room-temperature van der Waals (vdW) ferromagnet Fe3GaTe2 and ferroelectric α-In2Se3 provides possibilities for constructing room-temperature vdW MFTJs. In this work, by using first-principles calculations, we investigate the spin-dependent transport in vdW MFTJs with structure of Fe3GaTe2/bilayer α-In2Se3/Fe3GaTe2. We predict a giant tunneling magnetoresistance (TMR) high up to 10 000% by switching the magnetic alignments of Fe3GaTe2 and tunneling electroresistance (TER) exceeding 300% by controlling the ferroelectric configurations of bilayer α-In2Se3 in the proposed MFTJs. Furthermore, by introducing interface asymmetry and inserting monolayer of h-BN on one interface, the TMR and TER ratios of the MFTJs can be substantially enhanced. In consequence, the multi-level resistance states can be achieved by applying magnetic and electric field in asymmetric MFTJs. Our results highlight full vdW MFTJs for their potential applications in spintronic devices, particularly in the field of multilevel nonvolatile memories. [ABSTRACT FROM AUTHOR]

Node localization in two-dimensional (2D) and three-dimensional (3D) space for wireless sensor networks (WSNs) remains a hot research topic. To improve the localization accuracy and applicability, we first propose a quantum annealing bat algorithm (QABA) for node localization in WSNs. QABA incorporates quantum evolution and annealing strategy into the framework of the bat algorithm to improve local and global search capabilities, achieve search balance with the aid of tournament and natural selection, and finally converge to the best optimized value. Additionally, we use trilateral localization and geometric feature principles to design 2D (QABA-2D) and 3D (QABA-3D) node localization algorithms optimized with QABA, respectively. Simulation results show that, compared with other heuristic algorithms, the convergence speed and solution accuracy of QABA are greatly improved, with the highest average error of QABA-2D reduced by 90.35% and the lowest by 17.22%, and the highest average error of QABA-3D reduced by 75.26% and the lowest by 7.79%. [ABSTRACT FROM AUTHOR]

Like other high-latitude seas, the subpolar Bering and Okhotsk Seas in the northernmost Pacific Ocean changed rapidly from 1998 to 2018. The sea surface temperature (SST) increased by 0.62 and 0.41 °C/decade, respectively, much higher than the global rate of 0.108 °C/decade from 2000 until 2015. Despite this rapid warming, the chlorophyll content did not change significantly in the Bering Sea but increased by 0.047 μg/L/decade in the Okhotsk Sea. The Secchi disk depth (SDD) increased by 0.43 and 0.46 m/decade, respectively. Similar to other warm bodies of water, the SST of the subtropical/tropical South China Sea (SCS) also began rising, by 0.089 °C/decade, albeit more slowly than the global average. The chlorophyll content increased at 0.15 μg/L/decade from 1998 to 2006 but decreased by 0.11 μg/L/decade between 2007 and 2018. The SDD increased by 0.29 m/decade between 1998 and 2018. Although the SDD increased in all three seas, the chlorophyll concentration was maximum around 2006–2009, reflecting different phytoplankton responses to seawater warming. [ABSTRACT FROM AUTHOR]

Seawater pH is a direct proxy of ocean acidification, and monitoring the global pH distribution and long-term series changes is critical to understanding the changes and responses of the marine ecology and environment under climate change. Owing to the lack of sufficient global-scale pH data and the complex relationship between seawater pH and related environmental variables, generating time-series products of satellite-derived global sea surface pH poses a great challenge. In this study, we solved the problem of the lack of sufficient data for pH algorithm development by using the massive underway sea surface carbon dioxide partial pressure (pCO2) dataset to structure a large data volume of near in situ pH based on carbonate calculation between underway pCO2 and calculated total alkalinity from sea surface salinity and relevant parameters. The remote sensing inversion model of pH was then constructed through this massive pH training dataset and machine learning methods. After several tests of machine learning methods and groups of input parameters, we chose the random forest model with longitude, latitude, sea surface temperature (SST), chlorophyll a (Chla), and Mixed layer depth (MLD) as model inputs with the best performance of correlation coefficient (R2 = 0.96) and root mean squared error (RMSE = 0.008) in the training set and R2 = 0.83 (RMSE = 0.017) in the testing set. The sensitivity analysis of the error variation induced by the uncertainty of SST and Chla (SST ≤ ±0.5 °C and Chla ≤ ±20%; RMSESST ≤ 0.011 and RMSEChla ≤ 0.009) indicated that our sea surface pH model had good robustness. Monthly average global sea surface pH products from 2004 to 2019 with a spatial resolution of 0.25° × 0.25° were produced based on the satellite-derived SST and Chla products and modeled MLD dataset. The pH model and products were validated using another independent station-measured pH dataset from the Global Ocean Data Analysis Project (GLODAP), showing good performance. With the time-series pH products, refined interannual variability and seasonal variability were presented, and trends of pH decline were found globally. Our study provides a new method of directly using remote sensing to invert pH instead of indirect calculation based on the construction of massive underway calculated pH data, which would be made useful by comparing it with satellite-derived pCO2 products to understand the carbonate system change and the ocean ecological environments responding to the global change. [ABSTRACT FROM AUTHOR]

Sea surface salinity (SSS) retrieval from satellite-based microwave radiometer is hampered by uncertainties due to atmospheric and surface scattering contributions. This study presents a comprehensive vector radiative transfer model (VRTM) for estimation of brightness temperature (TB) (for SSS retrieval) from L-band radiometry based on a matrix-operator method. It includes an efficient two-scale model (TSM) that combines a geometrical optics (GO) model and a small-perturbation model (SPM) for computing both the small- and large-scale scattering components of the sea surface. Moreover, it considers the influence of rain effects on TB by including the radiation extinction term (scattering and attenuation). The simulation results using the VRTM were validated with those obtained from the RT4 model for flat sea surface conditions and the RTTOV model for rough sea surface conditions. The relative difference in the estimated TB among the models was small (<1%) for low wind speeds (<1 m/s) and increased up to 3% for high wind speeds and observation angles. Simulations on the influence of wind speed on TB with various parameterizations were further examined. Compared with SMOS-MIRAS and Aquarius measurements, the VRTM simulations agreed well with satellite measurements for both vertically and horizontally polarized TBs with biases of less than 2.2 K for observation angles from 20° to 65°. The binned TBs showed even better results, with a standard deviation of less than 1.45 K and an absolute mean error of less than 1.2 K. [ABSTRACT FROM AUTHOR]

Based on time-series satellite-retrieved records of the marine ecological environment from Aqua/MODIS, we investigated changes in the sea surface temperature (SST), photosynthetically active radiation (PAR), Secchi Disc depth (SDD), and chlorophyll-a concentration (Chla) in 12 Eurasian marginal seas from 2003 to 2014. Results showed that the SST increased in all 12 marginal seas, with the enclosed marginal seas (i.e., Black Sea, Baltic Sea, Japan Sea, Mediterranean Sea, and Persian Gulf) exhibiting relatively higher rates of increase. The PAR generally decreased, except in the European marginal seas, though not significantly. Similar to the changes in the SST, the SDD increased in all 12 marginal seas, with a maximum rate of 3.02%/year (or 0.25 m/year, p = 0.0003) found in the Persian Gulf. As expected, Chla generally decreased in the tropical marginal seas, but increased in the high-latitude marginal seas. The different relationships between SST and Chla changes indicate the complexity of global warming effects on marine phytoplankton in different marginal seas. [ABSTRACT FROM AUTHOR]