Stable Porous Organic Cage Nanocapsules for pH-Responsive Anticancer Drug Delivery for Precise Tumor Therapy.

The search for drug nanocarriers with stimuli-responsive properties and high payloads for targeted drug delivery and precision medicine is currently a focal point of biomedical research, but this endeavor still encounters various challenges. Herein, a porous organic cage (POC) is applied to paclitaxel (PTX) drug delivery for cancer therapy for the first time. Specifically, water-soluble, stable, and biocompatible POC-based nanocapsules (PTX@POC@RH40) with PTX encapsulation efficiency over 98% can be synthesized by simply grafting nonionic surfactant (Polyoxyl 40 hydrogenated castor oil, RH40) on the POC surface.

Enhanced forecasting of chlorophyll-a concentration in coastal waters through integration of Fourier analysis and Transformer networks.

The accurate prediction of chlorophyll-a (chl-a) concentration in coastal waters is essential to coastal economies and ecosystems as it serves as the key indicator of harmful algal blooms. Although powerful machine learning methods have made strides in forecasting chl-a concentrations, there remains a gap in effectively modeling the dynamic temporal patterns and dealing with data noise and unreliability. To wiggle out of quagmires, we introduce an innovative deep learning prediction model (termed ChloroFormer) by integrating Transformer networks with Fourier analysis within a decomposition architecture, utilizing coastal in-situ data from two distinct study areas.

Beneficial associations between outdoor visible greenness at the workplace and metabolic syndrome in Chinese adults.

Background: Greenness surrounding residential places has been found to significantly reduce the risk of diseases such as hypertension, obesity, and metabolic syndrome (MetS). However, it is unclear whether visible greenness exposure at the workplace has any impact on the risk of MetS.

Methods: Visible greenness exposure was assessed using a Green View Index (GVI) based on street view images through a convolutional neural network model.

Satellite-Based Global Sea Surface Oxygen Mapping and Interpretation with Spatiotemporal Machine Learning.

The assessment of dissolved oxygen (DO) concentration at the sea surface is essential for comprehending the global ocean oxygen cycle and associated environmental and biochemical processes as it serves as the primary site for photosynthesis and sea-air exchange. However, limited comprehensive measurements and imprecise numerical simulations have impeded the study of global sea surface DO and its relationship with environmental challenges. This paper presents a novel spatiotemporal information embedding machine-learning framework that provides explanatory insights into the underlying driving mechanisms.

A spatiotemporally weighted intelligent method for exploring fine-scale distributions of surface dissolved silicate in coastal seas.

The transfer of dissolved silicate (DSi) from land to coastal environments is a crucial part of global biogeochemical cycling. However, the retrieval of coastal DSi distribution is challenging due to the spatiotemporal non-stationarity and nonlinearity of modeling processes and the low resolution of in situ sampling. To explore the coastal DSi changes in a higher spatiotemporal resolution, this study developed a spatiotemporally weighted intelligent method based on a geographically and temporally neural network weighted regression (GTNNWR) model, a Data-Interpolating Empirical Orthogonal Functions (DINEOF) model, and satellite observations.

Ultrafast 3D histological imaging based on a minutes-time scale tissue clearing and multidirectional selective plane illumination microscopy.

Conventional histopathological examinations are time-consuming and labor-intensive, and are insufficient to depict 3D pathological features intuitively. Here we report an ultrafast 3D histological imaging scheme based on optimized selective plane illumination microscopy (mSPIM), a minutes-time scale clearing method (FOCM), and a deep learning-based image enhancement algorithm (SRACNet) to realize histological preparation and imaging of clinical tissues. Our scheme enables 1-minute clearing and fast imaging (up to 900 mm/min) of 200 µm-thick mouse kidney slices at micron-level resolution.

Contextual Fear Learning and Extinction in the Primary Visual Cortex of Mice.

Fear memory contextualization is critical for selecting adaptive behavior to survive. Contextual fear conditioning (CFC) is a classical model for elucidating related underlying neuronal circuits. The primary visual cortex (V1) is the primary cortical region for contextual visual inputs, but its role in CFC is poorly understood.

[Overexpression of circ_LARP4 inhibits the proliferation and migration of MCF-7 human breast cancer cells and its mechanism].

Objective To investigate the effect of overexpression of circRNA La-associated protein 4 (circ_LARP4) on malignant biological behaviors of MCF-7 breast cancer cells. Methods MCF-7 cells were transfected with circ_LARP4 plasmid pcDNA-circ_LARP4, and the expression of circ_LARP4 was detected by real-time quantitative PCR(qRT-PCR). After circ_LARP4 overexpression, CCK-8 assay was used to detect the proliferation of MCF-7 cells, and mRNAs of ki67, p21, inducible nitric oxide synthase (iNOS) and interleukin-1β (IL-1β) were detected by qRT-PCR.

A theory-guided graph networks based PM forecasting method.

The theory-guided air quality model solves the mathematical equations of chemical and physical processes in pollution transportation numerically. While the data-driven model, as another scientific research paradigm with powerful extraction of complex high-level abstractions, has shown unique advantages in the PM prediction applications. In this paper, to combine the two advantages of strong interpretability and feature extraction capability, we integrated the partial differential equation of PM dispersion with deep learning methods based on the newly proposed DPGN model.

Double cropping and cropland expansion boost grain production in Brazil.

Brazilian grain production increased more than fourfold from 1980 to 2016. The grain boom was achieved primarily by soybean-corn double cropping and cropland expansion-both show changing spatiotemporal patterns since the 1980s. Here, we quantified the contributions of these two strategies to corn and soybean production in Brazil using municipality-level data from 1980 to 2016.

A Spatially Weighted Neural Network Based Water Quality Assessment Method for Large-Scale Coastal Areas.

The accurate assessment of large-scale and complex coastal waters is a grand challenge due to the spatial nonstationarity and complex nonlinearity involved in integrating remote sensing and data. We developed a water quality assessment method based on a newly proposed geographically neural network weighted regression (GNNWR) model to address that challenge and obtained a highly accurate and realistic water quality distribution on the basis of the comprehensive index of Chinese Water Quality Classification Standards. Using geostationary ocean color imager (GOCI) data and observations from 1240 water quality sampling sites, we conducted experiments for a typical large-scale coastal area of the Zhejiang Coastal Sea (ZCS), People's Republic of China.

Forecasting PM using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability.

Air pollution is a complex process and is affected by meteorological conditions and other chemical components. Numerous studies have demonstrated that data-driven spatio-temporal prediction models of PM concentration are comparable with the model-driven model. However, data-driven models are usually depending on the statistical correlation between PM and other factors and have challenges in dealing with causality in complex systems.

Spatiotemporal mapping and assessment of daily ground NO concentrations in China using high-resolution TROPOMI retrievals.

Nitrogen dioxide (NO) is an important air pollutant that causes direct harms to the environment and human health. Ground NO mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates.

Variation in Concentration and Sources of Black Carbon in a Megacity of China During the COVID-19 Pandemic.

Black carbon (BC) not only warms the atmosphere but also affects human health. The nationwide lockdown due to the Coronavirus Disease 2019 (COVID-19) pandemic led to a major reduction in human activity during the past 30 years. Here, the concentration of BC in the urban, urban-industry, suburb, and rural areas of a megacity Hangzhou were monitored using a multiwavelength Aethalometer to estimate the impact of the COVID-19 lockdown on BC emissions.

Estimating spatial and temporal variation in ocean surface pCO in the Gulf of Mexico using remote sensing and machine learning techniques.

Research on the carbon cycle of coastal marine systems has been of wide concern recently. Accurate knowledge of the temporal and spatial distributions of sea-surface partial pressure (pCO) can reflect the seasonal and spatial heterogeneity of CO flux and is, therefore, essential for quantifying the ocean's role in carbon cycling. However, it is difficult to use one model to estimate pCO and determine its controlling variables for an entire region due to the prominent spatiotemporal heterogeneity of pCO in coastal areas.

Modeling spatially anisotropic nonstationary processes in coastal environments based on a directional geographically neural network weighted regression.

Quantifying the spatial association between ecological indicators (e.g., chlorophyll-a) and environmental parameters is crucial for explaining the ecological status in coastal ecosystems.

The total number of prelaryngeal and pretracheal lymph node metastases: is it a reliable predictor of contralateral central lymph node metastasis in papillary thyroid carcinoma?

Background: Central lymph node (CLN) metastasis in papillary thyroid carcinoma (PTC) is common and being able to predict CLN metastasis helps surgeons determine individualized therapy. However, the relationship between contralateral CLN metastasis and the total number of positive lymph nodes (LNs) in the combined prelaryngeal and pretracheal region remains unclear. This study aimed to investigate whether the total number of positive LNs in the combined prelaryngeal and pretracheal region has clinical significance as a predictor for contralateral CLN metastasis.

Granular cell tumor of the thyroid: Clinical and pathological characteristics of a rare case in a 14-year-old girl.

Granular cell tumors (GCTs) are soft tissue neoplasms that originate in the nervous system, which may arise anywhere in the body. However, GCTs are extremely uncommon in thyroid tumors, with a favorable prognosis. The diagnosis of GCTs is dependent on pathological and immunohistochemical analysis and at present, surgical resection is considered the only suitable treatment.

Autotransplantation of Inferior Parathyroid glands during central neck dissection for papillary thyroid carcinoma: a retrospective cohort study.

Introduction: The management of inferior parathyroid glands during central neck dissection (CND) for papillary thyroid carcinoma (PTC) remains controversial. Most surgeons preserve inferior parathyroid glands in situ. Autotransplantation is not routinely performed unless devascularization or inadvertent parathyroidectomy occurs.