Development potential of nanoenabled agriculture projected using machine learning

The controllability and targeting of nanoparticles (NPs) offer solutions for precise and sustainable agriculture. However, the development potential of nanoenabled agriculture remains unknown. Here, we build an NP-plant database containing 1,174 datasets and predict (R2 higher than 0.8 for 13 random forest models) the response and uptake/transport of various NPs by plants using a machine learning approach. Multiway feature importance analysis quantitatively shows that plant responses are driven by the total NP exposure dose and duration and plant age at exposure, as well as the NP size and zeta potential. Feature interaction and covariance analysis further improve the interpretability of the model and reveal hidden interaction factors (e.g., NP size and zeta potential). Integration of the model, laboratory, and field data suggests that Fe2O3 NP application may inhibit bean growth in Europe due to low night temperatures. In contrast, the risks of oxidative stress are low in Africa because of high night temperatures. According to the prediction, Africa is a suitable area for nanoenabled agriculture. The regional differences and temperature changes make nanoenabled agriculture complicated. In the future, the temperature increase may reduce the oxidative stress in African bean and European maize induced by NPs. This study projects the development potential of nanoenabled agriculture using machine learning, although many more field studies are needed to address the differences at the country and continental scales

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management

Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA

Intronic enhancers of the human SNCA gene predominantly regulate its expression in brain in vivo.

Evidence from patients with Parkinson's disease (PD) and our previously reported α-synuclein (SNCA) transgenic rat model support the idea that increased SNCA protein is a substantial risk factor of PD pathogenesis. However, little is known about the transcription control of the human SNCA gene in the brain in vivo. Here, we identified that the DYT6 gene product THAP1 (THAP domain-containing apoptosis-associated protein 1) and its interaction partner CTCF (CCCTC-binding factor) act as transcription regulators of SNCA. THAP1 controls SNCA intronic enhancers' activities, while CTCF regulates its enhancer-promoter loop formation. The SNCA intronic enhancers present neurodevelopment-dependent activities and form enhancer clusters similar to "super-enhancers" in the brain, in which the PD-associated single-nucleotide polymorphisms are enriched. Deletion of the SNCA intronic enhancer clusters prevents the release of paused RNA polymerase II from its promoter and subsequently reduces its expression drastically in the brain, which may provide new therapeutic approaches to prevent its accumulation and thus related neurodegenerative diseases defined as synucleinopathies

A Unified Algorithm for Channel Imbalance and Antenna Phase Center Position Calibration of a Single-Pass Multi-Baseline TomoSAR System

The multi-baseline synthetic aperture radar (SAR) tomography (TomoSAR) system is employed in such applications as disaster remote sensing, urban 3-D reconstruction, and forest carbon storage estimation. This is because of its 3-D imaging capability in a single-pass platform. However, a high 3-D resolution of TomoSAR is based on the premise that the channel imbalance and antenna phase center (APC) position are precisely known. If this is not the case, the 3-D resolution performance will be seriously degraded. In this paper, a unified algorithm for channel imbalance and APC position calibration of a single-pass multi-baseline TomoSAR system is proposed. Based on the maximum likelihood method, as well as the least squares and the damped Newton method, we can calibrate the channel imbalance and APC position. The algorithm is suitable for near-field conditions, and no phase unwrapping operation is required. The effectiveness of the proposed algorithm has been verified by simulation and experimental results

Intratumoral perfusion may affect microwave ablation area of hepatocellular carcinoma

AbstractObjectives We aimed to evaluate the effect of intratumoral perfusion on microwave ablation (MWA) area in hepatocellular carcinoma (HCC).Methods Patients who underwent curative MWA for HCC between October 2013 and May 2015 were enrolled. Three days before MWA, contrast-enhanced ultrasound (CEUS) was performed to illustrate the perfusion characteristics of the target lesion. Using the Sonoliver quantification software, time-intensity curves of dynamic CEUS were obtained, and quantitative parameters were extracted. Two microwave antennae were inserted into the center of the tumor and MWA was performed with a continuous power output of 50 W for 5 min. A second CEUS was performed to measure the size of the ablated region. Thereafter, an additional MWA procedure was performed until complete ablation with a 5–10-mm safety margin was achieved.Results A total of 38 patients who underwent curative MWA for 39 HCC nodules were enrolled. The mean age was 57 years (34–80 years), and the median maximum diameter of the HCC was 3.4 cm (interquartile range, 2–6.8 cm). Time-intensity curves were obtained and the area under the curve (AUC) was selected as a parameter for intratumoral perfusion. The AUC was inversely and linearly correlated with the size of the MWA area, including long- and short-axis diameters and ablation volume. A 1,000-dB·s change in the AUC produced an average change of 1.17 ± 0.44 mm, 0.725 ± 0.355 mm, and 2.4995 ± 0.6575 cm³ in the long- and short-axis diameters and ablation volume, respectively.Conclusions The intratumoral perfusion of HCC was inversely correlated with MWA area size