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Ferroelectric field effect transistors (Fe-FETs) offer promising candidates for neuromorphic computing. However, it is still challenging to achieve a light-tunable field effect, which limits the function of photo-synapse. In this work, a ferroelectric semiconductor floating-gate transistor (FSF-FET) is proposed based on MoS2/h-BN/α-In2Se3 van der Waals heterojunctions (vdWHs), in which the two-dimensional ferroelectric semiconducting α-In2Se3 and dielectric h-BN serve as the trapped layer of charges and prevent layer, respectively. The excellent memory performances are exhibited, including a high programming/erasing ratio of over 107, a large memory window ratio of 74.69%, and good non-volatility. Moreover, the FSF-FETs also possess the light-tunable synapse behaviors, including the high paired-pulse facilitation of 236% and an obvious transition from short-term plasticity to long-term plasticity. The high recognition rate of 93.9% is achieved with dual-mode modulation of light and electrical pulses. The ferroelectric semiconductor floating-gate design opens up a strategy to realize the light-tunable field effect of Fe-FETs for photo-synapse. [ABSTRACT FROM AUTHOR]

Camellia oleifera is a crop of high economic value, yet it is particularly susceptible to various diseases and pests that significantly reduce its yield and quality. Consequently, the precise segmentation and classification of diseased Camellia leaves are vital for managing pests and diseases effectively. Deep learning exhibits significant advantages in the segmentation of plant diseases and pests, particularly in complex image processing and automated feature extraction. However, when employing single-modal models to segment Camellia oleifera diseases, three critical challenges arise: (A) lesions may closely resemble the colors of the complex background; (B) small sections of diseased leaves overlap; (C) the presence of multiple diseases on a single leaf. These factors considerably hinder segmentation accuracy. A novel multimodal model, CNN–Transformer Dual U-shaped Network (CTDUNet), based on a CNN–Transformer architecture, has been proposed to integrate image and text information. This model first utilizes text data to address the shortcomings of single-modal image features, enhancing its ability to distinguish lesions from environmental characteristics, even under conditions where they closely resemble one another. Additionally, we introduce Coordinate Space Attention (CSA), which focuses on the positional relationships between targets, thereby improving the segmentation of overlapping leaf edges. Furthermore, cross-attention (CA) is employed to align image and text features effectively, preserving local information and enhancing the perception and differentiation of various diseases. The CTDUNet model was evaluated on a self-made multimodal dataset compared against several models, including DeeplabV3+, UNet, PSPNet, Segformer, HrNet, and Language meets Vision Transformer (LViT). The experimental results demonstrate that CTDUNet achieved an mean Intersection over Union (mIoU) of 86.14%, surpassing both multimodal models and the best single-modal model by 3.91% and 5.84%, respectively. Additionally, CTDUNet exhibits high balance in the multi-class segmentation of Camellia oleifera diseases and pests. These results indicate the successful application of fused image and text multimodal information in the segmentation of Camellia disease, achieving outstanding performance. [ABSTRACT FROM AUTHOR]

This article discusses a study conducted to investigate the humoral immune response to the COVID-19 vaccine in patients with psoriasis who are receiving biologics and small molecule medications. The study found that patients treated with different medications had similar levels of neutralizing antibodies (NAbs) after receiving the vaccine. Additionally, the study showed that a 3-dose vaccine regimen was more effective in inducing NAbs production compared to a 2-dose regimen. However, some patients experienced a decrease in NAb titers after the booster-dose vaccination, possibly due to a longer interval between doses or poor compliance. The study acknowledges limitations such as a small sample size and the absence of healthy individuals as controls. [Extracted from the article]

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a novel MRI technology to image certain compounds at extremely low concentrations. Long acquisition time to measure signals at a set of offset frequencies of the Z-spectra and to repeat measurements to reduce noise pose significant challenges to its applications. This study explores correlations of CEST MR images along the spatial and Z-spectral dimensions to improve MR image quality and robustness of magnetization transfer ratio (MTR) asymmetry estimation via a joint k- ω reconstruction model. The model was formulated as an optimization problem with respect to MR images at all frequencies ω , while incorporating regularizations along the spatial and spectral dimensions. The solution was subject to a self-consistency condition that the Z-spectrum of each pixel follows a multi-peak data fitting model corresponding to different CEST pools. The optimization problem was solved using the alternating direction method of multipliers. The proposed joint reconstruction method was evaluated on a simulated CEST MRI phantom and semi-experimentally on choline and iopamidol phantoms with added Gaussian noise of various levels. Results demonstrated that the joint reconstruction method was more tolerable to noise and reduction in number of offset frequencies by improving signal-to-noise ratio (SNR) of the reconstructed images and reducing uncertainty in MTR asymmetry estimation. In the choline and iopamidol phantom cases with 10.5% noise in the measurement data, our method achieved an averaged SNR of 31.0 dB and 32.2 dB compared to the SNR of 24.7 dB and 24.4 dB in the conventional reconstruction approach. It reduced uncertainty of the MTR asymmetry estimation over all regions of interest by 54.4% and 43.7%, from 1.71 and 2.38 to 0.78 and 1.71, respectively. [ABSTRACT FROM AUTHOR]

The source and pumping mechanism of magnesium play crucial roles in dolomitization. The preferential dolomitization of burrows has been extensively documented in geological archives. Although burrows are abundantly preserved in Cretaceous carbonates, burrow‐selective dolomitization is uncommon in normal salinity marine environments due to the low Mg/Ca ratio of seawater. However, burrow‐selective dolomitization occurred in the Cenomanian Rumaila Formation carbonates of the Arabian Platform providing an excellent example to further clarify the mechanism and explore other potential Mg sources of burrow‐elective dolomitization. Integrated studies of petrography, stable isotope geochemistry and laser ablation–inductively coupled plasma–mass spectrometry‐based in situ element geochemistry were conducted. It was found that the burrow‐selective dolomitization exclusively occurred in echinoderm fragment‐filled Thalassinoides networks which occurred as Glossifungites ichnofacies. Burrow dolomites showed fine to medium crystalline and planar subhedral to euhedral textures with cloudy centres and clear rims. They exhibited relatively greater Mn, lower Sr and Fe concentrations, no or very weak negative Ce anomaly and middle rare earth element‐bulge patterns, and are slightly enriched with occasionally depleted δ13C and comparable δ18O relative to the surrounding calcite matrix. The initial high‐Mg calcite echinoderm fragments in burrows have been stabilized to low‐Mg calcite, and echinoderm syntaxial overgrowth calcite cement was practically nonexistent. Echinoderm fragments were frequently replaced by dolomite in part or whole. Undolomitized echinoderms have negative Ce anomaly and seawater‐like rare earth element patterns, as well as very low Mn, Fe and relatively greater Sr concentrations. These suggest that echinoderm stabilization occurred in fluid unsaturated with respect to high‐Mg calcite driven by aerobic decomposition of organic matter in oxic seawater near the sediment–water interface, meanwhile, Mg ions were liberated into pore water. This process pre‐dated the dolomitization allowing the Mg derived from echinoderm to raise the Mg/Ca ratio of burrow interstitial water. The dolomites in burrows were generated by initial replacement and subsequent overgrowth cementation associated with bacterial sulphate reduction and methanogenesis in low‐temperature and suboxic to anoxic fluids in the near‐surface realm, and faintly recrystallized as burial depth increased. This study sheds light on the echinoderm stabilization process, links the early diagenesis of skeletons to burrow dolomitization, and proposes a conceptual model illustrating that high‐Mg calcite skeletons could act as a major Mg source for burrow‐selective dolomitization, which compensates for the deficiency of Mg in normal low Mg/Ca ratio Cretaceous seawater. This study implies the interaction effect and element cycle among components in early diagenetic systems, and verifies that high‐Mg calcite is indeed a non‐negligible potential Mg source for partial or elective dolomitization. [ABSTRACT FROM AUTHOR]