3D Bioprinting of Artificial Skin Substitute with Improved Mechanical Property and Regulated Cell Behavior through Integrating Patterned Nanofibrous Films.

Three-dimensional (3D) bioprinting has advantages for constructing artificial skin tissues in replicating the structures and functions of native skin. Although many studies have presented improved effect of printing skin substitutes in wound healing, using hydrogel inks to fabricate 3D bioprinting architectures with complicated structures, mimicking mechanical properties, and appropriate cellular environments is still challenging. Inspired by collagen nanofibers withstanding stress and regulating cell behavior, a patterned nanofibrous film was introduced to the printed hydrogel scaffold to fabricate a composite artificial skin substitute (CASS). The artificial dermis was printed using gelatin-hyaluronan hybrid hydrogels containing human dermal fibroblasts with gradient porosity and integrated with patterned nanofibrous films simultaneously, while the artificial epidermis was formed by seeding human keratinocytes upon the dermis. The collagen-mimicking nanofibrous film effectively improved the tensile strength and fracture resistance of the CASS, making it sewable for firm implantation into skin defects. Meanwhile, the patterned nanofibrous film also provided the biological cues to guide cell behavior. Consequently, CASS could effectively accelerate the regeneration of large-area skin defects in mouse and pig models by promoting re-epithelialization and collagen deposition. This research developed an effective strategy to prepare composite bioprinting architectures for enhancing mechanical property and regulating cell behavior, and CASS could be a promising skin substitute for treating large-area skin defects.

Cycle contrastive adversarial learning with structural consistency for unsupervised high-quality image deraining transformer.

In overcoming the challenges faced in adapting to paired real-world data, recent unsupervised single image deraining (SID) methods have proven capable of accomplishing notably acceptable deraining performance. However, the previous methods usually fail to produce a high quality rain-free image due to neglecting sufficient attention to semantic representation and the image content, which results in the inability to completely separate the content from the rain layer. In this paper, we develop a novel cycle contrastive adversarial framework for unsupervised SID, which mainly consists of cycle contrastive learning (CCL) and location contrastive learning (LCL). Specifically, CCL achieves high-quality image reconstruction and rain-layer stripping by pulling similar features together while pushing dissimilar features further in both semantic and discriminant latent spaces. Meanwhile, LCL implicitly constrains the mutual information of the same location of different exemplars to maintain the content information. In addition, recently inspired by the powerful Segment Anything Model (SAM) that can effectively extract widely applicable semantic structural details, we formulate a structural-consistency regularization to fine-tune our network using SAM. Apart from this, we attempt to introduce vision transformer (VIT) into our network architecture to further improve the performance. In our designed transformer-based GAN, to obtain a stronger representation, we propose a multi-layer channel compression attention module (MCCAM) to extract a richer feature. Equipped with the above techniques, our proposed unsupervised SID algorithm, called CCLformer, can show advantageous image deraining performance. Extensive experiments demonstrate both the superiority of our method and the effectiveness of each module in CCLformer. The code is available at https://github.com/zhihefang/CCLGAN.

Hierarchical processing enabled by 2D ferroelectric semiconductor transistor for low-power and high-efficiency AI vision system.

The growth of data and Internet of Things challenges traditional hardware, which encounters efficiency and power issues owing to separate functional units for sensors, memory, and computation. In this study, we designed an α-phase indium selenide (α-In2Se3) transistor, which is a two-dimensional ferroelectric semiconductor as the channel material, to create artificial optic-neural and electro-neural synapses, enabling cutting-edge processing-in-sensor (PIS) and computing-in-memory (CIM) functionalities. As an optic-neural synapse for low-level sensory processing, the α-In2Se3 transistor exhibits a high photoresponsivity (2855 A/W) and detectivity (2.91 × 1014 Jones), facilitating efficient feature extraction. For high-level processing tasks as an electro-neural synapse, it offers a fast program/erase speed of 40 ns/50 µs and ultralow energy consumption of 0.37 aJ/spike. An AI vision system using α-In2Se3 transistors has been demonstrated. It achieved an impressive recognition accuracy of 92.63% within 12 epochs owing to the synergistic combination of the PIS and CIM functionalities. This study demonstrates the potential of the α-In2Se3 transistor in future vision hardware, enhancing processing, power efficiency, and AI applications.

Ensemble learning-based applied research on heavy metals prediction in a soil-rice system.

Accurate prediction of heavy metal accumulation in soil ecosystems is crucial for maintaining healthy soil environments and ensuring high-quality agricultural products, as well as a challenging scientific task. In this study, we constructed a dataset containing 490 sets of multidimensional environmental covariate data and proposed prediction models for heavy metal concentrations (HMC) in a soil-rice system, EL-HMC (including RF-HMC and GBM-HMC), based on Random Forest (RF) and Gradient Boosting Machine (GBM) ensemble learning (EL) techniques. To reasonably evaluate the effectiveness of each model, Multiple linear and Bayesian regressions were selected as benchmark models (BM), and mean absolute error (MAE), root mean square error (RMSE), and determination coefficient R2 were selected as evaluation indicators. In addition, sensitivity and spatial autocorrelation (SAC) analyses were used to examine the robustness of the model. The results showed that the R2 values of RF-HMC and GBM-HMC for modeling available cadmium (Cd) concentrations in soil were 0.654 and 0.690, respectively, with an average increase of 48.0 % compared to the BMs. The R2 values of RF-HMC and GBM-HMC for predicting Cd, lead (Pb), chromium (Cr), and mercury (Hg) concentrations in rice ranged from 0.618 to 0.824 and 0.645 to 0.850, respectively, with an average increase of 58.2 % compared with the BMs. The corresponding MAEs and RMSEs of RF-HMC and GBM-HMC had low error levels. Sensitivity analysis of the input features and the SAC of the prediction bias showed that the EL-HMC models have excellent robustness. Therefore, the EL technology-based prediction models for HMCs proposed herein are practical and feasible, demonstrating better accuracy and stability than the traditional model. This study verifies the application potential of EL technology in pollution ecology and provides a new perspective and solution for sustainable management and precise prevention of heavy metal pollution in farmland soil at the regional scale.

Excellent thermoelectric properties of the Tl2S3 monolayer for medium-temperature applications.

Exploring materials with high thermoelectric (TE) performance can alleviate energy pressure and protect the environment, and thus, TE materials have attracted extensive attention in the new energy field. In this paper, we systematically study the TE properties of Tl2S3 using first-principles combined with Boltzmann transport theory (BTE). The calculation results show an excellent power factor (1.12 × 10-2 W m-1 K-2) and ultra-low lattice thermal conductivity (kl = 0.88 W m-1 K-1) at room temperature. Through analysis, we attribute the ultra-low kl of Tl2S3 to the lower phonon group velocity (vg) and larger phonon anharmonicity. Meanwhile, discussion of chemical bonding showed that the filling of the anti-bonding state leads to the weakening of the Tl-S chemical bond, resulting in low vg. Furthermore, this research also investigates the scattering processes (the out-of-plane acoustic mode (ZA) + optical mode (O) → O (ZA + O → O), the in-plane transverse acoustic mode (TA) + O → O (TA + O → O), and the in-plane longitudinal acoustic mode (LA) + O → O (LA + O → O)), from which we find that 2D Tl2S3 possesses strong acoustic-optical scattering. Based on the analysis of electron transport properties under electron-phonon coupling, 2D Tl2S3, as a novel TE material, exhibits a ZT value as high as 2.8 at 400 K. Our calculations suggest that Tl2S3 is a potential TE material at medium temperature.

A comparison of screening methods to identify waterlogging tolerance in the field in Brassica napus L. during plant ontogeny.

Waterlogging tolerance is typically evaluated at a specific development stage, with an implicit assumption that differences in waterlogging tolerance expressed in these systems will result in improved yield performance in fields. It is necessary to examine these criteria in fields. In the present study, three experiments were conducted to screen waterlogging tolerance in 25 rapeseed (Brassica napus L.) varieties at different developmental stages, such as seedling establishment stage and seedling stage at controlled environment, and maturity stage in the fields. The assessments for physiological parameters at three growth stages suggest that there were difference of waterlogging tolerance at all the development stages, providing an important basis for further development of breeding more tolerant materials. The results indicated that flash waterlogging restricts plant growth and growth is still restored after removal of the stress. Correlation analysis between waterlogging tolerance coefficient (WTC) of yield and other traits revealed that there was consistency in waterlogging tolerance of the genotypes until maturity, and good tolerance at seedling establishment stage and seedling stage can guarantee tolerance in later stages. The waterlogging-tolerant plants could be selected using some specific traits at any stage, and selections would be more effective at the seedling establishment stage. Thus, our study provides a method for screening waterlogging tolerance, which would enable the suitable basis for initial selection of a large number of germplasm or breeding populations for waterlogging tolerance and help for verifying their potential utility in crop-improvement.

The Transcriptome of Brassica napus L. Roots under Waterlogging at the Seedling Stage.

Although rapeseed (Brassica napus L.) is known to be affected by waterlogging, the genetic basis of waterlogging tolerance by rapeseed is largely unknown. In this study, the transcriptome under 0 h and 12 h of waterlogging was assayed in the roots of ZS9, a tolerant variety, using digital gene expression (DGE). A total of 4432 differentially expressed genes were identified, indicating that the response to waterlogging in rapeseed is complicated. The assignments of the annotated genes based on GO (Gene Ontology) revealed there were more genes induced under waterlogging in "oxidation reduction", "secondary metabolism", "transcription regulation", and "translation regulation"; suggesting these four pathways are enhanced under waterlogging. Analysis of the 200 most highly expressed genes illustrated that 144 under normal conditions were down-regulated by waterlogging, while up to 191 under waterlogging were those induced in response to stress. The expression of genes involved under waterlogging is mediated by multiple levels of transcriptional, post-transcriptional, translational and post-translational regulation, including phosphorylation and protein degradation; in particular, protein degradation might be involved in the negative regulation in response to this stress. Our results provide new insight into the response to waterlogging and will help to identify important candidate genes.