Highly sensitive and selective fluorescent "turn-on" sensor for Ag+ detection using MAPbBr3@PCN-221(Fe): An efficient Ag+-bridged energy transfer from perovskite to MOF.

Metal ion-induced water pollution is attracting increasing public attention. Perovskite quantum dots and metal-organic frameworks (MOFs), owing to their outstanding properties, hold promise as ideal probes for detecting metal ions. In this study, a composite material, MAPbBr3@PCN-221(Fe), was prepared by encapsulating MAPbBr3 quantum dots with PCN-221(Fe), demonstrating high chemical stability and good reusability. The composite material shows a sensitive fluorescence turn-on signal in the presence of silver ions. The fluorescence intensity of the composite material exhibits a linear relationship with the concentration of Ag+ in the solution, with a low detection limit of 8.68 µM. Moreover, the fluorescence signal exhibits a strong selectivity for Ag+, enabling the detection of Ag+ concentration. This fluorescence turn-on signal originates from the Ag+-bridged energy transfer from the conductive band of MAPbBr3 to the excited state of the MOF, which is directly proportional to the concentration of silver ions. Simultaneously, this finding may open up a new possibility in artificial controlled energy transfer from perovskite to MOF for future development.

Human iPSC and CRISPR targeted gene knock-in strategy for studying the somatic TIE2L914F mutation in endothelial cells.

Induced pluripotent stem cell (iPSC) derived endothelial cells (iECs) have emerged as a promising tool for studying vascular biology and providing a platform for modelling various vascular diseases, including those with genetic origins. Currently, primary ECs are the main source for disease modelling in this field. However, they are difficult to edit and have a limited lifespan. To study the effects of targeted mutations on an endogenous level, we generated and characterized an iPSC derived model for venous malformations (VMs). CRISPR-Cas9 technology was used to generate a novel human iPSC line with an amino acid substitution L914F in the TIE2 receptor, known to cause VMs. This enabled us to study the differential effects of VM causative mutations in iECs in multiple in vitro models and assess their ability to form vessels in vivo. The analysis of TIE2 expression levels in TIE2L914F iECs showed a significantly lower expression of TIE2 on mRNA and protein level, which has not been observed before due to a lack of models with endogenous edited TIE2L914F and sparse patient data. Interestingly, the TIE2 pathway was still significantly upregulated and TIE2 showed high levels of phosphorylation. TIE2L914F iECs exhibited dysregulated angiogenesis markers and upregulated migration capability, while proliferation was not affected. Under shear stress TIE2L914F iECs showed reduced alignment in the flow direction and a larger cell area than TIE2WT iECs. In summary, we developed a novel TIE2L914F iPSC-derived iEC model and characterized it in multiple in vitro models. The model can be used in future work for drug screening for novel treatments for VMs.

CO and HCHO Sensing by Single Au Atom-Decorated WS2 Monolayer for Diagnosis of Thermal Aging Faults in the Dry-Type Reactor: A First-Principles Study.

CO and HCHO are the main pyrolysis gases in long-term running dry-type reactors, and thus the diagnosis of thermal insulation faults inside such devices can be realized by sensing these gases. In this paper, a single Au atom-decorated WS2 (Au-WS2) monolayer is proposed as an original sensing material for CO or HCHO detection to evaluate the operation status of dry-type reactors. It was found that the Au atom prefers to be adsorbed at the top of the S atom of the pristine WS2 monolayer, wherein the binding force is calculated as -3.12 eV. The Au-WS2 monolayer behaves by chemisorption upon the introduction of CO and HCHO molecules, with the adsorption energies of -0.82 and -1.01 eV, respectively. The charge density difference was used to analyze the charge-transfer and bonding behaviors in the gas adsorptions, and the analysis of density of state as well as band structure indicate gas-sensing mechanisms. As calculated, the sensing responses of the Au-WS2 monolayer upon CO and HCHO molecule introduction were 58.7% and -74.4%, with recovery times of 0.01 s and 11.86 s, respectively. These findings reveal the favorable potential of the Au-WS2 monolayer to be a reusable and room-temperature sensing candidate for CO and HCHO detections. Moreover, the work function of the Au-WS2 monolayer was decreased by 13.0% after the adsorption of CO molecules, while it increased by 1.2% after the adsorption of HCHO molecules, which implies its possibility to be a work-function-based gas sensor for CO detection. This theoretical report paves the way for further investigations into WS2-based gas sensors in some other fields, and it is our hope that our findings can stimulate more reports on novel gas-sensing materials for application in evaluating the operation conditions of dry-type reactors.

Corrigendum: Active substances of myxobacteria against plant diseases and their action mechanisms.

[This corrects the article DOI: 10.3389/fmicb.2023.1294854.].

Water-stable perovskite CsPb2Br5/CdSe quantum dot-based photoelectrochemical sensors for the sensitive determination of dopamine.

A heterojunction of CdSe quantum dots in situ grown on the perovskite CsPb2Br5 (CsPb2Br5/CdSe) for water-stable photoelectrochemical (PEC) sensing was simply synthesized using the hot-injection method. Due to the inherent built-in electric field and the matching band structure between CsPb2Br5 and CdSe, the CsPb2Br5/CdSe p-n heterojunction demonstrates enhanced photoelectrochemical properties. Accelerated interfacial charge transfer and increased electron-hole pair separation enable hydrolysis-resistant CsPb2Br5/CdSe sensors to exhibit heightened sensitivity with an ultra-low detection limit (0.0124 µM) and a wide linear range (0.4-303.9 µM) in subsequent dopamine detection. Moreover, the CsPb2Br5/CdSe sensors show excellent anti-interference ability, as well as remarkable stability and reproducibility in water solvent. It is noteworthy that this work is conducted in an aqueous environment, which provides an inspiring and convenient way for photoelectric and photoelectrocatalysis applications based on water-resistant perovskites.

IDNet: Information Decomposition Network for Fast Panoptic Segmentation.

Traditional CNN-based pipelines for panoptic segmentation decompose the task into two subtasks, i.e., instance segmentation and semantic segmentation. In this way, they extract information with multiple branches, perform two subtasks separately and finally fuse the results. However, excessive feature extraction and complicated processes make them time-consuming. We propose IDNet to decompose panoptic segmentation at information level. IDNet only extracts two kinds of information and directly completes panoptic segmentation task, saving the efforts to extract extra information and to fuse subtasks. By decomposing panoptic segmentation into category information and location information and recomposing them with a serial pipeline, the process for panoptic segmentation is simplified greatly and unified with regard to stuff and things. We also adopt two correction losses specially designed for our serial pipeline, guaranteeing the overall predicting performance. As a result, IDNet strikes a better balance between effectiveness and efficiency, achieving the fastest inference speed of 24.2 FPS at a resolution of 800×1333 on a Tesla V100 GPU and a PQ of 43.8, which is comparable in one-stage CNN-based methods. The code will be released at https://github.com/AronLin/IDNet.

A Type II-B Cas9 nuclease with minimized off-targets and reduced chromosomal translocations in vivo.

Streptococcus pyogenes Cas9 (SpCas9) and derived enzymes are widely used as genome editors, but their promiscuous nuclease activity often induces undesired mutations and chromosomal rearrangements. Several strategies for mapping off-target effects have emerged, but they suffer from limited sensitivity. To increase the detection sensitivity, we develop an off-target assessment workflow that uses Duplex Sequencing. The strategy increases sensitivity by one order of magnitude, identifying previously unknown SpCas9's off-target mutations in the humanized PCSK9 mouse model. To reduce off-target risks, we perform a bioinformatic search and identify a high-fidelity Cas9 variant of the II-B subfamily from Parasutterella secunda (PsCas9). PsCas9 shows improved specificity as compared to SpCas9 across multiple tested sites, both in vitro and in vivo, including the PCSK9 site. In the future, while PsCas9 will offer an alternative to SpCas9 for research and clinical use, the Duplex Sequencing workflow will enable a more sensitive assessment of Cas9 editing outcomes.

Effect of Laser-Textured Cu Foil with Deep Ablation on Si Anode Performance in Li-Ion Batteries.

Si is a highly promising anode material due to its superior theoretical capacity of up to 3579 mAh/g. However, it is worth noting that Si anodes experience significant volume expansion (>300%) during charging and discharging. Due to the weak adhesion between the anode coating and the smooth Cu foil current collector, the volume-expanded Si anode easily peels off, thus damaging anode cycling performance. In the present study, a femtosecond laser with a wavelength of 515 nm is used to texture Cu foils with a hierarchical microstructure and nanostructure. The peeling and cracking phenomenon in the Si anode are successfully reduced, demonstrating that volume expansion is effectively mitigated, which is attributed to the high specific surface area of the nanostructure and the protection of the deep-ablated microgrooves. Moreover, the hierarchical structure reduces interfacial resistance to promote electron transfer. The Si anode achieves improved cycling stability and rate capability, and the influence of structural features on the aforementioned performance is studied. The Si anode on the 20 µm-thick Cu current collector with a groove density of 75% and a depth of 15 µm exhibits a capacity of 1182 mAh/g after 300 cycles at 1 C and shows a high-rate capacity of 684 mAh/g at 3 C.

Simultaneous inhibition of DNA-PK and PolÏ´ improves integration efficiency and precision of genome editing.

Genome editing, specifically CRISPR/Cas9 technology, has revolutionized biomedical research and offers potential cures for genetic diseases. Despite rapid progress, low efficiency of targeted DNA integration and generation of unintended mutations represent major limitations for genome editing applications caused by the interplay with DNA double-strand break repair pathways. To address this, we conduct a large-scale compound library screen to identify targets for enhancing targeted genome insertions. Our study reveals DNA-dependent protein kinase (DNA-PK) as the most effective target to improve CRISPR/Cas9-mediated insertions, confirming previous findings. We extensively characterize AZD7648, a selective DNA-PK inhibitor, and find it to significantly enhance precise gene editing. We further improve integration efficiency and precision by inhibiting DNA polymerase theta (PolÏ´). The combined treatment, named 2iHDR, boosts templated insertions to 80% efficiency with minimal unintended insertions and deletions. Notably, 2iHDR also reduces off-target effects of Cas9, greatly enhancing the fidelity and performance of CRISPR/Cas9 gene editing.

MRI features of pituitary adenoma apoplexy and their relationship with hypoxia, proliferation, and pathology.

OBJECTIVE: We aim to study the MRI features of pituitary adenoma (PA) apoplexy and their relationship with hypoxia, proliferation, and pathology. METHODS: Sixty-seven patients with MRI signs of PA apoplexy were selected. According to the MRI signs, they were divided into the parenchymal group and the cystic group. The parenchymal group had a low signal area on T2WI without cyst >2 mm and this area was not significantly enhanced on the corresponding TW1 enhancement. The cystic group had a cyst >2 mm on T2WI, and the cyst showed liquid stratification on T2WI or high signal on T1WI. The relative T1WI (rT1WI) enhancement value and relative T2WI (rT2WI) value of non-apoplexy areas were measured. Protein levels of hypoxia-inducible factor-1 (HIF-1α), pyruvate dehydrogenase kinase 1 (PDK1), and Ki67 were detected with immunohistochemistry and Western blot. Nuclear morphology was observed with HE staining. RESULTS: The rT1WI enhancement average value, rT2WI average value, Ki67 protein expression level, and the number of abnormal nuclear morphology of non-apoplexy lesions in the parenchymal group were significantly lower than those in the cystic group. The protein expression levels of HIF-1α and PDK1 in the parenchymal group were significantly higher than those in the cystic group. HIF-1α protein was positively correlated with PDK1 but negatively correlated with Ki67. CONCLUSION: When there is PA apoplexy, the ischemia and hypoxia of the cystic group are lesser than those of the parenchymal group, but the proliferation is stronger.

Elastic Knowledge Distillation by Learning From Recollection.

Model performance can be further improved with the extra guidance apart from the one-hot ground truth. To achieve it, recently proposed recollection-based methods utilize the valuable information contained in the past training history and derive a "recollection" from it to provide data-driven prior to guide the training. In this article, we focus on two fundamental aspects of this method, i.e., recollection construction and recollection utilization. Specifically, to meet the various demands of models with different capacities and at different training periods, we propose to construct a set of recollections with diverse distributions from the same training history. After that, all the recollections collaborate together to provide guidance, which is adaptive to different model capacities, as well as different training periods, according to our similarity-based elastic knowledge distillation (KD) algorithm. Without any external prior to guide the training, our method achieves a significant performance gain and outperforms the methods of the same category, even as well as KD with well-trained teacher. Extensive experiments and further analysis are conducted to demonstrate the effectiveness of our method.

Active substances of myxobacteria against plant diseases and their action mechanisms.

Myxobacteria have a complex life cycle and unique social behavior, and obtain nutrients by preying on bacteria and fungi in soil. Chitinase, ß-1,3 glucanase and ß-1,6 glucanase produced by myxobacteria can degrade the glycosidic bond of cell wall of some plant pathogenic fungi, resulting in a perforated structure in the cell wall. In addition, isooctanol produced by myxobacteria can lead to the accumulation of intracellular reactive oxygen species in some pathogenic fungi and induce cell apoptosis. Myxobacteria can also perforate the cell wall of some plant pathogenic oomycetes by ß-1,3 glucanase, reduce the content of intracellular soluble protein and protective enzyme activity, affect the permeability of oomycete cell membrane, and aggravate the oxidative damage of pathogen cells. Small molecule compounds such as diisobutyl phthalate and myxovirescin produced by myxobacteria can inhibit the formation of biofilm and lipoprotein of bacteria, and cystobactamids can inhibit the activity of DNA gyrase, thus changing the permeability of bacterial cell membrane. Myxobacteria, as a new natural compound resource bank, can control plant pathogenic fungi, oomycetes and bacteria by producing carbohydrate active enzymes and small molecular compounds, so it has great potential in plant disease control.

Alloy-Type Lithium Anode Prepared by Laser Microcladding and Dealloying for Improved Cycling/Rate Performance.

Nanosized alloy-type materials (Si, Ge, Sn, etc.) present superior electrochemical performance in rechargeable batteries. However, they fail to guarantee cycling capacity and stability under high mass loading required by industrial applications due to low electric contact and adhesive strength, which has long been a challenge. This work proposes a rational design for an alloy-type anode via facile and versatile laser microcladding and dealloying. The proposed anode features a large-area porous network composed of continuous nano-ligaments, which consist of evenly distributed nanosized alloy-type material metallurgically bonded with conductive material. The fabrication of the structure is validated using Ge-Cu and Sn-Cu anodes, both exhibiting enhanced cycling stability at high areal capacity and rate performance in lithium-ion batteries. The enhancement is attributed to the structural features, which contribute to lithiation-delithiation stability and intact electron/Li ion transference path, as verified by in situ and ex situ transmission electron microscopy observations. More importantly, the critical solidification conditions of laser microcladding are provided by a multiphysics simulation, allowing for a thorough understanding of the structural formation mechanism. The study provides a possible approach to improve mass loading and performance of an alloy-type anode for practical application.

MLMG-SGG: Multi-Label Scene Graph Generation with Multi-Grained Features.

As an important and challenging problem in computer vision, scene graph generation (SGG) aims to find out the underlying semantic relationships among objects from a given image for scene understanding. Usually, prevalent SGG approaches adopt a learning pipeline with the assumption that there exists only a single relationship for a particular object pair. Considering the common phenomenon that a pair of objects can be attached by multiple relationships, we propose a multi-label scene graph generation pipeline with multi-grained features (MLMG-SGG), which formulates the relationship detection as a multi-label classification problem during training while generating multigraphs at inference time. In order to better model the fine-grained relationships, the proposed pipeline encodes the feature representation of SGG on different spatial scales by a specially designed Multi-Grained Module (MGM), resulting in the multi-grained (i.e., object-level and region-level) features of objects. Experimental results over the benchmark dataset demonstrate the significant performance gain of the proposed pipeline used as a plug-in for the state-of-the-art methods.

Harnessing DSB repair to promote efficient homology-dependent and -independent prime editing.

Prime editing recently emerged as a next-generation approach for precise genome editing. Here we exploit DNA double-strand break (DSB) repair to develop two strategies that install precise genomic insertions using an SpCas9 nuclease-based prime editor (PEn). We first demonstrate that PEn coupled to a regular prime editing guide RNA (pegRNA) efficiently promotes short genomic insertions through a homology-dependent DSB repair mechanism. While PEn editing leads to increased levels of by-products, it can rescue pegRNAs that perform poorly with a nickase-based prime editor. We also present a small molecule approach that yields increased product purity of PEn editing. Next, we develop a homology-independent PEn editing strategy, which installs genomic insertions at DSBs through the non-homologous end joining pathway (NHEJ). Lastly, we show that PEn-mediated insertions at DSBs prevent Cas9-induced large chromosomal deletions and provide evidence that continuous Cas9-mediated cutting is one of the mechanisms by which Cas9-induced large deletions arise. Altogether, this work expands the current prime editing toolbox by leveraging distinct DNA repair mechanisms including NHEJ, which represents the primary pathway of DSB repair in mammalian cells.

TapLab: A Fast Framework for Semantic Video Segmentation Tapping Into Compressed-Domain Knowledge.

Real-time semantic video segmentation is a challenging task due to the strict requirements of inference speed. Recent approaches mainly devote great efforts to reducing the model size for high efficiency. In this paper, we rethink this problem from a different viewpoint: using knowledge contained in compressed videos. We propose a simple and effective framework, dubbed TapLab, to tap into resources from the compressed domain. Specifically, we design a fast feature warping module using motion vectors for acceleration. To reduce the noise introduced by motion vectors, we design a residual-guided correction module and a residual-guided frame selection module using residuals. TapLab significantly reduces redundant computations of the state-of-the-art fast semantic image segmentation models, running 3 to 10 times faster with controllable accuracy degradation. The experimental results show that TapLab achieves 70.6 percent mIoU on the Cityscapes dataset at 99.8 FPS with a single GPU card for the 1024×2048 videos. A high-speed version even reaches the speed of 160+ FPS. Code will be available soon at https://github.com/Sixkplus/TapLab.

An optimized genome-wide, virus-free CRISPR screen for mammalian cells.

Pooled CRISPR screens have been widely applied to mammalian and other organisms to elucidate the interplay between genes and phenotypes of interest. The most popular method for delivering the CRISPR components into mammalian cells is lentivirus based. However, because lentivirus is not always an option, virus-free protocols are starting to emerge. Here, we demonstrate an improved virus-free, genome-wide CRISPR screening platform for Chinese hamster ovary cells with 75,488 gRNAs targeting 15,028 genes. Each gRNA expression cassette in the library is precisely integrated into a genomic landing pad, resulting in a very high percentage of single gRNA insertions and minimal clonal variation. Using this platform, we perform a negative selection screen on cell proliferation that identifies 1,980 genes that affect proliferation and a positive selection screen on the toxic endoplasmic reticulum stress inducer, tunicamycin, that identifies 77 gene knockouts that improve survivability.

F³A-GAN: Facial Flow for Face Animation With Generative Adversarial Networks.

Formulated as a conditional generation problem, face animation aims at synthesizing continuous face images from a single source image driven by a set of conditional face motion. Previous works mainly model the face motion as conditions with 1D or 2D representation (e.g., action units, emotion codes, landmark), which often leads to low-quality results in some complicated scenarios such as continuous generation and large-pose transformation. To tackle this problem, the conditions are supposed to meet two requirements, i.e., motion information preserving and geometric continuity. To this end, we propose a novel representation based on a 3D geometric flow, termed facial flow, to represent the natural motion of the human face at any pose. Compared with other previous conditions, the proposed facial flow well controls the continuous changes to the face. After that, in order to utilize the facial flow for face editing, we build a synthesis framework generating continuous images with conditional facial flows. To fully take advantage of the motion information of facial flows, a hierarchical conditional framework is designed to combine the extracted multi-scale appearance features from images and motion features from flows in a hierarchical manner. The framework then decodes multiple fused features back to images progressively. Experimental results demonstrate the effectiveness of our method compared to other state-of-the-art methods.

Multitask Identity-Aware Image Steganography via Minimax Optimization.

High-capacity image steganography, aimed at concealing a secret image in a cover image, is a technique to preserve sensitive data, e.g., faces and fingerprints. Previous methods focus on the security during transmission and subsequently run a risk of privacy leakage after the restoration of secret images at the receiving end. To address this issue, we propose a framework, called Multitask Identity-Aware Image Steganography (MIAIS), to achieve direct recognition on container images without restoring secret images. The key issue of the direct recognition is to preserve identity information of secret images into container images and make container images look similar to cover images at the same time. Thus, we introduce a simple content loss to preserve the identity information, and design a minimax optimization to deal with the contradictory aspects. We demonstrate that the robustness results can be transferred across different cover datasets. In order to be flexible for the secret image restoration in some cases, we incorporate an optional restoration network into our method, providing a multitask framework. The experiments under the multitask scenario show the effectiveness of our framework compared with other visual information hiding methods and state-of-the-art high-capacity image steganography methods. The code is available at https://github.com/jiabaocui/MIAIS.

CoDiNet: Path Distribution Modeling with Consistency and Diversity for Dynamic Routing.

Dynamic routing networks, aimed at finding the best routing paths in the networks, have achieved significant improvements to neural networks in terms of accuracy and efficiency. In this paper, we see dynamic routing networks in a fresh light, formulating a routing method as a mapping from a sample space to a routing space. From the perspective of space mapping, prevalent methods of dynamic routing did not take into account how inference paths would be distributed in the routing space. Thus, we propose a novel method, termed CoDiNet, to model the relationship between a sample space and a routing space by regularizing the distribution of routing paths with the properties of consistency and diversity. Specifically, samples with similar semantics should be mapped into the same area in routing space, while those with dissimilar semantics should be mapped into different areas. Moreover, we design a customizable dynamic routing module, which can strike a balance between accuracy and efficiency. When deployed upon ResNet models, our method achieves higher performance and effectively reduces average computational cost on four widely used datasets.