Generating minimum set of gRNA to cover multiple targets in multiple genomes with MINORg.

MINORg is an offline gRNA design tool that generates the smallest possible combination of gRNA capable of covering all desired targets in multiple non-reference genomes. As interest in pangenomic research grows, so does the workload required for large screens in multiple individuals. MINORg aims to lessen this workload by capitalising on sequence homology to favour multi-target gRNA while simultaneously screening multiple genetic backgrounds in order to generate reusable gRNA panels. We demonstrated the practical application of MINORg by knocking out 11 homologous genes tandemly arrayed in a multi-gene cluster in two Arabidopsis thaliana lineages using three gRNA output by MINORg. We also described a new PCR-free modular cloning system for multiplexing gRNA, and used it to knockout three tandemly arrayed genes in another multi-gene cluster with gRNA designed by MINORg. Source code is freely available at https://github.com/rlrq/MINORg.

FSP1-mediated ferroptosis in cancer: from mechanisms to therapeutic applications.

Ferroptosis is a new discovered regulated cell death triggered by the ferrous ion (Fe2+)-dependent accumulation of lipid peroxides associated with cancer and many other diseases. The mechanism of ferroptosis includes oxidation systems (such as enzymatic oxidation and free radical oxidation) and antioxidant systems (such as GSH/GPX4, CoQ10/FSP1, BH4/GCH1 and VKORC1L1/VK). Among them, ferroptosis suppressor protein 1 (FSP1), as a crucial regulatory factor in the antioxidant system, has shown a crucial role in ferroptosis. FSP1 has been well validated to ferroptosis in three ways, and a variety of intracellular factors and drug molecules can alleviate ferroptosis via FSP1, which has been demonstrated to alter the sensitivity and effectiveness of cancer therapies, including chemotherapy, radiotherapy, targeted therapy and immunotherapy. This review aims to provide important frameworks that, bring the regulation of FSP1 mediated ferroptosis into cancer therapies on the basis of existing studies.

TS-1@MCM-48 Core-Shell Catalysts for Efficient Oxidation of p-Diethylbenzene to High Value-Added Derivatives.

To expand the market capacity of p-diethylbenzene (PDEB), core-shell zeolite (TS-1@MCM-48) is designed as a catalyst for PDEB oxidation. TS-1@MCM-48 catalyst is synthesized by in-situ crystallization method and characterized by X-ray diffraction (XRD), transmission electron microscope (TEM), scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS), N2 adsorption-desorption, in-situ electron paramagnetic resonance (EPR) and 29Si nuclear magnetic resonance (29Si MAS-NMR). Oxidation of PDEB by H2O2 was investigated systematically in liquid phase. The conversion of PDEB over TS-1@MCM-48 was 28.1 % and the total selectivity was 72.6 %, where the selectivity of EAP (p-ethylacetophenone) and EPEA (4-ethyl-α-methylbenzyl alcohol) was 28.6 % and 44.0 %, respectively. Compared with TS-1 and MCM-48 zeolite, the conversion rate of reactants and the selectivity of products have been significantly improved. The catalytic performance of TS-1@MCM-48 is derived from its well-crystallized microporous core and mesoporous shell with regular channels, which make active sites of TS-1 zeolite in the catalyst be fully utilized and mass transfer resistance be largely reduced. Further through theoretical calculation, we propose that the oxidation of PDEB is the result of the combination and mutual transformation of free radical process and carbocation process. Core-shell structure ensures the conversion rate of raw materials and improves the selectivity of products.

Ultrasensitive Ratiometric Electrochemiluminescence Sensor with an Efficient Antifouling and Antibacterial Interface of PSBMA@SiO2-MXene for Oxytetracycline Trace Detection in the Marine Environment.

The sensitivity and accuracy of electrochemiluminescence (ECL) sensors for detecting small-molecule pollutants in environmental water are affected not only by nonspecific adsorption of proteins and other molecules but also by bacterial interference. Therefore, there is an urgent need to develop an ECL sensor with antifouling and antibacterial functions for water environment monitoring. Herein, a highly efficient antifouling sensing interface (PSBMA@SiO2-MXene) based on zwitterionic sulfobetaine methacrylate (SBMA) antifouling nanospheres (NPs) and two-dimensional MXene nanosheets was designed for the sensitive detection of oxytetracycline (OTC), an antibiotic small-molecule pollutant. Specifically, SBMA with good hydrophilicity and electrical neutrality was connected to SiO2 NPs, thus effectively reducing protein and bacterial adsorption and improving stability. Second, MXene with a high specific surface area was selected as the carrier to load more antifouling NPs, which greatly improves the antifouling performance. Meanwhile, the introduction of MXene also enhances the conductivity of the antifouling interface. In addition, a ratio-based sensing strategy was designed to further improve the detection accuracy and sensitivity of the sensor by utilizing Au@luminol as an internal standard factor. Based on antifouling and antibacterial interfaces, as well as internal standard and ratiometric sensing strategies, the detection range of the proposed sensor was 0.1 ng/mL to 100 µg/mL, with a detection limit of 0.023 ng/mL, achieving trace dynamic monitoring of antibiotics in complex aqueous media.

A Truncated Singleton NLR Causes Hybrid Necrosis in Arabidopsis thaliana.

Hybrid necrosis in plants arises from conflict between divergent alleles of immunity genes contributed by different parents, resulting in autoimmunity. We investigate a severe hybrid necrosis case in Arabidopsis thaliana, where the hybrid does not develop past the cotyledon stage and dies 3 weeks after sowing. Massive transcriptional changes take place in the hybrid, including the upregulation of most NLR (nucleotide-binding site leucine-rich repeat) disease-resistance genes. This is due to an incompatible interaction between the singleton TIR-NLR gene DANGEROUS MIX 10 (DM10), which was recently relocated from a larger NLR cluster, and an unlinked locus, DANGEROUS MIX 11 (DM11). There are multiple DM10 allelic variants in the global A. thaliana population, several of which have premature stop codons. One of these, which has a truncated LRR-PL (leucine-rich repeat [LRR]-post-LRR) region, corresponds to the DM10 risk allele. The DM10 locus and the adjacent genomic region in the risk allele carriers are highly differentiated from those in the nonrisk carriers in the global A. thaliana population, suggesting that this allele became geographically widespread only relatively recently. The DM11 risk allele is much rarer and found only in two accessions from southwestern Spain-a region from which the DM10 risk haplotype is absent-indicating that the ranges of DM10 and DM11 risk alleles may be nonoverlapping.

Characterization of the Relationship between the Loess Moisture and Image Grayscale Value.

This paper presents a model for estimating the moisture of loess from an image grayscale value. A series of well-controlled air-dry tests were performed on saturated Malan loess, and the moisture content of the loess sample during the desiccation process was automatically recorded while the soil images were continually captured using a photogrammetric device equipped with a CMOS image sensor. By converting the red, green, and blue (RGB) image into a grayscale one, the relationship between the water content and grayscale value, referred to as the water content-gray value characteristic curve (WGCC), was obtained; the impacts of dry density, particle size distribution, and illuminance on WGCC were investigated. It is shown that the grayscale value increases as the water content decreases; based on the rate of increase of grayscale value, the WGCC can be segmented into three stages: slow-rise, rapid-rise, and asymptotically stable stages. The influences that dry density and particle size distribution have on WGCC are dependent on light reflection and transmission, and this dependence is closely related to soil water types and their relative proportion. Besides, the WGCC for a given soil sample is unique if normalized with illuminance. The mechanism behind the three stages of WGCC is discussed in terms of visible light reflection. A mathematical model was proposed to describe WGCC, and the physical meaning of the model parameters was interpreted. The proposed model is validated independently using another six different types of loess samples and is shown to match well the experimental data. The results of this study can provide a reference for the development of non-contact soil moisture monitoring methods as well as relevant sensors and instruments.

Dihydromethysticin, a natural molecule from Kava, suppresses the growth of colorectal cancer via the NLRC3/PI3K pathway.

Dihydromethysticin (DHM), a natural compound derived from Kava, has been reported to be effective against mental disorders and some malignant tumors. However, little is known about the inhibitory effect of DHM on colorectal cancer (CRC). First, we examined the impact of DHM on human colon cancer cell lines, which demonstrated that DHM inhibits proliferation, migration, and invasion and promotes apoptosis and cell cycle arrest in colon cancer cells in vitro. Using small hairpin RNA, we inhibited nucleotide-oligomerization domain-like receptor subfamily C3 (NLRC3)/phosphoinositide 3-kinase (PI3K) pathway to elucidate the partial signaling of DHM-mediated tumor suppression. Additionally, using an ectopic human CRC model, we verified whether DHM inhibits tumor growth and angiogenesis via the NLRC3/PI3K pathway in vivo. Overall, DHM showed an inhibitory effect on CRC by altering cell proliferation, migration, invasion, apoptosis, cell cycle, and angiogenesis, possibly via the NLRC3/PI3K pathway. Thus, DHM may be a promising candidate for CRC therapy.

Optimal stretching in the reacting wake of a bluff body.

We experimentally study spreading of the Belousov-Zhabotinsky reaction behind a bluff body in a laminar flow. Locations of reacted regions (i.e., regions with high product concentration) correlate with a moderate range of Lagrangian stretching and that range is close to the range of optimal stretching previously observed in topologically different flows [T. D. Nevins and D. H. Kelley, Phys. Rev. Lett. 117, 164502 (2016)]. The previous work found optimal stretching in a closed, vortex dominated flow, but this article uses an open flow and only a small area of appreciable vorticity. We hypothesize that optimal stretching is common in advection-reaction-diffusion systems with an excitation threshold, including excitable and bistable systems, and that the optimal range depends on reaction chemistry and not on flow shape or characteristic speed. Our results may also give insight into plankton blooms behind islands in ocean currents.

Anti-inflammatory and anti-apoptotic effect of combined treatment with methylprednisolone and amniotic membrane mesenchymal stem cells after spinal cord injury in rats.

This study was undertaken to investigate the synergistic effects of methylprednisolone (MP) administration and transplantation of amniotic membrane mesenchymal stem cells (AM-MSCs) following T11 spinal cord clip compressive injury in rats. The combination treatment with MP (50 mg/kg) and delayed transplantation of AM-MSCs after rat spinal cord injury, significantly reduced (1) myeloperoxidase activity, (2) the proinflammatory cytokines: tumor necrosis factor-α, interleukin (IL)-1ß, IL-6, IL-17, interferon-γ and (3) the cell apoptosis [terminal deoxynucleotidyl transferase, dUTP nick end labeling (TUNEL) staining, and caspase-3, Bax and Bcl-2 expressions]; increased: (1) the levels of the anti-inflammatory cytokines (IL-10 and transforming growth factor-ß1) and (2) the survival rate of AM-MSCs in the injury site. The combination therapy significantly ameliorated the recovery of limb function (evaluated by Basso, Beattie and Bresnahan score). Taken together, our results demonstrate that MP in combination with AM-MSCs transplantation is a potential strategy for reducing secondary damage and promoting functional recovery following spinal cord injury.

Bioinformatics and biomedical informatics with ChatGPT: Year one review.

The year 2023 marked a significant surge in the exploration of applying large language model (LLM) chatbots, notably ChatGPT, across various disciplines. We surveyed the applications of ChatGPT in bioinformatics and biomedical informatics throughout the year, covering omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bioinformatics programming, and bioinformatics education. Our survey delineates the current strengths and limitations of this chatbot in bioinformatics and offers insights into potential avenues for future developments.

A Fish-like Binocular Vision System for Underwater Perception of Robotic Fish.

Biological fish exhibit a remarkably broad-spectrum visual perception capability. Inspired by the eye arrangement of biological fish, we design a fish-like binocular vision system, thereby endowing underwater bionic robots with an exceptionally broad visual perception capacity. Firstly, based on the design principles of binocular visual field overlap and tangency to streamlined shapes, a fish-like vision system is developed for underwater robots, enabling wide-field underwater perception without a waterproof cover. Secondly, addressing the significant distortion and parallax of the vision system, a visual field stitching algorithm is proposed to merge the binocular fields of view and obtain a complete perception image. Thirdly, an orientation alignment method is proposed that draws scales for yaw and pitch angles in the stitched images to provide a reference for the orientation of objects of interest within the field of view. Finally, underwater experiments evaluate the perception capabilities of the fish-like vision system, confirming the effectiveness of the visual field stitching algorithm and the orientation alignment method. The results show that the constructed vision system, when used underwater, achieves a horizontal field of view of 306.56°. The conducted work advances the visual perception capabilities of underwater robots and presents a novel approach to and insight for fish-inspired visual systems.

Scientific figures interpreted by ChatGPT: strengths in plot recognition and limits in color perception.

Emerging studies underscore the promising capabilities of large language model-based chatbots in conducting basic bioinformatics data analyses. The recent feature of accepting image inputs by ChatGPT, also known as GPT-4V(ision), motivated us to explore its efficacy in deciphering bioinformatics scientific figures. Our evaluation with examples in cancer research, including sequencing data analysis, multimodal network-based drug repositioning, and tumor clonal evolution, revealed that ChatGPT can proficiently explain different plot types and apply biological knowledge to enrich interpretations. However, it struggled to provide accurate interpretations when color perception and quantitative analysis of visual elements were involved. Furthermore, while the chatbot can draft figure legends and summarize findings from the figures, stringent proofreading is imperative to ensure the accuracy and reliability of the content.

An Efficient Muscle Segmentation Method Via Bayesian Fusion of Probabilistic Shape Modeling and Deep Edge Detection.

OBJECTIVE: Paraspinal muscle segmentation and reconstruction from MR images are critical to implement quantitative assessment of chronic and recurrent low back pains. Due to unclear muscle boundaries and shape variations, current segmentation methods demonstrate suboptimal performance with insufficient training samples. This work proposes a novel approach to modeling and inferring muscle shapes that enhances segmentation accuracy and efficiency with few training data. METHODS: Firstly, a probabilistic shape model (PSM) based on Fourier basis functions and Gaussian processes (GPs) is designed to encode 3D muscle shapes, where anatomical meanings are attributed to the model's geometric parameters. Muscle shape variations and correlations are described by the GPs of the geometric parameters, which allow a small size of parameters to model the distribution of muscle shapes. Secondly, a Bayesian framework is developed to achieve entire muscle segmentation by posterior estimations. The framework fuses the geometric prior of the PSM with observations of deep-learning-based edge detections (DED) and sparse manual annotations, by which issues of unclear boundaries and shape variations can be compensated. RESULTS AND CONCLUSION: Experiments on public and clinical datasets demonstrate that, with just three manually annotated slices, our method achieves a Dice similarity coefficient exceeding 90%, which outperforms other methods. Meanwhile, our method needs only a small training dataset and offers rapid inference speeds in clinical applications. SIGNIFICANCE: Our study enables precise assessment of paraspinal muscles in 2D and 3D, aiding clinicians and researchers in understanding muscle changes in various conditions, potentially enhancing treatment outcomes.

A critical period for developing face recognition.

Face learning has important critical periods during development. However, the computational mechanisms of critical periods remain unknown. Here, we conducted a series of in silico experiments and showed that, similar to humans, deep artificial neural networks exhibited critical periods during which a stimulus deficit could impair the development of face learning. Face learning could only be restored when providing information within the critical period, whereas, outside of the critical period, the model could not incorporate new information anymore. We further provided a full computational account by learning rate and demonstrated an alternative approach by knowledge distillation and attention transfer to partially recover the model outside of the critical period. We finally showed that model performance and recovery were associated with identity-selective units and the correspondence with the primate visual systems. Our present study not only reveals computational mechanisms underlying face learning but also points to strategies to restore impaired face learning.

Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus.

Recognizing familiar faces and learning new faces play an important role in social cognition. However, the underlying neural computational mechanisms remain unclear. Here, we record from single neurons in the human amygdala and hippocampus and find a greater neuronal representational distance between pairs of familiar faces than unfamiliar faces, suggesting that neural representations for familiar faces are more distinct. Representational distance increases with exposures to the same identity, suggesting that neural face representations are sharpened with learning and familiarization. Furthermore, representational distance is positively correlated with visual dissimilarity between faces, and exposure to visually similar faces increases representational distance, thus sharpening neural representations. Finally, we construct a computational model that demonstrates an increase in the representational distance of artificial units with training. Together, our results suggest that the neuronal population geometry, quantified by the representational distance, encodes face familiarity, similarity, and learning, forming the basis of face recognition and memory.

Co-designing interventions to strengthen the primary health care system for the management of hypertension and type 2 diabetes in China.

Background: Policy makers and researchers are tasked with exploring ways to strengthen primary health care (PHC) to address the growing burden of non-communicable diseases (NCDs). This study aims to use a co-design approach (i.e., meaningful involvement of research end users in study planning and design) to develop PHC interventions to improve the management of hypertension and type 2 diabetes (T2DM) in four study sites in China. Methods: The study adopted a three-step co-design approach, including (1) a two-round Delphi panel with health system and NCD professionals to identify prioritised health system challenges, (2) three co-design workshops (in each study site) with local health administrators, PHC providers, and residents with hypertension and/or T2DM, respectively, to develop interventions and identify factors influencing implementation, and (3) another round of co-design workshops with local health administrators to summarise findings and reach consensus. Qualitative synthesis was conducted to analyse results from the workshops. Findings: Thirteen experts were involved in the two-round Delphi panel, which identified three prioritised health system challenges, including limited capacities of PHC providers, suboptimal service quality and evaluation mechanisms, and unreliable health information systems. The co-design workshops involved 116 local stakeholders in 16 sessions (four in each site), and developed three groups of interventions to address the challenges: (1) empowering PHC providers through on-the-job training for capacity building; (2) empowering patient communities through health education on healthy lifestyles and NCD self-management; and (3) empowering health administrators through local health data monitoring and strengthening governance for local PHC programs. Site-specific interventions were also considered to cater for different local contexts. Several recommendations were further identified for the implementation of these interventions, emphasising the importance of local customisation, community participation, and cross-sectoral collaborations. Interpretation: By engaging multiple stakeholders in priority setting and solution generation, this study summarised several key areas for change in health workforce, service delivery, and health information. Future research should examine the effectiveness and implementation of these interventions to improve NCD management in PHC in China. Funding: This study is funded by National Health and Medical Research Council (NHMRC) Global Alliance for Chronic Diseases funding (APP1169757) and National Natural Science Foundation of China (72074065). Shangzhi Xiong is supported by University of New South Wales tuition fee scholarship.

A three-in-one nanoplatform with photo/chemodynamic activities combined with glutathione depletion for treating bacterial infections.

The misuse of antibiotics leading to bacterial multidrug resistance is responsible for severe infectious diseases and a significant cause of mortality worldwide, resulting in numerous human disasters. Photodynamic antibacterial therapy (PDAT) is a promising strategy against multiantibiotic-resistant bacteria, but its antibacterial activity is greatly limited by reduced glutathione (GSH) in bacteria. In this study, we constructed a nanoplatform through the formation of metal chelating complexes (FeP) between ferric and pyrophosphate ions, with subsequent adsorption of the photosensitizer ZnPc(COOH)8 (octa-carboxyl substituted zinc phthalocyanine) mediated by polylysine (PL) on the surface. The nanocomplexes FeP@PL:ZnPc(COOH)8 exhibited the capacity of GSH depletion and chemodynamic activity, which synergistically enhanced PDAT efficacy. FeP@PL:ZnPc(COOH)8 possessed the excellent antibacterial activity in vivo and in vitro, which might be attributed to the increased production of reactive oxygen species (ROS), reduced GSH level in bacteria, improved bacterial uptake, and enhanced destruction of the bacterial outer membrane. Moreover, FeP@PL:ZnPc(COOH)8 exhibited accelerated wound healing efficacy and the ability to recognize bacteria-infected wounds, rendering it an effective theranostic nanoplatform for bacterial infections. The construction strategy of nanocomplexes in this study holds theoretical and practical significance for high-efficiency synergistic photodynamic and chemodynamic antibacterial therapy.

Bioinformatics Illustrations Decoded by ChatGPT: The Good, The Bad, and The Ugly.

Emerging studies underscore the promising capabilities of large language model-based chatbots in conducting fundamental bioinformatics data analyses. The recent feature of accepting image-inputs by ChatGPT motivated us to explore its efficacy in deciphering bioinformatics illustrations. Our evaluation with examples in cancer research, including sequencing data analysis, multimodal network-based drug repositioning, and tumor clonal evolution, revealed that ChatGPT can proficiently explain different plot types and apply biological knowledge to enrich interpretations. However, it struggled to provide accurate interpretations when quantitative analysis of visual elements was involved. Furthermore, while the chatbot can draft figure legends and summarize findings from the figures, stringent proofreading is imperative to ensure the accuracy and reliability of the content.

Three anthocyanin-rich berry extracts regulate the in vitro digestibility of corn starch: Physicochemical properties, structure and α-amylase.

This study aimed to compare the regulatory effects of blue honeysuckle anthocyanins (BHA), blueberry anthocyanins (BBA), and blackcurrant anthocyanins (BCA) on the in vitro digestibility of corn starch in terms of starch physicochemical properties and structure, as well as α-amylase inhibition. The results revealed that adding all three anthocyanins lowered digestibility in the following order: BHA > BCA > BBA. The terminal digestibility (C∞) decreased from 73.84 % to 57.3 % with the addition of 10 % BHA, while the resistant starch (RS) content increased from 4.39 % to 48.82 %. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis indicated that anthocyanins and starch interacted through noncovalent bonds. Differential scanning calorimetry (DSC) analysis showed that the gelatinization enthalpy was dramatically lowered in all three anthocyanin groups, with 10 % BHA producing a 38.58 % drop. Rheological property analysis showed that anthocyanins increased the apparent viscosity and modulus with starch. The interaction between anthocyanin and α-amylase was mainly through the formation of hydrogen bonds and hydrophobic forces. This research provides theoretical guidance for developing low glycemic index (GI) anthocyanin starch-based foods.

Construction of chitosan-based thermosensitive composite hydrogels for recognizing and combined chemo-photodynamic elimination of Gram-negative bacterial infections.

Recently, rapid acquisition of bacterial resistance and consequent slow healing of infected wounds threaten human life and health. In this study, chitosan-based hydrogels and nanocomplexes ZnPc(COOH)8:PMB composed of photosensitizer ZnPc(COOH)8 and antibiotic polymyxin B (PMB) were integrated into a thermosensitive antibacterial platform ZnPc(COOH)8:PMB@gel. Interestingly, fluorescence and reactive oxygen species (ROS) of ZnPc(COOH)8:PMB@gel can be triggered by E. coli bacteria at 37 °C, but not by S. aureus bacteria, which gave the potential to simultaneously detect and treat Gram-negative bacteria. The survival rate for a certain amount of E. coli bacteria treated with ZnPc(COOH)8:PMB (ZnPc(COOH)8 2 µM) was decreased by approximately fivefold than that with either ZnPc(COOH)8 or PMB alone, indicating combined antibacterial efficacy. ZnPc(COOH)8:PMB@gel facilitated the complete healing of wounds infected with E. coli bacteria in about seven days, while over 10 % wounds treated with ZnPc(COOH)8 or PMB remained unhealed on the 9th day. ZnPc(COOH)8:PMB resulted in a threefold increase of ZnPc(COOH)8 fluorescence in E. coli bacteria suggesting enhanced uptake of ZnPc(COOH)8 for the intervention of PMB on membrane permeability. The construction principle of the thermosensitive antibacterial platform and the combined antimicrobial strategy can be applied to other photosensitizers and antibiotics for detection and treatment of wound infections.