U1 snRNP regulates cancer cell migration and invasion in vitro

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Language models can identify enzymatic binding sites in protein sequences

Recent advances in language modeling have had a tremendous impact on how we handle sequential data in science. Language architectures have emerged as a hotbed of innovation and creativity in natural language processing over the last decade, and have since gained prominence in modeling proteins and chemical processes, elucidating structural relationships from textual/sequential data. Surprisingly, some of these relationships refer to three-dimensional structural features, raising important questions on the dimensionality of the information encoded within sequential data. Here, we demonstrate that the unsupervised use of a language model architecture to a language representation of bio-catalyzed chemical reactions can capture the signal at the base of the substrate-binding site atomic interactions. This allows us to identify the three-dimensional binding site position in unknown protein sequences. The language representation comprises a reaction-simplified molecular-input line-entry system (SMILES) for substrate and products, and amino acid sequence information for the enzyme. This approach can recover, with no supervision, 52.13% of the binding site when considering co-crystallized substrate-enzyme structures as ground truth, vastly outperforming other attention-based models.

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

Computational pathology: A survey review and the way forward

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

Exploring the impact of circRNAs on cancer glycolysis: Insights into tumor progression and therapeutic strategies

Cancer cells exhibit altered metabolic pathways, prominently featuring enhanced glycolytic activity to sustain their rapid growth and proliferation. Dysregulation of glycolysis is a well-established hallmark of cancer and contributes to tumor progression and resistance to therapy. Increased glycolysis supplies the energy necessary for increased proliferation and creates an acidic milieu, which in turn encourages tumor cells' infiltration, metastasis, and chemoresistance. Circular RNAs (circRNAs) have emerged as pivotal players in diverse biological processes, including cancer development and metabolic reprogramming. The interplay between circRNAs and glycolysis is explored, illuminating how circRNAs regulate key glycolysis-associated genes and enzymes, thereby influencing tumor metabolic profiles. In this overview, we highlight the mechanisms by which circRNAs regulate glycolytic enzymes and modulate glycolysis. In addition, we discuss the clinical implications of dysregulated circRNAs in cancer glycolysis, including their potential use as diagnostic and prognostic biomarkers. All in all, in this overview, we provide the most recent findings on how circRNAs operate at the molecular level to control glycolysis in various types of cancer, including hepatocellular carcinoma (HCC), prostate cancer (PCa), colorectal cancer (CRC), cervical cancer (CC), glioma, non-small cell lung cancer (NSCLC), breast cancer, and gastric cancer (GC). In conclusion, this review provides a comprehensive overview of the significance of circRNAs in cancer glycolysis, shedding light on their intricate roles in tumor development and presenting innovative therapeutic avenues.

Local Self-Assembly of Dissipative Structures Sustained by Substrate Diffusion

The coupling between energy-consuming molecular processes and the macroscopic dimension plays an important role in nature and in the development of active matter. Here, we study the temporal evolution of a macroscopic system upon the local activation of a dissipative self-assembly process. Injection of surfactant molecules in a substrate-containing hydrogel results in the local substrate-templated formation of assemblies, which are catalysts for the conversion of substrate into waste. We show that the system develops into a macroscopic (pseudo-)non-equilibrium steady state (NESS) characterized by the local presence of energy-dissipating assemblies and persistent substrate and waste concentration gradients. For elevated substrate concentrations, this state can be maintained for more than 4 days. The studies reveal an interdependence between the dissipative assemblies and the concentration gradients: catalytic activity by the assemblies results in sustained concentration gradients and, vice versa, continuous diffusion of substrate to the assemblies stabilizes their size. The possibility to activate dissipative processes with spatial control and create long lasting non-equilibrium steady states enables dissipative structures to be studied in the space-time domain, which is of relevance for understanding biological systems and for the development of active matter.

Selective electrochemical nitrogen fixation to ammonia catalyzed by a novel microporous vanadium phosphonate via the distal pathway

Herein, a microporous organic-inorganic hybrid, vanadium phosphonate (VPn) material has been developed. With the combined advantages of the periodic organic-inorganic skeleton, a regular microporous channel with a crystalline pore wall, and good surface area, VPn displays electrocatalytic NRR activity with a selective NH3 yield (11.84 μg h-1 mgcat-1), faradaic efficiency of 26.29% at -0.6 V and high stability up to 15 h. The isotopic labeling experiment also verifies the electrosynthesis of NH3 both qualitatively and quantitatively. The theoretical simulation reveals that the associative distal route serves as the most favourable pathway during the NRR, with the first protonation step of *N2 leading to *NNH as the potential determining step.

The Microenvironment of Solid Tumors: Components and current challenges of Tumor-on-a-Chip models

Solid tumors represent the most common type of cancer in humans and are classified into sarcomas, lymphomas, and carcinomas based on the originating cells. Among these, carcinomas, which arise from epithelial and glandular cells lining the body's tissues, are the most prevalent. Around the world, a significant increase in the incidence of solid tumors is observed during recent years. In this context, efforts to discover more effective cancer treatments have led to a deeper understanding of the tumor microenvironment and its components. Currently, the interactions between cancer cells and elements of the tumor microenvironment are being intensely investigated. Remarkable progress in research is noted, largely due to the development of advanced in vitro models, such as Tumor-on-a-Chip models that assist in understanding and ultimately discovering new effective treatments for a specific type of cancer. The purpose of this paper is to provide a review of the tumor microenvironment and cancer cell components, along with the advances on tumor-on-a-chip models designed to mimic tumors, offering a perspective on the current state-of-the-art. Recent studies using this kind of microdevices that reproduce the tumor microenvironment have allowed a better understanding of the cancer and its treatments. Nevertheless, current applications of this technology present some limitations that must be overcome in order to achieve a broad application by researchers looking for a deeper knowledge of cancer and new strategies to improve current therapies.

Definitive engineering strength and fracture toughness of graphene through on-chip nanomechanics

Fail-safe design of devices requires robust integrity assessment procedures which are still absent for 2D materials, hence affecting transfer to applications. Here, a combined on-chip tension and cracking method, and associated data reduction scheme have been developed to determine the fracture toughness and strength of monolayer-monodomain-freestanding graphene. Myriads of specimens are generated providing statistical data. The crack arrest tests provide a definitive fracture toughness of 4.4 MPam . Tension on-chip provides Young's modulus of 950 GPa, fracture strain of 11%, and tensile strength up to 110 GPa, reaching a record of stored elastic energy ~6 GJ m-3 as confirmed by thermodynamics and quantized fracture mechanics. A ~ 1.4 nm crack size is often found responsible for graphene failure, connected to 5-7 pair defects. Micron-sized graphene membranes and smaller can be produced defect-free, and design rules can be based on 110 GPa strength. For larger areas, a fail-safe design should be based on a maximum 57 GPa strength.

Mass spectrometry-based proteomic landscape of rice reveals a post-transcriptional regulatory role of N6-methyladenosine

Rice is one of the most important staple food and model species in plant biology, yet its quantitative proteomes are largely uncharacterized. Here we quantify the relative protein levels of over 15,000 genes across major rice tissues using a tandem mass tag strategy followed by intensive fractionation and mass spectrometry. We identify tissue-specific and tissue-enriched proteins that are linked to the functional specificity of individual tissues. Proteogenomic comparison of rice and Arabidopsis reveals conserved proteome expression, which differs from mammals in that there is a strong separation of species rather than tissues. Notably, profiling of N6-methyladenosine (m6A) across the rice major tissues shows that m6A at untranslated regions is negatively correlated with protein abundance and contributes to the discordance between RNA and protein levels. We also demonstrate that our data are valuable for identifying novel genes required for regulating m6A methylation. Taken together, this study provides a paradigm for further research into rice proteogenome.

Deformation under flow and morphological recovery of cancer cells

The metastatic cascade includes a blood circulation step for cells detached from the primary tumor. This stage involves significant shear stress as well as large and fast deformation as the cells circulate through the microvasculature. These mechanical stimuli are well reproduced in microfluidic devices. However, the recovery dynamics after deformation is also pivotal to understand how a cell can pass through the multiple capillary constrictions encountered during a single hemodynamic cycle. The microfluidic system developed in this work allows single cell recovery to be studied under flow-free conditions following pressure-actuated cell deformation inside constricted microchannels. We used three breast cancer cell lines - namely MCF-7, SK-BR3 and MDA-MB231 - as cellular models representative of different cancer phenotypes. Changing the size of the constriction allows exploration of moderate to strong deformation regimes, the latter being associated with the formation of plasma membrane blebs. In the regime of moderate deformation, all cell types display a fast elastic recovery behavior followed by a slower viscoelastic regime, well described by a double exponential decay. Among the three cell types, cells of the mesenchymal phenotype, i.e. the MDA-MB231 cells, are softer and the most fluid-like, in agreement with previous studies. Our main finding here is that the fast elastic recovery regime revealed by our novel microfluidic system is under the control of cell contractility ensured by the integrity of the cell cortex. Our results suggest that the cell cortex plays a major role in the transit of circulating tumor cells by allowing their fast morphological recovery after deformation in blood capillaries.

Architecture design and advanced manufacturing of heart-on-a-chip: scaffolds, stimulation and sensors

Heart-on-a-chip (HoC) has emerged as a highly efficient, cost-effective device for the development of engineered cardiac tissue, facilitating high-throughput testing in drug development and clinical treatment. HoC is primarily used to create a biomimetic microphysiological environment conducive to fostering the maturation of cardiac tissue and to gather information regarding the real-time condition of cardiac tissue. The development of architectural design and advanced manufacturing for these "3S" components, scaffolds, stimulation, and sensors is essential for improving the maturity of cardiac tissue cultivated on-chip, as well as the precision and accuracy of tissue states. In this review, the typical structures and manufacturing technologies of the "3S" components are summarized. The design and manufacturing suggestions for each component are proposed. Furthermore, key challenges and future perspectives of HoC platforms with integrated "3S" components are discussed. Architecture design concepts of scaffolds, stimulation and sensors in chips.

Chemo-, Regio- and Stereoselective Preparation of (Z)-2-Butene-1,4-Diol Monoesters via Pd-Catalyzed Decarboxylative Acyloxylation

(Z)-alkenes are useful synthons but thermodynamically less stable than their (E)-isomers and typically more difficult to prepare. The synthesis of 1,4-hetero-bifunctionalized (Z)-alkenes is particularly challenging due to the inherent regio- and stereoselectivity issues. Herein we demonstrate a general, chemoselective and direct synthesis of (Z)-2-butene-1,4-diol monoesters. The protocol operates within a Pd-catalyzed decarboxylative acyloxylation regime involving vinyl ethylene carbonates (VECs) and various carboxylic acids as the reaction partners under mild and operationally attractive conditions. The newly developed process allows access to a structurally diverse pool of (Z)-2-butene-1,4-diol monoesters in good yields and with excellent regio- and stereoselectivity. Various synthetic transformations of the obtained (Z)-2-butene-1,4-diol monoesters demonstrate how these synthons are of great use to rapidly diversify the portfolio of these formal desymmetrized (Z)-alkenes.

Recent advances in graphene-based electroanalytical devices for healthcare applications

Graphene, with its outstanding mechanical, electrical, and biocompatible properties, stands out as an emerging nanomaterial for healthcare applications, especially in building electroanalytical biodevices. With the rising prevalence of chronic diseases and infectious diseases, such as the COVID-19 pandemic, the demand for point-of-care testing and remote patient monitoring has never been greater. Owing to their portability, ease of manufacturing, scalability, and rapid and sensitive response, electroanalytical devices excel in these settings for improved healthcare accessibility, especially in resource-limited settings. The development of different synthesis methods yielding large-scale graphene and its derivatives with controllable properties, compatible with device manufacturing - from lithography to various printing methods - and tunable electrical, chemical, and electrochemical properties make it an attractive candidate for electroanalytical devices. This review article sheds light on how graphene-based devices can be transformative in addressing pressing healthcare needs, ranging from the fundamental understanding of biology in in vivo and ex vivo studies to early disease detection and management using in vitro assays and wearable devices. In particular, the article provides a special focus on (i) synthesis and functionalization techniques, emphasizing their suitability for scalable integration into devices, (ii) various transduction methods to design diverse electroanalytical device architectures, (iii) a myriad of applications using devices based on graphene, its derivatives, and hybrids with other nanomaterials, and (iv) emerging technologies at the intersection of device engineering and advanced data analytics. Finally, some of the major hurdles that graphene biodevices face for translation into clinical applications are discussed.

Extraction and characterization of exosomes from the exhaled breath condensate and sputum of lung cancer patients and vulnerable tobacco consumers-potential noninvasive diagnostic biomarker source

Noninvasive sample sources of exosomes, such as exhaled breath and sputum, which are in close proximity to the tumor microenvironment and may contain biomarkers indicative of lung cancer, are far more permissive than invasive sample sources for biomarker screening. Standardized exosome extraction and characterization approaches for low-volume noninvasive samples are critically needed. We isolated and characterized exhaled breath condensate (EBC) and sputum exosomes from healthy nonsmokers (n= 30), tobacco smokers (n= 30), and lung cancer patients (n= 40) and correlated the findings with invasive sample sources. EBC samples were collected by using commercially available R-Tubes. To collect sputum samples the participants were directed to take deep breaths, hold their breath, and cough in a collection container. Dynamic light scattering, nanoparticle tracking analysis, and transmission electron microscopy were used to evaluate the exosome morphology. Protein isolation, western blotting, exosome quantification via EXOCET, and Fourier transform infrared spectroscopy were performed for molecular characterization. Exosomes were successfully isolated from EBC and sputum samples, and their yields were adequate and sufficiently pure for subsequent downstream processing and characterization. The exosomes were confirmed based on their size, shape, and surface marker expression. Remarkably, cancer exosomes were the largest in size not only in the plasma subgroups, but also in the EBC (p < 0.05) and sputum (p= 0.0036) subgroups, according to our findings. A significant difference in exosome concentrations were observed between the control sub-groups (p < 0.05). Our research confirmed that exosomes can be extracted from noninvasive sources, such as EBC and sputum, to investigate lung cancer diagnostic biomarkers for research, clinical, and early detection in smokers.

Evaluating the probiotic effects of spraying lactiplantibacillus plantarum P-8 in neonatal piglets

BACKGROUND

Gut microbes play an important role in the growth and health of neonatal piglets. Probiotics can promote the healthy growth of neonatal piglets by regulating their gut microbes. The study investigated the effects of spraying Lactiplantibacillus plantarum P-8 (L. plantarum P-8) fermentation broth on the growth performance and gut microbes of neonatal piglets.

RESULTS

The animals were randomly divided into probiotics groups (109 neonatal piglets) and control groups (113 neonatal piglets). The probiotics group was sprayed with L. plantarum P-8 fermented liquid from 3 day before the expected date of the sow to the 7-day-old of piglets, while the control group was sprayed with equal dose of PBS. Average daily gain (ADG), immune and antioxidant status and metagenome sequencing were used to assess the changes in growth performance and gut microbiota of neonatal piglets. The results showed that L. plantarum P-8 treatment significantly improved the average daily gain (P < 0.05) of neonatal piglets. L. plantarum P-8 increased the activities of CAT and SOD but reduced the levels of IL-2 and IL-6, effectively regulating the antioxidant capacity and immunity in neonatal piglets. L. plantarum P-8 adjusted the overall structure of gut microflora improving gut homeostasis to a certain extent, and significantly increased the relative abundance of gut beneficial bacteria such as L. mucosae and L. plantarum.

CONCLUSION

Spraying L. plantarum P-8 can be a feasible and effective probiotic intervention not only improving the growth of neonatal piglets, regulating the antioxidant capacity and immunity of neonatal piglets, but also improving the gut homeostasis to a certain extent.

Species-level identification of enterotype-specific microbial markers for colorectal cancer and adenoma

Enterotypes have been shown to be an important factor for population stratification based on gut microbiota composition, leading to a better understanding of human health and disease states. Classifications based on compositional patterns will have implications for personalized microbiota-based solutions. There have been limited enterotype based studies on colorectal adenoma and cancer. Here, an enterotype-based meta-analysis of fecal shotgun metagenomic studies was performed, including 1579 samples of healthy controls (CTR), colorectal adenoma (ADN) and colorectal cancer (CRC) in total. Gut microbiota of healthy people were clustered into three enterotypes (Ruminococcus-, Bacteroides- and Prevotella-dominated enterotypes). Reference-based enterotype assignments were performed for CRC and ADN samples, using the supervised machine learning algorithm, K-nearest neighbors. Differential abundance analyses and random forest classification were conducted on each enterotype between healthy controls and CRC-ADN groups, revealing novel enterotype-specific microbial markers for non-invasive CRC screening strategies. Furthermore, we identified microbial species unique to each enterotype that play a role in the production of secondary bile acids and short-chain fatty acids, unveiling the correlation between cancer-associated gut microbes and dietary patterns. The enterotype-based approach in this study is promising in elucidating the mechanisms of differential gut microbiome profiles, thereby improving the efficacy of personalized microbiota-based solutions.

Device modeling of high performance and eco-friendly FAMASnI 3 based perovskite solar cell

Developing environmentally friendly and highly efficient inverted perovskite solar cells (PSCs) encounters significant challenges, specifically the potential toxicity and degradation of thin films in hybrid organic-inorganic photovoltaics (PV). We employed theoretical design strategies that produce hysteresis-reduced, efficient, and stable PSCs based on composition and interface engineering. The devices include a mixed-organic-cation perovskite formamidinium methylammonium tin iodide ( FAMASnI 3 ) as an absorber layer and zinc oxide (ZnO) together with a passivation film phenyl-C61-butyric acid methyl ester (PC 61 BM ) as a double-electron transport layer (DETL). Furthermore, a nickel oxide (NiO) layer and a trap-free junction copper iodide (CuI) are used as a double-hole transport layer (DHTL). The optoelectronic characterization measurements were carried out to understand the physical mechanisms that govern the operation of the devices. The high power conversion efficiencies (PCEs) of 24.27% and 23.50% were achieved in 1D and 2D simulations, respectively. This study illustrates that composition and interface engineering enable eco-friendly perovskite solar cells, improving performance and advancing clean energy.

Surface charge density and induced currents by self-charging sliding drops

Spontaneous charge separation in drops sliding over a hydrophobized insulator surface is a well-known phenomenon and lots of efforts have been made to utilize this effect for energy harvesting. For maximizing the efficiency of such devices, a comprehensive understanding of the dewetted surface charge would be required to quantitatively predict the electric current signals, in particular for drop sequences. Here, we use a method based on mirror charge detection to locally measure the surface charge density after drops move over a hydrophobic surface. For this purpose, we position a metal electrode beneath the hydrophobic substrate to measure the capacitive current induced by the moving drop. Furthermore, we investigate drop-induced charging on different dielectric surfaces together with the surface neutralization processes. The surface neutralizes over a characteristic time, which is influenced by the substrate and the surrounding environment. We present an analytical model that describes the slide electrification using measurable parameters such as the surface charge density and its neutralization time. Understanding the model parameters and refining them will enable a targeted optimization of the efficiency in solid-liquid charge separation.

Evaluating regression and probabilistic methods for ECG-based electrolyte prediction

Imbalances in electrolyte concentrations can have severe consequences, but accurate and accessible measurements could improve patient outcomes. The current measurement method based on blood tests is accurate but invasive and time-consuming and is often unavailable for example in remote locations or an ambulance setting. In this paper, we explore the use of deep neural networks (DNNs) for regression tasks to accurately predict continuous electrolyte concentrations from electrocardiograms (ECGs), a quick and widely adopted tool. We analyze our DNN models on a novel dataset of over 290,000 ECGs across four major electrolytes and compare their performance with traditional machine learning models. For improved understanding, we also study the full spectrum from continuous predictions to a binary classification of extreme concentration levels. Finally, we investigate probabilistic regression approaches and explore uncertainty estimates for enhanced clinical usefulness. Our results show that DNNs outperform traditional models but model performance varies significantly across different electrolytes. While discretization leads to good classification performance, it does not address the original problem of continuous concentration level prediction. Probabilistic regression has practical potential, but our uncertainty estimates are not perfectly calibrated. Our study is therefore a first step towards developing an accurate and reliable ECG-based method for electrolyte concentration level prediction-a method with high potential impact within multiple clinical scenarios.

Enhancing sorption kinetics by oriented and single crystalline array-structured ZSM-5 film on monoliths

To enhance the reaction kinetics without sacrificing activity in porous materials, one potential solution is to utilize the anisotropic distribution of pores and channels besides enriching active centers at the reactive surfaces. Herein, by designing a unique distribution of oriented pores and single crystalline array structures in the presence of abundant acid sites as demonstrated in the ZSM-5 nanorod arrays grown on monoliths, both enhanced dynamics and improved capacity are exhibited simultaneously in propene capture at low temperature within a short duration. Meanwhile, the ZSM-5 array also helps mitigate the long-chain HCs and coking formation due to the enhanced diffusion of reactants in and reaction products out of the array structures. Further integrating the ZSM-5 array with Co3O4 nanoarray enables comprehensive propene removal throughout a wider temperature range. The array structured film design could offer energy-efficient solutions to overcome both sorption and reaction kinetic restrictions in various solid porous materials for various energy and chemical transformation applications.

Electrolytic Hydrogen Release from Hydrogen Boride Sheets

Solid-state hydrogen storage materials are safe and lightweight hydrogen carriers. Among the various solid-state hydrogen carriers, hydrogen boride (HB) sheets possess a high gravimetric hydrogen capacity (8.5 wt%). However, heating at high temperatures and/or strong ultraviolet illumination is required to release hydrogen (H2) from HB sheets. In this study, the electrochemical H2 release from HB sheets using a dispersion system in an organic solvent without other proton sources is investigated. H2 molecules are released from the HB sheets under the application of a cathodic potential. The Faradaic efficiency for H2 release from HB sheets reached >90%, and the onset potential for H2 release is -0.445 V versus Ag/Ag+, which is more positive than those from other proton sources, such as water or formic acid, under the same electrochemical conditions. The total electrochemically released H2 in a long-time experiment reached ≈100% of the hydrogen capacity of HB sheets. The H2 release from HB sheets is driven by a small bias; thus, they can be applied as safe and lightweight hydrogen carriers with economical hydrogen release properties.

Embedding Bifurcations into Pneumatic Artificial Muscle

Harnessing complex body dynamics has long been a challenge in robotics, particularly when dealing with soft dynamics, which exhibit high complexity in interacting with the environment. Recent studies indicate that these dynamics can be used as a computational resource, exemplified by the McKibben pneumatic artificial muscle, a common soft actuator. This study demonstrates that bifurcations, including periodic and chaotic dynamics, can be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics not present in training data can be embedded through bifurcation embedment, implying the capability to incorporate various qualitatively different patterns into pneumatic artificial muscle without the need to design and learn all required patterns explicitly. Thus, this study introduces a novel approach to simplify robotic devices and control training by reducing reliance on external pattern generators and the amount and types of training data needed for control.

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative models. These advancements are reshaping data-driven planning strategies, practices, and approaches, thereby facilitating the achievement of environmental sustainability goals. This transformative wave signals a fundamental shift - marked by the synergistic operation of artificial intelligence (AI), artificial intelligence of things (AIoT), and urban digital twin (UDT) technologies. While previous research has largely explored urban AI, urban AIoT, and UDT in isolation, a significant knowledge gap exists regarding their synergistic interplay, collaborative integration, and collective impact on data-driven environmental planning in the dynamic context of sustainable smart cities. To address this gap, this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies, models, and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart cities. Central to this study are four guiding research questions: 1. What theoretical and practical foundations underpin the convergence of AI, AIoT, UDT, data-driven planning, and environmental sustainability in sustainable smart cities, and how can these components be synthesized into a novel comprehensive framework? 2. How does integrating AI and AIoT reshape the landscape of data-driven planning to improve the environmental performance of sustainable smart cities? 3. How can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities? 4. What challenges and barriers arise in integrating and implementing AI, AIoT, and UDT in data-driven environmental urban planning, and what strategies can be devised to surmount or mitigate them? Methodologically, this study involves a rigorous analysis and synthesis of studies published between January 2019 and December 2023, comprising an extensive body of literature totaling 185 studies. The findings of this study surpass mere interdisciplinary theoretical enrichment, offering valuable insights into the transformative potential of integrating AI, AIoT, and UDT technologies to advance sustainable urban development practices. By enhancing data-driven environmental planning processes, these integrated technologies and models offer innovative solutions to address complex environmental challenges. However, this endeavor is fraught with formidable challenges and complexities that require careful navigation and mitigation to achieve desired outcomes. This study serves as a comprehensive reference guide, spurring groundbreaking research endeavors, stimulating practical implementations, informing strategic initiatives, and shaping policy formulations in sustainable urban development. These insights have profound implications for researchers, practitioners, and policymakers, providing a roadmap for fostering resiliently designed, technologically advanced, and environmentally conscious urban environments.

Carbon Dots: A Bright Future as Anticounterfeiting Encoding Agents

Counterfeit products and data vulnerability present significant challenges in contemporary society. Hence, various methods and technologies are explored for anticounterfeiting encoding, with luminescent tracers, particularly luminescent carbon dots (CDs), emerging as a notable solution. CDs offer promising contributions to product security, environmental sustainability, and the circular economy. This critical review aims to highlight the luminescence responsiveness of CDs to physical and chemical stimuli, achieved through nanoengineering their chemical structure. The discussion will delve into the various tunable luminescence mechanisms and decay times of CDs, investigating preferential excitations such as up-conversion, delayed fluorescence, fluorescence, room temperature phosphorescence, persistent luminescence, energy and charge transfer, as well as photo-chemical interactions. These insights are crucial for advancing anticounterfeiting solutions. Following this exploration, a systematic review will focus on the research of luminescent CDs' smart encoding applications, encompassing anticounterfeiting, product tracing, quality certification, and information encryption. Finally, the review will address key challenges in implementing CDs-based technology, providing specific insights into strategies aimed at maximizing their stability and efficacy in anticounterfeiting encoding applications.

Reprogrammable Magnetic Soft Actuators with Microfluidic Functional Modules via Pixel-Assembly

Magnetic soft actuators and robots have attracted considerable attention in biomedical applications due to their speedy response, programmability, and biocompatibility. Despite recent advancements, the fabrication process of magnetic actuators and the reprogramming approach of their magnetization profiles continue to pose challenges. Here, a facile fabrication strategy is reported based on arrangements and distributions of reusable magnetic pixels on silicone substrates, allowing for various magnetic actuators with customizable architectures, arbitrary magnetization profiles, and integration of microfluidic technology. This approach enables intricate configurations with decent deformability and programmability, as well as biomimetic movements involving grasping, swimming, and wriggling in response to magnetic actuation. Moreover, microfluidic functional modules are integrated for various purposes, such as on/off valve control, curvature adjustment, fluid mixing, dynamic microfluidic architecture, and liquid delivery robot. The proposed method fulfills the requirements of low-cost, rapid, and simplified preparation of magnetic actuators, since it eliminates the need to sustain pre-defined deformations during the magnetization process or to employ laser heating or other stimulation for reprogramming the magnetization profile. Consequently, it is envisioned that magnetic actuators fabricated via pixel-assembly will have broad prospects in microfluidics and biomedical applications.

A High-Entropy Single-Atom Catalyst Toward Oxygen Reduction Reaction in Acidic and Alkaline Conditions

The design of high-entropy single-atom catalysts (HESAC) with 5.2 times higher entropy compared to single-atom catalysts (SAC) is proposed, by using four different metals (FeCoNiRu-HESAC) for oxygen reduction reaction (ORR). Fe active sites with intermetallic distances of 6.1 Å exhibit a low ORR overpotential of 0.44 V, which originates from weakening the adsorption of OH intermediates. Based on density functional theory (DFT) findings, the FeCoNiRu-HESAC with a nitrogen-doped sample were synthesized. The atomic structures are confirmed with X-ray photoelectron spectroscopy (XPS), X-ray absorption (XAS), and scanning transmission electron microscopy (STEM). The predicted high catalytic activity is experimentally verified, finding that FeCoNiRu-HESAC has overpotentials of 0.41 and 0.37 V with Tafel slopes of 101 and 210 mVdec-1 at the current density of 1 mA cm-2 and the kinetic current densities of 8.2 and 5.3 mA cm-2, respectively, in acidic and alkaline electrolytes. These results are comparable with Pt/C. The FeCoNiRu-HESAC is used for Zinc-air battery applications with an open circuit potential of 1.39 V and power density of 0.16 W cm-2. Therefore, a strategy guided by DFT is provided for the rational design of HESAC which can be replaced with high-cost Pt catalysts toward ORR and beyond.

Electrogenic sulfur oxidation mediated by cable bacteria and its ecological effects

At the sediment-water interfaces, filamentous cable bacteria transport electrons from sulfide oxidation along their filaments towards oxygen or nitrate as electron acceptors. These multicellular bacteria belonging to the family Desulfobulbaceae thus form a biogeobattery that mediates redox processes between multiple elements. Cable bacteria were first reported in 2012. In the past years, cable bacteria have been found to be widely distributed across the globe. Their potential in shaping the surface water environments has been extensively studied but is not fully elucidated. In this review, the biogeochemical characteristics, conduction mechanisms, and geographical distribution of cable bacteria, as well as their ecological effects, are systematically reviewed and discussed. Novel insights for understanding and applying the role of cable bacteria in aquatic ecology are summarized.

Innovative approaches to wound healing: insights into interactive dressings and future directions

The objective of this review is to provide an up-to-date and all-encompassing account of the recent advancements in the domain of interactive wound dressings. Considering the gap between the achieved and desired clinical outcomes with currently available or under-study wound healing therapies, newer more specific options based on the wound type and healing phase are reviewed. Starting from the comprehensive description of the wound healing process, a detailed classification of wound dressings is presented. Subsequently, we present an elaborate and significant discussion describing interactive (unconventional) wound dressings. Latter includes biopolymer-based, bioactive-containing and biosensor-based smart dressings, which are discussed in separate sections together with their applications and limitations. Moreover, recent (2-5 years) clinical trials, patents on unconventional dressings, marketed products, and other information on advanced wound care designs and techniques are discussed. Subsequently, the future research direction is highlighted, describing peptides, proteins, and human amniotic membranes as potential wound dressings. Finally, we conclude that this field needs further development and offers scope for integrating information on the healing process with newer technologies.

Electrocatalytic oxidation of pyrrole on a quasi-reversible silver nanodumbbell particle surface for supramolecular porphyrin production

Photoactive supramolecular porphyrin assemblies are attractive molecules for light-harvesting applications. This is due to their relatively non-toxicity, biological activities and charge and energy exchange characteristics. However, the extreme cost associated with their synthesis and requirements for toxic organic solvents during purification pose a challenge to the sustainability characteristics of their applications. This work presents the first report on the sustainable synthesis, spectroscopic and photophysical characterizations of a near-infrared (NIR) absorbing Ca(II)-meso-tetrakis (4-hydroxyphenyl)porphyrin using an electrolyzed pyrrole solution. The latter was obtained by cycling the pyrrole solution across the silver nanodumbbell particle surface at room temperature. The electrolyzed solution condensed readily with acidified p-hydroxybenzaldehyde, producing the targeted purple porphyrin. The non-electrolyzed pyrrole solution formed a green substance with significantly different optical properties. Remarkable differences were observed in the voltammograms of the silver nanodumbbell particles and those of the conventional gold electrode during the pyrrole cycling, suggesting different routes of porphyrin formation. The rationale behind these formations and the associated mechanisms were extensively discussed. Metalation with aqueous Ca2+ ion caused a Stokes shift of 38.75 eV. The current study shows the advantage of the electrochemical method towards obtaining sustainable light-harvesting porphyrin at room temperature without the need for high-energy-dependent conventional processes.

Time Refraction and Time Reflection above Critical Angle for Total Internal Reflection

We study the time reflection and time refraction of waves caused by a spatial interface with a medium undergoing a sudden temporal change in permittivity. We show that monochromatic waves are transformed into a pulse by the permittivity change, and that time reflection is enhanced at the vicinity of the critical angle for total internal reflection. In this regime, we find that the evanescent field is transformed into a propagating pulse by the sudden change in permittivity. These effects display enhancement of the time reflection and high sensitivity near the critical angle, paving the way to experiments on time reflection and photonic time crystals at optical frequencies.

Advances in biomimetic nanomaterial delivery systems: harnessing nature's inspiration for targeted drug delivery

The properties of nanomaterials make them promising and advantageous for use in drug delivery systems, but challenges arise from the immune system's recognition of exogenous nanoparticles, leading to their clearance and reduced targeting efficiency. Drawing inspiration from nature, this paper explores biomimetic strategies to transform recognizable nanomaterials into a "camouflaged state." The focal point of this paper is the exploration of bionic nanoparticles, with a focus on cell membrane-coated nanoparticles. These biomimetic structures, particularly those mimicking red blood cells (RBCs), white blood cells (WBCs), platelets, and cancer cells, demonstrate enhanced drug delivery efficiency and prolonged circulation. This article underscores the versatility of these biomimetic structures across diverse diseases and explores the use of hybrid cell membrane-coated nanoparticles as a contemporary trend. This review also investigated exosomes and protein bionic nanoparticles, emphasizing their potential for specific targeting, immune evasion, and improved therapeutic outcomes. We expect that this continued development based on biomimetic nanomaterials will contribute to the efficiency and safety of disease treatment.

Hole transport layer engineering in high performance quasi-2D perovskite blue light emitting diodes

Quasi-2D perovskites have emerged as highly promising materials for application in perovskite light-emitting diodes (PeLEDs), garnering significant attention due to their outstanding semiconductor properties. These materials boast an inherent multi-quantum well structure that imparts a robust confinement effect, particularly advantageous for blue emission. However, the development of blue emitters utilizing quasi-2D perovskites encounters challenges, notably colour instability, multipeak emission, and suboptimal fluorescence yield. The hole transfer layer (HTL) on which the perovskite layer is deposited in PeLEDs further affects the performance and efficiency. In this review, we delve into the evolution of blue PeLEDs and elucidate the optical properties of quasi-2D perovskites with the primary focus on HTL materials. We explore different HTL materials like PEDOT:PSS, metal oxides, and conjugated polyelectrolytes as well as ionic liquids, and their role in enhancing the colour stability, minimizing interfacial defects and increasing the fluorescence yield. This review endeavours to provide a holistic perspective of the different HTLs and serve as a valuable reference for researchers navigating the realm of HTL engineering towards the realization of high-performance blue quasi-2D PeLEDs.

A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has been widely used to characterize cell types based on their average gene expression profiles. However, most studies do not consider cell type-specific variation across donors. Modelling this cell type-specific inter-individual variation could help elucidate cell type-specific biology and inform genes and cell types underlying complex traits. We therefore develop a new model to detect and quantify cell type-specific variation across individuals called CTMM (Cell Type-specific linear Mixed Model). We use extensive simulations to show that CTMM is powerful and unbiased in realistic settings. We also derive calibrated tests for cell type-specific interindividual variation, which is challenging given the modest sample sizes in scRNA-seq. We apply CTMM to scRNA-seq data from human induced pluripotent stem cells to characterize the transcriptomic variation across donors as cells differentiate into endoderm. We find that almost 100% of transcriptome-wide variability between donors is differentiation stage-specific. CTMM also identifies individual genes with statistically significant stage-specific variability across samples, including 85 genes that do not have significant stage-specific mean expression. Finally, we extend CTMM to partition interindividual covariance between stages, which recapitulates the overall differentiation trajectory. Overall, CTMM is a powerful tool to illuminate cell type-specific biology in scRNA-seq.

The neuroprotective effects of SFGDI on sirtuin 3-related oxidative stress by regulating the Sirt3/SOD/ROS pathway and energy metabolism in BV2 cells

Recently, the investigation of neuroprotective peptides has gained attention in addressing memory impairment and cognitive decline. Although the potential neuroprotective peptide Serine-Phenylalanine-Glycine-Aspartic acid-Isoleucine (SFGDI) has been identified from sea cucumber, the molecular mechanisms remain unclear. This study was conducted to explore the neuroprotection of SFGDI against 3-TYP-induced oxidative stress in BV2 cells. The results showed a retention rate of 76.70% during in vitro simulated gastrointestinal digestion and an absorption rate of 10.41% in a rat-everted gut sac model for SFGDI. Two hours following the administration of SFGDI via gavage in mice, a notable fluorescence was observed in the brain, indicating a potential neuroprotection of SFGDI through its interactions with nerve cells. By utilizing a model of oxidative stress injury induced by 3-TYP in BV2 cells, it was determined that pretreatment with SFGDI (50-200 μg mL-1) resulted in a dose-dependent reduction in the acetylated SOD level, leading to enhanced SOD activity and reduced levels of ROS and MDA. In addition, this pretreatment triggered an increase in unsaturated lipid levels, which helped maintain the intracellular lipid metabolism balance and preserve the mitochondrial function and glycolysis levels to regulate energy metabolism. The results of this study indicate that SFGDI demonstrates neuroprotective properties through its modulation of the Sirt3/SOD/ROS pathway, regulation of lipid metabolism, and enhancement of energy metabolism in BV2 cells. These findings suggest potential novel therapeutic approaches for addressing Sirt3-related memory deficits and neurodegenerative disorders.

Current Developments in Emerging Lanthanide-Doped Persistent Luminescent Scintillators and Their Applications

Lanthanide-doped scintillators have the ability to convert the absorbed X-ray irradiation into ultraviolet (UV), visible (Vis), or near-infrared (NIR) light. Lanthanide-doped scintillators with excellent persistent luminescence (PersL) are emerging as a new class of PersL materials recently. They have attracted great attention due to their unique "self-luminescence" characteristic and potential applications. In this review, we comb through and focus on current developments of lanthanide-doped persistent luminescent scintillators (PersLSs), including their PersL mechanism, synthetic methods, tuning of PersL properties (e. g. emission wavelength, intensity, and duration time), as well as their promising applications (e. g. information storage, encryption, anti-counterfeiting, bio-imaging, and photodynamic therapy). We hope this review will provide valuable guidance for the future development of PersLSs.

Chip collection of hepatocellular carcinoma based on O2 heterogeneity from patient tissue

Hepatocellular carcinoma frequently recurs after surgery, necessitating personalized clinical approaches based on tumor avatar models. However, location-dependent oxygen concentrations resulting from the dual hepatic vascular supply drive the inherent heterogeneity of the tumor microenvironment, which presents challenges in developing an avatar model. In this study, tissue samples from 12 patients with hepatocellular carcinoma are cultured directly on a chip and separated based on preference of oxygen concentration. Establishing a dual gradient system with drug perfusion perpendicular to the oxygen gradient enables the simultaneous separation of cells and evaluation of drug responsiveness. The results are further cross-validated by implanting the chips into mice at various oxygen levels using a patient-derived xenograft model. Hepatocellular carcinoma cells exposed to hypoxia exhibit invasive and recurrent characteristics that mirror clinical outcomes. This chip provides valuable insights into treatment prognosis by identifying the dominant hepatocellular carcinoma type in each patient, potentially guiding personalized therapeutic interventions.

Directional Localization from a Magnetic Field in Moiré Systems

Moiré materials provide a highly tunable platform in which novel electronic phenomena can emerge. We study strained moiré materials in a uniform magnetic field and predict highly anisotropic electrical conductivity that switches easy axis as magnetic field or strain is varied. The dramatic anisotropy reflects one-dimensional localization (directional localization) of the electron wave functions along a crystal axis due to quantum interference effects. This can be understood in an effective one-dimensional quasiperiodic Aubry-André-Harper-like model, or in a complementary semiclassical picture. This phenomenon should be observable in strained moiré materials at realistic fields and low strain disorder, as well as unstrained systems with anisotropic Fermi surfaces.

Testing Theories of the Glass Transition with the Same Liquid but Many Kinetic Rules

We study the glass transition by exploring a broad class of kinetic rules that can significantly modify the normal dynamics of supercooled liquids while maintaining thermal equilibrium. Beyond the usual dynamics of liquids, this class includes dynamics in which a fraction (1-f_{R}) of the particles can perform pairwise exchange or "swap moves," while a fraction f_{P} of the particles can move only along restricted directions. We find that (i) the location of the glass transition varies greatly but smoothly as f_{P} and f_{R} change and (ii) it is governed by a linear combination of f_{P} and f_{R}. (iii) Dynamical heterogeneities (DHs) are not governed by the static structure of the material; their magnitude correlates instead with the relaxation time. (iv) We show that a recent theory for temporal growth of DHs based on thermal avalanches holds quantitatively throughout the (f_{R},f_{P}) diagram. These observations are negative items for some existing theories of the glass transition, particularly those reliant on growing thermodynamic order or locally favored structure, and open new avenues to test other approaches, as we illustrate.

Dynamics of the Molecular Geometric Phase

The fate of the molecular geometric phase in an exact dynamical framework is investigated with the help of the exact factorization of the wave function and a recently proposed quantum hydrodynamical description of its dynamics. An instantaneous, gauge-invariant phase is introduced for arbitrary paths in nuclear configuration space in terms of hydrodynamical variables, and shown to reduce to the adiabatic geometric phase when the state is adiabatic and the path is closed. The evolution of the closed-path phase over time is shown to adhere to a Maxwell-Faraday induction law, with nonconservative forces arising from the electron dynamics that play the role of electromotive forces. We identify the pivotal forces that are able to change the value of the phase, thereby challenging any topological argument. Nonetheless, negligible changes in the phase occur when the local dynamics along the probe loop is approximately adiabatic. That is, the geometric phase effects that arise in an adiabatic limiting situation remain suitable to effectively describe certain dynamic observables.

CryoEM reveals the structure of an archaeal pilus involved in twitching motility

Amongst the major types of archaeal filaments, several have been shown to closely resemble bacterial homologues of the Type IV pili (T4P). Within Sulfolobales, member species encode for three types of T4P, namely the archaellum, the UV-inducible pilus system (Ups) and the archaeal adhesive pilus (Aap). Whereas the archaellum functions primarily in swimming motility, and the Ups in UV-induced cell aggregation and DNA-exchange, the Aap plays an important role in adhesion and twitching motility. Here, we present a cryoEM structure of the Aap of the archaeal model organism Sulfolobus acidocaldarius. We identify the component subunit as AapB and find that while its structure follows the canonical T4P blueprint, it adopts three distinct conformations within the pilus. The tri-conformer Aap structure that we describe challenges our current understanding of pilus structure and sheds new light on the principles of twitching motility.

Lanthanide-based nanomaterials for temperature sensing in the near-infrared spectral region: illuminating progress and challenges

Being first proposed as a method to overcome limitations associated with conventional contact thermometers, luminescence thermometry has been extensively studied over the past two decades as a sensitive and fast approach to remote and minimally invasive thermal sensing. Herein, lanthanide (Ln)-doped nanoparticles (Ln-NPs) have been identified as particularly promising candidates, given their outstanding optical properties. Known primarily for their upconversion emission, Ln-NPs have also been recognized for their ability to be excited with and emit in the near-infrared (NIR) regions matching the NIR transparency windows. This sparked the emergence of the development of NIR-NIR Ln-NPs for a wide range of temperature-sensing applications. The shift to longer excitation and emission wavelengths resulted in increased efforts being put into developing nanothermometers for biomedical applications, however most research is still preclinical. This mini-review outlines and addresses the challenges that limit the reliability and implementation of luminescent nanothermometers to real-life applications. Through a critical look into the recent developments from the past 4 years, we highlight attempts to overcome some of the limitations associated with excitation wavelength, thermal sensitivity, calibration, as well as light-matter interactions. Strategies range from use of longer excitation wavelengths, brighter emitters through strategic core/multi-shell architectures, exploitation of host phonons, and a shift from double- to single-band ratiometric as well as lifetime-based approaches to innovative methods based on computation and machine learning. To conclude, we offer a perspective on remaining gaps and where efforts should be focused towards more robust nanothermometers allowing a shift to real-life, e.g., in vivo, applications.

Bonding states of gold/silver plasmonic nanostructures and sulfur-containing active biological ingredients in biomedical applications: a review

As one of the most instrumental components in the architecture of advanced nanomedicines, plasmonic nanostructures (mainly gold and silver nanomaterials) have been paid a lot of attention. This type of nanomaterial can absorb light photons with a specific wavelength and generate heat or excited electrons through surface resonance, which is a unique physical property. In innovative biomaterials, a significant number of theranostic (therapeutic and diagnostic) materials are produced through the conjugation of thiol-containing ingredients with gold and silver nanoparticles (Au and Ag NPs). Hence, it is essential to investigate Au/Ag-S interfaces precisely and determine the exact bonding states in the active nanobiomaterials. This study intends to provide useful insights into the interactions between Au/Ag NPs and thiol groups that exist in the structure of biomaterials. In this regard, the modeling of Au/Ag-S bonding in active biological ingredients is precisely reviewed. Then, the physiological stability of Au/Ag-based plasmonic nanobioconjugates in real physiological environments (pharmacokinetics) is discussed. Recent experimental validation and achievements of plasmonic theranostics and radiolabelled nanomaterials based on Au/Ag-S conjugation are also profoundly reviewed. This study will also help researchers working on biosensors in which plasmonic devices deal with the thiol-containing biomaterials (e.g., antibodies) inside blood serum and living cells.

The interplay of group size and flow velocity modulates fish exploratory behaviour

Social facilitation is a well-known phenomenon where the presence of organisms belonging to the same species enhances an individual organism's performance in a specific task. As far as fishes are concerned, most studies on social facilitation have been conducted in standing-water conditions. However, for riverine species, fish are most commonly located in moving waters, and the effects of hydrodynamics on social facilitation remain largely unknown. To bridge this knowledge gap, we designed and performed flume experiments where the behaviour of wild juvenile Italian riffle dace (Telestes muticellus) in varying group sizes and at different mean flow velocities, was studied. An artificial intelligence (AI) deep learning algorithm was developed and employed to track fish positions in time and subsequently assess their exploration, swimming activity, and space use. Results indicate that energy-saving strategies dictated space use in flowing waters regardless of group size. Instead, exploration and swimming activity increased by increasing group size, but the magnitude of this enhancement (which quantifies social facilitation) was modulated by flow velocity. These results have implications for how future research efforts should be designed to understand the social dynamics of riverine fish populations, which can no longer ignore the contribution of hydrodynamics.

Adoption of CRISPR-Cas for crop production: present status and future prospects

Background

Global food systems in recent years have been impacted by some harsh environmental challenges and excessive anthropogenic activities. The increasing levels of both biotic and abiotic stressors have led to a decline in food production, safety, and quality. This has also contributed to a low crop production rate and difficulty in meeting the requirements of the ever-growing population. Several biotic stresses have developed above natural resistance in crops coupled with alarming contamination rates. In particular, the multiple antibiotic resistance in bacteria and some other plant pathogens has been a hot topic over recent years since the food system is often exposed to contamination at each of the farm-to-fork stages. Therefore, a system that prioritizes the safety, quality, and availability of foods is needed to meet the health and dietary preferences of everyone at every time.

Methods

This review collected scattered information on food systems and proposes methods for plant disease management. Multiple databases were searched for relevant specialized literature in the field. Particular attention was placed on the genetic methods with special interest in the potentials of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and Cas (CRISPR associated) proteins technology in food systems and security.

Results

The review reveals the approaches that have been developed to salvage the problem of food insecurity in an attempt to achieve sustainable agriculture. On crop plants, some systems tend towards either enhancing the systemic resistance or engineering resistant varieties against known pathogens. The CRISPR-Cas technology has become a popular tool for engineering desired genes in living organisms. This review discusses its impact and why it should be considered in the sustainable management, availability, and quality of food systems. Some important roles of CRISPR-Cas have been established concerning conventional and earlier genome editing methods for simultaneous modification of different agronomic traits in crops.

Conclusion

Despite the controversies over the safety of the CRISPR-Cas system, its importance has been evident in the engineering of disease- and drought-resistant crop varieties, the improvement of crop yield, and enhancement of food quality.

Catalyst durability in electrocatalytic H2O2 production: key factors and challenges

On-demand electrocatalytic hydrogen peroxide (H2O2) production is a significant technological advancement that offers a promising alternative to the traditional anthraquinone process. This approach leverages electrocatalysts for the selective reduction of oxygen through a two-electron transfer mechanism (ORR-2e-), holding great promise for delivering a sustainable and economically efficient means of H2O2 production. However, the harsh operating conditions during the electrochemical H2O2 production lead to the degradation of both structural integrity and catalytic efficacy in these materials. Here, we systematically examine the design strategies and materials typically utilized in the electroproduction of H2O2 in acidic environments. We delve into the prevalent reactor conditions and scrutinize the factors contributing to catalyst deactivation. Additionally, we propose standardised benchmarking protocols aimed at evaluating catalyst stability under such rigorous conditions. To this end, we advocate for the adoption of three distinct accelerated stress tests to comprehensively assess catalyst performance and durability.

Ultrasensitive Monolithic Dopamine Microsensors Employing Vertically Aligned Carbon Nanofibers

Brain-on-Chip devices, which facilitate on-chip cultures of neurons to simulate brain functions, are receiving tremendous attention from both fundamental and clinical research. Consequently, microsensors are being developed to accomplish real-time monitoring of neurotransmitters, which are the benchmarks for neuron network operation. Among these, electrochemical sensors have emerged as promising candidates for detecting a critical neurotransmitter, dopamine. However, current state-of-the-art electrochemical dopamine sensors are suffering from issues like limited sensitivity and cumbersome fabrication. Here, a novel route in monolithically microfabricating vertically aligned carbon nanofiber electrochemical dopamine microsensors is reported with an anti-blistering slow cooling process. Thanks to the microfabrication process, microsensors is created with complete insulation and large surface areas. The champion device shows extremely high sensitivity of 4.52× 104 µAµM-1·cm-2, which is two-orders-of-magnitude higher than current devices, and a highly competitive limit of detection of 0.243 nM. These remarkable figures-of-merit will open new windows for applications such as electrochemical recording from a single neuron.

Modular photoorigami-based 4D manufacturing of vascular junction elements

Four-dimensional (4D) printing, combining three-dimensional (3D) printing with time-dependent stimuli-responsive shape transformation, eliminates the limitations of the conventional 3D printing technique for the fabrication of complex hollow constructs. However, existing 4D printing techniques have limitations in terms of the shapes that can be created using a single shape-changing object. In this paper, we report an advanced 4D fabrication approach for vascular junctions, particularly T-junctions, using the 4D printing technique based on coordinated sequential folding of two or more specially designed shape-changing elements. In our approach, the T-junction is split into two components, and each component is 4D printed using different synthesized shape memory polyurethanes and their nanohybrids, which have been synthesized with varying hard segment contents and by incorporating different weight percentages of photo-responsive copper sulfide-polyvinyl pyrrolidone nanoparticles. The formation of a T-junction is demonstrated by assigning different shape memory behaviors to each component of the T-junction. A cell culture study with human umbilical vein endothelial cells reveals that the cells proliferate over time, and almost 90% of cells remain viable on day 7. Finally, the formation of the T-junction in the presence of near-infrared light has been demonstrated after seeding the endothelial cells on the programmed flat surface of the two components and fluorescence microscopy at day 3 and 7 reveals that the cells adhered well and continue to proliferate over time. Hence, the proposed alternative approach has huge potential and can be used to fabricate vascular junctions in the future.

Stretchable OLEDs based on a hidden active area for high fill factor and resolution compensation

Stretchable organic light-emitting diodes (OLEDs) have emerged as promising optoelectronic devices with exceptional degree of freedom in form factors. However, stretching OLEDs often results in a reduction in the geometrical fill factor (FF), that is the ratio of an active area to the total area, thereby limiting their potential for a broad range of applications. To overcome these challenges, we propose a three-dimensional (3D) architecture adopting a hidden active area that serves a dual role as both an emitting area and an interconnector. For this purpose, an ultrathin OLED is first attached to a 3D rigid island array structure through quadaxial stretching for precise, deformation-free alignment. A portion of the ultrathin OLED is concealed by letting it 'fold in' between the adjacent islands in the initial, non-stretched condition and gradually surfaces to the top upon stretching. This design enables the proposed stretchable OLEDs to exhibit a relatively high FF not only in the initial state but also after substantial deformation corresponding to a 30% biaxial system strain. Moreover, passive-matrix OLED displays that utilize this architecture are shown to be configurable for compensation of post-stretch resolution loss, demonstrating the efficacy of the proposed approach in realizing the full potential of stretchable OLEDs.