U1 snRNP regulates cancer cell migration and invasion in vitro

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.

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.

WASP: weighted average of sequential projections for ptychographic phase retrieval

We introduce the weighted average of sequential projections, or WASP, an algorithm for ptychography. Using both simulations and real-world experiments, we test this new approach and compare performance against several alternative algorithms. These tests indicate that WASP effectively combines the benefits of its competitors, with a rapid initial convergence rate, robustness to noise and poor initial conditions, a small memory footprint, easy tuning, and the ability to reach a global minimum when provided with noiseless data. We also show how WASP can be parallelised to split operation across several different computation nodes.

The Efficacy of Risk Factor Modification Compared to NAD+ Repletion in Diastolic Heart Failure

Heart failure (HF) with left ventricular diastolic dysfunction is a growing global concern. This study evaluated myocardial oxidized nicotinamide adenine dinucleotide (NAD+) levels in human systolic and diastolic HF and in a murine model of HF with preserved ejection fraction, exploring NAD+ repletion as therapy. We quantified myocardial NAD+ and nicotinamide phosphoribosyltransferase levels, assessing restoration with nicotinamide riboside (NR). Findings show significant NAD+ and nicotinamide phosphoribosyltransferase depletion in human diastolic HF myocardium, but NR successfully restored NAD+ levels. In murine HF with preserved ejection fraction, NR as preventive and therapeutic intervention improved metabolic and antioxidant profiles. This study underscores NAD+ repletion's potential in diastolic HF management.

A new insight into the role of pericytes in ischemic stroke

The functional structure of the blood-brain barrier (BBB) deteriorates after stroke by developing diffuse microvascular and neurovascular dysfunction and loss of white matter integrity. This causes nervous tissue injury and causes sensory and motor disabilities in stroke patients. Improving the integrity of the BBB and neurovascular remodeling after stroke can promote post-stroke injury conditions. Pericytes are contractile cells abundant in the BBB and sandwiched between astrocytes and endothelial cells of the microvessels. Stroke could lead to the degeneration of pericytes in the BBB. However, recent evidence shows that promoting pericytes enhances BBB integrity and neurovascular remodeling. Furthermore, pericytes achieve multipotent properties under hypoxic conditions, allowing them to transdifferentiate into the brain resident cells such as microglia. Microglia regulate immunity and inflammatory response after stroke. The current review studies recent findings in the intervening mechanisms underlying the regulatory effect of pericytes in BBB recovery after stroke.

Antibiotic resistance profiles of sentinel bacteria isolated from aquaculture in Cambodia

The misuse of antibiotics and the emergence of antimicrobial resistance (AMR) is a concern in the aquaculture industry because it contributes to global health risks and impacts the environment. This study analyzed the AMR of sentinel bacteria associated with striped catfish (Pangasisanodon hypophthalmus) and giant snakehead (Channa micropeltes), the two main fish species reared in the pond culture in Cambodia. Phenotypic and genotypic characterization of the recovered isolates from fish, water, and sediment samples revealed the presence of bacteria, such as 22 species belonging to families Aeromonadaceae, Enterobacteriaceae, and Pseudomonadaceae. Among 48 isolates, Aeromonas caviae (n = 2), Aeromonas hydrophila (n = 2), Aeromonas ichthiosmia (n = 1), Aeromonas salmonicida (n = 4) were detected. A. salmonicida and A. hydrophilla are known as fish pathogens that occur worldwide in both fresh and marine water aquaculture. Antibiotic susceptibility testing revealed antibiotic resistance patterns of 24 (50 %) isolates among 48 isolates with higher multiple antibiotic resistance index (> 0.2). All the isolates of Enterobacteriaceae were susceptible to ciprofloxacin. Ciprofloxacin is a frontline antibiotic that is not recommended to use in aquaculture. Therefore, its use has to be strictly controlled. This study expands our knowledge of the AMR status in aquaculture farms which is very limited in Cambodia.

Coupling Exponential to Linear Amplification for Endpoint Quantitative Analysis

Exponential DNA amplification techniques are fundamental in ultrasensitive molecular diagnostics. These systems offer a wide dynamic range, but the quantification requires real-time monitoring of the amplification reaction. Linear amplification schemes, despite their limited sensitivity, can achieve quantitative measurement from a single end-point readout, suitable for low-cost, point-of-care, or massive testing. Reconciling the sensitivity of exponential amplification with the simplicity of end-point readout would thus break through a major design dilemma and open a route to a new generation of massively scalable quantitative bioassays. Here a hybrid nucleic acid-based circuit design is introduced to compute a logarithmic function, therefore providing a wide dynamic range based on a single end-point measurement. CELIA (Coupling Exponential amplification reaction to LInear Amplification) exploits a versatile biochemical circuit architecture to couple a tunable linear amplification stage - optionally embedding an inverter function - downstream of an exponential module in a one-pot format. Applied to the detection of microRNAs, CELIA provides a limit of detection in the femtomolar range and a dynamic range of six decades. This isothermal approach bypasses thermocyclers without compromising sensitivity, thereby opening the way to applications in various diagnostic assays, and providing a simplified, cost-efficient, and high throughput solution for quantitative nucleic acid analysis.

A comparison of gender diversity in transgender young people with and without autistic traits from the Trans 20 cohort study

Background

There is an elevated co-occurrence of autism in trans individuals, with recent meta-analyses suggesting that 11% of trans individuals are autistic. The presence of autism in trans young people can create clinical challenges by adding complexity to the presentation, assessment and management of those presenting to gender clinics. Although many trans young people display traits of autism, how these traits relate to the nature of their gender diversity is unclear.

Methods

This study compared gender identity, gender expression and gender dysphoria (GD) in trans young people with and without autistic traits. Baseline data from a cohort study of trans children and adolescents who first attended the Royal Children's Hospital Gender Service (Victoria, Australia) between February 2017 and January 2020 were analysed cross-sectionally. Autistic traits were assessed via the Social Responsiveness Scale-2. Gender was assessed using tools that measure gender identity, social transition, GD, body dissatisfaction, voice dysphoria, and chest dysphoria.

Findings

522 participants were included, of whom 239 (45.8%) exhibited autistic traits (SRS total T-score ≥60). Those with and without autistic traits were similar in their age (mean (SD) age 14.0 (2.9) and 13.1 (3.6) years respectively) and gender identity: the majority (73.7% (n = 174) and 70.5% (n = 198) respectively) identified in a binary way. Higher rates of social transition (specifically, changing pronouns) were noted in those with autistic traits (Difference in proportion 11.7, 95% confidence interval [CI] 2.4-21.1, p = 0.014). GD was high in both groups with ∼95% displaying clinically relevant levels of GD. Chest dysphoria was similar between groups, while voice dysphoria was higher in those with autistic traits (standardised mean difference [SMD] = 0.3, 95% confidence interval [CI]: 0.1-0.5 p = 0.00087). Dissatisfaction with secondary gendered characteristics (SMD = 0.3, CI: 0.1-0.5 p = 0.0011) and hormonally unresponsive body characteristics (SMD = 0.2, CI: 0.1-0.4 p = 0.016) was higher in trans young people with autistic traits.

Interpretation

The similarly high severity of GD in those with and without autistic traits reinforces the importance of trans young people with and without autistic traits being availed the same opportunities to access gender-affirming care. Subtle differences identified between the groups in other areas of gender diversity suggest trans young people with autistic traits may have distinct needs and that gender-affirming care may need to be tailored accordingly.

Funding

The Royal Children's Hospital Foundation, Hugh D.T. Williamson Foundation; Australian National Health and Medical Research Council-Clinical Trials and Cohort Studies scheme (GNT 2006529).

Construction of a pathological model of skin lesions in acute herpes zoster virus infection and its molecular mechanism

Varicella-zoster virus (VZV), a common pathogen with humans as the sole host, causes primary infection and undergoes a latent period in sensory ganglia. The recurrence of VZV is often accompanied by severe neuralgia in skin tissue, which has a serious impact on the life of patients. During the acute infection of VZV, there are few related studies on the pathophysiological mechanism of skin tissue. In this study, transcriptome sequencing data from the acute response period within 2 days of VZV antigen stimulation of the skin were used to explore a model of the trajectory of skin tissue changes during VZV infection. It was found that early VZV antigen stimulation caused activation of mainly natural immune-related signaling pathways, while in the late phase activation of mainly active immune-related signaling pathways. JAK-STAT, NFκB, and TNFα signaling pathways are gradually activated with the progression of infection, while Hypoxia is progressively inhibited. In addition, we found that dendritic cell-mediated immune responses play a dominant role in the lesion damage caused by VZV antigen stimulation of the skin. This study provides a theoretical basis for the study of the molecular mechanisms of skin lesions during acute VZV infection.

The enhancement of M13 phage titration by optimizing the origin of replication

Background and purpose

M13KO7, a modified M13 phage variant, carries the p15A replication origin and Tn903 kanamycin resistance gene. This study aimed to optimize M13KO7's replication by substituting the p15A origin with the higher-copy pMB1 origin (500-700 copy numbers).

Experimental approach

A 6431-nucleotide fragment from the M13KO7 plasmid lacking the p15A replication origin and kanamycin resistance gene was amplified using a long polymerase chain reaction (PCR). The modified M13AMB1 plasmid was created by adding adenine to the 3' ends of this fragment and ligating it to the pMB1-containing fragment using T/A cloning. Afterward, to prepare the phage, pM13AMB1 was transformed into E. coli TG1 bacteria, and then, using the PEG-NaCl precipitation, the modified phage was propagated. The modified phage titer was determined utilizing the serial dilution and the qPCR methods, compared with the M13KO7 phage.

Findings/Results

The results showed that in the serial dilution method, the titers of modified phage and M13KO7 phage were 4.8 × 1014 and 7 × 1012 pfu/mL, respectively. Besides, the phage titer calculated by the qPCR method for the modified phage was equal to 1.3 × 109 pfu/mL, whereas it was 4.08 × 108 pfu/mL for the M13KO7 phage.

Conclusion and implications

This study provides evidence that replication origin replacement led to a significant increase in phage titers. It highlights the importance of replication optimization for molecular biology applications.

Biocontrol of plant pathogens in omics era-with special focus on endophytic bacilli

Nearly all plants and their organs are inhabited by endophytic microbes which play a crucial role in plant fitness and stress resilience. Harnessing endophytic services can provide effective solutions for a sustainable increase in agriculture productivity and can be used as a complement or alternative to agrochemicals. Shifting agriculture practices toward the use of nature-based solutions can contribute directly to the global challenges of food security and environmental sustainability. However, microbial inoculants have been used in agriculture for several decades with inconsistent efficacy. Key reasons of this inconsistent efficacy are linked to competition with indigenous soil microflora and inability to colonize plants. Endophytic microbes provide solutions to both of these issues which potentially make them better candidates for microbial inoculants. This article outlines the current advancements in endophytic research with special focus on endophytic bacilli. A better understanding of diverse mechanisms of disease control by bacilli is essential to achieve maximum biocontrol efficacy against multiple phytopathogens. Furthermore, we argue that integration of emerging technologies with strong theoretical frameworks have the potential to revolutionize biocontrol approaches based on endophytic microbes.

Current research hotspots and frontier trends on carbon budget of coastal wetlands: A bibliometric analysis

Coastal wetlands are the main distribution of blue carbon in coastal zones and well known for their high carbon sequestration capacity. Investigating the variation of carbon budget is crucial for understanding the functionality of coastal wetlands and effectively addressing climate change. In this study, a bibliometric analysis of 4,509 articles was conducted to reveal research progress, hot issues, and emerging trends in the coastal wetland carbon budget field. The number of publications and citations in this field increased exponentially from 1991 to 2022. The leading subject category was Environmental Sciences with 1,844 articles (40.9%). At present, studies have been focused on blue carbon, the effects of climate change and man-made disturbances on carbon cycle, and the restoration of coastal wetlands. Based on the hotspots and trends in this field, the future researches should include (1) exploring the functional mechanisms of various factors affecting carbon cycle and establishing a methodological system for the estimation of blue carbon in coastal wetlands; (2) researching restoration techniques of coastal wetland and constructing wetland restoration evaluation index system; and (3) formulating enforceable carbon trading policy and strengthening international cooperation.

Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

This paper reviews the key building blocks needed to develop a mechanistic model for use as an operational production tool. The Chinese Hamster Ovary (CHO) cell, one of the most widely used hosts for antibody production in the pharmaceutical industry, is considered as a case study. CHO cell metabolism is characterized by two main phases, exponential growth followed by a stationary phase with strong protein production. This process presents an appropriate degree of complexity to outline the modeling strategy. The paper is organized into four main steps: (1) CHO systems and data collection; (2) metabolic analysis; (3) formulation of the mathematical model; and finally, (4) numerical solution, calibration, and validation. The overall approach can build a predictive model of target variables. According to the literature, one of the main current modeling challenges lies in understanding and predicting the spontaneous metabolic shift. Possible candidates for the trigger of the metabolic shift include the concentration of lactate and carbon dioxide. In our opinion, ammonium, which is also an inhibiting product, should be further investigated. Finally, the expected progress in the emerging field of hybrid modeling, which combines the best of mechanistic modeling and machine learning, is presented as a fascinating breakthrough. Note that the modeling strategy discussed here is a general framework that can be applied to any bioprocess.

Palm-Sized Lab-In-A-Magnetofluidic Tube Platform for Rapid and Sensitive Virus Detection

Simple, sensitive, and accurate molecular diagnostics are critical for preventing rapid spread of infection and initiating early treatment of diseases. However, current molecular detection methods typically rely on extensive nucleic acid sample preparation and expensive instrumentation. Here, a simple, fully integrated, lab-in-a-magnetofluidic tube (LIAMT) platform is presented for "sample-to-result" molecular detection of virus. By leveraging magnetofluidic transport of micro/nano magnetic beads, the LIAMT device integrates viral lysis, nucleic acid extraction, isothermal amplification, and CRISPR detection within a single engineered microcentrifuge tube. To enable point-of-care molecular diagnostics, a palm-sized processor is developed for magnetofluidic separation, nucleic acid amplification, and visual fluorescence detection. The LIAMT platform is applied to detect SARS-CoV-2 and HIV viruses, achieving a detection sensitivity of 73.4 and 63.9 copies µL-1, respectively. Its clinical utility is further demonstrated by detecting SARS-CoV-2 and HIV in clinical samples. This simple, affordable, and portable LIAMT platform holds promise for rapid and sensitive molecular diagnostics of infectious diseases at the point-of-care.

Development and validation of AI/ML derived splice-switching oligonucleotides

Splice-switching oligonucleotides (SSOs) are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of functional, verifiable, and therapeutic SSOs. We trained XGboost tree models using splicing factor (SF) pre-mRNA binding profiles and spliceosome assembly information to identify modulatory SSO binding sites on pre-mRNA. Using Shapley and out-of-bag analyses we also predicted the identity of specific SFs whose binding to pre-mRNA is blocked by SSOs. This step adds considerable transparency to AI/ML-driven drug discovery and informs biological insights useful in further validation steps. We applied this approach to previously established functional SSOs to retrospectively identify the SFs likely to regulate those events. We then took a prospective validation approach using a novel target in triple negative breast cancer (TNBC), NEDD4L exon 13 (NEDD4Le13). Targeting NEDD4Le13 with an AI/ML-designed SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGFβ pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data.

Recent updates on structural insights of MAO-B inhibitors: a review on target-based approach

Parkinson's disease is a neurodegenerative disorder characterized by slow movement, tremors, and stiffness caused due to loss of dopaminergic neurons caused in the brain's substantia nigra. The concentration of dopamine is decreased in the brain. Parkinson's disease may be happened because of various genetic and environmental factors. Parkinson's disease is related to the irregular expression of the monoamine oxidase (MAO) enzyme, precisely type B, which causes the oxidative deamination of biogenic amines such as dopamine. MAO-B inhibitors, available currently in the market, carry various adverse effects such as dizziness, nausea, vomiting, lightheadedness, fainting, etc. So, there is an urgent need to develop new MAO-B inhibitors with minimum side effects. In this review, we have included recently studied compounds (2018 onwards). Agrawal et al. reported MAO-B inhibitors with IC50 0.0051 µM and showed good binding affinity. Enriquez et al. reported a compound with IC50 144 nM and bind with some critical amino acid residue Tyr60, Ile198, and Ile199. This article also describes the structure-activity relationship of the compounds and clinical trial studies of related derivatives. These compounds may be used as lead compounds to develop potent compounds as MAO-B inhibitors.

An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. In contrast to individual-level data, GWAS summary statistics are usually publicly available, and thus methods using only summary statistics have greater usage. Although many methods have been developed for joint analysis of multiple traits using summary statistics, there are many issues, including inconsistent performance, computational inefficiency, and numerical problems when considering lots of traits. To address these challenges, we propose a multi-trait adaptive Fisher method for summary statistics (MTAFS), a computationally efficient method with robust power performance. We applied MTAFS to two sets of brain imaging derived phenotypes (IDPs) from the UK Biobank, including a set of 58 Volumetric IDPs and a set of 212 Area IDPs. Through annotation analysis, the underlying genes of the SNPs identified by MTAFS were found to exhibit higher expression and are significantly enriched in brain-related tissues. Together with results from a simulation study, MTAFS shows its advantage over existing multi-trait methods, with robust performance across a range of underlying settings. It controls type 1 error well and can efficiently handle a large number of traits.

Dichroic switching of core-shell plasmonic nanoparticles on reflective surfaces

Plasmonic metal nanostructures can simultaneously scatter and absorb light, with resonance wavelength and strength depending on their morphology and composition. This work demonstrates that unique dichroic effects and high-contrast colour-switching can be achieved by leveraging the resonant scattering and absorption of light by plasmonic nanostructures and the specular reflection of the resulting transmitted light. Using core/shell nanostructures comprising a metal core and a dielectric shell, we show that their spray coating on reflective substrates produces dichroic films that can display colour switching at different viewing angles. The high-contrast colour switching, high flexibility in designing multicolour patterns, and convenience for large-scale production promise their wide range of applications, including anticounterfeiting, mechanochromic sensing, colour display, and printing.

Individual low-carbon behaviors and influencing factors: Insights from a behavior survey study in China

Climate change is a major global concern. Greenhouse gas emissions that cause global climate change are directly or indirectly affected by human activities. Individual low-carbon behaviors are crucial in reducing CO2 emissions and improving environmental and ecological health. To effectively promote individual low-carbon behavior, this study designed a questionnaire on the factors influencing individual low-carbon intentions and behavior based on theoretical models of environmental behavior. A total of 2430 valid questionnaires were collected in China. This study focuses on analyzing the impact of demographic characteristics, internal and external factors on individual low-carbon behaviors and their interrelationships. The research shows correlations between internal and external factors in determining low-carbon intention or behaviors. Internal factors-related low-carbon behavior is not closely linked with demographic variables, whereas the external factors-related low-carbon behavior vary significantly by age, residence, education, marital status, occupation, and income. The findings have important implications for designing effective policies to promote low-carbon behaviors, such as creating a more favorable external environment and increasing the use of policy tools for reducing CO2 emission.

The dawn of MXene duo: revolutionizing perovskite solar cells with MXenes through computational and experimental methods

Integrating MXene into perovskite solar cells (PSCs) has heralded a new era of efficient and stable photovoltaic devices owing to their supreme electrical conductivity, excellent carrier mobility, adjustable surface functional groups, excellent transparency and superior mechanical properties. This review provides a comprehensive overview of the experimental and computational techniques employed in the synthesis, characterization, coating techniques and performance optimization of MXene additive in electrodes, hole transport layer (HTL), electron transport layer (ETL) and perovskite photoactive layer of the perovskite solar cells (PSCs). Experimentally, the synthesis of MXene involves various methods, such as selective etching of MAX phases and subsequent delamination. At the same time, characterization techniques encompass X-ray diffraction, scanning electron microscopy, and X-ray photoelectron spectroscopy, which elucidate the structural and chemical properties of MXene. Experimental strategies for fabricating PSCs involving MXene include interfacial engineering, charge transport enhancement, and stability improvement. On the computational front, density functional theory calculations, drift-diffusion modelling, and finite element analysis are utilized to understand MXene's electronic structure, its interface with perovskite, and the transport mechanisms within the devices. This review serves as a roadmap for researchers to leverage a diverse array of experimental and computational methods in harnessing the potential of MXene for advanced PSCs.

Tight-binding model predicts exciton energetics and structure for photovoltaic molecules

Conjugated molecules and polymers are being designed as acceptor and donor materials for organic photovoltaic (OPV) cells. OPV performance depends on generation of free charge carriers through dissociation of excitons, which are electron-hole pairs created when a photon is absorbed. Here, we develop a tight-binding model to describe excitons on homo-oligomers, alternating co-oligomers, and a non-fullerene acceptor - IDTBR. We parameterize our model using density functional theory (DFT) energies of neutral, anion, cation, and excited states of constituent moieties. A symmetric molecule like IDTBR has two ends where an exciton can sit; but the product wavefunction approximation for the exciton breaks symmetry. So, we introduce a tight-binding model with full correlation between electron and hole, which allows the exciton to coherently explore both ends of the molecule. Our approach predicts optical singlet excitation energies for oligomers of varying length as well as IDTBR in good agreement with time-dependent DFT and spectroscopic results.

Materials discovery with extreme properties via reinforcement learning-guided combinatorial chemistry

The goal of most materials discovery is to discover materials that are superior to those currently known. Fundamentally, this is close to extrapolation, which is a weak point for most machine learning models that learn the probability distribution of data. Herein, we develop reinforcement learning-guided combinatorial chemistry, which is a rule-based molecular designer driven by trained policy for selecting subsequent molecular fragments to get a target molecule. Since our model has the potential to generate all possible molecular structures that can be obtained from combinations of molecular fragments, unknown molecules with superior properties can be discovered. We theoretically and empirically demonstrate that our model is more suitable for discovering better compounds than probability distribution-learning models. In an experiment aimed at discovering molecules that hit seven extreme target properties, our model discovered 1315 of all target-hitting molecules and 7629 of five target-hitting molecules out of 100 000 trials, whereas the probability distribution-learning models failed. Moreover, it has been confirmed that every molecule generated under the binding rules of molecular fragments is 100% chemically valid. To illustrate the performance in actual problems, we also demonstrate that our models work well on two practical applications: discovering protein docking molecules and HIV inhibitors.

Integrating microfluidics and synthetic biology: advancements and diverse applications across organisms

Synthetic biology is the design and modification of biological systems for specific functions, integrating several disciplines like engineering, genetics, and computer science. The field of synthetic biology is to understand biological processes within host organisms through the manipulation and regulation of their genetic pathways and the addition of biocontrol circuits to enhance their production capabilities. This pursuit serves to address global challenges spanning diverse domains that are difficult to tackle through conventional routes of production. Despite its impact, achieving precise, dynamic, and high-throughput manipulation of biological processes is still challenging. Microfluidics offers a solution to those challenges, enabling controlled fluid handling at the microscale, offering lower reagent consumption, faster analysis of biochemical reactions, automation, and high throughput screening. In this review, we diverge from conventional focus on automating the synthetic biology design-build-test-learn cycle, and instead, focus on microfluidic platforms and their role in advancing synthetic biology through its integration with host organisms - bacterial cells, yeast, fungi, animal cells - and cell-free systems. The review illustrates how microfluidic devices have been instrumental in understanding biological systems by showcasing microfluidics as an essential tool to create synthetic genetic circuits, pathways, and organisms within controlled environments. In conclusion, we show how microfluidics expedite synthetic biology applications across diverse domains including but not limited to personalized medicine, bioenergy, and agriculture.

Carboxymethylated Lignin Reinforcement of SPI Adhesive: Enhancing Strength, Antimicrobial, and Flame-Retardant Properties without Excessive Alkali Introduction

To address the challenges associated with formaldehyde emissions in engineered wood adhesives and simultaneously enhance adhesive properties related to water resistance, fire resistance, and mold resistance, a novel environmentally sustainable biomass-based adhesive was formulated. In this work, kraft lignin was carboxymethylated and then blended with the soy protein isolate (SPI)-based adhesive, the dry and wet shear strength of the plywood bonded by the resultant adhesive was enhanced from 1.10 and 0.63 MPa to 1.73 and 1.23 MPa, respectively, resulting in improvements of 157% and 195%. Carboxymethylated lignin (CML) significantly improved the mold resistance and flame-resistance residual rate of the adhesive and decreased the water absorption rate from 190% to 108%. Furthermore, the adhesive exhibits outstanding flame-retardancy, with self-extinguishing capability rendering it suitable for industrial production. In addition, we also evaluated the performances of resulting adhesives cured with different diepoxides and triepoxides, and the comparisons of the adhesive in this work to commercial urea glue and soy protein-based adhesives were conducted. To our delight, the SPI-10CML adhesive presented comparable or even improved performances, showing its promising practical applications such as for fire doors.

Optimal Control of Short-Time Attractors in Active Nematics

Objective Eulerian coherent structures (OECSs) and instantaneous Lyapunov exponents (iLEs) govern short-term material transport in fluid flows as Lagrangian coherent structures and the finite-time Lyapunov exponent do over longer times. Attracting OECSs and iLEs reveal short-time attractors and are computable from the Eulerian rate-of-strain tensor. Here, we devise for the first time an optimal control strategy to create short-time attractors in compressible, viscosity-dominated active nematic flows. By modulating the active stress intensity, our framework achieves a target profile of the minimum eigenvalue of the rate-of-strain tensor, controlling the location and shape of short-time attractors. We show that our optimal control strategy effectively achieves desired short-time attractors while rejecting disturbances. Combining optimal control and coherent structures, our work offers a new perspective to steer material transport in compressible active nematics, with applications to morphogenesis and synthetic active matter.

Quantum-Enhanced Metrology with Network States

Armed with quantum correlations, quantum sensors in a network have shown the potential to outclass their classical counterparts in distributed sensing tasks such as clock synchronization and reference frame alignment. On the other hand, this analysis was done for simple and idealized networks, whereas the correlation shared within a practical quantum network, captured by the notion of network states, is much more complex. Here, we prove a general bound that limits the performance of using quantum network states to estimate a global parameter, establishing the necessity of genuine multipartite entanglement for achieving a quantum advantage. The bound can also serve as an entanglement witness in networks and can be generalized to states generated by shallow circuits. Moreover, while our bound prohibits local network states from achieving the Heisenberg limit, we design a probabilistic protocol that, once successful, attains this ultimate limit of quantum metrology and preserves the privacy of involved parties. Our work establishes both the limitation and the possibility of quantum metrology within quantum networks.

Intranasal vaccination with an NDV-vectored SARS-CoV-2 vaccine protects against Delta and Omicron challenges

The rapid development and deployment of vaccines following the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been estimated to have saved millions of lives. Despite their immense success, there remains a need for next-generation vaccination approaches for SARS-CoV-2 and future emerging coronaviruses and other respiratory viruses. Here we utilized a Newcastle Disease virus (NDV) vectored vaccine expressing the ancestral SARS-CoV-2 spike protein in a pre-fusion stabilized chimeric conformation (NDV-PFS). When delivered intranasally, NDV-PFS protected both Syrian hamsters and K18 mice against Delta and Omicron SARS-CoV-2 variants of concern. Additionally, intranasal vaccination induced robust, durable protection that was extended to 6 months post-vaccination. Overall, our data provide evidence that NDV-vectored vaccines represent a viable next-generation mucosal vaccination approach.

Chemical passivation of 2D transition metal dichalcogenides: strategies, mechanisms, and prospects for optoelectronic applications

The interest in obtaining high-quality monolayer transition metal dichalcogenides (TMDs) for optoelectronic device applications has been growing dramatically. However, the prevalence of defects and unwanted doping in these materials remain challenges, as they both limit optical properties and device performance. Surface chemical treatments of monolayer TMDs have been effective in improving their photoluminescence yield and charge transport properties. In this scenario, a systematic understanding of the underlying mechanism of chemical treatments will lead to a rational design of passivation strategies in future research, ultimately taking a step toward practical optoelectronic applications. We will therefore describe in this mini-review the strategies, progress, mechanisms, and prospects of chemical treatments to passivate and improve the optoelectronic properties of TMDs.

Physics to system-level modeling of silicon-organic-hybrid nanophotonic devices

The continuous growth in data volume has sparked interest in silicon-organic-hybrid (SOH) nanophotonic devices integrated into silicon photonic integrated circuits (PICs). SOH devices offer improved speed and energy efficiency compared to silicon photonics devices. However, a comprehensive and accurate modeling methodology of SOH devices, such as modulators corroborating experimental results, is lacking. While some preliminary modeling approaches for SOH devices exist, their reliance on theoretical and numerical methodologies, along with a lack of compatibility with electronic design automation (EDA), hinders their seamless and rapid integration with silicon PICs. Here, we develop a phenomenological, building-block-based SOH PICs simulation methodology that spans from the physics to the system level, offering high accuracy, comprehensiveness, and EDA-style compatibility. Our model is also readily integrable and scalable, lending itself to the design of large-scale silicon PICs. Our proposed modeling methodology is agnostic and compatible with any photonics-electronics co-simulation software. We validate this methodology by comparing the characteristics of experimentally demonstrated SOH microring modulators (MRMs) and Mach Zehnder modulators with those obtained through simulation, demonstrating its ability to model various modulator topologies. We also show our methodology's ease and speed in modeling large-scale systems. As an illustrative example, we use our methodology to design and study a 3-channel SOH MRM-based wavelength-division (de)multiplexer, a widely used component in various applications, including neuromorphic computing, data center interconnects, communications, sensing, and switching networks. Our modeling approach is also compatible with other materials exhibiting the Pockels and Kerr effects. To our knowledge, this represents the first comprehensive physics-to-system-level EDA-compatible simulation methodology for SOH modulators.

Biosynthesis of fungal terpenoids

Covering: up to August 2023Terpenoids, which are widely distributed in animals, plants, and microorganisms, are a large group of natural products with diverse structures and various biological activities. They have made great contributions to human health as therapeutic agents, such as the anti-cancer drug paclitaxel and anti-malarial agent artemisinin. Accordingly, the biosynthesis of this important class of natural products has been extensively studied, which generally involves two major steps: hydrocarbon skeleton construction by terpenoid cyclases and skeleton modification by tailoring enzymes. Additionally, fungi (Ascomycota and Basidiomycota) serve as an important source for the discovery of terpenoids. With the rapid development of sequencing technology and bioinformatics approaches, genome mining has emerged as one of the most effective strategies to discover novel terpenoids from fungi. To date, numerous terpenoid cyclases, including typical class I and class II terpenoid cyclases as well as emerging UbiA-type terpenoid cyclases, have been identified, together with a variety of tailoring enzymes, including cytochrome P450 enzymes, flavin-dependent monooxygenases, and acyltransferases. In this review, our aim is to comprehensively present all fungal terpenoid cyclases identified up to August 2023, with a focus on newly discovered terpenoid cyclases, especially the emerging UbiA-type terpenoid cyclases, and their related tailoring enzymes from 2015 to August 2023.

Epigenetic mechanisms linking early-life adversities and mental health

Early-life adversities, whether prenatal or postnatal exposure, have been linked to adverse mental health outcomes later in life increasing the risk of several psychiatric disorders. Research on its neurobiological consequences demonstrated an association between exposure to adversities and persistent alterations in the structure, function, and connectivity of the brain. Consistent evidence supports the idea that regulation of gene expression through epigenetic mechanisms are involved in embedding the impact of early-life experiences in the genome and mediate between social environments and later behavioral phenotypes. In addition, studies from rodent models and humans suggest that these experiences and the acquired risk factors can be transmitted through epigenetic mechanisms to offspring and the following generations potentially contributing to a cycle of disease or disease risk. However, one of the important aspects of epigenetic mechanisms, unlike genetic sequences that are fixed and unchangeable, is that although the epigenetic markings are long-lasting, they are nevertheless potentially reversible. In this review, we summarize our current understanding of the epigenetic mechanisms involved in the mental health consequences derived from early-life exposure to malnutrition, maltreatment and poverty, adversities with huge and pervasive impact on mental health. We also discuss the evidence about transgenerational epigenetic inheritance in mammals and experimental data suggesting that suitable social and pharmacological interventions could reverse adverse epigenetic modifications induced by early-life negative social experiences. In this regard, these studies must be accompanied by efforts to determine the causes that promote these adversities and that result in health inequity in the population.

Fe-based cyclically catalyzing double free radical nanogenerator for tumor-targeted chemodynamic therapy

The prosperity of chemodynamic therapy provides a new strategy for tumor treatment. However, the lack of reactive oxygen species and the specific reductive tumor microenvironment have limited the further development of chemodynamic therapy. Herein, we reported a Fe-based cyclically catalyzing double free radical system for tumor therapy by catalyzing exogenous potassium persulfate (K2S2O8) and endogenous hydrogen peroxide (H2O2). Sufficient amounts of Fe3+ and S2O82- were delivered to tumor sites via tumor-targeted hyaluronic acid (HA) encapsulated mesoporous silica nanoparticles (MSNs) and released under the dual stimulation of acid and hyaluronidase (HAase) in the tumor microenvironment. Fe3+ was reduced to Fe2+ by the reducing agents of loaded tannic acid (TA) and intracellular glutathione (GSH), and Fe2+ was subsequently reacted with S2O82- and endogenous H2O2 to produce two types of ROS (˙OH and SO4-˙), showing an excellent anti-tumor effect. This process not only supplied Fe2+ for the catalysis of active substances, but also reduced the concentration of reduced substances in cells, which was conducive to the existence of free radicals for the efficient killing of tumor cells. Therefore, this iron-based catalysis of exogenous and exogenous active substances to realize a dual-radical oncotherapy nanosystem would provide a new perspective for chemodynamic therapy.

Clarifying the association between Parkinson's disease and vitiligo: a population-based large-scale study

Objective

Our knowledge about the association between vitiligo and Parkinson's disease (PD) is sparse. We sought to investigate the bidirectional epidemiological association between vitiligo and PD.

Methods

A population-based study was conducted using Clalit Health Services (CHS) database (2002-2019) using both a cohort study and a case-control study design. Adjusted hazard ratio (HR) and odds ratio (OR) were calculated by multivariate Cox and logistic regressions, respectively.

Results

Overall, 20,851 vitiligo patients and 102,475 controls were included. The incidence of new-onset PD was 2.9 (95% CI, 2.1-4.1) and 4.3 (95% CI, 3.8-4.9) cases per 10,000 person-years among patients with vitiligo and controls, respectively. Patients with vitiligo had a significantly decreased risk of developing new-onset PD [adjusted HR, 0.62; 95% confidence interval (CI), 0.43-0.89, p = 0.009]. On the other hand, the likelihood of having vitiligo after a preexisting diagnosis of PD was not statistically different (adjusted OR, 0.80; 95% CI, 0.61-1.06; p = 0.117). Relative to the remaining patients with vitiligo, those with vitiligo and comorbid PD experienced an elevated risk of all-cause mortality (adjusted HR, 2.63; 95% CI, 1.82-3.80; p < 0.001) and higher prevalence of cardiometabolic comorbidities.

Conclusion

Vitiligo is associated with a lower risk of developing PD. The presence of comorbid PD predisposes patients with vitiligo to elevated mortality and cardiometabolic outcomes.

The Impact of Metagenomics on Biocatalysis

In the ever-growing demand for sustainable ways to produce high-value small molecules, biocatalysis has come to the forefront of greener routes to these chemicals. As such, the need to constantly find and optimise suitable biocatalysts for specific transformations has never been greater. Metagenome mining has been shown to rapidly expand the toolkit of promiscuous enzymes needed for new transformations, without requiring protein engineering steps. If protein engineering is needed, the metagenomic candidate can often provide a better starting point for engineering than a previously discovered enzyme on the open database or from literature, for instance. In this review, we highlight where metagenomics has made substantial impact on the area of biocatalysis in recent years. We review the discovery of enzymes in previously unexplored or 'hidden' sequence space, leading to the characterisation of enzymes with enhanced properties that originate from natural selection pressures in native environments.

Magnetic transverse unidirectional scattering and longitudinal displacement sensing in silicon nanodimer

Unidirectional scattering, crucial for manipulating light at the nanoscale, has wide-ranging applications from optical manipulation to sensing. While traditionally achieved through interactions between electric multipoles or between electric and magnetic multipoles, reports on unidirectional scattering driven purely by magnetic multipoles are limited. In this study, we undertake a theoretical exploration of transverse unidirectional scattering induced by magnetic multipoles, employing tightly focused azimuthally polarized beams (APBs) in interaction with a silicon nanodimer comprising two non-concentric nanorings. Through numerical simulations and theoretical analysis, we validate the transverse unidirectional scattering, predominantly governed by magnetic dipolar and quadrupolar resonances. Moreover, the directionality of this unidirectional scattering shows a strong correlation with the longitudinal displacement of the nanodimer within a specific range, showcasing its potential for longitudinal displacement sensing. Our study advances optical scattering control in nanostructures and guides the design of on-chip longitudinal displacement sensors.

Preventing lead leakage in perovskite solar cells and modules with a low-cost and stable chemisorption coating

Despite the promising commercial prospects of perovskite solar cells, the issue of lead toxicity continues to hinder their future industrial applications. Here, we report a low-cost and rapidly degraded sulfosuccinic acid-modified polyvinyl alcohol (SMP) coating that prevents lead leakage and enhances device stability without compromising device performance. Even under different strict conditions (simulated heavy rain, acid rain, high temperatures, and competing ions), the coatings effectively prevent lead leakage by over 99%. After 75 days of outdoor exposure, the coating still demonstrates similar lead sequestration efficiency (SQE). In addition, it can be applied to different device structures (n-i-p and p-i-n) and modules, with over 99% SQE, making it a general method for preventing lead leakage.

Layer-by-layer thinning of two-dimensional materials

Etching technology - one of the representative modern semiconductor device makers - serves as a broad descriptor for the process of removing material from the surfaces of various materials, whether partially or entirely. Meanwhile, thinning technology represents a novel and highly specialized approach within the realm of etching technology. It indicates the importance of achieving an exceptionally sophisticated and precise removal of material, layer-by-layer, at the nanoscale. Notably, thinning technology has gained substantial momentum, particularly in top-down strategies aimed at pushing the frontiers of nano-worlds. This rapid development in thinning technology has generated substantial interest among researchers from diverse backgrounds, including those in the fields of chemistry, physics, and engineering. Precisely and expertly controlling the layer numbers of 2D materials through the thinning procedure has been considered as a crucial step. This is because the thinning processes lead to variations in the electrical and optical characteristics. In this comprehensive review, the strategies for top-down thinning of representative 2D materials (e.g., graphene, black phosphorus, MoS2, h-BN, WS2, MoSe2, and WSe2) based on conventional plasma-assisted thinning, integrated cyclic plasma-assisted thinning, laser-assisted thinning, metal-assisted splitting, and layer-resolved splitting are covered in detail, along with their mechanisms and benefits. Additionally, this review further explores the latest advancements in terms of the potential advantages of semiconductor devices achieved by top-down 2D material thinning procedures.

The carbon footprint of predicting CO2 storage capacity in metal-organic frameworks within neural networks

While artificial intelligence drives remarkable progress in natural sciences, its broader societal implications are mostly disregarded. In this study, we evaluate environmental impacts of deep learning in materials science through extensive benchmarking. In particular, a set of diverse neural networks is trained for a given supervised learning task to assess greenhouse gas (GHG) emissions during training and inference phases. A chronological perspective showed diminishing returns, manifesting themselves as a 28% decrease in mean absolute error and nearly a 15,000% increase in the carbon footprint of model training in 2016-2022. By means of up-to-date graphics processing units, it is possible to partially offset the immense growth of GHG emissions. Nonetheless, the practice of employing energy-efficient hardware is overlooked by the materials informatics community, as follows from a literature analysis in the field. On the basis of our findings, we encourage researchers to report GHG emissions together with standard performance metrics.

Coherence Manipulation in Asymmetry and Thermodynamics

In the classical regime, thermodynamic state transformations are governed by the free energy. This is also called as the second law of thermodynamics. Previous works showed that, access to a catalytic system allows us to restore the second law in the quantum regime when we ignore coherence. However, in the quantum regime, coherence and free energy are two independent resources. Therefore, coherence places additional nontrivial restrictions on the state transformations that remain elusive. In order to close this gap, we isolate and study the nature of coherence, i.e., we assume access to a source of free energy. We show that allowing catalysis along with a source of free energy allows us to amplify any quantum coherence present in the quantum state arbitrarily. Additionally, any correlations between the system and the catalyst can be suppressed arbitrarily. Therefore, our results provide a key step in formulating a fully general law of quantum thermodynamics.

Effects of common co-occurring pesticides (a neonicotinoid and fungicide) on honey bee colony health in a semi-field study

Multiple stressors are linked to declines of insects and important pollinators, such as bees. Recently, interactive effects of multiple agrochemicals on bees have been highlighted, including fungicides, which increase toxicity of neonicotinoid insecticides. Here, we use a semi-field study across two seasons in controlled foraging tunnels to test the effects of a field application of a commercial fungicide product with two active ingredients (pyraclostrobin and metconazole) applied at label rates. We also examine its interactive effects with the neonicotinoid insecticide clothianidin, at a conservative field-realistic dose of 2.23 ppb, on 48 honey bee colonies. We found combined effects of pesticide exposure, including additive 2.93-fold increases in mortality, and an additional effect of increased infestation levels of the ectoparasitic mite, Varroa destructor. Pesticide treatments also reduced colony activity, reduced colony weight, and increased sugar consumption of whole colonies. These findings indicate that typical sublethal exposure levels to common, co-occurring agrochemicals in the field significantly affect the health of whole honey bee colonies, highlighting an unintended consequence of increasing pesticide applications.

From NAFLD to NASH: Understanding the spectrum of non-alcoholic liver diseases and their consequences

Non-alcoholic fatty liver disease (NAFLD) has become one of the most frequent chronic liver diseases worldwide in recent decades. Metabolic diseases like excessive blood glucose, central obesity, dyslipidemia, hypertension, and liver function abnormalities cause NAFLD. NAFLD significantly increases the likelihood of liver cancer, heart disease, and mortality, making it a leading cause of liver transplants. Non-alcoholic steatohepatitis (NASH) is a more advanced form of the disease that causes scarring and inflammation of the liver over time and can ultimately result in cirrhosis and hepatocellular carcinoma. In this review, we briefly discuss NAFLD's pathogenic mechanisms, their progression into NASH and afterward to NASH-related cirrhosis. It also covers disease epidemiology, metabolic mechanisms, glucose and lipid metabolism in the liver, macrophage dysfunction, bile acid toxicity, and liver stellate cell stimulation. Additionally, we consider the contribution of intestinal microbiota, genetics, epigenetics, and ecological factors to fibrosis progression and hepatocellular carcinoma risk in NAFLD and NASH patients.

N-glycosylation of immunoglobulin A in children and adults with type 1 diabetes mellitus

Aims

To identify N-glycan structures on immunoglobulin A related to type 1 diabetes mellitus among children at the disease onset and adults with type 1 diabetes mellitus.

Methods

Human polyclonal IgA N-glycans were profiled using hydrophilic interaction ultra performance liquid chromatography in two cohorts. The first cohort consisted of 62 children at the onset of type 1 diabetes mellitus and 86 of their healthy siblings. The second cohort contained 84 adults with the disease and 84 controls. Associations between N-glycans and type 1 diabetes mellitus were tested using linear mixed model for the paediatric cohort, or general linear model for the adult cohort. False discovery rate was controlled by Benjamini-Hochberg method modified by Li and Ji.

Results

In children, an increase in a single oligomannose N-glycan was associated with type 1 diabetes mellitus (B = 0.529, p = 0.0067). N-glycome of the adults displayed increased branching (B = 0.466, p = 0.0052), trigalactosylation (B = 0.466, p = 0.0052), trisialylation (B = 0.629, p < 0.001), and mannosylation (B = 0.604, p < 0.001). The strongest association with the disease was a decrease in immunoglobulin A core fucosylation (B = -0.900, p < 0.001).

Conclusions

Changes in immunoglobulin N-glycosylation patterns in type 1 diabetes point to disruptions in immunoglobulin A catabolism and dysregulated inflammatory capabilities of the antibody, potentially impacting immune responses and inflammation.

Atomically dispersed asymmetric cobalt electrocatalyst for efficient hydrogen peroxide production in neutral media

Electrochemical hydrogen peroxide (H2O2) production (EHPP) via a two-electron oxygen reduction reaction (2e- ORR) provides a promising alternative to replace the energy-intensive anthraquinone process. M-N-C electrocatalysts, which consist of atomically dispersed transition metals and nitrogen-doped carbon, have demonstrated considerable EHPP efficiency. However, their full potential, particularly regarding the correlation between structural configurations and performances in neutral media, remains underexplored. Herein, a series of ultralow metal-loading M-N-C electrocatalysts are synthesized and investigated for the EHPP process in the neutral electrolyte. CoNCB material with the asymmetric Co-C/N/O configuration exhibits the highest EHPP activity and selectivity among various as-prepared M-N-C electrocatalyst, with an outstanding mass activity (6.1 × 105 A gCo-1 at 0.5 V vs. RHE), and a high practical H2O2 production rate (4.72 mol gcatalyst-1 h-1 cm-2). Compared with the popularly recognized square-planar symmetric Co-N4 configuration, the superiority of asymmetric Co-C/N/O configurations is elucidated by X-ray absorption fine structure spectroscopy analysis and computational studies.

PNA-Functionalized, Silica Nanowires-Filled Glass Microtube for Ultrasensitive and Label-Free Detection of miRNA-21

MicroRNAs (miRNAs) are endogenous and noncoding single-stranded RNA molecules with a length of approximately 18-25 nucleotides, which play an undeniable role in early cancer screening. Therefore, it is very important to develop an ultrasensitive and highly specific method for detecting miRNAs. Here, we present a bottom-up assembly approach for modifying glass microtubes with silica nanowires (SiNWs) and develop a label-free sensing platform for miRNA-21 detection. The three-dimensional (3D) networks formed by SiNWs make them abundant and highly accessible sites for binding with peptide nucleic acid (PNA). As a receptor, PNA has no phosphate groups and exhibits an overall electrically neutral state, resulting in a relatively small repulsion between PNA and RNA, which can improve the hybridization efficiency. The SiNWs-filled glass microtube (SiNWs@GMT) sensor enables ultrasensitive, label-free detection of miRNA-21 with a detection limit as low as 1 aM at a detection range of 1 aM-100 nM. Noteworthy, the sensor can still detect miRNA-21 in the range of 102-108 fM in complex solutions containing 1000-fold homologous interference of miRNAs. The high anti-interference performance of the sensor enables it to specifically recognize target miRNA-21 in the presence of other miRNAs and distinguish 1-, 3-mismatch nucleotide sequences. Significantly, the sensor platform is able to detect miRNA-21 in the lysate of breast cancer cell lines (e.g., MCF-7 cells and MDA-MB-231 cells), indicating that it has good potential in the screening of early breast cancers.

Thermoelectric transport in Weyl semimetals under a uniform concentration of torsional dislocations

In this article, we present an effective continuum model for a Weyl semimetal, to calculate its thermal and thermoelectric transport coefficients in the presence of a uniform concentration of torsional dislocations. We model each dislocation as a cylindrical region of finite radius a, where the corresponding elastic strain is described as a gauge field leading to a local pseudo-magnetic field. The transport coefficients are obtained by a combination of scattering theory, Green's functions and the Kubo formulae in the linear response regime. We applied our theoretical results to predict the electrical and thermal conductivities as well as the Seebeck coefficient for several transition metal monopnictides, i.e. TaAs, TaP, NbAs and NbP.

Evidence for a finite-momentum Cooper pair in tricolor d-wave superconducting superlattices

Fermionic superfluidity with a nontrivial Cooper-pairing, beyond the conventional Bardeen-Cooper-Schrieffer state, is a captivating field of study in quantum many-body systems. In particular, the search for superconducting states with finite-momentum pairs has long been a challenge, but establishing its existence has long suffered from the lack of an appropriate probe to reveal its momentum. Recently, it has been proposed that the nonreciprocal electron transport is the most powerful probe for the finite-momentum pairs, because it directly couples to the supercurrents. Here we reveal such a pairing state by the non-reciprocal transport on tricolor superlattices with strong spin-orbit coupling combined with broken inversion-symmetry consisting of atomically thin d-wave superconductor CeCoIn5. We find that while the second-harmonic resistance exhibits a distinct dip anomaly at the low-temperature (T)/high-magnetic field (H) corner in the HT-plane for H applied to the antinodal direction of the d-wave gap, such an anomaly is absent for H along the nodal direction. By carefully isolating extrinsic effects due to vortex dynamics, we reveal the presence of a non-reciprocal response originating from intrinsic superconducting properties characterized by finite-momentum pairs. We attribute the high-field state to the helical superconducting state, wherein the phase of the order parameter is spontaneously spatially modulated.