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Choroidal vascular alterations in diabetic retinopathy and their association with macular microvascular parameters
Pandemic preparedness in shaping psychosocial working conditions – insights for occupational safety and health from a longitudinal mixed-methods study during the COVID-19 pandemic at six company sites of one organization in Germany
Objectives This longitudinal mixed-methods study explores how one company group dealt with COVID-19 pandemic-related challenges and how employees and managers perceived work-related psychosocial demands in Germany. Materials and methods We analyzed panel survey data from 322 employees and managers across diverse working fields, e.g., assembly line/production, office, company medical service, and factory security service. Employees and managers were recruited from six German company sites in the federal states of Bavaria, Baden-Wurttemberg, and Lower Saxony of one large company group. The survey was conducted in August-October 2020, January 2021, and October-November 2021. Participants self-reported their perceived psychosocial demands from aspects of work organization, work environment, work content, and social relations at work during and retrospectively before the COVID-19 pandemic on a 5-point Likert scale. Additionally, nine managers were interviewed in September-October 2020 and April-May 2021 about pandemic-related changes in working conditions, organizational processes to adapt working conditions, and a culture of trust. For contextualization, we performed a comprehensive document analysis of prevailing national and federal laws and OSH-regulations for infection control in Germany. Results Quantitative results revealed that participants perceived stable psychosocial demands during the pandemic. Psychosocial demands relating to work organization were perceived as stressful, especially for managers and company's medical service personnel. No statistically significant changes were identified with respect to psychosocial demand ratings during compared to before the pandemic. Qualitative results highlighted that a crisis management team, a culture of trust, extensive and transparent communication, and participatory approaches in change processes were key to managing pandemic-related challenges. Conclusion Our findings contribute to a contextualized understanding of how the design of good and sustainable working conditions ensuredwork ability during the COVID-19 pandemic in Germany. They highlight the importance of pandemic preparedness in an organization's psychosocial work design to successfully manage challenging times. The derived learnings for organizational pandemic preparedness on a meso-, macro-, and micro-level are of lasting significance with a view to future global challenges in terms of the design of good psychosocial working conditions.
DNA based nanoscale optoelectronic devices enabled by THz driven piezo vibrotronic effect
Abstract Investigating the piezo-vibrotronics effect in DNA chains, focusing on the interplay between mechanical strain, electronic properties, and photonic interactions paves the way for innovative applications of DNA in nanoscale electronic and optical devices. By applying varying degrees of mechanical deformation to DNA molecules, we observe significant changes in their charge transport properties. Strain-induced polarization within the DNA affects on carrier generation and transport, leading to improved optoelectronic performance. Additionally, photonic excitation under strain conditions demonstrates modulation of electronic responses, highlighting the potential of DNA-based materials in advanced piezo-vibro(photo)tronics devices. I-V characterization and multifractal analysis employ to elucidate these effects, providing a comprehensive understanding of the piezo-phototronics phenomenon in biologic systems.
Genetic and morphological variation in the Colombian Bombyx mori germplasm: A first SSR-based assessment
Centuries of domestication have produced over 4000 Bombyx mori L. ecotypes, many of which share similar morphological and reproductive traits that hinder genetic improvement and differentiation. In Colombia, 67 silkworm lines are maintained; however, their industrial potential remains underutilized due to the lack of genetic and phenotypic data. To address this, we evaluated the genetic and morphological variability of the Colombian B. mori germplasm using 13 phenotypic traits and 23 simple sequence repeat (SSR) markers, 17 of which were polymorphic. Morphological traits such as voltinism (H’ = 4.22), larval markings (H’ = 4.057), and cocoon color (H’ = 3.849) were the most informative in differentiating breeds. The SSR markers revealed 2–11 alleles per locus, with PIC values ranging from 0.05 to 0.72 (median = 0.46). The AMOVA results revealed that 96% of the genetic variation occurred within populations, with only 4% among them. Commercial lines presented greater molecular homogeneity, likely due to intensive selective breeding, whereas ICA and unknown-origin (NC) lines presented no significant deviation from Hardy‒Weinberg equilibrium, suggesting greater genetic stability and potential for improving inbred breeds. Additionally, the molecular markers allowed for the classification of lines according to breeder-defined categories. Although several lines retained ancestral morphological characteristics, Japanese and Chinese lines presented low heterozygosity, likely due to bottlenecks associated with hybrid development programs. Morphological markers also revealed patterns on the basis of line provenance, offering clues to the origins of unclassified strains. A few lines clustered similarly across both the morphological and molecular dendrograms, indicating conserved trait patterns. The combined use of morphological and molecular markers allowed for a more comprehensive classification of the silkworm germplasm, identifying candidate lines for future breeding efforts. These findings provide new insights into the genetic diversity of B. mori in Colombia and offer a valuable baseline for conservation and hybrid development programs aimed at enhancing vigor and productivity.
Bioinformatic screen with clinical validation for the identification of novel stool based mRNA biomarkers for the detection of colorectal lesions including advanced adenoma
Characterization and simulation of conglomerate reservoirs using core data of Triassic Baikouquan Formation, Mahu Depression
Abstract The 1 Gt oilfield discovery solidified the Mahu oilfield as the world’s largest conglomerate oil region, underscoring the exploration potential of these reservoirs. However, optimizing and selecting the target interval for hydraulic fracturing remains challenging due to the significant heterogeneity of the structure and composition of conglomerate reservoirs. This study addresses key gaps in understanding conglomerate reservoir characteristics and their impact on hydrocarbon production, focusing on the Baikouquan (T1 b) Formation (Fm) on the Mahu Depression’s northern slope. It introduces a new classification to better manage these complexities. In contrast to other classification methods, the proposed approach incorporates key factors influencing hydraulic fracture (HF) propagation, including grain size, cementation, supporting forms, and gravel composition, the latter of which is introduced for the first time. Based on core and test results, the conglomerate reservoirs are categorized into two main groups—fan delta front and fan delta plain conglomerates—and further divided into eight lithofacies types. Fan delta front conglomerates are subdivided into four types: A-1 (tuff, metamorphic, and magmatic rocks-dominated gravel-supported cobble-to-boulder lithofacies), A-2 (tuff and magmatic rocks-dominated matrix-supported pebble-to-cobble lithofacies), A-3 (tuff-dominated matrix-supported granule-to-pebble lithofacies), and A-4 (tuff-dominated gravel-supported granule-to-pebble lithofacies). Fan delta plain conglomerates are further divided into four types: B-1 (tuff and magmatic rocks-dominated gravel-supported granule-to-pebble lithofacies), B-2 (tuff and sedimentary rocks-dominated gravel-supported pebble-to-cobble lithofacies), B-3 (tuff-dominated gravel-supported cobble-to-boulder lithofacies), and B-4 (tuff, magmatic, and sedimentary rocks-dominated matrix-supported pebble-to-cobble lithofacies). The novelty of this classification method lies in its integration of both geological and engineering perspectives, particularly in optimizing hydraulic fracturing strategies. The study evaluates lithofacies from geological factors such as bedding, composition, and poroperm characteristics, as well as engineering considerations like fracturing potential and flow capacity. The results reveal that certain lithofacies types correlate strongly with higher fracturing success, providing insights that can guide more efficient hydraulic fracturing practices. By addressing the challenge of heterogeneity of the structure and composition in conglomerate reservoirs, this study offers a comprehensive framework for selecting optimal target intervals for hydraulic fracturing, which can significantly enhance hydrocarbon exploration and production strategies. This approach is expected to be valuable for similar complex conglomerate reservoirs worldwide.
Amitriptyline increases food intake via prdx-2 without altering other physiological parameters in C. elegans under a modified bacterial diet
Cudratricusxanthone A exhibits antitumor activity and enhances chemosensitivity to cisplatin against NSCLC via targeting EGFR
First dercetid fish from the Upper Cretaceous of Antarctica
Machine learning-based approach for reduction of energy consumption in hybrid energy storage electric vehicle
Abstract This research introduces a novel machine learning-based strategy for generating supercapacitor (SC) reference current to optimize energy distribution in Battery Electric Vehicles (BEV) and Hybrid Battery Electric Vehicles (HBEV). A Long Short-Term Memory (LSTM) neural network is trained using real-world drive cycle data and exported in Open Neural Network Exchange (ONNX) format for real-time deployment within a Simulink-based control environment. This enables adaptive SC current prediction to dynamically offload high transient loads from the battery. The system is modeled using the Nissan Sakura EV and evaluated under EUDC and IM240 drive cycles. With SC support, the EUDC cycle exhibits a 21.3% reduction in battery peak current, 18.1% reduction in peak power demand, and 5.75% lower battery energy consumption. In the IM240 cycle, peak battery current is reduced by 33.5%, peak power by 31.6%, and energy consumption by 12.36%. These improvements validate the proposed LSTM-ONNX framework in reducing battery stress, improving thermal performance, and enhancing energy efficiency. Additionally, SC assistance leads to smoother traction motor torque, reduced current ripple, and optimized power delivery. A comprehensive model is developed and tested in Simscape to confirm the real-time applicability of this data-driven control strategy for electric vehicles.
Polyphasic characterization and mating type allele distribution of Monilinia laxa in Iranian stone fruit orchards
Integrating horticultural practices with smallholder resources improves Rosa roxburghii yield and nitrogen use efficiency
Abstract Rosa roxburghii Tratt is one of the main cash crops in mountainous areas of Guizhou where production is a large contributor to the income of smallholders. In Guizhou Province, cultivation of R. roxburghii is largely dominated by smallholders employing extensive management practices, which typically results in low yield and economic challenges. Moreover, the effect of integrated and localized horticultural practices and smallholder resource endowment on yield and resource use efficiency is not well understood. In this study, localized technology and nitrogen use efficiency were assessed for 121 smallholders growing R. roxburghii in the main production zone in Guizhou Province. The average yield of R. roxburghii among smallholders was 5.9 t ha-1. Based on yield and nutrient use efficiency, smallholders were categorized into four groups. The yield of the high-yielding and high-efficiency (HH) group was 8.4%, 99.1%, and 97.8% higher than that of the high-yielding and low-efficiency (HL), low-yielding and high-efficiency (LH), and low-yielding and low-efficiency (LL) groups, respectively. The partial factor productivity of the HH group’s Nitrogen fertilizer (PFP-N) was 501.6%, 31.7%, and 589.0% higher than that of the HL, LH, and LL groups, respectively. Educated smallholders in the HH group employed optimized horticultural practices, including chemical fertilizer, pruning, pest control, planting density, and weed control. Thus, smallholder farm-led R. roxburghii crop production demonstrates great potential for high yield and efficient nitrogen use through the coupling of localized technology and the resource endowment of smallholders.
Reward boosts cognitive control during working memory maintenance
Abstract Working memory (WM) involves short-term maintenance and manipulation of goal-relevant information, with cognitive control playing a crucial role in these processes due to WM’s limited capacity. Pupillometry studies show distinct pupillary changes for WM stages, reflecting cognitive effort and load. Motivational incentives enhance WM performance by potentially improving encoding, maintenance, or retrieval, though the specific components influenced by reward remain unclear. This study specifically tested whether reward modulates cognitive control processes during WM maintenance using pupillometry. Participants performed a delayed-estimation orientation WM task with reward cues indicating reward levels at the beginning of trials. The results revealed that motivational incentives significantly improved WM performance and increased pupillary dilation during maintenance. These findings provide evidence for the modulation of WM maintenance by reward through enhanced top-down cognitive control processes.
Exploring the application value of ultrasound in animal studies of stem cell therapy for intrauterine adhesions
ELABELA targets the MEK/ERK axis to enhance trophoblast invasion in early-onset preeclampsia
Dual-loaded niosome-dendrimer nanoplatform enhances Tirapazamine delivery to hypoxic breast cancer cells
Design of a novel and robust 2-DOF PIDA controller based on enzyme action optimizer for ball position regulation in magnetic levitation systems
Anti-protein arginine methyltransferase 5 (PRMT5) antibodies is associated with interstitial lung disease in rheumatoid arthritis
Abstract Recently, anti-protein arginine methyltransferase 5 (PRMT5) antibodies has been identified a novel marker for systemic sclerosis (SSc). The present study aimed to to explore the association between anti-PRMT5 antibodies and rheumatoid arthritis (RA). The study recruited a cohort of 33 patients diagnosed with SSc, 87 patients with RA, and 31 healthy control subjects. Serum levels of the anti-PRMT5 antibodies were measured using a double-antigen sandwich enzyme-linked immunosorbent assay (ELISA). Patients with SSc exhibited higher serum levels (p < 0.001) and seropositivity rates (48.5% vs. 0%, p < 0.0001) of anti-PRMT5 antibodies compared to healthy subjects. Notably, serum levels of anti-PRMT5 antibodies were significantly elevated in patients with RA compared to healthy controls (p < 0.001), with a corresponding increase in the rate of seropositivity (14.9% vs. 0%, p < 0.05). Receiver operating curve analysis for anti-PRMT5 antibody levels yielded area under the curve values of 0.768 for distinguishing RA patients from healthy controls, and 0.903 for distinguishing SSc patients from controls. Furthermore, elevated levels of anti-PRMT5 antibodies were significantly associated with the presence of interstitial lung disease (ILD) (p < 0.0001) and antinuclear antibody positivity (p < 0.01). This study not only confirms the association of the anti-PRMT5 antibodies with SSc but also, for the first time, demonstrates a significant association between the anti-PRMT5 antibodies and RA, particularly in the context of RA-associated ILD.