Browse Articles

Optimization and induction effect evaluation of complex inducer of Aquilaria sinensis based on factorial design

Amino acids catalyse RNA formation under ambient alkaline conditions

Abstract RNA and proteins are the foundation of life and a natural starting point to explore its origins. However, the prebiotic relationship between the two is asymmetric. While RNA evolved to assemble proteins from amino acids, a significant mirror-symmetric effect of amino acids to trigger the synthesis of RNA was missing. We describe ambient alkaline conditions where amino acids, without additional chemical activators, promote RNA copolymerisation more than 100-fold, starting from prebiotically plausible ribonucleoside-2′,3′-cyclic phosphates. The observed effect is explained by acid-base catalysis, with optimal efficiency at pH values near the amine pK aH. The fold-change in oligomerisation yield is nucleobase-selective, resulting in increased compositional diversity necessary for subsequent molecular evolution and favouring the formation of natural 3′−5′ linkages. The elevated pH offers recycling of oligonucleotide sequences back to 2′,3′-cyclic phosphates, providing conditions for high-fidelity replication by templated ligation. The findings reveal a clear functional role of amino acids in the evolution of RNA earlier than previously assumed.

A location privacy protection method based on blockchain and threshold cryptography

To address privacy leakage risks arising from low collaborative user engagement, third-party trust deficits, and insufficient collaboration timeliness in location-based services (LBS), this paper proposes a dual-protection framework integrating blockchain technology and threshold cryptography for safeguarding location privacy. The framework employs asymmetric encryption with Shamir’s (t, n) secret sharing to encrypt user queries, distributing decryption key fragments to collaborative users while generating n anonymous service requests through location generalization strategies. A temporary private blockchain constructed using smart contracts ensures confidential data transmission, supported by a dynamic privacy parameter configuration system based on Byzantine fault tolerance. The framework implements a priority-response consensus mechanism through Token-based equity proof-of-stake, prioritizing service for users with higher Token values. To mitigate privacy breaches caused by unresponsive collaborators, a competitive incentive mechanism ensures timely information submission. Through ciphertext fragment verification algorithms and Lagrange interpolation-based key reconstruction, the framework enables secure query decryption and service matching in untrusted third-party environments, guaranteeing information security, integrity, and non-repudiation. Experimental validation using real-world datasets confirms the framework’s feasibility and operational effectiveness.

Female sterilization paradoxical association with premature menopause in Bihar

Abstract Premature menopause is a growing public health crisis with serious implications for women’s well-being. While premature menopause prevalence varies across India, Bihar’s rates are exceptionally high (11%), warranting specific investigation. Analyzing data from National Family Health Survey-5, this study examines state-level variations and predictors of premature menopause, focusing on the context of Bihar. Bihar’s premature menopause rates were significantly higher than in other states, even after controlling for hysterectomy, suggesting unique regional drivers. While lower education, more children, and younger age at first and last birth were risk factors across India, this is especially concerning in Bihar. I found a strong association between female sterilization and both naturally occurring and hysterectomy induced premature menopause in Bihar, despite its intended role in family planning. This paradoxical finding, coupled with the protective effect of contraceptive use, raises critical questions about the potential unintended consequences of sterilization practices in Bihar and highlights the need for comprehensive reproductive healthcare services. The study underscores the urgent need for targeted public health interventions, including investments in girls’ education, expanded reproductive healthcare options beyond sterilization, improved sterilization practices through stricter regulation and provider training, and further investigation into the complex factors driving Bihar’s high premature menopause rates.

Intracellular accumulation of amyloid-ß is a marker of selective neuronal vulnerability in Alzheimer’s disease

Abstract Defining how amyloid-β and pTau together lead to neurodegeneration is fundamental to understanding Alzheimer’s disease (AD). We used imaging mass cytometry to identify neocortical neuronal subtypes lost with AD in post-mortem brain middle temporal gyri from non-diseased and AD donors. Here we showed that L5,6 RORB+FOXP2+ and L3,5,6 GAD1+FOXP2+ neurons, which accumulate amyloid-β intracellularly from early Braak stages, are selectively vulnerable to degeneration in AD, while L3 RORB+GPC5+ neurons, which accumulate pTau but not amyloid-β, are not lost even at late Braak stages. We discovered spatial associations between activated microglia and these vulnerable neurons and found that vulnerable RORB+FOXP2+ neuronal transcriptomes are enriched selectively for pathways involved in inflammation and glycosylation and, with progression to AD, also protein degradation. Our results suggest that the accumulation of intraneuronal amyloid-β, which is associated with glial inflammatory pathology, may contribute to the initiation of degeneration of these vulnerable neurons.

Localization and characterization of cutaneous neurogenic inflammation in acute gastric mucosal injury in rats: A possible morphological explanation for visceral sensitization?

This investigation transcends traditional methodologies by providing a quantitative analysis of the dynamic relationship between visceral pathologies and neurogenic spots, employing an acute gastric mucosal injury (AGMI) rat model to map the somatotopic distribution of visceral sensitization. Through hydrochloric acid-induced plasma extravasation and Evans Blue dye (EB) marking, coupled with a geospatial grid system and multivariate statistical analysis, we identified Feature Regions (FRs) with distinct neurogenic responses. Notably, the right T10-13 dermatomere, or FR-11’, exhibited elevated levels of nociceptive neuropeptides and serotonin, indicative of its significant role in pain perception. The application of electroacupuncture at FR-11’ revealed enhanced therapeutic outcomes compared to the conventional acupoint BL-21, positioning it as a promising modality for the management of visceral pain. These findings contribute substantially to our understanding of the mechanisms underlying visceral-somatic pain and pave the way for innovative pain management interventions in clinical settings.

Overconfidence in nutritional knowledge is linked to unnecessary gluten-free consumption

Assembly and breakage of head-to-head double hexamer reveals mpox virus E5-catalyzed DNA unwinding initiation

Diagnostic performance of microvascular flow imaging for noninvasive assessment of liver fibrosis in chronic liver disease

Background and aims Chronic liver disease (CLD) represents a significant global health challenge necessitating the evaluation of liver fibrosis. This study aimed to evaluate the diagnostic performance of microvascular flow (MV-flow) imaging in evaluating liver fibrosis and compare it with other non-invasive tools. Methods Between July 2020 and June 2022, we prospectively enrolled patients scheduled for liver biopsy, concurrently measuring MV-flow imaging, transient elastography (TE), and two-dimensional shear wave elastography (2D-SWE) as part of the assessment process. We evaluated the diagnostic performance of MV-flow imaging, 2D-SWE, and TE based on histologic staging of liver fibrosis using the area under the receiver operating characteristic curve (AUROC), and calculated the optimal cut-off value. Results A total of 89 participants were included. Non-alcoholic fatty liver disease was the most common etiology of CLD (32.6%). The liver fibrosis stage distribution was as follows: stage 0 (11.2%), stage 1 (31.5%), stage 2 (25.8%), stage 3 (13.5%), and stage 4 (18.0%). The MV-flow scoring system’s cut-off values and AUROCs for predicting stage 2, stage 3, and cirrhosis were 2.1 (0.836), 2.5 (0.955), and 2.9 (0.942), respectively. The MV-flow scoring system’s performance in predicting advanced fibrosis (stage 3) was comparable to TE ( p  = 0.170) and 2D-SWE ( p  = 0.456). MV-flow imaging misclassified 9.0% of patients in predicting advanced fibrosis. A sequential combination of 2D-SWE and MV-flow imaging, following the specified cut-off, minimized the risk of missing advanced fibrosis to 1.2%. Conclusion MV-flow imaging is an effective tool for predicting liver fibrosis stage. Integrating MV-flow imaging with 2D-SWE can enhance the assessment of liver fibrosis in patients with CLD.

Pig models reveal the interplay between fatty acids and cytokines in skeletal muscle

Evolution and transmission dynamics of wild poliovirus in Pakistan and Afghanistan (2012-2023)

Abstract Despite concerted global vaccination efforts, wild poliovirus remains endemic in two countries in 2024, Pakistan and Afghanistan. This study uses phylogeographic analysis of poliovirus genetic and epidemiological data from clinical and wastewater surveillance to identify the causes of poliovirus persistence and routes of spread over the last decade (2012 to 2023). Poliovirus genetic diversity declined after 2020, with one of two major genetic clusters dying out, and recent detections are now closely related genetically. High-risk and hard-to-access regions have sustained polio transmission over the past decade, even when interrupted elsewhere. Karachi, one of the most densely populated cities globally, has acted as a hub for the amplification and spread of poliovirus to other regions, many of which we show to be dead-end for onwards transmission despite frequent virus detection. Phylogenetic analysis has long been central to the polio surveillance network, and advancing the approaches used can provide critical epidemiological insights to accelerate eradication efforts.

Model parameterization of robotic systems through the bio-inspired optimization

The accurate modeling of dynamic systems, particularly robotic ones, is crucial in the industry. It enables simulation-based approaches that facilitate various tasks without requiring the physical system, thereby reducing risks and costs. These approaches range from model-in-the-loop (MiL), where a simulated model of the real plant is used for controller design, to hardware-in-the-loop (HiL), which provides more realistic simulations on specialized real-time hardware. Among these, MiL is widely adopted due to its simplicity and effectiveness in developing control strategies. However, to fully leverage the advantages of MiL, developing a robust and accurate system model parameterization methodology is essential. This methodology should be adaptable to a wide range of applications, adopt a holistic approach, and balance the cost-benefit trade-offs in model characteristics. Achieving this, however, introduces additional challenges related to system complexity and the inherent properties of the model. To address these challenges, this work proposes a model parameterization approach for robotic systems using bio-inspired optimization to develop accurate and practical models for system design. The approach formulates an optimization problem to determine the dynamic model parameters of a robot, ensuring its behavior closely resembles that of the real system. Due to the complexity of this problem, bio-inspired optimization techniques are particularly well-suited. The proposed method is validated using a theoretical, non-conservative model of a three-degree-of-freedom serial robot. The dynamic parameters of its three links were identified to effectively generalize the real system. To solve the optimization problem, three bio-inspired algorithms were employed: the genetic algorithm, particle swarm optimization, and differential evolution. The optimal parameterization obtained for the robot model demonstrated the effectiveness of the proposed approach in a MiL simulation environment, achieving an overall correlation of 0.9019 in the experiments. This correlation highlights the model’s ability to predict the robot’s behavior accurately. Additionally, the methodology’s efficacy was further validated in another electromechanical system, the reaction force-sensing series elastic actuator, yielding a correlation of 0.8379 in the resulting model.

Optimal allocation of STATCOM for multi-objective ORPD problem on thermal wind solar hydro scheduling using driving training based optimization

Abstract On IEEE 30, 57, 118 & 300-bus experimental networks, this work aims to solve the optimal reactive power dispatch (ORPD) problem. Initially, the conventional network is countered, and subsequently, renewable energy sources (RESs) such as wind power (WP), solar photovoltaic (PV) sources, and hydro power (HP) are combined with the traditional network. This study examines both single and multiple type objective functions (OFs). The Objectives include lowering active power loss (APL), lowering aggregated voltage deviation (AVD), lowering the voltage stability index (VSI), lowering reactive power loss and concurrently lowering AVD, APL & VSI. There are five test modules that comprise a total of 30 cases. Cases 5-8 and 13-30 are being conducted using STATCOM in conjunction with the test setup. The Driving Training Based Optimization (DTBO) method has been used to achieve the goals, and its performance has been compared to that of other optimization algorithms that have been reported in recent ORPD studies. Both stable load demand and uncertain changing load demand scenarios are included in the study. Appropriate probability density functions (PDF) are employed to estimate the uncertain WP, PV source, HP, and load demand. Uncertain scenarios with variable load demand, wind speed (WS), solar irradiance (SI), and water flow rate (WFR) are created using Monte Carlo simulations (MCS). Based on a range of studied cases, the experiment results show that the DTBO has a significantly stronger ability to solve ORPD challenges than the optimization methods discovered in the most recent ORPD literature. The usage of STATCOM improves power network performance for the ORPD issue, which is another significant finding. From simulation results it has been observed that for IEEE 30 bus the average power loss (APL) is 4.5086 MW, utilizing STATCOM the APL is reduced by 5.3% MW, with integrating renewable sources the APL is reduced 41%, and for both STATCOM and renewable sources (RESs) system it decreases to 43.6%. Hence, STATCOM and RES help to reduce the power losses using DTBO approach. Furthermore, average voltage deviation (AVD) improved by 97.4 % with incorporating STATCOM-RESs. Voltage stability index (VSI) improved by 26.9% with scheduling STATCOM and renewable sources (RESs). For the multi-objective situation APL & AVD both simultatiously improved to 5.0701(MW) & 0.1221 (p.u.), respectively, with incorporating STATCOM and RESs using DTBO. Voltage deviation converges at 40 iterations for with STATCOM but for without STATCOM it takes 80 iterations to converge. Similarly for voltage stability index with STATCOM converge 4 iterations earlier rather than without STATCOM system. Again for large scale IEEE 57 bus system The DTBO approach incorporating STATCOM and RESs provided optimal results. So, for IEEE 30, 57, 118 & 300 bus systems DTBO proves its superiority and robustness satisfactorily. From simulation results it has been observed that for IEEE 30 bus the average power loss (APL) is 4.5086 MW, utilizing STATCOM the APL is reduced by 5.3% MW, with integrating renewable sources the APL is reduced 41%, and for both STATCOM and renewable sources (RESs) system it decreases to 43.6%. Hence, STATCOM and RES help to reduce the power losses using DTBO approach. Furthermore, average voltage deviation (AVD) improved by 97.4 % with incorporating STATCOM-RESs. Voltage stability index (VSI) improved by 26.9% with scheduling STATCOM and renewable sources (RESs). For the multi-objective situation APL & AVD both simultatiously improved to 5.0701(MW) & 0.1221 (p.u.), respectively, with incorporating STATCOM and RESs using DTBO. Voltage deviation converge at 40 iterations for with STATCOM but for without STATCOM it takes 80 iterations to converge. So, for IEEE 30, 57, 118 & 300 bus systems DTBO proof its superiority and robustness satisfactorily.

Carbon trade biases and the emerging mesoscale structure of the European Emissions Trading System network

MAGNET-seq: A tandem PCR and hybrid capture method for enhanced target enrichment

Hybrid-capture based target enrichment and multiplex PCR methods enhance sequencing efficiency by focusing on specific genomic regions, while struggling to enrich tens of regions spanning hundreds to thousands of base pairs. We developed MAGNET-seq (Multiplex Amplification and tarGeted eNrichment of sElecTed sequences), a streamlined method that integrates targeted multiplex PCR with hybrid capture. We evaluated its performance using two primer sets: a Drug Resistance Targeting Primers with 43 targets and a Reference Primers set with 7 targets, including clinically relevant mutations such as EGFR c.2369C > T (p.T790M) and KRAS c.35G > T (p.G12C). Using a set of 43 target primers, MAGNET-seq demonstrated higher on-target ratios (average 86.2%) compared to standard targeted multiplex PCR (average 2.2%). Furthermore, MAGNET-seq with 7 target primers showed concordant variant allele frequencies (VAF) in low-VAF (≤ 1%) reference cell-free DNA (cfDNA) samples (0.05% to 1%), supporting its reproducibility. This approach provides a simplified and cost-efficient solution for targeted sequencing, particularly well-suited for applications that require detection of low-allele frequency variants such as somatic cancer mutations.

Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury

Unveiling ongoing biogeochemical dynamics of CDOM from surface to deep ocean

Spatial autocorrelation of environmental factors influencing dengue outbreaks using Moran’s I: A study from Nepal (2020–2023)

Background Dengue fever, a mosquito-borne viral infection caused by the dengue virus, has become a significant global public health concern, especially in tropical and subtropical regions. Nepal, with its diverse geography and climate, has witnessed a rapid escalation in dengue cases in recent years, with the highest number of cases and fatalities reported in 2022. Objectives This study analyzed the spatial distribution of dengue in Nepal from 2020 to 2023, using Moran’s I spatial statistics to explore the relationship between environmental factors (such as vegetation indices, land surface temperature and precipitation) and dengue incidence. Methods By utilizing Geographic Information System (GIS) and spatial analysis techniques, the study seeks to identify high-incidence clusters and examine environmental factors contributing to the spread of dengue. Results This study examined dengue incidence in Nepal from 2020 to 2023, uncovering significant variations in disease patterns and their environmental correlations. Dengue cases peaked in 2022 (Moran’s I; 0.634, P-value; 0.001) before declining in 2023 (Moran’s I; 0.144, P-value; 0.036), likely due to targeted public health interventions. Spatial analysis revealed no significant patterns in 2020 (Moran’s I; −0.004, P-value; 0.288) and 2021 (Moran’s I; 0.006, P-value; 0.186), however, a focused spatial distribution emerged in 2022 and 2023. Environmental factors showed evolving relationships with dengue transmission: NDVI and LST showed negative correlations in 2020–2021, while NDWI and precipitation shifted from negative to positive correlations over the study period. Conclusion The findings showed significant spatial clustering of dengue cases in urban areas with correlations between higher precipitation and increased dengue incidence. These results highlighted the importance of adaptive public health strategies that account for environmental factors.

Distribution characteristics of microbial aerosols and optimization of protective methods during ultrasonic scaling procedure

Perinatal serotonin signalling dynamically influences the development of cortical GABAergic circuits with consequences for lifelong sensory encoding

Abstract Serotonin plays a prominent role in neurodevelopment, regulating processes from cell division to synaptic connectivity. Clinical studies suggest that alterations in serotonin signalling such as genetic polymorphisms or antidepressant exposure during pregnancy are risk factors for neurodevelopmental disorders. However, an understanding of how dysfunctional neuromodulation alters systems level activity over neocortical development is lacking. Here, we use a longitudinal imaging approach to investigate how genetics, pharmacology, and aversive experience disrupt state-dependent serotonin signalling with pathological consequences for sensory processing. We find that all three factors lead to increased neocortical serotonin levels during the initial postnatal period. Genetic deletion of the serotonin transporter or antidepressant dosing results in a switch from hypo- to hyper-cortical activity that arises as a consequence of altered cortical GABAergic microcircuitry. However, the trajectories of these manipulations differ with postnatal exposure to antidepressants having effects on adult sensory encoding. The latter is not seen in the genetic model despite a similar early phenotype, and a distinct influence of maternal genotype on the development of supragranular layers. These results reveal the dynamics and critical nature of serotonin signalling during perinatal life; pharmacological targeting of which can have profound life-long consequences for cognitive development of the offspring.