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Non-invasive detection of choroidal melanoma via tear-derived protein corona on gold nanoparticles: a machine learning approach

Blockchain-based trusted traceability and sustainability certification of leather products

The leather supply chain comprises numerous organizations and stakeholders, particularly when sustainability aspects are taken into account, making it a complex system. The complexity inherent in such systems can lead to inaccurate information, lack of transparency, and limited data provenance. Moreover, there has been a surge in the call for sustainable practices within leather production, propelled by growing environmental consciousness and ethical considerations. In this paper, we address these challenges by proposing a blockchain-based solution designed to ensure trusted and secure traceability and sustainability throughout the entire life cycle of leather products. By harnessing the inherent capabilities of Ethereum smart contracts and blockchain technology, such as decentralization, immutability, data integrity, and transparency, we guarantee the secure and reliable tracing of materials from the farm to the final consumer. Moreover, we provide proof of sustainability by which certification agencies monitor, audit, and approve the sustainable processes and practices carried out by the different stakeholders at all stages of production to ensure compliance with industry standards and regulations. The paper presents the blockchain-based system architecture, implementation, and validation of algorithms and smart contracts. It also evaluates the security measures and cost-effectiveness of the system to offer valuable insights into its robustness and efficiency. We have made the developed smart contracts code publicly available on GitHub.

Reviving the past and designing the future: Books in brief

Structure-guided design of a novel, stable, and soluble Cecropin A variant for antimicrobial therapeutic applications

Forecasting electricity consumption of India through nighttime satellite imagery

Amidst a growing need for effective energy management, government policies increasingly rely on accurate electricity consumption forecasts to make informed decisions on renewable energy adoption. This study investigates the predictive capabilities of night light satellite imagery in forecasting electricity usage in India, aligning with Sustainable Development Goals 7 and 10. Utilizing data from the VIIRS satellite and NASA’s Black Marble product, the research employs various LSTM models to analyse electricity consumption trends. Additionally, state-wise analyses have been conducted by applying k-means clustering to capture spatial consumption variations. By demonstrating the strong correlation between night lights and electricity consumption, the study emphasizes the utility of satellite imagery for actionable insights into energy dynamics. The results emphasize the viability of night light data as a dependable indicator of electricity demand, with MAPE values below 10% and RMSE values below 20 MU. It also highlights the transformative impact of remote sensing technologies in advancing sustainable development agendas and highlights the pivotal role of night light imagery in energy forecasting initiatives.

Catalytic enantioselective synthesis of alkylidenecyclopropanes

Abstract The enantioselective construction of small-ring carbocycles provides organic chemists with an enduring challenge1. Despite their commercial importance, enantioselective synthetic routes towards alkylidenecyclopropanes, a class of small-ring carbocycles, remain underdeveloped2,3. Alkylidenecyclopropanes can be converted into cyclopropanes, a common feature in drug molecules (for example, Nirmatrelvir 1)4, as well as both naturally occurring and synthetic agrochemicals (for example, permethrin 2)5,6. Here we describe the facile synthesis of highly enantioenriched alkylidenecyclopropanes through the use of a bifunctional iminophosphorane catalysed, stereo-controlled, strain-relieving deconjugation. Small modifications to the basic catalyst system were used to broaden the scope of the reaction to substrates containing ester, amide, phosphine oxide and ketone functionalities. Through the design of a suitable substrate and retuning of the catalyst’s iminophosphorane moiety, the transformation was effectively applied to the synthesis of a single stereoisomer of the commonplace insecticide permethrin as well as a range of cyclopropane-based insecticide cores. State-of-the-art computational studies were performed to provide detailed insights into the mechanistic pathway and origin of both diastereoselectivities and enantioselectivities.

An innovative approach to enhancing the strength and durability of recycled aggregate concrete through fly ash-silica fume coating and rice husk ash supplementation

Abstract The building industry responded to the growing imperative to reduce the global ecological footprint by developing new inventive methods of utilizing waste materials. Concrete waste is one of the leading contributors to global waste streams and a great opportunity for sustainable reuse. Recent research has demonstrated its potential for structural applications in concrete, although its use for nonstructural component construction has been well established. This study introduces an innovative approach, combining the impact of S-FA (silica fume-fly ash) slurry coated recycled aggregates (RCA) and supplementary cementitious material (Rice husk) simultaneously, for the first time to be evaluated for structural performance. These findings present a major improvement in the reuse of concrete waste for structural applications by overcoming the constraints recognized by the mechanical characteristics of RCA. The study is based on an extensive experimental program which evaluated critical parameters such as compressive and tensile strength, water absorption and acid resistance. The incorporation of 100% treated RCA aggregates improved compressive strength by 23% compared to the control mix of non-treated 100% RCA aggregates. Similarly for the same mix acid resistance strength demonstrated a 7% increase while the Non-destructive test results revealed that the treatment enhanced RCA performance by providing improved results through ultrasonic pulse velocity (UPV) readings that increased by 19% compared to the control mix of non-treated RCA aggregates (100%).

Metabolic diversity in sorghum: Mechanism underlying grain color variation and fermentation quality

Sorghum (Sorghum bicolor L. Moench) serves as a critical staple cereal, forage crop, and primary raw material for baijiu (Chinese distilled spirits) production and vinegar fermentation. In this study, we performed comprehensive untargeted metabolomic profiling on widely cultivated sorghum accessions exhibiting diverse grain color phenotypes, followed by in-depth characterization of their metabolic signatures. The results demonstrated significant inter-accession variability in metabolite composition, with GL18 showing the most pronounced accumulation of metabolites within the phenylpropanoids and polyketides class. KEGG pathway enrichment analysis revealed substantial divergence in flavonoid biosynthetic pathways among accessions, particularly in the biosynthesis of naringenin, delphinidin, cyanidin, and pelargonidin 3-glucoside—key pigments correlated with grain color variations. Metabolite profiling further identified distinct accumulation patterns of flavor precursors (e.g., β-phenylethanol precursors), amino acids (e.g., tryptophan, L-leucine), and glycosides (e.g., L-rhamnofuranose) that significantly influence baijiu sensory quality attributes. For vinegar fermentation, significant inter-accession differences were observed in carbohydrate (sucrose, mannitol), amino acid (L-proline, arginine), and organic acid (lactic acid, quinic acid) accumulation profiles, which correlated with fermentation efficiency and final product quality. This study provides novel insights into the metabolic basis of sorghum grain color diversity and highlights the potential for tailored sorghum accessions to enhance the quality and diversity of baijiu and vinegar products, thereby contributing to the optimization of crop quality and agricultural resource efficiency.

Tabersonine inhibits inflammation and apoptosis through the JAK1/STAT3 signaling pathway to alleviate LPS-induced acute lung injury

Trends in pediatric cochlear implants: The dual impact of COVID-19 and Lebanon’s crisis

Background Hearing loss is a major public health issue globally, especially in low- and middle-income countries (LMICs), where access to cochlear implants (CIs) is restricted by cost and limited healthcare infrastructure. In Lebanon, the 2020 economic crisis and the COVID-19 pandemic further reduced access to essential services, including pediatric CIs. Aim This study assesses the impact of the 2020 Lebanese economic crisis on the number and funding sources of pediatric CI surgeries. Methods A retrospective review was conducted on 228 pediatric patients who underwent 235 CI surgeries between 2017 and 2023. The number of surgeries and funding sources were compared before and after the 2020 crisis. Funding categories included government, private insurance, donations, or a combination. Data were analyzed using R software. Results There was no significant difference in the number of surgeries before (113) and after (122) the crisis (p = 0.56). However, a marked shift in funding occurred. Government-funded procedures dropped from 45.87% to 12.61% (p < 0.001), while private and donation-based funding rose from 32.11% to 66.39% (p < 0.001). The mean age at surgery declined from 5.86 to 3.57 years post-crisis (p < 0.05), indicating greater awareness of early intervention benefits. Conclusion Despite economic hardship, the demand for pediatric CIs persisted, with families turning to private and charitable sources. Enhanced government support and the classification of CIs as essential health services are vital. Early diagnosis and intervention should be prioritized to improve outcomes in children with hearing loss.

New records of <i>Lispe aquamarina</i> and <i>Lispe pseudohirsutipes</i> (Diptera: Muscidae) in Kanto region, Japan

Variational quantum recommendation system with embedded latent vectors

Investigating the role of mood induction on emotional facial recognition in social anxiety

Individuals with high trait social anxiety (SA) experience multiple challenges when interacting with others. Social skills abilities like accurate emotional facial expression recognition are particularly impaired in this population. Ambiguous and angry facial expressions are most often miscategorized and met with uncertainty. Part of this confusion may be attributable to increased state anxiety when approaching social situations. However, little is known about the influencing role of state anxiety on emotional facial expression recognition among those with social anxiety. The present study aimed to evaluate the impact of state anxiety on emotional facial recognition. Sixty-eight undergraduate students with high trait social anxiety participated in a pre-post emotional facial recognition task. Participants were presented with happy, neutral, and angry facial expressions in random order and asked to categorize the expressed emotion among six basic emotion categories. In between emotional facial recognition tasks, participants engaged in a mood induction procedure (i.e., mock discussion with a confederate) aimed to increase state anxiety. The results suggest that individuals with high-trait SA were significantly worse at recognizing happy facial expressions post-affect induction. Furthermore, individuals with high-trait SA showed significant difficulty in accurately recognizing neutral facial expressions across pre- and post-conditions. An error rate analysis revealed that neutral and happy facial expressions were most often miscategorized as either surprise, angry, sad, or disgust. This study highlights that positively-valenced expressions are met with increased uncertainty particularly when experiencing elevations in state anxiety.

Invading toward the North: Status of the spread of the native mosquito species <i>Aedes albopictus</i> in northern Japan

Revealing parthenogenetic reproduction in a praying mantis inhabiting South American grasslands

Prevalence of congenital missing permanent teeth and its association with side and gender in a Saudi subpopulation

Background This study’s objectives are to ascertain the frequency of congenital tooth missing and evaluate the relationship between gender and side characteristics. Method This retrospective study involved participants who attended private dental clinics as well as a dental college in Hail, Saudi Arabia. Out of the 1,150 patients examined, 494 (220 males and 274 females) fulfilled the inclusion criteria. Different types of tooth agenesis were evaluated and classified into the following categories: mild (one or two teeth missing), moderate (three to five teeth missing), and severe (six or more teeth missing). The analysis did not include third molars. The existence of retained primary teeth was noted, along with any dental abnormalities accompanying tooth agenesis. Statistical analysis was conducted utilizing the chi-square test and Fisher’s exact test to explore possible relationships between variables. A significance level of 5% (alpha = 0.05) was utilized. Results Out of the 1150 panoramic radiographs that were examined, 494 individuals (220 (44.5%) males and 274 (55.5%) females) met the criteria for inclusion. The prevalence of congenitally missing permanent teeth was 65 (13.2%) in the study sample. The prevalence of congenitally missing teeth were more incidence in maxilla 36 (56.3%) than mandible 28 (43.8%). There is statistically significant association between the occurrence of congenitally missing teeth and gender or arch (p < 0.05). The prevalence of dental anomalies was similar in both sides. There are not a statistically significant association (p > 0.05) between the occurrence of congenitally missing teeth and side. The second premolar was the most commonly missing (7.3%). The retained deciduous teeth was shown to be the most common dental anomaly, with a prevalence of 15 (23.4%). Conclusions The prevalence of congenitally missing teeth fell within the range reported in previous studies. Second premolars were the most frequently congenitally missing teeth, with maxillary teeth more commonly affected than mandibular ones.

Soft robot steers itself down the human airway

Recent Occurrence of Tabanid Flies in the Arimine Area of the Toyama Prefecture and Its Relationship with the Invasion and Expansion of Shika Deer the Distribution

The independent and combined effects of smoking and chronic obstructive pulmonary disease on body mass index trajectories

A hybrid deep learning framework combining transformer and logistic regression models for automatic marine mucilage detection using sentinel-1 SAR data: A case study in Armutlu-Zeytinbağı, Marmara Sea

The identification of various objects and species found in nature is of great importance today. Active and passive imaging systems are in a beneficial position in this direction, both in terms of cost and convenience. Recently, mucilage events in our country pose a great risk for both marine life and human life. In this study, water areas in one of the regions affected by the mucilage event that occurred in May 2021 were chosen as the object. The region between Armutlu-Zeytinbağı in the Marmara Sea was chosen as the study area. 1300 samples were selected from the mucilage region and recorded with the help of GPS. After these selected samples were chosen as mucilage area for 17 May–22 May and as a clean area for 21 June-22 June (2600 samples in total), image analyses were made using time series with the help of Sentinel-1 satellite images. These image analyses were performed using Sentinel-1 band parameters (VV-VH). A unique data set was created by recording the numerical data showing the backscattering values of the VV-VH polarization band images. It is aimed to automatically detect the mucilage area by applying deep learning and machine learning to the obtained data set. It has been observed that the accuracies of our applied hybrid (Transformer Method + Logistic Regression), deep learning (RNN, CNN) and machine learning models (Decision Tree, Naive Bayes, SVM) are high (96%−100%). With the applied deep learning and machine learning methods, it is thought that regions can be detected more easily and intervened early in these regions.