Browse Articles
Discover research articles across all indexed journals
Canonical PKCα-immunoreactive rod bipolar cells are present in nocturnal snakes but not in diurnal snakes
Abstract A series of studies have described aspects of outer-retinal morphology and visual opsins in different snake species. Among squamate reptiles, snakes stand out for their diversity in photoreceptor morphotypes and pronounced differences in retinal architecture between diurnal and nocturnal species. Diurnal snakes have a cone-dominated retina, lacking typical rods, but containing a transmuted, cone-like rod and exhibiting a low photoreceptor density. Nocturnal species have rod-dominated retinas with high rod densities and two or three types of cones. Despite these striking differences, no previous study has examined inner-retinal neuron types or aspects of neural circuitry in snakes. In this study, we used immunohistochemistry to investigate a specific type of bipolar cell immunoreactive to the anti-protein kinase C alpha (PKCα) antibody, a well-established marker for rod bipolar cells, and to assess its connectivity with photoreceptors in eight nocturnal and 13 diurnal species of snakes from eight families. Nocturnal snakes exhibited a canonical rod-selective PKCα-immunoreactive rod bipolar cell, whereas in diurnal species, the anti-PKCα antibody labeled a distinct population of long-wavelength-sensitive, cone-selective bipolar cells. This study reveals clear differences between diurnal and nocturnal species and highlights the remarkable plasticity and evolutionary innovation of the visual system in this group.
Development of 1-(substituted benzofuran-2-yl)-3-(halogen-substituted phenyl)prop-2-en-1-ones as potent, reversible, and selective MAO-B inhibitors for Parkinson’s Disease
Early detection and community-based surveillance of Aedes albopictus in the Razmian region of Qazvin Province, Iran
Genome-wide identification and expression analysis of the LBD gene family in Jasminum sambac
Species-specific assessment of climate change vulnerability in Himalayan Pikas and identification of at-risk elevational and latitudinal zones
Intercomparison case study of data-driven reconstructions of a cloud-obscured Saharan dust plume in Europe
Abstract On 15 March 2022 an intense, but partially cloud-obscured, Saharan dust plume was transported towards Europe by an atmospheric river. Ten-year statistics of dust plumes co-occurring with cloud cover over Europe highlights that regionally up to 100% of dust plumes are obscured by clouds, which poses challenges for reconstructing dust plumes from satellite images. The European dust case on 15 March 2022 is used to investigate whether data-driven machine-learning techniques for restoring the spatial extent of dust plumes in SEVIRI satellite images can be alleviated by exploiting the rich ground-based data sets in Europe. Satellite images were paired with different combinations of ground-based observation data, stemming from ground-based remote sensing, weather reports and measurements of particulate matter, using a k-nearest neighbours approach. Combining ceilometer and photometer data with satellite images added the most value for restoring the dust plume extent and are recommended for future reconstructions of cloud-obscured dust plumes.
The predictive value of systemic inflammation response index and albumin-to-globulin ratio for prognosis in non-small cell lung cancer
LiMPtTl (M = Ti, Zr) Heusler alloys for high-performance energy harvesting: a DFT study
Erratum: Morishita et al., “Two-Step Actions of Testicular Androgens in the Organization of a Male-Specific Neural Pathway from the Medial Preoptic Area to the Ventral Tegmental Area for Modulating Sexually Motivated Behavior”
Enhancing the discharge capacity of contracted weirs using novel models
Correction: Comprehensive performance assessment of the BMIA-12 a system for bone marrow cell quantification in normal and hematological malignancy samples
Spectral residual augmented classical least squares for simultaneous green determination of cetirizine, fexofenadine and loratadine by UV spectrophotometry in pharmaceuticals and environmental samples
Green synthesis of ZnO nanoparticles using bioactive compounds from the Amycolatopsis roodepoortensis strain EA7 and their effects in the HT-29 cell line
Multiplexing antibiotic screening assay in droplet microfluidics
Abstract Environmental samples contain complex microbial communities hiding a treasure trove of active compounds. However, screening for active natural products from environmental samples is challenging due to inefficient cultivation techniques and a lack of proper screening platforms. For empowering antibiotic screening assays from complex microbial communities, we have developed and optimized a droplet-based platform with multiplexing capability. A cultivation strategy for bacteria in picoliter droplets was combined with phenotypic screening using multiple whole-cell reporter species. A mixture of two different fluorescently labelled reporter strains, one Gram-positive and one Gram-negative, was picoinjected to each of millions of picoliter cultures, which were screened for inhibiting activity based on the independent survival signals of each reporter species. Proof-of-concept experiments demonstrate efficient detection, selection, and recovery of a model Streptomyces strain from a synthetic mixture according to the specific inhibition of the reporter strains by the produced antibiotic. Subsequently, the established platform was successfully applied to screen environmental microbial communities from soil samples. This approach showcases multiplexing capabilities for screening assays in microfluidic droplets in order to simultaneously screen for new bioactive compounds with various inhibition profiles.
Machine learning–driven prediction of mechanical properties of lightweight concrete based on experimental data
Abstract Lightweight concrete (LWC) is increasingly used in structural and non-structural applications due to its ability to reduce self-weight while maintaining adequate mechanical performance. However, predicting the mechanical behavior of EPS-based lightweight concrete remains challenging because of the complex and nonlinear interactions between mix composition, density, curing age, and aggregate–matrix characteristics. This study proposes an integrated experimental and machine learning–based framework to predict the mechanical properties of lightweight concrete incorporating expanded polystyrene (EPS) particles (Addipor 55) as a partial volumetric replacement of natural coarse aggregate. An experimental program was conducted using EPS replacement levels ranging from 0 to 500 L/m³, combined with a fixed silica fume content of 60 kg/m³ and a polycarboxylate-based superplasticizer at a constant water–cement ratio of 0.35. Compressive strength, splitting tensile strength, and density were measured at curing ages of 3, 7, and 28 days. The results showed a systematic reduction in density from 2380 kg/m³ for the control mix to 1720 kg/m³ at the highest EPS content. The 28-day compressive strength decreased from 41.2 MPa to 28.6 MPa, while the splitting tensile strength declined from 4.30 MPa to 2.65 MPa. To enhance model robustness, the experimental dataset was expanded using a physically constrained data augmentation approach based on experimentally observed trends. An Artificial Neural Network (ANN) model was then developed and validated using independent data subsets, demonstrating excellent predictive performance with coefficients of determination (R²) of approximately 0.998 for both compressive and splitting tensile strengths. The proposed ANN model, supported by a graphical user interface (GUI), provides a reliable and efficient tool for predicting the mechanical performance of EPS-based lightweight concrete and optimizing mix design with reduced experimental effort.
Aroma compound production and probiotic functionality of Bacillus paralicheniformis isolates from Berma, a fermented fish product of Tripura, India
Blockchain-enabled secure authentication and privacy-preserving information sharing in VANETs using adaptive echo state networks and dual trapdoor homomorphic encryption
Abstract A blockchain-driven approach is presented to ensure robust authentication and private data distribution in Vehicular Ad Hoc Networks (VANETs). A primary objective to develop a robust authentication procedure using an Adaptive Echo State Network (AESNet) and a dual trapdoor homomorphic encryption method (DTHE-OMK) to enhance secure data transmission in VANETs. This work employs a combination of optimization strategies, including the Position Updated Enhanced Frilled Lizard Optimization (PUE-FLO) algorithm, to fine-tune the parameters of the AESNet for node authentication. The authentication analysis on accuracy provided results in the range of 6.323% of CWO-AESNet, 5.828% of TOT-AESNet, 8.353% of WOA-AESNet, 6.323% of FLO-AESNet for the optimization algorithms and 7.838% of DNN, 4.729% of LSTM, 4.248% of GRU, and 4.729% of ESNet for various literature models, respectively. This work concludes that the developed approach improves both integrity and safety of information shared across VANETs.
Investigation of the insulation performance of lightweight concrete with silica aerogel additives produced using volcanic tuff waste
Abstract This paper examined how the addition of silica aerogel would impact the performance of lightweight concrete made using Bayburt stone (volcanic tuff) aggregate in insulation. A silica-based gel material was produced by the synthesis of Bayburt stone by the sol-gel process and then added to lightweight concrete mixtures prepared by Taguchi L8 experimental matrix. Signal-to-noise ratios and ANOVA were used to determine factors’ contribution within the chosen design space by analysing the effects of aerogel ratio, cement dosage and water to cement ratio, on unit weight, thermal conductivity, ultrasonic pulse velocity and compressive strength. The findings revealed that the higher the content of aerogel, the lower the unit weight and thermal conductivity by up to 46 per cent at the expense of the reference mixture, which signified better thermal insulation. The porosity percentage of the tested mixtures rose to 31.48%, and compressive strength dropped with the increase in the content of aerogel. Thermogravimetric data indicated that mixtures with aerogel experienced lower total mass loss, but these data are not taken as evidence of long-term stability at all, but only as the differences in thermal degradation behaviour during the test range. The combination of volcanic tuff aggregate and silica-based incorporation of aerogel can be a prospective approach to non-structural lightweight concrete use within the constraints of the current experimental programme, where the minimisation of density and thermal conductivity should be prioritised. The implications related to economics and sustainability are addressed qualitatively.