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Black soldier fly larvae meal influences the growth, reproduction and related gene expression in farm-raised and growth-trait-selected Clarias magur brooders
Abstract A 120-day feeding trial was conducted to evaluate the effects of Black Soldier Fly Larvae (BSFL) meal on gonadal development, reproductive performance, and gene expression in two populations of Clarias magur : Farm-raised stock (FR) and genetically selected stock (GS), Maha Magur. Two diets, FM (14.65% fishmeal) and IM (20% BSFL, 0% fishmeal), were employed in a 2 × 2 factorial design. A total of 168 brooders were fed respective diets on a satiation basis. GS showed significantly better growth and nutrient utilization (final weight, weight gain %, SGR, PER) than FR ( p < 0.05). Conversely, FR showed superior reproductive indices (GSI, Relative fecundity), reproductive performances (fertilization %, hatching % and larval survival %) ( p < 0.05). Hormonal profiles varied among stocks, with FR having higher serum 17α20βDHP and GS females having increased estradiol. Serum 11-Keto-testosterone was increased in FR females and GS males. Gene expression indicated upregulation of fshr , cyp19a1a , and vtg in GS females, which correlated with increased estradiol, while increased lhr expression, correlating with serum 17α20β-DHP found in FR females. Histology indicated mature oocytes and numerous spermatozoa in FR, with GS females having vitellogenic oocytes. BSFL addition had little effect on the reproductive characters of either stock. In general, FR exhibited better reproductive performance and maturity irrespective of GS exhibiting improved growth.
Creation of a rich vascular subcutaneous space for cell transplantation via injectable biological hydrogels
Abstract The subcutaneous space offers an attractive and accessible site for cell transplantation. However, its clinical utility is often hindered by insufficient vascularization. This study evaluated the vascularization potential of three hydrogels; human collagen type I, human fibrin, and alginate implanted subcutaneously in Sprague-Dawley rats. Polylactide-co-glycolide (PLG) scaffolds used as positive controls for foreign body response. Sequential injections were performed at 1, 2, and 4 weeks, and tissues were retrieved for histopathological examination. Human collagen type I and fibrin induced robust neovascularization compared to controls, with peak vessel density at week 1 (2.79-fold and 3.18-fold; P < 0.001) and sustained increases through week 4 (1.94-fold and 2.3-fold, respectively). No significant differences were observed between human collagen type I and fibrin at any time point. Both biomaterials were well tolerated without evident of fibrosis or foreign body reaction. Alginate produced the strongest early angiogenic effect (3.96-fold at week 1; P < 0.001) but was associated with marked inflammation and fibrosis by week 4. PLG scaffolds induced modest vascularization but consistently provoked inflammatory reactions and fibrosis. Human-derived hydrogels thus combine rapid and durable vascularization with excellent biocompatibility, providing a minimally invasive strategy to enhance subcutaneous cell transplantation outcomes.
Enhancing surveillance of antimicrobial resistant organisms in British Columbia through community-level wastewater testing
Geometry-aware point cloud clustering for spherical-component aggregate modeling
Abstract This paper proposes a method for obtaining independent mesh models of individual components from a point cloud representing an aggregate. An aggregate consists of a collection of small, similar components, such as individual grapes in a bunch. Typical shape reconstruction creates a rough shape of the entire bunch, but fails to recover individual components from the bunch due to occlusion and missing points for shapes. To achieve this type of modeling, we assume that each component can be approximated as a spherical shape. Leveraging this assumption, we develop geometry-aware clustering that identifies and segments individual components from the aggregate. During this procedure, we search for the optimal position and size of a predefined aggregate component that best fits the cluster. When overlapping components are detected, the corresponding clusters are merged. We demonstrate the effectiveness of the proposed method by applying it to several types of aggregates, such as grapes and tomatoes.
The hemopexin domain of matrix metalloproteinase-9 attenuates lipopolysaccharide-induced interleukin-6 secretion in liver
Plasma microRNAs and chemokines as biomarkers for rejection in liver transplantation with score verification and tissue correlation
MapReduce-based deep learning framework for potato leaf disease detection in sustainable precision agriculture
Integrated anaerobic membrane bioreactor-yeast biorefinery for co-production of hydrogen, volatile fatty acids, and microbial oil from food waste
Location and capacity optimization of urban sanitation robot base stations using improved NSGA-II
Impact of N-terminal domain on the sHSP Lo18 function
Solvatoalignment index and solvent-dependent behavior of asymmetric discotic liquid crystals: effect of polarity and alkyl chain length
MAR-YOLO: multi-scale feature adaptive selection and asymptotic pyramid for oriented Building detection in remote sensing images
Population-based national incidence of thyroid dysfunction in Spain
Orchestrating machine learning models in a swarm architecture for IoT inline malware detection
Abstract The Internet of Things (IoT) represents a vast network of interconnected devices engaged in continuous data exchange, real-time information processing, and autonomous decision-making through the Internet. The pervasive presence of sensitive data on IoT devices highlights their indispensable role in our daily lives. The rapid evolution of Information and Communications Technology (ICT) has ushered in a new era of interconnected devices, reshaping the computing landscape. With the expanding IoT ecosystem, cyberspace has become increasingly susceptible to frequent cyber threats. While IoT devices have greatly simplified and automated daily tasks, these devices have simultaneously introduced significant security vulnerabilities. The existing inadequacies in safeguarding these smart devices have rendered IoT the most vulnerable entry point for potential breaches, posing a tempting target for malicious actors. In response to these critical challenges, our study introduces an innovative solution known as Swarm-based Inline Machine Learning (SIML). This approach leverages the coordinated data processing capabilities of a swarm to effectively address and counter emerging malware threats. SIML represents a divergence from conventional standalone threat detection systems, offering a promise of more robust, distributed, and end-to-end security solutions for IoT environments. This approach significantly reduces the risk of malicious exploitation of IoT devices for launching cyber-attacks. The effectiveness of our proposed method was validated through rigorous testing using the UNSW-NB15 dataset. The results are compelling, boasting an impressive accuracy rate of 93.7% and a precision rate of 95%, achieved through the application of the Gradient-Boosting Tree algorithm under the proposed framework. Our comparative analysis reveals that the Gradient Boosting algorithm outperforms traditional methods without compromising efficiency when deployed in an inline setting. Furthermore, the proposed method has been benchmarked against the BoT-Iot and Edge-IIoTset datasets, and outperformance is noted with a minor degradation at higher throughput. This innovative approach not only enhances security in IoT but also paves the way for a safer and more resilient digital future.
Typhoon short-term heavy precipitation warning based on dual-polarization radar observations of precipitation particle vertical distribution
Dynamic power and frequency domain allocation for dedicated sensing signals in downlink ISAC
Mixing of a binary passive particle system using smart active particles
Abstract The controlled activity of active entities interacting with a passive environment can generate emergent system-level phenomena, positioning such systems as promising platforms for potential downstream applications in targeted drug delivery, adaptive and reconfigurable materials, microfluidic transport, and related fields. The present work aims to realise an optimal mixing of two segregated species of passive particles by introducing a small fraction of active particles ( $$2\%$$ by composition) with adaptive and intelligent behaviour, directed by a trained Artificial Neural Network-based agent. While conventional run-and-tumble particles can induce mixing in the system, the smart active particles demonstrate enhanced performance, achieving faster and more efficient mixing. Interestingly, an optimal mixing strategy doesn’t involve a uniform dispersion of active particles in the domain, but rather limiting their motion to an eccentrically placed zone of activity, inducing a global rotational motion of the passive particles about the system centre. A transition in the directionality of the passive particles’ motion is observed along the radius towards the centre, likening the active particles’ motion to an ellipse-shaped void with a defined surface speed. Situated at the intersection of active matter and machine learning, this work highlights the potential of integrating adaptive learning frameworks into traditional models of active matter.
Federated learning-based trust and energy-aware routing in Fog–Cloud computing environments for the Internet of Things
Simulation and experimental study of calcium sulfate and barium sulfate scale formation and Inhibition in petroleum engineering
Abstract Petroleum engineering could engage numerous challenges caused by sulfate mineral precipitation and deposition. This severe critical issue which could directly lead to production reduction, was investigated in this study. Accordingly, the precipitation, deposition, and inhibition of calcium and barium sulfate were scrutinized from the simulation and experimental perspectives. The simulation tools, PHREEQC and Aspen Plus, were first used to corroborate the results of the high-temperature (90 °C) standard static experimental tests conducted for calcium and barium sulfate precipitation and deposition phenomena. In the experimental phase of the inhibition study, folic acid was evaluated as a green scale inhibitor (SI) and compared to a phosphonate-based commercial SI regarding inhibition efficiency (IE%) and inhibition mechanisms. The findings indicated that folic acid reduced calcium and barium sulfate precipitation as much as 50.8% and 44.8% respectively at the specific critical mixing ratios through the crystal modification inhibition mechanism provided by scanning electron microscopy (SEM) analysis. Moreover, folic acid could perform effectively in the mitigation of calcium and barium deposition by 53.7% and 47.2% in the presence of 5 g of dolomite rock. However, a comparison of a commercial SI and folic acid showed that the commercial SI was weaker than folic acid for mitigation of sulfate deposition through the threshold inhibition mechanism.