Open source scientific bottle roller.

Proprietary bottle rolling systems automate some laboratory applications, however, their high costs limit accessibility. This study provides designs of an open source bottle roller that is compatible with distributed digital manufacturing using 3-D printed parts and readily-available commercial components. The experimental results show that the open source bottle roller can be fabricated for CAD$210 (about USD$150) in materials, which is 86% less expensive than the most affordable proprietary bottle roller on the market. The design, however, is more robust with enhanced capabilities. The design can be adapted to the user's needs, but is already compatible with incubators with a low profile (dimensions 50 cm x46 cm x8.8 cm) and capable of being operated at elevated temperatures. The systems can be adjusted to revolves from 1 to 200 RPM, exceeding the rotational speed of most commercial systems. The open source bottle roller as tested has a capacity greater than 1.2 kg and can roll twelve 100 mL bottles simultaneously. Validation testing showed that it can operate for days at 80 RPM without human intervention or monitoring for days at both room temperature and elevated temperatures (50 °C). Future work includes adapting the designs for different sizes and for different fabrication techniques to further reduce costs and increase flexibility.

Direct Selection of DNA-Encoded Libraries for Biased Agonists of GPCRs on Live Cells.

G protein-coupled receptors (GPCRs) are the largest superfamily of human membrane target proteins for approved drugs. GPCR ligands can have a complex array of pharmacological activities. Among these activities, biased agonists have potential to serve as both chemical probes to understand specific aspects of receptor signaling and therapeutic leads with more specific, desired activity. Challenges exist, however, in the development of new biased activators due, in part, to the low throughput of traditional screening approaches. DNA-encoded chemical libraries (DELs) dramatically improve the throughput of drug discovery by allowing a collective selection, rather than discrete screening, of large compound libraries. The use of DELs has been largely limited to affinity-based selections against purified protein targets, which identify binders only. Herein, we report a split protein complementation approach that allows direct identification of DNA-linked molecules that induce the dimerization of two proteins. We used this selection with a DEL against opioid receptor GPCRs on living cells for the identification of small molecules that possess the specific function of activation of either ß-arrestin or G protein signaling pathways. This approach was applied to δ-, µ-, and κ-opioid receptors and enabled the discovery of compound [66,66], a selective, G-protein-biased agonist of the κ-opioid receptor (EC50 = 100 nM, E max = 82%, Gi bias factor = 6.6). This approach should be generally applicable for the direct selection of chemical inducers of dimerization from DELs and expand the utility of DELs to enrich molecules with a specific and desired biochemical function.

Personalized Fair Split Learning for Resource-Constrained Internet of Things.

With the flourishing development of the Internet of Things (IoT), federated learning has garnered significant attention as a distributed learning method aimed at preserving the privacy of participant data. However, certain IoT devices, such as sensors, face challenges in effectively employing conventional federated learning approaches due to limited computational and storage resources, which hinder their ability to train complex local models. Additionally, in IoT environments, devices often face problems of data heterogeneity and uneven benefit distribution between them. To address these challenges, a personalized and fair split learning framework is proposed for resource-constrained clients. This framework first adopts a U-shaped structure, dividing the model to enable resource-constrained clients to offload subsets of the foundational model to a central server while retaining personalized model subsets locally to meet the specific personalized requirements of different clients. Furthermore, to ensure fair benefit distribution, a model-aggregation method with optimized aggregation weights is used. This method reasonably allocates model-aggregation weights based on the contributions of clients, thereby achieving collaborative fairness. Experimental results demonstrate that, in three distinct data heterogeneity scenarios, employing personalized training through this framework exhibits higher accuracy compared to existing baseline methods. Simultaneously, the framework ensures collaborative fairness, fostering a more balanced and sustainable cooperation among IoT devices.

Effect Analysis of GNSS/INS Processing Strategy for Sufficient Utilization of Urban Environment Observations.

The occlusion of buildings in urban environments leads to the intermittent reception of satellite signals, which limits the utilization of observations. This subsequently results in a decline of the positioning and attitude accuracy of Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated system (GNSS/INS). This study implements a smooth post-processing strategy based on a tightly coupled differential GNSS/INS. Specifically, this strategy used the INS-estimated position to reinitialize integer ambiguity. The GNSS raw observations were input into the Kalman filter to update the measurement. The Rauch-Tung-Striebel smoothing (RTSS) algorithm was used to process the observations of the entire period. This study analyzed the performance of loosely coupled and tightly coupled systems in an urban environment and the improvement of the RTSS algorithm on the navigation solution from the perspective of fully mining the observations. The experimental results of the simulation data and real data show that, compared with the traditional tightly coupled processing strategy which does not use INS-aided integer ambiguity resolution and RTSS algorithm, the strategy in this study sufficiently utilized INS observations and GNSS observations to effectively improve the accuracy of positioning and attitude and ensure the continuity of navigation results in an obstructed environment.

Enhanced Oral Bioavailability of Felodipine from Solid Lipid Nanoparticles Prepared Through Effervescent Dispersion Technique.

Felodipine (FLD), a dihydropyridine calcium channel blocker with excellent antihypertensive effect, is poorly soluble and undergoes extensive hepatic metabolism, which lead to poor oral bioavailability (about 15%) and limit its clinic application. The goal of this study was to develop solid lipid nanoparticles (SLNs) loading FLD to improve the oral bioavailability. The FLD loaded solid lipid nanoparticles (FLD-SLNs) were prepared by the effervescent dispersion technique developed by our laboratory, which might have some advantages over traditional methods. The FLD-SLNs showed desired particle characteristics with particle size (198.15 ± 1.82 nm), poly dispersity index (0.26 ± 0.02), zeta-potential (- 25.53 ± 0.60 mV), entrapment efficiency (95.65 ± 0.70%), drug loading (2.33 ± 0.10%), and a spherical appearance. Pharmacokinetic results showed that the FLD-SLNs presented 3.17-fold increase in area under the curve (AUC(0-t)) compared with free FLD after oral administration in beagle dogs, which indicated that SLNs prepared using the effervescent dispersion technique can improve the bioavailability of lipophilic drugs like felodipine by enhancement of absorption and reduction first-pass metabolism.

In situ one-pot synthesis of polydopamine/octadecylamine co-deposited coating in capillary for open-tubular capillary electrochromatography.

Mussel-inspired polydopamine (PDA) based materials are attractive as stationary phase for open-tubular capillary electrochromatography (OT-CEC) due to their many fascinating properties. However, all of the existing strategies for fabricating PDA based OT-CEC columns are limited in aqueous solutions. Consequently, it is a challenge work to directly immobilize the hydrophobic functional materials onto the inner wall of PDA modified capillary. Herein, by using the organic amine-inducing co-deposition strategy, a novel preparative method was developed for in situ one-pot synthesis of PDA/octadecylamine (ODA) co-deposited coating inside capillary as OT-CEC stationary phase. The formation and morphology of the PDA/ODA co-deposited coating were characterized by field emission scanning electron microscopy, atomic force microscope, attenuated total reflectance Fourier transform infrared spectroscopy and contact angle measurements. The separation performance of the fabricated PDA/ODA modified columns was validated by the separation of alkylbenzenes and steroids, which could achieve baseline separation with high separation efficiency. Their separation was found to follow the reversed phase chromatographic retention mechanism. The co-deposited column showed good stability and long lifetime. The repeatability of the PDA/ODA co-deposited column was also evaluated, with the relative standard deviations for intra-day and inter-day runs less than 5% and column-to-column runs less than 6%.