Enhancing the efficiency of human pancreatic islet dissociation

Over half a million children worldwide are affected by type 1 diabetes, an autoimmune disease characterized by the destruction of insulin-producing pancreatic beta cells. Islet transplantation is a treatment that is currently limited by the lack of vascular network to support large islets post-transplantation. A promising proposal is to disperse native islets into single-cell suspensions and re-aggregate them into smaller, uniform “pseudo-islets”. Substantial cell loss during islet dispersion, however, remains an important obstacle that limits the yield of pseudo-islet aggregates, especially considering the scarcity of donor islets. To optimize the dissociation protocol, we experimented with different cell dissociation reagents, concentrations, and times in order to establish standards for future pseudo-islet formation procedure.Isolated human islets were dissociated using Trypsin, TrypLE, Accutase, Accumax, and Dispase. These dissociation reagents were identified through literature and the concentrations as well as dissociation times used were in ranges previously outlined. Cell counts of viable cells were recorded using Trypan Blue and PicoGreen DNA assay to quantify cell loss during islet dispersion, filtration and post-culture. Assessment of the viability of the re-aggregated pseudo-islets post-culture was performed using the Alamar Blue assay.Preliminary results showed the potential for 5.8-fold increase in cell recovery which provides evidence of the significant need to optimize the dissociation protocol. TrypLE showed the highest recovery of cells both post-dissociation and filtration. Results from the project are promising and further investigations will allow the results to become applicable to clinical trials. Improving the recovery and quality of dissociated islet cells will directly help increase the number of treatable patients from the limited supply of donor islets

Corrosion-induced deterioration and fracture mechanisms in ultra-high-performance fiber-reinforced concretet

Ultra-high-performance fiber-reinforced concrete (UHPFRC) is an excellent material for harsh environments, but corrosion will change its internal microstructure and complicate the fracture evolution, bringing great difficulties in evaluating the long-term service life. Limited attention has been paid to the fracture mechanism of the UHPFRC upon corrosion. In the present study, integrating acoustic emission (AE) and digital image correlation (DIC) techniques are used to assess the micro/macrocracking characteristics of the specimens upon various corrosion degrees. Results show that the 56-day corroded UHPFRC with 2 vol% presents a remarkable decrease rate of 32%, 29% and 30% in the flexural stiffness, flexural strength and compressive strength. During the loading process, compaction of the original defects induced by fiber corrosion is concentrated in the elastic stage, the newborn cracks triggered by loading mainly occur in the strain-hardening stage, and the expansion of cracks mainly lies in the strain-softening stage. Corroded UHPFRC specimens with higher corrosion damage have a greater maximum strain value at the crack. In addition, the failure mode changes from shear crack failure to a brittle failure of tensile crack as corrosion damage increases. The macroscopic destruction of the corroded UHPFRC is a manifestation of internal microdamage evolution in fiber corrosion and matrix deterioration.</p

CIR at the NTCIR-17 ULTRE-2 Task

The Chinese academy of sciences Information Retrieval team (CIR) has participated in the NTCIR-17 ULTRE-2 task. This paper describes our approaches and reports our results on the ULTRE-2 task. We recognize the issue of false negatives in the Baidu search data in this competition is very severe, much more severe than position bias. Hence, we adopt the Dual Learning Algorithm (DLA) to address the position bias and use it as an auxiliary model to study how to alleviate the false negative issue. We approach the problem from two perspectives: 1) correcting the labels for non-clicked items by a relevance judgment model trained from DLA, and learn a new ranker that is initialized from DLA; 2) including random documents as true negatives and documents that have partial matching as hard negatives. Both methods can enhance the model performance and our best method has achieved nDCG@10 of 0.5355, which is 2.66% better than the best score from the organizer.Comment: 5 pages, 1 figure, NTCIR-1

Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank

Unbiased learning to rank (ULTR) aims to mitigate various biases existing in user clicks, such as position bias, trust bias, presentation bias, and learn an effective ranker. In this paper, we introduce our winning approach for the "Unbiased Learning to Rank" task in WSDM Cup 2023. We find that the provided data is severely biased so neural models trained directly with the top 10 results with click information are unsatisfactory. So we extract multiple heuristic-based features for multi-fields of the results, adjust the click labels, add true negatives, and re-weight the samples during model training. Since the propensities learned by existing ULTR methods are not decreasing w.r.t. positions, we also calibrate the propensities according to the click ratios and ensemble the models trained in two different ways. Our method won the 3rd prize with a DCG@10 score of 9.80, which is 1.1% worse than the 2nd and 25.3% higher than the 4th.Comment: 5 pages, 1 figure, WSDM Cup 202

Carbonation of photocatalytic aluminosilicate cement composites: Evolution of self-cleaning and radiative cooling properties

This study aims to understand the impact of carbonation on the photocatalytic self-cleaning and radiative cooling properties of autoclaved cement composites. The composites are prepared with varying silica contents and sources, with and without alumina addition under autoclave conditions. The phase composition, microstructure, optical properties, and photocatalytic self-cleaning performance are systematically characterized and analyzed. Outdoor measurements are conducted to validate the cooling performance of the material under ambient conditions. Cracking behaviors during autoclave/carbonation treatments and the performance degradation in functionalities upon carbonation are observed. The cement/nanosilica/alumina ternary system demonstrates the best overall performance, considering productibility during autoclave curing, effectiveness, and carbonation durability of functionalities. C-A-S-H minerals, mainly Al-Foshagite, in this system, serve as precursors for the generation of γ-Al2O3 upon carbonation, mitigating the deterioration of photocatalytic properties. The findings are expected to provide new insights for the development and engineering practice of functional cementitious materials for durable cooling applications