SALI: A Scalable Adaptive Learned Index Framework based on Probability Models

The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is learned indexes, which use a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04x compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+.Comment: Accepted by Conference SIGMOD 24, June 09-15, 2024, Santiago, Chil

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat

Wheat is a very important food crop for mankind. Many new varieties are bred every year. The accurate judgment of wheat varieties can promote the development of the wheat industry and the protection of breeding property rights. Although gene analysis technology can be used to accurately determine wheat varieties, it is costly, time-consuming, and inconvenient. Traditional machine learning methods can significantly reduce the cost and time of wheat cultivars identification, but the accuracy is not high. In recent years, the relatively popular deep learning methods have further improved the accuracy on the basis of traditional machine learning, whereas it is quite difficult to continue to improve the identification accuracy after the convergence of the deep learning model. Based on the ResNet and SENet models, this paper draws on the idea of the bagging-based ensemble estimator algorithm, and proposes a deep learning model for wheat classification, CMPNet, which is coupled with the tillering period, flowering period, and seed image. This convolutional neural network (CNN) model has a symmetrical structure along the direction of the tensor flow. The model uses collected images of different types of wheat in multiple growth periods. First, it uses the transfer learning method of the ResNet-50, SE-ResNet, and SE-ResNeXt models, and then trains the collected images of 30 kinds of wheat in different growth periods. It then uses the concat layer to connect the output layers of the three models, and finally obtains the wheat classification results through the softmax function. The accuracy of wheat variety identification increased from 92.07% at the seed stage, 95.16% at the tillering stage, and 97.38% at the flowering stage to 99.51%. The model’s single inference time was only 0.0212 s. The model not only significantly improves the classification accuracy of wheat varieties, but also achieves low cost and high efficiency, which makes it a novel and important technology reference for wheat producers, managers, and law enforcement supervisors in the practice of wheat production

Injectable supramolecular hydrogel formed from a-cyclodextrin and PEGylated arginine-functionalized poly(L-lysine) dendron for sustained MMP-9 shRNA plasmid delivery

© 2016 Acta Materialia Inc.Hydrogels have attracted much attention in cancer therapy and tissue engineering due to their sustained gene delivery ability. To obtain an injectable and high-efficiency gene delivery hydrogel, methoxypolyethylene glycol (MPEG) was used to conjugate with the arginine-functionalized poly(L-lysine) dendron (PLLD-Arg) by click reaction, and then the synthesized MPEG-PLLD-Arg interacted with a-cyclodextrin (a-CD) to form the supramolecular hydrogel by the host-guest interaction. The gelation dynamics, hydrogel strength and shear viscosity could be modulated by a-CD content in the hydrogel. MPEG-PLLD-Arg was confirmed to bind and deliver gene effectively, and its gene transfection efficiency was significantly higher than PEI-25k under its optimized condition. After gelation, MMP-9 shRNA plasmid (pMMP-9) could be encapsulated into the hydrogel matrix in situ and be released from the hydrogels sustainedly, as the release rate was dependent on a-CD content. The released MPEG-PLLD-Arg/pMMP-9 complex still showed better transfection efficiency than PEI-25k and induced sustained tumor cell apoptosis. Also, in vivo assays indicated that this pMMP-9-loaded supramolecular hydrogel could result in the sustained tumor growth inhibition meanwhile showed good biocompatibility. As an injectable, sustained and high-efficiency gene delivery system, this supramolecular hydrogel is a promising candidate for long-term gene therapy. Statement of Significance To realize the sustained gene delivery for gene therapy, a supramolecular hydrogel with high-efficiency gene delivery ability was prepared through the host-guest interaction between a-cyclodextrin and PEGylated arginine-functionalized poly(L-lysine) dendron. The obtained hydrogel was injectable and biocompatible with adjustable physicochemical property. More importantly, the hydrogel showed the high-efficiency and sustained gene transfection to our used cells, better than PEI-25k. The supramolecular hydrogel resulted in the sustained tumor growth inhibition meanwhile keep good biocompatibility. As an injectable, sustained and high-efficiency gene delivery system, this supramolecular hydrogel is a promising candidate in long-term gene therapy and tissue engineering

Chirality-specific growth of single-walled carbon nanotubes on solid alloy catalysts

Carbon nanotubes have many material properties that make them attractive for applications(1,2). In the context of nanoelectronics(3), interest has focused on single-walled carbon nanotubes(4) (SWNTs) because slight changes in tube diameter and wrapping angle, defined by the chirality indices (n,m), will shift their electrical conductivity from one characteristic of a metallic state to one characteristic of a semiconducting state, and will also change the bandgap. However, this structure-function relationship can be fully exploited only with structurally pure SWNTs. Solution-based separation methods(5) yield tubes within a narrow structure range, but the ultimate goal of producing just one type of SWNT by controlling its structure during growth has proved to be a considerable challenge over the last two decades(6-9). Such efforts aim to optimize the composition(6,10-17) or shape(18-21) of the catalyst particles that are used in the chemical vapour deposition synthesis process to decompose the carbon feedstock and influence SWNT nucleation and growth(22-25). This approach resulted in the highest reported proportion, 55 per cent, of single-chirality SWNTs in an as-grown sample(11). Here we show that SWNTs of a single chirality, (12, 6), can be produced directly with an abundance higher than 92 per cent when using tungsten-based bimetallic alloy nanocrystals as catalysts. These, unlike other catalysts used so far, have such high melting points that they maintain their crystalline structure during the chemical vapour deposition process. This feature seems crucial because experiment and simulation both suggest that the highly selective growth of (12, 6) SWNTs is the result of a good structural match between the carbon atom arrangement around the nanotube circumference and the arrangement of the catalytically active atoms in one of the planes of the nanocrystal catalyst. We anticipate that using high-melting-point alloy nanocrystals with optimized structures as catalysts paves the way for total chirality control in SWNT growth and will thus promote the development of SWNT applications