Curvature-based Transformer for Molecular Property Prediction

The prediction of molecular properties is one of the most important and challenging tasks in the field of artificial intelligence-based drug design. Among the current mainstream methods, the most commonly used feature representation for training DNN models is based on SMILES and molecular graphs, although these methods are concise and effective, they also limit the ability to capture spatial information. In this work, we propose Curvature-based Transformer to improve the ability of Graph Transformer neural network models to extract structural information on molecular graph data by introducing Discretization of Ricci Curvature. To embed the curvature in the model, we add the curvature information of the graph as positional Encoding to the node features during the attention-score calculation. This method can introduce curvature information from graph data without changing the original network architecture, and it has the potential to be extended to other models. We performed experiments on chemical molecular datasets including PCQM4M-LST, MoleculeNet and compared with models such as Uni-Mol, Graphormer, and the results show that this method can achieve the state-of-the-art results. It is proved that the discretized Ricci curvature also reflects the structural and functional relationship while describing the local geometry of the graph molecular data

HIBEChain: A Hierarchical Identity-based Blockchain System for Large-Scale IoT

Internet-of-Things enables interconnection of billions of devices, which perform autonomous operations and collect various types of data. These things, along with their generated huge amount of data, need to be handled efficiently and securely. Centralized solutions are not desired due to security concerns and scalability issue. In this paper, we propose HIBEChain, a hierarchical blockchain system that realizes scalable and accountable management of IoT devices and data. HIBEChain consists of multiple permissioned blockchains that form a hierarchical tree structure. To support the hierarchical structure of HIBEChain, we design a decentralized hierarchical identity-based signature (DHIBS) scheme, which enables IoT devices to use their identities as public keys. Consequently, HIBEChain achieves high scalability through parallel processing as blockchain sharding schemes, and it also implements accountability by use of identity-base keys. Identity-based keys not only make HIBEChain more user-friendly, they also allow private key recovery by validators when necessary. We provide detailed analysis of its security and performance, and implement HIBEChain based on Ethereum source code. Experiment results show that a 6-ary, (7,10)-threshold, 4-level HIBEChain can achieve 32,000 TPS, and it needs only 9 seconds to confirm a transaction

Liver Transplantation in an Adult with Citrullinaemia Type 2

Citrullinaemia is a urea cycle defect that results from a deficiency of the enzyme arginosuccinate synthetase. Type 1 disease is diagnosed in childhood, whereas Type 2 disease is adult onset. We report the outcome of a patient with citrullinemia Type 2 who received a liver transplant at our center and the implications of this diagnosis in liver transplantation

Simultaneous splicing of multiple DNA fragments in one PCR reaction

BACKGROUND: Rapid and simultaneous splicing of multiple DNA fragments is frequently required in many recombinant DNA projects. However, former overlap extension PCRs, the most common methods for splicing DNA fragments, are not really simultaneous fusing of multiple DNA fragments. RESULTS: We performed an optimized method which allowed simultaneous splicing of multiple DNA fragments in one PCR reaction. Shorter outermost primers were prior mixed with other PCR components at the same time. A sequential thermo cycling program was adopted for overlap extension reaction and amplification of spliced DNA. Annealing temperature was relatively higher in the overlap extension reaction stage than in the fused DNA amplification. Finally we successfully harvested target PCR products deriving from fusion of two to seven DNA fragments after 5–10 cycles for overlap extension reaction and then 30 cycles for fused DNA amplification. CONCLUSIONS: Our method provides more rapid, economical and handy approach to accurately splice multiple DNA fragments. We believe that our simultaneous splicing overlap extension PCR can be used to fuse more than seven DNA fragments as long as the DNA polymerase can match

An Improved Artificial Fish Swarm Algorithm

The purpose of this paper is to improve the performance of the original AFSA algorithm at the optimal accuracy rate and overcome the weakness of the algorithm which is also trapped in the local optimum. To this end, the original AFSA was further improved based on the tabu strategy. Specifically, the reproduction and death were introduced to protect the best individuals and eliminate poor quality fish, so as to increase convergence and accuracy. Through simulation, it is proved that our solution can achieve high accuracy, good global convergence, and strong resistance to local minimum. The findings bring new light on the application of AFSA and provide valuable reference to studies in related fields

Non-heavy-metal ZnS quantum dots with bright blue photoluminescence by a one-step aqueous synthesis

Nanotechnology, 18(20): pp. 205604-1 - 205604-6.We have examined the aqueous synthesis of non-heavy-metal ZnS quantum dots (QDs) using 3-mercaptopropionic acid (MPA) as the capping molecule at various pH and MPA:Zn:S ratios. Transmission electron microscopy (TEM) and x-ray diffraction (XRD) indicated that the aqueous ZnS QDs were 3–5 nm in size with a zinc blende structure. We showed that, at pH 12 with a MPA:Zn:S = 8:4:1 ratio, the ZnS QDs with optimal blue emission could be obtained in a one-step, room-temperature aqueous process that exhibited a quantum yield of 31%, higher than that of the commercial CdSe/ZnS core–shell QDs. The present ZnS QDs could pass through a 50 kD filter. This indicated that they were smaller than 5 nm in size, consistent with those estimated from the UV–vis absorption edge and the TEM image. At a lower pH (e.g. pH = 8), the room-temperature synthesized ZnS QDs exhibited no photoluminescence. Although further hydrothermal annealing at 100 ◦C could improve the photoluminescence of the ZnS QDs, the resultant emission was not as bright as that obtained at pH 12 at room temperature. The blue emission of aqueous ZnS QDs was likely the result of trap-state emissions involving the defect states of the QDs. The present ZnS QDs were bright, small and contained non-heavy-metal elements, thus offering the potential for in vivo bioimaging

Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study

Objective: Due to heterogeneity and limited medical data in primary healthcare services (PHS), assessing the psychological risk of type 2 diabetes mellitus (T2DM) patients in PHS is difficult. Using unsupervised contrastive pre-training, we proposed a deep learning framework named depression and anxiety prediction (DAP) to predict depression and anxiety in T2DM patients. // Materials and Methods: The DAP model consists of two sub-models. Firstly, the pre-trained model of DAP used unlabeled discharge records of 85 085 T2DM patients from the First Affiliated Hospital of Nanjing Medical University for unsupervised contrastive learning on heterogeneous electronic health records (EHRs). Secondly, the fine-tuned model of DAP used case–control cohorts (17 491 patients) selected from 149 596 T2DM patients’ EHRs in the Nanjing Health Information Platform (NHIP). The DAP model was validated in 1028 patients from PHS in NHIP. Evaluation included receiver operating characteristic area under the curve (ROC-AUC) and precision-recall area under the curve (PR-AUC), and decision curve analysis (DCA). // Results: The pre-training step allowed the DAP model to converge at a faster rate. The fine-tuned DAP model significantly outperformed the baseline models (logistic regression, extreme gradient boosting, and random forest) with ROC-AUC of 0.91±0.028 and PR-AUC of 0.80±0.067 in 10-fold internal validation, and with ROC-AUC of 0.75 ± 0.045 and PR-AUC of 0.47 ± 0.081 in external validation. The DCA indicate the clinical potential of the DAP model. // Conclusion: The DAP model effectively predicted post-discharge depression and anxiety in T2DM patients from PHS, reducing data fragmentation and limitations. This study highlights the DAP model’s potential for early detection and intervention in depression and anxiety, improving outcomes for diabetes patients