Chromosome 2p14 Is Linked to Susceptibility to Leprosy

BACKGROUND: A genetic component to the etiology of leprosy is well recognized but the mechanism of inheritance and the genes involved are yet to be fully established. METHODOLOGY: A genome-wide single nucleotide polymorphism (SNP) based linkage analysis was carried out using 23 pedigrees, each with 3 to 7 family members affected by leprosy. Multipoint parametric and non-parametric linkage analyses were performed using MERLIN 1.1.1. PRINCIPAL FINDINGS: Genome-wide significant evidence for linkage was identified on chromosome 2p14 with a heterogeneity logarithm of odds (HLOD) score of 3.51 (rs1106577) under a recessive model of inheritance, while suggestive evidence was identified on chr.4q22 (HLOD 2.92, rs1349350, dominant model), chr. 8q24 (HLOD 2.74, rs1618523, recessive model) and chr.16q24 (HLOD 1.93, rs276990 dominant model). Our study also provided moderate evidence for a linkage locus on chromosome 6q24-26 by non-parametric linkage analysis (rs6570858, LOD 1.54, p = 0.004), overlapping a previously reported linkage region on chromosome 6q25-26. CONCLUSION: A genome-wide linkage analysis has identified a new linkage locus on chromosome 2p14 for leprosy in Pedigrees from China

Antibody Responses and the Effects of Clinical Drugs in COVID-19 Patients

The coronavirus disease 2019 (COVID-19) emerged around December 2019 and have become a global epidemic disease currently. Specific antibodies against SAS-COV-2 could be detected in COVID-19 patients’ serum or plasma, but the clinical values of these antibodies as well as the effects of clinical drugs on humoral responses have not been fully demonstrated. In this study, 112 plasma samples were collected from 36 patients diagnosed with laboratory-confirmed COVID-19 in the Fifth Affiliated Hospital of Sun Yat-sen University. The IgG and IgM antibodies against receptor binding domain (RBD) and spike protein subunit 1 (S1) of SAS-COV-2 were detected by ELISA. We found that COVID-19 patients generated specific antibodies against SARS-CoV-2 after infection, and the levels of anti-RBD IgG within 2 to 3 weeks from onset were negatively associated with the time of positive-to-negative conversion of SARS-CoV-2 nucleic acid. Patients with severe symptoms had higher levels of anti-RBD IgG in 2 to 3 weeks from onset. The use of chloroquine did not significantly influence the patients’ antibody titer but reduced C-reaction protein (CRP) level. Using anti-viral drugs (lopinavir/ritonavir or arbidol) reduced antibody titer and peripheral lymphocyte count. While glucocorticoid therapy developed lower levels of peripheral lymphocyte count and higher levels of CRP, lactate dehydrogenase (LDH), α-Hydroxybutyrate dehydrogenase(α-HBDH), total bilirubin (TBIL), direct bilirubin (DBIL). From these results, we suggested that the anti-RBD IgG may provide an early protection of host humoral responses against SAS-COV-2 infection within 2 to 3 weeks from onset, and clinical treatment with different drugs displayed distinct roles in humoral and inflammatory responses

Decision-Behavior Based Online Shopping

The explosive popularity of e-commerce sites has reshaped users' shopping habits and an increasing number of users prefer to spend more time shopping online. This evolution allows e-commerce sites to collect rich data about users. The majority of traditional recommender systems have focused on the macro interactions between users and items, particularly the purchase history of customers. However, decision support only achieved limited performance due to the mismatch between the causality and the interaction sequence. It is especially challenging for products with low purchase frequency, such as refrigerators, or new users with little history data. To address the problem, we investigated how to leverage the heterogenous information, including decision making information to improve recommender systems, helping users approach their items easier and more accurately. Specifically, we propose to model users' purchasing reason information and knowledge graph of items to provide personalized recommendation. The new recommend model, called Decision-Behavior Knowledge Graph (DBKG), captures the decision-making knowledge during users' online purchasing, and update the decision making knowledge in the process of supporting users' purchase decision

Omega motion, rolling, and active standing of a worm-inspired robot under the action of the magnetic field

With the rapid development of origami technologies, worm-inspired robots have attracted a great deal of attention due to their flexible locomotion characteristics. In the present work, we have prepared a soft robot inspired by the worms, which can achieve various locomotion patterns under the actuation of magnetic field. First, the origami technique is used to form the backbone of the robot, and two NdFeB discs are adhered on its two ends. Next, the experiments for controlling the Omega motion and rolling of the robot are performed, and the mechanical analyses are given. In the experiments, the Omega locomotion speed and rolling speed can reach ∼5 mm/s and 2π rad/s, respectively. Then, two typical examples on the composite motion, including the Omega motion and rolling, are demonstrated, where the robot can realize the tasks of sweeping objects and obstacle crossing in unstructured environments. We further design a system to mimic the situation when the worm-like robot detects and responds to the dangerous signal, and the power of the electromagnet can be accurately controlled. These findings cast a new light on engineering intelligent robots and devices originating from the inspirations of living creatures

Aspect-guided syntax graph learning for explainable recommendation

Explainable recommendation systems provide explanations for recommendation results to improve their transparency and persuasiveness. The existing explainable recommendation methods generate textual explanations without explicitly considering the user's preferences on different aspects of the item. In this paper, we propose a novel explanation generation framework, namely Aspect-guided Explanation generation with Syntax Graph (AESG), for explainable recommendation. Specifically, AESG employs a review-based syntax graph to provide a unified view of the user/item details. An aspect-guided graph pooling operator is proposed to extract the aspect-relevant information from the review-based syntax graphs to model the user's preferences on an item at the aspect level. Then, an aspect-guided explanation decoder is developed to generate aspects and aspect-relevant explanations based on the attention mechanism. The experimental results on three real datasets indicate that AESG outperforms state-of-the-art explanation generation methods in both single-aspect and multi-aspect explanation generation tasks, and also achieves comparable or even better preference prediction accuracy than strong baseline methods.AI SingaporeNational Research Foundation (NRF)Submitted/Accepted versionThis research is supported, in part, by the National Research Foundation (NRF), Singapore under its AI Singapore Programme (AISG Award No: AISG-GC-2019-003), and by the Development of Cryptographic Library and Support Systems (LP180101062), Australian Research Council

A new mechanism and kinetic analysis for the efficient conversion of inorganic bromide in waste printed circuit board smelting ash via traditional sulfated roasting

The waste printed circuit board smelting ash (WPCB-SA) produced in the waste printed circuit board smelting process is a hazardous material that not only contains valuable metals, but also contains a large amount of toxic and harmful inorganic bromides. The utilization of metals has received considerable attention in previous studies, but the recovery of hazardous bromides requires further study. In this article, a new idea of converting inorganic bromine in WPCB-SA by traditional sulfated roasting is proposed. Debromination kinetics under simulated experimental conditions are discussed, and kinetic equations are established. The kinetic results show that during low-temperature sulfated roasting, the conversion of Br in CuBr and PbBr2 conforms to the chemical reaction diffusion model and diffusion control the product layer model, respectively. A possible reaction mechanism is also proposed. Our research shows that the conversion of Br in CuBr is divided into three processes: covalent bond decomposition, hydrogen ion form acid, copper ion form salt, and HBr oxidation conversion, whereas the conversion of Br in PbBr2 is divided into two processes: sulfuric acid ionization, lead ion form salt and HBr oxidation conversion. This work provides the theoretical basis for the improvement and application of inorganic bromide recovery technology in WPCB-SA

Development of sustainable and efficient recycling technology for spent Li-ion batteries: Traditional and transformation go hand in hand

Clean and efficient recycling of spent lithium-ion batteries (LIBs) has become an urgent need to promote sustainable and rapid development of human society. Therefore, we provide a critical and comprehensive overview of the various technologies for recycling spent LIBs, starting with lithium-ion power batteries. Recent research on raw material collection, metallurgical recovery, separation and purification is highlighted, particularly in terms of all aspects of economic efficiency, energy consumption, technology transformation and policy management. Mechanisms and pathways for transformative full-component recovery of spent LIBs are explored, revealing a clean and efficient closed-loop recovery mechanism. Optimization methods are proposed for future recycling technologies, with a focus on how future research directions can be industrialized. Ultimately, based on life-cycle assessment, the challenges of future recycling are revealed from the LIBs supply chain and stability of the supply chain of the new energy battery industry to provide an outlook on clean and efficient short process recycling technologies. This work is designed to support the sustainable development of the new energy power industry, to help meet the needs of global decarbonization strategies and to respond to the major needs of industrialized recycling

An integrated and sustainable hydrometallurgical process for enrichment of precious metals and selective separation of copper, zinc, and lead from a roasted sand

This study developed an efficient and sustainable hydrometallurgical process for the enrichment of gold and silver and the stepwise separation of copper, zinc, and lead from sulfated roasted sand of waste printed circuit board smelting ash. Selective separation of copper and zinc was achieved by water leaching, and silver dispersion was reduced by controlling the amount of NaCl added during the leaching process. The results of the water leaching showed that the copper and zinc leaching rates were 99.85% and 99.47%, respectively, whereas the loss rate of silver was 2.1% with optimal leaching parameters. The high-chloride-complex method was used to study the efficient conversion and separation of lead from the leached residue, and the leaching kinetics and conversion mechanism of lead were discussed. The results showed that under the optimal conditions, the leaching rate of lead was 99.79%. Leaching kinetics analysis showed that lead leaching in the high - chlorine system was controlled by a chemical reaction; the apparent activation energy was 53.63 kJ/mol. After the leaching of copper, zinc, and lead, 1.66% Ag and 213 g/t Au were enriched in the leached residue; and the precious metal enrichment goal was reached. The chlorinated leachate showed good recycling performance, and a lead leaching rate of 97.93% was obtained after three circulations. After cooling, crystallization, and purification, lead chloride with a purity of 99.89% and high economic and industrial value was obtained from the lead-rich leachate. This process has favorable and sustainable industrial application prospects