Topological Phases in Magnonics: A Review

Magnonics or magnon spintronics is an emerging field focusing on generating, detecting, and manipulating magnons. As charge-neutral quasi-particles, magnons are promising information carriers because of their low energy dissipation and long coherence length. In the past decade, topological phases in magnonics have attracted intensive attention due to their fundamental importance in condensed-matter physics and potential applications of spintronic devices. In this review, we mainly focus on recent progress in topological magnonics, such as the Hall effect of magnons, magnon Chern insulators, topological magnon semimetals, etc. In addition, the evidence supporting topological phases in magnonics and candidate materials are also discussed and summarized. The aim of this review is to provide readers with a comprehensive and systematic understanding of the recent developments in topological magnonics.Comment: 17 pages, 12 figure

2-(3-Methyl-2-nitro­phen­yl)-4,5-dihydro-1,3-oxazole

In the title compound, C10H10N2O3, an inter­mediate in the synthesis of anthranilamide insecticides, all the non-H atoms except the nitro-group O atom lie on a crystallographic mirror plane. The H atoms of the methyl group are disordered over two sets of sites with equal occupancies. In the crystal structure, C—H⋯N links lead to chains of mol­ecules propagating in [100]

Multi-objective optimization based network control principles for identifying personalized drug targets with cancer

It is a big challenge to develop efficient models for identifying personalized drug targets (PDTs) from high-dimensional personalized genomic profile of individual patients. Recent structural network control principles have introduced a new approach to discover PDTs by selecting an optimal set of driver genes in personalized gene interaction network (PGIN). However, most of current methods only focus on controlling the system through a minimum driver-node set and ignore the existence of multiple candidate driver-node sets for therapeutic drug target identification in PGIN. Therefore, this paper proposed multi-objective optimization-based structural network control principles (MONCP) by considering minimum driver nodes and maximum prior-known drug-target information. To solve MONCP, a discrete multi-objective optimization problem is formulated with many constrained variables, and a novel evolutionary optimization model called LSCV-MCEA was developed by adapting a multi-tasking framework and a rankings-based fitness function method. With genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, the effectiveness of LSCV-MCEA was validated. The experimental results indicated that compared with other advanced methods, LSCV-MCEA can more effectively identify PDTs with the highest Area Under the Curve score for predicting clinically annotated combinatorial drugs. Meanwhile, LSCV-MCEA can more effectively solve MONCP than other evolutionary optimization methods in terms of algorithm convergence and diversity. Particularly, LSCV-MCEA can efficiently detect disease signals for individual patients with BRCA cancer. The study results show that multi-objective optimization can solve structural network control principles effectively and offer a new perspective for understanding tumor heterogeneity in cancer precision medicine.Comment: 15 pages, 8 figures; This work has been submitted to IEEE Transactions on Evolutionary Computatio

Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal Scenarios Like a Lawyer?

Large Language Models (LLMs), such as ChatGPT, have drawn a lot of attentions recently in the legal domain due to its emergent ability to tackle a variety of legal tasks. However, it is still unknown if LLMs are able to analyze a legal case and perform reasoning in the same manner as lawyers. Therefore, we constructed a novel corpus consisting of scenarios pertain to Contract Acts Malaysia and Australian Social Act for Dependent Child. ChatGPT is applied to perform analysis on the corpus using the IRAC method, which is a framework widely used by legal professionals for organizing legal analysis. Each scenario in the corpus is annotated with a complete IRAC analysis in a semi-structured format so that both machines and legal professionals are able to interpret and understand the annotations. In addition, we conducted the first empirical assessment of ChatGPT for IRAC analysis in order to understand how well it aligns with the analysis of legal professionals. Our experimental results shed lights on possible future research directions to improve alignments between LLMs and legal experts in terms of legal reasoning.Comment: EMNLP 2023 Finding

(5-Bromo-2-hy­droxy­phen­yl)(4-propyl­cyclo­hex­yl)methanone

In the title compound, C16H21BrO2, the cyclo­hexane ring adopts a chair conformation. The hy­droxy and carbonyl groups are involved in an intra­molecular O—H⋯O hydrogen bond. In the crystal, weak C—H⋯O inter­actions link the mol­ecules into zigzag chains along [010]

Urban flood inundation probability assessment based on an improved Bayesian model

Urban flood inundation is spatially uncertain. To quantify this uncertainty, it is necessary to explore the spatial probability of urban flood inundation in different return periods. In this study, an urban flood spatial inundation probability assessment method based on an improved Bayesian model is proposed, which comprises three parts: data reconstruction based on undersampling; optimal Bayesian sample planning; and spatial inundation probability assessment. A case study of the central urban area of Jingdezhen City, China, is presented in this paper. The results indicate that (1) the inundation probabilities generated based on various return periods (20-, 50-, and 100-year return periods) are accurately determined and can provide more detailed inundation information. (2) The adoption of the random undersampling data reconstruction method solves the problem of an imbalanced number of inundations/noninundations during Bayesian modeling and substantially enhances the prediction accuracy compared with the traditional Bayesian modeling approach. (3) A sensitivity analysis reveals that inundation probability is sensitive to the drainage network and elevation rather than soil water retention and distance to river. With an increase in the return period, the inundation probability gradually increases. As the proposed method can quantify flood inundation uncertainty, it is valuable in supporting specific flood risk assessments

Further evidence that CP-AMPARs are critically involved in synaptic tag and capture at hippocampal CA1 synapses

The synaptic tag and capture (STC) hypothesis provides an important theoretical basis for understanding the synaptic basis of associative learning. We recently provided pharmacological evidence that calcium-permeable AMPA receptors (CP-AMPARs) are a crucial component of this form of heterosynaptic metaplasticity. Here we have investigated two predictions that arise on the basis of CP-AMPARs serving as a trigger of STC. Firstly, we compared the effects of the order in which we delivered a strong theta burst stimulation (TBS) protocol (75 pulses) and a weak TBS protocol (15 pulses) to two independent inputs. We only observed significant heterosynaptic metaplasticity when the strong TBS preceded the weak TBS. Second, we found that pausing stimulation following either the sTBS or the wTBS for ~20 min largely eliminates the heterosynaptic metaplasticity. These observations are consistent with a process that is triggered by the synaptic insertion of CP-AMPARs and provide a framework for establishing the underlying molecular mechanisms.This work was supported by the CIHR (GLC), the EJLB-CIHR Michael Smith Chair in Neurosciences and Mental Health, Canada Research Chair, and Cana‑dian Institute for Health Research operating Grants (CIHR66975 and 84256) (MZ) and the National Honor Scientist Program of the National Research Foundation funded by the South Korea Government (B-KK). This work was also supported by the Brain Canada Foundation through the Canada Brain Research Fund, with the fnancial support of Health Canada

Comprehensive analysis of SSRs and database construction using all complete gene-coding sequences in major horticultural and representative plants

Simple sequence repeats (SSRs) are one of the most important genetic markers and widely exist in most species. Here, we identified 249,822 SSRs from 3,951,919 genes in 112 plants. Then, we conducted a comprehensive analysis of these SSRs and constructed a plant SSR database (PSSRD). Interestingly, more SSRs were found in lower plants than in higher plants, showing that lower plants needed to adapt to early extreme environments. Four specific enriched functional terms in the lower plant Chlamydomonas reinhardtii were detected when it was compared with seven other higher plants. In addition, Guanylate_cyc existed in more genes of lower plants than of higher plants. In our PSSRD, we constructed an interactive plotting function in the chart interface, and users can easily view the detailed information of SSRs. All SSR information, including sequences, primers, and annotations, can be downloaded from our database. Moreover, we developed Web SSR Finder and Batch SSR Finder tools, which can be easily used for identifying SSRs. Our database was developed using PHP, HTML, JavaScript, and MySQL, which are freely available at http://www.pssrd.info/. We conducted an analysis of the Myb gene families and flowering genes as two applications of the PSSRD. Further analysis indicated that whole-genome duplication and whole-genome triplication played a major role in the expansion of the Myb gene families. These SSR markers in our database will greatly facilitate comparative genomics and functional genomics studies in the future