4-[(2-Chloro-4-nitro­phen­yl)diazen­yl]-N,N-diethyl­aniline

In the title compound, C16H17ClN4O2, the aromatic ring is twisted slightly with respect to the plane of the diazene group [N—N—C—C torsion angle = −3.9 (4)°]. The NO2 group is twisted by 16.2 (4)° relative to the aromatic ring. The two ethyl chains are positioned such that one ethyl chain lies above and the other below the ring

Phase transformation and deformation behavior of NiTi-Nb eutectic joined NiTi wires

NiTi wires were brazed together via eutectic reaction between NiTi and Nb powder deposited at the wire contact region. Phase transformation and deformation behavior of the NiTi-Nb eutectic microstructure were investigated using transmission electron microscopy (TEM) and cyclic loading-unloading tests. Results show that R phase and B19′ martensite transformation are induced by plastic deformation. R phase transformation, which significantly contributes to superelasticity, preferentially occurs at the interfaces between NiTi and eutectic region. Round-shaped Nb-rich phase with rod-like and lamellar-type eutectics are observed in eutectic regions. These phases appear to affect the deformation behavior of the brazed NiTi-Nb region via five distinct stages in stress-strain curves: (I) R phase reorientation, (II) R phase transformation from parent phase, (III) elastic deformation of reoriented martensite accompanied by the plastic deformation of Nb-rich phase and lamellar NiTi-Nb eutectic, (IV) B19′ martensitic transformation, and (V) plastic deformation of the specimen

Practical Quantum Simulation of Non-Hermitian Dynamics

Non-Hermitian quantum systems have recently attracted considerable attentions due to their exotic properties. Though many experimental realizations of non-Hermitian systems have been reported, the non-Hermiticity usually resorts to the hard-to-control environments. An alternative approach is to use quantum simulation with the closed system, whereas how to simulate general non-Hermitian Hamiltonian dynamics remains a great challenge. To tackle this problem, we propose a protocol by combining a dilation method with the variational quantum algorithm. The dilation method is used to transform a non-Hermitian Hamiltonian into a Hermitian one through an exquisite quantum circuit, while the variational quantum algorithm is for efficiently approximating the complex entangled gates in this circuit. As a demonstration, we apply our protocol to simulate the dynamics of an Ising chain with nonlocal non-Hermitian perturbations, which is an important model to study quantum phase transition at nonzero temperatures. The numerical simulation results are highly consistent with the theoretical predictions, revealing the effectiveness of our protocol. The presented protocol paves the way for practically simulating general non-Hermitian dynamics in the multi-qubit case.Comment: 9 pages, 5 figure

Measuring Quantum Entanglement from Local Information by Machine Learning

Entanglement is a key property in the development of quantum technologies and in the study of quantum many-body simulations. However, entanglement measurement typically requires quantum full-state tomography (FST). Here we present a neural network-assisted protocol for measuring entanglement in equilibrium and non-equilibrium states of local Hamiltonians. Instead of FST, it can learn comprehensive entanglement quantities from single-qubit or two-qubit Pauli measurements, such as R\'enyi entropy, partially-transposed (PT) moments, and coherence. It is also exciting that our neural network is able to learn the future entanglement dynamics using only single-qubit traces from the previous time. In addition, we perform experiments using a nuclear spin quantum processor and train an adoptive neural network to study entanglement in the ground and dynamical states of a one-dimensional spin chain. Quantum phase transitions (QPT) are revealed by measuring static entanglement in ground states, and the entanglement dynamics beyond measurement time is accurately estimated in dynamical states. These precise results validate our neural network. Our work will have a wide range of applications in quantum many-body systems, from quantum phase transitions to intriguing non-equilibrium phenomena such as quantum thermalization.Comment: 5 pages, 4 figures. All comments are welcom

EVNet: An Explainable Deep Network for Dimension Reduction

Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful properties of the original data. It is used in various applications, such as image recognition, single-cell sequencing analysis, and biomarker discovery. However, contemporary parametric-free and parametric DR techniques suffer from several significant shortcomings, such as the inability to preserve global and local features and the pool generalization performance. On the other hand, regarding explainability, it is crucial to comprehend the embedding process, especially the contribution of each part to the embedding process, while understanding how each feature affects the embedding results that identify critical components and help diagnose the embedding process. To address these problems, we have developed a deep neural network method called EVNet, which provides not only excellent performance in structural maintainability but also explainability to the DR therein. EVNet starts with data augmentation and a manifold-based loss function to improve embedding performance. The explanation is based on saliency maps and aims to examine the trained EVNet parameters and contributions of components during the embedding process. The proposed techniques are integrated with a visual interface to help the user to adjust EVNet to achieve better DR performance and explainability. The interactive visual interface makes it easier to illustrate the data features, compare different DR techniques, and investigate DR. An in-depth experimental comparison shows that EVNet consistently outperforms the state-of-the-art methods in both performance measures and explainability.Comment: 18 pages, 15 figures, accepted by TVC

Photoperiod-responsive changes in chromatin accessibility in phloem companion and epidermis cells of Arabidopsis leaves

Photoperiod plays a key role in controlling the phase transition from vegetative to reproductive growth in flowering plants. Leaves are the major organs perceiving day-length signals, but how specific leaf cell types respond to photoperiod remains unknown. We integrated photoperiod-responsive chromatin accessibility and transcriptome data in leaf epidermis and vascular companion cells of Arabidopsis thaliana by combining isolation of nuclei tagged in specific cell/tissue types with assay for transposase-accessible chromatin using sequencing and RNA-sequencing. Despite a large overlap, vasculature and epidermis cells responded differently. Long-day predominantly induced accessible chromatin regions (ACRs); in the vasculature, more ACRs were induced and these were located at more distal gene regions, compared with the epidermis. Vascular ACRs induced by long days were highly enriched in binding sites for flowering-related transcription factors. Among the highly ranked genes (based on chromatin and expression signatures in the vasculature), we identified TREHALOSE-PHOSPHATASE/SYNTHASE 9 (TPS9) as a flowering activator, as shown by the late flowering phenotypes of T-DNA insertion mutants and transgenic lines with phloem-specific knockdown of TPS9. Our cell-type-specific analysis sheds light on how the long-day photoperiod stimulus impacts chromatin accessibility in a tissue-specific manner to regulate plant development

Efficacy and safety of zanubrutinib plus R-CHOP in treatment of non-GCB DLBCL with extranodal involvement

IntroductionTreatment with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) shows poor response rates in non–germinal center B cell–like (non-GCB) diffuse large B-cell lymphoma (DLBCL) patients with multiple extranodal involvement. This study aims to evaluate anti-tumor activity and safety of zanubrutinib with R-CHOP (ZR-CHOP) in treatment naïve non-GCB DLBCL with extranodal involvement.MethodsIn this single-arm, phase 2, prospective, single-center study, patients with newly diagnosed non-GCB DLBCL with extranodal involvement enrolled between October 2020 to March 2022 received ZR-CHOP for 6 cycles followed by 2 cycles of maintenance treatment with rituximab and zanubrutinib. The primary endpoint included progression-free survival (PFS) in the intent-to-treat (ITT) population whereas the secondary endpoints included overall response rate (ORR), complete response (CR), and duration of response. Further, next-generation sequencing (NGS) was used for detection of different oncogenic mutations closely related to DLBCL pathogenesis.ResultsFrom October 2020 to March 2022, 26 patients were enrolled, and 23 of them were evaluated for efficacy after receiving 3 cycles of ZR-CHOP treatment. 1-year PFS and OS were 80.8% and 88.5% respectively while expected PFS and OS for 2-years are 74.0% and 88.5% respectively with median follow-up of 16.7 months and ORR was 91.3% (CR: 82.61%; PR: 8.70%). Oncogenic mutations closely related to DLBCL pathogenesis were assessed in 20 patients using NGS. B-cell receptor and NF-κB pathway gene mutations were detected in 10 patients, which occurred in MYD88 (7/19), CD79B (4/19), CARD11 (5/19), and TNFAIP3 (2/19). Hematological adverse events (AEs) ≥ grade 3 included neutropenia (50%), thrombocytopenia (23.1%), and anemia (7.7%) whereas non-hematological AEs ≥ grade 3 included pulmonary infection (19.2%).ConclusionZR-CHOP is safe and effective for treating treatment naïve non-GCB DLBCL patients with extranodal involvement.Clinical Trial RegistrationClinicaltrials.gov, NCT0483587