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Elsholtzia fruticosa is an ornamental plant with high medicinal value. In this study, we sequenced and analyzed the complete chloroplast (cp) genome of the species. The complete cp sequence is 151,550 bp, including the large single-copy (LSC) region of 82,778 bp, the small single-copy (SSC) region of 17,492 bp, and a pair of invert repeats (IRs) regions of 25,640 bp. It encodes 132 unique genes in total, including 87 protein-coding genes, 37 transfer RNA genes (tRNAs), and eight ribosomal RNA genes (rRNAs). The comparative analysis of complete cp genomes showed that the genomic structure and gene order of E. fruticosa cps were conserved. The sequences of rps15, rps19, ycf1, ycf3, ycf15, psbL, psaI, trnG-UCC, trnS-GCU, trnR-UCU, trnL-UAG, trnP-UG, and trnL-UAA serve as hotspots for developing the DNA barcoding of Elsholtzia species. There are 49 SSR loci in the cp genome of E. fruticosa, among which the repeat numbers of mononucleotide, dinucleotide, trinucleotide, tetranucleotide, and pentanucleotides SSR are 37, 9, 3, 0, and 0, respectively. A total of 50 repeats were detected, including 15 forward repeats, seven reverse repeats, 26 palindromic repeats, and two complementary repeats. Phylogenetic analysis based on the complete cp genome and protein-coding DNA sequences of 26 plants indicates that E. fruticosa has a dose relationship with E. splendens and E. byeonsanensis.

Prior to the onset of the Covid-19 pandemic in Hong Kong, there had been an extended period of civic unrest which began as opposition to an extradition bill but which later grew into a larger socio-political movement. We consider the relationship between ongoing political sentiments and the Covid-19 pandemic as evident through public signs; particularly, how commercial signage aligns and disaligns with government discourse on solidarity evident through recontextualization of the official tagline “Together, we fight the virus!” We suggest three analytical categories: (1) signs which demonstrate an alignment stance with the institutional sense of solidarity; (2) alignment with the pro-democracy movement, and thus disalignment from the institutional sense; and (3) politically ambiguous stances appealing to a broad understanding of solidarity for commercial gain. The pandemic occurring at a politically sensitive time has caused and made evident fractures in the way solidarity is construed, resulting in (dis)alignment and differing stances arising from the various social actors who have emplaced signs.

In this work, a three-quark picture is constructed using a bottom-up approach for baryons in light-front quark model. The shape parameters, which characterize the momentum distribution inside a baryon, are determined with the help of the pole residue of the baryon. The relation between the three-quark picture and the diquark picture is clarified. In model construction, we find that Lorentz boost effect plays an important role, and the bottom-up modeling approach can be generalized to multiquark states. Based on this, a unified theoretical framework for describing multiquark states may be established. As a by-product of model construction, we can easily obtain a new and possibly better definition of baryon interpolating current. The hadron interpolating currents are the starting point of Lattice QCD and QCD sum rules, and therefore have important significance., Comment: 17 pages

The SEL1L-HRD1 protein complex represents the most conserved branch of endoplasmic reticulum (ER)-associated degradation (ERAD); however, definitive evidence for the importance of SEL1L in HRD1 ERAD is lacking. Here we report that attenuation of the interaction between SEL1L and HRD1 impairs HRD1 ERAD function and has pathological consequences in mice. Our data show that SEL1L variant p.Ser658Pro ( SEL1L ( S 658 P ) ) previously identified in Finnish Hound suffering cerebellar ataxia is a recessive hypomorphic mutation, causing partial embryonic lethality, developmental delay, and early-onset cerebellar ataxia in homozygous mice carrying the bi-allelic variant. Mechanistically, SEL1L ( S 658 P ) variant attenuates the SEL1L-HRD1 interaction and causes HRD1 dysfunction by generating electrostatic repulsion between SEL1L F668 and HRD1 Y30 residues. Proteomic screens of SEL1L and HRD1 interactomes revealed that the SEL1L-HRD1 interaction is prerequisite for the formation of a functional HRD1 ERAD complex, as SEL1L recruits not only the lectins OS9 and ERLEC1, but the E2 UBE2J1 and retrotranslocon DERLIN, to HRD1. These data underscore the pathophysiological importance and disease relevance of the SEL1L-HRD1 complex, and identify a key step in organizing the HRD1 ERAD complex.

In this work, we perform a QCD sum rules analysis on the $\Xi_{Q}-\Xi_{Q}^{\prime}$ mixing. Contributions from up to dimension-6 four-quark operator are considered. However, it turns out that, only dimension-4 and dimension-5 operators contribute, which reveals the non-perturbative nature of mixing. Especially we notice that only the diagrams with the two light quarks participating in gluon exchange contribute to the mixing. Our results indicate that the mixing angle $\theta_{c}=(1.2\sim2.8)^{\circ}$ for the $Q=c$ case and $\theta_{b}=(0.28\sim0.34)^{\circ}$ for the $Q=b$ case. Our prediction of $\theta_{c}$ is consistent with the most recent Lattice QCD result within error. Such a small mixing angle seems unlikely to resolve the tension between experimental measurement and Lattice QCD calculation for the semileptonic decay $\Xi_{c}^{0}\to\Xi^{-} e^{+}\nu_{e}$., Comment: 7 pages, 3 figures

Prompt learning has become one of the most efficient paradigms for adapting large pre-trained vision-language models to downstream tasks. Current state-of-the-art methods, like CoOp and ProDA, tend to adopt soft prompts to learn an appropriate prompt for each specific task. Recent CoCoOp further boosts the base-to-new generalization performance via an image-conditional prompt. However, it directly fuses identical image semantics to prompts of different labels and significantly weakens the discrimination among different classes as shown in our experiments. Motivated by this observation, we first propose a class-aware text prompt (CTP) to enrich generated prompts with label-related image information. Unlike CoCoOp, CTP can effectively involve image semantics and avoid introducing extra ambiguities into different prompts. On the other hand, instead of reserving the complete image representations, we propose text-guided feature tuning (TFT) to make the image branch attend to class-related representation. A contrastive loss is employed to align such augmented text and image representations on downstream tasks. In this way, the image-to-text CTP and text-to-image TFT can be mutually promoted to enhance the adaptation of VLMs for downstream tasks. Extensive experiments demonstrate that our method outperforms the existing methods by a significant margin. Especially, compared to CoCoOp, we achieve an average improvement of 4.03% on new classes and 3.19% on harmonic-mean over eleven classification benchmarks.

Cancer stem cells (CSCs) are a subset of cancer cells with stem cell characteristics. The discovery of CSCs has opened a new era for cancer research. CSCs not only play a critical role in tumorigenesis, but also are responsible for the failure of cancer treatments. Here, we performed weighted gene co-expression network analysis (WGCNA) to identify key stemness genes and prognostic signatures using the data of an Asian liver cancer patient cohort and a White liver cancer patient cohort in The Cancer Genome Atlas (TCGA) database. To compare the difference in tumorigenesis between the Asian patients and the White patients, the prognostic value of the key genes from the Asian patients was evaluated in the White patient cohort and vice versa. We found that some key genes could predict the survival of the patients regardless of race. In addition, the key genes, NUCB2 and KLF4A, were selected from Asian patients and White patients, respectively, for further experimental validation. Knocking down NUCB2 could inhibit the activity of the AKT/mTOR signaling pathway and reverse the epithelial-mesenchymal transition (EMT) in liver cancer cells. We also confirmed that the knockdown of KLF4A suppressed ABCG2 activity and reduced the side population (SP) in liver cancer cells for the first time. Our results suggest that the stemness index is a useful method to identify key genes in tumorigenesis. Compared to the analysis for all patients, applying this index to the analysis of the patients of different races will provide more potential therapeutic targets for cancer treatment.

Water jet-guided laser is a new type of processing technology. With the continuous emergence of high hardness, high strength materials and the demand for high-efficiency processing, water jet coupled high power laser has become an inevitable trend in the development of the water-guided laser, and high-power laser energy and water jet coupling transmission and processing process will bring a series of problems such as transmission loss, these will become one of the key issues limiting the development of this technology. Therefore, studying the transmission loss of high-power laser energy guided by water jets is necessary. In this paper, we address this problem by numerically solving Maxwell's set of equations using the finite element (FEM) method and performing wave optical simulations of high-power laser transmission in a water jet to obtain the electric field distribution of the laser beam in the water jet, and explore the relationship between laser wavelength, laser spot diameter, water jet diameter and length, and laser energy loss. Finally, laser energy transfer efficiency measurement experiments were conducted to verify the model's validity by comparing it with simulation data. The study's results will provide some implications for regulating the transmission efficiency of high-power lasers in water jets.

Objective: To investigate the oral health related quality of life in elderly diabetic patients and analyze its influencing factors. Methods: Convenience sampling was used to select 190 elderly patients with diabetes under the Department of Endocrinology and Department of Integrated Traditional Chinese and Western Medicine in the Affiliated Hospital of Hebei University as the research subjects. The Chinese version of the Oral Health Influence Scale (OHIP-14) was used to conduct a questionnaire survey, and univariate analysis and multiple stepwise regression analysis were used to analyze the influencing factors of oral health related quality of life in elderly diabetic patients. Results: The elderly diabetic patients’ oral health related quality of life score was 34.48 ± 3.23, which is in the middle-lower range. The findings of multivariate stepwise regression analysis revealed that the course of disease, regular visits to the dentist, sleep quality, oral health knowledge, and oral health attitude together explained 58.9% of the total variance in elderly diabetic patients in terms of their oral health related quality of life (p < 0.05). Conclusion: The oral health related quality of life of elderly diabetic patients is generally low, and is affected by the duration of diabetes, sleep quality, and oral health knowledge, attitude, and behavior (regular visits to the dentist). Improving patients’ attention to oral health problems by improving sleep as well as their own oral health knowledge, attitude, and behavior is an effective way to enhance oral health related quality of life.

One new 6a,11a-dehydropterocarpan derivative, 6-O-methyl-anhydrotuberosin (1), one new 6a-hydroxypterocarpan, (6aR,11aR,11bR)-hydroxytuberosone (7), and seven known compounds including two 6a,11a-dehydropterocarpans (2 and 4), two coumestans (3 and 5), one isoflavonoid (6) and two other phenolic compounds (8 and 9) were isolated from the roots of Pueraria lobata. The structures of the isolated compounds were elucidated with spectroscopic and spectrometric methods (1 D and 2DNMR, HRESIMS). Compounds 1, 2, 4–5 showed potent LSD1 inhibitory activities with IC50 values ranging from 1.73 to 4.99 μM. Furthermore, compound 2 showed potent cytotoxicity against gastric cancer cell lines MGC-803 and BGC-823, and lung cancer cell lines H1299 and H460.

Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down. Early and accurate detection is critical for treatment. In recent years, deep-learning-based approaches have achieved great success in Alzheimer's disease diagnosis. The main objective of this paper is to review some popular conventional machine learning methods used for the classification and prediction of AD using Magnetic Resonance Imaging (MRI). The methods reviewed in this paper include support vector machine (SVM), random forest (RF), convolutional neural network (CNN), autoencoder, deep learning, and transformer. This paper also reviews pervasively used feature extractors and different types of input forms of convolutional neural network. At last, this review discusses challenges such as class imbalance and data leakage. It also discusses the trade-offs and suggestions about pre-processing techniques, deep learning, conventional machine learning methods, new techniques, and input type selection.

Introduction: Chronic severe aortic regurgitation (AR) has a poor long-term prognosis, especially among old-age patients. Considering their advancing age, the surgical approach of aortic valve replacement may not always be the best alternative modality of treatment in such patients. Therefore, this study’s primary goal was to provide an initial summary of the medium- and short-term clinical effectiveness of transcatheter aortic valve replacement (TAVR) guided by accurate multi-detector computed tomography (MDCT) measurements in patients with severe and chronic AR, especially in elderly patients. Methods: The study enrolled retrospectively and prospectively patients diagnosed with severe AR who eventually underwent TAVR procedure from January 2019 to September 2022 at Fuwai cardiovascular Hospital, Beijing. Baseline information, MDCT measurements, anatomical classification, perioperative, and 1-year follow-up outcomes were collected and analyzed. Based on a novel anatomical categorization and dual anchoring theory, patients were divided into four categories according to the level of anchoring area. Type 1, 2, and 3 patients (with at least two anchoring regions) will receive TAVR with a transcatheter heart valve (THV), but Type 4 patients (with zero or one anchoring location) will be deemed unsuitable for TAVR and will instead receive medical care (retrospectively enrolled patients who already underwent TAVR are an exception). Results: The mean age of the 37 patients with severe chronic AR was 73.1 ± 8.7 years, and 23 patients (62.2%) were male. The American Association of Thoracic Surgeons’ score was 8.6 ± 2.1%. The MDCT anatomical classification included 17 cases of type 1 (45.9%), 3 cases of type 2 (8.1%), 13 cases of type 3 (35.1%), and 4 cases of Type 4 (10.8%). The VitaFlow valve (MicroPort, Shanghai, China) was implanted in 19 patients (51.3%), while the Venus A valve (Venus MedTech, Hangzhou, China) was implanted in 18 patients (48.6%). Immediate TAVR procedural and device success rates were 86.5% and 67.6%, respectively, while eight cases (21.6%) required THV-in-THV implantation, and nine cases (24.3%) required permanent pacemaker implantation. Univariate regression analysis revealed that the major factors affecting TAVR device failure were sinotubular junction diameter, THV type, and MDCT anatomical classification (p < 0.05). Compared with the baseline, the left ventricular ejection fraction gradually increased, while the left ventricular end-diastolic diameter remained small, and the N-terminal-pro hormone B-type natriuretic peptide level significantly decreased within one year. Conclusion: According to the results of our study, TAVR with a self-expanding THV is safe and feasible for patients with chronic severe AR, particularly for those who meet the criteria for the appropriate MDCT anatomical classification with intact dual aortic anchors, and it has a significant clinical effect for at least a year.

Background. In-stent restenosis (ISR) is a common complication after percutaneous coronary intervention (PCI) surgery for patients with coronary atherosclerotic heart disease (CHD). Reports indicate alprostadil may reduce ISR, and this study aimed at reviewing and summarizing the effect of nanoliposome alprostadil on ISR by meta-analysis. Methods. Articles were searched from databases, and meta-analysis was performed in Review Manager software. Funnel plots were performed to evaluate the publication bias, and sensitivity analysis was performed to determine the robustness of the overall treatment effects. Results. Initially, 113 articles were identified, and 5 studies of 463 subjects were included for analysis eventually. The primary endpoint, i.e., the occurrence of ISR after PCI, occurred in 11.91% of the alprostadil treatment group (28 from 235 patients) vs. 21.49% of the conventional treatment group (49 from 228 patients) and showed a statistical significance in our pooled data (χ2 = 7.654, P=0.006), while there was no statistically significant difference in all of the separate studies. We observed no statistical methodological heterogeneity among the studies (P=0.64, I2 ≈ 0%). The pooled odds ratio (OR) of the occurrence of ISR was 49% in a fixed-effect model, and the 95% confidence boundary (95% CI) was 29% to 81%. The funnel plot did not show serious publication bias, and sensitivity analysis showed well robustness of the overall treatment effect. Discussion. In conclusion, the early application of nanoliposome alprostadil after PCI could effectively reduce the occurrence of ISR, and the overall effect of alprostadil treatment in reducing ISR after PCI was relatively stable.

Semi-supervised semantic segmentation (SSS) has recently gained increasing research interest as it can reduce the requirement for large-scale fully-annotated training data. The current methods often suffer from the confirmation bias from the pseudo-labelling process, which can be alleviated by the co-training framework. The current co-training-based SSS methods rely on hand-crafted perturbations to prevent the different sub-nets from collapsing into each other, but these artificial perturbations cannot lead to the optimal solution. In this work, we propose a new conflict-based cross-view consistency (CCVC) method based on a two-branch co-training framework which aims at enforcing the two sub-nets to learn informative features from irrelevant views. In particular, we first propose a new cross-view consistency (CVC) strategy that encourages the two sub-nets to learn distinct features from the same input by introducing a feature discrepancy loss, while these distinct features are expected to generate consistent prediction scores of the input. The CVC strategy helps to prevent the two sub-nets from stepping into the collapse. In addition, we further propose a conflict-based pseudo-labelling (CPL) method to guarantee the model will learn more useful information from conflicting predictions, which will lead to a stable training process. We validate our new CCVC approach on the SSS benchmark datasets where our method achieves new state-of-the-art performance. Our code is available at https://github.com/xiaoyao3302/CCVC., Comment: accepted by CVPR2023

Additional file 2. Fig. S1: Sugar consumption by M. thermophila strains WT, ΔMtlat-1, and ΔMtara-1, when grown in 1 × VMM with 2% d-glucose (A), 2% d-xylose (B), or 2% d-galactose (C). Error bars indicate the SD from at least three biological replicates. Fig. S2: Growth phenotypes of the M. thermophila ΔMtlat-1 mutant under xylan condition. A Cell dry weight of M. thermophila strains WT and ΔMtlat-1 after growth on 2% xylan for 2 d. B Protein concentrations, C xylanase activity, and D arabinanase activity of the culture supernatants for the M. thermophila strains grown in 2% xylanase medium. Error bars indicate the SD from at least three biological replicates. Fig. S3: Growth phenotypes of the complementation strain of the ΔMtlat-1 mutant. A l-arabinose transport rates of mycelia from the complementation strain Pn-Mtlat-1. B Cell dry weight of the M. thermophila complementation strain after growth on 2% arabinan for 4 days. C Protein concentrations, D arabinanase activity, and E xylanase activity of the culture supernatants for M. thermophila grown in 2% arabinan medium. Error bars indicate the SD from at least three biological replicates. Fig. S4: Comparative transcriptomic analysis of the WT and ΔMtlat-1 M. thermophila strains grown in arabinan medium for 2 d. A Total expression of genes encoding major hemicellulases from RNA-Seq data. B Transcriptional profiles of genes encoding arabinanolytic enzymes in the ΔMtlat-1 and WT strains when grown on 2% arabinan for 4 days. Fig. S5: Cell dry weight of M. thermophila strains after growth on 2% arabinan for 4 days. Fig. S6: Heatmap analysis of expression profiles for putative sugar transporter genes with statistically significant differences in transcript levels between ΔMtara-1 and the WT under l-arabinose condition. Log-transformed expression values are color-coded. Fig. S7: Protein concentrations and hemicellulase/cellulase activity of the culture supernatants for M. thermophila strain ΔMtara-1 grown in 1 × VMM with 2% xylan (A) or 2% Avicel (B) for 4 days.

The exchange-correlation potential formalism previously introduced and applied to the one-dimensional Hubbard model has been extended to spin systems and applied to the case of the one-dimensional antiferromagnetic spin$-\frac{1}{2}$ Heisenberg model. Within the spin exchange-correlation potential formulation, a new sum rule for spin-systems is derived. The exchange-correlation potential for the Heisenberg model is extrapolated from exact diagonalization results of small antiferromagnetic Heisenberg clusters. This procedure is also employed to revisit and computationally improve the previous investigation of the exchange-correlation potential of the half-filled Hubbard model, which was based on the exchange-correlation potential of the dimer. Numerical comparisons with exact benchmark calculations for both the Heisenberg and the Hubbard models indicate that, starting from the exchange-correlation potential of a finite cluster, the extrapolation procedure yields a one-particle spectral function with favorable accuracy at a relatively low computational cost. In addition, a comparison between the ground state energies for the one-dimensional Hubbard and Heisenberg models displays how the well known similarity in behavior of the two models at large interactions manifests within the exchange-correlation potential formalism., Comment: 17 pages, 9 figures

In this work, we perform a QCD sum rules analysis on the $Ξ_{Q}-Ξ_{Q}^{\prime}$ mixing. Contributions from up to dimension-6 four-quark operator are considered. However, it turns out that, only dimension-4 and dimension-5 operators contribute, which reveals the non-perturbative nature of mixing. Especially we notice that only the diagrams with the two light quarks participating in gluon exchange contribute to the mixing. Our results indicate that the mixing angle $θ_{c}=(1.2\sim2.8)^{\circ}$ for the $Q=c$ case and $θ_{b}=(0.28\sim0.34)^{\circ}$ for the $Q=b$ case. Our prediction of $θ_{c}$ is consistent with the most recent Lattice QCD result within error. Such a small mixing angle seems unlikely to resolve the tension between experimental measurement and Lattice QCD calculation for the semileptonic decay $Ξ_{c}^{0}\toΞ^{-} e^{+}ν_{e}$., 7 pages, 3 figures

Supplemental material, sj-docx-1-tan-10.1177_17562864231165561 for The severity of corneal nerve loss differentiates motor subtypes in patients with Parkinson’s disease by Ning-Ning Che, Qiu-Huan Jiang, Shuai Chen, Si-Yuan Chen, Zhen-Xiang Zhao, Xue Li, Jian-Jun Ma, Jie-Wen Zhang, Rayaz A. Malik and Hong-Qi Yang in Therapeutic Advances in Neurological Disorders

Electronic nematicity that spontaneously breaks rotational symmetry has been shown as a generic phenomenon in correlated quantum systems including high-temperature superconductors and the AV3Sb5 (A = K, Rb, Cs) family with a kagome network. Identifying the driving force has been a central challenge for understanding nematicity. In iron-based superconductors, the problem is complicated because the spin, orbital and lattice degrees of freedom are intimately coupled. In vanadium-based kagome superconductors AV3Sb5, the electronic nematicity exhibits an intriguing entanglement with the charge density wave order (CDW), making understanding its origin difficult. Recently, a new family of titanium-based kagome superconductors ATi3Bi5 has been synthesized. In sharp contrast to its vanadium-based counterpart, the electronic nematicity occurs in the absence of CDW. ATi3Bi5 provides a new window to explore the mechanism of electronic nematicity and its interplay with the orbital degree of freedom. Here, we combine polarization-dependent angle-resolved photoemission spectroscopy with density functional theory to directly reveal the band topology and orbital characters of the multi-orbital RbTi3Bi5. The promising coexistence of flat bands, type-II Dirac nodal line and nontrivial Z2 topological states is identified in RbTi3Bi5. Remarkably, our study clearly unveils the orbital character change along the G-M and G-K directions, implying a strong intrinsic inter-orbital coupling in the Ti-based kagome metals, reminiscent of iron-based superconductors. Furthermore, doping-dependent measurements directly uncover the orbital-selective features in the kagome bands, which can be well explained by the d-p hybridization. The suggested d-p hybridization, in collaboration with the inter-orbital coupling, could account for the electronic nematicity in ATi3Bi5.

Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress. Despite their promising performance, current state-of-the-art methods tend to increasingly complex designs at the cost of introducing more network components and additional training procedures. Differently, in this work, we follow a standard teacher-student framework and propose AugSeg, a simple and clean approach that focuses mainly on data perturbations to boost the SSS performance. We argue that various data augmentations should be adjusted to better adapt to the semi-supervised scenarios instead of directly applying these techniques from supervised learning. Specifically, we adopt a simplified intensity-based augmentation that selects a random number of data transformations with uniformly sampling distortion strengths from a continuous space. Based on the estimated confidence of the model on different unlabeled samples, we also randomly inject labelled information to augment the unlabeled samples in an adaptive manner. Without bells and whistles, our simple AugSeg can readily achieve new state-of-the-art performance on SSS benchmarks under different partition protocols., 10 pages, 8 tables

The aggregator, an emerging entity in the electricity market, gathers and creates market power for the small flexible resources, which traditionally contain distributed generation, electric storage, and flexible load. Recently, the exploding growth of information technology demand gives rise to the data centers, whose participation in demand response (DR) is becoming increasingly significant for its remarkable performance in spatiotemporal transfer characteristics compared to conventional facilities. The high-quality DR potential of data centers provides a new opportunity for the profit-seeking data center aggregators (DCAs) to optimize their bidding plans in the day-ahead electricity market. Therefore, this article targets the formulation of the optimal bidding strategy of demand response aggregators (DRAs) to achieve the maximization of the joint benefit of both DCA and data centers. A spatiotemporal transfer characteristics-based optimization strategy is formulated to obtain the electricity purchasing scheme and arrangement of data centers. Meanwhile, the Shapley value method is introduced to design the appropriate and equitable DR reward mechanism that each DCA should compensate its customers, which would serve as a guarantee to ensure data centers’ active participation in DR events and lay a solid foundation for the implementation of the DCA's optimal bidding strategy. Numerical tested results demonstrate the effectiveness of the proposed optimal bidding model and the benefit allocation mechanism, which profit both DRA and data centers when participating in DR programs while preserving their daily power demand.

Fabrication of new types of superconductors with novel physical properties has always been a major thread in the research of superconducting materials. An example is the enormous interests generated by the cascade of correlated topological quantum states in the newly discovered vanadium-based kagome superconductors AV3Sb5 (A=K, Rb, and Cs) with a Z2 topological band structure. Here we report the successful fabrication of single-crystals of titanium-based kagome metal CsTi3Bi5 and the observation of superconductivity and electronic nematicity. The onset of the superconducting transition temperature Tc is around 4.8 K. In sharp contrast to the charge density wave superconductor AV3Sb5, we find that the kagome superconductor CsTi3Bi5 preserves translation symmetry, but breaks rotational symmetry and exhibits an electronic nematicity. The angular-dependent magnetoresistivity shows a remarkable two-fold rotational symmetry as the magnetic field rotates in the kagome plane. The scanning tunneling microscopy and spectroscopic imaging detect rotational-symmetry breaking C2 quasiparticle interference patterns (QPI) at low energies, providing further microscopic evidence for electronic nematicity. Combined with first-principle calculations, we find that the nematic QPI is orbital-selective and dominated by the Ti dxz and dyz orbitals, possibly originating from the intriguing orbital bond nematic order. Our findings in the new "135" material CsTi3Bi5 provide new directions for exploring the multi-orbital correlation effect and the role of orbital or bond order in the electron liquid crystal phases evidenced by the symmetry breaking states in kagome superconductors.

Vision transformers have achieved significant improvements on various vision tasks but their quadratic interactions between tokens significantly reduce computational efficiency. Many pruning methods have been proposed to remove redundant tokens for efficient vision transformers recently. However, existing studies mainly focus on the token importance to preserve local attentive tokens but completely ignore the global token diversity. In this paper, we emphasize the cruciality of diverse global semantics and propose an efficient token decoupling and merging method that can jointly consider the token importance and diversity for token pruning. According to the class token attention, we decouple the attentive and inattentive tokens. In addition to preserving the most discriminative local tokens, we merge similar inattentive tokens and match homogeneous attentive tokens to maximize the token diversity. Despite its simplicity, our method obtains a promising trade-off between model complexity and classification accuracy. On DeiT-S, our method reduces the FLOPs by 35% with only a 0.2% accuracy drop. Notably, benefiting from maintaining the token diversity, our method can even improve the accuracy of DeiT-T by 0.1% after reducing its FLOPs by 40%.

ImportanceMost healthcare institutions require employees to be vaccinated against SARS-CoV-2 and many also require at least one booster.ObjectiveWe determine the impact of vaccine type, demographics, and health conditions on COVID-19 vaccine side effects in healthcare professionals.DesignA COVID-19 immunity study was performed at the 2021 American Association for Clinical Chemistry Annual Scientific meeting. As part of this study, a REDCap survey with cascading questions was administered from September 9, 2021 to October 20, 2021. General questions included participant demographics, past and present health conditions, smoking, exercise, and medications. COVID-19 specific questions asked about SARS-CoV-2 vaccine status and type, vaccine-associated side effects after each dose including any boosters, previous infection with COVID-19, diagnostic testing performed, and type and severity symptoms of COVID-19.ResultsThere were 975 participants (47.1% male, median age of 50 years) who completed the survey. Pfizer was the most commonly administered vaccine (56.4%) followed by Moderna (32.0%) and Johnson & Johnson (7.1%). There were no significant differences in vaccine type received by age, health conditions, smoking, exercise, or type or number of prescription medications. Side effects were reported more frequently after second dose (e.g., Moderna or Pfizer) (54.1%) or single/only dose of Johnson & Johnson (47.8%). Males were significantly more likely to report no side effects (p < 0.001), while females were significantly more likely to report injection site reactions (p < 0.001), fatigue (p < 0.001), headache (p < 0.001), muscle pain (p < 0.001), chills (p = 0.001), fever (p = 0.007), and nausea (p < 0.001). There was a significant upward trend in participants reporting no side effects with increasing age (p < 0.001). There were no significant trends in side effects among different races, ethnicities, health conditions, medications, smoking status or exercise. In multivariate logistic regressions analyses, the second dose of Moderna was associated with a significantly higher risk of side effects than both the second dose of Pfizer and the single dose of Johnson & Johnson.Conclusions and relevanceYounger people, females, and those receiving the second dose of Moderna had more COVID-19 vaccine side effects that per self-report led to moderate to severe limitations. As reported in other studies, the increase in side effects from Moderna may be explained by higher viral mRNA concentrations but be associated with additional protective immunity.

The identification and preservation of parathyroid glands (PGs) during thyroid surgery can be challenging. Many techniques have been developed to help surgeons find PGs. We have developed a novel mitoxantrone hydrochloride injection that can be used for lymphatic targeting. After local application during surgery, mitoxantrone hydrochloride injection for tracing (MHI) helps surgeons better identify and preserve PGs and helps pathologists find more lymph nodes. We conducted an open-label, multicenter, randomized clinical trial (CTR20171137) in six centers in China from 08/2017 to 12/2018. Patients with thyroid carcinoma were randomized to the MHI group or the control group. All patients received total thyroidectomy and bilateral central compartment lymph node dissection. The primary outcomes were the PG resection rate and lymph node staining rate. The full analysis set (FAS) included 461 patients, of which 228 were assigned to the MHI group, and 233 were assigned to the control group. The PG resection rates of the MHI group and the control group were 6.6% (15/228) and 26.6% (62/233), respectively, with a significant difference (P < 0.001). No PGs were stained blue with MHI. The central lymph nodes were stained blue with MHI, and the staining rate was 90.5%±12.0%. More lymph nodes were detected in the MHI group than in the control group (13.0±7.3 vs. 10.1±6.4 nodes/patient, P < 0.001). No adverse events related to MHI were observed. MHI is a safe and effective tracer that may help to preserve PGs and identify more central lymph nodes in patients with thyroid cancer.

It has been reported that extramammary malignant tumors metastasize to the breast, but cervical cancer metastasis to the breast is very rare. At present, there are only dozens of reports about cervical cancer metastasis to breast in the world. It is difficult to distinguish between primary breast cancer and metastatic breast cancer. We report a 44-year-old woman who underwent surgery, chemotherapy, and radiotherapy for cervical cancer 5 years ago. Then, she was hospitalized for finding a left breast mass measured 2.9 × 2.7 cm in chest CT. Pathological examination combined with immunohistochemical staining showed that the mass came from the cervix. Then, the patient received systematic chemotherapy and interstitial brachytherapy (IB) for the breast mass and got a great result. Cervical cancer rarely metastasizes to the breast. In this case, we confirmed the diagnosis of breast mass by histopathological examination and immunohistochemistry. IB achieved a good result in the treatment of the breast mass. We hope to provide reference of prognosis and treatment when facing this situation by presenting this case.

Although chirality is an ever-present characteristic in biology and some artificial molecules, controlling the chirality and demystifying the chirality origin of complex assemblies remain challenging. Herein, we report two homochiral Ag14 nanoclusters with inherent chirality originated from identical rotation of six square faces on a Ag8 cube driven by intra-cluster π···π stacking interaction between pntp− (Hpntp = p-nitrothiophenol) ligands. The spontaneous resolution of the racemic (SD/rac-Ag14a) to homochiral nanoclusters (SD/L-Ag14 and SD/R-Ag14) can be realized by re-crystallizing SD/rac-Ag14a in acetonitrile, which promotes the homochiral crystallization in solid state by forming C–H···O/N hydrogen bonds with nitro oxygen atoms in pntp− or aromatic hydrogen atoms in dpph (dpph = 1,6-bis(diphenylphosphino)hexane) on Ag14 nanocluster. This work not only provides strategic guidance for the syntheses of chiral silver nanoclusters in an all-achiral environment, but also deciphers the origin of chirality at molecular level by identifying the special effects of intra- and inter-cluster supramolecular interactions., The preparation of chiral monolayer-protected metal clusters is interesting for their potential applications in a variety of fields, including catalysis. Here, the authors synthesize chiral Ag14 nanoclusters in an all-achiral environment, and decipher the origin of chirality at the molecular level; the solvent choice is key to achieve homochiral crystallization.

The precise minute time scale forecasting of an individual PV power station output relies on accurate prediction of cloud distribution, which can lead to dramatic fluctuation of PV power generation. Precise cloud distribution information is mainly achieved by ground-based total sky imager, then the future cloud distribution can also be achieved by sky image prediction. In previous studies, traditional digital image processing technology (DIPT) has been widely used in predicting sky images. However, DIPT has two deficiencies: relatively limited input spatiotemporal information and linear extrapolation of images. The first deficiency makes the input spatiotemporal information not rich enough, while the second creates the prediction error from the beginning. To avoid these two deficiencies, convolutional autoencoder (CAE) based sky image prediction models are proposed due to the spatiotemporal feature extraction ability of two-dimensional (2-D) CAEs and 3-D CAEs. For 2-D CAEs and 3-D CAEs, four architectures are given respectively. To verify the effectiveness of the proposed models, two typical DIPT methods, including particle image velocimetry and Fourier phase correlation theory are introduced to build the benchmark models. Besides, five different scenarios are also set and the results show that the proposed models outperform the benchmark models in all scenarios.

Residential customers account for an indispensable part in the demand response (DR) program for their capability to provide flexibility when the system required. However, their available DR capacity has not been fully comprehended by the aggregator, who needs the information to bid accurately on behalf of the residential customers in the market transaction. To this end, this article devised an optimal bidding strategy for the aggregator considering the bottom-up responsiveness of residential customers. First, we attempt to establish the customers’ responsiveness function in relation to different incentives, during which a home energy management system is introduced to implement load adjustment for electrical appliances. Second, the functional relation is applied to the aggregator's decision-making process to formulate the optimal bidding strategy in the day-ahead market and the optimal scheduling scheme for the energy storage system with the aim to maximize its own revenue. Finally, the validity of the proposed method is verified using the dataset from the Pecan Street experiment in Austin. The obtained outcome demonstrates the practical rationality of the proposed method.

Ultra-short-term photovoltaic (PV) power forecasting can support the real-time dispatching of the power grid. However, PV power has great fluctuations due to various meteorological factors, which increase energy prices and cause difficulties in managing the grid. This article proposes an ultra-short-term PV power forecasting model based on the optimal frequency-domain decomposition and deep learning. First, the optimal frequency demarcation points for decomposition components are obtained through frequency-domain analysis. Then, the PV power is decomposed into the low-frequency and high-frequency components, which supports the rationality of decomposition results and solves the problem that the current decomposition model only uses the direct decomposition method and the decomposition components are not physical. Then, a convolutional neural network (CNN) is used to forecast the low-frequency and high-frequency components, and the final forecasting result is obtained by addition reconstruction. Based on the actual PV data in heavy rain days, the mean absolute percentage error (MAPE) of the proposed forecasting model is decreased by 52.97%, 64.07%, and 31.21%, compared with discrete wavelet transform, variational mode decomposition, and direct prediction models. In addition, compared with recurrent neural network and long–short-term memory model, the MAPE of the CNN forecasting model is decreased by 23.64% and 46.22%, and the training efficiency of the CNN forecasting model is improved by 85.63% and 87.68%. The results fully show that the proposed model in this article can improve both forecasting accuracy and time efficiency significantly.

We calculate the W-exchange contribution to the Ξcc++→Ξc+(′)π+ decay using light-cone sum rules. The two-particle light-cone distribution amplitudes of the pion are used as nonperturbative input for the sum rules calculation, and the perturbative kernel is calculated at the leading order. We obtain the corresponding decay branching fractions by combining our W-exchange amplitudes with the factorizable amplitudes given by various theoretical methods from the literature. It is shown that with the factorizable amplitudes from heavy quark effective theory, we obtain the branching fraction ratio B(Ξcc++→Ξc+′π+)/B(Ξcc++→Ξc+π+)=1.42±0.78, which is consistent with the experimental value of 1.41±0.17±0.1.