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\"One morning, so early that the fog still clings to the surface of the river, a young boy accompanies his yeye seven miles to the grassy field behind their home in order to cut satintail to feed the livestock.-- Provided by publisher.

Quantum dimer models are known to host topological quantum spin liquid phases, and it has recently become possible to simulate such models with Rydberg atoms trapped in arrays of optical tweezers. Here, we present large-scale quantum Monte Carlo simulation results on an extension of the triangular lattice quantum dimer model with terms in the Hamiltonian annihilating and creating single dimers. We find distinct odd and even Z 2 spin liquids, along with several phases with no topological order: a staggered crystal, a nematic phase, and a trivial symmetric phase with no obvious broken symmetry. We also present dynamic spectra of the phases, and note implications for experiments on Rydberg atoms. Quantum dimer models are known to host topological quantum spin liquid phases, and it has recently become possible to simulate related Z 2 gauge theories with Rydberg atoms. Yan et al. compute the phase diagram of an experimentally motivated quantum dimer model on a triangular lattice with fluctuating dimer density.

In the recent decades, with rapid development in computing power and algorithms, machine learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the history of ML is described and the basic process of ML accelerated polymer discovery is summarized. Next, the four steps in this process are reviewed, that is, dataset selection, fingerprinting, ML framework, and new polymer generation. Finally, a couple of main challenges for ML accelerated polymer discovery is presented and the outlooks in this field are prospected. It is expected that this review can service as a useful tool for the people who just step into this field and deepen the understanding for the people who are already in this field. Basic flow for machine learning‐assisted polymer discovery, which consists of nine steps, including Step 1: data collection, Step 2: fingerprinting, Step 3: machine learning prediction, Step 4: machine learning model, Step 5: new data points, Step 6: new polymer structures, Step 7: screening with threshold, Step 8: screening with chemical expertise, and Step 9: desired polymer.

Avian influenza viruses continue to present challenges to animal and human health. Viruses bearing the hemagglutinin (HA) gene of the H5 subtype and H7 subtype have caused 2634 human cases around the world, including more than 1000 deaths. These viruses have caused numerous disease outbreaks in wild birds and domestic poultry, and are responsible for the loss of at least 422 million domestic birds since 2005. The H5 influenza viruses are spread by migratory wild birds and have caused three waves of influenza outbreaks across multiple continents, and the third wave that started in 2020 is ongoing. Many countries in Europe and North America control highly pathogenic avian influenza by culling alone, whereas some countries, including China, have adopted a \"cull plus vaccination\" strategy. As the largest poultry-producing country in the world, China lost relatively few poultry during the three waves of global H5 avian influenza outbreaks, and nearly eliminated the pervasive H7N9 viruses that emerged in 2013. In this review, we briefly summarize the damages the H5 and H7 influenza viruses have caused to the global poultry industry and public health, analyze the origin, evolution, and spread of the H5 viruses that caused the waves, and discuss how and why the vaccination strategy in China has been a success. Given that the H5N1 viruses are widely circulating in wild birds and causing problems in domestic poultry around the world, we recommend that any unnecessary obstacles to vaccination strategies should be removed immediately and forever.

Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing, a distributed framework that brings computation and data storage closer to the sources of data. In addition to the capability of static image sensing and processing, the hardware implementation of a neuro-inspired vision system also requires the fulfilment of detecting and recognizing moving targets. Here, we demonstrated a neuro-inspired optical sensor based on two-dimensional NbS 2 /MoS 2 hybrid films, which featured remarkable photo-induced conductance plasticity and low electrical energy consumption. A neuro-inspired optical sensor array with 10 × 10 NbS 2 /MoS 2 phototransistors enabled highly integrated functions of sensing, memory, and contrast enhancement capabilities for static images, which benefits convolutional neural network (CNN) with a high image recognition accuracy. More importantly, in-sensor trajectory registration of moving light spots was experimentally implemented such that the post-processing could yield a high restoration accuracy. Our neuro-inspired optical sensor array could provide a fascinating platform for the implementation of high-performance artificial vision systems. Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing. Here the authors, develop a neuro-inspired optical sensor based on NbS 2 /MoS 2 films that can operate with monolithically integrated functions of static image enhancement and dynamic trajectory registration.

Background Liver hepatocellular carcinoma (LIHC) ranks sixth among the most common types of cancer with a high mortality rate. Cuproptosis is a newly discovered type of cell death in tumor, which is characterized by accumulation of intracellular copper leading to the aggregation of mitochondrial lipoproteins and destabilization of proteins. Thus, understanding the exact effects of cuproptosis-related genes in LIHC and determining their prognosticvalue is critical. However, the prognostic model of LIHC based on cuproptosis-related genes has not been reported. Methods Firstly, we downloaded transcriptome data and clinical information of LIHC patients from TCGA and GEO (GSE76427), respectively. We then extracted the expression of cuproptosis-related genes and established a prognostic model by lasso cox regression analysis. Afterwards, the prediction performance of the model was evaluated by Kaplan–Meier survival analysis and receiver operating characteristic curve (ROC). Then, the prognostic model and the expression levels of the three genes were validated using the dataset from GEO. Subsequently, we divided LIHC patients into two subtypes by non-negative matrix factorization (NMF) classification and performed survival analysis. We constructed a Sankey plot linking different subtypes and prognostic models. Next, we calculate the drug sensitivity of each sample from patients in the high-risk group and low-risk group by the R package pRRophetic. Finally, we verified the function of LIPT1 in LIHC. Results Using lasso cox regression analysis, we developed a prognostic risk model based on three cuproptosis-related genes (GCSH, LIPT1 and CDKN2A). Both in the training and in the test sets, the overall survival (OS) of LIHC patients in the low-risk group was significantly longer than that in the high-risk group. By performing NMF cluster, we identified two molecular subtypes of LIHC (C1 and C2), with C1 subtype having significantly longer OS and PFS than C2 subtype. The ROC analysis indicated that our model had a precisely predictive capacity for patients with LIHC. The multivariate Cox regression analysis indicated that the risk score is an independent predictor. Subsequently, we identified 71 compounds with IC50 values that differed between the high-risk and low-risk groups. Finally, we determined that knockdown of LIPT1 gene expression inhibited proliferation and invasion of hepatoma cells. Conclusion In this study, we developed a novel prognostic model for hepatocellular carcinoma based on cuproptosis-related genes that can effectively predict the prognosis of LIHC patients. The model may be helpful for clinicians to make clinical decisions for patients with LIHC and provide valuable insights for individualized treatment. Two distinct subtypes of LIHC were identified based on cuproptosis-related genes, with different prognosis and immune characteristics. In addition, we verified that LIPT1 may promote proliferation, invasion and migration of LIHC cells. LIPT1 might be a new potential target for therapy of LIHC.

Oxygen-anion redox in lithium-rich layered oxides can boost the capacity of lithium-ion battery cathodes. However, the over-oxidation of oxygen at highly charged states aggravates irreversible structure changes and deteriorates cycle performance. Here, we investigate the mechanism of surface degradation caused by oxygen oxidation and the kinetics of surface reconstruction. Considering Li 2 MnO 3 , we show through density functional theory calculations that a high energy orbital (lO 2 p ’ ) at under-coordinated surface oxygen prefers over-oxidation over bulk oxygen, and that surface oxygen release is then kinetically favored during charging. We use a simple strategy of turning under-coordinated surface oxygen into polyanionic (SO 4 ) 2− , and show that these groups stabilize the surface of Li 2 MnO 3 by depressing gas release and side reactions with the electrolyte. Experimental validation on Li 1.2 Ni 0.2 Mn 0.6 O 2 shows that sulfur deposition enhances stability of the cathode with 99.0% capacity remaining (194 mA h g −1 ) after 100 cycles at 1 C. Our work reveals a promising surface treatment to address the instability of highly charged layered cathode materials. Oxygen-anion redox in lithium-rich layered oxides can boost the capacity of lithium-ion battery cathodes. Here, the authors investigate the mechanism of surface degradation caused by oxygen oxidation and the kinetics of surface reconstruction.

The experimental discovery of the fractional Hall conductivity in two-dimensional electron gases revealed new types of quantum particles, called anyons, which are beyond bosons and fermions as they possess fractionalized exchange statistics. These anyons are usually studied deep inside an insulating topological phase. It is natural to ask whether such fractionalization can be detected more broadly, say near a phase transition from a conventional to a topological phase. To answer this question, we study a strongly correlated quantum phase transition between a topological state, called a Z 2 quantum spin liquid, and a conventional superfluid using large-scale quantum Monte Carlo simulations. Our results show that the universal conductivity at the quantum critical point becomes a simple fraction of its value at the conventional insulator-to-superfluid transition. Moreover, a dynamically self-dual optical conductivity emerges at low temperatures above the transition point, indicating the presence of the elusive vison particles. Our study opens the door for the experimental detection of anyons in a broader regime, and has ramifications in the study of quantum materials, programmable quantum simulators, and ultra-cold atomic gases. In the latter case, we discuss the feasibility of measurements in optical lattices using current techniques. Conventional quantum particles can break up into fractionalized excitations under the right conditions; however, their direct experimental observation is challenging. Here, the authors predict strong optical conductivity signatures of such excitations in the vicinity of a topological phase transition.

Subcritical water refers to high-temperature and high-pressure water. A unique and useful characteristic of subcritical water is that its polarity can be dramatically decreased with increasing temperature. Therefore, subcritical water can behave similar to methanol or ethanol. This makes subcritical water a green extraction fluid used for a variety of organic species. This review focuses on the subcritical water extraction (SBWE) of natural products. The extracted materials include medicinal and seasoning herbs, vegetables, fruits, food by-products, algae, shrubs, tea leaves, grains, and seeds. A wide range of natural products such as alkaloids, carbohydrates, essential oil, flavonoids, glycosides, lignans, organic acids, polyphenolics, quinones, steroids, and terpenes have been extracted using subcritical water. Various SBWE systems and their advantages and drawbacks have also been discussed in this review. In addition, we have reviewed co-solvents including ethanol, methanol, salts, and ionic liquids used to assist SBWE. Other extraction techniques such as microwave and sonication combined with SBWE are also covered in this review. It is very clear that temperature has the most significant effect on SBWE efficiency, and thus, it can be optimized. The optimal temperature ranges from 130 to 240 °C for extracting the natural products mentioned above. This review can help readers learn more about the SBWE technology, especially for readers with an interest in the field of green extraction of natural products. The major advantage of SBWE of natural products is that water is nontoxic, and therefore, it is more suitable for the extraction of herbs, vegetables, and fruits. Another advantage is that no liquid waste disposal is required after SBWE. Compared with organic solvents, subcritical water not only has advantages in ecology, economy, and safety, but also its density, ion product, and dielectric constant can be adjusted by temperature. These tunable properties allow subcritical water to carry out class selective extractions such as extracting polar compounds at lower temperatures and less polar ingredients at higher temperatures. SBWE can mimic the traditional herbal decoction for preparing herbal medication and with higher extraction efficiency. Since SBWE employs high-temperature and high-pressure, great caution is needed for safe operation. Another challenge for application of SBWE is potential organic degradation under high temperature conditions. We highly recommend conducting analyte stability checks when carrying out SBWE. For analytes with poor SBWE efficiency, a small number of organic modifiers such as ethanol, surfactants, or ionic liquids may be added.

Virtual and augmented reality (VAR) represents a combination of current state-of-the-art computer and imaging technologies and has the potential to be a revolutionary technology in many surgical fields. An increasing number of investigators have developed and applied VAR in hip-related surgery with the aim of using this technology to reduce hip surgery-related complications, improve surgical success rates, and reduce surgical risks. These technologies are beginning to be widely used in hip-related preoperative operation simulation and training, intraoperative navigation tools in the operating room, and postoperative rehabilitation. With the aim of reviewing the current status of virtual reality (VR) and augmented reality (AR) in hip-related surgery and summarizing its benefits, we discussed and briefly described the applicability, advantages, limitations, and future perspectives of VR and AR techniques in hip-related surgery, such as preoperative operation simulation and training; explored the possible future applications of AR in the operating room; and discussed the bright prospects of VR and AR technologies in postoperative rehabilitation after hip surgery. We searched the PubMed and Web of Science databases using the following key search terms: (\"virtual reality\" OR \"augmented reality\") AND (\"pelvis\" OR \"hip\"). The literature on basic and clinical research related to the aforementioned key search terms, that is, studies evaluating the key factors, challenges, or problems of using of VAR technology in hip-related surgery, was collected. A total of 40 studies and reports were included and classified into the following categories: total hip arthroplasty, hip resurfacing, femoral neck fracture, pelvic fracture, acetabular fracture, tumor, arthroscopy, and postoperative rehabilitation. Quality assessment could be performed in 30 studies. Among the clinical studies, there were 16 case series with an average score of 89 out of 100 points (89%) and 1 case report that scored 81 (SD 10.11) out of 100 points (81%) according to the Joanna Briggs Institute Critical Appraisal Checklist. Two cadaveric studies scored 85 of 100 points (85%) and 92 of 100 points (92%) according to the Quality Appraisal for Cadaveric Studies scale. VR and AR technologies hold great promise for hip-related surgeries, especially for preoperative operation simulation and training, feasibility applications in the operating room, and postoperative rehabilitation, and have the potential to assist orthopedic surgeons in operating more accurately and safely. More comparative studies are necessary, including studies focusing on clinical outcomes and cost-effectiveness.