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Topological insulators are materials that behave as insulators in the bulk and as conductors at the edge or surface due to the particular configuration of their bulk band dispersion. However, up to date possible practical applications of this band topology on materials’ bulk properties have remained abstract. Here, we propose and experimentally demonstrate a topological bulk laser. We pattern semiconductor nanodisk arrays to form a photonic crystal cavity showing topological band inversion between its interior and cladding area. In-plane light waves are reflected at topological edges forming an effective cavity feedback for lasing. This band-inversion-induced reflection mechanism induces single-mode lasing with directional vertical emission. Our topological bulk laser works at room temperature and reaches the practical requirements in terms of cavity size, threshold, linewidth, side-mode suppression ratio and directionality for most practical applications according to Institute of Electrical and Electronics Engineers and other industry standards. We believe this bulk topological effect will have applications in near-field spectroscopy, solid-state lighting, free-space optical sensing and communication.The interface between photonic crystals with distinct in-band topologies confines electromagnetic modes and gives rise to lasing emission in the bulk.

Conventional laser cavities require discontinuity of material property or disorder to localize a light field for feedback. Recently, an emerging class of materials, twisted van der Waals materials, have been explored for applications in electronics and photonics. Here we propose and develop magic-angle lasers, where the localization is realized in periodic twisted photonic graphene superlattices. We reveal that the confinement mechanism of magic-angle lasers does not rely on a full bandgap but on the mode coupling between two twisted layers of photonic graphene lattice. Without any fine-tuning in structure parameters, a simple twist can result in nanocavities with strong field confinement and a high quality factor. Furthermore, the emissions of magic-angle lasers allow direct imaging of the wavefunctions of magic-angle states. Our work provides a robust platform to construct high-quality nanocavities for nanolasers, nano light-emitting diodes, nonlinear optics and cavity quantum electrodynamics at the nanoscale.Twisted photonic graphene superlattices enable the realization of high-performance room-temperature magic-angle lasers.

استنادا إلى نظرة عامة على أوضاع الفقر، يقدم هذا الكتاب مسار التخفيف من حدة الفقر والتنمية في الصين، ويشرح نموذج التنمية والتخفي من حدة الفقر بخصائص صينية والتمسك بمباديء (سيطرة الحكومة ومشاركة المجتمع والاعتماد على الذات والتنمية الموجهة والتنمية الشاملة) كما يقدم الكتاب تلخيصا شاملا لإنجازات الصين العظيمة وخبراتها الهامة وإسهاماتها الرئيسية في قضية التخفيف من حدة الفقر في العالم، ويعرض بإيجاز نظرات وممارسات التخفيف المستهدف من الفقر في العصر الجديد من أجل توفير مراجع لكسب المعركة ضد الفقر في الصين وقضية التخفيف من حدة الفقر في العالم.

Point cloud data generated by LiDAR sensors play a critical role in 3D sensing systems, with applications encompassing object classification, part segmentation, and point cloud recognition. Leveraging the global learning capacity of dot product attention, transformers have recently exhibited outstanding performance in point cloud learning tasks. Nevertheless, existing transformer models inadequately address the challenges posed by uncertainty features in point clouds, which can introduce errors in the dot product attention mechanism. In response to this, our study introduces a novel global guidance approach to tolerate uncertainty and provide a more reliable guidance. We redefine the granulation and lower-approximation operators based on neighborhood rough set theory. Furthermore, we introduce a rough set-based attention mechanism tailored for point cloud data and present the rough set transformer (RST) network. Our approach utilizes granulation concepts derived from token clusters, enabling us to explore relationships between concepts from an approximation perspective, rather than relying on specific dot product functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method. Our method establishes concepts based on granulation generated from clusters of tokens. Subsequently, relationships between concepts can be explored from an approximation perspective, instead of relying on specific dot product or addition functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method.

PurposeMicrovascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI preoperatively.MethodsIn total, 405 patients were included. A total of 7302 radiomic features and 17 radiological features were extracted by a radiomics feature extraction package and radiologists, respectively. We developed a XGBoost model based on radiomics features, radiological features and clinical variables and a three-dimensional convolutional neural network (3D-CNN) to predict MVI status. Next, we compared the efficacy of the two models.ResultsOf the 405 patients, 220 (54.3%) were MVI positive, and 185 (45.7%) were MVI negative. The areas under the receiver operating characteristic curves (AUROCs) of the Radiomics-Radiological-Clinical (RRC) Model and 3D-CNN Model in the training set were 0.952 (95% confidence interval (CI) 0.923–0.973) and 0.980 (95% CI 0.959–0.993), respectively (p = 0.14). The AUROCs of the RRC Model and 3D-CNN Model in the validation set were 0.887 (95% CI 0.797–0.947) and 0.906 (95% CI 0.821–0.960), respectively (p = 0.83). Based on the MVI status predicted by the RRC and 3D-CNN Models, the mean recurrence-free survival (RFS) was significantly better in the predicted MVI-negative group than that in the predicted MVI-positive group (RRC Model: 69.95 vs. 24.80 months, p < 0.001; 3D-CNN Model: 64.06 vs. 31.05 months, p = 0.027).ConclusionThe RRC Model and 3D-CNN models showed considerable efficacy in identifying MVI preoperatively. These machine learning models may facilitate decision-making in HCC treatment but requires further validation.

There is a wide variety of kinds of lipids, and complex structures which determine the diversity and complexity of their functions. With the basic characteristic of water insolubility, lipid molecules are independent of the genetic information composed by genes to proteins, which determine the particularity of lipids in the human body, with water as the basic environment and genes to proteins as the genetic system. In this review, we have summarized the current landscape on hormone regulation of lipid metabolism. After the well-studied PI3K-AKT pathway, insulin affects fat synthesis by controlling the activity and production of various transcription factors. New mechanisms of thyroid hormone regulation are discussed, receptor α and β may mediate different procedures, the effect of thyroid hormone on mitochondria provides a new insight for hormones regulating lipid metabolism. Physiological concentration of adrenaline induces the expression of extrapituitary prolactin in adipose tissue macrophages, which promotes fat weight loss. Manipulation of hormonal action has the potential to offer a new therapeutic horizon for the global burden of obesity and its associated complications such as morbidity and mortality.

Long-term potentiation (LTP) in the hippocampus is the most studied form of synaptic plasticity. Temporal integration of synaptic inputs is essential in synaptic plasticity and is assumed to be achieved through Ca2+ signaling in neurons and astroglia. However, whether these two cell types play different roles in LTP remain unknown. Here, we found that through the integration of synaptic inputs, astrocyte inositol triphosphate (IP3) receptor type 2 (IP3R2)-dependent Ca2+ signaling was critical for late-phase LTP (L-LTP) but not early-phase LTP (E-LTP). Moreover, this process was mediated by astrocyte-derived brain-derived neurotrophic factor (BDNF). In contrast, neuron-derived BDNF was critical for both E-LTP and L-LTP. Importantly, the dynamic differences in BDNF secretion play a role in modulating distinct forms of LTP. Moreover, astrocyte- and neuron-derived BDNF exhibited different roles in memory. These observations enriched our knowledge of LTP and memory at the cellular level and implied distinct roles of astrocytes and neurons in information integration.

Electronic communication (e-communication), referring to communication through electronic platforms such as the web, patient portal, or mobile phone, has become increasingly important, as it extends traditional in-person communication with fewer limitations of timing and locations. However, little is known about the current status of patients' use of e-communication with clinicians and whether the use is related to the better patient-perceived quality of care at the population level. The aim of this study was to explore the prevalence of and the factors associated with e-communication use and the association of e-communication use with patient-perceived quality of care by using the nationally representative sample of the 2019 Health Information National Trends Survey 5 (HINTS 5)-Cycle 3. Data from 5438 adult responders (mean age 49.04 years, range 18-98 years) were included in this analysis. Multiple logistic and linear regressions were conducted to explore responders' personal characteristics related to their use of e-communication with clinicians in the past 12 months and how their use was related to perceived quality of care. Descriptive analyses for e-communication use according to age groups were also performed. All analyses considered the complex survey design using the jackknife replication method. The overall prevalence of e-communication use was 60.3%, with a significantly lower prevalence in older adults (16.6%) than that in <45-year-old adults (41%) and 45-65-year-old adults (42.4%). All percentages are weighted; therefore, absolute values are not shown. American adults who used e-communication were more likely to be high school graduates (odds ratio [OR] 1.95, 95% CI 1.14-3.34; P=.02), some college degree holders (OR 3.34, 95% CI 1.84-6.05; P<.001), and college graduates or more (OR 4.89, 95% CI 2.67-8.95; P<.001). Further, people who were females (OR 1.47, 95% CI 1.18-1.82; P=.001), with a household income ≥US $50,000 (OR 1.63, 95% CI 1.23-2.16; P=.001), with more comorbidities (OR 1.22, 95% CI 1.07-1.40; P=.004), or having a regular health care provider (OR 2.62, 95% CI 1.98-3.47; P<.001), were more likely to use e-communication. In contrast, those who resided in rural areas (OR 0.61, 95% CI 0.43-0.88; P=.009) were less likely to use e-communication. After controlling for the sociodemographics, the number of comorbidities, and relationship factors (ie, having a regular provider and trusting a doctor), e-communication use was found to be significantly associated with better perceived quality of care (β=.12, 95% CI 0.02-0.22; P=.02). This study confirmed the positive association between e-communication use and patient-perceived quality of care and suggested that policy-level attention should be raised to engage the socially disadvantaged (ie, those with lower levels of education and income, without a regular health care provider, and living in rural areas) to maximize e-communication use and to support better patient-perceived quality of care among American adults.