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Bulk–boundary correspondence, a guiding principle in topological matter, relates robust edge states to bulk topological invariants. Its validity, however, has so far been established only in closed systems. Recent theoretical studies indicate that this principle requires fundamental revisions for a wide range of open systems with effective non-Hermitian Hamiltonians. Therein, the intriguing localization of nominal bulk states at boundaries, known as the non-Hermitian skin effect, suggests a non-Bloch band theory in which non-Bloch topological invariants are defined in generalized Brillouin zones, leading to a general bulk–boundary correspondence beyond the conventional framework. Here, we experimentally observe this fundamental non-Hermitian bulk–boundary correspondence in discrete-time non-unitary quantum-walk dynamics of single photons. We demonstrate pronounced photon localizations near boundaries even in the absence of topological edge states, thus confirming the non-Hermitian skin effect. Facilitated by our experimental scheme of edge-state reconstruction, we directly measure topological edge states, which are in excellent agreement with the non-Bloch topological invariants. Our work unequivocally establishes the non-Hermitian bulk–boundary correspondence as a general principle underlying non-Hermitian topological systems and paves the way for a complete understanding of topological matter in open systems. Measurements of non-Hermitian photon dynamics show boundary-localized bulk eigenstates given by the non-Hermitian skin effect. A fundamental revision of the bulk–boundary correspondence in open systems is required to understand the underlying physics.

Background The World Health Organization (WHO) called for global action towards the elimination of cervical cancer. One of the main strategies is to screen 70% of women at the age between 35 and 45 years and 90% of women managed appropriately by 2030. So far, approximately 85% of cervical cancers occur in low- and middle-income countries (LMICs). The colposcopy-guided biopsy is crucial for detecting cervical intraepithelial neoplasia (CIN) and becomes the main bottleneck limiting screening performance. Unprecedented advances in artificial intelligence (AI) enable the synergy of deep learning and digital colposcopy, which offers opportunities for automatic image-based diagnosis. To this end, we discuss the main challenges of traditional colposcopy and the solutions applying AI-guided digital colposcopy as an auxiliary diagnostic tool in low- and middle- income countries (LMICs). Main body Existing challenges for the application of colposcopy in LMICs include strong dependence on the subjective experience of operators, substantial inter- and intra-operator variabilities, shortage of experienced colposcopists, consummate colposcopy training courses, and uniform diagnostic standard and strict quality control that are hard to be followed by colposcopists with limited diagnostic ability, resulting in discrepant reporting and documentation of colposcopy impressions. Organized colposcopy training courses should be viewed as an effective way to enhance the diagnostic ability of colposcopists, but implementing these courses in practice may not always be feasible to improve the overall diagnostic performance in a short period of time. Fortunately, AI has the potential to address colposcopic bottleneck, which could assist colposcopists in colposcopy imaging judgment, detection of underlying CINs, and guidance of biopsy sites. The automated workflow of colposcopy examination could create a novel cervical cancer screening model, reduce potentially false negatives and false positives, and improve the accuracy of colposcopy diagnosis and cervical biopsy. Conclusion We believe that a practical and accurate AI-guided digital colposcopy has the potential to strengthen the diagnostic ability in guiding cervical biopsy, thereby improves cervical cancer screening performance in LMICs and accelerates the process of global cervical cancer elimination eventually.

Disorder and non-Hermiticity dramatically impact the topological and localization properties of a quantum system, giving rise to intriguing quantum states of matter. The rich interplay of disorder, non-Hermiticity, and topology is epitomized by the recently proposed non-Hermitian topological Anderson insulator that hosts a plethora of exotic phenomena. Here we experimentally simulate the non-Hermitian topological Anderson insulator using disordered photonic quantum walks, and characterize its localization and topological properties. In particular, we focus on the competition between Anderson localization induced by random disorder, and the non-Hermitian skin effect under which all eigenstates are squeezed toward the boundary. The two distinct localization mechanisms prompt a non-monotonous change in profile of the Lyapunov exponent, which we experimentally reveal through dynamic observables. We then probe the disorder-induced topological phase transitions, and demonstrate their biorthogonal criticality. Our experiment further advances the frontier of synthetic topology in open systems. The authors report an experimental observation of a non-Hermitian topological Anderson insulator using photonic quantum walks, revealing the competition between Anderson localization induced by random disorder and the non-Hermitian skin effect.

Matter transport is a fundamental process in nature. Understanding and manipulating flow in a synthetic media often have rich implications for modern device design. Here we experimentally demonstrate directional transport of photons in a two-dimensional quantum walk, where the light propagation is highly tunable through dissipation and synthetic magnetic flux. The directional flow hereof underlies the emergence of the non-Hermitian skin effect, with its orientation continuously adjustable through the photon-loss parameters. By contrast, the synthetic magnetic flux originates from an engineered geometric phase, which, by inducing localized cyclotron orbits, suppresses the bulk flow through magnetic confinement. We further demonstrate how the directional flow and synthetic flux impact the dynamics of the Floquet topological edge modes along an engineered boundary. Our results exemplify an intriguing strategy for engineering directed light transport, highlighting the interplay of non-Hermiticity and gauge fields in synthetic systems of higher dimensions. Non-Hermitian phenomena such as non-Hermitian skin effect have a strong impact on open system dynamics. Here, the authors use a photonic quantum walk including a synthetic gauge field to show that the interplay of synthetic flux and dissipation enables the full control over the directional transport.

An increase in the number of people leaving metropolitan areas (MAs) was observed in various countries in the early years of the COVID-19 pandemic. While considerable attention has been paid to the impacts of health risks and teleworking, two prominent topics related to health-crisis-led migration, empirical evidence remains inadequate. This study aims to empirically investigate the impacts and temporal changes of these two factors on migration leaving MAs (LMA migration). It utilizes survey data from the Japanese government and employs fixed effects logit models. (1) By using infection rates in a more accurate measurement than previous studies, this study confirms the health-risk-aversion motives in LMA migration. (2) Teleworking’s influence on LMA migration is found to be insignificant over the long term. Nevertheless, it increases the likelihood of formal employees staying in MAs and strengthens the tendency of the self-employed to leave for local areas . (3) Temporally, the significant impact of lower COVID-19 infection rates attracting metropolitan residents persisted beyond the pandemic stringency and continued for several months afterward, though it eventually reversed. Teleworking shows a positive influence on LMA migration only in the later stage of COVID-19. These findings suggest a tendency of ‘deferred decisions’ in LMA migration due to people’s unfamiliarity with an unprecedented health crisis. However, the negative impact of infection risks emerges sooner than the significant effect of teleworking, indicating that safety is a pressing priority for LMA migration in the early stages of a major health crisis. (4) Self-employed individuals, homeworkers, and the unemployed are more likely to engage in LMA migration, while employees (whether formal or informal) are less likely, highlighting the role of opportunity costs. Policy implications suggest that local governments should focus on attracting the self-employed from MAs during health crises and on enhancing the teleworking environment for the long term.

Methyl-coenzyme M reductase (MCR) is the key enzyme of methanogenesis and anaerobic methane oxidation. The activity of MCR is dependent on the unique nickel-containing tetrapyrrole known as coenzyme F430. We used comparative genomics to identify the coenzyme F430 biosynthesis (cfb) genes and characterized the encoded enzymes from Methanosarcina acetivorans C2A. The pathway involves nickelochelation by a nickel-specific chelatase, followed by amidation to form Ni-sirohydrochlorin a,c-diamide. Next, a primitive homolog of nitrogenase mediates a six-electron reduction and γ-lactamization reaction before a Mur ligase homolog forms the six-membered carbocyclic ring in the final step of the pathway. These data show that coenzyme F430 can be synthesized from sirohydrochlorin using Cfb enzymes produced heterologously in a nonmethanogen host and identify several targets for inhibitors of biological methane formation.

In this paper, we investigate the (2+1)-dimensional nonlinear Schrödinger equation (NLSE) which characterizes the transmission of optical pulses through optical fibers exhibiting refractive index variations corresponding to light intensity changes. Traditional numerical methods typically require a substantial amount of data to ensure the accuracy when solving high-dimensional NLSE, resulting in high experimental costs as well as a significant demand for storage space and computing power. With physical knowledge embedded into deep neural networks, physics-informed neural network (PINN) has been widely applied to solve various complex nonlinear problems and achieved significant results with small amount of data. Setting different groups of initial conditions and boundary conditions with hyperbolic and exponential functions, we construct the corresponding loss functions which will be further applied to train PINN. All data studied here is generated on Python. Based on the predicted results, we depict different types of optical pulses. According to our data experiments, lower prediction errors can be achieved with small volume of data, which fully demonstrates the effectiveness of the PINN. In the meantime, we also perform data-driven parameter discovery to the (2+1)-dimensional NLSE to study the coefficients of the group velocity dispersion and self-phase modulation terms. It can be seen that the PINN has high accuracy and robustness for parameter discovery to the (2+1)-dimensional NLSE. In brief, the use of PINN greatly enriches the diversity of solving methods, providing a reference for research of (2+1)-dimensional solitons in the field of optical fiber communications.

Phase‐change random‐access memory (PCRAM) devices suffer from pronounced resistance drift originating from considerable structural relaxation of phase‐change materials (PCMs), which hinders current developments of high‐capacity memory and high‐parallelism computing that both need reliable multibit programming. This work realizes that compositional simplification and geometrical miniaturization of traditional GeSbTe‐like PCMs are feasible routes to suppress relaxation. While to date, the aging mechanisms of the simplest PCM, Sb, at nanoscale, have not yet been unveiled. Here, this work demonstrates that in an optimal thickness of only 4 nm, the thin Sb film can enable a precise multilevel programming with ultralow resistance drift coefficients, in a regime of ≈10−4–10−3. This advancement is mainly owed to the slightly changed Peierls distortion in Sb and the less‐distorted octahedral‐like atomic configurations across the Sb/SiO2 interfaces. This work highlights a new indispensable approach, interfacial regulation of nanoscale PCMs, for pursuing ultimately reliable resistance control in aggressively‐miniaturized PCRAM devices, to boost the storage and computing efficiencies substantially. The 4 nm‐thick monoatomic Sb film enables the ultralow resistance drift coefficient v, ranging from ≈10−4 to ≈10−3, which will benefit a further promotion in multibit programming accuracy to develop high‐capacity universal memory and high‐efficiency computing chips.

•Structural and functional evidence supports the enhancement of connectivity diversity on connector hubs after sleep deprivation.•Enhanced diversity, which may potentially signify a compensatory mechanism within the brain, is accompanied by an increased brain network cost and a more random-like network structure, yet it is associated with enhanced global efficiency.•The significantly affected connector hubs were primarily observed in the Control Network and Salience Network. Sleep deprivation has been demonstrated to exert widespread and intricate impacts on the brain network. The human brain network is a modular network composed of interconnected nodes. This network consists of provincial hubs and connector hubs, with provincial hubs having diverse connectivities within their own modules, while connector hubs distribute their connectivities across different modules. The latter is crucial for integrating information from various modules and ensuring the normal functioning of the modular brain. However, there has been a lack of systematic investigation into the impact of sleep deprivation on brain connector hubs. In this study, we utilized functional connectivity from resting-state functional magnetic resonance imaging, as well as structural connectivity from diffusion-weighted imaging, to systematically explore the variation of connector hub properties in the cerebral cortex after one night of sleep deprivation. The normalized participation coefficients (PCnorm) were utilized to identify connector hubs. In both the functional and structural networks, connector hubs exhibited a significant increase in average PCnorm, indicating the diversity enhancement of the connector hub following sleep deprivation. This enhancement is associated with increased network cost, reduced modularity, and decreased small-worldness, but enhanced global efficiency. This may potentially signify a compensatory mechanism within the brain following sleep deprivation. The significantly affected connector hubs were primarily observed in both the Control Network and Salience Network. We believe that the observed results reflect the increasing demand on the brain to invest more effort at preventing performance deterioration after sleep loss, in exchange for increased communication efficiency, especially involving systems responsible for neural resource allocation and cognitive control. These results have been replicated in an independent dataset. In conclusion, this study has enhanced our understanding of the compensatory mechanism in the brain response to sleep deprivation. This compensation is characterized by an enhancement in the connector hubs responsible for inter-modular communication, especially those related to neural resource and cognitive control. As a result, this compensation comes with a higher network cost but leads to an improvement in global communication efficiency, akin to a more random-like network manner.

Prunus mume is one of the most ancient medicinal herbs and health foods commonly used in Asian countries. It is widely used as a constituent of many medicinal preparations and as a food ingredient for its beneficial health effects. In this review, we retrieved reports from PubMed, embase, Scopus, and SciFinder databases, to collect extensive scientific evidence on the phytochemical constituents, pharmacological properties, and clinical applications of Prunus mume . The literature review revealed that approximately 192 compounds have been isolated from different parts of the plant, and their molecular structures have been identified. The pharmacological properties of the plant, including anti-diabetic, liver-protective, antitumor, antimicrobial, antioxidant, and anti-inflammatory activities, as well as their underlying mechanisms, have been clarified by in vitro and in vivo studies. Clinical studies, although very limited, have been highlighted in this review to provide a reference for further exploration on therapeutic applications of the plant.