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Nationwide nonpharmaceutical interventions (NPIs) have been effective at mitigating the spread of the novel coronavirus disease (COVID-19), but their broad impact on other diseases remains under-investigated. Here we report an ecological analysis comparing the incidence of 31 major notifiable infectious diseases in China in 2020 to the average level during 2014-2019, controlling for temporal phases defined by NPI intensity levels. Respiratory diseases and gastrointestinal or enteroviral diseases declined more than sexually transmitted or bloodborne diseases and vector-borne or zoonotic diseases. Early pandemic phases with more stringent NPIs were associated with greater reductions in disease incidence. Non-respiratory diseases, such as hand, foot and mouth disease, rebounded substantially towards the end of the year 2020 as the NPIs were relaxed. Statistical modeling analyses confirm that strong NPIs were associated with a broad mitigation effect on communicable diseases, but resurgence of non-respiratory diseases should be expected when the NPIs, especially restrictions of human movement and gathering, become less stringent. Non-pharmaceutical interventions implemented to mitigate COVID-19 transmission are likely to have impacted spread of other infectious diseases. Here, the authors investigate changes in the incidence of 31 notifiable infectious diseases using surveillance data from China.

Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world. Systems for automatic detection of a single disease may miss other important conditions. Here, the authors show a deep learning platform can detect 39 common retinal diseases and conditions.

Many experiments show that the numbers of mRNA and protein are proportional to the cell volume in growing cells. However, models of stochastic gene expression often assume constant transcription rate per gene and constant translation rate per mRNA, which are incompatible with these experiments. Here, we construct a minimal gene expression model to fill this gap. Assuming ribosomes and RNA polymerases are limiting in gene expression, we show that the numbers of proteins and mRNAs both grow exponentially during the cell cycle and that the concentrations of all mRNAs and proteins achieve cellular homeostasis; the competition between genes for the RNA polymerases makes the transcription rate independent of the genome number. Furthermore, by extending the model to situations in which DNA (mRNA) can be saturated by RNA polymerases (ribosomes) and becomes limiting, we predict a transition from exponential to linear growth of cell volume as the protein-to-DNA ratio increases. For various organisms, mRNA and protein copy numbers scale with cell volume. Here, the authors show that this result emerges naturally when ribosomes and RNAPs limit expression. Furthermore, the authors show that within their model this result breaks down for a sufficiently high volume/DNA ratio.

Yield stress materials flow if a sufficiently large shear stress is applied. Although such materials are ubiquitous and relevant for industry, there is no accepted microscopic description of how they yield, even in the simplest situations in which temperature is negligible and in which flow inhomogeneities such as shear bands or fractures are absent. Here we propose a scaling description of the yielding transition in amorphous solids made of soft particles at zero temperature. Our description makes a connection between the Herschel–Bulkley exponent characterizing the singularity of the flow curve near the yield stress Σ c, the extension and duration of the avalanches of plasticity observed at threshold, and the density P ( x ) of soft spots, or shear transformation zones, as a function of the stress increment x beyond which they yield. We argue that the critical exponents of the yielding transition may be expressed in terms of three independent exponents, θ, d f, and z , characterizing, respectively, the density of soft spots, the fractal dimension of the avalanches, and their duration. Our description shares some similarity with the depinning transition that occurs when an elastic manifold is driven through a random potential, but also presents some striking differences. We test our arguments in an elasto-plastic model, an automaton model similar to those used in depinning, but with a different interaction kernel, and find satisfying agreement with our predictions in both two and three dimensions. Significance Yield stress solids flow if a sufficiently large shear stress is applied. Although such materials are ubiquitous and relevant for industry, there is no accepted microscopic description of how they yield. Here we propose a scaling description of the yielding transition that relates the flow curve, the statistics of the avalanches of plasticity observed at threshold, and the density of local zones that are about to yield. Our description shares some similarity with the depinning transition that occurs when an elastic manifold is driven through a random potential, but also presents some striking differences. Numerical simulations on a simple elasto-plastic model find good agreement with our predictions.

Purpose Openness to external knowledge has recently gained popularity as a means for firms to complement and leverage internal knowledge in the pursuit of innovation outcomes. However, conflicting evidence exists regarding the role of openness in external knowledge acquisition. This paper aims to propose that openness to external knowledge has a nonlinear effect on innovation performance and that this nonlinear relationship is contingent on an ambidextrous knowledge search strategy. Design/methodology/approach Based on original large-scale survey of 246 interfirm collaborations in the high-technology industry, it is found that the impact of openness to external knowledge on innovation performance exhibits an inverted-U shape and that this relationship is affected by an ambidextrous knowledge search strategy. Findings The results indicate that an ambidextrous knowledge strategy that addresses the depth and breadth of external knowledge significantly influences a firm’s ability to derive benefits from increased openness to external knowledge. Empirically, the authors provide an original contribution to high-technology firms by exploring how and why an ambidextrous knowledge strategy can be a critical catalyst spurring innovation performance. Research limitations/implications The research scope is limited to a single industry. Further research could extend the theoretical framework to multiple industries, which may increase the likelihood of innovation theory development. Practical implications The results suggest that firms opening up the boundaries of their innovation activity to engage in external knowledge are able to leverage their in-house innovation to enhance their innovation performance. The authors advocate that in innovation management domains, greater emphasis is needed on how openness to external knowledge has more positive impacts not only on innovation performance but also on innovation implemented management. Originality/value This study is among the first to investigate the ambidextrous knowledge search effect on the external knowledge of high-technology firms. This paper contributes to the theoretical and practical literature concerning openness innovation and knowledge management by reflecting on the ambidextrous knowledge search strategy.

Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artificial intelligence algorithm, SERA algorithm, which uses both structured data and unstructured clinical notes to predict and diagnose sepsis. We test this algorithm with independent, clinical notes and achieve high predictive accuracy 12 hours before the onset of sepsis (AUC 0.94, sensitivity 0.87 and specificity 0.87). We compare the SERA algorithm against physician predictions and show the algorithm’s potential to increase the early detection of sepsis by up to 32% and reduce false positives by up to 17%. Mining unstructured clinical notes is shown to improve the algorithm’s accuracy compared to using only clinical measures for early warning 12 to 48 hours before the onset of sepsis. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. Here, the authors develop an artificial intelligence algorithm which uses both structured data and unstructured clinical notes to predict sepsis.

Continuous-variable quantum key distribution (CV QKD) protocols with discrete modulation are interesting due to their experimental simplicity and their great potential for massive deployment in the quantum-secured networks, but their security analysis is less advanced than that of Gaussian modulation schemes. In this work, we apply a numerical method to analyze the security of discrete-modulation protocols against collective attacks in the asymptotic limit, paving the way for a full security proof with finite-size effects. While our method is general for discrete-modulation schemes, we focus on two variants of the CV QKD protocol with quaternary modulation. Interestingly, thanks to the tightness of our proof method, we show that this protocol is capable of achieving much higher key rates over significantly longer distances with experimentally feasible parameters compared with previous security proofs of binary and ternary modulation schemes and also yielding key rates comparable to Gaussian modulation schemes. Furthermore, as our security analysis method is versatile, it allows us to evaluate variations of the discrete-modulated protocols, including direct and reverse reconciliation, and postselection strategies. In particular, we demonstrate that postselection of data in combination with reverse reconciliation can improve the key rates.

The “Peak-Carbon and Carbon Neutrality” targets proposed by China are formulated with a strategic consideration of coordinating both international and domestic contexts. Aligned with these goals, our study delves into Dongguan, a pivotal city in southern China, focusing on its challenges and opportunities in mitigating climate change and achieving carbon neutrality. Utilizing three periods of remote sensing image data (2002, 2012, and 2022) and applying the carbon emission coefficient method, the research specifically investigates the role of land-use types and transitions in influencing carbon emissions and emission intensity in Dongguan. Notably, the study identifies construction land-use as a significant contributor to carbon emissions, hindering the attainment of carbon neutrality and peak-carbon objectives. The findings prompt the proposal of targeted policies to address this issue and promote sustainable land-use practices, providing a more precise lens on the interplay between land use and carbon emissions in Dongguan.

The methodological advancements in surface‐enhanced Raman scattering (SERS) technique with nanoscale materials based on noble metals, Au, Ag, and their bimetallic alloy Au–Ag, has enabled the highly efficient sensing of chemical and biological molecules at very low concentration values. By employing the innovative various type of Au, Ag nanoparticles and especially, high efficiency Au@Ag alloy nanomaterials as substrate in SERS based biosensors have revolutionized the detection of biological components including; proteins, antigens antibodies complex, circulating tumor cells, DNA, and RNA (miRNA), etc. This review is about SERS‐based Au/Ag bimetallic biosensors and their Raman enhanced activity by focusing on different factors related to them. The emphasis of this research is to describe the recent developments in this field and conceptual advancements behind them. Furthermore, in this article we apex the understanding of impact by variation in basic features like effects of size, shape varying lengths, thickness of core–shell and their influence of large‐scale magnitude and morphology. Moreover, the detailed information about recent biological applications based on these core–shell noble metals, importantly detection of receptor binding domain (RBD) protein of COVID‐19 is provided. Bimetallic alloy Au–Ag surface‐enhanced Raman scattering (SERS) biosensors exhibit ultrahigh SERS sensitivity. Au–Ag SERS‐based biosensors have been utilized in biological components detection, including; proteins, antigens antibodies complex, circulating tumor cells, DNA, and RNA (miRNA), etc.

While most genes’ expression levels are proportional to cell volumes, some genes exhibit nonlinear scaling between their expression levels and cell volume. Therefore, their mRNA and protein concentrations change as the cell volume increases, which often have crucial biological functions such as cell-cycle regulation. However, the biophysical mechanism underlying the nonlinear scaling between gene expression and cell volume is still unclear. In this work, we show that the nonlinear scaling is a direct consequence of the heterogeneous recruitment abilities of promoters to RNA polymerases based on a gene expression model at the whole-cell level. Those genes with weaker (stronger) recruitment abilities than the average ability spontaneously exhibit superlinear (sublinear) scaling with cell volume. Analysis of the promoter sequences and the nonlinear scaling of Saccharomyces cerevisiae ’s mRNA levels shows that motifs associated with transcription regulation are indeed enriched in genes exhibiting nonlinear scaling, in concert with our model. Expression levels of most genes are proportional to cell volumes, although some genes exhibit nonlinear scaling of expression levels with cell volume. Here the authors provide a model that reveals nonlinear scaling is a direct consequence of heterogeneous recruitment abilities of promoters to RNA polymerases.