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Abstract Wave‐particle duality, intertwining two inherently contradictory properties of quantum systems, remains one of the most conceptually profound aspects of quantum mechanics. By using the concept of energy capacity, the ability of a quantum system to store and extract energy, a device‐independent uncertainty relation is derived for wave‐particle duality. This relation is shown to be independent of both the representation space and the measurement basis of the quantum system. Furthermore, it is experimentally validated that this wave‐particle duality relation using a photon‐based platform. 

Arabidopsis RESISTANCE TO POWDERY MILDEW 8.2 (RPW8.2) is specifically induced by the powdery mildew (PM) fungus (Golovinomyces cichoracearum) in the infected epidermal cells to activate immunity. However, the mechanism of RPW8.2-induction is not well understood. Here, we identify a G. cichoracearum effector that interacts with RPW8.2, named Gc-RPW8.2 interacting protein 1 (GcR8IP1), by a yeast two-hybrid screen of an Arabidopsis cDNA library. GcR8IP1 physically associated with RPW8.2 with its RING finger domain that is essential and sufficient for the association. GcR8IP1 was secreted and translocated into the nucleus of host cell infected with PM. Association of GcR8IP1 with RPW8.2 led to an increase of RPW8.2 in the nucleus. In turn, the nucleus-localised RPW8.2 promoted the activity of the RPW8.2 promoter, resulting in transcriptional self-amplification of RPW8.2 to boost immunity at infection sites. Additionally, ectopic expression or host-induced gene silencing of GcR8IP1 supported its role as a virulence factor in PM. Altogether, our results reveal a mechanism of RPW8.2-dependent defense strengthening via altered partitioning of RPW8.2 and transcriptional self-amplification triggered by a PM fungal effector, which exemplifies an atypical form of effector-triggered immunity. 

AIDS is a syndrome caused by the HIV. During the progression of AIDS, a patient's immune system is weakened, which increases the patient's susceptibility to infections and diseases. Although antiretroviral drugs can effectively suppress HIV, the virus mutates very quickly and can become resistant to treatment. In addition, the virus can also become resistant to other treatments not currently being used through mutations, which is known in the clinical research community as cross-resistance. Since a single HIV strain can be resistant to multiple drugs, this problem is naturally represented as a multilabel classification problem. Given this multilabel relationship, traditional single-label classification methods often fail to effectively identify the drug resistances that may develop after a particular virus mutation. In this work, we propose a novel multilabel Robust Sample Specific Distance (RSSD) method to identify multiclass HIV drug resistance. Our method is novel in that it can illustrate the relative strength of the drug resistance of a reverse transcriptase (RT) sequence against a given drug nucleoside analog and learn the distance metrics for all the drug resistances. To learn the proposed RSSDs, we formulate a learning objective that maximizes the ratio of the summations of a number of ℓ1-norm distances, which is difficult to solve in general. To solve this optimization problem, we derive an efficient, nongreedy iterative algorithm with rigorously proved convergence. Our new method has been verified on a public HIV type 1 drug resistance data set with over 600 RT sequences and five nucleoside analogs. We compared our method against several state-of-the-art multilabel classification methods, and the experimental results have demonstrated the effectiveness of our proposed method. 

Acquired immunodeficiency syndrome (AIDS) is a syndrome caused by the human immunodeficiency virus (HIV). During the progression of AIDS, a patient’s the immune system is weakened, which increases the patient’s susceptibility to infections and diseases. Although antiretroviral drugs can effectively suppress HIV, the virus mutates very quickly and can become resistant to treatment. In addition, the virus can also become resistant to other treatments not currently being used through mutations, which is known in the clinical research community as cross-resistance. Since a single HIV strain can be resistant to multiple drugs, this problem is naturally represented as a multi-label classification problem. Given this multi-class relationship, traditional single-label classification methods usually fail to effectively identify the drug resistances that may develop after a particular virus mutation. In this paper, we propose a novel multi-label Robust Sample Specific Distance (RSSD) method to identify multi-class HIV drug resistance. Our method is novel in that it can illustrate the relative strength of the drug resistance of a reverse transcriptase sequence against a given drug nucleoside analogue and learn the distance metrics for all the drug resistances. To learn the proposed RSSDs, we formulate a learning objective that maximizes the ratio of the summations of a number of ℓ1-norm distances, which is difficult to solve in general. To solve this optimization problem, we derive an efficient, non-greedy, iterative algorithm with rigorously proved convergence. Our new method has been verified on a public HIV-1 drug resistance data set with over 600 RT sequences and five nucleoside analogues. We compared our method against other state-of-the-art multi-label classification methods and the experimental results have demonstrated the effectiveness of our proposed method. 

We report the asymmetric reconstruction of the single-stranded RNA (ssRNA) content in one of the three otherwise identical virions of a multipartite RNA virus, brome mosaic virus (BMV). We exploit a sample consisting exclusively of particles with the same RNA content—specifically, RNAs 3 and 4—assembled in planta by agrobacterium-mediated transient expression. We find that the interior of the particle is nearly empty, with most of the RNA genome situated at the capsid shell. However, this density is disordered in the sense that the RNA is not associated with any particular structure but rather, with an ensemble of secondary/tertiary structures that interact with the capsid protein. Our results illustrate a fundamental difference between the ssRNA organization in the multipartite BMV viral capsid and the monopartite bacteriophages MS2 and Qβ for which a dominant RNA conformation is found inside the assembled viral capsids, with RNA density conserved even at the center of the particle. This can be understood in the context of the differing demands on their respective lifecycles: BMV must package separately each of several different RNA molecules and has been shown to replicate and package them in isolated, membrane-bound, cytoplasmic complexes, whereas the bacteriophages exploit sequence-specific “packaging signals” throughout the viral RNA to package their monopartite genomes.