Unconditional preparation of nonclassical states via linear-and-quadratic optomechanics

Reservoir engineering enables the robust and unconditional preparation of pure quantum states in noisy environments. We show how a family of nonclassical states of a mechanical oscillator can be stabilized in a cavity that is parametrically coupled to both the mechanical displacement and the displacement squared. The cavity is driven with three tones, on the red sideband, on the cavity resonance, and on the second blue sideband. The states so stabilized are (squeezed and displaced) superpositions of a finite number of phonons. They show the unique feature of encompassing two prototypes of nonclassicality for bosonic systems: by adjusting the strength of the drives, one can in fact move from a single-phonon- to a Schrödinger-cat-like state. The scheme is deterministic, supersedes the need for measurement-and-feedback loops, and does not require initialization of the oscillator to the ground state

Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data

Protein mimetic amyloid inhibitor potently abrogates cancer-associated mutant p53 aggregation and restores tumor suppressor function

Missense mutations in p53 are severely deleterious and occur in over 50% of all human cancers. The majority of these mutations are located in the inherently unstable DNA-binding domain (DBD), many of which destabilize the domain further and expose its aggregation-prone hydrophobic core, prompting self-assembly of mutant p53 into inactive cytosolic amyloid-like aggregates. Screening an oligopyridylamide library, previously shown to inhibit amyloid formation associated with Alzheimer\u2019s disease and type II diabetes, identified a tripyridylamide, ADH-6, that abrogates self-assembly of the aggregation-nucleating subdomain of mutant p53 DBD. Moreover, ADH-6 targets and dissociates mutant p53 aggregates in human cancer cells, which restores p53\u2019s transcriptional activity, leading to cell cycle arrest and apoptosis. Notably, ADH-6 treatment effectively shrinks xenografts harboring mutant p53, while exhibiting no toxicity to healthy tissue, thereby substantially prolonging survival. This study demonstrates the successful application of a bona fide small-molecule amyloid inhibitor as a potent\ua0anticancer agent

Information-theoretic active contour model for microscopy image segmentation using texture

High throughput technologies have increased the need for automated image analysis in a wide variety of microscopy techniques. Geometric active contour models provide a solution to automated image segmentation by incorporating statistical information in the detection of object boundaries. A statistical active contour may be defined by taking into account the optimisation of an information-theoretic measure between object and background. We focus on a product-type measure of divergence known as Cauchy-Schwartz distance which has numerical advantages over ratio-type measures. By using accurate shape derivation techniques, we define a new geometric active contour model for image segmentation combining Cauchy-Schwartz distance and Gabor energy texture filters. We demonstrate the versatility of this approach on images from the Brodatz dataset and phase-contrast microscopy images of cells

Bacterial surface lipoproteins mediate epithelial microinvasion by <i>Streptococcus pneumoniae</i>.

Streptococcus pneumoniae, a common colonizer of the upper respiratory tract, invades nasopharyngeal epithelial cells without causing disease in healthy participants of controlled human infection studies. We hypothesized that surface expression of pneumococcal lipoproteins, recognized by the innate immune receptor TLR2, mediates epithelial microinvasion. Mutation of lgt in serotype 4 (TIGR4) and serotype 6B (BHN418) pneumococcal strains abolishes the ability of the mutants to activate TLR2 signaling. Loss of lgt also led to the concomitant decrease in interferon signaling triggered by the bacterium. However, only BHN418 lgt::cm but not TIGR4 lgt::cm was significantly attenuated in epithelial adherence and microinvasion compared to their respective wild-type strains. To test the hypothesis that differential lipoprotein repertoires in TIGR4 and BHN418 lead to the intraspecies variation in epithelial microinvasion, we employed a motif-based genome analysis and identified an additional 525 a.a. lipoprotein (pneumococcal accessory lipoprotein A; palA) encoded by BHN418 that is absent in TIGR4. The gene encoding palA sits within a putative genetic island present in ~10% of global pneumococcal isolates. While palA was enriched in the carriage and otitis media pneumococcal strains, neither mutation nor overexpression of the gene encoding this lipoprotein significantly changed microinvasion patterns. In conclusion, mutation of lgt attenuates epithelial inflammatory responses during pneumococcal-epithelial interactions, with intraspecies variation in the effect on microinvasion. Differential lipoprotein repertoires encoded by the different strains do not explain these differences in microinvasion. Rather, we postulate that post-translational modifications of lipoproteins may account for the differences in microinvasion.IMPORTANCEStreptococcus pneumoniae (pneumococcus) is an important mucosal pathogen, estimated to cause over 500,000 deaths annually. Nasopharyngeal colonization is considered a necessary prerequisite for disease, yet many people are transiently and asymptomatically colonized by pneumococci without becoming unwell. It is therefore important to better understand how the colonization process is controlled at the epithelial surface. Controlled human infection studies revealed the presence of pneumococci within the epithelium of healthy volunteers (microinvasion). In this study, we focused on the regulation of epithelial microinvasion by pneumococcal lipoproteins. We found that pneumococcal lipoproteins induce epithelial inflammation but that differing lipoprotein repertoires do not significantly impact the magnitude of microinvasion. Targeting mucosal innate immunity and epithelial microinvasion alongside the induction of an adaptive immune response may be effective in preventing pneumococcal colonization and disease

Verification and Compliance in Collaborative Processes

Evidently, COVID-19 has changed our lives and is likely to make a lasting impact on our economic development and our industry and services. With the ongoing process of digital transformation in industry and services, Collaborative Networks (CNs) is required to be more efficient, productive, flexible, resilient and sustainable according to change of situations and related rules applied afterwards. Although the CN area is relatively young, it requires the previous research to be extended, i.e. business process management from dealing with processes within a single organization into processes across different organizations. In this paper, we review current business process verification and compliance research. Different tools approaches and limitations of them are compared. The further research issues and potential solutions of business process verification and compliance check are discussed in the context of CNs

Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature

Inborn errors of OAS-RNase L in SARS-CoV-2-related multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) is a rare and severe condition that follows benign COVID-19. We report autosomal recessive deficiencies of OAS1, OAS2, or RNASEL in five unrelated children with MIS-C. The cytosolic double-stranded RNA (dsRNA)-sensing OAS1 and OAS2 generate 2'-5'-linked oligoadenylates (2-5A) that activate the single-stranded RNA-degrading ribonuclease L (RNase L). Monocytic cell lines and primary myeloid cells with OAS1, OAS2, or RNase L deficiencies produce excessive amounts of inflammatory cytokines upon dsRNA or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stimulation. Exogenous 2-5A suppresses cytokine production in OAS1-deficient but not RNase L-deficient cells. Cytokine production in RNase L-deficient cells is impaired by MDA5 or RIG-I deficiency and abolished by mitochondrial antiviral-signaling protein (MAVS) deficiency. Recessive OAS-RNase L deficiencies in these patients unleash the production of SARS-CoV-2-triggered, MAVS-mediated inflammatory cytokines by mononuclear phagocytes, thereby underlying MIS-C

Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Interindividual clinical variability in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is vast. We report that at least 101 of 987 patients with life-threatening coronavirus disease 2019 (COVID-19) pneumonia had neutralizing immunoglobulin G (IgG) autoantibodies (auto-Abs) against interferon-w (IFN-w) (13 patients), against the 13 types of IFN-a (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 of the 101 were men. A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men