Investigation of the role of Streptococcus pneumoniae surface proteins PspA and PspC

S. pneumoniae has a relatively fast growth rate and can reach high cell densities in infections environments and can cause severe diseases, like hemolytic uremic syndrome (HUS). As part of its life cycle, S. pneumoniae remodel the genome by taking up and incorporating of exogenous DNA from other pneumococci or viruses. This can facilitate the spread of antibiotic resistance and evasion of vaccine-induced immunity and makes it necessary to search for novel targets, to understand the diversity, as well as the immune escape strategies of this pathogenic bacterium. S. pneumoniae has developed different strategies to evade or limit complement mediated opsonization and subsequent phagocytosis. Furthermore, sequence variation suggests that the two immune evasion proteins PspA and PspC are important for interaction of pneumococci with the host. Given the multifunctional characteristics and mosaic structure of PspA and PspC, it is important to investigate the domain composition of the proteins among different strains and in particular among clinical isolates. S. pneumoniae can induce pneumococcal hemolytic uremic syndrome (HUS). To characterize the role of HUS inducing strains, we evaluated 48 S. pneumoniae strains isolated from patients. These diseases associated isolates, Sp-HUS show strong complement resistance when challenged with complement active human serum. Sp-HUS strains show lower levels of surface C3 deposition, as compared to a pathogenic, strain D39. Consequently Sp-HUS strains evade host complement rather efficiently. In addition, I show that Sp-HUS strains have specific PspA and PspC variants which include unique domain profiles. By evaluating complement resistance of Sp-HUS, PspA interacting with the human complement regulator C3 and PspC binding with human Factor H together assisted Sp-HUS to resist and evade from the complement

The ternary Goldbach problem with the Piatetski-Shapiro primes

With the help of the transference principle, we prove that for any $c_1,c_2,c_3\in(1,73/64)$, every sufficiently large odd $n$ can be represented as the sum of three primes $p_1$, $p_2$ and $p_3$, where for each $1\leq i\leq 3$, $p_i$ is of the form $\lfloor n^{c_i}\rfloor$.Comment: This is a very preliminary manuscript, which maybe contains some mistake

DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting

End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although transformer-based methods eliminate the heuristic post-processing, they still suffer from the synergy issue between the sub-tasks and low training efficiency. In this paper, we present DeepSolo, a simple detection transformer baseline that lets a single Decoder with Explicit Points Solo for text detection and recognition simultaneously. Technically, for each text instance, we represent the character sequence as ordered points and model them with learnable explicit point queries. After passing a single decoder, the point queries have encoded requisite text semantics and locations and thus can be further decoded to the center line, boundary, script, and confidence of text via very simple prediction heads in parallel, solving the sub-tasks in text spotting in a unified framework. Besides, we also introduce a text-matching criterion to deliver more accurate supervisory signals, thus enabling more efficient training. Quantitative experiments on public benchmarks demonstrate that DeepSolo outperforms previous state-of-the-art methods and achieves better training efficiency. In addition, DeepSolo is also compatible with line annotations, which require much less annotation cost than polygons. The code will be released.Comment: The code will be available at https://github.com/ViTAE-Transformer/DeepSol

Diffraction-Free Bloch Surface Waves

In this letter, we demonstrate a novel diffraction-free Bloch surface wave (DF-BSW) sustained on all-dielectric multilayers that does not diffract after being passed through three obstacles or across a single mode fiber. It can propagate in a straight line for distances longer than 110 {\mu}m at a wavelength of 633 nm and could be applied as an in-plane optical virtual probe, both in air and in an aqueous environment. The ability to be used in water, its long diffraction-free distance, and its tolerance to multiple obstacles make this DF-BSW ideal for certain applications in areas such as the biological sciences, where many measurements are made on glass surfaces or for which an aqueous environment is required, and for high-speed interconnections between chips, where low loss is necessary. Specifically, the DF-BSW on the dielectric multilayer can be used to develop novel flow cytometry that is based on the surface wave, but not the free space beam, to detect the surface-bound targets

Optical Quantum Sensing for Agnostic Environments via Deep Learning

Optical quantum sensing promises measurement precision beyond classical sensors termed the Heisenberg limit (HL). However, conventional methodologies often rely on prior knowledge of the target system to achieve HL, presenting challenges in practical applications. Addressing this limitation, we introduce an innovative Deep Learning-based Quantum Sensing scheme (DQS), enabling optical quantum sensors to attain HL in agnostic environments. DQS incorporates two essential components: a Graph Neural Network (GNN) predictor and a trigonometric interpolation algorithm. Operating within a data-driven paradigm, DQS utilizes the GNN predictor, trained on offline data, to unveil the intrinsic relationships between the optical setups employed in preparing the probe state and the resulting quantum Fisher information (QFI) after interaction with the agnostic environment. This distilled knowledge facilitates the identification of optimal optical setups associated with maximal QFI. Subsequently, DQS employs a trigonometric interpolation algorithm to recover the unknown parameter estimates for the identified optical setups. Extensive experiments are conducted to investigate the performance of DQS under different settings up to eight photons. Our findings not only offer a new lens through which to accelerate optical quantum sensing tasks but also catalyze future research integrating deep learning and quantum mechanics

PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions

Relying on large-scale training data with pixel-level labels, previous edge detection methods have achieved high performance. However, it is hard to manually label edges accurately, especially for large datasets, and thus the datasets inevitably contain noisy labels. This label-noise issue has been studied extensively for classification, while still remaining under-explored for edge detection. To address the label-noise issue for edge detection, this paper proposes to learn Pixel-level NoiseTransitions to model the label-corruption process. To achieve it, we develop a novel Pixel-wise Shift Learning (PSL) module to estimate the transition from clean to noisy labels as a displacement field. Exploiting the estimated noise transitions, our model, named PNT-Edge, is able to fit the prediction to clean labels. In addition, a local edge density regularization term is devised to exploit local structure information for better transition learning. This term encourages learning large shifts for the edges with complex local structures. Experiments on SBD and Cityscapes demonstrate the effectiveness of our method in relieving the impact of label noise. Codes are available at https://github.com/DREAMXFAR/PNT-Edge.Comment: Accepted by ACM-MM 202