Blood-coated sensor for high-throughput ptychographic cytometry on a Blu-ray disc

Blu-ray drive is an engineering masterpiece that integrates disc rotation, pickup head translation, and three lasers in a compact and portable format. Here we integrate a blood-coated image sensor with a modified Blu-ray drive for high-throughput cytometric analysis of various bio-specimens. In this device, samples are mounted on the rotating Blu-ray disc and illuminated by the built-in lasers from the pickup head. The resulting coherent diffraction patterns are then recorded by the blood-coated image sensor. The rich spatial features of the blood-cell monolayer help down-modulate the object information for sensor detection, thus forming a high-resolution computational bio-lens with a theoretically unlimited field of view. With the acquired data, we develop a lensless coherent diffraction imaging modality termed rotational ptychography for image reconstruction. We show that our device can resolve the 435 nm linewidth on the resolution target and has a field of view only limited by the size of the Blu-ray disc. To demonstrate its applications, we perform high-throughput urinalysis by locating disease-related calcium oxalate crystals over the entire microscope slide. We also quantify different types of cells on a blood smear with an acquisition speed of ~10,000 cells per second. For in vitro experiment, we monitor live bacterial cultures over the entire Petri dish with single-cell resolution. Using biological cells as a computational lens could enable new intriguing imaging devices for point-of-care diagnostics. Modifying a Blu-ray drive with the blood-coated sensor further allows the spread of high-throughput optical microscopy from well-equipped laboratories to citizen scientists worldwide

Genome Characteristics of a Novel Phage from Bacillus thuringiensis Showing High Similarity with Phage from Bacillus cereus

Bacillus thuringiensis is an important entomopathogenic bacterium belongs to the Bacillus cereus group, which also includes B. anthracis and B. cereus. Several genomes of phages originating from this group had been sequenced, but no genome of Siphoviridae phage from B. thuringiensis has been reported. We recently sequenced and analyzed the genome of a novel phage, BtCS33, from a B. thuringiensis strain, subsp. kurstaki CS33, and compared the gneome of this phage to other phages of the B. cereus group. BtCS33 was the first Siphoviridae phage among the sequenced B. thuringiensis phages. It produced small, turbid plaques on bacterial plates and had a narrow host range. BtCS33 possessed a linear, double-stranded DNA genome of 41,992 bp with 57 putative open reading frames (ORFs). It had a typical genome structure consisting of three modules: the “late” region, the “lysogeny-lysis” region and the “early” region. BtCS33 exhibited high similarity with several phages, B. cereus phage Wβ and some variants of Wβ, in genome organization and the amino acid sequences of structural proteins. There were two ORFs, ORF22 and ORF35, in the genome of BtCS33 that were also found in the genomes of B. cereus phage Wβ and may be involved in regulating sporulation of the host cell. Based on these observations and analysis of phylogenetic trees, we deduced that B. thuringiensis phage BtCS33 and B. cereus phage Wβ may have a common distant ancestor

Research progress in microalloying of Al-Zn-Mg series aluminum alloys

Al-Zn-Mg series aluminum alloys have important applications in aerospace, transportation etc, for their excellent properties of low density and high strength. Further optimizing the microstructure to obtain higher mechanical properties and better corrosion resistance is the development direction of Al-Zn-Mg alloys. Microalloying has become an important means of improving the properties of aluminum alloys, owing to the limited space for alloy composition optimization and heat treatment processes improvement. The effects of microalloying elements on the mechanical properties, hot deformation behavior and corrosion resistance of Al-Zn-Mg alloys were briefly summarized, focusing on the different effects of the second phase particles formed by microalloying elements in different process stages, such as effectively refine grains and strongly hinder the movement of dislocations. The effects of pin grain boundaries, sub-grain boundaries and inhibiting recrystallization during hot deformation were discussed. The internal mechanism of improving the corrosion resistance of the alloy was explained. In addition, the further research direction of microalloying of Al-Zn-Mg aluminum alloy was prospected, understanding the interaction mechanism of microalloying elements and dual alloying-microalloying elements to realize the precise and accurate addition of microalloying elements will be one of the main research contents in the future. Clarifying the regulation effect of microalloying elements on deformation structures and dislocation configurations during hot working will provide a reference for improving the corrosion resistance of alloys

Meta metric learning for highly imbalanced aerial scene classification

Class imbalance is an important factor that affects the performance of deep learning models used for remote sensing scene classification. In this paper, we propose a random finetuning meta metric learning model (RF-MML) to address this problem. Derived from episodic training in meta metric learning, a novel strategy is proposed to train the model, which consists of two phases, i.e., random episodic training and all classes fine-tuning. By introducing randomness into the episodic training and integrating it with fine-tuning for all classes, the few-shot meta-learning paradigm can be successfully applied to class imbalanced data to improve the classification performance. Experiments are conducted to demonstrate the effectiveness of the proposed model on class imbalanced datasets, and the results show the superiority of our model, as compared with other state-of-the-art methods

Reusing Fine Silty Sand Excavated from Slurry Shield Tunnels as a Sustainable Raw Material for Synchronous Grouting

Using the Nanjing Dinghuaimen Yangtze River Tunnel project as a case study, we proposed a method to reuse the excavated silty-fine sand by adjusting the proportion of the waste sand to replace the commercial sand. This would address the issue of recycling the significant amount of waste sand generated when the slurry shield passes through the silty-fine sand stratum. Moreover, we have evaluated grout indicators such as density, fluidity, consistency, bleeding rate, volumetric shrinkage, setting time, and unconfined compressive strength and examined how the particle size and distribution of the sand affected the grout’s performance. The findings show that as the replacement ratio increases, the grout’s density, fluidity, consistency, and bleeding rate gradually increase; meanwhile, the volumetric shrinkage increases initially before decreasing; the setting time decreases gradually; the unconfined compressive strength initially decreases before increasing. The key factor altering the grout’s performance when the replacement ratio is less than 50% is the weakening of the adsorption effect of fine sand particles on water due to the increase in the sand’s fineness modulus. When it is greater than 50%, the particle size of the sand tends to be distributed nonuniformly and fine particles fill the voids between larger particles, thus contributing to the changes in grout properties

Meta metric learning for highly imbalanced aerial scene classification

Class imbalance is an important factor that affects the performance of deep learning models used for remote sensing scene classification. In this paper, we propose a random finetuning meta metric learning model (RF-MML) to address this problem. Derived from episodic training in meta metric learning, a novel strategy is proposed to train the model, which consists of two phases, i.e., random episodic training and all classes fine-tuning. By introducing randomness into the episodic training and integrating it with fine-tuning for all classes, the few-shot meta-learning paradigm can be successfully applied to class imbalanced data to improve the classification performance. Experiments are conducted to demonstrate the effectiveness of the proposed model on class imbalanced datasets, and the results show the superiority of our model, as compared with other state-of-the-art methods

Coupled size and temperature effects on intermittent plasticity of BCC micro-crystals

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