Self-partitioning SlipChip for slip-induced droplet formation and human papillomavirus viral load quantification with digital LAMP

Human papillomavirus (HPV) is one of the most common sexually transmitted infections worldwide, and persistent HPV infection can cause warts and even cancer. Nucleic acid analysis of HPV viral DNA can be very informative for the diagnosis and monitoring of HPV. Digital nucleic acid analysis, such as digital PCR and digital isothermal amplification, can provide sensitive detection and precise quantification of target nucleic acids, and its utility has been demonstrated in many biological research and medical diagnostic applications. A variety of methods have been developed for the generation of a large number of individual reaction partitions, a key requirement for digital nucleic acid analysis. However, an easily assembled and operated device for robust droplet formation without preprocessing devices, auxiliary instrumentation or control systems is still highly desired. In this paper, we present a self-partitioning SlipChip (sp-SlipChip) microfluidic device for the slip-induced generation of droplets to perform digital loop-mediated isothermal amplification (LAMP) for the detection and quantification of HPV DNA. In contrast to traditional SlipChip methods, which require the precise alignment of microfeatures, this sp-SlipChip utilized a design of “chain-of-pearls” continuous microfluidic channel that is independent of the overlapping of microfeatures on different plates to establish the fluidic path for reagent loading. Initiated by a simple slipping step, the aqueous solution can robustly self-partition into individual droplets by capillary pressure-driven flow. This advantage makes the sp-SlipChip very appealing for the point-of-care quantitative analysis of viral load. As a proof of concept, we performed digital LAMP on an sp-SlipChip to quantify human papillomaviruses (HPVs) 16 and 18 and tested this method with fifteen anonymous clinical samples

Learners’ engagement on a social networking platform: An ecological analysis

Language learners are actively engaging in language learning beyond the classroom. However, social networking sites, despite playing a major role in people’s lives, have been found to be rarely incorporated into language learners’ learning ecologies. An understanding of the factors that shape learners’ engagement on instruction-oriented social networking sites could inform platform design and enhance the likelihood of platforms being utilized. This study examined a group of language learners’ engagement on an instruction-oriented social networking site, Lang-8, over time. Using narrative data and learners’ behavioral data on the platform, the study revealed how various ecological resources on and outside the platform interacted with one another to shape the dynamic changes in different dimensions of learners’ engagement on the platform over time. The study also suggested that learners’ engagement on the platform further induced reconstruction of their language learning ecologies, providing additional learning opportunities both on and beyond the platform. The findings highlight the importance of supporting learner engagement on technological platforms in an informal learning context and provide insights into how such support could be achieved through system design

A Graph-Native Query Optimization Framework

Graph queries that combine pattern matching with relational operations, referred as PatRelQuery, are widely used in many real-world applications. It allows users to identify arbitrary patterns in a graph and further perform in-depth relational analysis on the results. To effectively support PatRelQuery, two key challenges need to be addressed: (1) how to optimize PatRelQuery in a unified framework, and (2) how to handle the arbitrary type constraints in patterns in PatRelQuery. In this paper, we present a graph-native query optimization framework named GOpt, to tackle these issues. GOpt is built on top of a unified intermediate representation (IR) that is capable of capturing both graph and relational operations, thereby streamlining the optimization of PatRelQuery. To handle the arbitrary type constraints, GOpt employs an automatic type inference approach to identify implicit type constraints. Additionally, GOpt introduces a graph-native optimizer, which encompasses an extensive collection of optimization rules along with cost-based techniques tailored for arbitrary patterns, to optimize PatRelQuery. Through comprehensive experiments, we demonstrate that GOpt can achieve significant query performance improvements, in both crafted benchmarks and real-world applications

WCGAN: Robust portrait watercolorization with adaptive hierarchical localized constraints

Deep learning has enabled image style transfer to make great strides forward. However, unlike many other styles, transferring the watercolor style to portraits is significantly challenging in image synthesis and style transfer. Pixel-correlation-based methods do not produce satisfactory watercolors. This is because portrait watercolors exhibit the sophisticated fusion of various painting techniques in local areas, which poses a problem for convolutional neural networks to accurately handle fine-grained features. Moreover, the common but problematic way of coping with multiple scales greatly impedes the performance of existing style transfer methods with fixed receptive fields. Although it is possible to develop an image processing pipeline mimicking various watercolor effects, such algorithms are slow and fragile, especially for inputs of different scales. As a remedy, this paper proposes WCGAN, a generative adversarial network (GAN) architecture dedicated to watercolorization of portraits. Specifically, a novel localized style loss suitable for watercolorization is proposed to deal with local details. To handle portraits of different scales and improve robustness, a novel discriminator architecture with three parallel branches of varying sizes of receptive fields is introduced. In addition, the application of WCGAN is expanded to video style transfer where a novel kind of video training data based on random crops is developed to efficiently capture temporal consistency. Extensive experimental results from qualitative and quantitative analyses demonstrate that WCGAN generates state-of-the-art, high quality watercolors from portraits

Structural Basis of β2 Integrin Inside—Out Activation

β2 integrins are expressed on all leukocytes. Precise regulation of the β2 integrin is critical for leukocyte adhesion and trafficking. In neutrophils, β2 integrins participate in slow rolling. When activated by inside–out signaling, fully activated β2 integrins mediate rapid leukocyte arrest and adhesion. The two activation pathways, starting with selectin ligand engagement and chemokine receptor ligation, respectively, converge on phosphoinositide 3-kinase, talin-1, kindlin-3 and Rap1. Here, we focus on recent structural insights into autoinhibited talin-1 and autoinhibited trimeric kindlin-3. When activated, both talin-1 and kindlin-3 can bind the β2 cytoplasmic tail at separate but adjacent sites. We discuss possible pathways for talin-1 and kindlin-3 activation, recruitment to the plasma membrane, and their role in integrin activation. We propose new models of the final steps of integrin activation involving the complex of talin-1, kindlin-3, integrin and the plasma membrane

Self-partitioning SlipChip for slip-induced droplet formation and human papillomavirus viral load quantification with digital LAMP

Human papillomavirus (HPV) is one of the most common sexually transmitted infections worldwide, and persistent HPV infection can cause warts and even cancer. Nucleic acid analysis of HPV viral DNA can be very informative for the diagnosis and monitoring of HPV. Digital nucleic acid analysis, such as digital PCR and digital isothermal amplification, can provide sensitive detection and precise quantification of target nucleic acids, and its utility has been demonstrated in many biological research and medical diagnostic applications. A variety of methods have been developed for the generation of a large number of individual reaction partitions, a key requirement for digital nucleic acid analysis. However, an easily assembled and operated device for robust droplet formation without preprocessing devices, auxiliary instrumentation or control systems is still highly desired. In this paper, we present a self-partitioning SlipChip (sp-SlipChip) microfluidic device for the slip-induced generation of droplets to perform digital loop-mediated isothermal amplification (LAMP) for the detection and quantification of HPV DNA. In contrast to traditional SlipChip methods, which require the precise alignment of microfeatures, this sp-SlipChip utilized a design of “chain-of-pearls” continuous microfluidic channel that is independent of the overlapping of microfeatures on different plates to establish the fluidic path for reagent loading. Initiated by a simple slipping step, the aqueous solution can robustly self-partition into individual droplets by capillary pressure-driven flow. This advantage makes the sp-SlipChip very appealing for the point-of-care quantitative analysis of viral load. As a proof of concept, we performed digital LAMP on an sp-SlipChip to quantify human papillomaviruses (HPVs) 16 and 18 and tested this method with fifteen anonymous clinical samples

Metabolic classification of microbial genomes using functional probes

<p>Abstract</p> <p>Background</p> <p>Microorganisms able to grow under artificial culture conditions comprise only a small proportion of the biosphere's total microbial community. Until recently, scientists have been unable to perform thorough analyses of difficult-to-culture microorganisms due to limitations in sequencing technology. As modern techniques have dramatically increased sequencing rates and rapidly expanded the number of sequenced genomes, in addition to traditional taxonomic classifications which focus on the evolutionary relationships of organisms, classifications of the genomes based on alternative points of view may help advance our understanding of the delicate relationships of organisms.</p> <p>Results</p> <p>We have developed a proteome-based method for classifying microbial species. This classification method uses a set of probes comprising short, highly conserved amino acid sequences. For each genome, <it>in silico </it>translation is performed to obtained its proteome, based on which a probe-set frequency pattern is generated. Then, the probe-set frequency patterns are used to cluster the proteomes/genomes.</p> <p>Conclusions</p> <p>Features of the proposed method include a high running speed in challenge of a large number of genomes, and high applicability for classifying organisms with incomplete genome sequences. Moreover, the probe-set clustering method is sensitive to the metabolic phenotypic similarities/differences among species and is thus supposed potential for the classification or differentiation of closely-related organisms.</p

Lattice Codes for Lattice-Based PKE

Existing error correction mechanisms in lattice-based public key encryption (PKE) rely on either trivial modulation or its concatenation with error correction codes (ECC). This paper demonstrates that lattice coding, as a combined ECC and modulation technique, can replace trivial modulation in current lattice-based PKEs, resulting in improved error correction performance. We model the FrodoPKE protocol as a noisy point-to-point communication system, where the communication channel resembles an additive white Gaussian noise (AWGN) channel. To utilize lattice codes for this specific channel with hypercube shaping, we propose an efficient labeling function that converts binary information bits to lattice codewords and vice versa. The parameter sets of FrodoPKE are enhanced to achieve higher security levels or smaller ciphertext sizes. For instance, the proposed Frodo-1344-E$_\text{8}$ offers a 10-bit classical security improvement over Frodo-1344. The code for reproducing our main experiments is available at https://github.com/shx-lyu/lattice-codes-for-pke

Anonymous Public Key Encryption under Corruptions

Anonymity of public key encryption (PKE) requires that, in a multi-user scenario, the PKE ciphertexts do not leak information about which public keys are used to generate them. Corruptions are common threats in the multi-user scenario but anonymity of PKE under corruptions is less studied in the literature. In TCC 2020, Benhamouda et al. first provide a formal characterization for anonymity of PKE under a specific type of corruption. However, no known PKE scheme is proved to meet their characterization. To the best of our knowledge, all the PKE application scenarios which require anonymity also require confidentiality. However, in the work by Benhamouda et al., different types of corruptions for anonymity and confidentiality are considered, which can cause security pitfalls. What\u27s worse, we are not aware of any PKE scheme which can provide both anonymity and confidentiality under the same types of corruptions. In this work, we introduce a new security notion for PKE called ANON-RSO$_k\&$C security, capturing anonymity under corruptions. We also introduce SIM-RSO$_k\&$C security which captures confidentiality under the same types of corruptions. We provide a generic framework of constructing PKE scheme which can achieve the above two security goals simultaneously based on a new primitive called key and message non-committing encryption (KM-NCE). Then we give a general construction of KM-NCE utilizing a variant of hash proof system (HPS) called Key-Openable HPS. We also provide Key-Openable HPS instantiations based on the matrix decisional Diffie-Hellman assumption. Therefore, we can obtain various concrete PKE instantiations achieving the two security goals in the standard model with compact ciphertexts. Furthermore, for some PKE instantiation, its security reduction is tight

Ultrasound volume projection image quality selection by ranking from convolutional RankNet.

Periodic inspection and assessment are important for scoliosis patients. 3D ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. With the generation of a 3D ultrasound volume projection spine image using our Scolioscan system, a series of 2D coronal ultrasound images are produced at different depths with different qualities. Selecting a high quality image from these 2D images is the crucial task for further scoliosis measurement. However, adjacent images are similar and difficult to distinguish. To learn the nuances between these images, we propose selecting the best image automatically, based on their quality rankings. Here, the ranking algorithm we use is a pairwise learning-to-ranking network, RankNet. Then, to extract more efficient features of input images and to improve the discriminative ability of the model, we adopt the convolutional neural network as the backbone due to its high power of image exploration. Finally, by inputting the images in pairs into the proposed convolutional RankNet, we can select the best images from each case based on the output ranking orders. The experimental result shows that convolutional RankNet achieves better than 95.5% top-3 accuracy, and we prove that this performance is beyond the experience of a human expert