Cometary implications of recent laboratory experiments on the photochemistry of the C2H and C3H2 radicals

Recent laboratory results on the photodissociation of the C2H and C3H2 radicals are described. These studies show that the C2 and C3 radicals are produced by the 193 nm photolysis of the C2H and C3H2 radicals, respectively. The quantum state distributions that were determined for the C2 radicals put certain constraints on the initial conditions for any models of the observed C2 cometary spectra. Experimental observations of C2 formed by the 212.8 nm photolysis of C2H are used to calculate a range of photochemical lifetimes for the C2H radical

Programmable base editing of zebrafish genome using a modified CRISPR-Cas9 system.

Precise genetic modifications in model animals are essential for biomedical research. Here, we report a programmable "base editing" system to induce precise base conversion with high efficiency in zebrafish. Using cytidine deaminase fused to Cas9 nickase, up to 28% of site-specific single-base mutations are achieved in multiple gene loci. In addition, an engineered Cas9-VQR variant with 5'-NGA PAM specificities is used to induce base conversion in zebrafish. This shows that Cas9 variants can be used to expand the utility of this technology. Collectively, the targeted base editing system represents a strategy for precise and effective genome editing in zebrafish.The use of base editing enables precise genetic modifications in model animals. Here the authors show high efficient single-base editing in zebrafish using modified Cas9 and its VQR variant with an altered PAM specificity

SUIT: Learning Significance-guided Information for 3D Temporal Detection

3D object detection from LiDAR point cloud is of critical importance for autonomous driving and robotics. While sequential point cloud has the potential to enhance 3D perception through temporal information, utilizing these temporal features effectively and efficiently remains a challenging problem. Based on the observation that the foreground information is sparsely distributed in LiDAR scenes, we believe sufficient knowledge can be provided by sparse format rather than dense maps. To this end, we propose to learn Significance-gUided Information for 3D Temporal detection (SUIT), which simplifies temporal information as sparse features for information fusion across frames. Specifically, we first introduce a significant sampling mechanism that extracts information-rich yet sparse features based on predicted object centroids. On top of that, we present an explicit geometric transformation learning technique, which learns the object-centric transformations among sparse features across frames. We evaluate our method on large-scale nuScenes and Waymo dataset, where our SUIT not only significantly reduces the memory and computation cost of temporal fusion, but also performs well over the state-of-the-art baselines.Comment: Accepted to IROS 202

Hepatitis E virus infection in swine workers: A meta‐analysis

Hepatitis E virus (HEV) infects both humans and animals. Swine has been confirmed to be the principal natural reservoir, which raises a concern that HEV infection would be substantially increasing among swine workers. The present study calculated the pooled prevalence of IgG antibodies against HEV among swine workers and the general population in previous cross‐sectional studies. We conducted a meta‐analysis comparing the prevalence of HEV infection between swine workers and the general population, including local residents, blood donors and non‐swine workers. Through searches in three databases (PubMed and OVID in English, and CNKI in Chinese) and after study selection, a total of 32 studies from 16 countries (from 1999 through 2018) were included in the meta‐analysis. A random‐effect model was employed in the study; an I 2 statistic assessed heterogeneity, and the Egger’s test detected publication bias. The comparative prevalence of anti‐HEV IgG was pooled from the studies. Compared to the general population, the prevalence ratio (PR) for swine workers was estimated to be 1.52 (95% CI 1.38–1.76) with the I 2 being 71%. No publication bias was detected (p = 0.40). A subgroup analysis further indicated increased prevalence of anti‐HEV IgG in the swine workers in Asia (PR = 1.49, 95% CI: 1.35–1.64), in Europe (PR = 1.93, 95% CI: 1.49–2.50) and in all five swine‐related occupations, including swine farmers, butchers, meat processors, pork retailers and veterinarians (PR ranged between 1.19 and 1.75). In summary, swine workers have a relatively higher prevalence of past HEV infection, and this finding is true across swine‐related occupations, which confirms zoonotic transmission between swine and swine workers.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147857/1/zph12548_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147857/2/zph12548.pd

Consecutive Inertia Drift of Autonomous RC Car via Primitive-based Planning and Data-driven Control

Inertia drift is an aggressive transitional driving maneuver, which is challenging due to the high nonlinearity of the system and the stringent requirement on control and planning performance. This paper presents a solution for the consecutive inertia drift of an autonomous RC car based on primitive-based planning and data-driven control. The planner generates complex paths via the concatenation of path segments called primitives, and the controller eases the burden on feedback by interpolating between multiple real trajectories with different initial conditions into one near-feasible reference trajectory. The proposed strategy is capable of drifting through various paths containing consecutive turns, which is validated in both simulation and reality.Comment: 9 pages, 10 figures, to appear to IROS 202

Metagenomic surveillance and comparative genomic analysis of Chlamydia psittaci in patients with pneumonia

Chlamydia psittaci, a strictly intracellular bacterium, is an underestimated etiologic agent leading to infections in a broad range of animals and mild illness or pneumonia in humans. In this study, the metagenomes of bronchoalveolar lavage fluids from the patients with pneumonia were sequenced and highly abundant C. psittaci was found. The target-enriched metagenomic reads were recruited to reconstruct draft genomes with more than 99% completeness. Two C. psittaci strains from novel sequence types were detected and these were closely related to the animal-borne isolates derived from the lineages of ST43 and ST28, indicating the zoonotic transmissions of C. psittaci would benefit its prevalence worldwide. Comparative genomic analysis combined with public isolate genomes revealed that the pan-genome of C. psittaci possessed a more stable gene repertoire than those of other extracellular bacteria, with ~90% of the genes per genome being conserved core genes. Furthermore, the evidence for significantly positive selection was identified in 20 virulence-associated gene products, particularly bacterial membrane-embedded proteins and type three secretion machines, which may play important roles in the pathogen-host interactions. This survey uncovered novel strains of C. psittaci causing pneumonia and the evolutionary analysis characterized prominent gene candidates involved in bacterial adaptation to immune pressures. The metagenomic approach is of significance to the surveillance of difficult-to-culture intracellular pathogens and the research into molecular epidemiology and evolutionary biology of C. psittaci

Should chronic hepatitis B mothers breastfeed? a meta analysis

<p>Abstract</p> <p>Background</p> <p>Hepatitis B virus (HBV) exists in the breast milk of chronic hepatitis B (CHB) mothers. The authors use a meta-analytic technique to quantify the evidence of an association between breastfeeding and risk of CHB infection among the infants vaccinated against HBV.</p> <p>Methods</p> <p>Literature search is performed up to 2010 on the relationship between infantile CHB infection within one-year follow up after immunization with the third-dose hepatitis B vaccine and breastfeeding. Two reviewers independently extract the data and evaluate the methodological quality. A random-effects model is employed to systematically combine the results of all included studies.</p> <p>Results</p> <p>Based on data from 32 studies, 4.32% (244/5650) of infants born of CHB mothers develop CHB infection. The difference in risk of the infection between breastfed and formula-fed infants (RD) is -0.8%, (95% confidence interval [CI]: -1.6%, 0.1%). Analysis of the data from 16 of the studies finds that RD for mothers who are positive for the HBeAg and/or the HBV DNA, 0.7% (95%CI: -2.0%, 3.5%), is similar to that for those who are negative for these infectivity markers, -0.5% (95%CI: -1.7%, 0.6%).</p> <p>Conclusions</p> <p>Breast milk is infectious; yet, breastfeeding, even by mothers with high infectivity, is not associated with demonstrable risk of infantile CHB infection, provided that the infants have been vaccinated against HBV at birth.</p

DiffDis: Empowering Generative Diffusion Model with Cross-Modal Discrimination Capability

Recently, large-scale diffusion models, e.g., Stable diffusion and DallE2, have shown remarkable results on image synthesis. On the other hand, large-scale cross-modal pre-trained models (e.g., CLIP, ALIGN, and FILIP) are competent for various downstream tasks by learning to align vision and language embeddings. In this paper, we explore the possibility of jointly modeling generation and discrimination. Specifically, we propose DiffDis to unify the cross-modal generative and discriminative pretraining into one single framework under the diffusion process. DiffDis first formulates the image-text discriminative problem as a generative diffusion process of the text embedding from the text encoder conditioned on the image. Then, we propose a novel dual-stream network architecture, which fuses the noisy text embedding with the knowledge of latent images from different scales for image-text discriminative learning. Moreover, the generative and discriminative tasks can efficiently share the image-branch network structure in the multi-modality model. Benefiting from diffusion-based unified training, DiffDis achieves both better generation ability and cross-modal semantic alignment in one architecture. Experimental results show that DiffDis outperforms single-task models on both the image generation and the image-text discriminative tasks, e.g., 1.65% improvement on average accuracy of zero-shot classification over 12 datasets and 2.42 improvement on FID of zero-shot image synthesis.Comment: ICCV202

Understanding Health Information Intent via Crowdsourcing: Challenges and Opportunities

Social Q&A sites have been emerging as a platform for people to seek information and social supports around health topics. Identifying users’ information needs from the questions can significantly help social Q&A sites serve their users better. Prior research had attempted to understand askers’ intentions and implicit needs by classifying hidden intent from questions, while the non-trivial categorization was only able to be conducted with a limited size of data. In this study, we aim to develop a scalable categorization method that can categorize the askers’ intent in a large set of health-related questions via crowdsourcing. We conducted a preliminary experiment on Amazon Mechanical Turk to evaluate our categorization method. Our results suggests both challenges and opportunities for understanding health information intent via crowdsourcing.ye