A benchmark and an algorithm for detecting germline transposon insertions and measuring de novo transposon insertion frequencies

Transposons are genomic parasites, and their new insertions can cause instability and spur the evolution of their host genomes. Rapid accumulation of short-read whole-genome sequencing data provides a great opportunity for studying new transposon insertions and their impacts on the host genome. Although many algorithms are available for detecting transposon insertions, the task remains challenging and existing tools are not designed for identifying de novo insertions. Here, we present a new benchmark fly dataset based on PacBio long-read sequencing and a new method TEMP2 for detecting germline insertions and measuring de novo \u27singleton\u27 insertion frequencies in eukaryotic genomes. TEMP2 achieves high sensitivity and precision for detecting germline insertions when compared with existing tools using both simulated data in fly and experimental data in fly and human. Furthermore, TEMP2 can accurately assess the frequencies of de novo transposon insertions even with high levels of chimeric reads in simulated datasets; such chimeric reads often occur during the construction of short-read sequencing libraries. By applying TEMP2 to published data on hybrid dysgenic flies inflicted by de-repressed P-elements, we confirmed the continuous new insertions of P-elements in dysgenic offspring before they regain piRNAs for P-element repression. TEMP2 is freely available at Github: https://github.com/weng-lab/TEMP2

Age-Specific Associations of Usual Blood Pressure Variability With Cardiovascular Disease and Mortality: 10-Year Diabetes Mellitus Cohort Study.

Background The detrimental effects of increased variability in systolic blood pressure (SBP) on cardiovascular disease (CVD) and mortality risk in patients with diabetes mellitus remains unclear. This study evaluated age-specific association of usual SBP visit-to-visit variability with CVD and mortality in patients with type 2 diabetes mellitus. Methods and Results A retrospective cohort study investigated 155 982 patients with diabetes mellitus aged 45 to 84 years without CVD at baseline (2008-2010). Usual SBP variability was estimated using SBP SD obtained from a mixed-effects model. Age-specific associations (45-54, 55-64, 65-74, 75-84 years) between usual SBP variability, CVD, and mortality risk were assessed by Cox regression adjusted for patient characteristics. After a median follow-up of 9.7 years, 49 816 events (including 34 039 CVD events and 29 211 mortalities) were identified. Elevated SBP variability was independently, positively, and log-linearly associated with higher CVD and mortality risk among all age groups, with no evidence of any threshold effects. The excess CVD and mortality risk per 5 mm Hg increase in SBP variability within the 45 to 54 age group is >3 times higher than the 70 to 79 age group (hazard ratio, 1.66; 95% CI, 1.49-1.85 versus hazard ratio, 1.19; 95% CI, 1.15-1.23). The significant associations remained consistent among all subgroups. Patients with younger age had a higher association of SBP variability with event outcomes. Conclusions The findings suggest that SBP visit-to-visit variability was strongly associated with CVD and mortality with no evidence of a threshold effect in a population with diabetes mellitus. As well as controlling overall blood pressure levels, SBP visit-to-visit variability should be monitored and evaluated in routine practice, in particular for younger patients

ReluDiff: Differential Verification of Deep Neural Networks

As deep neural networks are increasingly being deployed in practice, their efficiency has become an important issue. While there are compression techniques for reducing the network's size, energy consumption and computational requirement, they only demonstrate empirically that there is no loss of accuracy, but lack formal guarantees of the compressed network, e.g., in the presence of adversarial examples. Existing verification techniques such as Reluplex, ReluVal, and DeepPoly provide formal guarantees, but they are designed for analyzing a single network instead of the relationship between two networks. To fill the gap, we develop a new method for differential verification of two closely related networks. Our method consists of a fast but approximate forward interval analysis pass followed by a backward pass that iteratively refines the approximation until the desired property is verified. We have two main innovations. During the forward pass, we exploit structural and behavioral similarities of the two networks to more accurately bound the difference between the output neurons of the two networks. Then in the backward pass, we leverage the gradient differences to more accurately compute the most beneficial refinement. Our experiments show that, compared to state-of-the-art verification tools, our method can achieve orders-of-magnitude speedup and prove many more properties than existing tools.Comment: Extended version of ICSE 2020 paper. This version includes an appendix with proofs for some of the content in section 4.

Reduced binding activity of vaccine serum to omicron receptor-binding domain

Coronavirus disease 2019 (COVID-19) vaccination regimens contribute to limiting the spread of severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2). However, the emergence and rapid transmission of the SARS-CoV-2 variant Omicron raise a concern about the efficacy of the current vaccination strategy. Here, we expressed monomeric and dimeric receptor-binding domains (RBDs) of the spike protein of prototype SARS-CoV-2 and Omicron variant in E. coli and investigated the reactivity of anti-sera from Chinese subjects immunized with SARS-CoV-2 vaccines to these recombinant RBDs. In 106 human blood samples collected from 91 participants from Jiangxi, China, 26 sera were identified to be positive for SARS-CoV-2 spike protein antibodies by lateral flow dipstick (LFD) assays, which were enriched in the ones collected from day 7 to 1 month post-boost (87.0%) compared to those harvested within 1 week post-boost (23.8%) (P < 0.0001). A higher positive ratio was observed in the child group (40.8%) than adults (13.6%) (P = 0.0073). ELISA results showed that the binding activity of anti-SARS-CoV-2 antibody-positive sera to Omicron RBDs dropped by 1.48- to 2.07-fold compared to its homogeneous recombinant RBDs. Thus, our data indicate that current SARS-CoV-2 vaccines provide restricted humoral protection against the Omicron variant

DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks

Although deep neural networks have been very successful in image-classification tasks, they are prone to adversarial attacks. To generate adversarial inputs, there has emerged a wide variety of techniques, such as black- and whitebox attacks for neural networks. In this paper, we present DeepSearch, a novel fuzzing-based, query-efficient, blackbox attack for image classifiers. Despite its simplicity, DeepSearch is shown to be more effective in finding adversarial inputs than state-of-the-art blackbox approaches. DeepSearch is additionally able to generate the most subtle adversarial inputs in comparison to these approaches

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Dendritic Macrosurfactant Assembly for Physical Functionalization of HIPE-Templated Polymers

High-internal-phase emulsion-templated macroporous polymers (polyHIPEs) have attracted much interest, but their surface functionalization remains a primary concern. Thus, competitive surface functionalization via physical self-assembly of macrosurfactants was reviewed. Dendritic and diblock-copolymer macrosurfactants were tested, and the former appeared to be more topologically competitive in terms of solubility, viscosity, and versatility. In particular, hyperbranched polyethyleneimine (PEI) was transformed into dendritic PEI macrosurfactants through click-like N-alkylation with epoxy compounds. Free-standing PEI macrosurfactants were used as molecular nanocapsules for charge-selective guest encapsulation and robustly dictated the surface of a macroporous polymer through the HIPE technique, in which the macroporous polymer could act as a well-recoverable adsorbent. Metal nanoparticle-loaded PEI macrosurfactants could similarly lead to polyHIPE, whose surface was dictated by its catalytic component. Unlike conventional Pickering stabilizer, PEI macrosurfactant-based metal nanocomposite resulted in open-cellular polyHIPE, rendering the catalytic sites well accessible. The active amino groups on the polyHIPE could also be transformed into functional groups of aminopolycarboxylic acids, which could efficiently eliminate trace and heavy metal species in water

A Health Indicator for the Online Lifetime Estimation of an Electric Vehicle Power Li-Ion Battery

With the further development of the electric vehicle (EV) industry, the reliability of prediction and health management (PHM) systems has received great attention. The original Li-ion battery life prediction technology developed by offline training data can no longer meet the needs of use under complex working conditions. The existing methods pay insufficient attention to the dispersive information of health indicators (HIs) under EV driving conditions, and can only calculate through standard configuration files. To solve the problem that it is difficult to directly measure the capacity loss in real time, this paper proposes a battery HI called excitation response level (ERL) to describe the voltage variation at different lifetimes, which could be easily calculated according to the current and voltage under the actual load curve. In addition, in order to further optimize the proposed HI, Box–Cox transformation was used to enhance the linear correlation between the initially extracted HI and the capacity. Several Li-ion batteries were discharged to the 50% state of health (SOH) through profiles with different depths of discharge (DODs) and mean states of charge (SOCs) to verify the accuracy and robustness of the proposed method. The average estimation error of the tested batteries was less than 3%, which shows a good performance for accuracy and robustness