A Representation Learning Approach for Predicting circRNA Back-Splicing Event via Sequence-Interaction-Aware Dual Encoder

Circular RNAs (circRNAs) play a crucial role in generegulation and association with diseases because of their uniqueclosed continuous loop structure, which is more stable and conserved than ordinary linear RNAs. As fundamental work to clarifytheir functions, a large number of computational approaches foridentifying circRNA formation have been proposed. However, thesemethods fail to fully utilize the important characteristics of backsplicing events, i.e., the positional information of the splice sitesand the interaction features of its flanking sequences, for predicting circRNAs. To this end, we hereby propose a novel approachcalled SIDE for predicting circRNA back-splicing events using onlyraw RNA sequences. Technically, SIDE employs a dual encoderto capture global and interactive features of the RNA sequence,and then a decoder designed by the contrastive learning to fuseout discriminative features improving the prediction of circRNAsformation. Empirical results on three real-world datasets showthe effectiveness of SIDE. Further analysis also reveals that theeffectiveness of SIDE

Porous textured ceramics with controlled grain size and orientation

Texture in dense or porous ceramics can enhance their functional and structural properties. Current methods for texturation employ anisotropic particles as starting powders and processes that drive their orientation into specific directions. Using ultra-low magnetic fields combined with slip casting, it is possible to purposely orient magnetically responsive particles in any direction. When those particles are suspended with nanoparticles, templated grain growth occurs during sintering to yield a textured ceramic. Yet, the final grains are usually micrometric, leading weak mechanical properties. Here, we explore how to tune the grain orientation size using magnetic slip casting and templated grain growth. Our strategy consists in changing the size of the anisotropic powders and ensuring that magnetic alignment and densification occurs. The obtained ceramics featured a large range of grain anisotropy, with submicrometric thickness and anisotropic properties. Textured ceramics with tunable grains dimensions and porosity are promising for filtering, biomedical or composite applications

Natural variation in the prolyl 4-hydroxylase gene PtoP4H9 contributes to perennial stem growth in Populus

Perennial trees must maintain stem growth throughout their entire lifespan to progressively increase in size as they age. The overarching question of the molecular mechanisms that govern stem perennial growth in trees remains largely unanswered. Here we deciphered the genetic architecture that underlies perennial growth trajectories using genome-wide association studies (GWAS) for measures of growth traits across years in a natural population of Populus tomentosa. By analyzing the stem growth trajectory, we identified PtoP4H9, encoding prolyl 4-hydroxylase 9, which is responsible for the natural variation in the growth rate of diameter at breast height (DBH) across years. Quantifying the dynamic genetic contribution of PtoP4H9 loci to stem growth showed that PtoP4H9 played a pivotal role in stem growth regulation. Spatiotemporal expression analysis showed that PtoP4H9 was highly expressed in cambium tissues of poplars of various ages. Overexpression and knockdown of PtoP4H9 revealed that it altered cell expansion to regulate cell wall modification and mechanical characteristics, thereby promoting stem growth in Populus. We showed that natural variation in PtoP4H9 occurred in a BASIC PENTACYSTEINE transcription factor PtoBPC1-binding promoter element controlling PtoP4H9 expression. The geographic distribution of PtoP4H9 allelic variation was consistent with the modes of selection among populations. Altogether, our study provides important genetic insights into dynamic stem growth in Populus, and we confirmed PtoP4H9 as a potential useful marker for breeding or genetic engineering of poplars

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

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

Richtmyer-Meshkov instability of a single-mode heavy-light interface in cylindrical geometry

Richtmyer-Meshkov (RM) instability of a single-mode SF6-air interface subjected to a convergent shock is investigated experimentally. The convergent shock tube is specially designed with an opening tail to weaken the Rayleigh-Taylor effect and eliminate the reflected waves' effect. The gas layer scheme is used to create a heavy gas environment at the upstream side of the interface. Before phase inversion is finished, the amplitude reduction is accelerated, but the Bell-Plesset (BP) effect in this process is found to be negligible. After phase inversion is completed, the linear growth rate is generally predicted due to small amplitude and the weak BP effect. In nonlinear regime, an existing nonlinear model is revised based on the Pade approximation to give a better prediction of amplitude growth. The spike amplitude grows almost linearly, whereas the bubble amplitude gradually saturates and even reduces. For a heavy-light interface in convergent geometry, although both the spike and bubble amplitude growths are promoted by the BP effect, the spike growth is more promoted than the bubble. The BP effect enhances generation of the second-order harmonic, which results in saturation and reduction of the bubble amplitude. The discrepancy in the BP effect between light-heavy and heavy-light interfaces is qualitatively demonstrated for the first time

Optimization and Quality Assessment of Arrival Time Picking for Downhole Microseismic Events

Arrival-time picking is a critical step in microseismic data processing, and thus the quality control of arrival results is necessary. Conventional picking methods may be inaccurate or inconsistent due to varied signal-to-noise ratios (SNR) and waveform patterns of the events recorded in different time sections. To address this issue, we propose a quality assessment method based on waveform similarity coefficients to evaluate arrival results and also a global optimization algorithm based on iterative cross-correlation to refine arrival times. The recordings after moveout correction are applied to calculate the intra-event and inter-event waveform coefficients for the quality assessment of arrival results. The residual time differences of intra-event and inter-event traces are calculated sequentially using an enhanced iterative cross-correlation method. In addition, the stacked waveform of each event after the intra-event residual time correction is introduced for global optimization to obtain the inter-event residual time discrepancies. We use both synthetic data and field data to validate the proposed method. The results indicate that the proposed method yields more robust and reliable results. The quality assessment of the optimized arrivals is greatly enhanced compared to the adjusted picks obtained from single event-based processing methods