Discovery of entomopathogenic fungi across geographical regions in southern China on pine sawyer beetle Monochamus alternatus and implication for multi-pathogen vectoring potential of this beetle

Entomopathogen-based biocontrol is crucial for blocking the transmission of vector-borne diseases; however, few cross-latitudinal investigations of entomopathogens have been reported for vectors transmitting woody plant diseases in forest ecosystems. The pine sawyer beetle Monochamus alternatus is an important wood borer and a major vector transmitting pine wilt disease, facilitating invasion of the pinewood nematode Bursaphelenchus xylophilus (PWN) in China. Due to the limited geographical breadth of sampling regions, species diversity of fungal associates (especially entomopathogenic fungi) on M. alternatus adults and their potential ecological functions have been markedly underestimated. In this study, through traditional fungal isolation with morphological and molecular identification, 640 fungal strains (affiliated with 15 genera and 39 species) were isolated from 81 beetle cadavers covered by mycelia or those symptomatically alive across five regional populations of this pest in southern China. Multivariate analyses revealed significant differences in the fungal community composition among geographical populations of M. alternatus, presenting regionalized characteristics, whereas no significant differences were found in fungal composition between beetle genders or among body positions. Four region-representative fungi, namely, Lecanicillium attenuatum (Zhejiang), Aspergillus austwickii (Sichuan), Scopulariopsis alboflavescens (Fujian), and A. ruber (Guangxi), as well as the three fungal species Beauveria bassiana, Penicillium citrinum, and Trichoderma dorotheae, showed significantly stronger entomopathogenic activities than other fungi. Additionally, insect-parasitic entomopathogenic fungi (A. austwickii, B. bassiana, L. attenuatum, and S. alboflavescens) exhibited less to no obvious phytopathogenic activities on the host pine Pinus massoniana, whereas P. citrinum, Purpureocillium lilacinum, and certain species of Fusarium spp.—isolated from M. alternatus body surfaces—exhibited remarkably higher phytopathogenicity. Our results provide a broader view of the entomopathogenic fungal community on the vector beetle M. alternatus, some of which are reported for the first time on Monochamus spp. in China. Moreover, this beetle might be more highly-risk in pine forests than previously considered, as a potential multi-pathogen vector of both PWN and phytopathogenic fungi

Genomic sequencing combined with marker-assisted breeding effectively eliminates potential linkage drag of a target gene: a case study in tobacco

Linkage drag frequently impedes the utilization of beneficial genes from wild species in crop improvement. The N gene from Nicotiana glutinosa confers strong resistance to tobacco mosaic virus (TMV) but introduces linkage drag when introgressed into cultivated tobacco (Nicotiana tabacum). To address this issue, we sequenced the TMV-resistant flue-cured tobacco line 0970A and carried out comparative genomic analysis. Additionally, we used molecular markers to screen a BC4F1 population derived from the cross between 0970A and an elite flue-cured tobacco variety CB-1 (recurrent parent). As a result of sequencing 0970A, the N gene was located at the end of chromosome Nt11. The comparative genomic analysis showed that 0970A inherited approximately 3.74 Mb of N. glutinosa DNA (N-fragment) from its donor, Coker 176. From screening the BC4F1 population with molecular markers, a recombinant was identified. This recombinant had a significantly reduced N-fragment (~270 kb), which minimized the linkage drag while still maintaining resistance to TMV. This research indicates that the combination of genome sequencing and marker-assisted breeding can be successfully applied to reduce linkage drag. The findings offer valuable resources for breeding tobacco with resistance to TMV

Self-powered electrotactile textile haptic glove for enhanced human-machine interface.

Human-machine interface (HMI) plays an important role in various fields, where haptic technologies provide crucial tactile feedback that greatly enhances user experience, especially in virtual reality/augmented reality, prosthetic control, and therapeutic applications. Through tactile feedback, users can interact with devices in a more realistic way, thereby improving the overall effectiveness of the experience. However, existing haptic devices are often bulky due to cumbersome instruments and power modules, limiting comfort and portability. Here, we introduce a concept of wearable haptic technology: a thin, soft, self-powered electrotactile textile haptic (SPETH) glove that uses the triboelectric effect and gas breakdown discharge for localized electrical stimulation. Daily hand movements generate sufficient mechanical energy to power the SPETH glove. Its features-softness, lightweight, self-sustainability, portability, and affordability-enable it to provide tactile feedback anytime and anywhere without external equipment. This makes the SPETH glove an enhanced, battery-free HMI suitable for a wide range of applications

BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation

The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels, which real-world vision datasets rarely satisfy. While current synthetic data generators offer a promising alternative, particularly for embodied AI tasks, they often fall short for computer vision tasks due to low asset and rendering quality, limited diversity, and unrealistic physical properties. We introduce the BEHAVIOR Vision Suite (BVS), a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models, based on the newly developed embodied AI benchmark, BEHAVIOR-1K. BVS supports a large number of adjustable parameters at the scene level (e.g., lighting, object placement), the object level (e.g., joint configuration, attributes such as "filled" and "folded"), and the camera level (e.g., field of view, focal length). Researchers can arbitrarily vary these parameters during data generation to perform controlled experiments. We showcase three example application scenarios: systematically evaluating the robustness of models across different continuous axes of domain shift, evaluating scene understanding models on the same set of images, and training and evaluating simulation-to-real transfer for a novel vision task: unary and binary state prediction. Project website: https://behavior-vision-suite.github.io/Comment: CVPR 2024 (Highlight). Project website: https://behavior-vision-suite.github.io

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

JUNO Sensitivity to Invisible Decay Modes of Neutrons

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $n \rightarrow { inv}$ and $nn \rightarrow { inv}$. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$.Comment: 28 pages, 7 figures, 4 table

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

A BIM Platform for the Manufacture of Prefabricated Steel Structure

In the design phase, building information modeling (BIM) software has been widely employed due to its high efficiency, precision, and synergy among different teams. However, the advantages of BIM have not been fully explored in the manufacturing stage where the progress is not so transparent, and information exchange is not so smooth. To deal with these problems, a BIM platform for the manufacture of steel structures is developed in this article, which aims for the management and visualization of manufacturing progress in a steel structure factory in China. The proposed platform was developed and tested by using practical projects. The requirement is analyzed with different users involved in the manufacturing progress. The platform is web-based, where Node.js is adopted for server-side scripting, Neo4j is used for data storage, hyper text markup language (HTML), cascading style sheets (CSS), and JavaScript are used to compile user interface. Besides, a quick response (QR) code is attached to components for traceability. By parsing the BIM model exported in the design phase, essential information of components is imported into the platform, which are the data that form the basis of the following operation. By introducing the platform as a collaborative tool, the traceability and visibility of real-time manufacturing progress of each steel component are significantly enhanced. As a result, this platform can help managers make decisions, workers check quality problems, and other stakeholders grasp the manufacturing progress.</jats:p