Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe

FungalTraits:A user-friendly traits database of fungi and fungus-like stramenopiles

The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental studies. Over the past decades, rapid development and affordability of molecular tools have tremendously improved insights of the fungal diversity in all ecosystems and habitats. Yet, in spite of the progress of molecular methods, knowledge about functional properties of the fungal taxa is vague and interpretation of environmental studies in an ecologically meaningful manner remains challenging. In order to facilitate functional assignments and ecological interpretation of environmental studies we introduce a user friendly traits and character database FungalTraits operating at genus and species hypothesis levels. Combining the information from previous efforts such as FUNGuild and Fun(Fun) together with involvement of expert knowledge, we reannotated 10,210 and 151 fungal and Stramenopila genera, respectively. This resulted in a stand-alone spreadsheet dataset covering 17 lifestyle related traits of fungal and Stramenopila genera, designed for rapid functional assignments of environmental studies. In order to assign the trait states to fungal species hypotheses, the scientific community of experts manually categorised and assigned available trait information to 697,413 fungal ITS sequences. On the basis of those sequences we were able to summarise trait and host information into 92,623 fungal species hypotheses at 1% dissimilarity threshold

Multi-Residue Determination of 244 Chemical Contaminants in Chicken Eggs by Liquid Chromatography-Tandem Mass Spectrometry after Effective Lipid Clean-Up

In this study, we aimed to establish a multi-residue analytical method for the simultaneous detection of chemical contaminants in eggs. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), we developed an analytical method that can separate 244 compounds (including β-agonists (25), imidazole and benzimidazoles (31), sulfonamides (22), antihistamines (10), β-lactam (5), insecticides (7), quinolones (24), non-steroidal anti-inflammatory drugs (13), and steroidal hormones (38)) within 30 min. A new enhanced matrix removal-lipid (EMR-Lipid) material was used as a purified sorbent in the QuEChERS clean-up method. Excellent linearity (r > 0.9905) was achieved. Additionally, recoveries ranged between 51.33% and 118.28%, with repeatability (RSDr) and reproducibility (RSDwR) in the range of 1.01–14.22% and 1.08–14.96%, respectively. In all of the compounds, low limits of quantification (LOQs) ≤ 5 μg kg−1 were found. Meanwhile, the detection limit (CCα) and detection capability (CCβ) were 1.88–40.60 μg kg−1 and 2.85–407.19 μg kg−1, respectively. In conclusion, the evaluated method was shown to provide reliable screening, quantification, and identification of 244 multi-class chemicals in eggs and was successfully applied in real samples

Multi-Residue Determination of 244 Chemical Contaminants in Chicken Eggs by Liquid Chromatography-Tandem Mass Spectrometry after Effective Lipid Clean-Up

In this study, we aimed to establish a multi-residue analytical method for the simultaneous detection of chemical contaminants in eggs. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), we developed an analytical method that can separate 244 compounds (including β-agonists (25), imidazole and benzimidazoles (31), sulfonamides (22), antihistamines (10), β-lactam (5), insecticides (7), quinolones (24), non-steroidal anti-inflammatory drugs (13), and steroidal hormones (38)) within 30 min. A new enhanced matrix removal-lipid (EMR-Lipid) material was used as a purified sorbent in the QuEChERS clean-up method. Excellent linearity (r > 0.9905) was achieved. Additionally, recoveries ranged between 51.33% and 118.28%, with repeatability (RSDr) and reproducibility (RSDwR) in the range of 1.01–14.22% and 1.08–14.96%, respectively. In all of the compounds, low limits of quantification (LOQs) ≤ 5 μg kg−1 were found. Meanwhile, the detection limit (CCα) and detection capability (CCβ) were 1.88–40.60 μg kg−1 and 2.85–407.19 μg kg−1, respectively. In conclusion, the evaluated method was shown to provide reliable screening, quantification, and identification of 244 multi-class chemicals in eggs and was successfully applied in real samples

A Fast Method for the Simultaneous Analysis of 26 Beta-Agonists in Swine Muscle with a Multi-Functional Filter by Ultra-High Performance Liquid Chromatography-Tandem Mass Spectrometry

A rapid and simplified sample preparation method was developed for the simultaneous determination of 26 beta-agonists in swine muscle using a multi-functional filter (MFF) based on quick, easy, cheap, effective, rugged, and safe methods (QuEChERS). MFF integrated the cleanup and filter procedures, thereby significantly improving the efficiency of sample preparation compared with traditional solid-phase extraction. The sample was processed via enzymatic hydrolysis, purified with the optimized MFF containing 150 mg magnesium sulfate, 50 mg PSA, and 50 mg C18, and then analyzed using ultra-high performance liquid chromatography-tandem mass spectrometry. All procedures can be completed in 6.5 h. Good linearity (R2 > 0.99) was detected in all analytes. The recoveries ranged from 71.2% to 118.6%, with relative standard deviations (RSDs) of less than 18.37% in all spiked concentrations. The limits of detection (LOD) and the limits of quantitation (LOQ) were 0.01–0.10 and 0.10–0.50 μg/kg, respectively. The decision limit (CCα) and detection capacity (CCβ) values fluctuated in the range of 3.44–25.71 and 6.38–51.21 μg/kg, respectively. This method is a good alternative for detecting beta-agonist residues in swine muscle and can be successfully applied to the national risk monitoring of agro-product quality and safety in China

Multiclass Comparative Analysis of Veterinary Drugs, Mycotoxins, and Pesticides in Bovine Milk by Ultrahigh-Performance Liquid Chromatography–Hybrid Quadrupole–Linear Ion Trap Mass Spectrometry

A multiclass and multiresidue method for simultaneously screening and confirming veterinary drugs, mycotoxins, and pesticides in bovine milk was developed and validated with ultrahigh-performance liquid chromatography–hybrid quadrupole–linear ion trap mass spectrometry (UHPLC-Qtrap-MS). A total of 209 targeted contaminants were effectively extracted using an optimized QuEChERS method. Quantitative and qualitative confirmation were achieved simultaneously by multiple reaction monitoring–information-dependent acquisition–enhanced product ion (MRM-IDA-EPI) scan mode. The validation results exhibited a good sensitivity with the LOQs of 0.05–5 μg/kg, which was satisfactory for their MRLs in China or EU. The recoveries of in-house spiked samples were in the range of 51.20–129.76% with relative standard deviations (RSD) between replicates (n = 3) 0.82% and 19.76%. The test results of 140 milk samples from supermarkets and dairy farms in China showed that cloxacillin, aflatoxin M1, acetamiprid, and fipronil sulfone were found with lower concentrations. Combined with the residue results from the literature, penicillin G and cloxacillin (beta-lactams), enrofloxacin and ciprofloxacin (fluoroquinolones), and sulfamerazine (sulfonamides) were more frequently detected in different countries and need to receive more attention regarding their monitoring and control

Research on Airport Target Recognition under Low-Visibility Condition Based on Transfer Learning

Operational safety in the airport is the focus of the aviation industry. Target recognition under low visibility plays an essential role in arranging the circulation of objects in the airport field, identifying unpredictable obstacles in time, and monitoring aviation operation and ensuring its safety and efficiency. From the perspective of transfer learning, this paper will explore the identification of all targets (mainly including aircraft, humans, ground vehicles, hangars, and birds) in the airport field under low-visibility conditions (caused by bad weather such as fog, rain, and snow). First, a variety of deep transfer learning networks are used to identify well-visible airport targets. The experimental results show that GoogLeNet is more effective, with a recognition rate of more than 90.84%. However, the recognition rates of this method are greatly reduced under the condition of low visibility; some are even less than 10%. Therefore, the low-visibility image is processed with 11 different fog removals and vision enhancement algorithms, and then, the GoogLeNet deep neural network algorithm is used to identify the image. Finally, the target recognition rate can be significantly improved to more than 60%. According to the results, the dark channel algorithm has the best image defogging enhancement effect, and the GoogLeNet deep neural network has the highest target recognition rate