Comprehensive analysis of LRR-RLKs and key gene identification in Pinus massoniana resistant to pine wood nematode

Pinus massoniana is a pioneer tree widely planted for afforestation on barren hills in southern China where the total planted area is 8.04 million ha. The invasive pine wood nematode (Bursaphelenchus xylophilus) poses a serious threat to the survival of P. massoniana. Plant resistance genes encoded by leucine-rich repeat-containing transmembrane-receptor proteins play important roles in plant defense. Leucine-rich repeat receptor-like kinases (LRR-RLKs), the largest subfamily of the RLK protein family, play an important role in sensing stress signals in plants. However, the LRR-RLKs of P. massoniana have not been characterized previously, and their role in resistance to B. xylophilus is unknown. In this study, 185 members of the LRR-RLK subfamily were identified in P. massoniana and were categorized into 14 subgroups. Transcriptomic and quantitative real-time RT-PCR analyses showed that PmRLKs32 was highly expressed in the stem tissue after inoculation with B. xylophilus. The gene exhibited high homology with AtFLS2 of Arabidopsis thaliana. PmRLKs32 was localized to the plasma membrane and was significantly upregulated in nematode-resistant and nematode-susceptible individuals. The transient expression of PmRLKs32 resulted in a burst of reactive oxygen species production in P. massoniana and Nicotiana benthamiana seedlings. These results lay a foundation for further exploration of the regulatory mechanism of LRR-RLKs in response to biotic stress in P. massoniana

STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Large-scale models pre-trained on large-scale datasets have profoundly advanced the development of deep learning. However, the state-of-the-art models for medical image segmentation are still small-scale, with their parameters only in the tens of millions. Further scaling them up to higher orders of magnitude is rarely explored. An overarching goal of exploring large-scale models is to train them on large-scale medical segmentation datasets for better transfer capacities. In this work, we design a series of Scalable and Transferable U-Net (STU-Net) models, with parameter sizes ranging from 14 million to 1.4 billion. Notably, the 1.4B STU-Net is the largest medical image segmentation model to date. Our STU-Net is based on nnU-Net framework due to its popularity and impressive performance. We first refine the default convolutional blocks in nnU-Net to make them scalable. Then, we empirically evaluate different scaling combinations of network depth and width, discovering that it is optimal to scale model depth and width together. We train our scalable STU-Net models on a large-scale TotalSegmentator dataset and find that increasing model size brings a stronger performance gain. This observation reveals that a large model is promising in medical image segmentation. Furthermore, we evaluate the transferability of our model on 14 downstream datasets for direct inference and 3 datasets for further fine-tuning, covering various modalities and segmentation targets. We observe good performance of our pre-trained model in both direct inference and fine-tuning. The code and pre-trained models are available at https://github.com/Ziyan-Huang/STU-Net

Experimental Study on the Peeling Characteristics of Wax on the Surface of Flexible Composite Pipe and Plastic Alloy Tube

In this paper, the wax deposition stripping experiment on the surface of flexible composite tube and the surface of plastic alloy tube was carried out by using medical paraffin. Under different temperature and different thickness of wax, the peeling force of wax on the surface of flexible composite pipe and the surface of plastic alloy pipe lining was discussed. The experimental conclusions of this study are specific guidance for wax cleaning technology in nonmetallic pipes and device design

A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation

Although deep learning have revolutionized abdominal multi-organ segmentation, models often struggle with generalization due to training on small, specific datasets. With the recent emergence of large-scale datasets, some important questions arise: \textbf{Can models trained on these datasets generalize well on different ones? If yes/no, how to further improve their generalizability?} To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation. We employ training sets from four large-scale public datasets: FLARE22, AMOS, WORD, and TotalSegmentator, each providing extensive labels for abdominal multi-organ segmentation. For evaluation, we incorporate the validation sets from these datasets along with the training set from the BTCV dataset, forming a robust benchmark comprising five distinct datasets. We evaluate the generalizability of various models using the A-Eval benchmark, with a focus on diverse data usage scenarios: training on individual datasets independently, utilizing unlabeled data via pseudo-labeling, mixing different modalities, and joint training across all available datasets. Additionally, we explore the impact of model sizes on cross-dataset generalizability. Through these analyses, we underline the importance of effective data usage in enhancing models' generalization capabilities, offering valuable insights for assembling large-scale datasets and improving training strategies. The code and pre-trained models are available at \href{https://github.com/uni-medical/A-Eval}{https://github.com/uni-medical/A-Eval}

SAM-Med3D

Although the Segment Anything Model (SAM) has demonstrated impressive performance in 2D natural image segmentation, its application to 3D volumetric medical images reveals significant shortcomings, namely suboptimal performance and unstable prediction, necessitating an excessive number of prompt points to attain the desired outcomes. These issues can hardly be addressed by fine-tuning SAM on medical data because the original 2D structure of SAM neglects 3D spatial information. In this paper, we introduce SAM-Med3D, the most comprehensive study to modify SAM for 3D medical images. Our approach is characterized by its comprehensiveness in two primary aspects: firstly, by comprehensively reformulating SAM to a thorough 3D architecture trained on a comprehensively processed large-scale volumetric medical dataset; and secondly, by providing a comprehensive evaluation of its performance. Specifically, we train SAM-Med3D with over 131K 3D masks and 247 categories. Our SAM-Med3D excels at capturing 3D spatial information, exhibiting competitive performance with significantly fewer prompt points than the top-performing fine-tuned SAM in the medical domain. We then evaluate its capabilities across 15 datasets and analyze it from multiple perspectives, including anatomical structures, modalities, targets, and generalization abilities. Our approach, compared with SAM, showcases pronouncedly enhanced efficiency and broad segmentation capabilities for 3D volumetric medical images. Our code is released at https://github.com/uni-medical/SAM-Med3D

TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution

The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future reactor neutrino experiments, and to provide a benchmark measurement to test nuclear databases. A spherical acrylic vessel containing 2.8 ton gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full coverage. The photoelectron yield is about 4500 per MeV, an order higher than any existing large-scale liquid scintillator detectors. The detector operates at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The detector will measure about 2000 reactor antineutrinos per day, and is designed to be well shielded from cosmogenic backgrounds and ambient radioactivities to have about 10% background-to-signal ratio. The experiment is expected to start operation in 2022

Drivers of cropland abandonment in mountainous areas: A household decision model on farming scale and a case study of Southwest China

Cropland abandonment has emerged as a prevalent phenomenon in the mountainous areas of China.While there is a general understanding that this new trend is driven by the rising opportunity cost of rural labor, rigorous theoretical and empirical analyses are largely absent. This paper first develops a theoretical model to investigate household decisions on farming scale when off-farm labor market is accessible and there is heterogeneity of farmland productivity and distribution. The model is capable of explaining the hidden reasons of cropland abandonment in sloping and agriculturally less-favored locations. The model also unveils the impacts of heterogeneity of household labor on fallow decisions and the efficiency loss due to an imperfect labor market. The model is empirically tested by applying the Probit and Logit estimators to a unique household and land-plot survey dataset which contains 5258 plots of599 rural households in Chongqing, a provincial level municipality, in Southwest China. The survey shows that more than 30% of the sample plots have been abandoned, mainly since 1992. The econometric results are consistent with our theoretical expectations. This work would help policy-makers and stakeholders to identify areas with a high probability of land abandonment and farming practice which is less sustainable in the mountainous areas

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