Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning

We propose ADCLR: A ccurate and D ense Contrastive Representation Learning, a novel self-supervised learning framework for learning accurate and dense vision representation. To extract spatial-sensitive information, ADCLR introduces query patches for contrasting in addition with global contrasting. Compared with previous dense contrasting methods, ADCLR mainly enjoys three merits: i) achieving both global-discriminative and spatial-sensitive representation, ii) model-efficient (no extra parameters in addition to the global contrasting baseline), and iii) correspondence-free and thus simpler to implement. Our approach achieves new state-of-the-art performance for contrastive methods. On classification tasks, for ViT-S, ADCLR achieves 77.5% top-1 accuracy on ImageNet with linear probing, outperforming our baseline (DINO) without our devised techniques as plug-in, by 0.5%. For ViT-B, ADCLR achieves 79.8%, 84.0% accuracy on ImageNet by linear probing and finetune, outperforming iBOT by 0.3%, 0.2% accuracy. For dense tasks, on MS-COCO, ADCLR achieves significant improvements of 44.3% AP on object detection, 39.7% AP on instance segmentation, outperforming previous SOTA method SelfPatch by 2.2% and 1.2%, respectively. On ADE20K, ADCLR outperforms SelfPatch by 1.0% mIoU, 1.2% mAcc on the segm

P4HA2 hydroxylates SUFU to regulate the paracrine Hedgehog signaling and promote B-cell lymphoma progression.

Aberrations in the Hedgehog (Hh) signaling pathway are significantly prevailed in various cancers, including B-cell lymphoma. A critical facet of Hh signal transduction involves the dynamic regulation of the suppressor of fused homolog (SUFU)-glioma-associated oncogene homolog (GLI) complex within the kinesin family member 7 (KIF7)-supported ciliary tip compartment. However, the specific post-translational modifications of SUFU-GLI complex within this context have remained largely unexplored. Our study reveals a novel regulatory mechanism involving prolyl 4-hydroxylase 2 (P4HA2), which forms a complex with KIF7 and is essential for signal transduction of Hh pathway. We demonstrate that, upon Hh pathway activation, P4HA2 relocates alongside KIF7 to the ciliary tip. Here, it hydroxylates SUFU to inhibit its function, thus amplifying the Hh signaling. Moreover, the absence of P4HA2 significantly impedes B lymphoma progression. This effect can be attributed to the suppression of Hh signaling in stromal fibroblasts, resulting in decreased growth factors essential for malignant proliferation of B lymphoma cells. Our findings highlight the role of P4HA2-mediated hydroxylation in modulating Hh signaling and propose a novel stromal-targeted therapeutic strategy for B-cell lymphoma

Correction: Identification of soil P fractions that are associated with P loss from surface runoff under various cropping systems and fertilizer rates on sloped farmland.

[This corrects the article DOI: 10.1371/journal.pone.0179275.]

Identification of soil P fractions that are associated with P loss from surface runoff under various cropping systems and fertilizer rates on sloped farmland.

Soil phosphorus (P) fractions and runoff P concentration were measured to understand the fate of soil P entering surface runoff water during summer cropping season of different double cropping systems under two fertilizer regimes. The dominant form of runoff P was particulate P (PP). Runoff total P (TP) was higher at the vegetative growth stage and lower at the crop reproductive stage. TP and PP were derived mainly from soil Olsen-P, Al-P and Fe-P and amounts increased with sediment content in runoff water. Runoff P discharge was closely related to the changes in soil P forms. Soil Olsen-P, mainly consisting of some Ca2-P and Al-P, was increased by elevating fertilizer rate. Along with crop growth, there were active interconversions among Olsen-P, Org-P, Fe-P and O-Al-P in the soil, and some available P converted into Ca10-P, with O-Fe-P possibly being a transitional form for this conversion. The oilseed rape/corn system had less runoff TP at the early stage, and wheat/sweet potato system had a lower runoff P at the late stage. Intercropping corn with sweet potato in the field with oilseed rape as a previous crop may be helpful for alleviating runoff P load during the summer in this region

Construction of a highly saturated Genetic Map for Vitis by Next-generation Restriction Site-associated DNA Sequencing

Abstract Background High-saturate molecular linkage maps are an important tool in studies on plant molecular biology and assisted breeding. Development of a large set of single nucleotide polymorphisms (SNPs) via next-generation sequencing (NGS)-based methods, restriction-site associated DNA sequencing (RAD-seq), and the generation of a highly saturated genetic map help improve fine mapping of quantitative trait loci (QTL). Results We generated a highly saturated genetic map to identify significant traits in two elite grape cultivars and 176 F1 plants. In total, 1,426,967 high-quality restriction site-associated DNA tags were detected; 51,365, 23,683, and 70,061 markers were assessed in 19 linkage groups (LGs) for the maternal, paternal, and integrated maps, respectively. Our map was highly saturated in terms of marker density and average “Gap ≤ 5 cM” percentage. Conclusions In this study, RAD-seq of 176 F1 plants and their parents yielded 8,481,484 SNPs and 1,646,131 InDel markers, of which 65,229 and 4832, respectively, were used to construct a highly saturated genetic map for grapevine. This map is expected to facilitate genetic studies on grapevine, including an evaluation of grapevine and deciphering the genetic basis of economically and agronomically important traits. Our findings provide basic essential genetic data the grapevine genetic research community, which will lead to improvements in grapevine breeding

Spatially Regularized Streaming Sensor Selection

Sensor selection has become an active topic aimed at energy saving, information overload prevention, and communication cost planning in sensor networks. In many real applications, often the sensors' observation regions have overlaps and thus the sensor network is inherently redundant. Therefore it is important to select proper sensors to avoid data redundancy. This paper focuses on how to incrementally select a subset of sensors in a streaming scenario to minimize information redundancy, and meanwhile meet the power consumption constraint. We propose to perform sensor selection in a multi-variate interpolation framework, such that the data sampled by the selected sensors can well predict those of the inactive sensors. Importantly, we incorporate sensors' spatial information as two regularizers, which leads to significantly better prediction performance. We also define a statistical variable to store sufficient information for incremental learning, and introduce a forgetting factor to track sensor streams' evolvement. Experiments on both synthetic and real datasets validate the effectiveness of the proposed method. Moreover, our method is over 10 times faster than the state-of-the-art sensor selection algorithm

Correlation coefficients between soil Olsen-P and Ca₁₀-P and other P fractions in summer crop season of different cropping systems.

<p>Correlation coefficients between soil Olsen-P and Ca₁₀-P and other P fractions in summer crop season of different cropping systems.</p