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SoxB subfamily is an important branch of Sox family and plays a key role in animal physiological process, but little is known about their function in planarian regeneration. This study aims to evaluate the function of DjSoxB family genes in intact and regenerating planarians Dugesia japonica. Here, we amplify the full-length cDNA of DjSoxB1 and DjSoxB2 in D. japonica by rapid amplification of the cDNA ends (RACE), detect the expression of DjSoxB family genes in planarian. The results show that DjSoxBs are expressed in parenchymal tissue and the hybridization signals partially disappear after irradiation indicates DjSoxB family genes are expressed in neoblasts. After the RNA interference (RNAi) of DjSoxB1, DjSoxB2 and DjSoxB3 separately, the numbers of proliferative cells are all reduced that causes planarians show slower growth of blastema in the early stage of regeneration, and nerves of planarians are affected that the movement speed of planarians decreases in varying degrees. Specially, planarians in the DjSoxB3 RNAi group show shrinkage and twisting. Overall, this study reveals that DjSoxB family genes play a role in cell proliferation during regeneration. They also play an important role in the maintenance of normal nerve function and nerve regeneration. These results provide directions for the functional study of SoxB family genes and provide an important foundation for planarian regeneration. [ABSTRACT FROM AUTHOR]

The Oligocene-Miocene boundary Asian climatic reorganization linked to the northward migration of the East Asian monsoon into subtropical China is a potentially important but poorly constrained atmospheric CO2 consumption process. Here, we performed a first-order estimate of the CO2 consumption induced by silicate chemical weathering and organic carbon burial in subtropical China related to this climatic reorganization. Our results show that an increase in long-term CO2 consumption by silicate weathering varies from 0.06×1012 to 0.87×1012 mol yr−1 depending on erosion flux reconstructions, with an ~50% contribution of Mg-silicate weathering since the late Oligocene. The organic carbon burial flux is approximately 25% of the contemporary CO2 consumption by silicate weathering. The results highlight the significant role of weathering of the Mg-rich upper continental crust in East China, which would contribute to the rapid decline in atmospheric CO2 during the late Oligocene and the Neogene rise in the seawater Mg content. If this climatic reorganization was mainly induced by the Tibetan Plateau uplift, our study suggests that the growth of the Himalayan-Tibetan Plateau can lead to indirect modification of the global carbon and magnesium cycles by changing the regional hydrological cycle in areas of East Asia that are tectonically less active. [ABSTRACT FROM AUTHOR]

We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variational inference with implicit distributions, we put forth a statistical inference framework that enables the end-to-end training of surrogate models on paired input–output observations that may be stochastic in nature, originate from different information sources of variable fidelity, or be corrupted by complex noise processes. The resulting surrogates can accommodate high-dimensional inputs and outputs and are able to return predictions with quantified uncertainty. The effectiveness our approach is demonstrated through a series of canonical studies, including the regression of noisy data, multi-fidelity modeling of stochastic processes, and uncertainty propagation in high-dimensional dynamical systems. [ABSTRACT FROM AUTHOR]

In this paper, we focus on exploring effective methods for faster and accurate semantic segmentation. A common practice to improve the performance is to attain high-resolution feature maps with strong semantic representation. Two strategies are widely used: atrous convolutions and feature pyramid fusion, while both are either computationally intensive or ineffective. Inspired by the Optical Flow for motion alignment between adjacent video frames, we propose a Flow Alignment Module (FAM) to learn Semantic Flow between feature maps of adjacent levels and broadcast high-level features to high-resolution features effectively and efficiently. Furthermore, integrating our FAM to a standard feature pyramid structure exhibits superior performance over other real-time methods, even on lightweight backbone networks, such as ResNet-18 and DFNet. Then to further speed up the inference procedure, we also present a novel Gated Dual Flow Alignment Module to directly align high-resolution feature maps and low-resolution feature maps where we term the improved version network as SFNet-Lite. Extensive experiments are conducted on several challenging datasets, where results show the effectiveness of both SFNet and SFNet-Lite. In particular, when using Cityscapes test set, the SFNet-Lite series achieve 80.1 mIoU while running at 60 FPS using ResNet-18 backbone and 78.8 mIoU while running at 120 FPS using STDC backbone on RTX-3090. Moreover, we unify four challenging driving datasets (i.e., Cityscapes, Mapillary, IDD, and BDD) into one large dataset, which we named Unified Driving Segmentation (UDS) dataset. It contains diverse domain and style information. We benchmark several representative works on UDS. Both SFNet and SFNet-Lite still achieve the best speed and accuracy trade-off on UDS, which serves as a strong baseline in such a challenging setting. The code and models are publicly available at https://github.com/lxtGH/SFSegNets. [ABSTRACT FROM AUTHOR]

The carbon pool stored in soil carbonate is comparable to the soil organic carbon. Therefore, secondary calcite precipitation in supersaturated catchment could be an important, yet poorly constrained, carbon sink within the modern global carbon cycle. The chemical analysis of some dissolved species transported by rivers, such as elevated Sr/Ca and Mg/Ca ratios but also heavy stable Ca isotopic compositions, witness the formation of secondary calcite in rivers draining arid regions. However, in areas affected by active tectonics and rapid physical erosion, co-variations in the fluvial Sr/Ca and Mg/Ca ratios could also be related to incongruent carbonate weathering processes. Here, we present a model to assess the roles played by incongruent carbonate dissolution and secondary calcite precipitation in modern weathering processes. We tested and applied the model to rivers draining the Himalayan-Tibetan region. The results suggest that regional aridity in the drainage basin promotes carbon sequestration as secondary carbonate but that for a given runoff, incongruent dissolution of carbonate possibly related to rapid physical erosion amplifies such sequestration. The isotopic compositions (C/C and O/O) of detrital carbonate transported by the main rivers in South and South-East Tibet imply that around 1% of the suspended material transported by those rivers corresponds to secondary carbonate and can represent between 5% and 15% of the alkalinity flux. Most of these alkalinity transported as particulate material is, nevertheless related to the weathering of carbonate lithologies and is also subjected to dissolution prior its final storage in sedimentary basins. However, on glacial-interglacial timescale this will amplify the significant role of mountain weathering on climatic variations. [ABSTRACT FROM AUTHOR]

Using an alternative source decomposition, we propose new exact boundary conditions on numerical boundary of a square lattice for out-of-plane motion over the whole space. A set of recurrence relations are found for the resulting kernel functions, hence allow their efficient and accurate evaluation with a system of ordinary differential equations. Stability of the boundary conditions is proved rigorously. Numerical results illustrate effective suppression for spurious wave reflection, and elimination of corner effects. This approach may be extended to other lattice structures and in higher dimensions. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms underlying biodiversity patterns is a central issue in ecology, while how temperature and precipitation jointly control the elevational patterns of microbes is understudied. Here, we studied the effects of temperature, precipitation and their interactions on the alpha and beta diversity of soil archaea and bacteria in alpine grasslands along an elevational gradient of 4300–5200 m on the Tibetan Plateau. Alpha diversity was examined on the basis of species richness and evenness, and beta diversity was quantified with the recently developed metric of local contributions to beta diversity (LCBD). Typical alpine steppe and meadow ecosystems were distributed below and above 4850 m, respectively, which was consistent with the two main constraints of mean annual temperature (MAT) and mean annual precipitation (MAP). Species richness and evenness showed decreasing elevational patterns in archaea and nonsignificant or U-shaped patterns in bacteria. The LCBD of both groups exhibited significant U-shaped elevational patterns, with the lowest values occurring at 4800 m. For the three diversity metrics, soil pH was the primary explanatory variable in archaea, explaining over 20.1% of the observed variation, whereas vegetation richness, total nitrogen and the K/Al ratio presented the strongest effects on bacteria, with relative importance values of 16.1%, 12.5% and 11.6%, respectively. For the microbial community composition of both archaea and bacteria, the moisture index showed the dominant effect, explaining 17.6% of the observed variation, followed by MAT and MAP. Taken together, temperature and precipitation exerted considerable indirect effects on microbial richness and evenness through local environmental and energy supply-related variables, such as vegetation richness, whereas temperature exerted a larger direct influence on LCBD and the community composition. Our findings highlighted the profound influence of temperature and precipitation interactions on microbial beta diversity in alpine grasslands on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model predictions against experimental results. Experimental data is often reconstructed from indirect measurements causing the aggregate transformation from theoretical models to experimental data to be poorly-described analytically. Instead, numerical simulations are used at great computational cost. We introduce Optimal-Transport-based Unfolding and Simulation (OTUS), a fast simulator based on unsupervised machine-learning that is capable of predicting experimental data from theoretical models. Without the aid of current simulation information, OTUS trains a probabilistic autoencoder to transform directly between theoretical models and experimental data. Identifying the probabilistic autoencoder's latent space with the space of theoretical models causes the decoder network to become a fast, predictive simulator with the potential to replace current, computationally-costly simulators. Here, we provide proof-of-principle results on two particle physics examples, Z-boson and top-quark decays, but stress that OTUS can be widely applied to other fields. [ABSTRACT FROM AUTHOR]

Motivated by biological evolution, this paper explains the rationality of Vision Transformer by analogy with the proven practical evolutionary algorithm (EA) and derives that both have consistent mathematical formulation. Then inspired by effective EA variants, we propose a novel pyramid EATFormer backbone that only contains the proposed EA-based transformer (EAT) block, which consists of three residual parts, i.e., Multi-scale region aggregation, global and local interaction, and feed-forward network modules, to model multi-scale, interactive, and individual information separately. Moreover, we design a task-related head docked with transformer backbone to complete final information fusion more flexibly and improve a modulated deformable MSA to dynamically model irregular locations. Massive quantitative and quantitative experiments on image classification, downstream tasks, and explanatory experiments demonstrate the effectiveness and superiority of our approach over state-of-the-art methods. E.g., our Mobile (1.8 M), Tiny (6.1 M), Small (24.3 M), and Base (49.0 M) models achieve 69.4, 78.4, 83.1, and 83.9 Top-1 only trained on ImageNet-1K with naive training recipe; EATFormer-Tiny/Small/Base armed Mask-R-CNN obtain 45.4/47.4/49.0 box AP and 41.4/42.9/44.2 mask AP on COCO detection, surpassing contemporary MPViT-T, Swin-T, and Swin-S by 0.6/1.4/0.5 box AP and 0.4/1.3/0.9 mask AP separately with less FLOPs; Our EATFormer-Small/Base achieve 47.3/49.3 mIoU on ADE20K by Upernet that exceeds Swin-T/S by 2.8/1.7. Code is available at https://github.com/zhangzjn/EATFormer. [ABSTRACT FROM AUTHOR]

Fractional derivatives play a very important role in modeling physical phenomena involving long-range correlation effects. However, they raise challenges of computational cost and memory storage requirements when solved using current well developed numerical methods. In this paper, the differential operator multiplication method is proposed to address the issues by considering a reaction-advection-diffusion equation with a fractional derivative in time. The linear fractional differential equation is transformed into an integer order differential equation by the proposed method, which can fundamentally fix the aforementioned issues for select fractional differential equations. In such a transform, special attention should be paid to the initial conditions for the resulting differential equation of higher integer order. Through numerical experiments, we verify the proposed method for both fractional ordinary differential equations and partial differential equations. [ABSTRACT FROM AUTHOR]