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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]

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]

Neural architecture has been a research focus in recent years due to its importance in deciding the performance of deep networks. Representative ones include a residual network (ResNet) with skip connections and a dense network (DenseNet) with dense connections. However, a theoretical guidance for manual architecture design and neural architecture search (NAS) is still lacking. In this paper, we propose a manual architecture design framework, which is inspired by optimization algorithms. It is based on the conjecture that an optimization algorithm with a good convergence rate may imply a neural architecture with good performance. Concretely, we prove under certain conditions that forward propagation in a deep neural network is equivalent to the iterative optimization procedure of the gradient descent algorithm minimizing a cost function. Inspired by this correspondence, we derive neural architectures from fast optimization algorithms, including the heavy ball algorithm and Nesterov’s accelerated gradient descent algorithm. Surprisingly, we find that we can deem the ResNet and DenseNet as special cases of the optimization-inspired architectures. These architectures offer not only theoretical guidance, but also good performances in image recognition on multiple datasets, including CIFAR-10, CIFAR-100, and ImageNet. Moreover, we show that our method is also useful for NAS by offering a good initial search point or guiding the search space. [ABSTRACT FROM AUTHOR]

Chemical reaction kinetics can be evaluated by probing dynamic changes of chemical substrates or physical phenomena accompanied during the reaction process. Chemiluminescence, a light emitting exoenergetic process, involves random reaction positions and kinetics in solution that are typically characterized by ensemble measurements with nonnegligible average effects. Chemiluminescent reaction dynamics at the single-molecule level remains elusive. Here we report direct imaging of single-molecule chemiluminescent reactions in solution and probing of their reaction dynamics under catalytic conditions. Double-substrate Michaelis–Menten type of catalytic kinetics is found to govern the single-molecule reaction dynamics in solution, and a heterogeneity is found among different catalyst particles and different catalytic sites on a single particle. We further show that single-molecule chemiluminescence imaging can be used to evaluate the thermodynamics of the catalytic system, resolving activation energy at the single-particle level. Our work provides fundamental insights into chemiluminescent reactions and offers an efficient approach for evaluating catalysts.Research into the dynamics of chemical reactions at the single-molecule level is a pivotal undertaking. Here, the authors present a direct investigation of the chemiluminescent reaction dynamics of single molecules in solution, providing spatiotemporally resolved insights into chemical reactions. [ABSTRACT FROM AUTHOR]

This research proposed the modular prefabricated permanent formwork system made of ultra-high-performance concrete (UHPC). Two kinds of modular formwork shapes were designed: the flat formwork and the ribbed. The experimental investigation on the axial compression performance of the composite columns that consist of the normal strength concrete (NSC) core and the modular UHPC permanent formwork was demonstrated. Compared with the flat formwork, the ribbed formwork exhibited better bonding with the NSC core. As observed from the test results, the composite column with the ribbed formwork presented a similar axial behavior as the NSC column with a slight improvement in ultimate loads. Therefore, the modular UHPC ribbed permanent formwork could be regarded as the additional cover to the conventional NSC column. In addition, the finite element analysis (FEA) model was also developed to simulate the composite columns numerically. The predicted capacities agreed with the experimental results, which validated the numerical models. The crack pattern estimated by the FEA model revealed that the interaction between the permanent formwork and the inner concrete introduced many tiny cracks to the concrete core. However, as protected by the UHPC permanent formwork, the overall durability of the composite columns can still be enhanced. [ABSTRACT FROM AUTHOR]

Nowadays, convolutional neural networks (CNNs) have led the developments of machine learning. However, most CNN architectures are obtained by manual design, which is empirical, time-consuming, and non-transparent. In this paper, we aim at offering better insight into CNN models from the perspective of optimization theory. We propose a unified framework for understanding and designing CNN architectures with the family of Newton’s methods, which is referred to as Newton design. Specifically, we observe that the standard feedforward CNN model (PlainNet) solves an optimization problem via a kind of quasi-Newton method. Interestingly, residual network (ResNet) can also be derived if we use a more general quasi-Newton method to solve this problem. Based on the above observations, we solve this problem via a better method, the Newton-conjugate-gradient (Newton-CG) method, which inspires Newton-CGNet. In the network design, we translate binary-value terms in the optimization schemes to dropout layers, so dropout modules naturally appear in the derived CNN structures with specific locations, rather than being an empirical training strategy. Extensive experiments on image classification and text categorization tasks verify that Newton-CGNets perform very competitively. Particularly, Newton-CGNets surpass their counterparts ResNets by over 4% on CIFAR-10 and over 10% on CIFAR-100, respectively. [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]

To reduce the cost of lightweight concrete (LWC) partition panels and to address recycling concrete waste, this work utilized completely recycled fine aggregate (CRFA) to replace the natural fine aggregate and ceramsite in the preparation of LWC and LWC partition panels. To this end, an autoclave-free curing process and an air-entraining agent were used to prepare the CRFA-LWC. The workability, compressive strength, drying shrinkage, and pore structure of the CRFA-LWC and the performance of the CRFA-LWC partition panels were then investigated. The results show that the optimal ratio of the CRFA to the cement is 2.2 for the lightweight concrete, and the optimal panel cross section is a rounded rectangular one. All the pores in the CRFA-LWC have a diameter of smaller than 0.17 mm, and the diameter of 89% of them is less than 0.05 mm. In order to satisfy the drying shrinkage requirements stipulated by Chinese code JC/T 169-2016, the CRFA-LWC should be cured for at least 10 days. The economic analysis concludes that the material cost of CRFA-LWC is 40% lower than that of the autoclaved ceramsite concrete. In addition, utilizing CRFA in lightweight concrete can ease the shortage of natural aggregate. Highlights: CRFA is used for preparing the lightweight concrete partition panels. The optimal ratio of the CRFA to the cement is 2.2 for the lightweight concrete. CRFA lightweight concrete needs to be cured for at least 10 days. The cost of lightweight concrete is reduced by 40% by using CRFA. [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]

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]