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Aiming at the problems of low segmentation accuracy and inaccurate object boundary segmentation in current semantic segmentation algorithms, a semantic segmentation algorithm using multiple loss function constraints and multi-level cascading residual structure is proposed. The multi-layer cascaded residual unit was used to increase the range of the network layer receptive field. A parallel network was constructed to extract different depth feature information, then different depth feature information and encoder output features are fused to obtain multiple outputs feature which build multiple losses with the label, thereby constraining the model to optimize the network. The proposed network was evaluated on Cityscapes and CamVid datasets. The experimental results show that the mean Intersection over Union ratio (MIoU) of the proposed algorithm is 3.07% and 3.59% higher than the original Deeplabv3+ algorithm, respectively.

Although enzyme catalysis is typified by high specificity, enzymes can catalyze various substrates (substrate promiscuity) and/or different reaction types (catalytic promiscuity) using a single active site. This interesting phenomenon is widely distributed in enzyme catalysis, with both fundamental and applied importance. To date, the mechanistic understanding of enzyme promiscuity is very limited. Herein, we report the structural mechanism underlying the substrate and catalytic promiscuity of Vibrio dual lipase/transferase (VDLT). Crystal structures of the VDLT from Vibrio alginolyticus (ValDLT) and its fatty acid complexes were solved, revealing prominent structural flexibility. In particular, the “Ser−His−Asp” catalytic triad machinery of ValDLT contains an intrinsically flexible oxyanion hole. Analysis of ligand-bound structures and mutagenesis showed that the flexible oxyanion hole and other binding residues can undergo distinct conformational changes to facilitate substrate and catalytic promiscuity. Our study reveals a previously unknown flexible form of the famous catalytic triad machinery and proposes a “catalytic site tuning” mechanism to expand the mechanistic paradigm of enzyme promiscuity. Vibrio dual lipases/transferases are virulence-related enzymes, with both substrate and catalytic promiscuity. Wang et al reveal their prominent structural flexibility, proposing a catalytic site tuning mechanism underlying enzyme promiscuity.

As a major risk factor to human health, obesity presents a massive burden to people and society. Interestingly, the obese status of parents can cause progeny’s lipid accumulation through epigenetic inheritance in multiple species. To date, many questions remain as to how lipid accumulation leads to signals that are transmitted across generations. In this study, we establish a nematode model of C. elegans raised on a high-fat diet (HFD) that leads to measurable lipid accumulation, which can transmit the lipid accumulation signal to their multigenerational progeny. Using this model, we find that transcription factors DAF-16/FOXO and SBP-1/SREBP, nuclear receptors NHR-49 and NHR-80, and delta-9 desaturases ( fat-5 , fat-6 , and fat-7 ) are required for transgenerational lipid accumulation. Additionally, histone H3K4 trimethylation (H3K4me3) marks lipid metabolism genes and increases their transcription response to multigenerational obesogenic effects. In summary, this study establishes an interaction between a network of lipid metabolic genes and chromatin modifications, which work together to achieve transgenerational epigenetic inheritance of obesogenic effects. Transgenerational inheritance (TEI) mechanisms are to some extent conserved across species, but how TEI mediates lipid accumulation is unknown. Here the authors reveal that a network of lipid metabolic genes and chromatin modifications mediated by transcription factors and H3K4 trimethylation work together to achieve multigenerational obesogenic effects in C. elegans fed with a high-fat diet.

This paper presents a discussion on a new focus area in mathematics teacher education, namely the mathematics teacher in shadow education (SE mathematics teacher). It addresses three issues pertaining to SE mathematics teachers: special knowledge and expertise they need to possess, teacher qualification and selection and SE mathematics teacher professional development corresponding, respectively, to necessary specialized expertise. Some perspectives for future teacher education research are also addressed.

Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVITian) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R2 can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option.

DNA damage poses a serious threat to genome integrity and greatly affects growth and development. To maintain genome stability, all organisms have evolved elaborate DNA damage response mechanisms including activation of cell cycle checkpoints and DNA repair. Here, we show that the DNA repair protein SNI1, a subunit of the evolutionally conserved SMC5/6 complex, directly links these two processes in Arabidopsis. SNI1 binds to the activation domains of E2F transcription factors, the key regulators of cell cycle progression, and represses their transcriptional activities. In turn, E2Fs activate the expression of SNI1, suggesting that E2Fs and SNI1 form a negative feedback loop. Genetically, overexpression of SNI1 suppresses the phenotypes of E2F-overexpressing plants, and loss of E2F function fully suppresses the sni1 mutant, indicating that SNI1 is necessary and sufficient to inhibit E2Fs. Altogether, our study revealed that SNI1 is a negative regulator of E2Fs and plays dual roles in DNA damage responses by linking cell cycle checkpoint and DNA repair.

Tumble and stampede in a dense crowd may be caused by irrational behaviours of individuals and always troubles the safety management of crowd activities. Risk evaluation based on pedestrian dynamical models can be regarded as an effective method of preventing crowd disasters. Here, a method depending on a combination of collision impulses and pushing forces was used to model the physical contacts between individuals in a dense crowd, by which the acceleration error during physical contacts caused by a traditional dynamical equation can be avoided. The human domino effect in a dense crowd could be successfully reproduced, and the crushing and trampling risk of a microscopic individual in a crowd could be quantitatively evaluated separately. This method provides a more reliable and integral data foundation for evaluating individual risk that shows better portability and repeatability than macroscopic crowd risk evaluation methods and will also be conducive to preventing crowd disasters.

Gardeniae Fructus (GF) is one of the most widely used traditional Chinese medicines (TCMs). Its processed product, Gardeniae Fructus Praeparatus (GFP), is often used as medicine; hence, there is an urgent need to determine the stir-frying degree of GFP. In this paper, we propose a deep learning method based on transfer learning to determine the stir-frying degree of GFP. We collected images of GFP samples with different stir-frying degrees and constructed a dataset containing 9224 images. Five neural networks were trained, including VGG16, GoogLeNet, Resnet34, MobileNetV2, and MobileNetV3. While the model weights from ImageNet were used as initial parameters of the network, fine-tuning was used for four neural networks other than MobileNetV3. In the training of MobileNetV3, both feature transfer and fine-tuning were adopted. The accuracy of all five models reached more than 95.82% in the test dataset, among which MobileNetV3 performed the best with an accuracy of 98.77%. In addition, the results also showed that fine-tuning was better than feature transfer in the training of MobileNetV3. Therefore, we conclude that deep learning can effectively recognize the stir-frying degree of GFP.

Designable and ultrathin covalent organic framework nanosheets (CONs) with good photoelectric activity are promising candidates for the construction of photoelectrochemical (PEC) biosensors for the detection of low-abundance biological substrates. However, achieving highly sensitive PEC properties by using emerging covalent organic framework nanosheets (CONs) remains a great challenge due to the polymeric nature and poor photoelectric activity of CONs. Herein, we report for the first time the preparation of novel composites and their PEC sensing properties by electrostatic self-assembly of ultrathin CONs (called TTPA-CONs) with Ti3C2Tx. The prepared TTPA-CONs/Ti3C2Tx composites can be used as photocathodes for PEC detection of prostate-specific antigen (PSA) with high sensitivity, low detection limit, and good stability. This work not only expands the application of CONs but also opens new avenues for the development of efficient PEC sensing platforms.

When the surrounding rock is subjected to cyclic loading, its physical and mechanical properties show fatigue damage properties under the action of periodic engineering, disturbance stress. In this research, the fatigue life test of gypsum-like rocks was carried out by using a dynamic fatigue testing machine. During the cyclic loading process, the maximum fatigue loading times increased with the increase of frequency, but the fatigue life increased first and then decreased with the increase of frequency. The stress-circumferential strain hysteresis loop of the specimen tilts to the ε axis at low frequency and to the σ axis at high frequency, which indicates that the lower the frequency, the greater the damage of the specimen in one cycle. The strain of the sample increases with time, and the action mechanism of fatigue loading can be divided into three stages: initial stage, stable development stage, and failure stage. It was observed that under the conventional uniaxial compression test conditions, the failure mode of specimens is shear failure. The end effect of specimens after fatigue loading is more obvious, and the failure modes are mainly the fatigue damage of near vertical cracks. After fatigue loading, the specimen has obvious fatigue damage. With the expansion and penetration of the internal crack, the width, length and coverage of the crack gradually increase, leading to the continuous deterioration of the physical and mechanical properties.