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The exogenous application of melatonin by the root drenching method is an effective way to improve crop drought resistance. However, the optimal concentration of melatonin by root drenching and the physiological mechanisms underlying melatonin-induced drought tolerance in cotton (Gossypium hirsutum L.) roots remain elusive. This study determined the optimal concentration of melatonin by root drenching and explored the protective effects of melatonin on cotton roots. The results showed that 50 μmol L−1 melatonin was optimal and significantly mitigated the inhibitory effect of drought on cotton seedling growth. Exogenous melatonin promoted root development in drought-stressed cotton plants by remarkably increasing the root length, projected area, surface area, volume, diameter, and biomass. Melatonin also mitigated the drought-weakened photosynthetic capacity of cotton and regulated the endogenous hormone contents by regulating the relative expression levels of hormone-synthesis genes under drought stress. Melatonin-treated cotton seedlings maintained optimal enzymatic and non-enzymatic antioxidant capacities, and produced relatively lower levels of reactive oxygen species and malondialdehyde, thus reducing the drought stress damage to cotton roots (such as mitochondrial damage). Moreover, melatonin alleviated the yield and fiber length declines caused by drought stress. Taken together, these findings show that root drenching with exogenous melatonin increases the cotton yield by enhancing root development and reducing the root damage induced by drought stress. In summary, these results provide a foundation for the application of melatonin in the field by the root drenching method.

Abstract Background Root system architecture (RSA) exhibits significant genetic variability and is closely associated with drought tolerance. However, the evaluation of drought-tolerant cotton cultivars based on RSA in the field conditions is still underexplored. Results So, this study conducted a comprehensive analysis of drought tolerance based on physiological and morphological traits (i.e., aboveground and RSA, and yield) within a rain-out shelter, with two water treatments: well-watered (75 ± 5% soil relative water content) and drought stress (50 ± 5% soil relative water content). The results showed that principal component analysis identified six principal components, including highlighting the importance of root traits and canopy parameters in influencing drought tolerance. Moreover, the systematic cluster analysis was used to classify 80 cultivars into 5 categories, including drought-tolerant cultivars, relatively drought-tolerant cultivars, intermediate cultivars, relatively drought-sensitive cultivars, and drought-sensitive cultivars. Further validation of the drought tolerance index showed that the yield drought tolerance index and biomass drought tolerance index of the drought-tolerant cultivars were 8.97 and 5.05 times higher than those of the drought-sensitive cultivars, respectively. Conclusions The RSA of drought-tolerant cultivars was characterised by a significant increase in average length-all lateral roots, a significant decrease in average lateral root emergence angle and a moderate root/shoot ratio. In contrast, the drought-sensitive cultivars showed a significant decrease in average length-all lateral roots and a significant increase in both average lateral root emergence angle and root/shoot ratio. It is therefore more comprehensive and accurate to assess field crop drought tolerance by considering root performance.

IntroductionPlant responses to drought stress are influenced by various factors, including the lateral root angle (LRA), stomatal regulation, canopy temperature, transpiration rate and yield. However, there is a lack of research that quantifies their interactions, especially among different cotton varieties.MethodsThis experiment included two water treatments: well-watered (75 ± 5% soil relative water content) and drought stress (50 ± 5% soil relative water content) starting from the three-leaf growth stage. ResultsThe results revealed that different LRA varieties show genetic variation under drought stress. Among them, varieties with smaller root angles show greater drought tolerance. Varieties with smaller LRAs had significantly increased stomatal opening by 15% to 43%, transpiration rate by 61.24% and 62.00%, aboveground biomass by 54% to 64%, and increased seed cotton yield by 76% to 79%, and decreased canopy temperature by 9% to 12% under drought stress compared to the larger LRAs. Varieties with smaller LRAs had less yield loss under drought stress, which may be due to enhanced access to deeper soil water, compensating for heightened stomatal opening and elevated transpiration rates. The increase in transpiration rate promotes heat dissipation from leaves, thereby reducing leaf temperature and protecting leaves from damage. DiscussionDemonstrating the advantages conferred by the development of a smaller LRA under drought stress conditions holds value in enhancing cotton’s resilience and promoting its sustainable adaptation to abiotic stressors.

Potassium plays a significant role in the basic functions of plant growth and development. Potassium uptake is closely associated with morphological characteristics of the roots. However, the dynamic characteristics of phenotype and lifespan of cotton (Gossypium hirsutum L.) lateral roots and root hairs under low and high potassium stress remain unclear. In this study, potassium stress experiments (low and high potassium, medium potassium as control) were conducted using RhizoPot (an in situ root observation device) to determine the response characteristics of lateral roots and root hairs in cotton under potassium stress. The plant morphology, photosynthetic characteristics, root phenotypic changes, and lifespan of lateral roots and root hairs were measured. Potassium accumulation, aboveground phenotype, photosynthetic capacity, root length density, root dry weight, root diameter, lateral root lifespan, and root hair lifespan under low potassium stress were significantly decreased compared to medium potassium treatment. However, the root hair length of the former was significantly increased than that of the latter. Potassium accumulation and the lateral root lifespan were significantly increased under high potassium treatment, while root length density, root dry weight, root diameter, root hair length, and root hair lifespan were significantly decreased compared to the medium potassium treatment. Notably, there were no significant differences in aboveground morphology and photosynthetic characters. Principal component analysis revealed that lateral root lifespan, root hair lifespan of the first lateral root, and root hair length significantly correlated with potassium accumulation. The root had similar regularity responses to low and high potassium stress except for lifespan and root hair length. The findings of this study enhance the understanding of the phenotype and lifespan of cotton’s lateral roots and root hairs under low and high potassium stress.

BackgroundLiver cirrhosis is commonly accompanied by intestinal dysbiosis and metabolic defects. Many clinical trials have shown microbiota-targeting strategies represent promising interventions for managing cirrhosis and its complications. However, the influences of the intestinal metagenomes and metabolic profiles of patients have not been fully elucidated.MethodsWe administered lactulose, Clostridium butyricum, and Bifidobacterium longum infantis as a synbiotic and used shotgun metagenomics and non-targeted metabolomics to characterize the results.ResultsPatients treated with the synbiotic for 12 weeks had lower dysbiosis index (DI) scores than placebo-treated patients and patients at baseline (NIP group). We identified 48 bacterial taxa enriched in the various groups, 66 differentially expressed genes, 18 differentially expressed virulence factor genes, 10 differentially expressed carbohydrate-active enzyme genes, and 173 metabolites present at differing concentrations in the Synbiotic versus Placebo group, and the Synbiotic versus NIP group. And Bifidobacteria species, especially B. longum, showed positive associations with many differentially expressed genes in synbiotic-treated patients. Metabolites pathway enrichment analysis showed that synbiotic significantly affected purine metabolism and aminoacyl-tRNA biosynthesis. And the purine metabolism and aminoacyl-tRNA biosynthesis were no longer significant differences in the Synbiotic group versus the healthy controls group. In conclusion, although littles influence on clinical parameters in the early intervention, the synbiotic showed a potential benefit to patients by ameliorating intestinal dysbiosis and metabolic defects; and the DI of intestinal microbiota is useful for the evaluation of the effect of clinical microbiota-targeting strategies on cirrhotic patients.Clinical Trial Registrationhttps://www.clinicaltrials.gov, identifiers NCT05687409.

Abstract This study focusses on the target detection problem in the distributed multiple‐input multiple‐output (MIMO) radar. The observation environment is assumed to be partially homogeneous in one transmitter‐receiver path and simultaneously non‐homogeneous for different paths. By characterising the disturbance signals with a series of auto‐regressive (AR) processes, two parametric detectors based on the Wald test are developed. The first one utilises an iterative scheme to estimate the unknown parameters that were involved in the derivation, and the second one obtains these estimations sequentially. One of the most attractive features of the two distributed MIMO radar detectors is that they can be used in the presence/absence of the training data. More specifically, they can take advantage of the training data to boost the detection performances effectively even if the training data are very limited. The simulation results of several scenarios demonstrate the remarkable detection performances of authors’ proposed methods compared with that of the competitors. Furthermore, the amplitude estimation performances of the two detectors are evaluated via the corresponding Cramér–Rao bound (CRB).

Abstract Aim To assess the effects of health locus of control‐based education programme (HLCEP) on self‐management, health locus of control and glycated haemoglobin (HbA1c) among type 2 diabetes mellitus (T2DM). Design Quasi‐experimental study. Methods The study recruited 120 T2DM participants from May–September 2020. The control group received one‐week in‐hospital care and 12‐week follow‐up. The intervention group received additional HLCEP. The self‐management and the health locus of control were measured by using the Summary of Diabetes Self‐care Activities and the Multidimensional Health Locus of Control at baseline and the 4th and 12th week after discharge. The HbA1c was collected at baseline and the 12th week after discharge. The generalized estimating equation analysis was performed to assess the intervention effects. Results The intervention group has statistically significantly higher scores on the overall level of self‐management, dietary management, foot care, medication management and internal health locus of control, while a lower HbA1c level than the control group.

Abstract MicroRNAs (miRNAs) are one type of noncoding RNAs that interfere with mRNA translation to downregulate gene expression, which results in posttranscriptional gene silencing. Over the past two decades, miRNAs have been widely reported to impact the progression of malignant tumours by interfering with cancer initiation and progression; therefore, miRNAs represent potential new diagnostic and therapeutic tools. miR-650 is a newly identified miR, and increasing studies have demonstrated that miR-650 plays critical roles in cancer progression, such as mediating the Wnt signalling pathway/AXIN1 (axis inhibition protein 1) axis in hepatocellular carcinoma. Nevertheless, associations between the expression patterns and molecular mechanisms of miR-650 in cancer have not been comprehensively described. In this article, we review the existing evidence regarding the mechanisms by which miR-650 expression is altered and their relation to cancer. Moreover, the promising clinical application of miR-650 for diagnosis and treatment is highlighted.

The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems such as low analysis efficiency, high acquisition cost, and difficult deployment of image acquisition devices outdoors. Therefore, this study designed a precise extraction method of in situ roots based on semantic segmentation model and edge device deployment. It initially proposes two data expansion methods, pixel by pixel and equal proportion, expand 100 original images to 1600 and 53193 respectively. It then presents an improved DeeplabV3+ root segmentation model based on CBAM and ASPP in series is designed, and the segmentation accuracy is 93.01%. The root phenotype parameters were verified through the Rhizo Vision Explorers platform, and the root length error was 0.669%, and the root diameter error was 1.003%. It afterwards designs a time-saving Fast prediction strategy. Compared with the Normal prediction strategy, the time consumption is reduced by 22.71% on GPU and 36.85% in raspberry pie. It ultimately deploys the model to Raspberry Pie, realizing the low-cost and portable root image acquisition and segmentation, which is conducive to outdoor deployment. In addition, the cost accounting is only $247. It takes 8 hours to perform image acquisition and segmentation tasks, and the power consumption is as low as 0.051kWh. In conclusion, the method proposed in this study has good performance in model accuracy, economic cost, energy consumption, etc. This paper realizes low-cost and high-precision segmentation of in-situ root based on edge equipment, which provides new insights for high-throughput field research and application of in-situ root.

ObjectiveElderly people are less likely than younger patients to undergo curative surgery for early-stage lung cancer because of the greater risk of surgery and postoperative complications. We investigated the relationship between treatment modality and the risk of all-cause and lung cancer-specific mortality to compare the efficacy of surgical treatment with radiotherapy in patients with stage I and II non-small cell lung cancer (NSCLC) who were ≥80 years old.MethodsWe extracted data from the most recent Surveillance, Epidemiology, and End Results 9 registry study database (2010–2017). We mainly selected patients with stage I and II NSCLC who were ≥80 years old, and after screening, 7,045 cases were selected for our study. We used univariate analysis, stratified analysis, and multiple regression equation analysis to examine all-cause mortality and lung cancer-specific mortality in different treatment modalities. The overall and stratified populations' survival curves were plotted using the Kaplan–Meier method. The competing risk regression method of Fine and Gray was used to estimate mortality specific to lung cancer.ResultsIn the fully adjusted model, all-cause mortality was 1.97 times higher in the radiotherapy-only group (hazard ration (HR) = 1.97, 95% confidence interval (CI) = 1.81–2.14, p

Quantitative analysis of root development is becoming a preferred option in assessing the function of hidden underground roots, especially in studying resistance to abiotic stresses. It can be enhanced by acquiring non-destructive phenotypic information on roots, such as rhizotrons. However, it is challenging to develop high-throughput phenotyping equipment for acquiring and analyzing in situ root images of root development. In this study, the RhizoPot platform, a high-throughput in situ root phenotyping platform integrating plant culture, automatic in situ root image acquisition, and image segmentation, was proposed for quantitative analysis of root development. Plants (1-5) were grown in each RhizoPot, and the growth time depended on the type of plant and the experimental requirements. For example, the growth time of cotton was about 110 days. The imaging control software (RhizoAuto) could automatically and non-destructively image the roots of RhizoPot-cultured plants based on the set time and resolution (50-4800 dpi) and obtain high-resolution (>1200 dpi) images in batches. The improved DeepLabv3+ tool was used for batch processing of root images. The roots were automatically segmented and extracted from the background for analysis of information on radical features using conventional root software (WinRhizo and RhizoVision Explorer). Root morphology, root growth rate, and lifespan analysis were conducted using in situ root images and segmented images. The platform illustrated the dynamic response characteristics of root phenotypes in cotton. In conclusion, the RhizoPot platform has the characteristics of low cost, high-efficiency, and high-throughput, and thus it can effectively monitor the development of plant roots and realize the quantitative analysis of root phenotypes in situ.

Roots assist plants in absorbing water and nutrients from soil. Thus, they are vital to the survival of nearly all land plants, considering that plants cannot move to seek optimal environmental conditions. Crop species with optimal root system are essential for future food security and key to improving agricultural productivity and sustainability. Root systems can be improved and bred to acquire soil resources efficiently and effectively. This can also reduce adverse environmental impacts by decreasing the need for fertilization and fresh water. Therefore, there is a need to improve and breed crop cultivars with favorable root system. However, the lack of high-throughput root phenotyping tools for characterizing root traits in situ is a barrier to breeding for root system improvement. In recent years, many breakthroughs in the measurement and analysis of roots in a root system have been made. Here, we describe the major advances in root image acquisition and analysis technologies and summarize the advantages and disadvantages of each method. Furthermore, we look forward to the future development direction and trend of root phenotyping methods. This review aims to aid researchers in choosing a more appropriate method for improving the root system.

Melatonin (N-acetyl-5-methoxytryptamine, MT) can mitigate abotic stress, including drought stress on a number of crops. However, it is unclear whether and how seed priming with melatonin alleviates the effects of drought stress on seed germination and seedling growth of triticale (Triticale hexaploide L.). In this study, we investigated the effects of seed priming with MT on seed germination, protective enzyme activity, superoxide anion, and hydrogen peroxide in triticale under PEG-6000 induced drought stress. Seed priming with 20 μM MT alleviated the adverse effects of PEG-6000 induced drought stress on seed germination and seedling growth. Triticale seeds primed with 20 μM MT exhibited improved germination potential, germination rate, germ and radicle length. Specifically, MT priming increased the germination rate by 57.67% compared with unprimed seeds. Seed priming with melatonin also alleviated the adverse effects of PEG-6000 induced drought stress on triticale seedlings. MT pretreatment with 20 μM significantly increased the net photosynthetic rate, transpiration rate, stomatal conductance, plant height, leaf area, and relative chlorophyll concentration, enhanced the activities of superoxide dismutase and peroxidase, and decreased reactive oxygen species (ROS) and malonaldehyde content in the seeds (germ and radicle) and seedlings (leaf and root). Collectively, these results suggest that seed priming with melatonin promotes ROS scavenging capacity and enhances energy supply and antioxidant enzyme activities to alleviate the adverse effects of drought stress in triticale.

Late-sowing and high-density cultivation are typical cotton planting models that are widely applied in the Yellow River Valley of China. The model can easily lead to late maturity and reduced yields. Plant topping and ethephon spraying have been shown to improve cotton earliness and yields. However, the optimal topping date and ethephon concentration are yet to be established. A two-year field experiment (2020–2021) was thus conducted to assess the effects of spraying high-concentration ethephon and early topping on the growth, yield, and earliness of cotton under late-sowing and high-density cultivation models. The experiment employed a split-plot design, in which the main plots were sprayed with varying ethephon concentrations (2000 mL/ha, E2000; 4000 mL/ha, E4000; 6000 mL/ha, E6000), while subplots were plant topping dates (T13, 13 July, early-topped; T20, 20 July, suitable-topped; T27, 27 July, late-topped). Higher ethephon concentrations (E4000) increased the earliness of early-topped (T13) plants. Boll-setting and boll-opening rates increased by 12.00% and 16.83% in T13 plants, respectively, relative to T27 plants. However, the shedding rate for T13 plants decreased by 12.00%. Earliness and harvest index (HI) for the T13 plants were higher than those of the T27 plants by 8.01% and 18.91%, respectively. Even though the T13 plant yield decreased slightly, their boll weight increased by 3.99% and 12.27%, respectively, relative to those of the T20 and T27 plants. Earliness increased with increasing ethephon concentration. In conclusion, early topping and higher ethephon concentrations can effectively improve cotton yield and earliness.

Leaf senescence reduces the photosynthetic capacity of leaves, thus significantly affecting the growth, development, and yield formation of cotton. Melatonin (MT) is a multipotent substance proven to delay leaf senescence. However, its potential mechanism in delaying leaf senescence induced by abiotic stress remains unclear. This study aimed to explore the effect of MT on delaying drought-induced leaf senescence in cotton seedlings and to clarify its morphological and physiological mechanisms. Drought stress upregulated the leaf senescence marker genes, destroyed the photosystem, and led to excessive accumulation of reactive oxygen species (ROS, e.g., H2O2 and O2−), thus accelerating leaf senescence. However, leaf senescence was significantly delayed when 100 μM MT was sprayed on the leaves of the cotton seedlings. The delay was embodied by the increased chlorophyll content, photosynthetic capacity, and antioxidant enzyme activities, as well as decreased H2O2, O2−, and abscisic acid (ABA) contents by 34.44%, 37.68%, and 29.32%, respectively. MT significantly down-regulated chlorophyll degradation-related genes and senescence marker genes (GhNAC12 and GhWRKY27/71). In addition, MT reduced the chloroplast damage caused by drought-induced leaf senescence and maintained the integrity of the chloroplast lamellae structure under drought stress. The findings of this study collectively suggest that MT can effectively enhance the antioxidant enzyme system, improve photosynthetic efficiency, reduce chlorophyll degradation and ROS accumulation, and inhibit ABA synthesis, thereby delaying drought-induced leaf senescence in cotton.

Electro-optic modulation with a constant modulation depth plays important roles in quantum metrology, microwave photonics, and optical precision measurement and sensing, etc. To compensate the continuous drift of the half-wave voltage and maintain the modulation depth, we incidentally generated a reference laser to make intermedia optical beatings with the carrier and +1st sideband, and tightly locked the carrier to sideband ratio by extracting the relative microwave power of those intermedia beat signals using a home-made electronic control module. Experiments of the short-term test for locking bandwidth, the anti-jamming capability, and the long-term stability were demonstrated. As is verified that, the locking bandwidth was estimated up to tens of kHz, and the anti-jamming capability was confirmed despite of a strong hair-dryer agitation. The long-term stability for the modulation depth stabilization showed that the peak-to-peak relative stability for 150-minute was 5.67 × 10−6, and the Allan deviation reached a minimum of 2.19 × 10−7 at an averaging time of 400 s, which is improved by more than four orders of magnitude compared with the open-loop regime. We believe the reported method as well as the designed module is versatile and potential to precision measurement and sensing applications badly requiring a stable modulation depth, such as atom interferometry, optical interferometry and lidar detection.

Abstract The molluscan shell is a biomineral that comprises calcium carbonate and organic matrices controlling the crystal growth of calcium carbonate. The main components of organic matrices are insoluble chitin and proteins. Various kinds of proteins have been identified by solubilizing them with reagents, such as acid or detergent. However, insoluble proteins remained due to the formation of a solid complex with chitin. Herein, we identified these proteins from the nacreous layer, prismatic layer, and hinge ligament of Pinctada fucata using mercaptoethanol and trypsin. Most identified proteins contained a methionine-rich region in common. We focused on one of these proteins, NU-5, to examine the function in shell formation. Gene expression analysis of NU-5 showed that NU-5 was highly expressed in the mantle, and a knockdown of NU-5 prevented the formation of aragonite tablets in the nacre, which suggested that NU-5 was required for nacre formation. Dynamic light scattering and circular dichroism revealed that recombinant NU-5 had aggregation activity and changed its secondary structure in the presence of calcium ions. These findings suggest that insoluble proteins containing methionine-rich regions may be important for scaffold formation, which is an initial stage of biomineral formation.

As novel members of the noncoding RNA family, long noncoding RNAs (lncRNAs) have been widely reported to function as powerful regulators in gene expression processes, including chromosome remodeling, transcription interference and posttranscriptional modification. With the rapid development of metagenomic sequencing, numerous studies have indicated that the dysregulation of lncRNAs is closely associated with diverse human diseases, especially cancers. Prostate Gene Expression Marker 1 (PCGEM1), a recently identified lncRNA, has been reported to play a crucial role in the initiation and progression of multiple tumors by interacting with pivotal regulators of tumor-related signaling pathways. In this review, we will retrospectively review the recent studies of the expression of lncRNA PCGEM1 in human cancers and comprehensively describe the underlying regulatory mechanism by which PCGEM1 functions in tumors. More importantly, based on the relationship between PCGEM1 and cancers, the potential application of PCGEM1 in clinical diagnosis, prognosis and therapeutic treatment will also be highlighted.

Nitrogen (N) deficiency is one of the pivotal environmental factors that induce leaf senescence. However, little is known regarding the impact of low N on root senescence in cotton. Thus, the objective of this study was to investigate the effect of low nitrogen on root senescence. In this study, the molecular mechanism of cotton root senescence in response to nitrogen deficiency was investigated by combing physiological and transcriptomic analysis when no nitrogen and normal nitrogen (138mg N·kg−1 soil). The results showed that: (1) nitrogen starvation induced the premature senescence of leaf, while delaying root senescence. (2) The increase in catalase (CAT) activity at 60, 80, and 100days after emergence (DAE), combined with decrease of malonaldehyde content at 60, 80, and 100 DAE, and the content of abscisic acid (ABA), all of these contributed to the delay of root senescence by low nitrogen treatment. (3) To study the molecular mechanisms underlying root senescence, the gene expression profiling between low nitrogen and normal nitrogen treatments were compared pairwise at 20, 40, 60, 80, and 100 DAE. A total of 14,607 genes were identified to be differentially expressed at these five points. (5) Most genes involved in glutathione (GSH) and ascorbate peroxidase (APX) synthesis were upregulated, while ABA, apoptosis, caspase, and cell cycle-related differentially expressed genes (DEGs) were downregulated. Coupled with the physiology data, these results provide new insights into the effect of nitrogen starvation on root senescence.

Phosphorus (P) deficiency is a common challenge in crop production because of its poor mobility through the soil. The root system plays a significant role in P absorption from the soil and is the initial indicator of low P levels. However, the phenotypic dynamics and longevity of cotton roots under P stress remain unknown. In this study, RhizoPot, an improvised in situ root observation device, was used to monitor the dynamics of root phenotypes of cotton seedlings under P-deficient (PD) and P-replete (PR) conditions. Low P stress reduced P absorption and accumulation in the roots, leading to low dry weight accumulation. Cotton seedlings responded to low P stress by increasing the number of lateral roots, specific root length, branch density, root length density, and length of root hairs. Additionally, the life span of root hairs was prolonged. Low P stress also reduced the average diameter of roots, promoted root extension, expanded the root coverage area, and increased the range of P acquisition. Principal component analysis revealed that the net root growth rate, root length density, root dry weight, P absorption efficiency, average root hair length, and taproot daily growth significantly influenced the cotton root architecture. Collectively, these results show that low P stress reduces the net growth rate of cotton seedling roots and restricts plant growth. Plants respond to P deficiency by extending the life span of root hairs and increasing specific root length and lateral root branch density. This change in root system architecture improves the adaptability of plants to low P conditions. The findings of this study may guide the selection of cotton varieties with efficient P utilization.

The Rhizotrons method is an important means of detecting dynamic growth and development phenotypes of plant roots. However, the segmentation of root images is a critical obstacle restricting further development of this method. At present, researchers mostly use direct manual drawings or software-assisted manual drawings to segment root systems for analysis. Root systems can be segmented from root images obtained by the Rhizotrons method, and then, root system lengths and diameters can be obtained with software. This type of image segmentation method is extremely inefficient and very prone to human error. Here, we investigate the effectiveness of an automated image segmentation method based on the DeepLabv3+ convolutional neural network (CNN) architecture to streamline such measurements. We have improved the upsampling portion of the DeepLabv3+ network and validated it using in situ images of cotton roots obtained with a micro root window root system monitoring system. Segmentation performance of the proposed method utilizing WinRHIZO Tron MF analysis was assessed using these images. After 80 epochs of training, the final verification set F1-score, recall, and precision were 0.9773, 0.9847, and 0.9702, respectively. The Spearman rank correlation between the manually obtained Rhizotrons manual segmentation root length and automated root length was 0.9667 (p < 10–8), with r2 = 0.9449. Based on the comparison of our segmentation results with those of traditional manual and U-net segmentation methods, this novel method can more accurately segment root systems in complex soil environments. Thus, using the improved DeepLabv3+ to segment root systems based on micro-root images is an effective method for accurately and quickly segmenting root systems in a homogeneous soil environment and has clear advantages over traditional manual segmentation.

Lithium-ion batteries have been widely used in electric vehicles but may cause severe internal short circuit during extreme intrusion-type accidents. A well-defined homogenized model of battery or jellyroll is necessary for safety assessment and design on large-scale structure level. In our previous study, the jellyroll of prismatic lithium-ion battery cell shows anisotropic mechanical behavior and failure tolerance. For homogenized characterization of jellyroll, in the present paper, the user subroutine of a constitutive model taking anisotropy into account is implanted into Abaqus finite element analysis software, which is capable of capturing the force versus displacement responses along different loading directions before jellyroll failure. To extend the capability of the homogenized model, five single-parameter failure criteria and two combined failure criteria are examined in predicting the failure onsets in jellyroll along different directions. The result proves the combined failure criteria is competent to correctly predict the multidirectional failure onsets compared with the single-parameter ones.

With the development and universal application of satellite technology, an important way to expand the function of satellites is setting up inter-satellite networks to make them work together. Traditional satellite networking methods generally adopt a fixed time slot allocation method, which is not suitable for small satellite groups with low latency and high throughput requirements. In order to solve this problem, it has been proposed to apply the traditional Wifi protocol in satellite networking. As there are differences between satellite networks and terrestrial networks, it's necessary to improve the traditional 802.11 protocol. The Media Access Control (MAC) protocol in 802.11 is improved in this paper, which mainly includes the adaptive algorithm of maximum contention window size and the growth algorithm of Contention Window (CW) size. The maximum contention window is adjusted according to the conflict state of the current network, which makes the network accommodate more satellite nodes. The CW growth algorithm improves the traditional Binary Exponential Back-off (BEB) algorithm, where the CW is designed according to the priority of the data frame or the network load. In this way, high-priority satellite accusation information will have higher reliability or tolerate greater network load.

Hyperspectral anomaly detection plays an important role in the field of remote sensing. It provides a way to distinguish interested targets from the background without any prior knowledge. The majority of pixels in the hyperspectral dataset belong to the background, and they can be well represented by several endmembers, so the background has a low-rank property. Anomalous targets usually account for a tiny part of the dataset, and they are considered to have a sparse property. Recently, the low-rank and sparse matrix decomposition (LRaSMD) technique has drawn great attention as a method for solving anomaly detection problems. In this letter, a new anomaly detection method based on LRaSMD and cluster weighting is proposed. We concentrate on the sparse part, which contains most of anomaly information, and calculate the initial anomaly matrix based on this part. To suppress background regions and discriminate anomalies from the background more distinctly, a weighting strategy in terms of the clustering result is used, and then the anomaly matrix is updated. The judgement of anomalies is made according to the responses on the matrix. Our proposed method considers the characteristics of anomalies from the spectral dimension and the spatial distribution simultaneously. Experiments on three hyperspectral datasets demonstrate the outstanding performance of the proposed method.