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Programmed death-ligand 1 (PD-L1) is a promising target for cancer immunotherapy due to its ability to inhibit T cell activation; however, its expression on various noncancer cells may cause on-target off-tumor toxicity when designing PD-L1-targeting Chimeric Antigen Receptor (CAR) T cell therapies. Combining rational design and directed evolution of the human fibronectin-derived monobody scaffold, PDbody was engineered to bind to PD-L1 with a preference for a slightly lower pH, which is typical in the tumor microenvironment. PDbody was further utilized as a CAR to target the PD-L1-expressing triple negative MDA-MB-231 breast cancer cell line. To mitigate on-target off-tumor toxicity associated with targeting PD-L1, a Cluster of Differentiation 19 (CD19)-recognizing SynNotch IF THEN gate was integrated into the system. This CD19-SynNotch PDbody-CAR system was then expressed in primary human T cells to target CD19-expressing MDA-MB-231 cancer cells. These CD19-SynNotch PDbody-CAR T cells demonstrated both specificity and efficacy in vitro, accurately eradicating cancer targets in cytotoxicity assays. Moreover, in an in vivo bilateral murine tumor model, they exhibited the capability to effectively restrain tumor growth. Overall, CD19-SynNotch PDbody-CAR T cells represent a distinct development over previously published designs due to their increased efficacy, proliferative capability, and mitigation of off-tumor toxicity for solid tumor treatment.

The aim of this study was to compare the accuracies of cognitive fusion-guided targeted biopsy (TB), systematic biopsy (SB), and combined TB+SB for the detection of prostate cancer (PCa) and clinically significant PCa (csPCa) in males with lesions detected by magnetic resonance imaging (MRI). We conducted a retrospective analysis of individuals who underwent prostate biopsy at Peking University People’s Hospital (Beijing, China), with an emphasis on patients with both transrectal TB and SB. The main objective was to determine the precisions of SB, TB, and TB+SB for diagnosing PCa and csPCa. We also evaluated the detection rates of TB, SB, TB+ipsilateral-SB (ipsi-SB), TB+contralateral-SB (contra-SB), and TB+SB for PCa and csPCa in patients with unilateral MRI lesions. We compared the diagnostic yields of the various biopsy schemes using the McNemar’s test. A total of 180 patients were enrolled. The rates of PCa detection using TB, SB, and TB+SB were 52.8%, 62.2%, and 66.7%, respectively, and the corresponding rates for csPCa were 46.1%, 56.7%, and 58.3%, respectively. Among patients with unilateral MRI lesions, the PCa detection rates for TB, SB, TB+ipsi-SB, TB+contra-SB, and TB+SB were 53.3%, 64.8%, 65.6%, 61.5%, and 68.0%, respectively. TB+ipsi-SB detected 96.4% of PCa and 95.9% of csPCa cases. These findings suggest that the combination of TB+SB has better diagnostic accuracy compared with SB or TB alone. For patients with unilateral MRI lesions, the combination of TB+ipsi-SB may be suitable in clinical settings.

Abstract The Ideal Plant Architecture 1 (IPA1) transcription factor promotes rice yield and immunity through phosphorylation at its amino acid residue Ser163 as a switch. Although phosphorylated IPA1 mimic, IPA1(S163D), directly targets the promoter of immune response gene WRKY45, it cannot activate its expression. Here, we identified a co-activator of IPA1(S163D), a RING-finger E3 ligase IPA1 interactor 7 (IPI7), which fine-tunes the transcriptional activity of IPA1 to timely promote plant immunity and simultaneously maintain growth for yield. IPI7 interacts with IPA1 and promotes K29-polyubiquitination of IPA1 in vitro and in vivo. However, the stability of IPA1 protein is not affected by IPI7-mediated ubiquitination. The IPI7-promoted K29-polyubiquitination of IPA1 is induced by Magnaporthe oryzae infection and required for phosphorylated IPA1 to transactivate WRKY45 expression for immune response but not for plain IPA1 to transactivate DENSE AND ERECT PANICLES 1 (DEP1) expression for panicle development. IPI7 knockout impairs IPA1-mediated immunity but not yield. Our study reveals that plants utilize non-proteolytic K29-ubiquitination as a response to pathogen infection to fine-tune IPA1 transactivation activity for promoting immunity.

Urban vegetation in China has changed substantially in recent decades due to rapid urbanization and dramatic climate change. Nevertheless, the spatial differentiation of greenness among major cities of China and its evolution process and drivers are still poorly understood. This study examined the spatial patterns of vegetation greenness across 289 cities in China in 2000, 2005, 2010, 2015, and 2018 by using spatial autocorrelation analysis on the Normalized Difference Vegetation Index (NDVI); then, the influencing factors were analyzed by using the optimal parameters-based geographical detector (OPGD) model and 18 natural and anthropogenic indicators. The findings demonstrated a noticeable rise in the overall greenness of the selected cities during 2000–2018. The cities in northwest China and east China exhibited the rapidest and slowest greening, respectively, among the six sub-regions. A significant positive spatial correlation was detected between the greenness of the 289 cities in different periods, but the correlation strength weakened over time. The hot and very hot spots in southern and eastern China gradually shifted to the southwest. While the spatial pattern of urban greenness in China is primarily influenced by wind speed (WS) and precipitation (PRE), the interaction between PRE and gross domestic product (GDP) has the highest explanatory power. The explanatory power of most natural factors decreased and, conversely, the influence of anthropogenic factors generally increased. These findings emphasize the variations in the influence strength of multiple factors on urban greenness pattern, which should be taken into account to understand and adapt to the changing urban ecosystem.

Abstract Background The effects of blue light on photosynthetic organs have been studied. However, its effects on non-photosynthetic organs, in particular, on the early stages of rice caryopsis development, are unclear. Thus, we aimed to determine the metabolic characteristics of caryopsis development under blue light to improve the metabolic quality of crop kernels. Results We conducted a multi-omics analysis of each of the three periods from the beginning of cellular differentiation to the end of morphogenesis in post-pollination seeds of a japonica rice variety to explore the effect of blue light on metabolic levels during these metabolic changes and its genetic basis. It was found that blue light caused a gradual decrease in auxin content, a significant increase in the accumulation of JA and flavonoids, and a downregulation of the expression of many starch-related genes and proteins, leads to reduced starch synthesis and smaller starch granules. In addition, the gene co-expression network identified three transcription factors that may regulate starch and two that may regulate flavonoids. Conclusions It was found through multi-omics testing that hormones such as jasmonic acid and auxins, and metabolites including alkaloids, flavonoids, lipids, organic acids, phenolic acids, and terpenoids altered significantly. Transcriptome and proteome analyses showed that blue light affected the seed nutrient repository activity. Specifically, starch- and gluten-related genes and proteins were significantly downregulated. Co- and WGCNA analyses identified several transcription factors that were regulated under blue light and identified key regulators of starch. Our study provides an understanding of the effects of blue light on post-flowering development in Gramineae and provides a framework for blue light-induced synthesis of secondary metabolites. Graphical Abstract

Abstract Background As an emerging food crop with high nutritional value, quinoa has been favored by consumers in recent years; however, flooding, as an abiotic stress, seriously affects its growth and development. Currently, reports on the molecular mechanisms related to quinoa waterlogging stress responses are lacking; accordingly, the core genes related to these processes were explored via Weighted Gene Co-expression Network Analysis (WGCNA). Results Based on the transcriptome data, WGCNA was used to construct a co-expression network of weighted genes associated with flooding resistance-associated physiological traits and metabolites. Here, 16 closely related co-expression modules were obtained, and 10 core genes with the highest association with the target traits were mined from the two modules. Functional annotations revealed the biological processes and metabolic pathways involved in waterlogging stress, and four candidates related to flooding resistance, specifically AP2/ERF, MYB, bHLH, and WRKY-family TFs, were also identified. Conclusions These results provide clues to the identification of core genes for quinoa underlying quinoa waterlogging stress responses. This could ultimately provide a theoretical foundation for breeding new quinoa varieties with flooding tolerance.

Existing studies on road network recovery decision have ignored the impact of the differences in recovery speed and recovery degree of different road sections on the recovery performance of the road network. To address this problem, road network connectivity index based on section impedance tolerance was first constructed to evaluate road network performance under partial recovery of road section capacity. Then, a bi-level optimization model for emergency recovery decisions was constructed with the weighted road network performance resilience and recovery speed resilience as optimization objectives. When the optimal set and recovery time sequence of the road sections to be repaired are determined, the recovery degree and speed of the road sections to be restored are obtained through resource allocation and budget allocation at the road section level. Finally, based on the traditional parallel machine scheduling problem genetic algorithm, a new encoding and decoding method was constructed to solve the upper model. The lower level model was solved based on the Frank-Wolfe algorithm. Based on the data of a regional expressway network in Guizhou Province, the above models and algorithms were verified and analyzed. The results show that under certain resource and budget constraints, considering the difference in road section recovery degree can improve the road network performance resilience by 32.62%. Considering the difference in road section recovery speed can improve the road network performance resilience by 10.17%. The sensitivity analysis shows that taking into consideration the difference in road section recovery speed can improve the marginal benefits of increasing the number of recovery resources for improving road network performance resilience, recovery speed resilience, and weighted resilience by 12.69%, 5.47%, and 22.93% respectively. Considering the difference in road section recovery degree helps balance the improvement of road network performance resilience and the reduction of recovery speed resilience caused by the increase of recovery budget, so as to ensure the road network recovery performance. Therefore, it is important to consider the recovery differences in different road sections for road network recovery decision.

Population aging has become an inevitable trend and exerted profound influences on socio-economic development in China. In this study, we utilized data from national population census and statistical yearbooks in 2010 and 2020 to explore spatio-temporal patterns of aging population and its coupling correlations with socio-economic factors from both global and local perspectives. The results from Local Indicators of Spatial Association (LISA) uncover notable spatial disparities in aging population rates, with higher rates concentrated in the eastern regions and lower rates in the western areas of the Chinese mainland. The results from the global correlation analysis with the changes in aging population rates show significant positive correlations with government interventions and industrial structures, but negatively correlated with economic development, social consumption, and medical facilities. From a local perspective, a Geographically Weighted (GW) correlation analysis is employed to uncover local correlations between aging trends and socio-economic factors. The insights gained from this technique not only underscore the complexity and diversity of economic implications stemming from population aging, but also provide invaluable guidance for crafting region-specific economic policies tailored to various stages of population aging.

Phytopathogens represent an ongoing threat to crop production and a significant impediment to global food security. During the infection process, these pathogens spatiotemporally deploy a large array of effectors to sabotage host defense machinery and/or manipulate cellular pathways, thereby facilitating colonization and infection. However, besides their pivotal roles in pathogenesis, certain effectors, known as avirulence (AVR) effectors, can be directly or indirectly perceived by plant resistance (R) proteins, leading to race-specific resistance. An in-depth understanding of the intricate AVR-R interactions is instrumental for genetic improvement of crops and safeguarding them from diseases. Magnaporthe oryzae (M. oryzae), the causative agent of rice blast disease, is an exceptionally virulent and devastating fungal pathogen that induces blast disease on over 50 monocot plant species, including economically important crops. Rice-M. oryzae pathosystem serves as a prime model for functional dissection of AVR effectors and their interactions with R proteins and other target proteins in rice due to its scientific advantages and economic importance. Significant progress has been made in elucidating the potential roles of AVR effectors in the interaction between rice and M. oryzae over the past two decades. This review comprehensively discusses recent advancements in the field of M. oryzae AVR effectors, with a specific focus on their multifaceted roles through interactions with corresponding R/target proteins in rice during infection. Furthermore, we deliberated on the emerging strategies for engineering R proteins by leveraging the structural insights gained from M. oryzae AVR effectors.

Abstract Stripe rust of wheat, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most important diseases of wheat worldwide. Identification of new and elite Pst-resistance loci or genes has the potential to enhance overall resistance to this pathogen. Here, we conducted an integrated genome-wide association study (GWAS) and transcriptomic analysis to screen for loci associated with resistance to stripe rust in 335 accessions from Yunnan, including 311 landraces and 24 cultivars. Based on the environmental phenotype, we identified 113 protein kinases significantly associated with Pst resistance using mixed linear model (MLM) and generalized linear model (GLM) models. Transcriptomic analysis revealed that 52 of 113 protein kinases identified by GWAS were up and down regulated in response to Pst infection. Among these genes, a total of 15 receptor kinase genes were identified associated with Pst resistance. 11 candidate genes were newly discovered in Yunnan wheat germplasm. Our results revealed that resistance alleles to stripe rust were accumulated in Yunnan wheat germplasm, implying direct or indirect selection for improving stripe rust resistance in elite wheat breeding programs.

Adoptive cell therapy (ACT) has revolutionized the treatment of patients with cancer. The success of ACT depends largely on transferred T cell status, particularly their less-differentiated state with stem cell-like properties, which enhances ACT effectiveness. Stem cell-like memory T (TSCM) cells exhibit continuous self-renewal and multilineage differentiation similar to pluripotent stem cells. TSCM cells are promising candidates for cancer immunotherapies, whereas maintenance of a more stem-cell-like state before transfer is challenging. Here, we established a highly efficient protocol for generating CD8+ TSCM cells from peripheral blood mononuclear cells (PBMCs). The process involved activating PBMCs using anti-CD3 monoclonal antibody and RetroNectin, followed by a transient-resting culture period (24 h) and subsequent long-term expansion in vitro with interlukien-2. We report that this transient-resting culture after activation preserves CD8+ T cells in a stem memory phenotype (CD95+ CD45RA+ CCR7+) compared to the conventional culture method. Further, this approach reduces the expression of T cell immunoglobulin mucin-3, an exhaustion marker, and increases the expression of T cell factor-1, a master regulator of stemness even after long-term culture compared to the conventional culture method. In conclusion, our study presents a simplified and cost-effective method for generating and expanding CD8+ TSCM cells ex vivo. This approach streamlines the optimization of cancer immunotherapy using ACT.

Chinese baijiu is highly regarded for its unique flavor, and a variety of crops can be utilized as raw materials in its production. Waxy crops are essential ingredients in the brewing of high-quality baijiu; however, there is currently no comprehensive identification of volatile organic compounds (VOCs) and non-volatile compounds (N-VOCs) in waxy wheat baijiu (WWB). This study aims to investigate the dynamic changes of VOCs and N-VOCs in WWB during several important time periods from new to aged. A total of 25 amino acids underwent changes in the samples, with numerous physiologically active beneficial amino acids showing significant accumulation after aging. Additionally, 517 VOCs changed after aging, predominantly comprising esters and terpenoids, with 72 major VOCs being identified. A total of 718 metabolites were identified in the metabolome, primarily comprising alterations in lipids, amino acids, phenolic acids, organic acids, and alkaloids. These metabolites significantly influenced the levels of amino acids and VOCs. Our study is the first to provide a comprehensive examination of these aspects of WWB, highlighting its unique advantages over other crops. We believe that this research will establish a theoretical foundation for the application of waxy wheat in the baijiu industry, improve baijiu quality, and promote the development of functional baijius.

Ethnopharmacological relevance: Aster tataricus L.f., an extensively used herb in traditional Chinese medicine for more than 2000 years, is known as “Zi wan” or “Fan huncao”. Its dried root and rhizome hold great promise in the treatment of cough, asthma, tumor, inflammation, etc.Aim of the study: This literature review summarizes the morphology characteristics, ethnopharmacological use, phytochemical properties, pharmacological effects, and potential applications of Aster tataricus. Furthermore, this review will discuss the future research trends and development prospects of this plant. Materials and methods: Using “Aster tataricus L.f.”, “Traditional medicinal usage”, “Phytochemistry”, “Pharmacological effects” as the keywords and gathered relevant data on Aster tataricus L.f. using electronic databases (Elsevier, PubMed, ACS, CNKI, Google Scholar, Baidu Scholar, Web of Science), relevant books, and classic literature about Chinese herb. Result: A total of 186 compounds have been isolated and identified from Aster tataricus, including terpenes, organic acids, peptides, and flavonoids. And Aster tataricus has been widely used as a natural cough suppressant and has anti-oxidative, anti-inflammatory, anti-depressive, and anti-tumor effects. In addition, Aster tataricus has also been reported to have damaging effects on the liver as well as other toxicities were discussed in this review. Conclusions: Aster tataricus is an ancient herbal medicine with a broad spectrum of pharmaco logical activities that has been used for thousands of years in China, and has shown remarkable effectiveness in the treatment of various diseases, especially cough, asthma, inflammation. Although its rich chemical constituents have various pharmacological activities, the underlying mechanisms, as well as its toxicity and safety, remains unclear and warrant further investigation.

Cell reprogramming has wide applications in tissue regeneration, disease modelling and personalized medicine. In addition to biochemical cues, mechanical forces also contribute to the modulation of the epigenetic state and a variety of cell functions through distinct mechanisms that are not fully understood. Here we show that millisecond deformation of the cell nucleus caused by confinement into microfluidic channels results in wrinkling and transient disassembly of the nuclear lamina, local detachment of lamina-associated domains in chromatin and a decrease of histone methylation (histone H3 lysine 9 trimethylation) and DNA methylation. These global changes in chromatin at the early stage of cell reprogramming boost the conversion of fibroblasts into neurons and can be partially reproduced by inhibition of histone H3 lysine 9 and DNA methylation. This mechanopriming approach also triggers macrophage reprogramming into neurons and fibroblast conversion into induced pluripotent stem cells, being thus a promising mechanically based epigenetic state modulation method for cell engineering.

Background: The aim of this study was to explore the association between lactate/albumin ratio and the prognosis of sepsis patients. Methods: A computerized search was performed in Pubmed, EMbase, Ovid, Medline, and Google Scholar to collate relevant studies. The results were compared using standardized mean differences (SMD)/odds ratio (OR) and 95% confidence intervals (CI). Prospective and retrospective cohort studies were both included in this study. Results: A total of nine studies involving 3039 participants were included. Pooled analysis revealed that survivors had substantially lower lactate/albumin ratio than non-survivors (SMD=-2.02, 95% CI: -2.76 to -1.28, I2=97.4%). Further, our results also indicated that elevated lactate/albumin ratio is an independent risk factor for mortality (OR=2.16, 95% CI: 1.58 to 2.95, I2=76.2%) and multiple organ dysfunction syndrome (MODS) (OR=3.41, 95% CI: 1.78 to 6.50, I2=0.0%) in septic patients. Moreover, according to the area under curve (AUC) results, the lactate/albumin ratio also presented good discriminatory power to predict mortality (AUC=0.75, 95% CI: 0.68 to 0.84, I2=92.9%) and MODS (AUC=0.78, 95% CI: 0.68 to 0.91, I2= 65.1%) in septic patients. Begg's and Egger's tests suggested no publication bias in the included studies. Conclusion: Our results highlighted that the lactate/albumin ratio is an important prognostic factor for MODS and mortality in sepsis patients, having good capabilities in identifying MODS and mortality. Elevated lactate/albumin ratio is an independent risk factor for mortality and multiple organ dysfunction syndrome (MODS) in septic patients. The lactate/albumin ratio also presented good discriminatory power to predict mortality and MODS in septic patients.

In graph modeling, scarcity of labeled data is a challenging issue. To address this issue, state-of-the-art graph models learn the representation of graph data via contrastive learning. Those models usually use data augmentation techniques to generate positive pairs for contrastive learning, which aims to maximize the similarity of positive data pairs while minimizing the similarity of negative data pairs. However, samples with the same labels may be separately mapped in the feature space. To solve this problem, we introduce a novel model called Clustering Contrastive Graph Neural Network (CC-GNN), which develops a new kind of grouped contrastive learning that maximizes the similarity of positive data groups and minimizes the similarity of negative groups. That is, contrastive learning is defined on a group level rather than on an instant level. We assert that parameters learned by this kind of contrastive learning will lead to better performance of graph neural networks for downstream classification tasks. We combined the clustering contrastive learning technique with three baseline GNN models for graph classification. We found that the performance of these models was significantly improved, which strongly supports our assertion. We also testified the models for node classification on three popular citation networks. Finally, we conducted an ablation study to analyze how the clustering contrastive learning influence the performance of a graph model.

Abstract Background Although the concept of declined immune function associated with cancer has been accepted extensively, real-world clinical studies focusing on analysis of the peripheral blood immune changes underlying ageing, immunity and cancer are scarce. Methods In this case-control study, we retrospectively analysed 1375 cancer patients and enrolled 275 age and gender matched healthy individuals. Flow cytometry was conducted to assess the immune changes. Further analysis was examined by SPSS 17.0 and GraphPad Prism 9 software. Results Cancer patients showed obviously decreased CD3+ T, CD3+CD4+ Th, CD3+CD8+ CTL, CD19+ B, CD16+CD56+ NK cell counts and lower percentage of PD-1 (programmed cell death protein-1, PD-1) positive cells than healthy control (P

Online teacher professional development is widely regarded as a promising way to improve the quality of teachers and the effect of teaching. Micro-learning, which has the potential to fit the learner's personalized learning needs through a variety of small educational resources and flexible learning opportunities, could be an efficient way for the online teacher professional development. However, the scattered knowledge points and fragmented learning time in micro-learning may limit the learner's comprehension and the construction of knowledge. In this study, a knowledge map-based online micro-learning system was developed to solve this problem. To verify the effectiveness of the proposed system, a quasi-experiment was conducted during a three-week online course for teachers' professional development. A total of 42 in-service Mandarin teachers participated in the course, and were divided into two equal groups. An experimental group learned by using a knowledge map tool while a control group learned the same contents in the form of the conventional list type. The results indicate that the experimental group outperformed the control group in terms of learning engagement, and showed a clearer and more complete knowledge structure. Furthermore, micro-learning coupled with knowledge maps was found to improve the teachers' learning performance.

The antibiotics are generally regarded as the first choice approach to treat dairy mastitis, targeting the public health problems associated with the food safety and the emergence of antibioticresistant bacteria. The objective of the study was to evaluate the antibacterial efficacy of ursolic acid (UA) when used to treat Staphylococcus aureus and other isolates associated with bovine mastitis and to clarify the mechanistic basis for these effects. The bacteriostatic properties of UA extracted from Rosmarinus officinalis L. at four different purity levels were assessed by calculating minimum inhibitory concentration (MIC) values, while the synergistic effects of combining 98% UA with antibiotics were evaluated by measuring the fractional inhibitory concentration index (FICI). Changes in biofilm formation and the growth curves of the clinical isolates were assessed to clarify the bacteriostatic effect of UA. Furthermore, the cell wall integrity, protein synthesis, and reactive oxygen species (ROS) production were assessed to determine the antibacterial mechanism of UA treatment. Ultimately, UA was revealed to exhibit robust activity against Gram-positive bacteria including S. aureus (ATCC 25923), Streptococcus dysgalactiae (ATCC27957), Streptococcus agalactiae (ATCC13813), Enterococcus faecalis (ATCC29212), and Streptococcus mutans (ATCC25175). However, it did not affect Escherichia coli (ATCC 25922). The MIC values of UA preparations that were 98, 50, 30, and 10% pure against S. aureus were 39, 312, 625, and 625 μg/mL, respectively, whereas the corresponding MIC for E. coli was >5,000 μg/mL. The minimum bactericidal concentrations of 98% UA when used to treat three clinical S. aureus isolates (S4, S5, and S6) were 78, 78, and 156 μg/mL, respectively. Levels of biofilm formation for clinical S. aureus isolates decreased with increasing 98% UA concentrations. Above the MIC dose, UA treatment resulted in the dissolution of bacterial cell walls and membranes, with cells becoming irregularly shaped and exhibiting markedly impaired intracellular protein synthesis. S. aureus treated with 98% UA was able to rapidly promote intracellular ROS biogenesis. Together, these data highlight the promising utility of UA as a compound that can be used together with other antibiotics for the treatment of infections caused by S. aureus.

The use of the subharmonic signal from microbubbles exposed to ultrasound is a promising safe and cost-effective approach for the non-invasive measurement of blood pressure. Achieving a high sensitivity of the subharmonic amplitude to the ambient overpressure is crucial for clinical applications. However, currently used microbubbles have a wide size distribution and diverse shell properties. This causes uncertainty in the response of the subharmonic amplitude to changes in ambient pressure, which limits the sensitivity. The aim of this study was to use monodisperse microbubbles to improve the sensitivity of subharmonic-based pressure measurements. With the same shell materials and gas core, we used a flow-focusing microfluidic chip and a mechanical agitation method to fabricate monodisperse (∼2.45-µm mean radius and 4.7 % polydisperse index) and polydisperse microbubbles (∼1.51-µm mean radius and 48.4 % polydisperse index), respectively. We varied the ultrasound parameters (i.e., the frequency, peak negative pressure (PNP) and pulse length), and found that there was an optimal excitation frequency (2.8 MHz) for achieving maximal subharmonic emission for monodisperse microbubbles, but not for polydisperse microbubbles. Three distinct regimes (occurrence, growth, and saturation) were identified in the response of the subharmonic amplitude to increasing PNP for both monodisperse and polydisperse microbubbles. For the polydisperse microbubbles, the subharmonic amplitude decreased either monotonically or non-monotonically with ambient overpressure, depending on the PNP. By contrast, for the monodisperse microbubbles, there was only a monotonic decrease at all PNPs. The maximum sensitivity (1.18 dB/kPa, R2 = 0.97) of the subharmonic amplitude to ambient overpressure for the monodisperse microbubbles was ∼6.5 times higher than that for the polydisperse microbubbles (0.18 dB/kPa, R2 = 0.88). These results show that monodisperse microbubbles can achieve a more consistent response of the subharmonic signal to changes in ambient overpressure and greatly improve the measurement sensitivity.

In order to meet the increasing requirements of speed and distance, an advanced digital signal processing (DSP) algorithm is preferred without changing the system structure in intensity modulation and the direct detection (IM/DD) system. As the transmission distance increases, the power fading induced by dispersion must be mitigated. In addition, linear and nonlinear inter symbol interference (ISI) introduced by bandwidth limitation and device imperfections becomes an obstacle to achieving higher capacity. The Gerchberg–Saxton (GS) algorithm was recently used to compensate for dispersion. In this paper, GS-based pre- and post-compensation schemes in the IM/DD system with nonlinearity were investigated. We investigated and compared the performance of the GS-based pre- and post-compensation algorithm in a 28 GB aud four-level pulse amplitude modulation (PAM-4) transmission over 40 km standard single-mode fiber (SSMF). The bit error rate (BER) achieved a threshold of 3.8 × 10−3 using look-up-table (LUT), FFE, and the GS-based pre-compensation algorithm without iterations. Turning to the GS-based post-compensation scheme, 80 iterations are needed. However, the demand for FFE is reduced. The algorithm selection depends on the tolerance of the transmitter or receiver complexity in specific scenarios. The joint LUT and GS-based pre-compensation algorithm may be a preferable approach in scenarios where a low-complexity receiver is desired.

Settlement prediction based on monitoring data holds significant importance for engineering maintenance of seawalls. In practical engineering, the volume of the collected monitoring data is often limited due to the restrictions of devices and engineering budgets. Previous studies have applied the fractional-order grey model to time series prediction under the situation of limited data volume. However, the performance of the fractional-order grey model is easily affected by the inappropriate settings of fractional order. Also, the model cannot make dynamic predictions due to the characteristic of fixed step size. To solve the above problems, in this paper, the genetic algorithm with enhanced search capabilities was employed to solve the premature convergence problem. Additionally, to solve the problem of the fractional-order grey model associated with fixed step size, the real-time tracing algorithm was introduced to conduct equal-dimensionally recursive calculation. The proposed model was validated using monitoring data of four monitoring points at Haiyan seawall in Zhejiang province, China. The prediction performance of the proposed model was then compared with those of the fractional-order GM(1,1), integer-order GM(1,1), and fractal theory model. Results indicate that the proposed model significantly improves the prediction performance compared to other models.

Quinoa is a nutritious crop that is tolerant to extreme environmental conditions; however, low-temperature stress can affect quinoa growth, development, and quality. Considering the lack of molecular research on quinoa seedlings under low-temperature stress, we utilized a Weighted Gene Co-Expression Network Analysis to construct weighted gene co-expression networks associated with physiological indices and metabolites related to low-temperature stress resistance based on transcriptomic data. We screened 11 co-expression modules closely related to low-temperature stress resistance and selected 12 core genes from the two modules that showed the highest associations with the target traits. Following the functional annotation of these genes to determine the key biological processes and metabolic pathways involved in low-temperature stress, we identified four important transcription factors involved in resistance to low-temperature stress: gene-LOC110731664, gene-LOC110736639, gene-LOC110684437, and gene-LOC110720903. These results provide insights into the molecular genetic mechanism of quinoa under low-temperature stress and can be used to breed lines with tolerance to low-temperature stress.