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Abstract Multi-sequence magnetic resonance imaging is crucial in accurately identifying knee abnormalities but requires substantial expertise from radiologists to interpret. Here, we introduce a deep learning model incorporating co-plane attention across image sequences to classify knee abnormalities. To assess the effectiveness of our model, we collected the largest multi-sequence knee magnetic resonance imaging dataset involving the most comprehensive range of abnormalities, comprising 1748 subjects and 12 types of abnormalities. Our model achieved an overall area under the receiver operating characteristic curve score of 0.812. It achieved an average accuracy of 0.78, outperforming junior radiologists (accuracy 0.65) and remains competitive with senior radiologists (accuracy 0.80). Notably, with the assistance of model output, the diagnosis accuracy of all radiologists was improved significantly (p

Abstract Asparagus is a nutritionally dense stem vegetable whose growth and development are correlated with its quality and yield. To investigate the dynamic changes and underlying mechanisms during the elongation and growth process of asparagus stems, we documented the growth pattern of asparagus and selected stem segments from four consecutive elongation stages using physiological and transcriptome analyses. Notably, the growth rate of asparagus accelerated at a length of 25 cm. A significant decrease in the concentration of sucrose, fructose, glucose, and additional sugars was observed in the elongation region of tender stems. Conversely, the levels of auxin and gibberellins(GAs) were elevated along with increased activity of enzymes involved in sucrose degradation. A significant positive correlation existed between auxin, GAs, and enzymes involved in sucrose degradation. The ABA content gradually increased with stem elongation. The tissue section showed that cell elongation is an inherent manifestation of stem elongation. The differential genes screened by transcriptome analysis were enriched in pathways such as starch and sucrose metabolism, phytohormone synthesis metabolism, and signal transduction. The expression levels of genes such as ARF, GA20ox, NCED, PIF4, and otherswere upregulated during stem elongation, while DAO, GA2ox, and other genes were downregulated. The gene expression level was consistent with changes in hormone content and influenced the cell length elongation. Additionally, the expression results of RT-qPCR were consistent with RNA-seq. The observed variations in gene expression levels, endogenous hormones and sugar changes during the elongation and growth of asparagus tender stems offer valuable insights for future investigations into the molecular mechanisms of asparagus stem growth and development and provide a theoretical foundation for cultivation and production practices.

Abstract Sinonasal malignant tumors are a group of uncommon malignancies that account for less than 1% of all tumors. These tumors often involve the maxillary sinus and nasal cavity, with less cumulative incidence in the ethmoidal sinus, sphenoidal sinus, and frontal sinus. The lack of consensus on the management of sinonasal malignancies is due to their rarity, diagnostic challenges, and the heterogeneity of treatments. In this paper, we present a case of endoscopic-assisted medial canthus incision combined with radiotherapy in the treatment of sinonasal malignant tumors, with the aim of providing valuable insights to clinicians on the management of these tumors.

The growing interest in the flexibility and operational capabilities of soft manipulators in confined spaces emphasizes the need for precise modeling and accurate motion control. Conventional control methods encounter difficulties in modeling and involve intricate computations. This work introduces a novel deep reinforcement learning (DRL) control algorithm based on neural network modeling. Using the Whale Optimization Algorithm, an approximate dynamic model for the soft manipulator is established. The twin delayed deterministic policy gradient is employed for DRL control. Domain randomization is applied during pretraining in a simulated environment. The algorithm addresses issues related to dependency on measurement data quality and redundant mappings, outperforming other methods by 8–15 mm in control accuracy. The trained DRL controller achieves precise trajectory tracking within the soft manipulator's task space, enabling successful grasping tasks in various complex environments, including pipelines and other narrow spaces. Experimental results confirm the autonomy of our controller in performing these tasks without human intervention.

Abstract Several hematologic traits have been suggested to potentially contribute to the formation and rupture of intracranial aneurysms (IA). The purpose of this study is to explore the causal association between hematologic traits and the risk of IA. To explore the causal association between hematologic traits and the risk of IA, we employed two-sample Mendelian randomization (MR) analysis. Two independent summary-level GWAS data were used for preliminary and replicated MR analyses. The inverse variance weighted (IVW) method was employed as the primary method in the MR analyses. The stabilities of the results were further confirmed by a meta-analysis. In the preliminary MR analysis, hematocrit, hemoglobin concentration (p = 0.0047), basophil count (p = 0.0219) had a suggestive inverse causal relationship with the risk of aneurysm-associated subarachnoid hemorrhage (aSAH). The monocyte percentage of white cells (p = 0.00956) was suggestively positively causally correlated with the risk of aSAH. In the replicated MR analysis, only the monocyte percentage of white cells (p = 0.00297) remained consistent with the MR results in the preliminary analysis. The hematocrit, hemoglobin concentration, and basophil count no longer showed significant causal relationship (p > 0.05). Meta-analysis results further confirmed that only the MR result of monocyte percentage of white cells reached significance in the random effect model and fixed effect model. None of the 25 hematologic traits was causally associated with the risk of unruptured intracranial aneurysms (uIA). This study revealed a suggestive positive association between the monocyte percentage of white cells and the risk of aSAH. This finding contributes to a better understanding that monocytes/macrophages could participate in the risk of aSAH.

Abstract Background Triatomines (kissing bugs) are natural vectors of trypanosomes, which are single-celled parasitic protozoans, such as Trypanosoma cruzi, T. conorhini and T. rangeli. The understanding of the transmission cycle of T. conorhini and Triatoma rubrofasciata in China is not fully known. Methods The parasites in the faeces and intestinal contents of the Tr. rubrofasciata were collected, and morphology indices were measured under a microscope to determine the species. DNA was extracted from the samples, and fragments of 18S rRNA, heat shock protein 70 (HSP70) and glycosomal glyceraldehyde-3-phosphate dehydrogenase (gGAPDH) were amplified and sequenced. The obtained sequences were then identified using the BLAST search engine, followed by several phylogenetic analyses. Finally, laboratory infections were conducted to test whether Tr. rubrofasciata transmit the parasite to rats (or mice) through bites. Moreover, 135 Tr. rubrofasciata samples were collected from the Guangxi region and were used in assays to investigate the prevalence of trypanosome infection. Results Trypanosoma sp. were found in the faeces and intestinal contents of Tr. rubrofasciata, which were collected in the Guangxi region of southern China and mostly exhibited characteristics typical of epimastigotes, such as the presence of a nucleus, a free flagellum and a kinetoplast. The body length ranged from 6.3 to 33.9 µm, the flagellum length ranged from 8.7 to 29.8 µm, the nucleus index was 0.6 and the kinetoplast length was −4.6. BLAST analysis revealed that the 18S rRNA, HSP70 and gGAPDH sequences of Trypanosoma sp. exhibited the highest degree of similarity with those of T. conorhini (99.7%, 99.0% and 99.0%, respectively) and formed a well-supported clade close to T. conorhini and T. vespertilionis but were distinct from those of T. rangeli and T. cruzi. Laboratory experiments revealed that both rats and mice developed low parasitaemia after inoculation with Trypanosoma sp. and laboratory-fed Tr. rubrofasciata became infected after feeding on trypanosome-positive rats and mice. However, the infected Tr. rubrofasciata did not transmit Trypanosoma sp. to their offspring. Moreover, our investigation revealed a high prevalence of Trypanosoma sp. infection in Tr. rubrofasciata, with up to 36.3% of specimens tested in the field being infected. Conclusions Our study is the first to provide a solid record of T. conorhini from Tr. rubrofasciata in China with morphological and molecular evidence. This Chinese T. conorhini is unlikely to have spread through transovarial transmission in Tr. rubrofasciata, but instead, it is more likely that the parasite is transmitted between Tr. rubrofasciata and mice (or rats). However, there was a high prevalence of T. conorhini in the Tr. rubrofasciata from our collection sites and numerous human cases of Tr. rubrofasciata bites were recorded. Moreover, whether these T. conorhini strains are pathogenic to humans has not been investigated. Graphical Abstract

Actinobacillus pleuropneumoniae is the causative agent of pleuropneumonia, an economically important lung disease in pigs. In draft genomes of two Cypriot clinical A. pleuropneumoniae isolates (MIDG3457 and MIDG3459), we previously identified single genomic regions with homology to Mu-like bacteriophage and presented preliminary evidence of active phage. Here, updated Phastest genomic analysis identified two loci in both MIDG3457 and MIDG3459 that were predicted to encode proteins with high homology to, and whose organisation was characteristic of, Mu-like phages. Phylogenetically, the closest matches were with Mannheimia Vb and Glaesserella SuMu phages. Phastest scored the loci as “complete”, indicating they produced active phage. PCR amplification of the Mu-like phage c and tail genes from DNase-treated polyethylene glycol 8000 (PEG)-precipitated supernatants of MIDG3457 and MIDG3459 (grown in either Brain Heart Infusion-NAD or Grace’s Insect Medium-NAD broth) indicated the presence of intact virions. The phages from MIDG3457 and MIDG3459 were named PluMu 3457-1, 3457-2, and PluMu 3459-1 and PluMu 3459-2, respectively. Transmission electron microscopy (TEM) of the PEG-precipitated supernatants of broth-grown MIDG3459 identified virions with icosahedral heads and tails, consistent with other Mu-like phages. We conclude that MIDG3459 produces an active Mu-like phage.

The emergence of RPA technology has transformed warehouse management practices, presenting unparalleled prospects to improve business performance. This study explores the significant impact of RPA on improving warehouse operations, improving efficiency, accuracy, and cost-effectiveness. By conducting an inclusive examination of prevailing writings and case studies, this research explores how RPA automates repetitive tasks like inventory monitoring, order processing, and data input, ultimately reducing errors and streamlining processes. Furthermore, it evaluates the influence of RPA on workforce productivity, emphasizing the advantages of assigning tasks to robots and the potential for enhancing the skills of human employees in value-added tasks. Moreover, this study explores the combination of RPA with other tools such as AI and IoT, enhancing its capabilities in real-time data analysis, predictive maintenance, and demand prediction. The results highlight RPA’s pivotal role as a transformative tool in contemporary warehouse management, providing a competitive advantage through flexibility, scalability, and operational efficiency. Finally, this research contributes to a deeper conception of RPA’s implications for business development and sustainability in the ever-evolving logistics sector.

Asparagus, characterized by its high metabolic rate, is susceptible to quality degradation. Proanthocyanidins have antioxidant, antibacterial, antiviral, and other biological functions and can inhibit the production of reactive oxygen species in plants. To enhance the shelf life of asparagus, we investigated the impact of various concentrations of proanthocyanidins on its cold storage and preservation. The findings revealed that proanthocyanidins effectively mitigated water loss, delayed chlorophyll degradation, and prevented firmness decline. Furthermore, they enhanced the activity of antioxidant enzymes (superoxide dismutase, catalase, peroxidase, and polyphenol oxidase), bolstered DPPH free radical scavenging ability, and increased the levels of total phenol, total flavone, rutin, oligomeric procyanidins, proline, and soluble protein. Moreover, proanthocyanidins promoted the accumulation of vitamin C, amino acids, total saponins, and lignin in the later storage stage, contributing to increased mechanical tissue thickness. These results suggest that proanthocyanidins play a crucial role in retarding the deterioration of asparagus quality during storage by affecting the antioxidant capacity and phytochemical (polyphenol,amino acid, total saponin, and lignin) synthesis in asparagus.

Dwarf apple is one of the most important forms of garden economy, which has become a new engine for rural revitalization. The effective detection of coloring and ripening apples in complex environments is important for the sustainable development of smart agricultural operations. Addressing the issues of low detection efficiency in the greenhouse and the challenges associated with deploying complex target detection algorithms on low-cost equipment, we propose an enhanced lightweight model rooted in YOLOv7. Firstly, we enhance the model training performance by incorporating the Squeeze-and-Excite attention mechanism, which can enhance feature extraction capability. Then, an SCYLLA-IoU (SIoU) loss function is introduced to improve the ability of extracting occluded objects in complex environments. Finally, the model was simplified by introducing depthwise separable convolution and adding a ghost module after up-sampling layers. The improved YOLOv7 model has the highest AP value, which is 10.00%, 5.61%, and 6.00% higher compared to YOLOv5, YOLOv7, and YOLOX, respectively. The improved YOLOv7 model has an MAP value of 95.65%, which provides higher apple detection accuracy compared to other detection models and is suitable for potted dwarf anvil apple identification and detection.

In order to achieve efficient and accurate detection of common corn leaf diseases such as leaf blight, gray spot disease, and rust, a corn leaf disease detection method based on CA-YOLOv8 was proposed. In this method, the Coordinate Attention(CA) attention mechanism was added after the feature map output from the Neck part to enhance the feature extraction capability of the model. The experimental results showed that the precision, recall and mean average precision(mAP) of the CA-YOLOv8 model on the test set were 94.08%, 90.53% and 97.38%, respectively. Compared with the YOLOv8, YOLOv8+SE and YOLOv8+CBAM models, the mAP was improved by 2.15, 0.86 and 2.35 percentage points, respectively. Compared with Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8 models, the mAP has increased by 63.53, 29.24, 3.21, and 2.15 percentage points, respectively. The study showed that the CA-YOLOv8 model can provide a technical reference for the development of a portable intelligent corn leaf disease detection system. [ABSTRACT FROM AUTHOR]

Computer vision and deep learning are one of the main technologies for weed intelligent recognition in farmland. However, when using the deep learning technology to identify weeds in the field broomcorn millet growing at seedling stage, the weeds grow densely, which not only leads to the imbalance of positive and negative sample data, but also causes the problem of small area segmentation. In this paper, the combination of dice and focal loss was applied to Segformer semantic segmentation network with MiT-B3 as encoder block to identify intensive weeds in seedling broomcorn millet fields. The accuracy scores on the testing set were 95.23 %. The results show that the method proposed in this paper can effectively identify the seedling intensive weeds. [ABSTRACT FROM AUTHOR]

Abstract The presence of noncanonical open reading frames within lncRNAs (long non-coding RNAs) suggests their potential for translation, yielding various functional peptides or proteins. However, the existence and specific roles of these products in gastric cancer remain largely unclear. Here we identify the HOXA10-HOXA9-derived small protein (HDSP) in gastric cancer through comprehensive analysis and experimental validation, including mass spectrometry and western blotting. HDSP exhibits high expression and oncogenic roles in gastric cancer. Mechanistically, HDSP blocks TRIM25-mediated ubiquitination and degradation by interacting with MECOM, leading to MECOM accumulation and enhanced SPINK1 transcription-a gene promoting cancer via the EGFR signaling pathway. Furthermore, MECOM fosters HOXA10-HOXA9 transcription, establishing a feedback loop activating SPINK1-EGFR signaling. HDSP knockdown inhibits tumor growth in a PDX (patient-derived xenograft) model, and infusion of an artificially synthesized HDSP peptide as a neoantigen enhances immune cell-mediated anti-tumor efficacy against gastric cancer in vitro and in vivo. These findings propose HDSP as a potential therapeutic target or neoantigen candidate for gastric cancer treatment.

Abstract The prognosis for patients with colorectal cancer (CRC) remains worse than expected due to metastasis, recurrence, and resistance to chemotherapy. Colorectal cancer stem cells (CRCSCs) play a vital role in tumor metastasis, recurrence, and chemotherapy resistance. However, there are currently no prognostic markers based on CRCSCs-related genes available for clinical use. In this study, single-cell transcriptome sequencing was employed to distinguish cancer stem cells (CSCs) in the CRC microenvironment and analyze their properties at the single-cell level. Subsequently, data from TCGA and GEO databases were utilized to develop a prognostic risk model for CRCSCs-related genes and validate its diagnostic performance. Additionally, functional enrichment, immune response, and chemotherapeutic drug sensitivity of the relevant genes in the risk model were investigated. Lastly, the key gene RPS17 in the risk model was identified as a potential prognostic marker and therapeutic target for further comprehensive studies. Our findings provide new insights into the prognostic treatment of CRC and offer novel perspectives for a systematic and comprehensive understanding of CRC development.

To address the challenge of suboptimal object detection outcomes stemming from the deformability of marine flexible biological entities, this study introduces an algorithm tailored for detecting marine flexible biological targets. Initially, we compiled a dataset comprising marine flexible biological subjects and developed a Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, supplemented with a boundary detection enhancement module, to refine underwater image quality and accentuate the distinction between the images’ foregrounds and backgrounds. This enhancement mitigates the issue of foreground-background similarity encountered in detecting marine flexible biological entities. Moreover, the proposed adaptation incorporates a Deformable Convolutional Network (DCN) network module in lieu of the C2f module within the YOLOv8n algorithm framework, thereby augmenting the model’s proficiency in capturing geometric transformations and concentrating on pivotal areas. The Neck network module is enhanced with the RepBi-PAN architecture, bolstering its capability to amalgamate and emphasize essential characteristics of flexible biological targets. To advance the model’s feature information processing efficiency, we integrated the SimAM attention mechanism. Finally, to diminish the adverse effects of inferior-quality labels within the dataset, we advocate the use of WIoU (Wise-IoU) as a bounding box loss function, which serves to refine the anchor boxes’ quality assessment. Simulation experiments show that, in comparison to the conventional YOLOv8n algorithm, our method markedly elevates the precision of marine flexible biological target detection.

Objectives This study aimed to estimate the variation in disease burden associated with air pollutants and other respiratory viruses during the COVID-19 pandemic. Methods We adopted a machine learning approach to calculate the excess mortality attributable to air pollutants and influenza, during the pre-pandemic and pandemic period. Results In the first 2 years of the COVID-19 pandemic, there were 8762 (95% confidence interval, 7503–9993), and 12,496 (11,718–13,332) excess all-cause deaths in Hong Kong. These figures correspond to 117.4 and 167.9 per 100,000 population, and 12.6% and 8.5% of total deaths in 2020 and 2021, respectively. Compared to the period before the pandemic, excess deaths from all-causes, cardiovascular and respiratory diseases, pneumonia and influenza attributable to influenza A and B significantly decreased in all age groups. However, excess deaths associated with ozone increased in all age-disease categories, while the relative change of nitrogen dioxide (NO 2 ) and particular matters less than 10 µm (PM 10 ) associated burden showed a varied pattern. Conclusions A notable shift in disease burden attributable to influenza and air pollutants was observed in the pandemic period, suggesting that both direct and indirect impacts shall be considered when assessing the global and regional burden of the COVID-19 pandemic.

In entanglement theory, there are different methods to consider one state being more entangled than another. The “maximally” entangled states in a multipartite system can be defined from an axiomatic perspective. According to different criteria for selection, there are many specific types of quantum maximally entangled states, such as absolutely maximally entangled state, planar maximally entangled state and so on. In this paper we propose a new type of maximally entangled states, the planar two-region multipartite maximally entangled state. The requirement condition of this maximally entangled state is weak than that of the absolutely maximally entangled state and different from that of the planar maximally entangled state. We show that there are the two-region four-partite maximally entangled states in 4-qubit and 7-qubit planar systems, although there is no absolutely maximally entangled state in these systems. It is proved that there are the planar two-region four-partite maximally entangled states in both even particle quantum systems and odd particle quantum systems. Additionally, based on some planar two-region four-partite maximally entangled states, the new planar two-region four-partite maximally entangled states are generated. We also provide some important examples of the planar two-region multi-partite maximally entangled states.

Abstract Purpose Cytokines regulate the interaction between the immune system and malignant tumors. Among them, interleukin-10 (IL-10) is a multifunctional anti-inflammatory cytokine mainly produced by immune cells. The correlation between gastric cancer and T/C single nucleotide polymorphism (SNP) of interleukin-10 (IL-10) promoter−819(rs1800871)was opaque and remained to be determined. We aim to explore the pertinence of gastric cancer and SNP of interleukin 10–819 by meta-analysis via five statistical models. Methods Databases including PubMed, Cochrane Library, Embase, the Scopus, and Google Scholars were comprehensively retrieved for the eligible studies on the related topic from inception to March 2022. Odds ratios (ORs) were generated for dichotomous variants by meta-analysis in each model via STATA 17.0 MP. The statistical models comprised recessive model, over-dominant model, allele model, co-dominant model and dominant model. Subgroup analysis was performed to investigate the difference across races as well as the source of heterogeneity if necessary. Results Eventually a total of 15 articles reporting 7779 patients were enrolled in our study. There were 2383 patients and 5396 controls, collectively. There was no correlation between gastric cancer and IL-10 819 in recessive model, co-dominant model or dominant model, and subgroup analysis showed that Asian, Latin American and Caucasian had no correlation with the risk of gastric cancer. In the allelic model, there was significant correlation between gastric cancer and IL-10 819 (OR = 3.96%, 95%CI: 3.28 to 3.78). In the over-dominant model, there is no correlation between gastric cancer and IL-10 819, but subgroup analysis uncovered significant vulnerability of Asian people with regard to gastric cancer. Conclusions In our study, both Asians, Latin Americans, and Europeans showed an increased risk of gastric cancer in the allelic model, whereas only Asians showed significant susceptibility in the super dominant model. Of course, more large cohort studies are needed to confirm our results.

[Objective] Under unpredictable complex conditions such as natural disasters, failures of vehicle/track key components, or external impacts, metro train derailment is still highly prone to occurrence. Therefore, research on train passive derailment protection device schemes is essential. [Method] A dynamics nonlinear simulation model of metro train multi-body system is established. The typical shapes of both the wheel tread and the steel rail are matched. Based on the characteristics of metro train operating routes, three schemes for metro train derailment protection devices are proposed, and the derailment condition for each scheme is simulated under low-speed operating conditions. Regarding the simulation results, the effectiveness of preventing secondary rail climbing/derailment, the strength of the installation position, and the damage to vehicle caused by collision between rail and derailment protection device after derailment are taking into consideration comprehensively, a scheme of installing lateral anti-derailment stops at framework lower end as the derailment protection device is proposed. A dynamics simulation analysis is conducted on the metro train derailment protection device and the result is compared with the non-protection device working condition. [Result & Conclusion] The metro train derailment protection device exhibits a significant anti-derailment effect with certain feasibility. The dynamic envelope of the above device meets the clearance requirements of the metro line and does not affect train normal operation, making it suitable for engineering applications.

Crop change detection study is the foundation of agricultural sustainability. The inherent high spectral resolution of hyperspectral images, combined with multi-temporal datasets, facilitates the detection of subtle changes. To enhance the accuracy and applicability of hyperspectral change detection in agricultural scenes, this paper introduces a fast hyperspectral change detection approach for agricultural crops based on low-rank matrix and morphological feature extraction (FLRaMF). The goal is to improve detection precision and computational efficiency of the change detection process. The method initially employs rapid low-rank matrix extraction to separate changing and non-changing pixels in the spectral domain. Subsequently, spatial information is introduced using attribute profiles, restricting spectral anomalies through hyperspectral morphology, which ultimately improves the detection results. This study utilized four hyperspectral change detection datasets in agricultural crop scenarios, optimizing and analyzing parameters. Experimental results and analysis indicate that the FLRaMF method can achieve higher detection accuracy with reduced computation cost in unsupervised, default parameter scenarios when performing agricultural crop change detection tasks.

The ionosphere is one of the important error sources that affect the communication of radio signals. The international reference ionosphere (IRI) model is a commonly used model to describe ionospheric parameters. The driving parameter IG12 of the IRI-2016 model was optimally updated based on GNSS-TEC data from 2015 and 2019. The electron density profiles and NmF2 calculated by the IRI-2016 model (upda-IRI-2016) driven by the updated IG12 value (IG-up) were evaluated for their accuracy using ionosonde observations and COSMIC inversion data. The experiments show that both the electron density profiles and NmF2 calculated by upda-IRI-2016 driven by IG-up show significant optimization effects, compared to the IRI-2016 model driven by IG12. For electron density, the precision improvement (PI) for both MAE and RMSE at the Beijing station exceed 31.2% in January 2015 and 16.0% in January 2019. While the PI of MAE and RMSE at the Wuhan station, which is located at a lower latitude, both exceed 32.5% in January 2015, both exceed 42.1% in January 2019, which is significantly higher than that of the Beijing station. In 2015, the PI of MAE and RMSE compared with COSMIC are both higher than 20%. For NmF2, the PI is greater for low solar activity years and low latitude stations, with the Wuhan station showing a PI of more than 11.7% in January 2019 compared to January 2015. The PI compared to COSMIC was higher than 17.2% in 2015.

Previous studies have demonstrated that when the cyclin D2 (CCND2), a cell-cycle regulatory protein, is overexpressed in human-induced pluripotent stem cells (hiPSCs), cardiomyocytes (CMs) differentiated from these CCND2-overexpressing hiPSCs can proliferate after transplantation into infarcted hearts, which significantly improves the cells’ potency for myocardial regeneration. However, persistent CM proliferation could lead to tumor growth or the development of arrhythmogenic complications; thus, the goal of the current study was to generate a line of hiPSCs in which CCND2 overexpression could be tightly controlled. First, we transfected hiPSCs with vectors coding for a doxycycline-inducible Tet-On transactivator and S. pyogenes dCas9 fused to the VPR activation domain; then, the same hiPSCs were engineered to express guide RNAs targeting the CCND2 promotor. Thus, treatment with doxycycline (dox) activated dCas9-VPR expression, and the guide RNAs directed dCas9-VPR to the CCND2 promoter, which activated CCND2 expression. Subsequent experiments confirmed that CCND2 expression was dox-dependent in this newly engineered line of hiPSCs (doxCCND2-hiPSCs): CCND2 protein was abundantly expressed after 48 h of treatment with dox and declined to near baseline level ~96 h after dox treatment was discontinued.

Leading enterprises in the industry chain play a demonstrative role, and promoting green innovation among leading enterprises is a meaningful approach to unlocking industry chain dividends. According to an analysis of the game process between customers and leading firms that incorporates the open innovation theory, we find a nonlinear role for the consumer discourse power in the leading firms' innovation. Furthermore, using data from Chinese A-share listed companies between 2012 and 2021, we observe an inverted "U" relationship between customer discourse power and green innovation. Notably, this effect is more pronounced in leading non-technology-intensive enterprises, industries with a high degree of industrial innovation, or regions with a high marketization degree. Our analysis also reveals that leading enterprises' client leads to financial limitations that influence green innovation. Additionally, leading enterprises play a driving role in achieving "joint progress" in green innovation with local non-leading firms, and this effect exhibits spatial spillover.

Congenital birth defects (CBD) play a significant role in causing child mortality globally. The incidence and mortality of CBD vary widely across countries, and the underlying causes for this divergence remain incompletely comprehended. We conducted an analysis to investigate the relationship between the incidence and mortality of CBD in 189 countries and their Human Development Index (HDI). In this study, CBD data from 189 countries was used from the Global Burden of Diseases Study (GBD) 2019, and HDI data was collected for the same countries. Later, the relationship between CBD and HDI was analyzed, and the impact of gross national income (GNI) per capita, expected years of schooling, mean years of schooling and life expectancy at birth was quantified using principal component regression. The age-standardized incidence rate (ASIR) varied between 66.57 to 202.24 per 100,000, with a 95% uncertainty interval (UI) of 57.20-77.51 and 165.87-241.48 respectively. The age-standardized mortality rate (ASMR) also showed a rang from 1.38 to 26.53 (14.03-39.90) per 100,000, with the 95%UI of 0.91-2.09 and 14.03-39.90 respectively. Both the incidence and mortality rates of CBD decreased with the increased HDI (incidence: r = -0.38, p < 0.001, mortality: r = -0.77, p < 0.001). Our investigation revealed significant variations in the incidence and mortality of CBD among countries with different development levels. In conclusion, the global incidence and mortality of CBD vary significantly among countries, possibly due to differences in the accessibility of health services. [ABSTRACT FROM AUTHOR]