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Abstract Robust high-order optical vortices are much in demand for applications in optical manipulation, optical communications, quantum entanglement and quantum computing. However, in numerous experimental settings, a controlled generation of optical vortices with arbitrary orbital angular momentum remains a challenge. Here, we present a concept of “optical vortex ladder” for the stepwise generation of optical vortices through Sisyphus pumping of pseudospin modes in photonic graphene. The ladder is applicable in various lattices with Dirac-like structures. Instead of conical diffraction and incomplete pseudospin conversion under conventional Gaussian beam excitations, the vortices produced in the ladder arise from non-trivial topology and feature diffraction-free Bessel profiles, thanks to the refined excitation of the ring spectrum around the Dirac cones. By employing a periodic “kick” to the photonic graphene, effectively inducing the Sisyphus pumping, the ladder enables tunable generation of optical vortices of any order even when the initial excitation does not involve any orbital angular momentum. The optical vortex ladder stands out as an intriguing non-Hermitian dynamical system, and, among other possibilities, opens a pathway for applications of topological singularities in beam shaping and wavefront engineering.

In this study, WC-12Co cemented carbides were prepared by laser powder bed fusion (LPBF) on a WC-20Co substrate. The effects of the scanning speed and the substrate on the microstructure, mechanical properties and wear characteristics of the WC-12Co cemented carbide were investigated. The results showed that an alternating distribution of coarse and fine WC grains is observed in the LPBF-prepared WC-12Co cemented carbides. As the laser scanning speed decreases or the laser energy density increases, the WC phase gradually decomposes, accompanied by the loss of C and the formation of ƞ phases. WC-12Co cemented carbides produced at a lower scanning speed exhibit a higher density of thermal cracks, while those produced at a higher scanning speed have a larger number of pores. As the laser scanning speed increases, the transverse rupture strength (TRS) first increases to a maximum value of 823.13 MPa and then decreases. The variation of TRS is mainly attributed to the evolution of the brittle ƞ-phase and metallurgical defects. For WC-12Co cemented carbides formed at low scanning speeds, the brittle η-phase may fracture and cut through the matrix during the friction process, leading to an increase in coefficient of friction (COF) and wear mass loss. For samples formed at high scanning speeds, the formation and detachment of the shear layer could accelerate the friction process and undermine the wear resistance of the WC-12Co cemented carbide. Therefore, the sample prepared at the scanning speed of 400 mm/s shows the best wear performance.

Highlights LPS-induced ARDS rats and PMVECs have low Fcgr2b level and high Elk1 level; Fcgr2b overexpression mitigates LPS-induced ALI/ARDS in rats and PMVECs; Elk1 knockdown mitigates LPS-induced ALI/ARDS in rats and PMVECs; Elk1 represses Fcgr2b transcription by recruiting H3K9me3; Elk1/Fcgr2b axis aggravates LPS-induced ALI/ARDS in rats and PMVECs.

Abstract Bioprinting that can synchronously deposit cells and biomaterials has lent fresh impetus to the field of tissue regeneration. However, the unavoidable occurrence of cell damage during fabrication process and intrinsically poor mechanical stability of bioprinted cell-laden scaffolds severely restrict their utilization. As such, on basis of heart-inspired hollow hydrogel-based scaffolds (HHSs), a mechanical-assisted post-bioprinting strategy is proposed to load cells into HHSs in a rapid, uniform, precise and friendly manner. HHSs show mechanical responsiveness to load cells within 4 s, a 13-fold increase in cell number, and partitioned loading of two types of cells compared with those under static conditions. As a proof of concept, HHSs with the loading cells show an enhanced regenerative capability in repair of the critical-sized segmental and osteoporotic bone defects in vivo. We expect that this post-bioprinting strategy can provide a universal, efficient, and promising way to promote cell-based regenerative therapy.

[Introduction] Accurate load forecasting underpins the operation optimization of the electricity systems and is an indispensable aspect of energy management within such systems. Given the low accuracy and high computational complexity inherent in traditional methodologies that combine data decomposition and machine learning models, this study proposes a novel Empirical Mode Decomposition and Multi-Layer Perceptron (EMD-MLP) model for predicting day-ahead electrical load. [Method] Initially, the EMD method decomposed the original load time series into multiple Intrinsic Mode Function (IMF). These IMFs were then reconstructed into high-frequency and low-frequency components using extreme point partitioning, simplifying the prediction target. Subsequently, each reconstructed components was modeled separately for prediction, and the results were cumulatively used to provide the forecasted electrical load value. [Result] The proposed model is tested using real-world electrical load data of 2018 and 2019 from the Australian electricity market. [Conclusion] Comparing the extrapolative capabilities of our EMD-MLP model with persistence model, standalone MLP model and traditional EMD ensemble model confirms the effectiveness of our model in enhancing prediction accuracy. Moreover, while ensuring accuracy, the proposed EMD-MLP model simplifies the complexity and improves the efficiency of the forecasting process, thereby providing a practical solution for both day-ahead and real-time electrical load forecasting.

ABSTRACTGliomas are the most common type of primary brain tumor. Despite advances in treatment, it remains one of the most aggressive and deadly tumor of the central nervous system (CNS). Gliomas are characterized by high malignancy, heterogeneity, invasiveness, and high resistance to radiotherapy and chemotherapy. It is urgent to find potential new molecular targets for glioma. The TRPM channels consist of TRPM1-TPRM8 and play a role in many cellular functions, including proliferation, migration, invasion, angiogenesis, etc. More and more studies have shown that TRPM channels can be used as new therapeutic targets for glioma. In this review, we first introduce the structure, activation patterns, and physiological functions of TRPM channels. Additionally, the pathological mechanism of glioma mediated by TRPM2, 3, 7, and 8 and the related signaling pathways are described. Finally, we discuss the therapeutic potential of targeting TRPM for glioma.

The statistical mechanical behavior of weakly nonlinear multimoded optical settings has been attracting increased interest over the last few years. The main purpose of this work is to numerically investigate the main factors that affect the thermalization process in photonic lattices. In particular, we find that lattices with identically selected properties (such as temperature, coupling coefficient, lattice size, and excitation conditions) can exhibit very different thermalization dynamics and, thus, thermalization distances. Our investigation is focused on two different two-dimensional lattices: the honeycomb lattice and the triangular lattice. Our numerical results show that, independently of the excitation conditions, the honeycomb lattice always thermalizes faster than the triangular lattice. We mainly explain this behavior by the quasilinear spectrum that promotes wave-mixing in the honeycomb lattice in comparison to the power-like spectrum of the triangular lattice. In addition, we investigate the combined effects of temperature as well as the sign and magnitude of the nonlinearity. Switching either the sign of the Kerr nonlinear coefficient or the sign of the temperature can lead to significant differences in the thermalization dynamics, a phenomenon that can be physically explained in terms of wave instabilities. Larger absolute values of the temperature |T| result in more uniform distributions for the power occupation numbers and faster thermalization speeds. Finally, as expected, increasing the magnitude of the nonlinearity results in accelerated thermalization. Our findings provide valuable insights into optical thermalization in discrete systems, where experimental realization may bring about new possibilities for light manipulation and applications.

To enhance therapeutic approaches, we created a distinctive pattern utilizing the cell demise indicator (CDI) to predict the effectiveness of immunotherapy in individuals with bladder carcinoma (BLCA). Hub prognostic CDIs were identified from the TCGA database using differential gene expression and survival analysis, encompassing 763 genes across 13 death modes. The subtype assessment was employed to evaluate the impact of these genes on the prognosis and immunotherapeutic outcomes in patients with BLCA. The LASSO regression method was used to identify significant CDIs, while Cox regression and nomogram analyses were conducted to explore the impact of CDIs on prognosis. CHMP4C and GSDMB were selected as the hub genes for the following research. Subsequently, These two central genes underwent further investigation to explore their association with immunotherapy, followed by an analysis of their potential regulatory network. Subtype analysis showed that these CDIs were significantly associated with the prognosis and immunotherapy of BLCA patients. The regulatory network in BLCA was evaluated through the establishment of the lncRNA XIST/NEAT1-CDIs-miR-146a-5p/miR-429 axis. Immunohistochemical analysis revealed a significant up-regulation of CHMP4C in bladder cancer tissues, which was strongly associated with an unfavorable prognosis for BLCA patients. Moreover, our findings provide compelling evidence that CHMP4C plays a pivotal role in promoting BLCA progression through the activation of the epithelial-mesenchymal transition (EMT) pathway. These findings highlight the negative impact of CHMP4C on BLCA patient prognosis, while also providing insights into the oncogenic mechanisms and immunotherapy in which CHMP4C may be involved.

Synthetic gauge fields introduce an unconventional degree of freedom for studying many fundamental phenomena in different branches of physics. Here, we propose a scheme to use staggered synthetic gauge fields for control of the non-Hermitian skin effect (NHSE). A modified Su–Schrieffer–Heeger model is employed, where two dimer chains with non-reciprocal coupling phases are coupled, exhibiting non-trivial point-gap topology and the NHSE. In contrast to previous studies, the skin modes in our model are solely determined by the coupling phase terms associated with the staggered synthetic gauge fields. By manipulating such gauge fields, we can achieve maneuvering of skin modes as well as the bipolar NHSE. As a typical example, we set up a domain wall by imposing different synthetic gauge fields on two sides of the wall, thereby demonstrating flexible control of the non-Hermitian skin modes at the domain wall. Our scheme opens a new avenue for the creation and manipulation of NHSE by synthetic gauge fields, which may find applications in beam shaping and non-Hermitian topological devices.

Abstract Objective Sepsis is an organ malfunction disease that may become fatal and is commonly accompanied by severe complications such as multiorgan dysfunction. Patients who are already hospitalized have a high risk of death due to sepsis. Even though early diagnosis is very important, the technology and clinical approaches that are now available are inadequate. Hence, there is an immediate necessity to investigate biological markers that are sensitive, specific, and reliable for the prompt detection of sepsis to reduce mortality and improve patient prognosis. Mounting research data indicate that ferroptosis contributes to the occurrence, development, and prevention of sepsis. However, the specific regulatory mechanism of ferroptosis remains to be elucidated. This research evaluated the expression profiles of ferroptosis‐related genes (FRGs) and the diagnostic significance of the ferroptosis‐related classifiers in sepsis. Methods and Results We collected three peripheral blood data sets from septic patients, integrated the clinical examination data and mRNA expression profile of these patients, and identified 13 FRGs in sepsis through a co‐expression network and differential analysis. Then, an optimal classifier tool for sepsis was constructed by integrating a variety of machine learning algorithms. Two key genes, ATG16L1 and SRC, were shown to be shared between the algorithms, and thus were identified as the FRG signature of classifier. The tool exhibited satisfactory diagnostic efficiency in the training data set (AUC = 0.711) and two external verification data sets (AUC = 0.961; AUC = 0.913). In the rat cecal ligation puncture sepsis model, in vivo experiments verified the involvement of ATG16L1 and SRC in the early sepsis process. Conclusion These findings confirm that FRGs may participate in the development of sepsis, the ferroptosis related classifiers can provide a basis for the development of new strategies for the early diagnosis of sepsis and the discovery of new potential therapeutic targets for life‐threatening infections.

BackgroundThe treatment of unstable femoral neck fractures (FNFs) remains a challenge. In this study, a new cannulated screw for unstable FNFs was designed to provide a new approach for the clinical treatment of these injuries, and its biomechanical stability was analyzed using finite element analysis and mechanical tests.MethodsAn unstable FNF model was established. An internal fixation model with parallel inverted triangular cannulated screws (CSs) and a configuration with two superior cannulated screws and one inferior new cannulated screw (NCS) were used. The biomechanical properties of the two fixation methods were compared and analyzed by using finite element analysis and mechanical tests.ResultsThe NCS model outperformed the CSs model in terms of strain and stress distribution in computer-simulated reconstruction of the inverted triangular cannulated screw fixation model for unstable FNFs. In the biomechanical test, the NCS group showed significantly smaller average femoral deformation (1.08 ± 0.15 mm vs. 1.50 ± 0.37 mm) and fracture line displacement (1.43 ± 0.30 mm vs. 2.01 ± 0.47 mm). In the NCS group, the mean stiffness was significantly higher than that in the CSs group (729.37 ± 82.20 N/mm vs. 544.83 ± 116.07 N/mm), and the mean compression distance was significantly lower than that in the CSs group (2.87 ± 0.30 mm vs. 4.04 ± 1.09 mm).ConclusionThe NCS combined with two ordinary cannulated screws in an inverted triangle structure to fix unstable FNFs can provide better biomechanical stability than CSs and exhibit a length- and angle-stable construct to prevent significant femoral neck shortening.

Abstract The field of medical image segmentation, particularly in the context of brain tumor delineation, plays an instrumental role in aiding healthcare professionals with diagnosis and accurate lesion quantification. Recently, Convolutional Neural Networks (CNNs) have demonstrated substantial efficacy in a range of computer vision tasks. However, a notable limitation of CNNs lies in their inadequate capability to encapsulate global and distal semantic information effectively. In contrast, the advent of Transformers, which has established their prowess in natural language processing and computer vision, offers a promising alternative. This is primarily attributed to their self-attention mechanisms that facilitate comprehensive modeling of global information. This research delineates an innovative methodology to augment brain tumor segmentation by synergizing UNET architecture with Transformer technology (denoted as UT), and integrating advanced feature enhancement (FE) techniques, specifically Modified Histogram Equalization (MHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Modified Bi-histogram Equalization Based on Optimization (MBOBHE). This integration fosters the development of highly efficient image segmentation algorithms, namely FE1-UT, FE2-UT, and FE3-UT. The methodology is predicated on three pivotal components. Initially, the study underscores the criticality of feature enhancement in the image preprocessing phase. Herein, techniques such as MHE, CLAHE, and MBOBHE are employed to substantially ameliorate the visibility of salient details within the medical images. Subsequently, the UT model is meticulously engineered to refine segmentation outcomes through a customized configuration within the UNET framework. The integration of Transformers within this model is instrumental in imparting contextual comprehension and capturing long-range data dependencies, culminating in more precise and context-sensitive segmentation. Empirical evaluation of the model on two extensively acknowledged public datasets yielded accuracy rates exceeding 99%.

The distribution line is widely distributed and the span of pole-towers is small. However, the existing lightning positioning system has limited positioning accuracy, and the lightning strike coordinates it locates have certain deviations for the distribution network. The lightning hazard risk divided by lightning density is not sufficient to support the differentiated lightning protection transformation of the distribution network. The environment of the underlying surface where the pole-tower is located has a significant impact on lightning activity, so this paper combines terrain, climate, lightning current characteristics, and historical lightning trip data to finely evaluate the lightning risk level in the distribution line pole-tower area. This paper provides a detailed analysis of the relationship between different underlying surface environments and lightning current characteristics and utilizes the entropy weight method to construct a comprehensive lightning disaster risk index, then evaluates the risk level of lightning disaster in different regions, and corrects it through lightning fault data. Overlaying the risk level of the area and the distribution map of the pole-tower foundation coordinates, it will obtain the distribution of lightning hazard-pregnant risk at the pole-tower area, which would contribute to guiding the differentiated lightning protection transformation of the line. Compared to traditional unified lightning protection renovation methods, differentiated lightning protection methods that integrate factors such as pole-tower structure, wire type, and lightning risk level can achieve significant lightning protection effects at a lower economic cost. Taking Nanchang City as an example, risk assessment and lightning protection renovation were conducted on 21 distribution lines. The average number of lightning trips per year is 55 before the renovation, but it is only 4 after the renovation, which the number of lightning trips significantly reduce. This verifies the effectiveness of using lightning hazard-pregnant assessment results to guide differentiated lightning protection methods.

Backgroundsand Aims. Colorectal cancer (CRC) represents a major global health challenge, necessitating comprehensive investigations into its underlying molecular mechanisms to enhance diagnostic and therapeutic strategies. This study focuses on elucidating the oncogenic role of Membrane-Associated Ring-CH-Type Finger 9 (MARCHF9), a RING-Type E3 ubiquitin transferase, in CRC. We aim to assess MARCHF9’s clinical significance, functional impact on CRC progression, and its potential as a prognostic biomarker. Methods. We leveraged data from the Cancer Genome Atlas (TCGA) cohort to evaluate MARCHF9 expression profiles in CRC. In vitro experiments involved siRNA-mediated MARCHF9 knockdown in COAD cell lines (SW480 and LoVo). Cell proliferation and invasion assays were conducted to investigate MARCHF9’s functional relevance. Survival analyses were performed to assess its prognostic role. Results. Our analysis revealed significantly elevated MARCHF9 expression in CRC tissues compared to normal colorectal tissues (P

Background: There is a great obstacle in prenatal diagnosis of fetal anomalies due to their considerable genetic and clinical heterogeneity. Whole-exome sequencing (WES) has been confirmed as a successful option for genetic diagnosis in pediatrics, but its clinical utility for prenatal diagnosis remains to be limited.Methods: A total of 60 fetuses with abnormal ultrasound findings underwent karyotyping or chromosomal microarray analysis (CMA), and those with negative results were further subjected to WES. The identified variants were classified as pathogenic or likely pathogenic (P/LP) and the variant of uncertain significance (VUS). Pregnancy outcomes were obtained through a telephone follow-up.Results: Twelve (20%, 12/60) fetuses were diagnosed to have chromosomal abnormalities using karyotyping or CMA. Of the remaining 48 cases that underwent WES, P/LP variants were identified in 14 cases (29.2%), giving an additional diagnostic yield of 23.3% (14/60). The most frequently affected organ referred for prenatal WES was the head or neck system (40%), followed by the skeletal system (39.1%). In terms of pathogenic genes, FGFR3 was the most common diagnostic gene in this cohort. For the first time, we discovered five P/LP variants involved in SEC24D, FIG4, CTNNA3, EPG5, and PKD2. In addition, we identified three VUSes that had been reported previously. Outcomes of pregnancy were available for 54 cases, of which 24 cases were terminated.Conclusion: The results confirmed that WES is a powerful tool in prenatal diagnosis, especially for fetuses with ultrasonographic anomalies that cannot be diagnosed using conventional prenatal methods. Additionally, newly identified variants will expand the phenotypic spectrum of monogenic disorders and greatly enrich the prenatal diagnostic database.

Abstract The lack of available protons severely lowers the activity of alkaline hydrogen evolution reaction process than that in acids, which can be efficiently accelerated by tuning the coverage and chemical environment of protons on catalyst surface. However, the cycling of active sites by proton transfer is largely dependent on the utilization of noble metal catalysts because of the appealing electronic interaction between noble metal atoms and protons. Herein, an all-non-noble W/WO2 metallic heterostructure serving as an efficient solid-acid catalyst exhibits remarkable hydrogen evolution reaction performance with an ultra-low overpotential of −35 mV at −10 mA/cm2 and a small Tafel slope (−34 mV/dec), as well as long-term durability of hydrogen production (>50 h) at current densities of −10 and −50 mA/cm2 in alkaline electrolyte. Multiple in situ and ex situ spectroscopy characterizations combining with first-principle density functional theory calculations discover that a dynamic proton-concentrated surface can be constructed on W/WO2 solid-acid catalyst under ultra-low overpotentials, which enables W/WO2 catalyzing alkaline hydrogen production to follow a kinetically fast Volmer-Tafel pathway with two neighboring protons recombining into a hydrogen molecule. Our strategy of solid-acid catalyst and utilization of multiple spectroscopy characterizations may provide an interesting route for designing advanced all-non-noble catalytic system towards boosting hydrogen evolution reaction performance in alkaline electrolyte.

Abstract Background Thalassemia is an extremely prevalent monogenic inherited blood disorder in southern China. It is important to comprehensively understand the molecular spectrum of thalassemia in an area with such a high prevalence of thalassemia before taking appropriate actions for the prevention and treatment of this disorder. Herein, we explored the clinical feasibility of using next-generation sequencing (NGS) for large-scale population screening to illustrate the prevalence and spectrum of thalassemia in Southern Jiangxi. Methods Blood samples collected from 136,312 residents of reproductive age in Southern Jiangxi were characterized for thalassemia by NGS. A retrospective analysis was then conducted on blood samples determined to be positive for thalassemia. Results In total, 19,827 (14.545%) subjects were diagnosed as thalassemia carriers, and the thalassemia prevalence rate significantly varied by geographical region (p T accounting for 35.257% of these β-thalassemia genotypes. Furthermore, 74 genotypes were identified among 608 individuals with combined α- and β-thalassemia. Notably, most individuals with rare thalassemia mutations had mildly abnormal hematologic parameters including microcytic hypochromia. Conclusions Our findings demonstrate the great heterogeneity and diverse spectrum of thalassemia in Southern Jiangxi, emphasizing the importance and necessity of persistent prevention and control of thalassemia in this region. Additionally, our findings further suggest that NGS can effectively identify rare mutations and reduce the misdiagnosis rate of thalassemia.

A bacterium was isolated and identified from the secretion of a rhesus monkey with endometritis. The morphological results showed that the strain exhibited round, convex, gray-white colonies with smooth surfaces and diameters ranging from 1 to 2 mm when cultured on Columbia blood agar at 37 °C for 24 h; on salmonella–shigella agar (S.S.) at 37 °C for 24 h, the colonies appeared round, flat, and translucent. Gram staining showed negative results with blunt ends and non-spore-forming characteristics. Molecular biology results showed that the 16S rRNA sequence of the strain revealed over 96.9% similarity with published sequences of M. morganii from different sources in the NCBI GenBank database. Morphological and molecular biology analysis confirmed that the strain (RM2023) isolated from cervical secretions of rhesus monkey was M. morganii. Drug sensitivity testing demonstrated that the isolated strain (RM2023) was sensitive to ceftriaxone, amikacin, gentamicin, cefazolin, cefuroxime, ceftazidime, levofloxacin, cotrimoxazole, norfloxacin, and tetracycline; moderately sensitive to ampicillin; and resistant to penicillin, vancomycin, ciprofloxacin, and clindamycin. The research findings provide valuable insights for disease prevention in rhesus monkeys and contribute to molecular epidemiological studies.

Abstract Long non-coding RNAs (lncRNAs) are tissue-specific expression patterns and dysregulated in cancer. How they are regulated still needs to be determined. We aimed to investigate the functions of glioma-specific lncRNA LIMD1-AS1 activated by super-enhancer (SE) and identify the potential mechanisms. In this paper, we identified a SE-driven lncRNA, LIMD1-AS1, which is expressed at significantly higher levels in glioma than in normal brain tissue. High LIMD1-AS1 levels were significantly associated with a shorter survival time of glioma patients. LIMD1-AS1 overexpression significantly enhanced glioma cells proliferation, colony formation, migration, and invasion, whereas LIMD1-AS1 knockdown inhibited their proliferation, colony formation, migration, and invasion, and the xenograft tumor growth of glioma cells in vivo. Mechanically, inhibition of CDK7 significantly attenuates MED1 recruitment to the super-enhancer of LIMD1-AS1 and then decreases the expression of LIMD1-AS1. Most importantly, LIMD1-AS1 could directly bind to HSPA5, leading to the activation of interferon signaling. Our findings support the idea that CDK7 mediated-epigenetically activation of LIMD1-AS1 plays a crucial role in glioma progression and provides a promising therapeutic approach for patients with glioma.

Abstract Fibroadenomas (FAs) are the most common breast tumors in women. No pharmacological agents are currently approved for FA intervention owing to its unclear mechanisms and a shortage of reproducible human models. Here, using single-cell RNA sequencing of human FAs and normal breast tissues, we observe distinct cellular composition and epithelial structural changes in FAs. Interestingly, epithelial cells exhibit hormone-responsive functional signatures and synchronous activation of estrogen-sensitive and hormone-resistant mechanisms (ERBB2, BCL2 and CCND1 pathways). We develop a human expandable FA organoid system and observe that most organoids seem to be resistant to tamoxifen. Individualized combinations of tamoxifen with ERBB2, BCL2 or CCND1 inhibitors could significantly suppress the viability of tamoxifen-resistant organoids. Thus, our study presents an overview of human FA at single-cell resolution that outlines the structural and functional differences between FA and normal breast epithelium and, in particular, provides a potential therapeutic strategy for breast FAs.

α-globin gene triplication carriers were not anemic in general, while some studies found that α-globin gene triplication coinherited with heterozygous β-thalassemia may cause adverse clinical symptoms, which yet lacks sufficient evidence in large populations. In this study, we investigated the prevalence and distribution of α-globin gene triplication as well as the phenotypic characteristics of α-globin gene triplication coinherited with heterozygous β-thalassemia in Ganzhou city, southern China. During 2021-2022, a total of 73,967 random individuals who received routine health examinations before marriage were genotyped for globin gene mutations by high-throughput sequencing. Among them, 1,443 were α-globin gene triplication carriers, with a carrier rate of 1.95%. The most prevalent mutation was αααanti3.7/αα (43.10%), followed by αααanti4.2/αα (38.12%). 42 individuals had coinherited α-globin gene triplication and heterozygous β-thalassemia. However, they did not differ from the individuals with heterozygous β-thalassemia and normal α-globin (αα/αα) in terms of mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) levels. In addition, heterogenous clinical phenotypes were found in two individuals with the same genotype. Our study established a database of Ganzhou α-globin gene triplication and provided practical advice for the clinical diagnosis of α-globin gene triplication.

In modern healthcare, the precision of medical image segmentation holds immense significance for diagnosis and treatment planning. Deep learning techniques, such as CNNs, UNETs, and Transformers, have revolutionized this field by automating the previously labor-intensive manual segmentation processes. However, challenges like intricate structures and indistinct features persist, leading to accuracy issues. Researchers are diligently addressing these challenges to further unlock the potential of medical image segmentation in healthcare transformation. To enhance the precision of brain tumor MRI image segmentation, our study introduces three novel feature-enhanced hybrid UNet models (FE-HU-NET): FE1-HU-NET, FE2-HU-NET, and FE3-HU-NET. Our approach encompasses three main aspects. Initially, we emphasize feature enhancement during the image preprocessing stage. We apply distinct image enhancement techniques—CLAHE, MHE, and MBOBHE—to each model. Secondly, we tailor the architecture of the UNet model to enhance segmentation results, focusing on a personalized layered design. Lastly, we employ a CNN model in post-processing to refine segmentation outcomes through additional convolutional layers. The HU-Net module, shared across the three models, integrates a customized UNet layer and a CNN. We also introduce an alternative feature-enhanced variant, FE4-HU-NET, utilizing the DeepLABv3 model. Incorporating CLAHE for image enhancement and bolstered by CNN layers, this variant offers a distinct approach. Rigorous experimentation underscores the excellence of our proposed framework in distinguishing complex brain tissues, surpassing current state-of-the-art models. Impressively, we achieve accuracy rates exceeding 99% across two publicly available datasets. Performance metrics such as the Jaccard index, sensitivity, and specificity further substantiate the effectiveness of our Hybrid U-Net model.

Infrared technology holds significant importance in the detection of electrical equipment, as it has the capability to swiftly and securely identify electrical apparatus. To simplify the implementation of proficient detection frameworks for electrical equipment within constrained settings (like embedded apparatus), this study presents an enhanced, lightweight model of the single-shot multibox detector (SSD). This model specifically addresses the detection of multiple equipment objects within infrared imagery. The model realized the lightweight of the model by using the network structure characteristics of squeezenet to modify the backbone network of SSD, and compensated for the impact of the lightweight model on the detection accuracy by adding multiple convolutional layers and connecting branches to enhance the propagation ability and extraction ability of features. To ensure a comprehensive evaluation of the model’s detection capabilities, all the models discussed in this study employed the technique of random weight initialization. This approach was utilized to validate the optimal structure of the model and its performance. The experimentation was conducted on both the PASCAL VOC 2007 benchmark dataset and an infrared image dataset encompassing five distinct categories of electrical equipment found within substations. The experimental outcomes indicate that this model offers an efficient approach for achieving lightweight, real-time detection of electrical apparatus.

Abstract Background Super-enhancers (SEs), driving high-level expression of genes with tumor-promoting functions, have been investigated recently. However, the roles of super-enhancer-associated lncRNAs (SE-lncRNAs) in tumors remain undetermined, especially in gliomas. We here established a SE-lncRNAs expression-based prognostic signature to choose the effective treatment of glioma and identify a novel therapeutic target. Methods Combined analysis of RNA sequencing (RNA-seq) data and ChIP sequencing (ChIP-seq) data of glioma patient-derived glioma stem cells (GSCs) screened SE-lncRNAs. Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets served to construct and validate SE-lncRNA prognostic signature. The immune profiles and potential immuno- and chemotherapies response prediction value of the signature were also explored. Moreover, we verified the epigenetic activation mechanism of LINC00945 via the ChIP assay, and its effect on glioma was determined by performing the functional assay and a mouse xenograft model. Results 6 SE-lncRNAs were obtained and identified three subgroups of glioma patients with different prognostic and clinical features. A risk signature was further constructed and demonstrated to be an independent prognostic factor. The high-risk group exhibited an immunosuppressive microenvironment and was higher enrichment of M2 macrophage, regulatory T cells (Tregs), and Cancer-associated fibroblasts (CAFs). Patients in the high-risk group were better candidates for immunotherapy and chemotherapeutics. The SE of LINC00945 was further verified via ChIP assay. Mechanistically, BRD4 may mediate epigenetic activation of LINC00945. Additionally, overexpression of LINC00945 promoted glioma cell proliferation, EMT, migration, and invasion in vitro and xenograft tumor formation in vivo. Conclusion Our study constructed the first prognostic SE-lncRNA signature with the ability to optimize the choice of patients receiving immuno- and chemotherapies and provided a potential therapeutic target for glioma.

This paper aims to investigate the effects of several novel preserving methods on the quality of wet konjac noodles during storage. Wet konjac noodles were prepared by konjac flour, soy protein isolate and sweet potato starch. Fresh-keeping treatments by single acid (pH = 3) soaking (CA group), acid soaking followed by pasteurization and vacuum packing (CF group), and alkaline immersion followed by pasteurization and vacuum packing (CI group) were set. As a result, CF and CI groups could well inhibit the growth of microorganisms during storage at room temperature (28 ± 1 °C) for 8 weeks. The treated wet konjac noodles also had stable sensory quality, better odor and taste, and higher chewiness and elasticity compared with the control (CK) group. Compared to CI treatment, CF treatment exhibited more desirable performance in whiteness, sensory characteristics, textural properties and internal microstructure of the product. In summary, the use of acid soaking followed by pasteurization and vacuum packing technology is an efficacious way to ensure the expected shelf life of wet konjac noodles. This technology could also provide some theoretical and technical support for enterprises to process and produce wet konjac noodles and other high moisture foods.

The whole-grain, hulled Tartary buckwheat flour (HTBF) with outstanding bioactive functions was prepared, and the effects of partial substitution ratios (0, 30%, 51% and 70%) of wheat flour with HTBF on the characteristics of TB noodles (TBNs) were investigated, mainly including the cooking characteristics, sensory analysis, internal structure, bioactive components, and in vitro starch digestibility. With an increasing replacement level of HTBF, the water absorption index of the noodles decreased, whereas the cooking loss increased. A sensory analysis indicated that there were no off-flavors in all TBN samples. The scanning electron microscope images presented that the wheat noodles, 30% TBNs and 70% TBNs had dense and uniform cross sections. Meanwhile, the deepest color, V-type complexes, and lowest crystallinity (13.26%) could be observed in the 70% TBNs. A HTBF substitution increased the rutin content and the total phenolic and flavonoid contents in the TBNs, and higher values were found in the 70% TBNs. Furthermore, the lowest rapidly digestible starch content (16%) and highest resistant starch content (66%) were obtained in the 70% TBNs. Results demonstrated that HTBF could be successfully applied to make TBNs, and a 70% substitution level was suggested. This study provides consumers with a good option in the realm of special noodle-type products.

The upcoming wireless technology developments in the next generations are expected to substantially transform the vehicle-to-everything (V2X) communication network. The challenge of limited spectrum resources in V2X communication, caused by the need for high data rates, necessitates a thorough analysis of spectrum resource allocation and power control. This complex problem falls under the domain of mixed-integer nonlinear programming; a strategic approach is implemented to overcome these issues, which divides the main challenge into two sub-problems. The issue of resource allocation is addressed by implementing a multiaccess spectrum allocation method, which is deliberately designed to optimize the utilization of the spectrum resources that are currently accessible. Concurrently, the power control issue is resolved by employing a continuous convex approximation technique, which effectively converts non-convex power-allocation issues into convex equivalents. This approach helps to alleviate interference between users. Finally, the simulation results prove that the proposed approaches can improve vehicle performance. The algorithms proposed in this article significantly improve the system throughput and access rate of vehicular user equipment (VUEs) while ensuring the data rate of cellular user equipment (CUEs).

Lightning is one of the frequent natural disasters, which seriously affects the secure and stable operation of the power system, especially the distribution network lines with weak reliability. In order to improve the power supply reliability of the distribution network, higher requirements are put forward for the accuracy of lightning warning. Therefore, this paper establishes a lightning warning model based on comprehensive multi-meteorological factor thresholds and analyzes the meteorological factor data such as atmospheric field strength, echo intensity, echo-top height, and vertical cumulative liquid water content under thunderstorm weather. The threshold value of each factor warning is obtained, and the corresponding threshold weight is calculated by the entropy weight method. According to the weight of each threshold, the comprehensive threshold index of lightning warning is obtained, and the lightning warning is based on this index. A total of 105 lightning data from May to June 2022 in Nanchang city were analyzed as samples. The thresholds of atmospheric field strength, echo intensity, echo-top height, and vertical cumulative liquid water content were 1.2 kV/m, 40 dBZ, 8 km, and 5.2 kg·m−2, respectively. The corresponding weights of each factor were 0.4188, 0.2056, 0.2105, and 0.165, respectively. This model was used to warn a thunderstorm event in July 2022 in Nanchang area. The success rate of the model warning was 0.91, the false alarm rate (FAR) was 0.11, and the critical success index (CSI) was 0.80. Compared with the single-factor threshold lightning warning model, the warning FAR is decreased by 6%, and CSI is increased by 14% while ensuring the high warning success rate.