Ultrasound-Guided Microwave Ablation of Thyroid Schwannoma.

Thyroid schwannoma, a rare neoplasm of the thyroid gland, originates from Schwann cells that form the myelin sheath. A 47-year-old woman presented with a progressively enlarging thyroid nodule, which was monitored by repeated ultrasonography over the previous 2 years. Following a diagnosis of thyroid schwannoma by core needle biopsy and immunohistochemical staining, the patient underwent ultrasound-guided microwave ablation (MWA). Subsequent thyroid ultrasounds indicated a gradual decrease in the tumor's volume, achieving a 12-month volume reduction ratio of 79.20%. No complications were observed. Ultrasound-guided MWA may serve as an effective alternative to conventional surgery for managing thyroid schwannomas.

Engineering microvascular networks using a KLF2 reporter to probe flow-dependent endothelial cell function.

Shear stress generated by the flow of blood in the vasculature is a potent regulator of endothelial cell function and vascular structure. While vascular responses to flow are complex and context-dependent, endothelial cell signaling in response to shear stress induced by laminar flows is coordinated by the transcription factor KLF2. The flow-dependent expression of KLF2 in endothelial cells is associated with a quiescent, anti-inflammatory phenotype and has been well characterized in two-dimensional systems but has not been studied in three-dimensional in vitro systems. Here we develop engineered microvascular networks (MVNs) that incorporate a KLF2-based endothelial cell flow sensor within a microfluidic chip, apply continuous flow using an attached microfluidic pump, and study the effects of this flow on vascular structure and function. We found that application of flow to MVNs for 48 h resulted in increased expression of the KLF2 reporter, larger vessel diameters, and decreased vascular branching and resistance. Notably, vessel diameters after the application of flow were independent of initial MVN morphologies. Finally, we found that MVNs exposed to flow have improved vascular barrier function and decreased platelet adhesion. MVNs with KLF2-based flow sensors represent a novel, powerful tool for evaluating the structural and functional effects of flow on engineered three-dimensional vascular systems.

Genome-Wide Identification and Expression Analysis of ADK Gene Family Members in Cotton under Abiotic Stress.

Adenosine kinase (ADK) is a key enzyme widely distributed in plants, playing an important role in maintaining cellular energy homeostasis and regulating plant growth, development, and responses to environmental stresses. However, research on ADK genes in cotton (Gossypium hirsutum), an economically significant crop, has been limited. This study identified 92 ADK genes from four cotton species (G. arboreum, G. raimondii, G. hirsutum, and G. barbadense) using HMMER and Local BLASTP methods and classified them into six groups. Chromosomal localization revealed a random distribution of ADK genes in G. hirsutum, with 13 genes located on the At subgenome and 14 genes on the Dt subgenome. Gene structure analysis showed consistency in exon-intron organization within subgroups, while conserved motif analysis identified subgroup-specific motifs, indicating functional diversity. Synteny and collinearity mapping analysis revealed that the primary expansion mechanisms of the ADK gene family in cotton are polyploidy and segmental duplication. Cis-regulatory elements in GhADK promoters were classified into light response, hormone response, developmental regulation, and stress response. We also analyzed the expression patterns of GhADK genes under a low temperature (4 °C) and drought conditions. Most GhADK genes responded to cold stress with different expression patterns, indicating their roles in rapid response and long-term cold adaptation. Under drought stress, expression patterns varied, with some genes showing sustained high expression levels. The qRT-PCR validation of transcriptomic data confirmed the stress-induced expression patterns of selected GhADK genes. Functional analysis through the VIGS silencing of GhADK25 demonstrated its importance in cold and drought stress responses, with silencing resulting in poor growth under stress, highlighting its significance in stress tolerance. This study provides a basis for further understanding the evolutionary relationships and functions of the cotton ADK gene family.

Terpene synthases GhTPS6 and GhTPS47 participate in resistance to Verticillium dahliae in upland cotton.

Terpene synthases (TPSs) are enzymes responsible for catalyzing the production of diverse terpenes, the largest class of secondary metabolites in plants. Here, we identified 107 TPS gene loci encompassing 92 full-length TPS genes in upland cotton (Gossypium hirsutum L.). Phylogenetic analysis showed they were divided into six subfamilies. Segmental duplication and tandem duplication events contributed greatly to the expansion of TPS gene family, particularly the TPS-a and TPS-b subfamilies. Expression profile analysis screened out that GhTPSs may mediate the interaction between cotton and Verticillium dahliae. Three-dimensional structures and subcellular localizations of the two selected GhTPSs, GhTPS6 and GhTPS47, which belong to the TPS-a subfamily, demonstrated similarity in protein structures and nucleus and cytoplasm localization. Virus-induced gene silencing (VIGS) of the two GhTPSs yielded plants characterized by increased wilting and chlorosis, more severe vascular browning, and higher disease index than control plants. Additionally, knockdown of GhTPS6 and GhTPS47 led to the down-regulation of cotton terpene synthesis following V. dahliae infection, indicating that these two genes may positively regulate resistance to V. dahliae through the modulation of disease-resistant terpene biosynthesis. Overall, our study represents a comprehensive analysis of the G. hirsutum TPS gene family, revealing their potential roles in defense responses against Verticillium wilt.

Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Toward a Universal Model.

Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems with ab initio accuracy. Still, the community awaits universal interatomic models that can be applied to a wide range of materials without tuning neural network parameters. We develop a unified deep-learning interatomic potential (the DPA-Semi model) for 19 semiconductors ranging from group IIB to VIA, including Si, Ge, SiC, BAs, BN, AlN, AlP, AlAs, InP, InAs, InSb, GaN, GaP, GaAs, CdTe, InTe, CdSe, ZnS, and CdS. In addition, independent deep potential models for each semiconductor are prepared for detailed comparison. The training data are obtained by performing density functional theory calculations with numerical atomic orbitals basis sets to reduce the computational costs. We systematically compare various properties of the solid and liquid phases of semiconductors between different machine-learning models. We conclude that the DPA-Semi model achieves GGA exchange-correlation functional quality accuracy and can be regarded as a pretrained model toward a universal model to study group IIB to VIA semiconductors.

Tetrad stage transient cold stress skews auxin-mediated energy metabolism balance in Chinese cabbage pollen.

Changing ambient temperature often impairs plant development and sexual reproduction, particularly pollen ontogenesis. However, mechanisms underlying cold stress-induced male sterility are not well understood. Here, we exposed Chinese cabbage (Brassica campestris) to different cold conditions during flowering and demonstrated that the tetrad stage was the most sensitive. After completion of pollen development at optimal conditions, transient cold stress at the tetrad stage still impacted auxin levels, starch and lipid accumulation, and pollen germination, ultimately resulting in partial male sterility. Transcriptome and metabolome analyses and histochemical staining indicated that the reduced pollen germination rate was due to the imbalance of energy metabolism during pollen maturation. The investigation of ß-glucuronidase (GUS)-overexpressing transgenic plants driven by the promoter of DR5 (DR5::GUS report system) combined with cell tissue staining and metabolome analysis further validated that cold stress during the tetrad stage reduced auxin levels in mature pollen grains. Low-concentration auxin treatment on floral buds at the tetrad stage before cold exposure improved the cold tolerance of mature pollen grains. Artificially changing the content of endogenous auxin during pollen maturation by spraying chemical reagents and loss-of-function investigation of the auxin biosynthesis gene YUCCA6 by artificial microRNA technology showed that starch overaccumulation severely reduced the pollen germination rate. In summary, we revealed that transient cold stress at the tetrad stage of pollen development in Chinese cabbage causes auxin-mediated starch-related energy metabolism imbalance that contributes to the decline in pollen germination rate and ultimately seed set.

Microwave ablation for recurrent primary hyperparathyroidism in four patients with multiple endocrine neoplasia type 1: a case series report.

Multiple endocrine neoplasia type 1 (MEN1), a rare tumor syndrome, is inherited in an autosomal dominant pattern, mainly manifested as primary hyperparathyroidism (PHPT). Surgery is preferred for patients with MEN1 and PHPT. Thermal ablation has been widely applied for PHPT but rarely for postoperative recurrent PHPT in MEN1 patients. Based on a series of cases, we aimed to investigate the clinical efficacy and safety of ultrasound-guided percutaneous microwave ablation in the treatment of MEN1 patients with postoperative recurrence of PHPT.

Enhancing adversarial attacks with resize-invariant and logical ensemble.

In black-box scenarios, most transfer-based attacks usually improve the transferability of adversarial examples by optimizing the gradient calculation of the input image. Unfortunately, since the gradient information is only calculated and optimized for each pixel point in the image individually, the generated adversarial examples tend to overfit the local model and have poor transferability to the target model. To tackle the issue, we propose a resize-invariant method (RIM) and a logical ensemble transformation method (LETM) to enhance the transferability of adversarial examples. Specifically, RIM is inspired by the resize-invariant property of Deep Neural Networks (DNNs). The range of resizable pixel is first divided into multiple intervals, and then the input image is randomly resized and padded within each interval. Finally, LETM performs logical ensemble of multiple images after RIM transformation to calculate the final gradient update direction. The proposed method adequately considers the information of each pixel in the image and the surrounding pixels. The probability of duplication of image transformations is minimized and the overfitting effect of adversarial examples is effectively mitigated. Numerous experiments on the ImageNet dataset show that our approach outperforms other advanced methods and is capable of generating more transferable adversarial examples.

Mechanical Properties and Microscopic Mechanism of a Multi-Cementitious System Comprising Cement, Fly Ash, and Steel Slag Powder.

The objective of this study was to reduce the stockpile of steel slag, which is a solid waste generated in the steelmaking process, and promote the resource utilization of steel slag powder (SSP) in construction projects. Experimental research was conducted on SSP and fly ash (FA) as supplementary cementitious materials. Composite cement paste samples were prepared to investigate the effects of the water-to-binder ratio and cement-substitution rate on the macroscopic mechanical properties, including the setting time, fluidity, flexural strength, and compressive strength of the prepared paste. The mineral composition in the raw materials was measured using X-ray diffraction (XRD), and a micro-morphological and structural analysis of the hydrated cementitious material samples was performed using scanning electron microscopy (SEM); the SEM and Image Pro Plus (IPP) image analysis techniques were combined for a quantitative analysis of the microstructure. The results showed that the addition of FA and SSP delayed the hydration of cement, thereby improving the flowability of the composite paste. Under the same curing age and cement substitution rate, the sample strength decreased with increasing water-to-binder ratio. Under the same water-to-binder ratio and curing age, the variations in the flexural and compressive strengths of the SSP group samples were inconsistent in the early and later stages, and the sample group with 20% SSP exhibited optimal mechanical strength in the later stage. The microscopic results showed that the needle-like AFt crystals in the hydrated pores decreased in number with the increase in the SSP content. The hydration products of the FA-SSP admixture, such as C-S-H gel and RO phase, acted as pore fillers in alkaline environments. When the water-to-binder ratio was 0.4 and the FA-to-SSP ratio was 1:1 to replace 40% cement, the performance of the hardened cement paste was the best among all the test groups containing both FA and SSP. This study provides a theoretical basis for the practical application of SSP and FA as cementitious materials in construction-related fields.

Efficient spatial and channel net for lane marker detection based on self-attention and row anchor.

Lane detection is an important component of advanced driving aided system (ADAS). It is a combined component of the planning and control algorithms. Therefore, it has high standards for the detection accuracy and speed. Recently several researchers have worked extensively on this topic. An increasing number of researchers have been interested in self-attention-based lane detection. In difficult situations such as shadows, bright lights, and nights extracting global information is effective. Regardless of channel or spatial attention, it cannot independently extract all global information until a complicated model is used. Furthermore, it affects the run-time. However trading in this contradiction is challenging. In this study, a new lane identification model that combines channel and spatial self-attention was developed. Conv1d and Conv2d were introduced to extract the global information. The model is lightweight and efficient avoiding difficult model calculations and massive matrices, In particular obstacles can be overcome under certain difficult conditions. We used the Tusimple and CULane datasets as verification standards. The accuracy of the Tusimple benchmark was the highest at 95.49%. In the CULane dataset, the proposed model achieved 75.32% in F1, which is the highest result, particularly in difficult scenarios. For the Tusimple and CULane datasets, the proposed model achieved the best performance in terms of accuracy and speed.

Ultrasound-guided microwave ablation in the treatment of recurrent primary hyperparathyroidism in a patient with MEN1: a case report.

Background: Multiple endocrine neoplasia type 1 (MEN1) is an inherited endocrine syndrome caused by the mutation in the tumor suppressor gene MEN1. The recurrence rate of primary hyperparathyroidism (PHPT) in patients with MEN1 after parathyroidectomy remains high, and the management of recurrent hyperparathyroidism is still challenging. Case presentation: We reported a 44-year-old woman with MEN1 combined with PHPT who was diagnosed through genetic screening of the patient and her family members. After parathyroidectomy to remove one parathyroid gland, the patient suffered from persistent high levels of serum calcium and parathyroid hormone, which returned to normal at up to 8 months after ultrasound-guided microwave ablation (MWA) for bilateral parathyroid glands, suggesting an acceptable short-term prognosis. Conclusion: Ultrasound-guided MWA for parathyroid nodules may be an effective therapeutic strategy for recurrent PHPT in MEN1 patients.

ADDNS: An asymmetric dual deep network with sharing mechanism for medical image fusion of CT and MR-T2.

Medical images with different modalities have different semantic characteristics. Medical image fusion aiming to promotion of the visual quality and practical value has become important in medical diagnostics. However, the previous methods do not fully represent semantic and visual features, and the model generalization ability needs to be improved. Furthermore, the brightness-stacking phenomenon is easy to occur during the fusion process. In this paper, we propose an asymmetric dual deep network with sharing mechanism (ADDNS) for medical image fusion. In our asymmetric model-level dual framework, primal Unet part learns to fuse medical images of different modality into a fusion image, while dual Unet part learns to invert the fusion task for multi-modal image reconstruction. This asymmetry of network settings not only enables the ADDNS to fully extract semantic and visual features, but also reduces the model complexity and accelerates the convergence. Furthermore, the sharing mechanism designed according to task relevance also reduces the model complexity and improves the generalization ability of our model. In the end, we use the intermediate supervision method to minimize the difference between fusion image and source images so as to prevent the brightness-stacking problem. Experimental results show that our algorithm achieves better results on both quantitative and qualitative experiments than several state-of-the-art methods.

Performance of multigene testing in cytologically indeterminate thyroid nodules and molecular risk stratification.

Objective: Thyroid cancer is the third most prevalent cancer among females. Genetic testing based on next-generation sequencing may provide an auxiliary diagnosis to reduce cytologically diagnostic uncertainty. However, commercial multigene tests are not widely available and are not well-tested in the Chinese population. Methods: In this study, we designed a multigene testing panel and evaluated its performance in 529 cytologically indeterminate thyroid nodules (Bethesda III, IV and V). The molecular data of the DNA mutations and RNA fusions of fine needle aspiration samples were reviewed in conjunction with a clinical diagnosis, pathological reports, and definitive surgery for retrospective analysis. Then, the molecular risk stratification was investigated for its accuracy in malignant risk prediction. Results: The overall combined consistency revealed substantial agreement (Kappa = 0.726) with the sensitivity, specificity, positive predictive value, and negative predictive values of 97.80%, 82.14%, 98.99%, and 67.65%, respectively. The most common aberration was BRAFV600E (82.59%), followed by NRAS mutants (4.07%), RET fusions (3.70%), and KRAS mutants (3.15%). Two cases (0.44%) were categorized into a high-risk group, 426 cases (94.67%) were categorized into a BRAF-like group with totally histopathologic papillary patterned tumors, and 22 cases (4.89%) were categorized into a RAS-like group with 14 papillary and eight follicular patterned tumors when the cohort concurrent aberrations were excluded. Potentially aggressive features may be related to concurrent molecular alterations of BRAFV600E with TERTQ302R, and AKT1L52R, NRASG12C, NRASQ61R, and CCDC6-RET fusions. Conclusions: This study provided a multigene panel for identifying benign nodules from cytologically indeterminate thyroid nodules to avoid unnecessary surgery. We provide further evidence for using molecular risk stratification as a promising predictor of disease outcomes. The results of this study may be limited by the extremely high prevalence of cancer in the cohort for clinical reference.

DeePMD-kit v2: A software package for deep potential models.

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

The difference in the composition of gut microbiota is greater among bats of different phylogenies than among those with different dietary habits.

Bats have a very long evolutionary history and are highly differentiated in their physiological functions. Results of recent studies suggest effects of some host factors (e.g., phylogeny and dietary habit) on their gut microbiota. In this study, we examined the gut microbial compositions of 18 different species of bats. Results showed that Firmicutes, Gammaproteobacteria, and Actinobacteria were dominant in all fecal samples of bats. However, the difference in the diversity of gut microbiota among bats of different phylogenies was notable (p = 0.06). Various species of Firmicutes, Actinobacteria, and Gammaproteobacteria were found to contribute to the majority of variations in gut microbiota of all bats examined, and Aeromonas species were much more abundant in bats that feed on both insects and fish than in those of insectivores. The abundance of various species of Clostridium, Euryarchaeota, and ancient bacterial phyla was found to vary among bats of different phylogenies, and various species of Vibrio varied significantly among bats with different dietary habits. No significant difference in the number of genes involved in various metabolic pathways was detected among bats of different phylogenies, but the abundance of genes involved in 5 metabolic pathways, including transcription; replication, recombination, and repair; amino acid transport and metabolism; and signal transduction mechanisms, was different among bats with different dietary habits. The abundance of genes in 3 metabolic pathways, including those involved in stilbenoid, diarylheptanoid, and gingerol biosynthesis, was found to be different between insectivorous bats and bats that feed on both insects and fish. Results of this study suggest a weak association between dietary habit and gut microbiota in most bats but a notable difference in gut microbiota among bats of different phylogenies.

Extreme environments and human health: From the immune microenvironments to immune cells.

Exposure to extreme environments causes specific acute and chronic physiological responses in humans. The adaptation and the physiological processes under extreme environments predominantly affect multiple functional systems of the organism, in particular, the immune system. Dysfunction of the immune system affected by several extreme environments (including hyperbaric environment, hypoxia, blast shock, microgravity, hypergravity, radiation exposure, and magnetic environment) has been observed from clinical macroscopic symptoms to intracorporal immune microenvironments. Therefore, simulated extreme conditions are engineered for verifying the main influenced characteristics and factors in the immune microenvironments. This review summarizes the responses of immune microenvironments to these extreme environments during in vivo or in vitro exposure, and the approaches of engineering simulated extreme environments in recent decades. The related microenvironment engineering, signaling pathways, molecular mechanisms, clinical therapy, and prevention strategies are also discussed.

Finite-Length Analysis for Spatially Coupled LDPC Codes Based on Base Matrix.

Spatially coupled low density parity check (SC-LDPC) are prominent candidates for future communication standards due to their "threshold saturation" properties. To evaluate the finite-length performance of SC-LDPC codes, a general and efficient finite-length analysis from the perspective of the base matrix is proposed. We analyze the evolution of the residual graphs resulting at each iteration during the decoding process based on the base matrix and then derive the expression for the error probability. To verify the effectiveness of the proposed finite-length analysis, we consider the SC-LDPC code ensembles constructed by parallelly connecting multiple chains (PC-MSC-LDPC). The analysis results show that the predicted error probabilities obtained by using the derived expression for the error probability match the simulated error probabilities. The proposed finite-length analysis provides a useful engineering tool for practical SC-LDPC code design and for analyzing the effects of the code parameters on the performances.

Evaluation indices of the temperature difference of subgrade and the optimization of mitigation measures in cold regions.

With the construction and operation of railways in cold regions, the asymmetric deformation of subgrades due to the difference in the transverse ground temperature has become a prominent issue. A comprehensive evaluation of the transverse ground temperature difference and investigation of the corresponding mitigation measures should be conducted to avoid or minimize the damage resulting from this difference, thereby improving subgrade stability and reducing deformation. In this study, the time history variations in the homogeneity and symmetry indices of the ground temperature at typical instances that reflect the spatial and temporal changes in the temperature difference of the subgrade were proposed as evaluation indices. The feasibility of these evaluation indices was verified through numerical models with different types of anti-frost berms. Subsequently, the numerical models were used to analyze the ground temperature evaluation indices of a subgrade with expanded polystyrene (EPS) insulation board and polyurethane (PU) insulation board at different locations. Additionally, the performances of each mitigation measure in eliminating or reducing the ground temperature difference were assessed and compared. The results show that all the mitigation measures could improve the homogeneity and symmetry of the ground temperature distribution. The maximum mitigation rates for the homogeneity and symmetry are 97.87% and 45.90%, respectively. This study provides a comprehensive evaluation method for the temperature difference of subgrades constructed in cold regions and a theoretical reference for the selection of anti-frost measures in the design, operation, and maintenance of subgrades in cold regions.

Confirmation of the existence of Himalayan long-eared bats, Plecotushomochrous (Chiroptera, Vespertilionidae), in China.

The existence of Himalayan long-eared bats, Plecotushomochrous (Chiroptera, Vespertilionidae), in China has not been previously confirmed. In this study, four bats captured with harp traps from two sites in the Maoershan National Nature Reserve in Guangxi, China were investigated. These bats have long, wide auricles, each with a prominent tragus. The length of each auricle is about the same as that of a forearm. Hairs on the ventral fur have a dark base with mixed grey and yellowish tips; those on the dorsal fur also have a dark base and are bicolored with brown tips. The thumbs are very short. A concavity is present in the front of the dorsal side of the cranium. Based on morphological characteristics and phylogeny using Cyt b gene sequences, these bats were identified as P.homochrous, thus confirming the existence of Himalayan long-eared bats in China.

Synthesis and Sensing Performance of Chitin Fiber/MoS2 Composites.

In this study, chitin fibers (CFs) were combined with molybdenum sulfide (MoS2) to develop high-performance sensors, and chitin carbon materials were innovatively introduced into the application of gas sensing. MoS2/CFs composites were synthesized via a one-step hydrothermal method. The surface properties of the composites were greatly improved, and the fire resistance effect was remarkable compared with that of the chitin monomer. In the gas-sensitive performance test, the overall performance of the MoS2/CFs composite was more than three times better than that of the MoS2 monomer and showed excellent long-term stability, with less than 10% performance degradation in three months. Extending to the field of strain sensing, MoS2/CFs composites can realize real-time signal conversion in tensile and motion performance tests, which can help inspectors make analytical judgments in response to the analysis results. The extensive application of sensing materials in more fields is expected to be further developed. Based on the recycling of waste chitin textile materials, this paper expands the potential applications of chitin materials in the fields of gas monitoring, biomedicine, behavioral discrimination and intelligent monitoring.