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Abstract Background Carotid artery stenting (CAS) is a key treatment option for moderate to severe carotid artery stenosis. Carotid stent thrombosis (CST), a rare complication of CAS, has gained significant attention because of its catastrophic nature. More evidences are needed to guide the diagnosis and treatment of CST. Case presentation This study reports a rare case of sub-acute CST following CAS in a 50-year-old male patient who had experienced repeated cerebrovascular events on the premise of taking antiplatelet drugs. He also suffered an occlusion of the left middle cerebral artery (MCA) in the M2 segment, likely caused by an embolus detached from the stent thrombus. The cause of CST in this patient was presumed to be dual antiplatelet resistance (AR), as indicated by genetic testing. After treated with guide catheter-directed thrombolysis, thrombus aspiration, and a second round of thrombolysis, his in-stent thrombus was basically cleared. His M2 occlusion was resolved by mechanical thrombectomy using the Solitaire FR/Stent with Intermediate Catheter Assisting technique. The patient recovered well after replacement of antiplatelet drugs, and no new thromboembolic event occurred during the 13-month follow-up period. Conclusions The occurrence rate of AR-related CST may be underestimated as the cause of majority CST cases remains unclear. Implementation of genetic test for aspirin and clopidogrel resistance may be helpful to find the possible cause of CST and to avoid future repeated cerebrovascular events by replacement of antiplatelet drugs.

Burned forests were detected using remote sensing techniques. Yet, identifying long-term burned forests in the mountains, especially at a large spatial extent, remains a great challenge due to a lack of long-term high-resolution remote sensing data or the unsatisfactory performance of the moderate-resolution remotely sensed data in complex mountain landscapes. Efficient, robust, and cost-effective methods are urgently called for. In this study, we developed a novel method combining remote sensing and ecological mechanisms (short for RSEM-M) with a paired-pixel approach to identify the long-term burned forests in Southwest China, where the terrain is rugged. In mapping burned forests in 2010, the overall accuracy, producer accuracy, user accuracy, Kappa coefficient, and field validation accuracy were 92.27 %, 95.86 %, 88.36 %, 0.85, and 81.16 %, respectively. Compared to the standard procedure and FireCCI51 and MCD64A1 fire products, the RSEM-M shows higher accuracy and consistency in identifying burned forests of different sizes, with fewer invalid pixels, better spatial continuity, and more accurate boundary delineation. Then, using the RSEM-M, we mapped burned forests in Southwest China from 2002 to 2017. The cumulative areas were 37.69 × 104 ha. The RSEM-M improved the efficiency and accuracy of long-term burned forest identifications in mountainous landscapes at a large spatial extent.

Abstract Organoid and spheroid technology provide valuable insights into developmental biology and oncology. Optical coherence tomography (OCT) is a label-free technique that has emerged as an excellent tool for monitoring the structure and function of these samples. However, mature organoids are often too opaque for OCT. Access to multi-angle views is highly desirable to overcome this limitation, preferably with non-contact sample handling. To fulfil these requirements, we present an ultrasound-induced reorientation method for multi-angle-OCT, which employs a 3D-printed acoustic trap inserted into an OCT imaging system, to levitate and reorient zebrafish larvae and tumor spheroids in a controlled and reproducible manner. A model-based algorithm was developed for the physically consistent fusion of multi-angle data from a priori unknown angles. We demonstrate enhanced penetration depth in the joint 3D-recovery of reflectivity, attenuation, refractive index, and position registration for zebrafish larvae, creating an enabling tool for future applications in volumetric imaging.

Abstract Due to the high heterogeneity of cancers, it is rather essential to explore driver modules with the help of gene mutation data as well as known interactions between genes/proteins. Unfortunately, latent false positive interactions are inevitable in the Protein-Protein Interaction (PPI) network. Hence in the presented method, a new weight evaluation index, based on the gene-microRNA network as well as somatic mutation profile, is introduced for weighting the PPI network first. Subsequently, the vertices in the weighted PPI network are hierarchically clustered by measuring the Mahalanobis distance of their feature vectors, extracted with the graph embedding method Node2vec. Finally, a heuristic process with dropping and extracting is conducted on the gene clusters to produce a group of gene modules. Numerous experiment results demonstrate that the proposed method exhibits superior performance to four cutting-edge identification methods in most cases regarding the capability of recognizing the acknowledged cancer-related genes, generating modules having relatively high coverage and mutual exclusivity, and are significantly enriched for specific types of cancers. The majority of the genes in the identified modules are involved in cancer-related signaling pathways, or have been reported to be carcinogenic in the literature. Furthermore, many cancer related genes detected by the proposed method are actually omitted by the four comparison methods, which has been verified in the experiments.

Abstract Philadelphia chromosome (Ph)‐like acute lymphoblastic leukemia (ALL) is recognized for its genetic and clinical diversity. In this study, we identified a novel high‐risk subset of Ph‐like ALL, characterized by the activation of oncogenic signaling and the inactivation of the tumor suppressor gene IKZF1, resulting in a dismal outcome. The association between cytogenetic aberrations and clinical features was assessed on a cohort of 191 patients with Ph‐like ALL. Our findings revealed that patients with inactivation of IKZF1 combined with activation of oncogenic signaling (CRLF2/EPOR/JAK2 rearrangements or p‐CRKL/p‐STAT5 high expression) had the worst outcome (3‐year overall survival [OS] of 28.8% vs. 80.1% for others, p

Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively monitor organoid growth. Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis due to its high-resolution, label-free, non-destructive, and real-time 3D imaging capabilities, but accurately identifying and quantifying organoids in OCT images remain challenging due to various factors. Here, we propose an automatic deep learning-based pipeline with convolutional neural networks that synergistically includes optimized preprocessing steps, the implementation of a state-of-the-art deep learning model, and ad-hoc postprocessing methods, showcasing good generalizability and tracking capabilities over an extended period of 13 days. The proposed tracking algorithm thoroughly documents organoid evolution, utilizing reference volumes, a dual branch analysis, key attribute evaluation, and probability scoring for match identification. The proposed comprehensive approach enables the accurate tracking of organoid growth and morphological changes over time, advancing organoid analysis and serving as a solid foundation for future studies for drug screening and tumor drug sensitivity detection based on organoids.

Cells are highly sensitive to their geometrical and mechanical microenvironment that directly regulate cell shape, cytoskeleton and organelle, as well as the nucleus morphology and genetic expression. The emerging two-dimensional micropatterning techniques offer powerful tools to construct controllable and well-organized microenvironment for single-cell level investigations with qualitative analysis, cellular standardization, and in vivo environment mimicking. Here, we provide an overview of the basic principle and characteristics of the two most widely-used micropatterning techniques, including photolithographic micropatterning and soft lithography micropatterning. Moreover, we summarize the application of micropatterning technique in controlling cytoskeleton, cell migration, nucleus and gene expression, as well as intercellular communication.

In the era of multi-messenger astronomy, gamma-ray bursts (GRBs) with known redshifts, especially high-redshift GRBs, are a powerful tool for studying the structure and evolution of the early Universe. We review the background, the history, and the application of measurement methods of GRB redshifts in astronomy. Based on different observation targets, two measurement methods are mainly introduced. One is on GRB afterglow, the other is on GRB host galaxy. There are various processing methods belonging to measurement methods based on afterglow, including spectral measurement method of afterglow and afterglow spectral energy distribution fitting method with improved methods. There are also numerous measurement methods based on host galaxy, such as spectral measurement method of host galaxy, template matching method of host galaxy, some automatic spectroscopic redshift measurement methods, and machine learning methods. We subsequently introduce the principles, effects, and performance of these methods. We enumerate several detection and measurement instruments, which have been used in observation. The characteristics, advantages, disadvantages, and applicability of the GRB redshift measurement methods are summarized and analyzed. Furthermore, we provide a data set of 611 GRBs with measured redshift. The data set has been collected since 1997. Analysis and statistics are presented based on this data set. We summarize the characteristics of GRBs such as location, time, and accuracy. Finally, we introduce Space-based multi-band astronomical Variable Objects Monitor (SVOM) mission dedicated to searching high redshift GRBs. We also introduce the application prospect of various redshift measurement methods in SVOM mission.

Laser Time Transfer (LTT) has proven to be able to improve remote time transfer accuracy compared to microwave technology. The impact of satellite clock errors and atmospheric delays during LTT will be further reduced in the common-view mode. The challenge is presented as an optimization problem that is limited by satellite trajectories. This paper introduces an improved simulated annealing algorithm designed to maximize the common-view possibility among various station pairs within regional Satellite Laser Ranging (SLR) networks by optimizing satellite orbit trajectories. The study proposes a system model that integrates LTT principles with satellite visibility considerations. The simulated annealing algorithm is improved with new annealing strategies that incorporate control strategies, and modify the cooling function. Comparative simulation analyses demonstrate the effectiveness of the algorithm, resulting in a significant reduction in computation time by over 10 times. The optimized orbits exhibit common-view windows between 3.337 and 8.955 times longer than existing orbits. Further simulations are conducted to optimize the orbits, and common-view models are established for 45 pairs among 10 stations. The optimizations result in common-view times ranging from 6.183 to 60.633 min in the Asia-Pacific region and from 5.583 to 61.75 min in the Europe-to-Asia region. This can provide valuable references for designing satellite constellations.

The real-time light curve classification of transients is helpful in searching for rare transients. We propose a new algorithm based on machine learning, namely the Temporary Convective Network and Light Gradient Boosting Machine Combined with Weight Module Algorithm (TLW). The TLW algorithm can classify the photometric simulation transients data in g, r, i bands provided via PLAsTiCC, typing Tidal Disruption Event (TDE), Kilonova (KN), Type Ia supernova (SNIa), and Type I Super-luminous supernova (SLSN-I). When comparing the real-time classification results of the TLW algorithm and six other algorithms, such as Rapid, we found that the TLW algorithm has the best comprehensive performance indexes and has the advantages of high precision and high efficiency. The average accuracy of TLW is 84.54%. The average implementation timings of the TLW algorithm for classifying four types of transients is 123.09 s, which is based on TensorFlow’s architecture in windows and python. We use three indicators to prove that the TLW algorithm is superior to the classical Rapid algorithm, including Confusion Matrix, PR curve, and ROC curve. We also use the TLW algorithm to classify ZTF real transients. The real-time classification results for ZTF transients show that the accuracy of the TLW algorithm is higher than the other six algorithms.

Abstract Background Self-efficacy, as the vital determinant of behavior, influencing clinicians’ situation awareness, work performance, and medical decision-making, might affect the incidence of anesthesia-related adverse events (ARAEs). This study was employed to evaluate the association between perceived self-efficacy level and ARAEs. Methods A cross-sectional study was performed in the form of an online self-completion questionnaire-based survey. Self-efficacy was evaluated via validated 4-point Likert scales. Internal reliability and validity of both scales were also estimated via Cronbach’s alpha and validity analysis. According to the total self-efficacy score, respondents were divided into two groups: normal level group and high level group. Propensity score matching and multivariable logistic regression were employed to identify the relationship between self-efficacy level and ARAEs. Results The response rate of this study was 34%. Of the 1011 qualified respondents, 38% were women. The mean (SD) age was 35.30 (8.19) years. The Cronbach’s alpha of self-efficacy was 0.92. The KMO (KMO and Bartlett's test) value of the scale was 0.92. ARAEs occurred in 178 (33.0%) of normal level self-efficacy group and 118 (25.0%) of high level self-efficacy group. Before adjustment, high level self-efficacy was associated with a decreased incidence of ARAEs (RR [relative risk], 0.76; 95% CI [confidence interval], 0.62–0.92). After adjustment, high level self-efficacy was also associated with a decreased incidence of ARAEs (aRR [adjusted relative risk], 0.63, 95% CI, 0.51–0.77). In multivariable logistic regression, when other covariates including years of experience, drinking, and the hospital ranking were controlled, self-efficacy level (OR [odds ratio], 0.62; 95% CI, 0.46–0.82; P = 0.001) was significantly correlated with ARAEs. Conclusions Our results found a clinically meaningful and statistically significant correlation between self-efficacy and ARAEs. These findings partly support medical educators and governors in enhancing self-efficacy construction in clinical practice and training.

Actin cytoskeleton plays crucial roles in various cellular functions. Extracellular matrix (ECM) can modulate cell morphology by remodeling the internal cytoskeleton. To define how geometry of ECM regulates the organization of actin cytoskeleton, we plated individual NIH 3T3 cells on micropatterned substrates with distinct shapes and sizes. It was found that the stress fibers could form along the nonadhesive edges of T-shaped pattern, but were absent from the opening edge of V-shaped pattern, indicating that the organization of actin cytoskeleton was dependent on the mechanical environment. Furthermore, a secondary actin ring was observed on 50[Formula: see text][Formula: see text]m circular pattern while did not appear on 30[Formula: see text][Formula: see text]m and 40[Formula: see text][Formula: see text]m pattern, showing a size-dependent organization of actin cytoskeleton. Finally, osteoblasts, MDCK and A549 cells exhibited distinct organization of actin cytoskeleton on T-shaped pattern, suggesting a cell-type specificity in arrangement of actin cytoskeleton. Together, our findings brought novel insight into the organization of actin cytoskeleton on micropatterned environments.

Endovascular thrombectomy (EVT) is the recommended option for acute ischemic stroke (AIS) patients with large vessel occlusion (LVO) that within 6 h onset of stroke. The EVT treatment time window has been extended to 24 h in carefully selected patients by DAWN trial. Recent evidences indicated that some patients presented beyond 24 h still potentially benefit from EVT treatment. Herein, we describe one case of successful delayed EVT in a 50-year-old male AIS patient with an 8-day history of left middle cerebral artery occlusion. Before surgery, CT perfusion demonstrated a marked left hypoperfusion with penumbra volume of 127 mL and ischemic core volume of 10 mL. EVT was performed with complete recanalization and significant improvement in his neurological deficits at 90-days post-surgery follow-up. In future, more randomized clinical trials are warranted to further confirm the safety, efficacy, as well as the applicable population of delayed EVT.

FePS$_3$ is a prototype van der Waals layered antiferromagnet and a Mott insulator under ambient conditions, which has been recently reported to go through a pressure-induced dimensionality crossover and an insulator-to-metal transition. These transitions also lead to the appearance of a novel magnetic metallic state. To further understand these emergent structural and physical properties, we have performed a first-principles study using van der Waals and Hubbard $U$ corrected density functional theory including a random structure search. Our computational study attempts to interpret the experimental coexistence of the low- and intermediate-pressure phases and we predict a novel high-pressure phase with distinctive dimensionality and different possible origins of metallicity.

Background: Non-intubated video-assisted thoracic surgery (NIVATS) can be safely performed in lung volume reduction surgery for patients with severe pulmonary dysfunction. However, there is still no cohort observation on the effects of NIVATS on patients with pulmonary dysfunction undergoing different types of thoracic procedures. This retrospective study aimed to observe the effects of NIVATS for this kind of patients.Methods: Three hundred and twenty-eight patients with moderate to severe obstructive pulmonary dysfunction, who underwent video-assisted thoracic surgery (VATS), were retrospectively collected from June 1st, 2017 to September 30th, 2019. Patients in NIVATS were case-matched with those in intubated video-assisted thoracic surgery (IVATS) by a propensity score-matched analysis. The primary outcome was the comparison of perioperative values, the secondary outcome was the risk factors for postoperative clinical complications (PCP) which were identified by binary logistic regression analysis.Results: After being matched, there were no differences in demographics and preoperative values of pulmonary function between NIVATS and IVATS groups. The duration of surgery and anesthesia had no difference (P = 0.091 and P = 0.467). As for the postoperative recovery, except for the mean intensive care unit (ICU) stay was longer in the IVATS group than in the NIVATS group (P = 0.015), the chest tube removal time and the postoperative hospital stay had no difference (P = 0.394 and P = 0.453), and the incidence of PCP also had no difference (P = 0.121). The binary logistic regression analysis revealed that the history of pulmonary disease, anesthesia method, and surgical location were risk factors of PCP.Conclusion: For patients with pulmonary dysfunction when undergoing different types of thoracic procedures, the NIVATS can be performed as effectively and safely as the IVATS, and can reduce the ICU stay.

Rapeseed meal (RSM) is a major by-product of oil extraction from rapeseed, consists mainly of proteins and phenolic compounds. The use of RSM as protein feedstock for microbial fermentation is always hampered by phenolic compounds, which have antioxidant property with health-promoting benefits but inhibit bacterial growth. However, there is still not any good process that simultaneously improve extraction efficiency of phenolic compounds with conversion efficiency of protein residue into microbial production. Here we established a two-step strategy including fungal pretreatment followed by extraction of phenolic compounds. This could not only increase extraction efficiency and antioxidant property of phenolic compounds by about 2-fold, but also improve conversion efficiency of protein residue into iturin A production by Bacillus amyloliquefaciens CX-20 by about 33%. The antioxidant and antibacterial activities of phenolic extracts were influenced by both total phenolic content and profile, while microbial feedstock value of residue was greatly improved because protein content was increased by ∼5% and phenolic content was decreased by ∼60%. Moreover, this two-step process resulted in isolating more proteins from RSM, bringing iturin A production to 1.95 g/L. In conclusion, high-value-added and graded utilization of phenolic extract and protein residue from RSM with zero waste is realized by a two-step strategy, which combines both benefits of fungal pretreatment and phenolic extraction procedures.

Resonance coupling between plasmonic resonances in metallic nanostructures and excitons in two-dimensional (2D) semiconductors has attracted much recent attention. The 2D semiconductor excitons are sensitive to external stimulus, enabling active tuning on the resonance couplings by physical, such as applying electrostatic gating, thermal scanning, etc., or chemical approaches. Among the others, chemical tuning approach has the advantage of facile implementation, high efficiency, and being capable of large-area tuning. Here, we report on chemical tuning of resonance coupling in heterostructures consisted of individual gold nanorods integrated with monolayer WS2. We showed that by incubating the heterostructures into a bis (trifluoro-methane) sulfonimide (TFSI) solution, the exciton transition strength of the WS2 will be enhanced significantly. As a result, the resonance coupling in the heterostructures evolved from a weak coupling regime to a strong coupling one, with the mode splitting energy increases from 94.96 to 105.32 meV. These findings highlight the potential of chemical treatment as an efficient technique for tailoring the interactions between plasmonic nanostructures and 2D semiconductors.

In this work, a novel fiber optic sensor based on Fabry–Pérot interferometry is adopted in an optical coherence photoacoustic microscopy (OC-PAM) system to enable high-resolution in vivo imaging. The complete OC-PAM system is characterized using the fiber optic sensor for photoacoustic measurement. After characterization, the performance of the system is evaluated by imaging zebrafish larvae in vivo. With a lateral resolution of 3.4 μm and an axial resolution of 3.7 μm in air, the optical coherence microscopy subsystem visualizes the anatomy of the zebrafish larvae. The photoacoustic microscopy subsystem reveals the vasculature of the zebrafish larvae with a lateral resolution of 1.9 μm and an axial resolution of 37.3 μm. As the two modalities share the same sample arm, we obtain inherently co-registered morphological and vascular images. This OC-PAM system provides comprehensive information on the anatomy and vasculature of the zebrafish larvae. Featuring compactness, broad detection bandwidth, and wide detection angle, the fiber optic sensor enables a large field of view with a static sensor position. We verified the feasibility of the fiber optic sensor for dual-modality in vivo imaging. The OC-PAM system, as a non-invasive imaging method, demonstrates its superiority in the investigation of zebrafish larvae, an animal model with increasing significance in developmental biology and disease research. This technique can also be applied for functional as well as longitudinal studies in the future.

Ultrathin hexagonal boron nitride (h-BN) has recently attracted a lot of attention due to its excellent properties. With the rapid development of chemical vapor deposition (CVD) technology to synthesize wafer-scale single-crystal h-BN, the properties of h-BN have been widely investigated with a variety of material characterization techniques. However, the electronic properties of monolayer h-BN have rarely been quantitatively determined due to its atomically thin thickness and high sensitivity to the surrounding environment. In this work, by the combined use of AFM (atomic force microscope) PeakForce Tunneling (PF-TUNA) mode and Kevin probe force microscopy (KPFM) model, both the electrical resistivity (529 MΩ cm) and the inherent Fermi level (∼4.95 eV) of the as-grown monolayer h-BN flakes on the copper substrate have been quantitatively analyzed. Moreover, direct visualization of the high-temperature oxidation-resistance effect of h-BN nanoflakes has been presented. Our work demonstrates a direct estimation of the electronic properties for 2D materials on the initial growth substrate without transfer, avoiding any unwanted contaminations introduced during the transfer process. The quantitative analysis by state-of-the-art atomic force microscope techniques implies that monolayer h-BN can be employed as an atomically thin and high-quality insulator for 2D electronics, as well as a high-temperature antioxidation layer for electronic device applications.

The increasing production of manure is a challenge for livestock management systems as well as the global environment. Being traditionally, and still dominantly, used as fertilizers, land application of manure could preserve soil fertility and improve soil carbon sequestration. However, manure application also increases nitrous oxide (N _2 O) emissions that might outweigh the benefits of carbon gains. Here we quantify soil carbon change and greenhouse gas (GHG) emissions from corn production systems in the United States from a life-cycle perspective. We show that utilizing manure can reduce mineral fertilizers use, and therefore avoid GHG emissions that would otherwise occur due to mineral fertilizer production and application. As a result, corn produced under manure has a reduced intensity of GHG emissions (1.5 t CO _2 e ha ^−1 or 0.15 t CO _2 e t ^−1 corn grain), about 15% less than those under sole mineral fertilizers. Owing to a sizeable amount of avoided emissions counterbalancing N _2 O, the soil carbon gain derived from manure use can largely contribute to net climate change mitigation. It should be noted that GHG emissions estimation can be largely improved as more robust and recent data become available to better represent spatially specific land management and to integrate ecosystem models with life-cycle model. Future studies are merited to further assess the alternative fate of manure, and expand the system boundary to assess agriculture and livestock sectors holistically.

This editorial provides an overview of recent advancements in the understanding and treatment of neurological disorders, focusing on aging, immunity, and blood flow, as featured in this special issue. The first section explores the importance of identifying biomarkers of aging and aging-related diseases, such as Alzheimer's Disease, highlighting the emerging role of saliva-based biomarkers and the gut-brain axis in disease diagnosis and management. In the subsequent section, the dysregulated immune systems associated with aging are discussed, emphasizing the intricate landscape of the immune system during aging and its bidirectional relationship with neuroinflammation. Additionally, insights into the involvement of Myeloid-Derived Suppressor Cells (MDSCs) in Multiple Sclerosis (MS) pathogenesis are presented. The third section examines the role of microglia in neuroinflammation and various neurological diseases, including age-related macular degeneration (AMD) and Tuberculous Meningitis (TBM). Furthermore, the therapeutic potential of stem cell and extracellular vesicle-based therapies for stroke is explored, along with molecular mechanism of how inflammation regulates cerebral and myocardial ischemia. Finally, the importance of blood flow in maintaining vascular health and its impact on neurological disorders are discussed, highlighting the potential of novel assessment methods for optimizing patient care. Overall, this special issue offers valuable insights into the complex mechanisms underlying neurological disorders and identifies potential avenues for therapeutic intervention. [ABSTRACT FROM AUTHOR]

Ependymal cells are indispensable components of the central nervous system (CNS). They originate from neuroepithelial cells of the neural plate and show heterogeneity, with at least three types that are localized in different locations of the CNS. As glial cells in the CNS, accumulating evidence demonstrates that ependymal cells play key roles in mammalian CNS development and normal physiological processes by controlling the production and flow of cerebrospinal fluid (CSF), brain metabolism, and waste clearance. Ependymal cells have been attached to great importance by neuroscientists because of their potential to participate in CNS disease progression. Recent studies have demonstrated that ependymal cells participate in the development and progression of various neurological diseases, such as spinal cord injury and hydrocephalus, raising the possibility that they may serve as a potential therapeutic target for the disease. This review focuses on the function of ependymal cells in the developmental CNS as well as in the CNS after injury and discusses the underlying mechanisms of controlling the functions of ependymal cells. [ABSTRACT FROM AUTHOR]