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Abstract Background The accumulation of senescent microglia has been highlighted as a critical contributor to the progression of tauopathies. Irisin, a muscle-derived hormone produced by the proteolytic cleavage of Fibronectin-domain III containing 5 (FNDC5), mediates the pleiotropic effects of exercise on the physical body. Herein, we investigate the potential role of irisin in microglial senescence in tauopathies. Methods To model tauopathies both in vivo and in vitro, we utilized P301S tau transgenic mice and tau K18 fibril-treated microglia BV2 cells, respectively. We first examined the expression of the irisin expression and senescence phenotypes of microglia in tauopathies. Subsequently, we investigated the impact of irisin on microglial senescence and its underlying molecular mechanisms. Result We observed a reduction in irisin levels and an onset of premature microglial senescence both in vivo and in vitro. Irisin administration was found to counteract microglial senescence and ameliorate cognitive decline in P301S mice. Mechanistically, irisin effectively inhibited microglial senescence by stimulating the expression of mitochondrial transcription factor A (TFAM), a master regulator of mitochondrial respiratory chain biogenesis, thereby enhancing mitochondrial oxidative phosphorylation (OXPHOS). Silencing TFAM eliminated the inhibitory effect of irisin on microglial senescence as well as the restorative effect of irisin on mitochondrial OXPHOS. Furthermore, the SIRT1/PGC1α signaling pathway appeared to be implicated in irisin-mediated upregulation of TFAM. Conclusion Taken together, our study revealed that irisin mitigated microglial senescence via TFAM-driven mitochondrial biogenesis, suggesting a promising new avenue for therapeutic strategies targeting tauopathies.

Abstract The erratic, intermittent, and unpredictable nature of sweat production, resulting from physiological or psychological fluctuations, poses intricacies to consistently and accurately sample and evaluate sweat biomarkers. Skin‐interfaced microfluidic devices that rely on colorimetric mechanisms for semi‐quantitative detection are particularly susceptible to these inaccuracies due to variations in sweat secretion rate or instantaneous volume. This work introduces a skin‐interfaced colorimetric bifluidic sweat device with two synchronous channels to quantify sweat rate and biomarkers in real‐time, even during uncertain sweat activities. In the proposed bifluidic‐distance metric approach, with one channel to measure sweat rate and quantify collected sweat volume, the other channel can provide an accurate analysis of the biomarkers based on the collected sweat volume. The closed channel design also reduces evaporation and resists contamination from the external environment. The feasibility of the device is highlighted in a proof‐of‐the‐concept demonstration to analyze sweat chloride for evaluating hydration status and sweat glucose for assessing glucose levels. The low‐cost yet highly accurate device provides opportunities for clinical sweat analysis and disease screening in remote and low‐resource settings. The developed device platform can be facilely adapted for the other biomarkers when corresponding colorimetric reagents are exploited.

Abstract As sweat biomarker levels are continuously changing over metabolism and daily activities, pathological and physiological processes can be dynamically analyzed by wearable devices. The colorimetric skin-interfaced microfluidic devices that do not have external circuit modules exhibit enhanced deformability with a small footprint. However, it is difficult to achieve sampling over time and self-feedback for closed-loop systems. This review summarizes recent advances in microfluidic valves for biofluid management and chrono-sampling, as well as active triggers in microfluidics self-feedback. After enumerating the current limitations in temporal resolution and reliability, we further point out a few potential feasible strategies for future developments.

Cancer, ranks as the secondary cause of death, is a group of diseases that are characterized by uncontrolled tumor growth and distant metastasis, leading to increased mortality year-on-year. To date, targeted therapy to intercept the aberrant proliferation and invasion is crucial for clinical anticancer treatment, however, mutant expression of target genes often leads to drug resistance. Therefore, it is essential to identify more molecules that can be targeted to facilitate combined therapy. Previous studies showed that death associated protein 3 (DAP3) exerts a pivotal role in regulating apoptosis signaling of tumors, meanwhile, aberrant DAP3 expression is associated with the tumorigenesis and disease progression of various cancers. This review provides an overview of the molecule structure of DAP3 and the discrepant roles played by DAP3 in various types of tumors. Considering the molecular mechanism of DAP3-regulated cancer development, new potential treatment strategies might be developed in the future.

Abstract Cultural heritage identity management is the most basic and important work in the process of cultural heritage protection. It is of great significance to provide a unique and identifiable digital identity for ancient ceramics. At present, the identification information of ancient ceramics is mainly composed of external visual characteristics, and there is no report on feature identification method that can reflect the properties of ancient ceramics. Audible sound signals not only have advantages in non-destructive testing, but also can be used as voiceprint information to identify, monitor and analyze ancient ceramics. In this paper, seven ancient ceramics and 12 similar modern ceramic cups are taken as research objects, and an acoustic identifier (AID) is constructed. We put forward a reliable acoustic identification method for ancient ceramics, and established a digital code of acoustic characteristics of ancient ceramics. The results show that audible sound waves can reflect the attribute information of ancient ceramics. Sufficient acoustic data combined with deep learning can not only accurately match the identity of ancient ceramics, but also detect the real-time identity information of ancient ceramics, and make a comparative analysis of its cracks and whether it has caused damage. This method can provide a variety of practical applications for audible signal feature recognition technology in the exhibition, protection, trading, recognition and safety management of ancient ceramics and other cultural relics.

Intelligent fault diagnostics based on deep learning provides a favorable guarantee for the reliable operation of equipment, but a trained deep learning model generally has low prediction accuracy in cross-domain diagnostics. To solve this problem, a deep learning fault diagnosis method based on the reconstructed envelope spectrum is proposed to improve the ability of rolling bearing cross-domain fault diagnostics in this paper. First, based on the envelope spectrum morphology of rolling bearing failures, a standard envelope spectrum is constructed that reveals the unique characteristics of different bearing health states and eliminates the differences between domains due to different bearing speeds and bearing models. Then, a fault diagnosis model was constructed using a convolutional neural network to learn features and complete fault classification. Finally, using two publicly available bearing data sets and one bearing data set obtained by self-experimentation, the proposed method is applied to the data of the fault diagnostics of rolling bearings under different rotational speeds and different bearing types. The experimental results show that, compared with some popular feature extraction methods, the proposed method can achieve high diagnostic accuracy with data at different rotational speeds and different bearing types, and it is an effective method for solving the problem with cross-domain fault diagnostics for rolling bearings.

Abstract In the field of cultural heritage protection, it is significant to establish a reliable ID (identifier) for valuable cultural and artistic items. At present, the identification of ancient cultural relics is mainly based on image information, such as pictures, 3D (three-dimensional) scanning, X-ray and CT (computed tomography) data. However, in many cases, it is impossible to identify whether slight damage, partial restorations, or ancient cultural relics have been replaced by fakes by using image information. In the era of digital duplication, more reliable identity information is urgently needed. The main technical challenge of an acoustic analysis system for ancient coins based on artificial intelligence technology is to find a non-destructive, fast and accurate identification method for ancient cultural relics. The recognition method includes two main modules: the artificial audio data sampling device and deep learning. In addition, this paper has completed the analysis of the vibration spectrum features of 19 ancient coins and realized the whole process of acoustic ID construction. The open-source platform Easy DL was used to analyze the multidimensional vibration spectrum curve feature extraction and identification. This method enables audio signal signature recognition technology to be applied in the display, preservation, transaction and safety management of ancient coins and other cultural relics.

Allium victorialis L. (Family: Liliaceae) is an herb with nutritional and medicinal properties. In Jilin Province, China, A. victorialis is mainly distributed in the Changbai Mountains and grows in various habitat conditions. However, the relationship between habitat factors and the nutritional quality of A. victorialis in the Changbai Mountains has not yet been examined. We assessed the nutritional quality of five A. victorialis populations growing in five different habitats in the Changbai Mountains and analyzed the relationship between nutritional quality and habitat factors. Allium victorialis populations in this region were primarily found in the undergrowth at elevations above 500 m and within specific ranges of air temperature, air relative humidity, soil temperature, and soil water content. Among the habitat factors investigated, canopy density significantly affected several nutritional components of A. victorialis; however, elevation had a significant effect only on the total flavonoid content, and the vitamin C content was not strongly associated with the main habitat factors in this study. During germplasm selection and artificial cultivation, it is important to simulate the growth conditions of the original habitat. Our results provide useful information for site selection and environmental condition optimization for the artificial cultivation of A. victorialis.

Kimberlite in the Mengyin area serves as an excellent medium for studying the characteristics and evolutionary processes of the Paleozoic mantle. In order to determine the age of the primary calcite within the kimberlite, in situ carbonate U–Pb dating was conducted in the Mengyin area. The results indicate that the primary calcite in the kimberlite originated approximately 383 ± 18 Ma (MSWD = 6.6). This age constraint suggests that the eruption of the kimberlite took place during this period, leading to the thermal alteration of limestone xenoliths, ultimately forming marble. Consequently, it can be inferred that lithospheric thinning occurred no later than the Late Devonian period. Fluid inclusions found within the marble provide further insights into its formation. The recorded formation temperature of the marble ranges from 243°C to 370°C, with a salinity range of 2.57%–14.77% (NaCl). The pressure estimates fall within the range of 3.22–20.70 MPa, indicating a depth mainly between 900 and 1,000 m. Based on these findings, it can be inferred that the overall denudation depth in the west Shandong area, since the Late Devonian, is estimated to be approximately 900–1,000 m. Furthermore, the overall crustal rise rate is estimated to be approximately 3 m/Ma.

The bearing fault diagnosis based on instantaneous angular speed (IAS) has been developed rapidly. However, it is difficult to identify the weak fault of bearings by using the variable reluctance sensor (VRS) as an encoder because of the lack of the high-frequency information in the modulation frequency of IAS. To overcome this shortcoming, a novel method based on the raw signal of the VRS is proposed for bearing diagnosis in this paper. The mechanism of bearing local fault signal response is analyzed by establishing the bearing outer ring fault model and the sensor signal response model, which are explored and verified by the fault simulation experiments. The results show that the gearwheel tooth-pass frequency is modulated by the outer ring fault characteristic frequency, and the defect size can be estimated by the double peaks in the high-frequency band. Moreover, compared with the acceleration signals, the defect size estimations based on the VRS are closer to the actual defect size, which verifies the effectiveness of the proposed method.

The Changbai Mountains comprise one of the main distribution areas of A. victorialis in China, and this species is endangered owing to habitat changes and overexploitation. However, A. victorialis germplasms have not been systematically collected and studied. The aims of this study were to obtain some detailed genetic information, analyze the genetic diversity, and further promote the protection of A. victorialis germplasms from the Changbai Mountains. Transcriptomic analysis was performed with six A. victorialis samples collected from the Changbai Mountains. At least 146,759 genes for each sample were obtained after performing de novo assembly of the RNA-seq data, and at least 92% of these genes were found to have only one mRNA isoform. These sequences and their functional annotations provided a large-scale genetic resource of this species. Phylogenetic analysis showed that A. victorialis was genetically distant from some related species, e.g. Allium sativum, Allium fistulosum, and Allium cepa, but genetically close to Allium tuberosum. The two A. victorialis var. listera samples were phylogenetically separated from the other four samples, and these two samples should be regarded as Allium listera. In addition, two KASP markers for discriminating the Dongfeng samples from the other four A. victorialis samples were successfully developed. This study lays the foundation for future studies on the genetic diversity and evolution of Allium species, as well as for the conservation of A. victorialis germplasms from the Changbai Mountains and other populations of this species.

The Houge’zhuang gold deposit, located in the Penglai–Qixia gold belt of the Jiaodong peninsula, is a representative auriferous quartz vein-style deposit. Pyrite is the most common and main gold-bearing mineral and shows complex textures in the Houge’zhuang gold deposit. Study of ore-related pyrite is of great significance for understanding the metallogenesis of this deposit, especially the gold precipitation mechanism. The present study applied systematic microscopic observation, fluid inclusion analysis, electron microprobes,in situ LA-ICP-MS trace-element analysis, and in situ sulfur isotope analyses. Three types of fluid inclusions were identified, among which the ore-forming fluids exhibited medium–low salinity and temperature, with the fluid inclusions mainly comprising H2O and CO2. Three types of pyrites were identified: 1) Py0, characterized by low concentrations of As and Au and low δ34S values (5.51–6.86‰). 2) Py1, found in the gold-quartz-pyrite veins and homogeneous in chemical composition with no obvious zonal growth but with notably more gold and chalcopyrite inclusions. Py1 contained medium and uniform concentrations of As and was Au-rich, with δ34S values ranging from 7.13 to 7.89‰ (mean 7.44‰). 3) Py2 contained arsenic-bearing pyrite and was found in quartz-polymetallic sulfide veins, with distinct As enrichment and As-rich rims of pyrite and growth zoning. Consequently, the primary ore-forming fluids passed through some arsenic and δ34S-rich sedimentary rocks, such as the Jingshan, Fenzishan, and Penglai groups. With the occurrence of stable water-rock interaction, the extracted fluids were enriched for As and δ34S. Furthermore, Au was closely associated with As, visible gold grains tended to occur in association with Py1 at stage II, and invisible gold was related to the Au-As-rich Py2 of stage III. The As-bearing pyrites shared a close spatiotemporal relationship with gold, playing an important role in the formation and exploration of high-grade gold deposits.

Taking the reducer for robot as the research object，a reducer test device composed of drive system，mechanical system，control system and data acquisition system is developed to test its mechanical transmission performance. In the process of testing，because of the traditional test method is difficult to capture the instantaneous change of the data，this is not conducive to the study of precision gear reducer. In order to solve this problem，the solving method is put forward. The software system based on JUCE libraries is designed，combined with the movement control cards of data transmission，the performance test of speed reducer is finished. The results show that the test system can quickly and accurately measure the transmission performance of the reducer，and it is expected to analyze and optimize to improve the overall performance of the reducer.

Epilepsy is one of the most common neurological disorders characterized by recurrent seizures. The mechanism of epilepsy remains unclear and previous studies suggest that N-methyl-D-aspartate receptors (NMDARs) play an important role in abnormal discharges, nerve conduction, neuron injury and inflammation, thereby they may participate in epileptogenesis. NMDARs belong to a family of ionotropic glutamate receptors that play essential roles in excitatory neurotransmission and synaptic plasticity in the mammalian CNS. Despite numerous studies focusing on the role of NMDAR in epilepsy, the relationship appeared to be elusive. In this article, we reviewed the regulation of NMDAR and possible mechanisms of NMDAR in epilepsy and in respect of onset, development, and treatment, trying to provide more evidence for future studies.

Inverse opal zirconia pigment prepared by traditional process has the disadvantages of low color saturation and variegated color which restrict its further application. In this work, the inverse opal zirconia pigment was prepared by colloidal crystal template fabricated using polystyrene microspheres with particle size of 340 ± 10 nm as raw material and further modified by sintering at 600 °C for 2 h with heating rate of 2 °C/min in an atmosphere tube of 0.8 L min-1 nitrogen flow. The morphology, phase crystallinity and color performance of the inverse opal zirconia pigment before and after modification were characterized in detail and the modified mechanism was investigated. The results showed that morphology of the inverse opal zirconia pigment before modification was basically a highly ordered porous structure, the phase was relatively pure, and the overall appearance was variegated color. Some parts of the samples exhibited low color saturation and different areas showed various colors, indicating that the samples had certain angle dependence. After modification, the samples showed bigger-area single blue-green color, suggesting that the color saturation was significantly improved and the angle dependence was reduced evidently. The mechanism for modification was that the zirconia precursor and polystyrene templates were carbonized when sintered in nitrogen atmosphere. The generated in-situ carbon remained in the samples and absorbed the tray background light, which significantly suppressed the multiple scattering of structural defects.

Optical remote sensing is one of the most attractive options for generating crop cover maps because it enables computation of vegetation indices, which are useful for assessing the condition of vegetation. The Sentinel-2A Multispectral Instrument (MSI), which is a multispectral sensor with 13 bands covering the visible, near infrared and short-wave infrared (SWIR) wavelength regions, offers a vast number of vegetation indices. Spectral indices, which are combinations of spectral measurements at different wavelengths, have been used in the previous studies and they sometimes contributed to improve classification accuracies. In this study, 91 published spectral indices were calculated from the MSI data. Additionally, classification algorithms are essential for generating accurate maps and the random forests classifier is one of which possesses the five hyperparameters were applied. The improvements in classification accuracies were confirmed achieving an overall accuracy of 93.1% based on the reflectance at 4 bands and 8 spectral indices.

Natural hydraulic lime (NHL) is a cementitious material widely used in the restoration of stone cultural relics and maintenance of historic buildings, the practical use of which is mainly hindered by its poor fluidity. Due to the multilayer (double-layer) adsorption that isobutylene–maleic anhydride (IBMA) has on the surface of NHL, the effects that IBMA copolymer have on the fluidity and hydration of NHL were thus investigated. Moreover, the yield stress and plastic viscosity of NHL pastes were found to be reduced significantly by the incorporation of IBMA. Combined with the effects of electrostatic repulsion and steric hindrance, the flocculated structures in NHL pastes were gradually dismantled, releasing the trapped water and leading to a significant enhancement in the fluidity of NHL. IBMA was found to postpone the early hydration of NHL. In particular, it showed that adding specific content of IBMA can significantly improve the early strength of NHL.

The Jiaodong Peninsula is the most important gold mineralization area in China, and the formation of gold deposits is closely related to granitoids. The isotopic ages of the Early Cretaceous granodiorites in the northwestern Jiaodong Peninsula are concentrated in the range of 111~123 Ma, and are coeval with the formation of the gold deposits in the area. However, the studies on the geotectonic settings of the granodiorites, especially their petrogenesis and relationship with gold deposits in the northwestern Jiaodong Peninsula, are scarce. Based on field and petrographic observations, geochemistry, EPMA analysis, zircon U-Pb chronology, and Sr-Nd isotopes of the Early Cretaceous Zhouguan granodiorite in the Jiaodong area, the formation age of Zhouguan granodiorite is determined as 115 Ma ± 0.77 Ma; the analysis of EPMA shows that biotite is mainly composed of Fe-biotite and Mg-biotite, with its MgO content ranging from 9.797% to 11.635%. The crystallization temperature of biotite is in the range of 500 °C~625 °C and the emplacement depth of the rock mass is 3.98~8.71 km. The amphibole in the mass mainly includes magnesiohornblende, pargasite, and magnesiosadanagaite; among them, the former two are of crustal origin, while magnesiosadanagaite is of mantle origin. The crystallization pressure and depth of the former two are in the range of 0.75~3.02 kbar and 2.81~11.4 km, respectively, while the crystallization pressure and depth for the latter is 4.64 kbar and 17.53 km, respectively. The (87Sr/86Sr) values range from 0.710424 to 0.711074 and the (143Nd/144Nd) values range from 0.511530 to 0.511808. The parental magma of the Zhouguan granodiorite is highly oxidized with high-water content that is favorable for Au enrichment. Combined with the Nb-Y and Yb-Ta diagrams, a model describing the formation of Zhouguan granodiorite is proposed.

Abstract Grinding burn monitoring is of great importance to guarantee the surface integrity of the workpiece. Existing methods monitor overall signal variation. However, the signals produced by metal burn are always weak. Therefore, the detection rate of grinding burn still needs to be improved. The paper presents a novel grinding burn detection method basing on acoustic emission (AE) signals. It is achieved by establishing the coherence relationship of pure metal burn and grinding burn signals. Firstly, laser and grinding experiments were carried out to produce pure metal burn signals and grinding burn signals. No-burn and burn surfaces were generated and AE signals were captured separately. Then, the cross wavelet transform (XWT) and wavelet coherence (WTC) were applied to reveal the coherence relationship of the pure metal burn signal and grinding burn signal. The methods can reduce unwanted AE sources and background noise. Novel parameters based on XWT and WTC are proposed to quantify the degree of coherence and monitor the grinding burn. The grinding burn signals were recognized successfully by the proposed indexes under same grinding condition.

Abstract Background Plant phospholipase D (PLD), which can hydrolyze membrane phospholipids to produce phosphatidic acid (PA), a secondary signaling molecule, has been proposed to function in diverse plant stress responses. Both PLD and PA play key roles in plant growth, development, and cellular processes. PLD was suggested to mediate the regulation of stomatal movements by abscisic acid (ABA) as a response to water deficit. In this research, we characterized the roles of the cucumber phospholipase D alpha gene (CsPLDα, GenBank accession number EF363796) in the growth and tolerance of transgenic tobacco (Nicotiana tabacum) to drought stress. Results The CsPLDα overexpression in tobacco lines correlated with the ABA synthesis and metabolism, regulated the rapid stomatal closure in drought stress, and reduced the water loss. The NtNCED1 expression levels in the transgenic lines and wild type (WT) were sharply up-regulated after 16 days of drought stress compared with those before treatment, and the expression level in the transgenic lines was significantly higher than that in the WT. The NtAOG expression level evidently improved after 8 and 16 days compared with that at 0 day of treatment and was significantly lower in the transgenic lines than in the WT. The ABA content in the transgenic lines was significantly higher than that in the WT. The CsPLDα overexpression could increase the osmolyte content and reduce the ion leakage. The proline, soluble sugar, and soluble protein contents significantly increased. By contrast, the electrolytic leakage and malondialdehyde accumulation in leaves significantly decreased. The shoot and root fresh and dry weights of the overexpression lines significantly increased. These results indicated that a significant correlation between CsPLDα overexpression and improved resistance to water deficit. Conclusions The plants with overexpressed CsPLDα exhibited lower water loss, higher leaf relative water content, and heavier fresh and dry matter accumulation than the WT. We proposed that CsPLDα was involved in the ABA-dependent pathway in mediating the stomatal closure and preventing the elevation of intracellular solute potential.

Nickel cobaltite (NiCo2O4) is a promising sensing material for H2O2. However, the poor electronic conductivity and agglomeration of NiCo2O4 in aqueous medium easily lead to poor sensitivity and selectivity, which are still two formidable challenges. Herein, the poly (3, 4-ethylene dioxythiophene)-coated NiCo2O4 rod-like micro-nano structures/reduced graphene oxide (NiCo2O4 RMNs@PEDOT2/RGO) hybrid is synthesized. The one-dimension hierarchical NiCo2O4 RMNs serve as a catalyst, while the RGO acting as support and charge collector. Particularly, the PEDOT is employed as a conductive wrapped layer and caking agent reduced interface resistance and quicken transport electrons from NiCo2O4 to the surface of RGO. Benefitting from the above synergistic effect of three components, the hybrid demonstrates high sensitivity (0.679 mA mM-1 cm-2), the low detection limit (it is estimated to be 0.031 µM), and a wide linear detection ranges of 0.388 µM–140.068 mM for H2O2 detection, which observably outperforms detection performances of previously reported other composites. The influence of morphology and interface structure of the hybrid towards performances is detailed. Besides, the practicality detection of H2O2 in merchant hydrogen peroxide disinfectant has been realized. The work provided a new strategy for improving the bio-sensing performance and accelerating the practical application of Co3O4. Keywords: Nickel cobaltite, H2O2 biosensor, Micro-nano composite, Reduced graphene oxide

We used OCO-2 products and considered three factors that potentially affect CO2 concentration in Indonesia: sea surface temperature (SST), forest fires and vegetation. From 2014 to 2016, CO2 concentration in Indonesia showed a trend of increase, which is consistent with the global increase reported by the Greenhouse Gases Observing Satellite (GOSAT) Project. As an archipelago country, the results indicate that SST has a direct effect on the CO2 concentration in Indonesia. Their changing exhibits similar fluctuations; meanwhile, CO2 concentration and SST also presented positive correlation. In 2015, the number of fire hotspots suddenly increased to 140,699, because of occurrence of the worst forest fire. Due to special geographic conditions, forest fires did not induce CO2 concentration changes in Indonesia, but CO2 concentration in the corresponding islands showed a trend of increase. CO2 concentration increased in Kalimantan during the occurrence of forest fire in September–October 2014, and CO2 concentration increased in Kalimantan and Sumatra during the occurrence of forest fire in September–October 2015. Vegetation indices were stable and presented no correlation with CO2 concentration. This study demonstrated that OCO-2 is capable of monitoring CO2 concentration at a regional scale; additionally, an effective method for using OCO-2 Level 2 products is proposed.

Background/Aims: Acute myeloid leukemia (AML) of French-American-British (FAB) subtypes M0 and M1 are both poorly differentiated AML, but their mutational spectrum and molecular characteristics remain unknown. This study aimed to explore the mutational spectrum and prognostic factors of AML-M0 and M1. Methods: Sixty-five AML patients derived from The Cancer Genome Atlas (TCGA) database were enrolled in this study. Whole-genome sequencing was performed to depict the mutational spectrum of each patient. Clinical characteristics at diagnosis, including peripheral blood (PB) white blood cell counts (WBC), blast percentages in PB and bone marrow (BM), FAB subtypes and the frequencies of known recurrent genetic mutations were described. Survival was estimated using the Kaplan-Meier methods and log-rank test. Univariate and multivariate Cox proportional hazard models were constructed for event-free survival (EFS) and overall survival (OS), using a limited backward elimination procedure. Results: Forty-six patients had more than five recurrent genetic mutations. FLT3 had the highest mutation frequency (n=20, 31%), followed by NPM1 (n=18, 28%), DNMT3A (n=16, 25%), IDH1 (n=14, 22%), IDH2 (n=12, 18%), RUNX1 (n=11, 17%) and TET2 (n=7, 11%). Univariate analysis showed that age ≥60 years and TP53 mutations had adverse effect on EFS (P=0.015, P=0.036, respectively) and OS (P=0.003, P=0.004, respectively), WBC count ≥50×109/L and FLT3-ITD negatively affected EFS (P=0.003, P=0.034, respectively), whereas NPM1 mutations had favorable effect on OS (P=0.035) and allogeneic hematopoietic stem cell transplantation (allo-HSCT) on EFS and OS (all P< 0.001). Multivariate analysis suggested that allo-HSCT and NPM1 mutations were independent favorable prognostic factors for EFS and OS (all P< 0.05), WBC count ≥50×109/L was an independent risk factor for EFS (P=0.002) and TP53 mutations for OS (P=0.043). Conclusions: Our study provided new insights into the mutational spectrum and molecular signatures of AML-M0 and M1. We proposed that FLT3-ITD, NPM1 and TP53 be identified as markers for risk stratification of AML-M0 and M1. Patients with AML-M0 and M1 would likely benefit from allo-HSCT.

Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches namely kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarization data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification.

Type 2 diabetes mellitus (T2DM) is a metabolic disease accompanied by a series of diseases such as diabetic nephropathy. The drug pair (HS) of Astragalus Radix (HQ) and Dioscoreae Rhizoma (SY) was designed by Dr. Shi Jinmo to improve the treatment of T2DM. However, the exact mechanism involved requires further clarification. In this work, 1H-NMR−based metabonomics and network pharmacology were adopted. Metabolic profiling indicated that the metabolic perturbation was reduced after HS treatment. The results found 21 biomarkers. According to the network pharmacology, we found that the regulation of T2DM was primarily associated with 18 active compounds in HS. These active compounds mainly had an effect on 135 targets. Subsequently, combining network pharmacology and metabonomics, we found four target proteins, which indicated that HS has potential hypoglycemic effects through regulating monoamine oxidases B (MAOB), acetyl-CoA carboxylase 1 (ACACA), carbonic anhydrase 2 (CA2), and catalase (CAT). In conclusion, the result showed that these four targets might be the most relevant targets for the treatment of T2DM with HS. This study clarified the mechanism of HS in the treatment of T2DM and also confirmed the feasibility of combining metabonomics and network pharmacology to study the mechanisms of traditional Chinese medicine (TCM). In the future, this approach may be a potentially powerful tool to discovery active components of traditional Chinese medicines and elucidate their mechanisms.

Road-based directional broadcast protocols are proposed in the literatures to offset efficiency of message dissemination of traditional broadcast protocol in urban vehicular ad hoc networks. However, these protocols cannot provide enough reliability and efficiency for vehicles’ misclassification at intersection or on straight road. Therefore, we present regular-hexagon-equilateral-triangle area grouping–based broadcast protocol for urban vehicular ad hoc networks. The area covered by relay node is averagely divided using regular-hexagon-equilateral-triangle, and vehicles are grouped according to the area that they reside. The algorithm for constructing regular-hexagon-equilateral-triangle is proposed. We adopt same vehicles’ grouping method at intersection and on straight road, and no neighbor list is maintained to identify road intersection. We design waiting time formula to calculate time for node forwarding message. It is ruled that node with waiting time dropping to zero first is defined as the relay node, and this relay node transmits message. So there is only one relay node which forwards message in each group. It also rules in regular-hexagon-equilateral-triangle area grouping–based broadcast protocol that each relay node forwards the same message only once, therefore, it limits redundant message retransmission. Using the vehicles’ grouping method and selection strategy of relay node as mentioned above, the proposed protocol enables message to be transmitted in different directions along different roads at the same time. Simulation indicates that our protocol has a better performance

Aim of study: The ambrosia beetle, Platypus quercivorus, is a vector of Japanese oak wilt, which causes massive mortality of oak trees in Japan. ALOS/AVNIR-2 true color images can be used to help detect areas of oak wilt, although such detection by inventory surveys is not realistic. Applying pan-sharpening techniques, a higher spatial resolution multispectral image can be generated from lower-resolution multispectral images and higher-resolution panchromatic images. In this study, some pan-sharpening algorithms were considered and evaluated for the detection of damage points.Area of study: The oak forests in Kanazawa prefecture, Japan.Materials and methods: The ALOS/AVNIR-2 and ALOS/PRISM sensors were used. The pan-sharpening algorithms adopted were: Brovey transformation, Modified IHS transformation, Wavelet transformation, Ehlers fusion and High Pass Filter Resolution Merge. Four types of quantitative spectral analyses and visual detection were conducted to evaluate these algorithms.Main results: The Brovey transformation was the most useful algorithm to detect damage points, although it had an issue with the preservation of spectral characteristics.Research highlights: The detection rate of damage points was improved in 50% by applying the Brovey algorithm to a 10 m panchromatic image and 62.5 m multispectral image.Key words: ambrosia beetle; oak wilt; pan-sharpening; satellite imagery; visual detection.

In this paper, an Urban Light Index (ULI) is constructed to facilitate analysis and quantitative evaluation of the process of urbanization and expansion rate by using DMSP/OLS Nighttime Light Data during the years from 1992 to 2010. A unit circle urbanization evaluation model is established to perform a comprehensive analysis of the urbanization process of 34 prefecture-level cities in Northeast China. Furthermore, the concept of urban light space is put forward. In this study, urban light space is divided into four types: the core urban area, the transition zone between urban and suburban areas, suburban area and fluorescent space. Proceeding from the temporal and spatial variation of the four types of light space, the pattern of morphologic change and space-time evolution of the four principal cities in Northeast China (Harbin, Changchun, Shenyang, Dalian) is analyzed and given particular attention. Through a correlation analysis between ULI and the traditional urbanization indexes (urban population, proportion of the secondary and tertiary industries in the regional GDP and the built-up area), the advantages and disadvantages as well as the feasibility of using the ULI in the study of urbanization are evaluated. The research results show that ULI has a strong correlation with urban built-up area (R2 ＝ 0.8277). The morphologic change and history of the evolving urban light space can truly reflect the characteristics of urban sprawl. The results also indicate that DMSP/OLS Nighttime Light Data is applicable for extracting urban space information and has strong potential to urbanization research.

The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features.

Research Highlights: In this study, we classified natural forest into four forest types using time-series multi-source remotely sensed data through a proposed semi-supervised model developed and validated for mapping forest types and assessing forest transition in Vietnam. Background and Objectives: Data on current forest state and changes detection are always essential for forest management and planning. There is, therefore, a need for improved tools to classify and evaluate forest dynamics more accurately and effectively. Our objective is to develop such tools using a semi-supervised model and landscape metrics to classify and map changes in natural forest types by using multi-source remotely sensed data. Materials and Methods: A combination of Landsat data with PALSAR and PALSAR-2 was used for forest classification through the proposed semi-supervised model. This model turned a kernel least square into a self-learning algorithm, trained by a small number of samples with given labels, and then used this classifier to assign labels to the unlabeled data. The overall accuracy, kappa, user’s accuracy, and producer’s accuracy were used to evaluate the classification accuracy by comparing the classified image with the results of ground truth interpretation. Based on the classified images, forest transition was evaluated using certain landscape metrics at the class and landscape levels. Results: The multi-source data approach achieved improved discrimination of forest types compared to only using single data (optical or radar data). Good classification accuracies were obtained, with kappas of 0.81, 0.76, and 0.74 for the years 2007, 2010, and 2016, respectively. The analysis of landscape metrics indicated that there were different behaviors in the four forest types, as well as provided much information about the trends in spatial pattern changes. Conclusions: This study highlights the utilization of a semi-supervised model in forest classification, and the analysis of forest transition using landscape metrics. However, future research should include a comparison of different models to estimate the improvement of the proposed model. Another important study that should be conducted is to test the proposed method on larger areas.

Kidney-yang deficiency syndrome (KYDS) is a metabolic disease caused by a neuro-endocrine disorder. The You-gui pill (YGP) is a classic traditional Chinese medicine (TCM) formula for the treatment of KYDS and has been widely used to warm and recuperate KYDS clinically for hundreds of years in China. However, it is unknown whetherthe corresponding targets and metabolic pathways can also be found via using metabonomics based on one platform (e.g., 1H NMR) to study different biological samples of KYDS. At the same time, relevant reports on further molecular verification (e.g., RT-qPCR analysis) of these targets associated with biomarkers and metabolic pathways have not yet, to our knowledge, been seen in KYDS’s research. In the present study, a comprehensive strategy integrating systems pharmacology and 1H NMR-based urinary metabonomics analysis was proposed to identify the target proteins and metabolic pathways that YGP acts on KYDS. Thereafter, further validation of target proteins in kidney tissue was performed through quantitative real-time PCR analysis (RT-qPCR). Furthermore, biochemical parameters and histopathological analysis were studied. As a result, seven target proteins (L-serine dehydratase; phosphoenolpyruvate carboxykinase; spermidine synthase; tyrosyl-tRNA synthetase, glutamine synthetase; 3-hydroxyacyl-CoA dehydrogenase; glycine amidinotransferase) in YGP were discovered to play a therapeutic role in KYDS via affecting eight metabolic pathways (glycine, serine and threonine metabolism; butanoate metabolism; TCA cycle, etc.). Importantly, three target proteins (i.e., 3-hydroxyacyl-CoA dehydrogenase; glutamine synthetase; and glycine amidinotransferase) and two metabolic pathways (butanoate metabolism and dicarboxylate metabolism) related to KYDS, to our knowledge, had been newly discovered in our study. The mechanism of action mainly involved energy metabolism, oxidative stress, ammonia metabolism, amino acid metabolism, and fatty acid metabolism. In short, our study demonstrated that targets and metabolic pathways for the treatment of KYDS by YGP can be effectively found via combining with systems pharmacology and urinary metabonomics. In addition to this, common and specific targets and metabolic pathways of KYDS treated by YGP can be found effectively by integration with the analysis of different biological samples (e.g., serum, urine, feces, and tissue). It is; therefore, important that this laid the foundation for deeper mechanism research and drug-targeted therapy of KYDS in future.

This paper describes the long-term effects on vegetation following the catastrophic fire in 1987 on the northern Great Xing’an Mountain by analyzing the AVHRR GIMMS 15-day composite normalized difference vegetation index (NDVI) dataset. Both temporal and spatial characteristics were analyzed for natural regeneration and tree planting scenarios from 1984 to 2006. Regressing post-fire NDVI values on the pre-fire values helped identify the NDVI for burnt pixels in vegetation stands. Stand differences in fire damage were classified into five levels: Very High (VH), High (H), Moderate (M), Low (L) and Slight (S). Furthermore, intra-annual and inter-annual post-fire vegetation recovery trajectories were analyzed by deriving a time series of NDVI and relative regrowth index (RRI) values for the entire burned area. Finally, spatial pattern and trend analyses were conducted using the pixel-based post-fire annual stands regrowth index (SRI) with a nonparametric Mann-Kendall (MK) statistics method. The results show that October was a better period compared to other months for distinguishing the post- and pre-fire vegetation conditions using the NDVI signals in boreal forests of China because colored leaves on grasses and shrubs fall down, while the leaves on healthy trees remain green in October. The MK statistics method is robustly capable of detecting vegetation trends in a relatively long time series. Because tree planting primarily occurred in the severely burned area (approximately equal to the Medium, High and Very High fire damage areas) following the Daxing’anling fire in 1987, the severely burned area exhibited a better recovery trend than the lightly burned regions. Reasonable tree planting can substantially quicken the recovery and shorten the restoration time of the target species. More detailed satellite analyses and field data will be required in the future for a more convincing validation of the results.

Carbon dioxide (CO2) is the most important greenhouse gas (GHG) in the atmosphere and is the greatest contributor to global warming. CO2 concentration data are usually obtained from ground observation stations or from a small number of satellites. Because of the limited number of observations and the short time series of satellite data, it is difficult to monitor CO2 concentrations on regional or global scales for a long time. The use of the remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) or Moderate Resolution Imaging Spectroradiometer (MODIS) data can overcome these problems, particularly in areas with low densities of CO2 concentration watch stations. A model based on temperature (MOD11C3), vegetation cover (MOD13C2 and MOD15A2) and productivity (MOD17A2) of MODIS (which we have named the TVP model) was developed in the current study to assess CO2 concentrations on a global scale. We assumed that CO2 concentration from the Thermal And Near infrared Sensor for carbon Observation (TANSO) aboard the Greenhouse gases Observing SATellite (GOSAT) are the true values and we used these values to check the TVP model accuracy. The results indicate that the accuracy of the TVP model is different in different continents: the greatest Pearson’s correlation coefficient (R2) was 0.75 in Eurasia (RMSE = 1.16) and South America (RMSE = 1.17); the lowest R2 was 0.57 in Australia (RMSE = 0.73). Compared with the TANSO-observed CO2 concentration (XCO2), we found that the accuracy throughout the World is between −2.56~3.14 ppm. Potential sources of TVP model uncertainties were also analyzed and identified.

Link duration between two vehicles is considered an important quality of service metric in designing a network protocol for vehicular networks. There exist many works that study the probability density functions of link duration in a vehicular network given various vehicle mobility models, for example, the random waypoint model. None of them, however, provides a practical solution to estimating the link duration between two vehicles on the road. This is in part because link duration between vehicles is affected by many factors including the distance between vehicles, their turning directions at intersections, and the impact of traffic lights. Considering these factors, we propose the extended link duration prediction (ELDP) model which allows a vehicle to accurately estimate how long it will be connected to another vehicle. The ELDP model does not assume that vehicles follow certain mobility models; instead, it assumes that a vehicle's velocity follows the Normal distribution. We validate the ELDP model in both highway and city scenarios in simulations. Our detailed simulations illustrate that relative speed between vehicles plays a vital role in accurately predicting link duration in a vehicular network. On the other hand, we find that the turning directions of a vehicle at intersections have subtle impact on the prediction results.

Link duration prediction is one of the most fundamental problems in vehicular ad hoc networks (VANETs) as it determines the network performance of many vehicular applications. Existing analytical analysis about link duration in both mobile ad hoc network (MANETs) and VANETs is too complicated to be applied in a practical setting. Assuming vehicle's velocity follows the normal distribution, we propose a practical model which considers the distribution of relative velocity, intervehicle distance, and impact of traffic lights to estimate the expected link duration between any pair of connected vehicles. Such model is implemented on each vehicle along with (1) a relative velocity estimation approach and (2) an exponential moving average- (EMA-) based data processing procedure. Furthermore, the proposed model assumes that the events of two consecutive vehicles encountering traffic lights combination are dependent, which make the model more practical. Simulation results show that the link duration model predicts link duration with the average accuracy of 10% and 20% in highway and city scenarios, respectively.

Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 μm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI2.1)-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AODAERONET).

In this paper, we introduced a new algorithm for retrieving aerosol optical depth (AOD) over land, from the Cloud and Aerosol Imager (CAI), which is one of the instruments on the Greenhouse Gases Observing Satellite (GOSAT) for detecting and correcting cloud and aerosol interference. We used the GOSAT and AErosol RObotic NETwork (AERONET) collocated data from different regions over the globe to analyze the relationship between the top-of-atmosphere (TOA) reflectance in the shortwave infrared (1.6 μm) band and the surface reflectance in the red (0.67 μm) band. Our results confirmed that the relationships between the surface reflectance at 0.67 μm and TOA reflectance at 1.6 μm are not constant for different surface conditions. Under low AOD conditions (AOD at 0.55 μm < 0.1), a Normalized Difference Vegetation Index (NDVI) based regression function for estimating the surface reflectance of 0.67 μm band from the 1.6 μm band was summarized, and it achieved good performance, proving that the reflectance relations of the 0.67 μm and 1.6 μm bands are typically vegetation dependent. Since the NDVI itself is easily affected by aerosols, we combined the advantages of the Aerosol Free Vegetation Index (AFRI), which is aerosol resistant and highly correlated with regular NDVI, with our regression function, which can preserve the various correlations of 0.67 μm and 1.6 μm bands for different surface types, and developed a new surface reflectance and aerosol-free NDVI estimation algorithm, which we named the Modified AFRI1.6 algorithm. This algorithm was applied to AOD retrieval, and the validation results for our algorithm show that the retrieved AOD has a consistent relationship with AERONET measurements, with a correlation coefficient of 0.912, and approximately 67.7% of the AOD retrieved data were within the expected error range (± 0.1 ± 0.15AOD(AERONET)).

Objective: To explore risk factors of electrical status epilepticus during sleep in children with benign childhood epilepsy with centro-temporal spikes (BECT). Methods: This is a clinical comparative study. The subjects of study were 67 children with BECT from the Outpatient Department of Pediatric Neurology in Xingtai People's Hospital from January 2019 to January 2022. According to the occurrence of ESES, the enrolled children were divided into control group which included BECT children without ESES and the observation group which included BECT children with ESES. Compared differences of the two groups in the age of first seizure, the frequency of seizures before treatment, the classification of treatment drugs, cranial MRI, and discharge side of electroencephalogram (EEG). Results: There was no statistical difference between the two groups in the frequency of seizures before treatment, the classification of treatment drugs, cranial MRI, and the distribution of EEG discharges in the left and right cerebral areas(P>0.05). Statistical differences were observed in the age of the first seizure, whether the seizures occurred after treatment, and EEG discharges in unilateral/bilateral cerebral areas (P<0.05). Furthermore, the collinearity test and Logistic regression analysis showed that the age of the first seizure, the frequency of seizures before treatment, and whether the seizures occurred after treatment were independent risk factors for the occurrence of ESES in BECT (P<0.05). Conclusion: Clinically, the occurrence of ESES in children with BECT may be related to the younger age of the first seizure, higher frequency of seizures before treatment, and the occurrence of seizures after treatment. [ABSTRACT FROM AUTHOR]