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ObjectiveTo comprehensively analyze the ADRs associated with Denosumab (Prolia) in the treatment of osteoporosis using data from the FAERS database, and gain a better understanding of the potential risks and side effects of Denosumab (Prolia) therapy.MethodsData of Denosumab (Prolia) were collected from the FAERS database covering the period from first quarter of 2010 to the third quarter of 2023. Disproportionality analysis was performed by calculating the reporting odds ratios (ROR), proportional reporting ratio (PRR), and Bayesian analysis confidence propagation neural network (BCPNN) to detect positive signals.ResultsTotally, 17,985,365 reports were collected from the FAERS database, 1,97,807 reports of Denosumab (Prolia) were identified as the “primary suspected (PS)” ADRs. Denosumab (Prolia) induced ADRs occurred in 27 organ systems. 38 significant disproportionality PTs satisfying with the three algorithms were retained at the same time. Unexpected significant ADRs such as bone density abnormal and immobile also occur. The majority of the ADRs occurred within the first 30 days after Denosumab (Prolia) initiation.ConclusionBased on the American FAERS database, the high frequency ADRs of Denosumab (Prolia) were hypocalcaemia, bone density abnormal, eczema, rebound effect, spinal deformity, etc. Clinical use of this drug should focus on this part of ADRs. Attention should also be paid to newly discovered ADRs, such as immobile, menopausal symptoms, etc., to avoid more serious consequences. Cohort studies, more detailed and comprehensive case information, and long-term clinical investigations are needed to confirm these results and to further understand the safety profile of Denosumab (Prolia).

Building segmentation has extensive research value and application prospects in high-resolution remote sensing image (HRSI) processing. However, complex architectural contexts, varied building morphologies, and non-building occlusions make building segmentation challenging. Compared with traditional methods, deep learning-based methods present certain advantages in terms of accuracy and intelligence. At present, the most popular option is to first apply a single neural network to encode an HRSI, then perform a decoding process through up-sampling or using a transposed convolution operation, and then finally obtain the segmented building image with the help of a loss function. Although effective, this approach not only tends to lead to a loss of detail information, but also fails to fully utilize the contextual features. As an alternative, we propose a novel network called NPSFF-Net. First, using an improved pseudo-Siamese network composed of ResNet-34 and ResNet-50, two sets of deep semantic features of buildings are extracted with the support of transfer learning, and four encoded features at different scales are obtained after fusion. Then, information from the deepest encoded feature is enriched using a feature enhancement module, and the resolutions are recovered via the operations of skip connections and transposed convolutions. Finally, the discriminative features of buildings are obtained using the designed feature fusion algorithm, and the optimal segmentation model is obtained by fitting a cross-entropy loss function. Our method obtained intersection-over-union values of 89.45% for the Aerial Imagery Dataset, 71.88% for the Massachusetts Buildings Dataset, and 68.72% for the Satellite Dataset I.

The ability of wheat to adapt to a wide range of environmental conditions is determined mostly by allelic diversity among genes regulating vernalization requirement. Vrn-1 is a major regulator of this requirement. In this study, two novel alleles of Vrn-A1 were discovered in Chinese cultivars: vrn-A1n was identified in two landraces, Jiunong 2 and Ganchun 16, and Vrn-A1o was detected in Duanhongmangmai. Both novel alleles showed a linked duplication in the promoter region. The common copy of these two alleles was identical to the recessive allele vrn-A1. Compared with the recessive allele vrn-A1, the other copy of vrn-A1n contained a 54-bp deletion in the promoter region and the distinct copy of Vrn-A1o contained an 11-bp deletion in the promoter region. In segregating populations in the greenhouse under nonvernalizing (20–25 °C) and long-day (16 h light) conditions, plants with the novel vrn-A1n allele did not head earlier than those with the recessive vrn-A1 allele. However, plants that were either homozygous or heterozygous for the novel Vrn-A1o allele headed earlier than those with the recessive vrn-A1 allele. To identify the novel allele with the small-sized product and facilitate screening, a DNA marker for the novel dominant allele Vrn-A1o was designed. Analysis of the novel-allele distribution showed that two cultivars carrying the vrn-A1n allele were dispersed in the northwestern spring wheat zone, and 12 cultivars carrying the dominant Vrn-A1o allele were widely distributed in the northwestern spring wheat zone, Xinjiang winter and spring wheat zone, Yellow and Huai River valley winter wheat zone, and Qinghai-Tibetan Plateau spring and winter wheat zone. Our study identifies useful germplasm and a DNA marker for wheat breeding.

Android has been the most widely used operating system for mobile phones over the past few years. Malicious attacks against android are a major privacy and security concern. Malware detection techniques for android applications are therefore significant. A class of methods using Function Call Graphs (FCGs) for android malware detection has shown great potential. The relationships between functions are limited to simple binary relationships (i.e., graphs) in these methods. However, one function often calls several other functions to produce specific effects in android applications, which cannot be captured with FCGs. In this paper, we propose to formalize android malware detection as a hypergraph-level classification task. A hypergraph is a topology capable of portraying complex relationships between multiple vertices, which can better characterize the functional behavior of android applications. We model android applications using hypergraphs and extract the embedded features of android applications using hypergraph neural networks to represent the functional behavior of android applications. Hypergraph neural networks can encode high-order data correlation in a hypergraph structure for data representation learning. In experiments, we validate the gaining effect of hypergraphs on detection performance across two open-source android application datasets. Especially, HGNNP obtains the best classification performance of 91.10% on the Malnet-Tiny dataset and 97.1% on the Drebin dataset, which outperforms all baseline methods.

A shale reservoir is a complex system with lots of nanoscale pore throat structures and variable permeability. Even though shale reservoirs contain both organic and inorganic matter, the slip effect and phase behavior complicate the two-phase flow mechanism. As a result, understanding how microscale effects occur is critical to effectively developing shale reservoirs. In order to explain the experimental phenomena that are difficult to describe using classical two-phase flow theory, this paper proposes a new simulation method for two-phase shale oil reservoirs that takes into account the microscale effects, including the phase change properties of oil and gas in shale micro- and nanopores, as well as the processes of dissolved gas escape, nucleation, growth and aggregation. The presented numerical simulation framework, aimed at comprehending the dynamics of the two-phase flow within fractured horizontal wells situated in macroscale shale reservoirs, is subjected to validation against real-world field data. This endeavor serves the purpose of enhancing the theoretical foundation for predicting the production capacity of fractured horizontal wells within shale reservoirs. The impact of capillary forces on the fluid dynamics of shale oil within micro- and nanoscale pores is investigated in this study. The investigation reveals that capillary action within these micro- and nanoscale pores of shale formations results in a reduction in the actual bubble point pressure within the oil and gas system. Consequently, the reservoir fluid persists in a liquid monophasic state, implying a constrained mobility and diminished flow efficiency of shale oil within the reservoir. This constrained mobility is further characterized by a limited spatial extent of pressure perturbation and a decelerated pressure decline rate, which are concurrently associated with a relatively elevated oil saturation level.

Trauma-induced osteonecrosis of the femoral head (TI-ONFH) is a pathological process in which the destruction of blood vessels supplying blood to the femoral head causes the death of bone tissue cells. Vascular cell adhesion molecule 1 (VCAM-1) has been shown to have potent proangiogenic activity, but the role in angiogenesis of TI-ONFH is unclear. In this work, we discovered that VCAM-1 was significantly downregulated in the bone marrow mesenchymal stem cells (BMSCs) derived from patients with TI-ONFH. Subsequently, we constructed BMSCs overexpressing VCAM-1 using a lentiviral vector. VCAM-1 enhances the migration and angiogenesis of BMSCs. We further performed mRNA transcriptome sequencing to explore the mechanisms by which VCAM-1 promotes angiogenesis. Gene ontology biological process enrichment analysis demonstrated that upregulated differentially expressed genes (DEGs) were related to blood vessel development. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that upregulated DEGs were engaged in the Apelin signaling pathway. Apelin-13 is the endogenous ligand of the APJ receptor and activates this G protein-coupled receptor. Treatment with Apelin-13 activated the Apelin signaling pathway and suppressed the expression of cellular communication network factor 2 in BMSCs. Furthermore, Apelin-13 also inhibits the migration and angiogenesis of VCAM-1-BMSCs. In summary, VCAM-1 plays an important role in vascular microcirculation disorders of TI-ONFH, which provides a new direction for the molecular mechanism and treatment of TI-ONFH.

Abstract Background Studies have shown that medial subluxation of the tibia occurs after anterior cruciate ligament (ACL) rupture. However, it is unclear whether anterior cruciate ligament reconstruction (ACLR) can correct tibial coronal subluxation. Purpose To determine whether the tibia is medially subluxated after ACL rupture, and whether ACLR can correct medial subluxation of the tibia. Study design Case series; Level of evidence, 4, Retrospective clinical study. Methods The distance of tibial coronal subluxation before and after ACLR surgery was measured in 48 patients with ACL rupture and meniscus injury. Tibiofemoral subluxation was defined as the perpendicular distance between the long axis of the tibia and a second parallel line originating at the most proximal aspect of the femoral intercondylar notch. To determine the long axis of the tibia, two circles separated by 5 cm were centered on the proximal tibia. The proximal circle is 5 cm from the tibial plateau, and the distal circle is 5 cm from the proximal circle. The line passing through the center of the two circles was considered the long axis of the proximal tibia. Care was taken to ensure that each patient lied on the back with their patellae facing upward, to minimize rotational variation among the radiographs. At the same time, 30 patients with simple meniscus injury who underwent arthroscopy during the same period were selected to determine the degree of tibiofemoral coronal subluxation as the baseline value. The changes before and after operation were compared, as well as the differences with the baseline data. Result The average follow-up period was 21.2 ± 5.8 months. The average distance of tibial coronal subluxation before ACLR was 5.5 ± 2.1 mm, which was significantly different from that of baseline group (7.3 ± 2.1 mm) (P

Surface upward longwave radiation (SULR) is one of the four components of the surface radiation budget, which is defined as the total surface upward radiative flux in the spectral domain of 4-100 μm. The SULR is an indicator of surface thermal conditions and greatly impacts weather, climate, and phenology. Big Earth data derived from satellite remote sensing have been an important tool for studying earth science. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-16) has greatly improved temporal and spectral resolution compared to the imager sensor of the previous GOES series and is a good data source for the generation of high spatiotemporal resolution SULR. In this study, based on the hybrid SULR estimation method and an upper hemisphere correction method for the SULR dataset, we developed a regional clear-sky land SULR dataset for GOES-16 with a half-hourly resolution for the period from 1st January 2018 to 30th June 2020. The dataset was validated against surface measurements collected at 65 Ameriflux radiation network sites. Compared with the SULR dataset of the Global LAnd Surface Satellite (GLASS) longwave radiation product that is generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the polar-orbiting Terra and Aqua satellites, the ABI/GOES-16 SULR dataset has commensurate accuracy (an RMSE of 15.9 W/m2 vs 19.02 W/m2 and an MBE of −4.4 W/m2 vs −2.57 W/m2), coarser spatial resolution (2 km at nadir vs 1 km resolution), less spatial coverage (most of the Americas vs global), fewer weather conditions (clear-sky vs all-weather conditions) and a greatly improved temporal resolution (48 vs 4 observations a day). The published data are available at http://www.dx.doi.org/10.11922/sciencedb.j00076.00062.

A low-profile, wideband, and high-gain antenna array, based on a novel double-H-shaped slot microstrip patch radiating element and robust against high temperature variations, is proposed in this work. The antenna element was designed to operate in the frequency range between 12 GHz and 18.25 GHz, with a 41.3% fractional bandwidth (FBW) and an obtained peak gain equal to 10.2 dBi. The planar array, characterized by a feed network with a flexible 1 to 16 power divider, comprised 4 × 4 antenna elements and generated a pattern with a peak gain of 19.1 dBi at 15.5 GHz. An antenna array prototype was fabricated, and the measurements showed good agreement with the numerical simulations as the manufactured antenna operated in the range of 11.4–17 GHz, with a 39.4% FBW, and the peak gain at 15.5 GHz was 18.7 dBi. The high-temperature simulated and experimental results, performed in a temperature chamber, demonstrated that the array performance was stable in a wide temperature range, from −50 °C to 150 °C.

Phage therapy is widely being reconsidered as an alternative to antibiotics for the treatment of multidrug-resistant bacterial infections, including salmonellosis caused by Salmonella. As facultative intracellular parasites, Salmonella could spread by vertical transmission and pose a great threat to both human and animal health; however, whether phage treatment might provide an optional strategy for controlling bacterial vertical infection remains unknown. Herein, we explored the effect of phage therapy on controlling the vertical transmission of Salmonella enterica serovar Gallinarum biovar Pullorum (S. Pullorum), a poultry pathogen that causes economic losses worldwide due to high mortality and morbidity. A Salmonella phage CKT1 with lysis ability against several S. enterica serovars was isolated and showed that it could inhibit the proliferation of S. Pullorum in vitro efficiently. We then evaluated the effect of phage CKT1 on controlling the vertical transmission of S. Pullorum in an adult broiler breeder model. The results demonstrated that phage CKT1 significantly alleviated hepatic injury and decreased bacterial load in the liver, spleen, heart, ovary, and oviduct of hens, implying that phage CKT1 played an active role in the elimination of Salmonella colonization in adult chickens. Additionally, phage CKT1 enabled a reduction in the Salmonella-specific IgG level in the serum of infected chickens. More importantly, the decrease in the S. Pullorum load on eggshells and in liquid whole eggs revealed that phage CKT1 effectively controlled the vertical transmission of S. Pullorum from hens to laid eggs, indicating the potential ability of phages to control bacterial vertical transmission.

Medium resolution spectral imager II (MERSI-2) is a payload for the Chinese meteorological satellite Feng Yun 3 (FY-3). China's satellite remote sensing observation capabilities such as climate change research can be improved during MERSI-2's operation in orbit, and the sensor is the world's first imaging instrument that can obtain a global infrared split-window data with a spatial resolution of 250 m. We developed an operational spilt-window (SW) algorithm to retrieve land surface temperature (LST) accurately from the MERSI-2 data. The SW algorithm coefficients were derived from a simulation dataset that was established with the Moderate spectral resolution atmospheric Transmittance model version 5.2 and the thermodynamic initial guess retrieval dataset. In the practical retrieval, the precise algorithm coefficients were determined by view zenith angle and atmospheric water vapor content (WVC), the atmospheric WVC were obtained from the ERA5 dataset, and the land surface emissivity was dynamically estimated using the advanced spaceborne thermal emission and reflection radiometer global emissivity dataset, considering the fractional of vegetation cover and snow cover. The retrieved LST compared with in situ LST, which was highly consistent with the in situ LST and that the root-mean-square error of the two is within 3 K. The retrieved LST was compared with the MYD11_L2 and MYD21_L2 LST products, and the results indicated that MERSI-2 LST was more consistent with the MYD21 LST. The operational SW algorithm for FY-3D MERSI-2 developed in this study could retrieve LST accurately and has a wide range of popularization and application values.

Abstract Background Studies have shown that the spherical shape of the lateral femoral condyle has a clear relationship with the relative axial movement of tibiofemoral joint and the anterior cruciate ligament (ACL) rupture. The purpose of this study was to describe the distal curvature of the lateral femoral condyle by ratio of height of lateral femoral condyle to anteroposterior diameter (HAPR), and evaluate its correlation with ACL rupture. Methods A retrospective case-control study of 64 patients was conducted. Two age-and sex-matched cohorts (each n = 32) were analyzed: primary ACL ruptures, and a control group consisting of isolated meniscal tears. On the radiograph, the distance from the intersection of the axis of the distal femur and the anteriorly diameter of the lateral femoral condyle to the lower point of the lateral femoral condyle divided by the anteriorly diameter of the lateral femoral condyle is HAPR. The HAPR was measured by digital radiograph imaging systems (DR) to quantify femoral sphericity. Cutoff values were defined; and diagnostic performance of the risk factors was assessed. Meanwhile, we measured the posterior tibial slope (PTS) on radiograph and compared the two methods to evaluate the significance of HAPR in predicting ACL rupture. Results A total of sixty-four patients who met the inclusion criteria were included in the final analysis (32 with primary ACL rupture, 32 controls). The HAPR was smaller in the knees with primary ACL rupture (0.31 ± 0.02) than that of the control group (0.33 ± 0.02) (p

Land surface component temperatures (LSCTs), i.e., the temperatures of soil and vegetation, are important parameters in many applications, such as estimating evapotranspiration and monitoring droughts. However, the multiangle algorithm is affected due to different spatial resolution between nadir and oblique views. Therefore, we propose a combined retrieval algorithm that uses dual-angle and multipixel observations together. The sea and land surface temperature radiometer onboard ESA's Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dual-angle and multipixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in the oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multiangle algorithm. The introduction of multipixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multiangle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors of 3.09 K and 1.91 K for soil and vegetation at a grass site, respectively, and corresponding values of 3.71 K and 3.42 K at a sparse forest site, respectively. Considering that the temperature differences between components can reach 20 K, the results confirm that, in addition to a pixel-average LST, the combined retrieval algorithm can provide information on LSCTs. This article demonstrates the potential of utilizing additional information sources for better LSCT results, which makes the presented combined strategy a promising option for deriving large-scale LSCT products.

Land surface temperature (LST) is a vital parameter in the surface energy budget and water cycle. One of the most important foundations for LST studies is a theory to understand how to model LST with various influencing factors, such as canopy structure, solar radiation, and atmospheric conditions. Both physical-based and empirical methods have been widely applied. However, few studies have compared these two categories of methods. In this paper, a physical-based method, soil canopy observation of photochemistry and energy fluxes (SCOPE), and two empirical methods, random forest (RF) and long short-term memory (LSTM), were selected as representatives for comparison. Based on a series of measurements from meteorological stations in the Heihe River Basin, these methods were evaluated in different dimensions, i.e., the difference within the same surface type, between different years, and between different climate types. The comparison results indicate a relatively stable performance of SCOPE with a root mean square error (RMSE) of approximately 2.0 K regardless of surface types and years but requires many inputs and a high computational cost. The empirical methods performed relatively well in dealing with cases either within the same surface type or changes in temporal scales individually, with an RMSE of approximately 1.50 K, yet became less compatible in regard to different climate types. Although the overall accuracy is not as stable as that of the physical method, it has the advantages of fast calculation speed and little consideration of the internal structure of the model.

Antibiotic resistance genes of Escherichia coli (E. coli) from companion animals were still poorly understood. Here, we investigated the extended-spectrum β-lactamases (ESBLs) resistance genes of E. coli from companion animals in Shandong, China. A total of 79 isolates (80.6%) were recovered from 98 healthy or diarrheal companion animals in 2021, among which ESBLs-producing isolates accounted for 43.0% (34/79), and more than half of ESBL E. coli (ESBL-EC) strains (n = 19) were isolated from healthy companion animals. Diarrheagenic E. coli isolates (45.6%, n = 36) were represented by enterotoxigenic (ETEC) (32.9%), enteropathogenic (EPEC) (10.1%) and enteroinvasive (EIEC) (2.6%), 20 isolates of which were from healthy pets. Among tested antibiotics, resistance to tetracycline (64.6%) was the most commonly observed, followed by doxycycline (59.5%) and ampicillin (53.2%). Notably, all isolates were susceptible to meropenem. The multidrug-resistant (MDR) rate was 49.4%, 20 isolates of which were ESBLs producers; moreover, 23.4%, 16.4% of ESBL-EC strains were resistant to 5 or more, 7 or more antibiotics, respectively. Among the 5 β-lactamase resistance genes, the most frequent gene was blaCTX-M (60.76%), followed by blaSHV (40.51%). The plasmid-mediated quinolone resistance (PMQR) gene aac(6’)-Ib-cr was detected in 35 isolates. Additionally, ESBL-associated genes (i.e., blaCTX-M, blaSHV) were found in 76.5% ESBL-EC strains, with six isolates carrying blaCTX-M and blaSHV. The marker gene of high-pathogenicity island gene irp2 (encoding iron capture systems) was the most frequency virulence gene. Our results showed that ESBL-EC were widespread in healthy or diarrhea companion animals, especially healthy pets, which may be a potential reservoir of antibiotic resistance, therefore, enhancing a risk to public and animal health.

With the continuous increase in wind power penetration, doubly fed wind turbines can quickly respond to changes in grid frequency, and have particularly important inertia-response characteristics. This article starts with the excitation control principle of a doubly fed induction generator, compares the transient frequency characteristics of the synchronous generator under fault, and proposes that the doubly fed induction generator can control the speed or active power of the generator through excitation. It proves the unique “active power transient frequency characteristics” of doubly fed induction generators. Under different wind speeds, the inertia response capability of the wind turbine is quantified, and the degrees of influence of the inertia time constant and frequency characteristic slope of the doubly fed induction generator on the transient change process are analyzed. Finally, simulation and experiments verify the correctness of the above theory, which provides a basis for significantly improving the transient stability of the power system and realizing the controllability of the transient stability of the power system.

As one category of vector boson fusion, photo-production is one important production mechanism at e-p colliders. A future e-p collider – Large Hadron-electron Collider (LHeC) has been discussed as a “Higgs factory” candidate where the Higgs boson produced via weak boson fusion (WBF) at the LHeC plays an important role in precision measurement of Yukawa couplings. On the other hand, a measurement of photo-production of the Higgs boson, if possible, might be complementary to the measurement of Higgs to di-photon partial decay width Γ(h→γγ). In this paper, we study the possibility of measuring this production process at the LHeC with the help of the photon PDFs published in recent years. This process has a clean final state without additional colored particles in the detectable region other than the decay products of the Higgs. We compute the cross sections of all related processes and find that the production rate is at the same order as the neutral current WBF production of Higgs boson with missing forward jets. However, a detailed phenomenological study of various Higgs decay channels shows that even in the most promising semi-leptonic WW channel, the feasibility of identifying such photo-production is negative due to an irreducible photo-production of W+W−.

To acquire high-resolution wide-swath (HRWS) imaging capacity, the displaced phase center multichannel azimuth beam (DPCMAB) technology is usually adopted in spaceborne synthetic aperture radar (SAR), while multichannel reconstruction must be carried out before imaging process due to azimuth nonuniform sampling. Up to now, almost all azimuth multichannel reconstruction algorithms have been mainly based on conventional hyperbolic range equation (CHRE), but the accuracy of the CHRE model is usually not suitable for the HRWS mode, especially for high resolution and large squint observation cases. In this study, the azimuth multichannel signal model based on the advanced hyperbolic range equation (AHRE) is established and analyzed. The major difference between multichannel signal models based on CHRE and AHRE is the additional time-varying phase error between azimuth channels. The time-varying phase error is small and can be ignored in the monostatic DPCMAB SAR system, but it must be considered and compensated in the distributed DPCMAB SAR system. In addition to the time-varying phase error, additional Doppler spectrum shift and extended Doppler bandwidth should be considered in the squint case during azimuth multichannel reconstruction. The azimuth multichannel reconstruction algorithm based on AHRE is proposed in this paper. Before multichannel reconstruction and combination, time-varying phase errors between azimuth channels were first compensated, and the range-frequency-dependent de-skewing function was derived to remove the two-dimension (2D) spectrum tilt to avoid azimuth under-sampling. Then, azimuth multichannel data were reconstructed according to the azimuth multichannel impulse response based on AHRE. Finally, the range-frequency dependent re-skewing function was introduced to recover the tilted 2D spectrum. Simulation results on both point and distributed targets validated the proposed azimuth multichannel reconstruction approach.

An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites.

Land surface reflectance (LSR) and aerosol types are the two main factors that affect aerosol inversions over land. According to LSR determination methods, Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products are produced using the Deep Blue (DB) and Dark Target (DT) algorithms. Five aerosol types that are determined from Aerosol Robotic Network (AERONET) ground measurements are used to describe the global distribution of aerosol types in each algorithm. To assess the influence of LSR and the method used to determine aerosol type from aerosol retrievals, 10-km global aerosol products that cover 2013 are selected for validation using Level 2.0 aerosol observations from 175 AERONET sites. The variations in the retrieval accuracy of the DB and DT algorithms for different LSR values are analyzed by combining them with a global 10-km LSR database. Meanwhile, the adaptability of the MODIS products over areas covered with different aerosols is also explored. The results are as follows. (1) Compared with DT retrievals, the DB algorithm yields lower root mean squared error (RMSE) and mean absolut error (MAE) values, and a greater number of appropriate sample points fall within the expected error (EE). The DB algorithm shows higher overall reliability; (2) The aerosol retrieval accuracy of the DB and DT algorithms decline irregularly as the surface reflectance increases; the DB algorithm displays relatively high accuracy; (3) Both algorithms have a high retrieval accuracy over areas covered by weak absorbing aerosols, whereas dust aerosols and continental aerosols produce a low retrieval accuracy. The DB algorithm shows good retrieval results for most aerosols, but a lower accuracy for strong absorbing aerosols.

ObjectivesThis study aimed to develop a deep learning radiomic model using multimodal imaging to differentiate benign and malignant breast tumours.MethodsMultimodality imaging data, including ultrasonography (US), mammography (MG), and magnetic resonance imaging (MRI), from 322 patients (112 with benign breast tumours and 210 with malignant breast tumours) with histopathologically confirmed breast tumours were retrospectively collected between December 2018 and May 2023. Based on multimodal imaging, the experiment was divided into three parts: traditional radiomics, deep learning radiomics, and feature fusion. We tested the performance of seven classifiers, namely, SVM, KNN, random forest, extra trees, XGBoost, LightGBM, and LR, on different feature models. Through feature fusion using ensemble and stacking strategies, we obtained the optimal classification model for benign and malignant breast tumours.ResultsIn terms of traditional radiomics, the ensemble fusion strategy achieved the highest accuracy, AUC, and specificity, with values of 0.892, 0.942 [0.886–0.996], and 0.956 [0.873–1.000], respectively. The early fusion strategy with US, MG, and MRI achieved the highest sensitivity of 0.952 [0.887–1.000]. In terms of deep learning radiomics, the stacking fusion strategy achieved the highest accuracy, AUC, and sensitivity, with values of 0.937, 0.947 [0.887–1.000], and 1.000 [0.999–1.000], respectively. The early fusion strategies of US+MRI and US+MG achieved the highest specificity of 0.954 [0.867–1.000]. In terms of feature fusion, the ensemble and stacking approaches of the late fusion strategy achieved the highest accuracy of 0.968. In addition, stacking achieved the highest AUC and specificity, which were 0.997 [0.990–1.000] and 1.000 [0.999–1.000], respectively. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity of 1.000 [0.999–1.000] under the early fusion strategy.ConclusionThis study demonstrated the potential of integrating deep learning and radiomic features with multimodal images. As a single modality, MRI based on radiomic features achieved greater accuracy than US or MG. The US and MG models achieved higher accuracy with transfer learning than the single-mode or radiomic models. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity under the early fusion strategy, showed higher diagnostic performance, and provided more valuable information for differentiation between benign and malignant breast tumours.

Atherosclerosis, a highly prevalent cardiovascular disease, has significantly contributed to morbidity and mortality worldwide. Despite the availability of various treatment options in clinical practice, complete reversal of the atherosclerotic pathological process remains challenging. Traditional anti-arteriosclerosis drugs often suffer from poor solubility, limited targeting ability and obvious adverse effects. Additionally, novel therapeutics such as genetic and protein-based drugs inherently suffer from instability issues, thereby compromising their actual curative efficacy and impeding further clinical application. Recently, nanomedicine-based drug delivery strategies have emerged as promising approaches for treating atherosclerosis due to their ability to specifically transport various drugs to target cells, microstructures and specific molecules within lesion sites. These advanced features enhance drug properties and bioavailability while minimizing off-target effects. Moreover, numerous studies have demonstrated that nanomedicine not only inhibits plaque progression but also reduces lesional areas and eventually reverses atherosclerosis. The utilization of nanomedicine is considered an important therapeutic tactic for further mitigating residual risks associated with atherosclerosis. This review provides a comprehensive overview of emerging nanomedicine-based strategies for targeted delivery of anti-atherosclerosis drugs, elucidating their distinct mechanisms and highlighting their remarkable efficacy in combating atherosclerosis. Additionally, critical challenges and future perspectives associated with the development and application of anti-arteriosclerosis nanomedicine are thoroughly discussed.

Vaccines has long been the focus of antiviral immunotherapy research. Viral epitopes are thought to be useful biomarkers for immunotherapy (both antibody-based and cellular). In this study, we designed a novel vaccine molecule, the Hantaan virus (HTNV) glycoprotein (GP) tandem Th epitope molecule (named the Gnc molecule), in silico. Subsequently, computer analysis was used to conduct a comprehensive and in-depth study of the various properties of the molecule and its effects as a vaccine molecule in the body. The Gnc molecule was designed for DNA vaccines and optimized with a lysosomal-targeting membrane protein (LAMP) strategy. The effects of GP-derived Th epitopes and multiepitope vaccines were initially verified in animals. Our research has resulted in the design of two vaccines based on effective antiviral immune targets. The effectiveness of molecular therapies has also been preliminarily demonstrated in silico and in laboratory animals, which lays a foundation for the application of a vaccines strategy in the field of antivirals.

(1) Background and Purpose: Ebola virus (EBOV) is the causative agent of Ebola virus disease (EVD), which causes extremely high mortality and widespread epidemics. The only glycoprotein (GP) on the surface of EBOV particles is the key to mediating viral invasion into host cells. DNA vaccines for EBOV are in development, but their effectiveness is unclear. The lack of immune characteristics resides in antigenic MHC class II reactivity. (2) Methods: We selected MHC-II molecules from four human leukocyte antigen II (HLA-II) superfamilies with 98% population coverage and eight mouse H2-I alleles. IEDB, NetMHCIIpan, SYFPEITHI, and Rankpep were used to screen MHC-II-restricted epitopes with high affinity for EBOV GP. Further immunogenicity and conservation analyses were performed using VaxiJen and BLASTp, respectively. EpiDock was used to simulate molecular docking. Cluster analysis and binding affinity analysis of EBOV GP epitopes and selected MHC-II molecules were performed using data from NetMHCIIpan. The selective GP epitopes were verified by the enzyme-linked immunospot (ELISpot) assay using splenocytes of BALB/c (H2d), C3H, and C57 mice after DNA vaccine pVAX-GPEBO immunization. Subsequently, BALB/c mice were immunized with Protein-GPEBO, plasmid pVAX-GPEBO, and pVAX-LAMP/GPEBO, which encoded EBOV GP. The dominant epitopes of BALB/c (H-2-I-AdEd genotype) mice were verified by the enzyme-linked immunospot (ELISpot) assay. It is also used to evaluate and explore the advantages of pVAX-LAMP/GPEBO and the reasons behind them. (3) Results: Thirty-one HLA-II-restricted and 68 H2-I-restricted selective epitopes were confirmed to have high affinity, immunogenicity, and conservation. Nineteen selective epitopes have cross-species reactivity with good performance in MHC-II molecular docking. The ELISpot results showed that pVAX-GPEBO could induce a cellular immune response to the synthesized selective peptides. The better immunoprotection of the DNA vaccines pVAX-LAMP/GPEBO coincides with the enhancement of the MHC class II response. (4) Conclusions: Promising MHC-II-restricted candidate epitopes of EBOV GP were identified in humans and mice, which is of great significance for the development and evaluation of Ebola vaccines.

The relative hostile media effect suggests that partisans tend to perceive the bias of slanted news differently depending on whether the news is slanted in favor of or against their sides. To explore the effect of an algorithmic vs. human source on hostile media perceptions, this study conducts a 3 (author attribution: human, algorithm, or human-assisted algorithm) x 3 (news attitude: pro-issue, neutral, or anti-issue) mixed factorial design online experiment (N = 511). This study uses a transformer-based adversarial network to auto-generate comparable news headlines. The framework was trained with a dataset of 364,986 news stories from 22 mainstream media outlets. The results show that the relative hostile media effect occurs when people read news headlines attributed to all types of authors. News attributed to a sole human source is perceived as more credible than news attributed to two algorithm-related sources. For anti-Trump news headlines, there exists an interaction effect between author attribution and issue partisanship while controlling for people’s prior belief in machine heuristics. The difference of hostile media perceptions between the two partisan groups was relatively larger in anti-Trump news headlines compared with pro-Trump news headlines.

Abstract Background The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high efficiency features, which helps to improve the level of computer-aided diagnosis (CAD). It can improve the performance of distinguishing benign and malignant breast ultrasound (BUS) tumor images, making rapid breast tumor screening possible. Results The classification model was evaluated with a different dataset of 100 BUS tumor images (50 benign cases and 50 malignant cases), which was not used in network training. Evaluation indicators include accuracy, sensitivity, specificity, and area under curve (AUC) value. The results in the Fus2Net model had an accuracy of 92%, the sensitivity reached 95.65%, the specificity reached 88.89%, and the AUC value reached 0.97 for classifying BUS tumor images. Conclusions The experiment compared the existing CNN-categorized architecture, and the Fus2Net architecture we customed has more advantages in a comprehensive performance. The obtained results demonstrated that the Fus2Net classification method we proposed can better assist radiologists in the diagnosis of benign and malignant BUS tumor images. Methods The existing public datasets are small and the amount of data suffer from the balance issue. In this paper, we provide a relatively larger dataset with a total of 1052 ultrasound images, including 696 benign images and 356 malignant images, which were collected from a local hospital. We proposed a novel CNN named Fus2Net for the benign and malignant classification of BUS tumor images and it contains two self-designed feature extraction modules. To evaluate how the classifier generalizes on the experimental dataset, we employed the training set (646 benign cases and 306 malignant cases) for tenfold cross-validation. Meanwhile, to solve the balance of the dataset, the training data were augmented before being fed into the Fus2Net. In the experiment, we used hyperparameter fine-tuning and regularization technology to make the Fus2Net convergence.

Abstract Influence, the ability to change the beliefs and behaviors of others, is the main currency on social media. Extant studies of influence on social media, however, are limited by publicly available data that record expressions (active engagement of users with content, such as likes and comments), but neglect impressions (exposure to content, such as views) and lack “ground truth” measures of influence. To overcome these limitations, we implemented a social media simulation using an original, web-based micro-blogging platform. We propose three influence models, leveraging expressions and impressions to create a more complete picture of social influence. We demonstrate that impressions are much more important drivers of influence than expressions, and our models accurately identify the most influential accounts in our simulation. Impressions data also allow us to better understand important social media dynamics, including the emergence of small numbers of influential accounts and the formation of opinion echo chambers.

Abstract Background Breast cancer is one of the most serious diseases threatening women’s health. Early screening based on ultrasound can help to detect and treat tumours in the early stage. However, due to the lack of radiologists with professional skills, ultrasound-based breast cancer screening has not been widely used in rural areas. Computer-aided diagnosis (CAD) technology can effectively alleviate this problem. Since breast ultrasound (BUS) images have low resolution and speckle noise, lesion segmentation, which is an important step in CAD systems, is challenging. Results Two datasets were used for evaluation. Dataset A comprises 500 BUS images from local hospitals, while dataset B comprises 205 open-source BUS images. The experimental results show that the proposed method outperformed its related classic segmentation methods and the state-of-the-art deep learning model RDAU-NET. Its accuracy (Acc), Dice similarity coefficient (DSC) and Jaccard index (JI) reached 96.25%, 78.4% and 65.34% on dataset A, and its Acc, DSC and sensitivity reached 97.96%, 86.25% and 88.79% on dataset B, respectively. Conclusions We proposed an adaptive morphological snake based on marked watershed (AMSMW) algorithm for BUS image segmentation. It was proven to be robust, efficient and effective. In addition, it was found to be more sensitive to malignant lesions than benign lesions. Methods The proposed method consists of two steps. In the first step, contrast limited adaptive histogram equalization (CLAHE) and a side window filter (SWF) are used to preprocess BUS images. Lesion contours can be effectively highlighted, and the influence of noise can be eliminated to a great extent. In the second step, we propose adaptive morphological snake (AMS). It can adjust the working parameters adaptively according to the size of the lesion. Its segmentation results are combined with those of the morphological method. Then, we determine the marked area and obtain candidate contours with a marked watershed (MW). Finally, the best lesion contour is chosen by the maximum average radial derivative (ARD).

Background/Aims: Partial splenic embolization (PSE) is used in the management of gastroesophageal variceal hemorrhage (GEVH). However, it is uncertain whether it has beneficial effects for GEVH patients in preventing variceal recurrence and variceal hemorrhage, as well as promoting overall survival (OS), when it is combined with conventional therapies. Materials and Methods: The databases including PubMed, EMBASE, Web of Science, Google scholar, and Cochrane Central Register of Controlled Trials were searched up to 11th of November, 2015. Meta-analyses were performed by using Review Manager 5.3 software for analyzing the risk of bias, Newcastle-Ottawa Scale for assessing the bias of cohort studies, and GRADEprofiler software for assessing outcomes obtained from the meta-analyses. Results: A total of 1505 articles were reviewed, and 1 randomized controlled trial and 5 cohort studies with 244 participants were eligible for inclusion. The pooled hazard ratio (HR) of variceal recurrence is 0.50 (95% confidence interval (CI) 0.37, 0.68; P< 0.00001; I2 = 0%). The pooled HR of variceal hemorrhage is 0.24 (95% CI 0.15, 0.39; P< 0.00001; I2 = 0%). The pooled HR of OS is 0.50 (95% CI 0.33, 0.67; P< 0.00001; I2 = 0%). Meta-analyses demonstrated statistically significant superiority of combinational therapies over conventional therapies in preventing variceal recurrence and variceal hemorrhage and prolonging OS. The complications related to PSE were mild or moderate and nonfatal. Conclusions: The results indicate that PSE has beneficial effects for GEVH patients, however, future investigation with a larger number of subjects in clinical trials is warranted.

This experiment was to investigate the effects of dietary leucine supplementation on the gene expression of mammalian target of rapamycin (mTOR) signaling pathway and intestinal development of broilers. A total of 384 one-day-old broilers were randomly assigned into 4 treatments with 6 replicates (16 broilers per replicate). Broilers in these treatment groups were offered the following diets with 1.37, 1.77, 2.17 and 2.57% of leucine. These diet treatments were named 1.37TM, 1.77TM, 2.17TM, and 2.57TM. The experiment lasted 21 days and all birds had free access to feed and water. Results indicated that there was no significant difference in body weight, average daily gain and average feed intake among all treatments (P > 0.05). The broiler duodenal villus height in 2.57TM was the lowest, but the highest occurred in 1.37TM on d 7 and 14 (P