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The Nanjing University of Information Science and Technology reviewed 7561 publications, identifying 1271 relevant works published between 2014 and 2022 based on data from the Web of Science database. The PRISMA 2020 technique, with appropriate inclusion and exclusion criteria to minimize errors, revealed significant annual increases in publishing volume during this period. The United States and China ranked first and second in terms of publication volume and citations, while the Peoples of China and the United States ranked first in terms of publication quality. Inequalities in network density and linkages were found, with the Chinese Academy of Sciences receiving the most citations and degrees. The document co-citation analysis network was developed to increase understanding of the frequency and amplitude of bursts of different publications in distinct clusters. Seneviratne SI in Cluster 1, (2014) had the most citations (18) and the highest strength (4.81). CiteSpace's analytical technology identifies high-frequency keywords, explores co-occurrence patterns, and highlights emerging concepts, shedding light on cutting-edge research dynamics and the impact of new ideas from policies or public events. In this review, bursts of new concepts highlight rapid growth in the field. Seneviratne SI (2014) and Seager R with Zhang Q (2015) were top contributors in citations and references. Notably, knowledge gaps exist in anthropogenic climate dynamics, environmental factors, drought risk mitigation, and attribution. Future scholars should focus on these and comparable issues, while Chinese and American authors should prioritize article quality above publication numbers. • This Drought bibliography looked into the connections between, nations, institutions, writers, references, and publisher. • Institutional collaboration analysis shows inequalities in network density, with Chinese Academy of Sciences leading. • CiteSpace's analytical technology gathered high-frequency keywords, co-occurrence, grouping, and emerging word identification. • This review found knowledge gaps in anthropogenic climate dynamics, environmental factors, and drought mitigation. [ABSTRACT FROM AUTHOR]

The clinical management of small gastric submucosal tumors (SMTs) (<2 cm) faces a non-negligible challenge because of the lack of guideline consensus and effective diagnostic tools. This article develops an automatically optimized radiomics modeling system (AORMS) based on EUS images to diagnose and evaluate SMTs. A total of 205 patients with EUS images of small gastric SMTs (<2 cm) were retrospectively enrolled in the development phase of AORMS for the diagnosis and the risk stratification of GI stromal tumor (GIST). A total of 178 patients with images from different centers were prospectively enrolled in the independent testing phase. The performance of AORMS was compared to that of endoscopists in the development set and evaluated in the independent testing set. AORMS demonstrated an area under the curve (AUC) of 0.762 for the diagnosis of GIST and 0.734 for the risk stratification of GIST, respectively. In the independent testing set, AORMS achieved an AUC of 0.770 and 0.750 for the diagnosis and risk stratification of small GISTs, respectively. In comparison, the AUCs of 5 experienced endoscopists ranged from 0.501 to 0.608 for diagnosing GIST and from 0.562 to 0.748 for risk stratification. AORMS outperformed experienced endoscopists by more than 20% in diagnosing GIST. AORMS implements automatic parameter selection, which enhances its robustness and clinical applicability. It has demonstrated good performance in the diagnosis and risk stratification of GISTs, which could aid endoscopists in the diagnosis of small gastric SMTs (<2 cm). [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Accurate preoperative identification of lateral cervical lymph node metastasis (LNM) is important for decision-making and clinical management of patients with papillary thyroid carcinoma (PTC). The aim of this study was to develop an ultrasound (US)-based radiomic nomogram to preoperatively predict the lateral LNM in PTC patients.Methods: In this retrospective study, a total of 886 patients were enrolled and randomly divided into 2 groups. Radiomic features were extracted from the preoperative US images. A radiomic signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set. Multivariate logistic regression was performed to develop the radiomic nomogram, which incorporating the radiomic signature and the selected clinical characteristics. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness in both the training and validation sets.Results: The radiomic signature was significantly associated with the lateral LNM in both cohorts (p< 0.001). The nomogram that consisted of radiomic signature, US-reported cervical lymph node (CLN) status, and CT-reported CLN status demonstrated good discrimination and calibration in the training and validation sets with an AUC of 0.946 and 0.914, respectively. The decision curve analysis indicated that the radiomic nomogram was worthy of clinical application.Conclusion: The radiomic nomogram proposed here has good performance for noninvasively predicting the lateral LNM and might be used to facilitate clinical decision-making and potentially improve the survival outcome in selected patients. [ABSTRACT FROM AUTHOR]

• An effective radiomic system is proposed to identify the early ischemic stroke that is invisible to the radiologist in ncCT images. • A novel multi-scale symmetry image patch classification model is designed to detect the stroke regions. • A MAP model integrating the multi-scale and spatial constraint information is built to improve the identification performance. Early identification of stroke is critical for the treatment and subsequent recovery. Non-contrast computed tomography (ncCT) is a routinely employed imaging modality for stroke evaluation. However, the early identification of stroke in ncCT images is very difficult, since there are subtle differences between lesion and healthy tissue during the hyperacute phase. In this paper, an image patch classification-based method was developed to detect the early ischemic stroke that is invisible to the radiologist in ncCT. First, we proposed radiomics-based patch classification model to identify whether each patch in ncCT is stroke region or not. To improve the identification accuracy, a symmetry image patch classification was developed, in which an image patch in one brain hemisphere was combined with its contralateral image patch for classification. Second, based on the spatial dependencies between neighboring patches, we built a maximum a posteriori identification model which integrated the spatial constraint information to improve the identification performance. Finally, after performing morphological post-processing, we defined the detected results containing more than 300 voxels as the stroke region. 108 stroke cases that were all invisible to radiologists in ncCT were enrolled in the study and divided into one training cohort and two independent testing cohorts to validate the proposed method. The proposed method has achieved identification accuracies of 76.67% and 75.00% on the two independent testing cohorts, respectively. The results proved the potential of the proposed radiomics model in the task which possessed great clinical values. [ABSTRACT FROM AUTHOR]

• A general and concise reinforcement supervision strategy is designed for the segmentation of multi-ROI in medical images. • A generative-adversarial structure with a reused decoder is proposed to implement our strategy, which further simplifies network structure, reduces the training difficulty and improves the inference efficiency. • Three modules including a dilated convolution chain, a fusion block, and a location encoder are proposed to strengthen the segmentation performance of the framework. • In the new version, we carefully revised the manuscript based on the comments, as detailed in the response letter. A new multiple region of interest (multi-ROI) segmentation framework, RsALUNet, is proposed in this paper, whose backbone was U-Net. Rs represented the reinforcement-supervision strategy by utilizing adversarial learning (AL) between U-Net's decoders and an additional discriminator, which was based on the differences among the segmentation results (diff S e g s ) and labels of multi-ROI (diff L a b e l s ). As the AL progressed, diff S e g s was increasingly similar to diff Labels , and this further contributed to a more accurate segmentation of each member in the multi-ROI. In addition to the Rs strategy, three blocks were proposed to enhance RsALUNet, namely, a dilated convolution chain providing diverse and large receptive fields to accommodate different target sizes, a fusion block integrating features of large targets to small ones to optimize the segmentation of the latter, and a location-encoder block extracting multi-scale positional information to enhance the model's attention to the ROI. RsALUNet was evaluated through three multi-ROI segmentation tasks using different imaging modalities, including X-ray, ultrasound, and magnetic-resonance (MR) imaging. The mean Dice coefficient (Dice) increased from 1.1% to 8.5% compared to the other frameworks. The results demonstrate the promising adaptability and extendibility of our strategy and RsALUNet for multi-ROI segmentation in X-ray, ultrasound, and MR images. [ABSTRACT FROM AUTHOR]

Fetal nuchal translucency (NT) thickness is one of the most important parameters in prenatal screening. Locating the mid-sagittal plane is one of the key points to measure NT. In this paper, an automatic method for the sagittal plane detection using 3-D ultrasound data is proposed. To avoid unnecessary massive searching and the corresponding huge computation load, a model is proposed to turn the sagittal plane detection problem into a symmetry plane and axis searching problem. The deep belief network (DBN) and a modified circle detection method provide prior knowledge for the searching. The experiments show that in most cases, the result plane has small distance error and angle error at the same time-88.6% of the result planes have a distance error less than 4 mm and 71.0% have angle error less than 20°. [ABSTRACT FROM AUTHOR]

• We propose a R-Transformer network to achieve precise brain tumor segmentation by combining R-Transformer with U-Net. • ERTN employs the ranking attention mechanism, which helps the model improve training efficiency and reduce computation cost. • ERTN can provide higher brain tumor segmentation accuracy than the CNNs or Transformers based methods. Gliomas are the most prevalent and destructive forms of primary brain tumors with a high mortality rate. Magnetic resonance imaging is extensively employed in the examination of gliomas. Segmenting brain glioma on magnetic resonance images has significant clinical value. However, the blur boundary of gliomas, variability in the shape, location, and size make segmentation extremely challenging. In this paper, we propose a new method to segment gliomas from three-dimensional brain magnetic resonance images accurately. We propose an efficient R-Transformer network with dual encoders (ERTN) to achieve precise segmentation by innovatively combining R-Transformer with U-Net. Specifically, ERTN constructs a feature branch and a patch branch, capturing complex semantic features and global context information. Moreover, features generated from the feature branch and patch branch are up-sampled and combined with low- and high-resolution CNN features in the decoder to enable precise localization. At last, ERTN employs the ranking attention mechanism in Transformer (R-Transformer), which helps the model focus on helpful information to improve training efficiency and reduce computation cost. Experiments on the 2017 BRATS dataset prove that ERTN achieves satisfactory performance, with a Dice similarity coefficient of 83.20%, 77.93%, and 72.59% on the whole tumor, tumor core, and enhanced tumor segmentation. For the Hausdorff distance index, we obtained the scores of 5.30, 4.60, and 5.50 for the whole tumor, tumor core, and enhanced tumor, respectively. Our results suggest that ERTN improves the segmentation accuracy and reduces computation cost, which performs better than the existing convolution- and transformer-based methods. [ABSTRACT FROM AUTHOR]

As a fundamental task for geographical information updating, 3D city modeling, and other critical applications, the automatic extraction of building footprints from high-resolution remote sensing images has been substantially explored and received increasing attention over recent years. Among different types of building extraction methods, the polygonal segmentation methods produce vector building polygons that are in a more realistic format compared with those obtained from pixel-wise semantic labeling and contour-based methods. However, existing polygonal building segmentation methods usually require a perfect segmentation map and a complex post-processing procedure to guarantee the polygonization quality, or produce inaccurate vertex prediction results that suffer from wrong vertex sequence, self-intersections, fixed vertex quantity, etc. In our previous work, we have proposed a method for polygonal building segmentation from remote sensing images that addresses the above limitations of existing methods. In this paper, we propose PolyCity, which further extends and improves our previous work in terms of the application scenario, methodology design, and experimental results. Our proposed PolyCity contains the following three components: (1) a pixel-wise multi-task network for learning the semantic and geometric information via three tasks, i.e., building segmentation, boundary prediction, and edge orientation prediction; (2) a simple but effective vertex selection module (VSM), which effectively bridges the gap between pixel-wise and graph-based models via transforming the segmentation map into valid polygon vertices; (3) a graph-based vertex refinement network (VRN) for automatically adjusting the coordinates of VSM-generated valid polygon vertices, producing the final building polygons with more precise vertices. Results on three large-scale building extraction datasets demonstrate that our proposed PolyCity generates vector building footprints with more accurate vertices, edges, shapes, etc., achieving significant vertex score improvements while maintaining high segmentation and boundary scores compared with the current state-of-the-art. The code of PolyCity will be released at https://github.com/liweijia/polycity. [ABSTRACT FROM AUTHOR]

Although many criteria were proposed to predict the glass forming ability (GFA) of alloys, the precise correlation between the minor addition of elements and the glass formation remains a challenge. To solve this problem, we propose a simple parameter α' derived from thermal expansion coefficient based on the details of the intrinsic interatomic potential. It has been found that α' has the capability of precisely predict the changes of GFA, indicating the important role of anharmonicity of atomic vibration in glass formation. This anharmonicity-related parameter α' relates to atomic mobility behavior, and could be well embodied by the competition between characteristic types of clusters in terms of the fragility concept upon the liquid-solid evolution. This work not only helps to uncover atomic and cluster origins of glass formation from interatomic potential, but also provides a novel route to precisely evaluate the effect of minor addition of elements on GFA. [Display omitted] [ABSTRACT FROM AUTHOR]

The nuchal translucency thickness is an important parameter for the diagnosis of fetal abnormalities during 11–14 weeks. Currently in clinical practice, it first requires manual scanning operations to determine the fetal standard sagittal-view plane and then the measurements can be performed in the corresponding plane images. Besides the difficulty of such standard plane detection, this also leads to the time-consuming and detection-variability problems. In the paper, a hierarchical model is proposed to automatically detect the standard sagittal-view plane based on 3D ultrasound data. In the model, Hessian-matrix based filtering is first applied for obtaining the plate-structure distribution in the data. Then the sphere distribution is calculated by convolving sphere detectors with the ultrasound data. Based on the two prior distributions, the sampling-based Hough transform is further performed for the plane detection. The performance of the proposed model is verified by the experimental results on 3D synthetic data and 241 clinical 3D ultrasound data in 11–14 weeks. [ABSTRACT FROM AUTHOR]

Statement of problem It is unknown whether dental implant systems with a platform-switched configuration have better periimplant bone stress distribution and lead to less periimplant bone level changes. Purpose The purpose of this study was to quantitatively investigate interfacial stress and stress distribution in implant bone in 2 implant abutment designs (platform-switched design and conventional diameter matching) by using a nonlinear finite element analysis method. Material and methods A finite element simulation study was applied to 2 commercially available dental implant systems: the Ankylos implant system with a reduced-diameter abutment (platform-switched implant) and the Anthogyr implant system with an abutment of the same diameter (regular platform implant). These 2 dental implant systems were positioned in a bone block, which was constructed based on a cross-sectional image of a human mandible in the molar region. In simulation, a single vertical load of 50 N, 100 N, or 150 N and horizontal loads of 50 N and 100 N were applied to the occlusal surface of the abutment. Results The finite element analysis found that the Ankylos implant system has a higher maximum von Mises stress in the implant abutment connection section and a lower maximum von Mises stress in the periimplant bone. The opposite results were found in the Anthogyr implant system. Conclusions Lower stress levels in the periimplant bone with a more uniform stress distribution were found for the Ankylos implant system with a platform-switched configuration. Although relatively higher stress was found in the abutment, premature implant failure is not anticipated because of the high strength of titanium alloy. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: Mineral trioxide aggregate (MTA) has been widely used in clinical apexification and apexogenesis. However, the effects of MTA on the stem cells from apical papilla (SCAPs) and the precise mechanism of apexogenesis have not been elucidated in detail. Methods: Multiple colony-derived stem cells were isolated from the apical papillae, and the effects of MTA on the proliferation and differentiation of SCAPs were investigated both in vitro and in vivo. Activation of nuclear factor kappa B (NFκB) pathway in MTA-treated SCAPs was analyzed by immunofluorescence assay and Western blot. Results: MTA at the concentration of 2 mg/mL did not affect the proliferation activity of SCAPs. However, 2 mg/mL MTA-treated SCAPs presented the ultrastructural changes, up-regulated alkaline phosphatase, increased calcium deposition, up-regulated expression of odontoblast markers (dentin sialoprotein and dentin sialophosphoprotein) and odonto/osteoblast markers (runt-related transcription factor 2 and osteocalcin), suggesting that MTA enhanced the odonto/osteoblastic differentiation of SCAPs in vitro. In vivo results confirmed that MTA can promote the regular dentinogenesis of SCAPs. Moreover, MTA-treated SCAPs exhibited the up-regulated cytoplasmic phos-IκBα and phos-P65, enhanced nuclear P65, and increased nuclear translocation of P65. When co-treated with BMS345541 (the specific NFκB inhibitor), MTA-mediated odonto/osteoblastic differentiation was significantly attenuated. Conclusions: MTA at the concentration of 2 mg/mL can improve the odonto/osteogenic capacity of SCAPs via the activation of NFκB pathway. [Copyright &y& Elsevier]

• A 3D UNet-based model was proposed to conduct cross-modality synthesis of MR images. • Multi-scale and residual learning framework were developed to improve the effect. • Experimental results on three datasets demonstrate the promising performance. Multi-modality MRI has been increasingly used for disease diagnosis and prognosis evaluation. However, the acquisitions of multi-modal images always face some practical difficulties such as the increased cost and patient discomfort. In this paper, we proposed an improved 3D UNet model for MRI modality synthesis and use the synthetic modalities to improve the performance of the following image analysis tasks such as tumor segmentation and tumor grading. We developed our model with three improvements on the basic 3D UNet model. First, we constructed a coarse-to-fine resolution fusion network to improve the ability of structural texture preservation. Second, we designed a convolutional fusion-based residual learning framework to learn mapping function, which leads to more detail preservation as well as less artifacts in the background. Third, for many-to-one synthesis, to combine the input multi-modalities more effectively, we introduced a new network module to fuse the UNet output feature maps of each modality. One private and two public datasets including a total of 493 cases were used for synthesis validation. Our proposed method surpasses the best existing method about 2.0 dB and 0.8 dB in PSNRs on the two datasets, respectively. Furthermore, two separate radiomics applications were used to demonstrate the value of our modality synthesis method. In the image segmentation application, integrating the modalities synthesized by our method has improved the segmentation Dice by 1.3%. In gliomas grading diagnosis application, integrating the synthesized modalities improved the diagnostic accuracy by 8.3%. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: The purpose of this study was to identify the influential factors responsible for clinical instrument separation of reused ProTaper Universal rotary instruments (Dentsply Maillefer, Ballaigues, Switzerland). Methods: Six thousand one hundred fifty-four root canals in 2,654 teeth were prepared using ProTaper Universal files in endodontic clinics. Separation incidence was determined based on the number of treated teeth or canals. Data were collected including the size of fractured instrument, the length and location of a broken segment within the root canal, and the curvature of canal. The chi-square test and independent samples t test were used to determine the statistical significance. Results: The overall instrument separation incidences were 2.6% according to the number of teeth and 1.1% according to the canal number, respectively. Separation incidences according to the number of teeth or canals were significantly higher (P < .05) in molars than those in premolars or anterior teeth. Because of its largest diameter, F3 file presented the highest separation incidence according to the number of teeth (1.0%) or canals (0.4%); 47.5% instrument separation of mandibular molars and 61.5% instrument separation of maxillary molars happened in the mesiobuccal canals. Moreover, 91.4% fragments were located in the apical third of root canals, and 54.2% instrument separation occurred in severely curved canals. There was a significant difference (P < .05) in the mean fracture length between shaping (2.42 ± 0.73 mm) and finishing files (3.32 ± 0.73 mm). Conclusions: Separation incidence according to the canal number is more reliable than that according to the number of teeth because of the variable canal number in different teeth. The tooth type, rotary file size, canal location, and anatomy were correlated with the instrument separation of reused ProTaper Universal files. [Copyright &y& Elsevier]

Abstract: An ultrasound speckle reduction method is proposed in this paper. The filter, which enhances the power of anisotropic diffusion with the Smallest Univalue Segment Assimilating Nucleus (SUSAN) edge detector, is referred to as the SUSAN-controlled anisotropic diffusion (SUSAN_AD). The SUSAN edge detector finds image features by using local information from a pseudo-global perspective. Thanks to the noise insensitivity and structure preservation properties of SUSAN, a better control can be provided to the subsequent diffusion process. To enhance the adaptability of the SUSAN_AD, the parameters of the SUSAN edge detector are calculated based on the statistics of a fully formed speckle (FFS) region. Different FFS estimation schemes are proposed for envelope-detected speckle images and log-compressed ultrasonic images. Adaptive diffusion threshold estimation and automatic diffusion termination criterion are employed to enhance the robustness of the method. Both synthetic and real ultrasound images are used to evaluate the proposed method. The performance of the SUSAN_AD is compared with four other existing speckle reduction methods. It is shown that the proposed method is superior to other methods in both noise reduction and detail preservation. [Copyright &y& Elsevier]

Abstract: In many applications of medical image analysis, the density of an object is the most important feature for isolating an area of interest (image segmentation). In this research, an object density-based image segmentation methodology is developed, which incorporates intensity-based, edge-based and texture-based segmentation techniques. The proposed method consists of three main stages: preprocessing, object segmentation and final segmentation. Image enhancement, noise reduction and layer-of-interest extraction are several subtasks of preprocessing. Object segmentation utilizes a marker-controlled watershed technique to identify each object of interest (OI) from the background. A marker estimation method is proposed to minimize over-segmentation resulting from the watershed algorithm. Object segmentation provides an accurate density estimation of OI which is used to guide the subsequent segmentation steps. The final stage converts the distribution of OI into textural energy by using fractal dimension analysis. An energy-driven active contour procedure is designed to delineate the area with desired object density. Experimental results show that the proposed method is 98% accurate in segmenting synthetic images. Segmentation of microscopic images and ultrasound images shows the potential utility of the proposed method in different applications of medical image processing. [Copyright &y& Elsevier]

Abstract: A simple cleft-shaped anion receptor 1 containing amide and hydroxyl as recognition units was synthesized and its anion recognition properties were investigated by naked-eye observation, UV–vis, fluorescence and 1H NMR spectra. Receptor 1 could effectively recognize F− via multiple H-bonding interactions, along with visible color change and remarkable fluorescence enhancement. Interestingly, reference compound 2 only containing amide groups did not display any interaction for all of the examined anions, including F−. [Copyright &y& Elsevier]

Abstract: A novel method is developed for the fetal abdominal contour extraction and measurement in ultrasound images. Fetal abdominal circumference (AC) is one of the standardized measurements in the antepartum ultrasound monitoring. Among several standardized measurements, AC is best correlated with fetal growth but is also the most difficult to be accurately measured. To overcome the difficulties in the abdominal contour extraction, the proposed method is a four-step procedure that integrates several image segmentation techniques. The proposed method is able to make the best use of the strength of different segmentation algorithms, while avoiding their deficiencies. An enhanced instantaneous coefficient of variation (ICOV) edge detector is first developed to detect edges of the abdominal contour and alleviate the effects of most speckle noise. Then, the Fuzzy C-Means clustering is employed to distinguish salient edges attributable to the abdominal contour from weak edges due to the other texture. Subsequently, the iterative Hough transform is applied to determine an elliptical contour and obtain an initial estimation of the AC. Finally, the gradient vector field (GVF) snake adapts the initial ellipse to the real edges of the abdominal contour. Quantitative validation of the proposed method on synthetic images under different imaging conditions achieves satisfactory segmentation accuracy (98.78% ± 0.16%). Experiments on 150 clinical images are carried out in three aspects: comparisons between inter-observer and inter-run variation, the fitness analysis between the automatically detected ellipse and the manual delineation, and the accuracy comparisons between automatic measurements and manual measurements in estimation of fetal weight (EFW). Experimental results show that the proposed method can provide consistent and accurate measurements. The reductions of the mean absolute difference and the standard deviation of EFW based on automatic measurements are about 1.2% and 2.1%, respectively, which indicate its potential in clinical antepartum monitoring application. (E-mail: yywang@fudan.edu.cn) [Copyright &y& Elsevier]

Purpose: To explore a new approach mainly based on deep learning residual network (ResNet) to detect infarct cores on non-contrast CT images and improve the accuracy of acute ischemic stroke diagnosis.Methods: We continuously enrolled magnetic resonance diffusion weighted image (MR-DWI) confirmed first-episode ischemic stroke patients (onset time: less than 9 h) as well as some normal individuals in this study. They all underwent CT plain scan and MR-DWI scan with same scanning range, layer thickness (4 mm) and interlayer spacing (4 mm) (The time interval between two examinations: less than 4 h). Setting MR-DWI as gold standard of infarct core and using deep learning ResNet combined with a maximum a posteriori probability (MAP) model and a post-processing method to detect the infarct core on non-contrast CT images. After that, we use decision curve analysis (DCA) establishing models to analyze the value of this new method in clinical practice.Results: 116 ischemic stroke patients and 26 normal people were enrolled. 58 patients were allocated into training dataset and 58 were divided into testing dataset along with 26 normal samples. The identification accuracy of our ResNet based approach in detecting the infarct core on non-contrast CT is 75.9%. The DCA shows that this deep learning method is capable of improving the net benefit of ischemic stroke patients.Conclusions: Our deep learning residual network assisted with optimization methods is able to detect early infarct core on non-contrast CT images and has the potential to help physicians improve diagnostic accuracy in acute ischemic stroke patients. [ABSTRACT FROM AUTHOR]

The detrimental impacts of climate change have drawn the interest of many climate scientists toward understanding the past climate for the sake of preparing for the future. The current study evaluates the performance of 12 rainfall models available in Coupled Model Intercomparison Project Phase 5 (CMIP5) in reproducing observed rainfall over Kenya from 1979 to 2005. Several statistical metrics were deployed in quantifying the disparities between CMIP5 models, in situ, and the Global Precipitation Climatology Centre (GPCC v7) rainfall datasets. The results show satisfactory skill of CMIP5 models' in simulating the bimodal rainfall regime despite exhibiting dry (wet) bias during March–May (MAM) and October–November (OND) season, respectively. The models' skills in reproducing the interannual variability is relatively weak. However, majority of the models captures the temporal pattern with reasonable skills in OND than in MAM and annual rainfall. The impacts of Indian Ocean Dipole (IOD) and El Niño southern oscillation (ENSO) are marked in observed OND rains with no significance link in MAM rains. Overall, CMIP5 models' skills in replicating the mean statistics and teleconnection links are relatively weak. Remarkably, the performance of models at different time scale, metrics, simulation of dynamical and teleconnection patterns are inconsistent among models. Nevertheless, based on skill score, the models are listed from top to bottom as; MPI-ESM-MR, CSIRO-MK3-6-0, GISS-E2-R, MRI-CGCM2.3.3, EC-EARTH, MIROC-ESM-CHEM, FGOALS-g2, BCC-CSM1.1-M, HADGEM-AO, CanESM2, GFDL-ESM2G, IPSL-CM5A-MR, and MME model. This study sheds light on the use of statistical metrics and teleconnection pattern to rank CMIP5 models and forms basis on model selection. Model parameterization over tropics is prudent, and bias correction is paramount in future projections and impacts studies. • Various ranking metrics of models leads to different types of models' selection. • Models performance differ from one locale to another and even within different climate variables. • Models evaluation may include teleconnection link apart from reproducing mean rainfall statistics. • Failure of a model should be based on the intended use of the models. • Caution should be exercised when using CMIP5 models' projection over Kenya. [ABSTRACT FROM AUTHOR]

Analyzing spatiotemporal dynamics of meteorological variables in evolving climate, especially in regions where rain-fed agribusiness is predominant is paramount. In this regard, this study examines the spatiotemporal variability, trend, and magnitude of observed rainfall from 33 synoptic stations during 1979–2017 over Kenya. Non-parametric techniques like Mann–Kendall test, Sequential Mann-Kendall (SMK), and Sen's slope estimator (SSE) were deployed in assessing trend, change points, and magnitude. Results indicate that significant net change of monthly total rainfall mean is observed in May and December. Boreal spring and annual rainfall are insignificantly diminishing at a rate of −1.58 and −0.93 mm/year respectively. During boreal autumn, rainfall is insignificantly increasing at a rate of 1.48 mm/year. High interannual variability was observed, but no abrupt change was recorded during the study period. Spatial distribution of trend reveals dynamic variations from station to station. Amidst boreal spring (autumn), 27 (29) of stations considered had negative (positive) trends wherein 4 (8) stations recorded significant trend at α ≤ 0.01, respectively. At the annual scale, 10 (23) of the stations reported positive (negative) with 1 (5) significant trends at α ≤ 0.01, respectively. At all-time scales, stations at lower eastern and northeastern parts of the country reported a negative trend. Spatial decadal analysis portrays anomalous dry event over the study domain with the first decade being the wettest during boreal spring. To date, anomalously wet conditions exist throughout the country except lower eastern parts during boreal autumn. Comprehensive knowledge about variability, trend, and change point in precipitation is of unprecedented interest to scientists since they are prerequisites for developing effective contingency strategies to curb the impacts of climate change and future preparedness. • Trend, change points and abrupt changes in seasonal and annual rainfall from 33 synoptic stations in Kenya from 1979 to 2017 are assessed. • Rainfall is decreasing (increasing) insignificantly during boreal springs (autumn) and decreasing at annual scale. • Spatial distribution of seasonal and annual trend revealed that trend is dynamic and varied from station to station. • At all-time scales, stations at lower eastern and northeastern parts of the country reported negative trend. • Drying decadal trend in rainfall is observed during boreal springs while current decades are becoming wetter during boreal autumn. [ABSTRACT FROM AUTHOR]

Early screening and frequent monitoring effectively decrease the risk of severe scoliosis, but radiation exposure is a consequence of traditional radiograph examinations. Additionally, traditional X-ray images on the coronal or sagittal plane have difficulty providing three-dimensional (3-D) information on spinal deformities. The Scolioscan system provides an innovative 3-D spine imaging approach via ultrasonic scanning, and its feasibility has been demonstrated in numerous studies. In this paper, to further examine the potential of spinal ultrasonic data for describing 3-D spinal deformities, we propose a novel deep-learning tracker named Si-MSPDNet for extracting widely employed landmarks (spinous process (SP)) in ultrasonic images of spines and establish a 3-D spinal profile to measure 3-D spinal deformities. Si-MSPDNet has a Siamese architecture. First, we employ two efficient two-stage encoders to extract features from the uncropped ultrasonic image and the patch centered on the SP cut from the image. Then, a fusion block is designed to strengthen the communication between encoded features and further refine them from channel and spatial perspectives. The SP is a very small target in ultrasonic images, so its representation is weak in the highest-level feature maps. To overcome this, we ignore the highest-level feature maps and introduce parallel partial decoders to localize the SP. The correlation evaluation in the traditional Siamese network is also expanded to multiple scales to enhance cooperation. Furthermore, we propose a binary guided mask based on vertebral anatomical prior knowledge, which can further improve the performance of our tracker by highlighting the potential region with SP. The binary-guided mask is also utilized for fully automatic initialization in tracking. We collected spinal ultrasonic data and corresponding radiographs on the coronal and sagittal planes from 150 patients to evaluate the tracking precision of Si-MSPDNet and the performance of the generated 3-D spinal profile. Experimental results revealed that our tracker achieved a tracking success rate of 100% and a mean IoU of 0.882, outperforming some commonly used tracking and real-time detection models. Furthermore, a high correlation existed on both the coronal and sagittal planes between our projected spinal curve and that extracted from the spinal annotation in X-ray images. The correlation between the tracking results of the SP and their ground truths on other projected planes was also satisfactory. More importantly, the difference in mean curvatures was slight on all projected planes between tracking results and ground truths. Thus, this study effectively demonstrates the promising potential of our 3-D spinal profile extraction method for the 3-D measurement of spinal deformities using 3-D ultrasound data. • A novel tracker is proposed to real-time extracting spinous processes in 3D ultrasonic images. • Vertebral anatomical prior and spinal continuity are introduced into our tracker. • Experimental results indicate that our tracker can effectively describe 3-D spinal deformities. [ABSTRACT FROM AUTHOR]

Azoles have been widely employed for the treatment of invasive fungal diseases; however, their efficacy is diminished as pathogenic fungi tolerate them due to their fungistatic properties. Geldanamycin (GdA) can render azoles fungicidal by inhibiting the ATPase and molecular chaperone activities of heat shock protein 90 (Hsp90). Nonetheless, the clinical applicability of GdA is restricted due to its cytotoxic ansamycin scaffold structure, its induction of cytoprotective heat shock responses, and the conservative nature of Hsp90. Hence, it is imperative to elucidate the mechanism of action of GdA to confer fungicidal properties to azoles and mitigate the toxic adverse effects associated with GdA. Through various experimental methods, including the construction of gene-deleted Candida albicans mutants, in vitro drug sensitivity experiments, Western blot analysis, reactive oxygen species (ROS) assays, and succinate dehydrogenase activity assays, we identified Hsp90 client proteins associated with the tolerance of C. albicans to azoles. It was observed that GdA effectively hindered the entry of Hsp90 into mitochondria, resulting in the alleviation of inhibitory effect of Hsp90 on succinate dehydrogenase. Consequently, the activation of succinate dehydrogenase led to an increased production of ROS. within the mitochondria, thereby facilitating the antifungal effects of azoles against C. albicans. This research presents a novel approach for conferring fungicidal properties to azoles, which involves specifically disrupting the interaction of between Hsp90 and succinate dehydrogenase rather than employing a non-specific inhibition of ATPase activity of Hsp90. [ABSTRACT FROM AUTHOR]

Ultrasonography is one of the main imaging methods for monitoring and diagnosing atherosclerosis due to its non-invasiveness and low-cost. Automatic differentiation of carotid plaque fibrous cap integrity by using multi-modal ultrasound videos has significant diagnostic and prognostic value for cardiovascular and cerebrovascular disease patients. However, the task faces several challenges, including high variation in plaque location and shape, the absence of analysis mechanism focusing on fibrous cap, the lack of effective mechanism to capture the relevance among multi-modal data for feature fusion and selection, etc. To overcome these challenges, we propose a new target boundary and perfusion feature guided video analysis network (BP-Net) based on conventional B-mode ultrasound and contrast-enhanced ultrasound videos for assessing the integrity of fibrous cap. Based on our previously proposed plaque auto-tracking network, in our BP-Net, we further introduce the plaque edge attention module and reverse mechanism to focus the dual video analysis on the fiber cap of plaques. Moreover, to fully explore the rich information on the fibrous cap and inside/outside of the plaque, we propose a feature fusion module for B-mode and contrast video to filter out the most valuable features for fibrous cap integrity assessment. Finally, multi-head convolution attention is proposed and embedded into transformer-based network, which captures semantic features and global context information to obtain accurate evaluation of fibrous caps integrity. The experimental results demonstrate that the proposed method has high accuracy and generalizability with an accuracy of 92.35% and an AUC of 0.935, which outperforms than the state-of-the-art deep learning based methods. A series of comprehensive ablation studies suggest the effectiveness of each proposed component and show great potential in clinical application. [Display omitted] • The first boundary and perfusion feature guided dual-modality ultrasound video analysis network for fibrous cap integrity assessment. • A plaque edge attention module is proposed to focus the dual video analysis on the fibrous cap of plaques. • The proposed multi-modal fusion block can explore the inside/outside of the plaque and rich inter/intra modal interactions. • The multi-head convolution attention can utilize the robustness of CNN and sophisticated global modeling of Transformer. [ABSTRACT FROM AUTHOR]

Amyloid-beta peptide (Aβ) ranks as a pivotal cause of Alzheimer’s disease (AD), a common devastating dementia form in elderly. Recent research corroborated the beneficial roles of tetrahydroxystilbene glucoside (TSG) in alleviating the learning and memory of AD model and aged mice. Unfortunately, the underlying mechanism remains poorly elucidated. Here, treatment with non-toxic TSG dose-dependently antagonized Aβ-induced cytotoxic death in hippocampal neuronal cells by increasing cell viability and decreasing cell apoptosis. Furthermore, TSG also alleviated cell oxidative stress injury in response to Aβ by attenuating lactate dehydrogenase (LDH) release, ROS levels and MDA leakage. Importantly, TSG administration abrogated Aβ-triggered loss of mitochondrial membrane potential (Δy m ), release of cytochrome c from mitochondrial to cytosol, increase in caspase-3 activity and pro-apoptotic protein Bax, and decrease in Bcl-2 protein, indicating that TSG could rescue mitochondrial dysfunctions of neuron cells under adverse Aβ condition. Subsequently, TSG induced the activation of Nrf2-HO-1 pathway. Importantly, blocking this pathway by si-Nrf2 transfection or HO-1 antagonist ZnPP notably muted the cytoprotective effects of TSG on neuronal cell cytotoxic injury upon Aβ stimulation. Together, this research substantiated a new mechanism that TSG protectively antagonized Aβ-induced hippocampal neuronal cell damage by restoring mitochondrial function via Nrf2-HO-1 pathway, implying a promising candidate against neurodegenerative diseases including AD. [ABSTRACT FROM AUTHOR]

This paper present a novel group sparse representation model to segment four cardiac chambers in fetal echocardiograms. By incorporating the group reconstruction error, sparsity and distinctive term in a unified framework, the model is able to exploit the inherent structure of echocardiograms, constructing a discriminative group dictionary. A novel adaptive group dictionary learning approach is developed to obtain a compact dictionary with high atoms’ utilization and low complexity. With the learned dictionary, the reconstruction residue is employed to discriminate the initial location of four chambers. The local appearances are used to regionally refine the final contours. Extensive experiments have been conducted to evaluate the performance of our proposed AGDL and its application in the fetal echocardiogram segmentation. The results demonstrate both representation and discriminative power of our approach and its superior performances to other competing state-of-the-art sparse representation methods and general intensity models. Our approach is capable of learning a more compact and discriminative group dictionary, providing robust and accurate four-chamber segmentation results. [ABSTRACT FROM AUTHOR]

A radiomics approach to sonoelastography, called "sonoelastomics," is proposed for classification of benign and malignant breast tumors. From sonoelastograms of breast tumors, a high-throughput 364-dimensional feature set was calculated consisting of shape features, intensity statistics, gray-level co-occurrence matrix texture features and contourlet texture features, which quantified the shape, hardness and hardness heterogeneity of a tumor. The high-throughput features were then selected for feature reduction using hierarchical clustering and three-feature selection metrics. For a data set containing 42 malignant and 75 benign tumors from 117 patients, seven selected sonoelastomic features achieved an area under the receiver operating characteristic curve of 0.917, an accuracy of 88.0%, a sensitivity of 85.7% and a specificity of 89.3% in a validation set via the leave-one-out cross-validation, revealing superiority over the principal component analysis, deep polynomial networks and manually selected features. The sonoelastomic features are valuable in breast tumor differentiation. [ABSTRACT FROM AUTHOR]

Objectives Insulin-like growth factor 1 (IGF-1) is a broad-spectrum growth-promoting factor that plays a key role in natural tooth development. Human dental pulp stem cells (hDPSCs) are multipotent and can influence the reparative regeneration of dental pulp and dentin. This study was designed to evaluate the effects of IGF-1 on the proliferation and differentiation of human dental pulp stem cells. Methods HDPSCs were isolated and purified from human dental pulps. The proliferation and osteo/odontogenic differentiation of hDPSCs treated with 100 ng/ml exogenous IGF-1 were subsequently investigated. Results MTT assays revealed that IGF-1 enhanced the proliferation of hDPSCs. ALP activity in IGF-1-treated group was obviously enhanced compared to the control group from days 3 to 9. Alizarin red staining revealed that the IGF-1-treated cells contained a greater number of mineralization nodules and had higher calcium concentrations. Moreover, western blot and qRT-PCR analyses demonstrated that the expression levels of several osteogenic genes (e.g., RUNX2, OSX, and OCN) and an odontoblast-specific marker (DSPP) were significantly up-regulated in IGF-1-treated hDPSCs as compared with untreated cells (P < 0.01). Interestingly, the expression of phospho-ERK and phospho-p38 were also up-regulated, indicating that the MAPK signaling pathway is activated during the differentiation of hDPSCs. Conclusions IGF-1 can promote the proliferation and osteo/odontogenic differentiation of hDPSCs by activating MAPK pathways. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: Traumatic pulp exposure can bring about some permanent damages to tooth tissues including dental pulps. This study was designed to evaluate the effects of traumatic pulp exposure on the osteo/odontogenic capacity of dental pulp stem cells (DPSCs). Methods: Rat incisors were artificially fractured and dental pulps were exposed to the oral environment for 48h. Then, multi-colony-derived DPSCs from the injured pulps (iDPSCs) were isolated. Their osteo/odontogenic differentiation and the involvement of NF-κB pathway were subsequently investigated. Results: iDPSCs presented a lower proliferative capacity than normal DPSCs (nDPSCs), as indicated by MTT and FCM assay. ALP levels in iDPSCs were significantly higher (P <0.01) than those in nDPSCs. Alizarin red staining revealed that iDPSCs exhibited an increased capacity of calcium deposition. Moreover, iDPSCs expressed stronger osteogenic markers (Runx2/RUNX2 and Ocn/OCN) and less odontogenic gene/protein (Dspp/DSP) than nDPSCs in vitro. In vivo transplantation showed that nDPSCs implants generated the typical dentine-pulp complex while all iDPSCs pellets formed the osteodentin-like tissues which were immunopositive for OCN. Mechanistically, iDPSCs expressed the higher levels of cytoplasmic phosphorylated IκBα/P65 and nuclear P65 than nDPSCs, indicating an active cellular NF-κB pathway in iDPSCs. After the inhibition of NF-κB pathway, the osteogenic potential in iDPSCs was significantly down-regulated while odontogenic differentiation was up-regulated, as indicated by the decreased Alp/Runx2/Ocn and uprised Dspp expression. Conclusions: Pulp exposure for 48h decreased the odontogenic capacity and enhanced the osteogenic potential of DPSCs via the NF-κB signalling pathway. [Copyright &y& Elsevier]

Abstract: Objective: To assess the odontogenic potential of bone marrow mesenchymal stem cells (BMMSCs) to differentiate into odontoblast-like cells under the morphogenetic influence of dentine matrix as a possible basis for new stem cell-mediated therapeutic approaches to pulp diseases. Design: BMMSCs were harvested from the whole bone marrow and cells at passages 3–5 were used for subsequent experiments. For in vitro studies, 1×104 cells were seeded on the surface of dentine slabs and co-cultured for 2 weeks in 24-well plates, then fixed, decalcified, embedded in paraffin and serial sections were processed for analyses. Haematoxylin-eosin (HE) staining was used for the morphological analysis of BMMSCs on the dentine slabs. The protein expression of dentine sialoprotein (DSP) in co-cultured BMMSCs was detected by immunohistochemical (IHC) staining. For in vivo studies, 5×106 cells were collected as cell pellets, seeded onto dentine slices and transplanted into renal capsules for 6 weeks. Histological analyses of harvested tissues were performed as described for the in vitro studies. Total RNA and protein were extracted from harvested tissues and Dspp/DSP expression was investigated by reverse transcription-polymerase chain reaction (RT-PCR) and Western blot, respectively. Results: After 2 weeks of co-culture with dentine slabs, BMMSCs demonstrated good viability in terms of morphological appearance and some showed polarization and extension of their cytoplasmic processes into dentine tubules with DSP expression. In vivo study demonstrated similar morphological changes and DSP expression in cells adjacent to dentine. RT-PCR and Western blot also demonstrated that the expression of Dspp/DSP in the co-cultured BMMSCs groups was higher than in the control groups. Conclusion: Dentine matrix can signal morphogenic induction of differentiation of BMMSCs into odontoblast-like cells in vivo and in vitro. [Copyright &y& Elsevier]