Assessment of Suitable Habitat of the Demoiselle Crane (Anthropoides virgo) in the Wake of Climate Change: A Study of Its Wintering Refugees in Pakistan.

The inevitable impacts of climate change have reverberated across ecosystems and caused substantial global biodiversity loss. Climate-induced habitat loss has contributed to range shifts at both species and community levels. Given the importance of identifying suitable habitats for at-risk species, it is imperative to assess potential current and future distributions, and to understand influential environmental factors. Like many species, the Demoiselle crane is not immune to climatic pressures. Khyber Pakhtunkhwa and Balochistan provinces in Pakistan are known wintering grounds for this species. Given that Pakistan is among the top five countries facing devastating effects of climate change, this study sought to conduct species distribution modeling under climate change using data collected during 4 years of field surveys. We developed a Maximum Entropy distribution model to predict the current and projected future distribution of the species across the study area. Future habitat projections for 2050 and 2070 were carried out using two representative concentration pathways (RCP 4.5 and RCP 8.5) under three global circulation models, including HADGEM2-AO, BCC-CSM1-1, and CCSM4. The most influential factors shaping Demoiselle Crane habitat suitability included the temperature seasonality, annual mean temperature, terrain ruggedness index, and human population density, all of which contributed significantly to the suitability (81.3%). The model identified 35% of the study area as moderately suitable (134,068 km2) and highly suitable (27,911 km2) habitat for the species under current climatic conditions. Under changing climate scenarios, our model predicted a major loss of the species' current suitable habitat, with shrinkage and shift towards western-central areas along the Pakistan-Afghanistan boarder. The RCP 8.5, which is the extreme climate change scenario, portrays particularly severe consequences, with habitat losses reaching 65% in 2050 and 85% in 2070. This comprehensive study provides useful insights into the Demoiselle Crane habitat's current and future dynamics in Pakistan.

Patterns and drivers of avian taxonomic and phylogenetic beta diversity in China vary across geographical backgrounds and dispersal abilities.

Geographical background and dispersal ability may strongly influence assemblage dissimilarity; however, these aspects have generally been overlooked in previous large-scale beta diversity studies. Here, we examined whether the patterns and drivers of taxonomic beta diversity (TBD) and phylogenetic beta diversity (PBD) of breeding birds in China vary across (1) regions on both sides of the Hu Line, which demarcates China's topographical, climatic, economic, and social patterns, and (2) species with different dispersal ability. TBD and PBD were calculated and partitioned into turnover and nestedness components using a moving window approach. Variables representing climate, habitat heterogeneity, and habitat quality were employed to evaluate the effects of environmental filtering. Spatial distance was considered to assess the impact of dispersal limitation. Variance partitioning analysis was applied to assess the relative roles of these variables. In general, the values of TBD and PBD were high in mountainous areas and were largely determined by environmental filtering. However, different dominant environmental filters on either side of the Hu Line led to divergent beta diversity patterns. Specifically, climate-driven species turnover and habitat heterogeneity-related species nestedness dominated the regions east and west of the line, respectively. Additionally, bird species with stronger dispersal ability were more susceptible to environmental filtering, resulting in more homogeneous assemblages. Our results indicated that regions with distinctive geographical backgrounds may present different ecological factors that lead to divergent assemblage dissimilarity patterns, and dispersal ability determines the response of assemblages to these ecological factors. Identifying a single universal explanation for the observed pattern without considering these aspects may lead to simplistic or incomplete conclusions. Consequently, a comprehensive understanding of large-scale beta diversity patterns and effective planning of conservation strategies necessitate the consideration of both geographical background and species dispersal ability.

Automatic Image Segmentation and Grading Diagnosis of Sacroiliitis Associated with AS Using a Deep Convolutional Neural Network on CT Images.

Ankylosing spondylitis (AS) is a chronic inflammatory disease that causes inflammatory low back pain and may even limit activity. The grading diagnosis of sacroiliitis on imaging plays a central role in diagnosing AS. However, the grading diagnosis of sacroiliitis on computed tomography (CT) images is viewer-dependent and may vary between radiologists and medical institutions. In this study, we aimed to develop a fully automatic method to segment sacroiliac joint (SIJ) and further grading diagnose sacroiliitis associated with AS on CT. We studied 435 CT examinations from patients with AS and control at two hospitals. No-new-UNet (nnU-Net) was used to segment the SIJ, and a 3D convolutional neural network (CNN) was used to grade sacroiliitis with a three-class method, using the grading results of three veteran musculoskeletal radiologists as the ground truth. We defined grades 0-I as class 0, grade II as class 1, and grades III-IV as class 2 according to modified New York criteria. nnU-Net segmentation of SIJ achieved Dice, Jaccard, and relative volume difference (RVD) coefficients of 0.915, 0.851, and 0.040 with the validation set, respectively, and 0.889, 0.812, and 0.098 with the test set, respectively. The areas under the curves (AUCs) of classes 0, 1, and 2 using the 3D CNN were 0.91, 0.80, and 0.96 with the validation set, respectively, and 0.94, 0.82, and 0.93 with the test set, respectively. 3D CNN was superior to the junior and senior radiologists in the grading of class 1 for the validation set and inferior to expert for the test set (P < 0.05). The fully automatic method constructed in this study based on a convolutional neural network could be used for SIJ segmentation and then accurately grading and diagnosis of sacroiliitis associated with AS on CT images, especially for class 0 and class 2. The method for class 1 was less effective but still more accurate than that of the senior radiologist.

Comparison Between the Stereoscopic Virtual Reality Display System and Conventional Computed Tomography Workstation in the Diagnosis and Characterization of Cerebral Arteriovenous Malformations.

It is difficult to accurately understand the angioarchitecture of cerebral arteriovenous malformations (CAVMs) before surgery using existing imaging methods. This study aimed to evaluate the ability of the stereoscopic virtual reality display system (SVRDS) to display the angioarchitecture of CAVMs by comparing its accuracy with that of the conventional computed tomography workstation (CCTW). Nineteen patients with CAVM confirmed on digital subtraction angiography (DSA) or during surgery were studied. Computed tomography angiography images in the SVRDS and CCTW were retrospectively analyzed by two experienced neuroradiologists using a double-blind method. Angioarchitectural parameters, such as the location and size of the nidus, type and number of the arterial feeders and draining veins, and draining pattern of the vessels, were recorded and compared. The diameter of the nidus ranged from 1.1 to 9 cm. Both CCTW and SVRDS correctly diagnosed the location of the nidus in 19 patients with CAVM. Among the 19 patients, 35 arterial feeders and 25 draining veins were confirmed on DSA and during surgery. With the DSA and intraoperative results as the gold standard bases, the CCTW misjudged one arterial feeder and one draining vein and missed three arterial feeders and two draining veins; meanwhile, the SVRDS missed only two arterial feeders. SVRDS had some advantages in displaying nidus, arterial branches, and draining veins of the CAVM compared with CCTW, as well as SVRDS could more intuitively display the overall angio-architectural spatial picture of CAVM.

Dual-energy CT Virtual Non-Calcium Image is Beneficial for the Detection of Non-displaced Knee Fractures.

BACKGROUND: Early and accurate diagnosis is vital for avoiding the development of nondisplaced fractures to displaced fractures. Dual-energy CT (Computed Tomography) can detect bone marrow edema (BME), which may help to detect non-displaced fractures. AIM: To evaluate the value of DECT (Dual-Energy Computed Tomography) VNCa (Virtual noncalcium) images for improving diagnostic performance and confidence in acute non-displaced knee fractures. METHODS: 125 patients with clinical suspicion of knee fractures underwent both DECT and MR. Conventional linear-blended CT and VNCa images were obtained from DECT. First, five readers with varying levels of experience evaluated the presence of fractures on conventional linear-blended CT and graded their diagnostic confidence on a scale of 1 to 10. Then BME with VNCa images was evaluated and compared with MR. Finally, the VNCa images combined with conventional linear-blended CT images were used to reassess the presence of fractures and diagnostic confidence. Diagnostic performance and matched pair analyses were performed. RESULTS: 20 non-displaced knee fractures were detected. The consistency test of VNCa images and MR by five radiologists showed Kappa values are 0.76, 0.79, 0.81,0.85,and 0.90,respectively. The diagnostic performance of all readers was improved when using VNCa images combined with conventional linear-blended CT compared with that with conventional linear-blended CT alone. Diagnostic confidence was improved with combined conventional linear-blended CT and VNCa images (median score:8,8,9,9, and 10, respectively) compared with conventional linear-blended CT alone (median score:7,7,8,9, and 9). CONCLUSION: DECT VNCa images could improve the radiologists' diagnostic performance and confidence with varying levels of experience in the detection of non-displaced knee fractures.

Deep Learning for Detection of Intracranial Aneurysms from Computed Tomography Angiography Images.

The accuracy of computed tomography angiography (CTA) image interpretation depends on the radiologist. This study aims to develop a new method for automatically detecting intracranial aneurysms from CTA images using deep learning, based on a convolutional neural network (CNN) implemented on the DeepMedic platform. Ninety CTA scans of patients with intracranial aneurysms are collected and divided into two datasets: training (80 subjects) and test (10 subjects) datasets. Subsequently, a deep learning architecture with a three-dimensional (3D) CNN model is implemented on the DeepMedic platform for the automatic segmentation and detection of intracranial aneurysms from the CTA images. The samples in the training dataset are used to train the CNN model, and those in the test dataset are used to assess the performance of the established system. Sensitivity, positive predictive value (PPV), and false positives are evaluated. The overall sensitivity and PPV of this system for detecting intracranial aneurysms from CTA images are 92.3% and 100%, respectively, and the segmentation sensitivity is 92.3%. The performance of the system in the detection of intracranial aneurysms is closely related to their size. The detection sensitivity for small intracranial aneurysms (≤ 3 mm) is 66.7%, whereas the sensitivity of detection for large (> 10 mm) and medium-sized (3-10 mm) intracranial aneurysms is 100%. The deep learning architecture with a 3D CNN model on the DeepMedic platform can reliably segment and detect intracranial aneurysms from CTA images with high sensitivity.

[Resident breeding bird responses to wind turbines: A functional and phylogenetic perspective]. / 基于功能和谱系视角分析繁殖留鸟对风机的响应.

As a clean and green renewable energy source, wind power attracts more and more attention globally, and rapidly expands its use scale. There are conflicts between wind power development and bird protection. The impacts of wind farms on birds is a hot topic for ecologists and conservationists. To estimate the effects of wind turbines on avian species diversity and their functional and phylogenetic diversity, we conducted line transect surveys in January, March, April and May 2019. Thirty-two line transects were divided into four gradients according to the distance from the wind turbines, i.e., 100-300 m (6 transects), 300-500 m (13 transects), 500-700 m (8 transects), >700 m (5 transects). The results showed that a total of 76 resident breeding birds were recorded, belonging to 11 orders and 31 families. Passeriformes and Timaliidae had the highest species richness (53 and 12 species, respectively). Bird species richness, functional richness (FRic), and phylogenetic diversity (Faith's PD) were increased with increasing distance from the wind turbines. Specifically, bird richness, FRic and phylogenetic diversity increased little within 500 m of the wind turbines, whereas a significant increase occurred over 500 m from wind turbines. The community-weighted mean of dispersal ability showed an increasing trend with distance from the wind turbines. The standardized effect size of both mean pairwise functional/phylogenetic distance (SES.MFD and SES.MPD) were less than 0 in all transects, with about half of which being significantly lower than expected at random. This finding suggested that the impacts of wind farms on bird species, functional and phylogenetic diversity occurred within 500 meters from wind turbines, with a pattern of functional and phylogenetic clustering. The impacts of wind turbines on birds were multi-dimensional. It is therefore difficult to provide complete perspective on assessing the impacts of wind farms on birds when only considering species diversity.

Climate-driven elevational variation in range sizes of vascular plants in the central Himalayas: A supporting case for Rapoport's rule.

A fundamental yet controversial topic in biogeography is how and why species range sizes vary along spatial gradients. To advance our understanding of these questions and to provide insights into biological conservation, we assessed elevational variations in the range sizes of vascular plants with different life forms and biogeographical affinities and explored the main drivers underlying these variations in the longest valley in China's Himalayas, the Gyirong Valley. Elevational range sizes of vascular plants were documented in 96 sampling plots along an elevational gradient ranging from 1,800 to 5,400 m above sea level. We assessed the elevational variations in range size by averaging the range sizes of all recorded species within each sampling plot. We then related the range size to climate, disturbance, and the mid-domain effect and explored the relative importance of these factors in explaining the range size variations using the Random Forest model. A total of 545 vascular plants were recorded in the sampling plots along the elevational gradient. Of these, 158, 387, 337, and 112 were woody, herbaceous, temperate, and tropical species, respectively. The range size of each group of vascular plants exhibited uniform increasing trends along the elevational gradient, which was consistent with the prediction of Rapoport's rule. Climate was the main driver of the increasing trends of vascular plant range sizes in the Gyirong Valley. The climate variability hypothesis and mean climate condition hypothesis could both explain the elevation-range size relationships. Our results reinforce the previous notion that Rapoport's rule applies to regions where the influence of climate is the most pronounced, and call for close attention to the impact of climate change to prevent species range contraction and even extinction due to global warming.

Diagnostic accuracy of dual-energy CT virtual non-calcium images with different related contrast material values for the detection of bone marrow edema in knee.

PURPOSE: The purpose of this study was to evaluate the diagnostic accuracy of different related contrast material (Rel.CM) values in dual-energy computed tomography (DECT) virtual non-calcium (VNCa) images for the detection of bone marrow edema (BME) in knee. METHOD: This prospective study was approved by the institutional research ethics board, and written informed consent was obtained from all participants. Twenty-three patients (24 knees) who underwent dual-energy CT and MRI within three weeks from July 2018 to June 2019 with a definite history of trauma were enrolled. Each knee was divided into 12 regions. First, MR images served as the reference standard, Receiver operating characteristic (ROC) curve was used and diagnostic accuracy of VNCa images corresponding to different Rel.CM values (1.25, 1.35, 1.45, 1.55, 1.65, 1.75) were analyzed, aimed to select an optimal Rel.CM value of VNCa images for detecting BME. Then, CT values of the normal areas and BME areas were measured on the VNCa images corresponding to the optimal Rel.CM value for preliminary quantitative analysis. The rank-sum test was used to compare the differences of CT values between BME areas and normal bone marrow areas on the VNCa images. RESULTS: The 24 knees were divided into 288 areas. MR Imaging showed BME in 121 areas. The areas under the ROC curve with different Rel.CM values (1.25, 1.35, 1.45, 1.55, 1.65, and 1.75) were 0.633, 0.674, 0.882, 0.684, 0.651, and 0.649, respectively. On the VNCa images of Rel.CM = 1.45, the diagnostic accuracy was the highest (up to 89.2 %), the CT values of the BME area and the normal area were -67.9 (1.7∼-100.1) HU and -94.5 (-69.7∼-144.9) HU, respectively, with statistical significance (Z=-9.804, P < 0.05). CONCLUSIONS: The VNCa images with a Rel.CM value of 1.45 is optimal for the detection of BME in knee.

Different responses of avian feeding guilds to spatial and environmental factors across an elevation gradient in the central Himalaya.

Although elevational patterns of species richness have been well documented, how the drivers of richness gradients vary across ecological guilds has rarely been reported. Here, we examined the effects of spatial factors (area and mid-domain effect; MDE) and environmental factors, including metrics of climate, productivity, and plant species richness on the richness of breeding birds across different ecological guilds defined by diet and foraging strategy. We surveyed 12 elevation bands at intervals of 300 m between 1,800 and 5,400 m a.s.l using line-transect methods throughout the wet season in the central Himalaya, China. Multiple regression models and hierarchical partitioning were used to assess the relative importance of spatial and environmental factors on overall bird richness and guild richness (i.e., the richness of species within each guild). Our results showed that richness for all birds and most guilds displayed hump-shaped elevational trends, which peaked at an elevation of 3,300-3,600 m, although richness of ground-feeding birds peaked at a higher elevation band (4,200-4,500 m). The Normalized Difference Vegetation Index (NDVI)-an index of primary productivity-and habitat heterogeneity were important factors in explaining overall bird richness as well as that of insectivores and omnivores, with geometric constraints (i.e., the MDE) of secondary importance. Granivore richness was not related to primary production but rather to open habitats (granivores were negatively influenced by habitat heterogeneity), where seeds might be abundant. Our findings provide direct evidence that the richness-environment relationship is often guild-specific. Taken together, our study highlights the importance of considering how the effects of environmental and spatial factors on patterns of species richness may differ across ecological guilds, potentially leading to a deeper understanding of elevational diversity gradients and their implications for biodiversity conservation.

Elevational pattern of bird species richness and its causes along a central Himalaya gradient, China.

This study examines the relative importance of six variables: area, the mid-domain effect, temperature, precipitation, productivity, and habitat heterogeneity on elevational patterns of species richness for breeding birds along a central Himalaya gradient in the Gyirong Valley, the longest of five canyons in the Mount Qomolangma National Nature Reserve. We conducted field surveys in each of twelve elevational bands of 300 m between 1,800 and 5,400 m asl four times throughout the entire wet season. A total of 169 breeding bird species were recorded and most of the species (74%) were small-ranged. The species richness patterns of overall, large-ranged and small-ranged birds were all hump-shaped, but with peaks at different elevations. Large-ranged species and small-ranged species contributed equally to the overall richness pattern. Based on the bivariate and multiple regression analyses, area and precipitation were not crucial factors in determining the species richness along this gradient. The mid-domain effect played an important role in shaping the richness pattern of large-ranged species. Temperature was negatively correlated with overall and large-ranged species but positively correlated with small-ranged species. Productivity was a strong explanatory factor among all the bird groups, and habitat heterogeneity played an important role in shaping the elevational richness patterns of overall and small-ranged species. Our results highlight the need to conserve primary forest and intact habitat in this area. Furthermore, we need to increase conservation efforts in this montane biodiversity hotspot in light of increasing anthropogenic activities and land use pressure.

[A preliminary study of dual-source computed tomography dual energy imaging in the diagnosis of acute traumatic bone marrow lesions in spine].

OBJECTIVE: To explore the feasibility of virtual noncalcium technique from dual-source computed tomography (DSCT) for the diagnosis of vertebral bone marrow lesions. METHODS: A total of 13 patients with acute vertebral bone marrow lesions underwent both DSCT and MRI within 3 days. And the DSCT dual-energy CT data were postprocessed for generating virtual noncalcium images and color-coded maps. Two radiologists analyzed the lesions of bone marrow by magnetic resonance (MR) imaging and virtual noncalcium images on a three-level. MR imaging interpretation served as the reference standard. Consistency check was conducted by using kappa statistics. And then the sensitivity of DSCT dual energy imaging was examined in the diagnosis of acute traumatic bone marrow lesions in spine. RESULTS: Among them, 13 vertebral body with bone marrow lesions were detected by MRI. The T2WI fat-suppression irregular high signal and slightly high density shadow under the background in noncalcium images corresponded to corresponding high signal areas on MRI. Interreader agreement was substantial for qualitative grading of DE CT images (κ = 0.629). The sensitivity of DSCT dual energy virtual noncalcium images in the diagnosis of acute traumatic bone marrow lesions in spine were 92.3% and 84.6% for observers 1 and 2. CONCLUSION: Distinct traumatic bone marrow lesions of spine may be diagnosed with a high sensitivity on virtual noncalcium images reconstructed from DSCT and color-coded maps. And it is worth further explorations.