1株来自丛生盔形珊瑚的球托霉鉴定及生理学特性研究

在丛生盔形珊瑚中分离得到1株形态特殊的共附生真菌XWC14-13,对其鉴定并进行生理学特性研究,为该类珊瑚共附生真菌的进一步研究提供参考。通过观察菌株XWC14-13在CA和PDA培养基的生长特性及个体特征,结合r DNA ITS1-ITS4基因序列的系统发育分析,将其鉴定为卵形孢球托霉(Gongronella butleri)。该菌株菌丝宽度为3.62~8.09μm,有膈膜,不产厚垣孢子,孢子囊大小为(5.15~7.57)μm×(23.53~25.74)μm,孢囊孢子大小为(2.02~3.03)μm×(1.52~2.01)μm。其生长温度、酸碱度和盐度的适应性研究结果表明,该菌最适生长条件为温度30℃,pH 8~8.5,盐度1%,具有一定的耐盐性及酸碱耐受性,属兼性海洋真菌。     

A rapid screening classifier for diagnosing COVID-19

Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach.Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction.Results: The clinical features of COVID-19 and influenza showed different patterns. Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability. Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0.909 and 0.919, respectively. The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0.952 with 91.5% sensitivity and 81.2% specificity. Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0.971 with 96.9% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT. In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians.Conclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic.     

Diagnosing colorectal abnormalities using scattering coefficient maps acquired from optical coherence tomography

Optical coherence tomography (OCT) has shown potential in differentiating normal colonic mucosa from neoplasia. In this study of 33 fresh human colon specimens, we report the first use of texture features and computer vision-based imaging features acquired from en face scattering coefficient maps to characterize colorectal tissue. En face scattering coefficient maps were generated automatically using a new fast integral imaging algorithm. From these maps, a gray-level cooccurrence matrix algorithm was used to extract texture features, and a scale-invariant feature transform algorithm was used to derive novel computer vision-based features. In total, 25 features were obtained, and the importance of each feature in diagnosis was evaluated using a random forest model. Two classifiers were assessed on two different classification tasks. A support vector machine model was found to be optimal for distinguishing normal from abnormal tissue, with 94.7% sensitivity and 94.0% specificity, while a random forest model performed optimally in further differentiating abnormal tissues (i.e., cancerous tissue and adenomatous polyp) with 86.9% sensitivity and 85.0% specificity. These results demonstrated the potential of using OCT to aid the diagnosis of human colorectal disease.     

Anatomical Feature Segmentation of Femur Point Cloud Based on Medical Semantics

Feature segmentation is an essential phase for geometric modeling and shape processing in anatomical study of human skeleton and clinical digital treatment of orthopedics. Due to various degrees of freedom of bone surface, the existing segmentation algorithms can hardly meet specific medical need. To address this, a novel segmentation methodology for anatomical features of femur model based on medical semantics is put forward. First, anatomical reference objects (ARO) are created to represent typical characteristics of femur anatomy by 3D point fitting in combination with medical priori knowledge. Then, local point clouds between adjacent anatomies are selected according to the AROs to extract boundary feature point (BFP)s. Finally, the complete model of femur is divided into anatomical regions by executing the enhanced watershed algorithm guided with BFPs. Experimental results show that the proposed method has the advantages of automatic segmentation of femoral head, neck and other complex areas, and the segmentation results have better medical semantics. In addition, the slight modification of segmentation results can be achieved by adjusting a few threshold parameter values, which improves the convenience of modification for ordinary users.     

Does Multifocal Papillary Thyroid Microcarcinoma With a Total Tumor Diameter >1 cm Indicate Poor Biological Behavior? The Evidence is Insufficient

ObjectiveMultifocal cancer is common in papillary thyroid microcarcinoma (PTMC). Our aim was to investigate the correlation between multifocal PTMC, total tumor diameter (TTD), and clinicopathologic features.MethodsIn total, 206 patients were included and grouped as stage cT1a or cT1b. The primary tumor diameter and TTD (the sum of the maximal diameter of each focus) were calculated. These patients were further subgrouped as TTD ≤1 cm or 1 cm < TTD ≤ 2 cm. The relationships of clinicopathological features between these groups were analyzed.ResultsMultifocal cancer was more likely to occur with stage cT1a than stage cT1b (P = .028). Stage cT1b papillary thyroid carcinoma was more prone to central lymph node metastasis (CLNM) and capsular invasion than stage cT1a. There was no difference in clinicopathological factors, such as sex, age, CLNM, number of CLNMs, capsular invasion, BRAF mutation, or recurrence between the multifocal PTMC and TTD >1 cm and primary tumor diameter + TTD ≤1 cm groups. Comparing stage cT1a and cT1b tumors with a 1 cm < TTD ≤ 2 cm using a multivariate analysis, stage cT1b tumors were more prone to capsular invasion than stage cT1a tumors, with an odds ratio of 19.013 (95% confidence interval, 2.295-157.478), but there was no significant correlation with CLNM.ConclusionStage cT1b tumors are more prone to capsular invasion and CLNM than than stage cT1a tumors. For multifocal PTMC, calculating the TTD to evaluate adverse biological behavior is insufficient and limited, and further research is needed.     

Clarifications on habitat complexity: A response to technical note by Madin et al.

In a critique of our recent review on measuring habitat complexity in ecology, Madin et al. (2023) advocate the use of fractal dimension in ecology and defend their geometric constraint theory of habitat complexity. We explain the flaws in their arguments and highlight points where they misinterpreted our statements.     

Grid origin affects scaling of species across spatial scales

Aim Distribution maps of species based on a grid are useful for investigating relationships between scale and the number or area of occupied grid cells. A species is scaled up simply by merging occupied grid cells on the observation grid to successively coarser cells. Scale–occupancy relationships (SORs) obtained in this way can be used to extrapolate species down, in other words to compute occupancies at finer scales than the observation scale. In this paper we demonstrate that the SOR is not unique but depends on where one positions the origin of the grid map. Innovation The effect of grid origin on SORs was explored with the aid of the Dutch national data base FLORBASE, which contains the observation records of all 1410 wild vascular plants in the Netherlands on a 1‐km square basis. For each species, we generated 2500 unique SORs by scaling up from 1 km, in steps of 1 km, to squares of 50 km. We computed the sensitivity of the SOR to the grid origin for each species, and subsequently analysed the factors that determined this sensitivity. The effect of grid origin on downscaling was demonstrated by means of a simple power function that we used to extrapolate down from both a 2‐km and a 5‐km grid, to the original 1‐km grid. It appeared that the position of grid origin could have a substantial effect on SORs. The sensitivity of SORs to the position of the grid origin depended on three characteristics of a species’ spatial distribution: rarity, degree of spatial clustering and the position of the distribution relative to the border of the investigated area. Rare species with a clustered distribution near the border were particularly highly sensitive. The dependence of SOR on grid origin caused unpredictable and non‐random errors in downscaled occupancies. Main conclusions In future, the whole bandwidth of scaled occupancies should be considered when testing and interpreting mathematical relationships between scale and occupancy. Moreover, downscaled occupancies should be interpreted cautiously.     

MCFSA-Net: A multi-scale channel fusion and spatial activation network for retinal vessel segmentation

As the only vascular tissue that can be directly viewed in vivo, retinal vessels are medically important in assisting the diagnosis of ocular and cardiovascular diseases. They generally appear as different morphologies and uneven thickness in fundus images. Therefore, the single-scale segmentation method may fail to capture abundant morphological features, suffering from the deterioration in vessel segmentation, especially for tiny vessels. To alleviate this issue, we propose a multi-scale channel fusion and spatial activation network (MCFSA-Net) for retinal vessel segmentation with emphasis on tiny ones. Specifically, the Hybrid Convolution-DropBlock (HC-Drop) is first used to extract deep features of vessels and construct multi-scale feature maps by progressive down-sampling. Then, the Channel Cooperative Attention Fusion (CCAF) module is designed to handle different morphological vessels in a multi-scale manner. Finally, the Global Spatial Activation (GSA) module is introduced to aggregate global feature information for improving the attention on tiny vessels in the spatial domain and realizing effective segmentation for them. Experiments are carried out on three datasets including DRIVE, CHASE_DB1, and STARE. Our retinal vessel segmentation method achieves Accuracy of 96.95%, 97.57%, and 97.83%, and F1 score of 82.67%, 81.82%, and 82.95% in the above datasets, respectively. Qualitative and quantitative analysis show that the proposed method outperforms current advanced vessel segmentation methods, especially for tiny vessels.