Optimized Two-Stage Ensemble Model for Mammography Mass Recognition

ObjectivesMammography mass recognition is considered as a very challenge pattern recognition problem due to the high similarity between normal and abnormal masses. Therefore, the main objective of this study is to develop an efficient and optimized two-stage recognition model to tackle this recognition task.Material and methodsBasically, the developed recognition model combines an ensemble of linear Support Vector Machine (SVM) classifiers with a Reinforcement Learning-based Memetic Particle Swarm Optimizer (RLMPSO) as RLMPSO-SVM recognition model. RLMPSO is used to construct a two-stage of an ensemble of linear SVM classifiers by performing simultaneous SVM parameters tuning, features selection, and training instances selection. The first stage of RLMPSO-SVM recognition model is responsible about recognizing the input ROI mammography masses as normal or abnormal mass pattern. Meanwhile, the second stage of RLMPSO-SVM model used to perform further recognition for abnormal ROIs as malignant or benign masses. In order to evaluate the effectiveness of RLMPSO-SVM, a total of 1187 normal ROIs, 111 malignant ROIs, and 135 benign ROIs were randomly selected from DDSM database images.ResultsReported results indicated that RLMPSO-SVM model was able to achieve performances of 97.57% sensitivity rate with 97.86% specificity rate for normal vs. abnormal recognition cases. For malignant vs. benign recognition performance it was reported of 97.81% sensitivity rate with 96.92% specificity rate.ConclusionReported results indicated that RLMPSO-SVM recognition model is an effective tool that could assist the radiologist during the diagnosis of the presented abnormalities in mammography images. The outcomes indicated that RLMPSO-SVM significantly outperformed various SVM-based models as well as other variants of computational intelligence models including multi-layer perceptron, naive Bayes classifier, and k-nearest neighbor.     

Seasonal shifts in the assembly dynamics of benthic macroinvertebrate and diatom communities in a subtropical river

Identifying seasonal shifts in community assembly for multiple biological groups is important to help enhance our understanding of their ecological dynamics. However, such knowledge on lotic assemblages is still limited. In this study, we used biological traits and functional diversity indices in association with null model analyses to detect seasonal shifts in the community assembly mechanisms of lotic macroinvertebrates and diatoms in an unregulated subtropical river in China. We found that functional composition and functional diversity (FRic, FEve, FDis, MNN, and SDNN) showed seasonal variation for macroinvertebrate and diatom assemblages. Null models suggested that environmental filtering, competitive exclusion, and neutral process were all important community assembly mechanisms for both biological groups. However, environmental filtering had a stronger effect on spring macroinvertebrate assemblages than autumn assemblages, but the effect on diatom assemblages was the same in both seasons. Moreover, macroinvertebrate and diatom assemblages were shaped by different environmental factors. Macroinvertebrates were filtered mainly by substrate types, velocity, and COD_Mn, while diatoms were mainly shaped by altitude, substrate types, and water quality. Therefore, our study showed (a) that different biological assemblages in a river system presented similarities and differences in community assembly mechanisms, (b) that multiple processes play important roles in maintaining benthic community structure, and (c) that these patterns and underlying mechanisms are seasonally variable. Thus, we highlight the importance of exploring the community assembly mechanisms of multiple biological groups, especially in different seasons, as this is crucial to improve the understanding of river community changes and their responses to environmental degradation.     

Disentangling the spatial and temporal causes of decline in a bird population

The difficulties in understanding the underlying reasons of a population decline lie in the typical short duration of field studies, the often too small size already reached by a declining population or the multitude of environmental factors that may influence population trend. In this difficult context, useful demographic tools such as integrated population models (IPM) may help disentangling the main reasons for a population decline. To understand why a hoopoe Upupa epops population has declined, we followed a three step model analysis. We built an IPM structured with respect to habitat quality (approximated by the expected availability of mole crickets, the main prey in our population) and estimated the contributions of habitat‐specific demographic rates to population variation and decline. We quantified how much each demographic rate has decreased and investigated whether habitat quality influenced this decline. We tested how much weather conditions and research activities contributed to the decrease in the different demographic rates. The decline of the hoopoe population was mainly explained by a decrease in first‐year apparent survival and a reduced number of fledglings produced, particularly in habitats of high quality. Since a majority of pairs bred in habitats of the highest quality, the decrease in the production of locally recruited yearlings in high‐quality habitat was the main driver of the population decline despite a homogeneous drop of recruitment across habitats. Overall, the explanatory variables we tested only accounted for 19% of the decrease in the population growth rate. Among these variables, the effects of spring temperature (49% of the explained variance) contributed more to population decline than spring precipitation (36%) and research activities (maternal capture delay, 15%). This study shows the power of IPMs for identifying the vital rates involved in population declines and thus paves the way for targeted conservation and management actions.     

Meta‐replication,sampling bias,and multi‐scale model selection: A case study on snow leopard (Panthera uncia) in western China

Replicated multiple scale species distribution models (SDMs) have become increasingly important to identify the correct variables determining species distribution and their influences on ecological responses. This study explores multi‐scale habitat relationships of the snow leopard (Panthera uncia) in two study areas on the Qinghai–Tibetan Plateau of western China. Our primary objectives were to evaluate the degree to which snow leopard habitat relationships, expressed by predictors, scales of response, and magnitude of effects, were consistent across study areas or locally landcape‐specific. We coupled univariate scale optimization and the maximum entropy algorithm to produce multivariate SDMs, inferring the relative suitability for the species by ensembling top performing models. We optimized the SDMs based on average omission rate across the top models and ensembles’ overlap with a simulated reference model. Comparison of SDMs in the two study areas highlighted landscape‐specific responses to limiting factors. These were dependent on the effects of the hydrological network, anthropogenic features, topographic complexity, and the heterogeneity of the landcover patch mosaic. Overall, even accounting for specific local differences, we found general landscape attributes associated with snow leopard ecological requirements, consisting of a positive association with uplands and ridges, aggregated low‐contrast landscapes, and large extents of grassy and herbaceous vegetation. As a means to evaluate the performance of two bias correction methods, we explored their effects on three datasets showing a range of bias intensities. The performance of corrections depends on the bias intensity; however, density kernels offered a reliable correction strategy under all circumstances. This study reveals the multi‐scale response of snow leopards to environmental attributes and confirms the role of meta‐replicated study designs for the identification of spatially varying limiting factors. Furthermore, this study makes important contributions to the ongoing discussion about the best approaches for sampling bias correction.     

国产西罗莫司与原研品在体内外对细胞影响的比较实验

目的探讨国产西罗莫司与原研品对移植宿主外周血中免疫细胞的影响效果。方法体外实验:人膀胱癌T24细胞体外培养,分别加入国产西罗莫司和原研品,CKK-8法检测并比较细胞增殖活性受抑制的情况。体内实验:建立小鼠异位心脏移植模型,设立对照无手术组(对照组)、移植无治疗组(Tx组)、移植+国产西罗莫司组(Tx+YXK组)、移植+原研品组(Tx+RAPA组)。观察移植心脏搏动情况,受者脾脏的流式细胞学检测,以及脾脏及移植物中免疫细胞浸润的病理检查。流式细胞检测树突状细胞(DC),CD8+细胞和调节性T细胞(Treg),病理组织学检测及免疫组化染色比较两组免疫细胞浸润情况。两组间比较采用独立样本t检验,多组间比较采用单因素方差分析,两两比较采用LSD-t检验。结果体外实验结果显示,国产西罗莫司与原研品对T24细胞活力影响的差异无统计学意义(P>0.05)。体内实验结果显示,Tx组移植心脏于第7天停止搏动,Tx+YXK组和Tx+RAPA组在第10天心脏搏动仍有力、节律正常。(1)脾脏流式细胞检测显示,与对照组、Tx组比较,Tx+RAPA组、Tx+YXK组CD11c+I-A+CD86+DC细胞(15.88±4.73、22.90±3.86比4.51±1.57、5.40±2.54)、CD8+淋巴细胞数量(6.32±0.98、6.75±1.34比3.03±1.12、3.23±0.97)均降低,而Tx+RAPA组CD4+CD25+Foxp3+阳性细胞数量(15.06±3.42比7.87±1.95,10.88±2.08)升高(P均<0.05)。Tx+YXK组和Tx+RAPA组3种免疫细胞数量差异均无统计学意义(P>0.05)。(2)移植心脏病理免疫细胞组化染色灰度分析,Tx组、Tx+YXK组和Tx+RAPA组CD4,CD8,IDO和CD11b数量差异无统计学意义(P>0.05),与Tx组比较,Tx+RAPA组和Tx+YXK组CD11c(25143.52±3525.12比12936.30±766.94、14240.60±3124.67)、Foxp3阳性细胞浸润数量(500.78±238.33比46.05±68.16、49.22±25.82)降低(P均<0.05),Tx+YXK组和Tx+RAPA组比较差异无统计学意义(P>0.05)。(3)模型动物脾脏病理免疫细胞组化染色灰度分析,Tx组CD 4和CD8阳性细胞浸润数量较Tx+YXK组和Tx+RAPA组少,但差异无统计学意义(P>0.05),Tx+YXK组和Tx+RAPA组比较,各种细胞染色的IOD值差异均无统计学意义。结论使用国产西罗莫司与原研品两种药物后受者移植心脏和脾脏中的细胞浸润变化一致;在体外对细胞增殖、移植后抗排斥作用和体内免疫细胞的影响表现均一致。     

Straightforward method for calibration of mechanistic cation exchange chromatography models for industrial applications

Mechanistic modeling of chromatography processes is one of the most promising techniques for the digitalization of biopharmaceutical process development. Possible applications of chromatography models range from in silico process optimization in early phase development to in silico root cause investigation during manufacturing. Nonetheless, the cumbersome and complex model calibration still decelerates the implementation of mechanistic modeling in industry. Therefore, the industry demands model calibration strategies that ensure adequate model certainty in a limited amount of time. This study introduces a directed and straightforward approach for the calibration of pH-dependent, multicomponent steric mass action (SMA) isotherm models for industrial applications. In the case investigated, the method was applied to a monoclonal antibody (mAb) polishing step including four protein species. The developed strategy combined well-established theories of preparative chromatography (e.g. Yamamoto method) and allowed a systematic reduction of unknown model parameters to 7 from initially 32. Model uncertainty was reduced by designing two representative calibration experiments for the inverse estimation of remaining model parameters. Dedicated experiments with aggregate-enriched load material led to a significant reduction of model uncertainty for the estimates of this low-concentrated product-related impurity. The model was validated beyond the operating ranges of the final unit operation, enabling its application to late-stage downstream process development. With the proposed model calibration strategy, a systematic experimental design is provided, calibration effort is strongly reduced, and local minima are avoided.     

Integrated analysis of ceRNA network and tumor-infiltrating immune cells in esophageal cancer

Background: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA.Methods: The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan–Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves.Results: The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules.Conclusion: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.     

全球变化格局下重要传粉昆虫大蜜蜂的潜在适生区变化

【目的】当前全球气候变化、土地利用改变、人类活动加剧等正威胁着传粉昆虫的多样性及分布;蜜蜂是生态系统中重要的传粉昆虫类群,对气候、环境变化响应敏感。本研究以重要的传粉昆虫大蜜蜂Apis dorsata为对象,探讨全球变化格局下其潜在适生区变化以及影响其分布的关键因子。【方法】通过文献、馆藏和野外调查系统收集了全球范围内大蜜蜂的物种分布数据,使用13个环境变量通过MaxEnt模型模拟了大蜜蜂当前的潜在适生区;使用9个气候变量并结合公共地球系统模型(CCSM4)模拟了大蜜蜂过去、当前和未来的潜在适生区。【结果】AUC比率显示MaxEnt模型对大蜜蜂的潜在适生区模拟具有较高的准确性,模拟结果表明大蜜蜂的中高潜在适生区主要分布在南亚和东南亚湿润的热带雨林、热带季节性雨林和低地雨林。人类影响、温度季节性变化、等温性、最冷季均温和海拔是影响大蜜蜂潜在适生区的5个最主要因子;在人类影响下大蜜蜂的潜在适生区向山区和连片的湿润常绿森林区收缩,中高潜在适生区显著减少且呈破碎化趋势。基于9个气候变量和CCSM4气候模型对过去、当前和未来的模拟结果显示：在过去的末次冰盛期,东南亚地区可能是大蜜蜂的避难所;在未来,广布于热带地区的大蜜蜂适生区与当前的相近,且部分地区适生指数升高。【结论】基于气候的模拟结果显示大蜜蜂能积极应对未来气候变暖,但随人类活动的加剧及全球气候变化,大蜜蜂仍然面临较大的威胁,需要加强其在南亚和东南亚的中高潜在适生区的重视和保护。     

Effects of 5-HT7 receptors on circadian rhythm of mice anesthetized with isoflurane

ABSTRACT

The role of the serotonin 7 receptor (5-HT₇ receptor) subtype in a number of domains has been widely recognized, but its role in the regulation of changes of the circadian rhythm after anesthesia is still unclear. We used intraperitoneal injection of 5-HT₇ receptor agonist LP-211 or antagonist SB-269970 in mice to influence the level of 5-HT₇ receptor protein in the SCN and to observe the role of this receptor on circadian rhythm changes after isoflurane anesthesia. Our results show the appropriate dose of SB-269970 significantly alleviated the circadian rhythm disorder induced by isoflurane anesthesia, while LP-211 significantly aggravated it after anesthesia, which is different from the phase shift that can be caused by the administration of LP-211 before anesthesia. These findings may indicate the 5-HT₇ receptor plays a complex role in the regulation of circadian rhythm after anesthesia. Our findings may provide some positive significance for alleviating circadian rhythm disorder in patients after anesthesia and ultimately promoting rapid postoperative recovery.     

Establishment of the biological control agent Hypocosmia pyrochroma for Dolichandra unguis-cati (Bignoniaceae) is limited by microclimate

Cat's claw creeper, Dolichandra unguis-cati (Bignoniaceae), a perennial woody vine native to tropical America, is a target for biological control in Australia and South Africa. The cat's claw creeper leaf-tying moth Hypocosmia pyrochroma (Lepidoptera: Pyralidae) from tropical South America was released as a biological control agent for cat's claw creeper in Australia from 2007 to 2010. A total of 2,277 adults, 837 pupae and 77,250 larvae were released at 40 sites in Queensland and New South Wales. Releases were made mostly in open fields (85%), and at limited sites (15%) in insect-proof cages erected over naturally occurring cat's claw creeper infestations in the field. Sampling was conducted annually in spring and autumn to monitor the establishment and dispersal of H. pyrochroma. Establishment of H. pyrochroma was first noticed in 2012 at three release sites and since then the number of established sites has increased to 80 in 2020. Establishment was evident on both ‘short-pod’ and ‘long-pod’ forms of cat's claw creeper and was more widespread in sites where releases were made within insect-proof field cages (50%) than in sites with open field releases (9%). The moth was active from late spring to late autumn with peak larval activity in late summer. To date, all field establishments have been in areas predicted by a CLIMEX model as climatically suitable but restricted mostly to riparian environment (93% of establishment), where the moth has continued to spread from 1.5 to 23 km from release sites. In contrast, there is the only limited establishment and spread in non-riparian corridors, highlighting the role of microclimate (riparian) as a limiting factor for establishment and spread. Future efforts will focus on redistribution of the agent to river/creek systems where the moth is currently not present.