A partnership project for a new Red List of the Italian Flora

Abstract

The IUCN Red Lists assessment provides an internationally accepted system to verify the extinction risk of species. Working Groups of the Italian Botanical Society have recently discussed the importance of producing a reliable list of species at the national level. This list could be the starting point for future in situ and ex situ plant conservation activities.     

A Bayesian multi-risks survival (MRS) model in the presence of double censorings

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.     

Evidential statistics as a statistical modern synthesis to support 21st century science

During the 20th century, population ecology and science in general relied on two very different statistical paradigms to solve its inferential problems: error statistics (also referred to as classical statistics and frequentist statistics) and Bayesian statistics. A great deal of good science was done using these tools, but both schools suffer from technical and philosophical difficulties. At the turning of the 21st century (Royall in Statistical evidence: a likelihood paradigm. Chapman & Hall, London, 1997 ; Lele in The nature of scientific evidence: statistical, philosophical and empirical considerations. The University of Chicago Press, Chicago, pp 191–216, 2004a ), evidential statistics emerged as a seriously contending paradigm. Drawing on and refining elements from error statistics, likelihoodism, Bayesian statistics, information criteria, and robust methods, evidential statistics is a statistical modern synthesis that smoothly incorporates model identification, model uncertainty, model comparison, parameter estimation, parameter uncertainty, pre-data control of error, and post-data strength of evidence into a single coherent framework. We argue that evidential statistics is currently the most effective statistical paradigm to support 21st century science. Despite the power of the evidential paradigm, we think that there is no substitute for learning how to clarify scientific arguments with statistical arguments. In this paper we sketch and relate the conceptual bases of error statistics, Bayesian statistics and evidential statistics. We also discuss a number of misconceptions about the paradigms that have hindered practitioners, as well as some real problems with the error and Bayesian statistical paradigms solved by evidential statistics.     

Ultra high risk status and transition to psychosis in 22q11.2 deletion syndrome

The 22q11.2 deletion syndrome (22q11DS) is characterized by high rates of psychotic symptoms and schizophrenia, making this condition a promising human model for studying risk factors for psychosis. We explored the predictive value of ultra high risk (UHR) criteria in a sample of patients with 22q11DS. We also examined the additional contribution of socio‐demographic, clinical and cognitive variables to predict transition to psychosis within a mean interval of 32.5 ± 17.6 months after initial assessment. Eighty‐nine participants with 22q11DS (age range: 8‐30 years; mean 16.1 ± 4.7) were assessed using the Structured Interview for Psychosis‐Risk Syndromes. Information on Axis I diagnoses, internalizing and externalizing symptoms, level of functioning and IQ was also collected. At baseline, 22 (24.7%) participants met UHR criteria. Compared to those without a UHR condition, they had a significantly lower functioning, more frequent anxiety disorders, and more severe psychopathology. Transition rate to psychosis was 27.3% in UHR and 4.5% in non‐UHR participants. Cox regression analyses revealed that UHR status significantly predicted conversion to psychosis. Baseline level of functioning was the only other additional predictor. This is the first study investigating the predictive value of UHR criteria in 22q11DS. It indicates that the clinical path leading to psychosis is broadly comparable to that observed in other clinical high‐risk samples. Nevertheless, the relatively high transition rate in non‐UHR individuals suggests that other risk markers should be explored in this population. The role of low functioning as a predictor of transition to psychosis should also be investigated more in depth.     

Proposing a Radial Basis Function and CSDM Indices to Predict the Traumatic Brain Injury Risk

BackgroundMany head injury indices and finite element (FE) head models have been proposed to predict traumatic brain injury (TBI). Although FE head models are suitable methods with high accuracy, they are computationally intensive. Head motion-based brain injury criteria are usually fast tools with lower accuracy. So, the objective of this study is to propose new criteria along with an artificial neural network model to predict TBI risks, which can be fast and accurate.MethodsFor this purpose, 250 FE head simulations have been carried out at 5 magnitudes and 50 rotational impact directions using the SIMon model. The effects of directions and magnitudes of rotational impacts were assessed for cumulative strain damage measure (CSDM) values. Next, statistical analysis and neural network were applied to predict CSDM values.ResultsThe results of the present research showed that the direction of rotation in the sagittal and frontal planes had a considerable effect on the CSDM values. Furthermore, new brain injury indices and a radial basis function neural network have been proposed to predict CSDM values which having high correlation coefficients with SIMon responses.ConclusionsThe results of this research demonstrated that rotational impact directions should be used to develop new head injury criteria being able to predict CSDM values. However, findings of present research proved that head motion-based brain injury criteria and RBF network can be used to predict FE head model responses with high speed and accuracy.     

VPMCD: variable interaction modeling approach for class discrimination in biological systems

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.     

The concept of mental disorder: diagnostic implications of the harmful dysfunction analysis

What do we mean when we say that a mental condition is a medical disorder rather than a normal form of human suffering or a problem in living? The status of psychiatry as a medical discipline depends on a persuasive answer to this question. The answers tend to range from value accounts that see disorder as a sociopolitical concept, used for social control purposes, to scientific accounts that see the concept as strictly factual. I have proposed a hybrid account, the harmful dysfunction (HD) analysis, that incorporates both value and scientific components as essential elements of the medical concept of disorder, applying to both physical and mental conditions. According to the HD analysis, a condition is a disorder if it is negatively valued ("harmful") and it is in fact due to a failure of some internal mechanism to perform a function for which it was biologically designed (i.e., naturally selected). The implications of this analysis for the validity of symptom-based diagnostic criteria and for challenges in cross-cultural use of diagnostic criteria are explored, using a comparison of the application of DSM diagnostic criteria in the U.S. and Taiwan.     

基于层次分析法的旗舰物种遴选方法

旗舰物种是保护生物学中一个广泛应用的概念, 用于获得公众对保护行为的支持, 在生物多样性保护领域发挥着重要作用。但目前尚未见量化分析遴选和确定旗舰物种的公开报道, 致使学术界对旗舰物种的定义和使用相对主观, 缺乏科学依据。本文对旗舰物种的定义进行了梳理, 探讨了旗舰物种的内涵, 确定了旗舰物种的属性, 制定了遴选标准和量化遴选方法, 以便读者更好地理解和运用这一概念。目前旗舰物种的定义仍然局限在其社会学属性, 作者建议今后应加强其生物生态学属性。作者认为除了公众的关注度和认可度之外, 旗舰物种还应具备以下3个生态生物学和社会文化特征: (1)物种在当地的濒危程度及生存现状; (2)物种在当地生态系统中的重要性; (3)物种在当地民众中的影响力和认可度。基于此, 本文确定了表征旗舰物种社会文化属性和生态生物学属性的8个要素, 基于层次分析法提出了一种量化的旗舰物种遴选方法。该方法综合上述8个要素进行评判和计算分值, 以塔吉克斯坦境内分布的14种大型濒危哺乳动物的生态生物学属性和社会文化影响力为基础, 描述基于层次分析法遴选塔吉克斯坦哺乳类旗舰物种的方法和过程, 最终依据每个备选物种所得分值高低排序, 得到塔吉克斯坦旗舰物种的优先备选物种。本文首次提出了一种量化遴选旗舰物种的方法, 期望今后可基于该方法开发更加合理的旗舰物种遴选模型和算法。     

植物子平台及中国数字植物标本馆(CVH)运维: 做法和经验

该文系统介绍了植物子平台所采取的以数字标本质量为导向的数字化技术规范和管理策略,以及CVH网站数据共享规则, 并指出存在的问题及今后努力方向。