Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping

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An Ecosystem Health Index for a large and variable river basin: Methodology,challenges and continuous improvement in Queensland’s Fitzroy Basin

Report cards are an increasingly popular method for summarising and communicating relative environmental performance and ecosystem health, including in aquatic environments. They are usually underpinned by an Ecosystem Health Index (EHI) that combines various individual indicators to produce an overall ecosystem health “score”. As a result of public water quality concerns, an integrated means of monitoring and reporting on aquatic ecosystem health was needed for the Fitzroy Basin in central Queensland, Australia. The Fitzroy Partnership for River Health was formed to address this need, and developed an EHI and report card for the Basin using existing monitoring data collected from various third parties including regulated companies operations and government. At 142,000 square kilometres, the Fitzroy Basin is the largest catchment draining to the World Heritage Listed Great Barrier Reef. The Fitzroy Basin provides an example of how to deliver an effective aquatic ecosystem health reporting system in a large and complex river basin. We describe the methodology used to develop an adaptive EHI for the Fitzroy Basin that addresses variability, complexity and scale issues associated with reporting across large areas. As well, we report how to manage the design and reporting stages given limitations in data collection and scientific understanding.     

Evaluation of environmentally conscious manufacturing programs using a three-hybrid multi-criteria decision analysis method

Environmental management, being an important component in strategies for achieving sustainable development of processes and products, has emerged as a proactive approach in majority of the manufacturing organizations. From the strategic perspective environmentally conscious manufacturing (ECM) programs lead to better environmental management practice. The objective of the current paper is to present an integrated and holistic framework to evaluate ECM programs. This framework combines three multi-criteria decision analysis (MCDA) methods to consider eight major environmentally conscious manufacturing indicators (ECMI) in order to identify the efficiency of each ECM program. First the interdependence relationship among the ECMIs is established using decision-making trail and evaluation laboratory (DEMATEL). Then a range of weightage (i.e. upper and lower bounds) is created for each ECMI using analytic network process (ANP) to include managerial preferences. Finally, this range of weightage for each indicator is applied to perform restricted multiplier data envelopment analysis (RMDEA). Results show that the technical efficiency of the inefficient ECM programs for integrated RMDEA, on average, is calculated as 53.2% whereas traditional input oriented DEA provides the same score as 72.3%. This clearly indicates that integrated RMDEA is better than the input oriented DEA because same level of output could be produced with lesser resources if the ECM programs perform on the frontier. Hence, the advantage of this methodology is that the managerial preferences are successfully implemented through this newly developed hybrid methodology that will help to reduce less resource consumption and lead to better environmental policy.     

典型矿区植被覆盖度时空分布特征及影响因素

植被状况可以直接或间接地反映采煤对生态环境的影响。以长河井工煤矿、离柳井工煤矿、平朔露天煤矿3个典型矿区为研究区域。以Landsat数据为数据源，基于地形调节植被指数的像元二分模型提取植被覆盖度；采用趋势分析、线性回归斜率、稳定性分析方法，分析了3个典型矿区2001-2016年植被覆盖度的时空变化特征；运用"以时间换空间"的方法，采用相关分析方法对植被覆盖度变化的自然影响因素进行了分析。结果表明：（1）近16年3个典型矿区植被覆盖度呈增加趋势，长河、离柳、平朔矿区的增长速率分别为0.09%/10 a、0.10%/10 a、0.08%/10 a（P > 0.05）。（2）空间上，长河、离柳、平朔矿区植被覆盖度变化不明显比例分别占到66.63%，59.90%，62.25%，呈增加趋势的比例仅分别占28.14%、32.55%、27.81%，而呈减少趋势的比例分别占到5.23%、7.55%、9.94%。长河矿区明显改善的区域位于自然植被和耕作区的北部和东北部，离柳矿区明显改善的区域位于以低植被覆盖度为主的北部，平朔矿区明显改善的区域位于复垦的中西部。（3）不区分植被类型时，3个矿区的植被覆盖度变化与高程、高程与温度的交互作用表现出显著相关性（P < 0.01），与各自然因素的相关性总体表现为长河 > 离柳 > 平朔矿区；区分植被类型时，草地与坡度的相关性不显著（P > 0.05），与降雨量、高程存在显著正相关（P < 0.05）；灌木林与温度相关性不显著，与高程和降雨量的交互作用存在显著正相关；旱地与高程、高程与温度的交互作用存在显著相关性；疏林地与坡向、降雨量与坡向坡度的交互作用均没有表现出相关性；有林地与高程降雨量的交互作用表现出显著正相关性。探讨不同植被类型对自然因素的响应，可为矿区植被结构的选择，矿区复垦提供参考依据。     

Extending the Horizon of Homology Detection with Coevolution-based Structure Prediction

Traditional sequence analysis algorithms fail to identify distant homologies when they lie beyond a detection horizon. In this review, we discuss how co-evolution-based contact and distance prediction methods are pushing back this homology detection horizon, thereby yielding new functional insights and experimentally testable hypotheses. Based on correlated substitutions, these methods divine three-dimensional constraints among amino acids in protein sequences that were previously devoid of all annotated domains and repeats. The new algorithms discern hidden structure in an otherwise featureless sequence landscape. Their revelatory impact promises to be as profound as the use, by archaeologists, of ground-penetrating radar to discern long-hidden, subterranean structures. As examples of this, we describe how triplicated structures reflecting longin domains in MON1A-like proteins, or UVR-like repeats in DISC1, emerge from their predicted contact and distance maps. These methods also help to resolve structures that do not conform to a “beads-on-a-string” model of protein domains. In one such example, we describe CFAP298 whose ubiquitin-like domain was previously challenging to perceive owing to a large sequence insertion within it. More generally, the new algorithms permit an easier appreciation of domain families and folds whose evolution involved structural insertion or rearrangement. As we exemplify with α1-antitrypsin, coevolution-based predicted contacts may also yield insights into protein dynamics and conformational change. This new combination of structure prediction (using innovative co-evolution based methods) and homology inference (using more traditional sequence analysis approaches) shows great promise for bringing into view a sea of evolutionary relationships that had hitherto lain far beyond the horizon of homology detection.     

The RAINFOR database: monitoring forest biomass and dynamics

Problem: Data from over 100 permanent sample plots which have been studied for 10–20 years need a suitable system for storage which allows simple data manipulation and retrieval for analysis. Methods: A relational database linking tree records, taxonomic nomenclature and corresponding environmental data has been built in MS Access as part of the RAINFOR project. Conclusion: The database allows flexible and long‐term use of a large amount of data: more than 100 tree plots across Amazonia, incorporating over 80 000 records of individual trees and over 300 000 total records of tree diameter measurements from successive censuses. The database is designed to enable linkages to existing soil, floristic or plant‐trait databases. This database will be a useful tool for exploring the impact of environmental factors on forest structure and dynamics at local to continental scales, and long term changes in forest ecology. As an early example of its potential, we explore the impact of different methodological assumptions on estimates of tropical forest biomass and carbon storage.