A comparison of methods for classifying clinical samples based on proteomics data: A case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems

Perspectives on scientific error.

Peer reviewed: TrueTheoretical arguments and empirical investigations indicate that a high proportion of published findings do not replicate and are likely false. The current position paper provides a broad perspective on scientific error, which may lead to replication failures. This broad perspective focuses on reform history and on opportunities for future reform. We organize our perspective along four main themes: institutional reform, methodological reform, statistical reform and publishing reform. For each theme, we illustrate potential errors by narrating the story of a fictional researcher during the research cycle. We discuss future opportunities for reform. The resulting agenda provides a resource to usher in an era that is marked by a research culture that is less error-prone and a scientific publication landscape with fewer spurious findings

No-till farming systems for sustainable agriculture: an overview

No-till (NT) farming systems have revolutionized agriculture by improving erosion control, soil water storage, soil quality and, in many instances, yield and net farm income. The adoption of NT systems has increased at an exponential rate since the 1990s and they are now used on 12.5% of global croplands. However, while the development of NT systems has seen much success, there can be significant agronomic, economic and/or social challenges associated with their use that limit large scale worldwide adoption. In addition, where NT is not implemented as part of an integrated system that incorporates stubble retention and appropriate crop rotations to help manage weeds, diseases, pests and soil fertility, decreases in yield can be observed. A combination of research, education and good policy development to remove economic/institutional and social barriers to uptake are required to ensure the continued success of NT. In particular, the tailoring of NT farming systems according to individual locations and the introduction of some flexibility in approach to tillage management can provide an opportunity to manage some of the challenges of NT farming systems

Land radiative management as contributor to regional-scale climate adaptation and mitigation

Greenhouse gas emissions urgently need to be reduced. Even with a step up in mitigation, the goal of limiting global temperature rise to well below 2 °C remains challenging. Consequences of missing these goals are substantial, especially on regional scales. Because progress in the reduction of carbon dioxide emissions has been slow, climate engineering schemes are increasingly being discussed. But global schemes remain controversial and have important shortcomings. A reduction of global mean temperature through global-scale management of solar radiation could lead to strong regional disparities and affect rainfall patterns. On the other hand, active management of land radiative effects on a regional scale represents an alternative option of climate engineering that has been little discussed. Regional land radiative management could help to counteract warming, in particular hot extremes in densely populated and important agricultural regions. Regional land radiative management also raises some ethical issues, and its efficacy would be limited in time and space, depending on crop growing periods and constraints on agricultural management. But through its more regional focus and reliance on tested techniques, regional land radiative management avoids some of the main shortcomings associated with global radiation management. We argue that albedo-related climate benefits of land management should be considered more prominently when assessing regional-scale climate adaptation and mitigation as well as ecosystem services

Resilient and dynamic soil biology

Agricultural intensification has delivered great gains in terms of food production but has come at great environmental cost. Consequently, there is growing societal demand for more sustainable farming systems, i.e., sustainable intensification. Within this, there is increasing recognition of the ecosystem services provided by soil organisms that contribute both to agricultural production and environmental sustainability. Conventional tillage-based farming systems experience frequent and significant soil disturbance, which negatively impacts many key soil organism groups, e.g., fungi and earthworms. Loss of these soil organisms results in loss of critical soil ecosystem services, including those related to soil nutrient cycling, crop nutrient uptake, and soil water management. Conversion of farming systems from conventional tillage to no-till can allow recovery of soil biology and restoration of soil ecosystem services. Thus, no-till farming systems can contribute positively towards sustainable intensification. However, important knowledge gaps and challenges remain. Greater knowledge of what soil organisms are present in soil and what services they provide is urgently needed. The ultimate goal is to understand how soil biology can be manipulated through management to provide desirable ecosystem services in space and time