Foodways have been a component of archaeological research for decades. However, cooking and food preparation, as specific acts that could reveal social information about life beyond the kitchen, only became a focus of archaeological inquiry more recently. A review of the literature on cooking and food preparation reveals a shift from previous studies on subsistence strategies, consumption, and feasting. The new research is different because of the social questions that are asked, the change in focus to preparation and production rather than consumption, and the interest in highlighting marginalized people and their daily experiences. The theoretical perspectives the literature addresses revolve around practice, agency, and gender. As a result, this new focus of archaeological research on cooking and preparing food is grounded in anthropology.

Evolutionary games model a common type of interactions in a variety of complex, networked, natural systems and social systems. Given such a system, uncovering the interacting structure of the underlying network is key to understanding its collective dynamics. Based on compressive sensing, we develop an efficient approach to reconstructing complex networks under game-based interactions from small amounts of data. The method is validated by using a variety of model networks and by conducting an actual experiment to reconstruct a social network. While most existing methods in this area assume oscillator networks that generate continuous-time data, our work successfully demonstrates that the extremely challenging problem of reverse engineering of complex networks can also be addressed even when the underlying dynamical processes are governed by realistic, evolutionary-game type of interactions in discrete time.

Alzheimer’s disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods – four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one “ball-and-stick” approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification.

Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.

Despite wide applications of high-throughput biotechnologies in cancer research, many biomarkers discovered by exploring large-scale omics data do not provide satisfactory performance when used to predict cancer treatment outcomes. This problem is partly due to the overlooking of functional implications of molecular markers. Here, we present a novel computational method that uses evolutionary conservation as prior knowledge to discover bona fide biomarkers. Evolutionary selection at the molecular level is nature's test on functional consequences of genetic elements. By prioritizing genes that show significant statistical association and high functional impact, our new method reduces the chances of including spurious markers in the predictive model. When applied to predicting therapeutic responses for patients with acute myeloid leukemia and to predicting metastasis for patients with prostate cancers, the new method gave rise to evolution-informed models that enjoyed low complexity and high accuracy. The identified genetic markers also have significant implications in tumor progression and embrace potential drug targets. Because evolutionary conservation can be estimated as a gene-specific, position-specific, or allele-specific parameter on the nucleotide level and on the protein level, this new method can be extended to apply to miscellaneous “omics” data to accelerate biomarker discoveries.

Scholarly communication is at an unprecedented turning point created in part by the increasing saliency of data stewardship and data sharing. Formal data management plans represent a new emphasis in research, enabling access to data at higher volumes and more quickly, and the potential for replication and augmentation of existing research. Data sharing has recently transformed the practice, scope, content, and applicability of research in several disciplines, in particular in relation to spatially specific data. This lends exciting potentiality, but the most effective ways in which to implement such changes, particularly for disciplines involving human subjects and other sensitive information, demand consideration. Data management plans, stewardship, and sharing, impart distinctive technical, sociological, and ethical challenges that remain to be adequately identified and remedied. Here, we consider these and propose potential solutions for their amelioration.

Rice is an essential crop in Ghana. Several aspects of rice have been studied to increase its production; however, the environmental aspects, including impact on climate change, have not been studied well. There is therefore a gap in knowledge, and hence the need for continuous research. By accessing academic portals, such as Springer Open, InTech Open, Elsevier, and the Kwame Nkrumah University of Science and Technology’s offline campus library, 61 academic publications including peer reviewed journals, books, working papers, reports, etc. were critically reviewed. It was found that there is a lack of data on how paddy rice production systems affect greenhouse gas (GHG) emissions, particularly emissions estimation, geographical location, and crops. Regarding GHG emission estimation, the review identified the use of emission factors calibrated using temperate conditions which do not suit tropical conditions. On location, most research on rice GHG emissions have been carried out in Asia with little input from Africa. In regard to crops, there is paucity of in-situ emissions data from paddy fields in Ghana. Drawing on the review, a conceptual framework is developed using Ghana as reference point to guide the discussion on fertilizer application, water management rice cultivars, and soil for future development of adaptation strategies for rice emission reduction.

Transdisciplinary research practice has become a core element of global sustainability science. Transdisciplinary research brings with it an expectation that people with different backgrounds and interests will learn together through collective problem solving and innovation. Here we introduce the concept of “transdisciplinary communities of practice, ” and draw on both situated learning theory and transdisciplinary practice to identify three key lessons for people working in, managing, or funding such groups. (1) Opportunities need to be purposefully created for outsiders to observe activities in the core group. (2) Communities of practice cannot be artificially created, but they can be nurtured. (3) Power matters in transdisciplinary communities of practice. These insights challenge thinking about how groups of people come together in pursuit of transdisciplinary outcomes, and call for greater attention to be paid to the social processes of learning that are at the heart of our aspirations for global sustainability science.

The conscientious are morally conflicted when their moral dilemmas or incommensurabilities, real or apparent, have not been resolved. But such doublemindedness need not lead to ethical disintegration or moral insensitivity. For one may develop the moral virtue of doublemindedness, the settled power to deliberate and act well while morally conflicted. Such action will be accompanied by both moral loss (perhaps 'dirty hands') and ethical gain (salubrious agental stability). In explaining the virtue's moral psychology I show, among other things, its consistency with wholeheartedness and the unity of the virtues. To broaden its claim to recognition, I show the virtue's consistency with diverse models of practical reason. In conclusion, Michael Walzer's interpretation of Hamlet's attitude toward Gertrude exemplifies this virtue in a fragmentary but nonetheless praiseworthy form.

In this paper, we propose an efficient and scalable low rank matrix completion algorithm. The key idea is to extend the orthogonal matching pursuit method from the vector case to the matrix case. We further propose an economic version of our algorithm by introducing a novel weight updating rule to reduce the time and storage complexity. Both versions are computationally inexpensive for each matrix pursuit iteration and find satisfactory results in a few iterations. Another advantage of our proposed algorithm is that it has only one tunable parameter, which is the rank. It is easy to understand and to use by the user. This becomes especially important in large-scale learning problems. In addition, we rigorously show that both versions achieve a linear convergence rate, which is significantly better than the previous known results. We also empirically compare the proposed algorithms with several state-of-the-art matrix completion algorithms on many real-world datasets, including the large-scale recommendation dataset Netflix as well as the MovieLens datasets. Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.