A Critique of the Use of the Balanced Scorecard in Multi-Enterprise Family Farm Businesses

Business strategy is very important to small and medium family businesses as many are both fragile and vulnerable; strategy provides a solid foundation for survival. Various studies have identified that businesses that engage in strategic management outperform those that do not. Despite this knowledge the uptake of many aspects of strategic management by farm businesses has been slow. Although the development of business plans is now common there is often a disconnect between monitoring and strategy. The Balanced Scorecard (BSC) was applied to case study farms during both the planning process and as they implemented and controlled their strategic choices to determine areas of difference that restrict or enhance it as a management tool for both family and farming businesses. The BSC was immediately applicable in the strategic management process for those businesses with current business plans. It could be used to test the degree of balance between the goals already identified in their plans. It was able to be used to critique the control measures they had in place and to determine how well they could be used to derive the causal chain from the operational level to family goals. In some instances either outcome or driver measures were recognized as being missing, in others the wiring within the balanced scorecard revealed some strategic measures without linkages.Farm Management,

Loss of intranetwork and internetwork resting state functional connections with Alzheimer\u27s disease progression

Alzheimer\u27s disease (AD) is the most common cause of dementia. Much is known concerning AD pathophysiology but our understanding of the disease at the systems level remains incomplete. Previous AD research has used resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) to assess the integrity of functional networks within the brain. Most studies have focused on the default-mode network (DMN), a primary locus of AD pathology. However, other brain regions are inevitably affected with disease progression. We studied rs-fcMRI in five functionally defined brain networks within a large cohort of human participants of either gender (n = 510) that ranged in AD severity from unaffected [clinical dementia rating (CDR) 0] to very mild (CDR 0.5) to mild (CDR 1). We observed loss of correlations within not only the DMN but other networks at CDR 0.5. Within the salience network (SAL), increases were seen between CDR 0 and CDR 0.5. However, at CDR 1, all networks, including SAL, exhibited reduced correlations. Specific networks were preferentially affected at certain CDR stages. In addition, cross-network relations were consistently lost with increasing AD severity. Our results demonstrate that AD is associated with widespread loss of both intranetwork and internetwork correlations. These results provide insight into AD pathophysiology and reinforce an integrative view of the brain\u27s functional organization

Cortex-wide, cellular-resolution two-photon microscopy

Functional imaging of the mouse brain in its extreme complexity involves substantial trade-offs. An optical intrinsic spectroscopy system can image the entire cortex but at the expense of spatial and temporal resolution [1]. A two-photon microscope (TPM) can image single neurons with high temporal resolution, but the field of view (FOV) is generally restricted. Advanced techniques like random-access scanning allow for imaging single neurons that are millimeters apart but only by ignoring the neurons and tissue in between [2]. By carefully considering the properties of the optical components as well as the imaging requirements, we present a TPM capable of imaging nearly the entire mouse cortex with 15 Hz frame rates and single neuron resolution. Please click Additional Files below to see the full abstract

Exploration in the wild

Making good decisions requires people to appropriately explore their available options and generalize what they have learned. While computational models have successfully explained exploratory behavior in constrained laboratory tasks, it is unclear to what extent these models generalize to complex real world choice problems. We investigate the factors guiding exploratory behavior in a data set consisting of 195,333 customers placing 1,613,967 orders from a large online food delivery service. We find important hallmarks of adaptive exploration and generalization, which we analyze using computational models. We find evidence for several theoretical predictions: (1) customers engage in uncertainty-directed exploration, (2) they adjust their level of exploration to the average restaurant quality in a city, and (3) they use feature-based generalization to guide exploration towards promising restaurants. Our results provide new evidence that people use sophisticated strategies to explore complex, real-world environments

Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer

With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is usually limited to only a few histopathological features. However, most of the tumor microenvironments used to describe patterns of aggressive tumor behavior are ignored. In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer. To do so, we propose a self-supervised learning method that jointly learns a representation of tissue regions as well as a metric of the clustering to obtain their underlying patterns. These histopathological patterns are then used to represent the interaction between complex tissues and predict clinical outcomes directly. We furthermore show that the proposed approach can benefit from linear predictors to avoid overfitting in patient outcomes predictions. To this end, we introduce a new well-characterized clinicopathological dataset, including a retrospective collective of 374 patients, with their survival time and treatment information. Histomorphological clusters obtained by our method are evaluated by training survival models. The experimental results demonstrate statistically significant patient stratification, and our approach outperformed the state-of-the-art deep clustering methods

Efficient Entropy Estimation for Mutual Information Analysis Using B-Splines

International audienceThe Correlation Power Analysis (CPA) is probably the most used side-channel attack because it seems to fit the power model of most standard CMOS devices and is very efficiently computed. However, the Pearson correlation coefficient used in the CPA measures only linear statistical dependences where the Mutual Information (MI) takes into account both linear and nonlinear dependences. Even if there can be simultaneously large correlation coefficients quantified by the correlation coefficient and weak dependences quantified by the MI, we can expect to get a more profound understanding about interactions from an MI Analysis (MIA). We study methods that improve the non-parametric Probability Density Functions (PDF) in the estimation of the entropies and, in particular, the use of B-spline basis functions as pdf estimators. Our results indicate an improvement of two fold in the number of required samples compared to a classic MI estimation. The B-spline smoothing technique can also be applied to the rencently introduced Cramér-von-Mises test

Defining principles for mobile apps and platforms development in citizen science

Apps for mobile devices and web-based platforms are increasingly used in citizen science projects. While extensive research has been done in multiple areas of studies, from Human-Computer Interaction to public engagement in science, we are not aware of a collection of recommendations specific for citizen science that provides support and advice for planning, design and data management of mobile apps and platforms that will assist learning from best practice and successful implementations. In two workshops, citizen science practitioners with experience in mobile application and web-platform development and implementation came together to analyse, discuss and define recommendations for the initiators of technology based citizen science projects. Many of the recommendations produced during the two workshops are applicable to citizen science project that do not use mobile devices to collect data. Therefore, we propose to closely connect the results presented here with ECSA’s Ten Principles of Citizen Science

The usefulness of case reports in managing emerging infectious disease

Emerging infectious diseases are an important problem in medicine. Case reports usually document episodes in the early emerging phase or in a small outbreak. Although the case report is considered weak evidence in medical literature, it is usually the first report when there is a new emerging infectious disease. There is no doubt that case reports can provide useful information for further case series, reviews and studies. This editorial focuses on the usefulness of the case report on emerging infectious disease to the medical society. Publication in this area is highly welcomed by the journal and can serve as a future point of reference