This research communication reports the results of a study aimed at investigating the effects of introducing Mycobacterium vaccae on paratuberculosis carriage in a dairy herd. M. vaccae is a non-pathogenic member of the Mycobacteriaceae, with immunomodulatory and immunotherapeutic capabilities, acting by stimulating the cellular immune system, important in protection against paratuberculosis. Starting in 2014 we administered, by gavage, 1010 live M. vaccae bacteria to all new-born heifers on a dairy farm, first within 24 h of birth and again 2 weeks later. Paratuberculosis carriage was monitored yearly by milk ELISA. Faecal samples of 50% of cows, aged 3 years, born 1, 2 or 3 years before the experiment's onset, were tested by qPCR for MAP shedding and compared to 100% treated cows of the same age. Within 3 years, milk ELISA positivity was reduced from 6 to 0% and remained unchanged for the subsequent 2 years. One qPCR positive control cow was found each year for a total of 3 animals (2.46%). One positive cow (1%) was found among the treated cows. Two of the 3 positive control animals, still present on the farm at the end of 2019, tested negative whereas the positive test cow continued shedding MAP. M. vaccae shedding heifers mixing with adult cows were the probable means of the microorganism's propagation. The results of this investigation indicate that the introduction of live M. vaccae may be an inexpensive and fast alternative to current paratuberculosis control practices, justifying further exploration of the topic.

Over the past decade, advances in the interdisciplinary field of network science have provided a framework for understanding the intrinsic structure and function of human brain networks. A particularly fruitful area of this work has focused on patterns of functional connectivity derived from noninvasive neuroimaging techniques such as functional magnetic resonance imaging. An important subset of these efforts has bridged the computational approaches of network science with the rich empirical data and biological hypotheses of neuroscience, and this research has begun to identify features of brain networks that explain individual differences in social, emotional, and cognitive functioning. The most common approach estimates connections assuming a single configuration of edges that is stable across the experimental session. In the literature, this is referred to as a static network approach, and researchers measure static brain networks while a subject is either at rest or performing a cognitively demanding task. Research on social and emotional functioning has primarily focused on linking static brain networks with individual differences, but recent advances have extended this work to examine temporal fluctuations in dynamic brain networks. Mounting evidence suggests that both the strength and flexibility of time-evolving brain networks influence individual differences in executive function, attention, working memory, and learning. In this review, we first examine the current evidence for brain networks involved in cognitive functioning. Then we review some preliminary evidence linking static network properties to individual differences in social and emotional functioning. We then discuss the applicability of emerging dynamic network methods for examining individual differences in social and emotional functioning. We close with an outline of important frontiers at the intersection between network science and neuroscience that will enhance our understanding of the neurobiological underpinnings of social behavior.

The Packing Simulation Model is a micro-computer application program designed for researchers, extension personnel, bankers, packing managers, or other specialists who plan the operations of a packing facility or simulate its financial performance. PACKSIM produces pro forma financial statements for packing facilities based on flexible crop mixes and packing assumptions. Variations can be made in the product harvest schedule, price, quality, quantity, input costs and requirements, packing efficiency, overhead, and loan terms.

Deflection missions to near-Earth asteroids will encounter non-negligible uncertainties in the physical and orbital parameters of the target object. In order to reliably assess future impact threat mitigation operations such uncertainties have to be quantified and incorporated into the mission design. The implementation of deflection demonstration missions offers the great opportunity to test our current understanding of deflection relevant uncertainties and their consequences, e.g., regarding kinetic impacts on asteroid surfaces. In this contribution, we discuss the role of uncertainties in the NEOTωIST asteroid deflection demonstration concept, a low-cost kinetic impactor design elaborated in the framework of the NEOShield project. The aim of NEOTωIST is to change the spin state of a known and well characterized near-Earth object, in this case the asteroid (25143) Itokawa. Fast events such as the production of the impact crater and ejecta are studied via cube-sat chasers and a flyby vehicle. Long term changes, for instance, in the asteroid's spin and orbit, can be assessed using ground based observations. We find that such a mission can indeed provide valuable constraints on mitigation relevant parameters. Furthermore, the here proposed kinetic impact scenarios can be implemented within the next two decades without threatening Earth's safety.