Direction of jaw movement in dairy cattle during the rumination period

Ultrafast chirality: the road to efficient chiral measurements

Today we are witnessing the electric-dipole revolution in chiral measurements. Here we reflect on its lessons and outcomes, such as the perspective on chiral measurements using the complementary principles of "chiral reagent" and "chiral observer", the hierarchy of scalar, vectorial and tensorial enantio-sensitive observables, the new properties of the chiro-optical response in the ultrafast and non-linear domains, and the geometrical magnetism associated with the chiral response in photoionization. The electric-dipole revolution is a landmark event. It has opened routes to extremely efficient enantio-discrimination with a family of new methods. These methods are governed by the same principles but work in vastly different regimes - from microwaves to optical light; they address all molecular degrees of freedom - electronic, vibrational and rotational, and use flexible detection schemes, i.e. detecting photons or electrons, making them applicable to different chiral phases, from gases to liquids to amorphous solids. The electric-dipole revolution has also enabled enantio-sensitive manipulation of chiral molecules with light. This manipulation includes exciting and controlling ultrafast helical currents in vibronic states of chiral molecules, enantio-sensitive control of populations in electronic, vibronic and rotational molecular states, and opens the way to efficient enantio-separation and enantio-sensitive trapping of chiral molecules. The word "perspective" has two meanings: an "outlook" and a "point of view". In this perspective article, we have tried to cover both meanings.

Short communication: Detection of mastication speed during rumination in cattle using 3-axis, neck-mounted accelerometers and fast Fourier transfer algorithm

There have been limited reports on mastication speed during cattle rumination. The objective of this study was to establish a method to detect mastication speed based on data obtained during rumination through the use of a 3-axis accelerometer attached to the neck. A 3-axis accelerometer was attached to 6 dry Holstein cattle. When rumination behavior was observed, the accelerometer and the high-speed camera simultaneously recorded acceleration at the neck and moving image of the head movement. Based on the number of mastication movements recorded on video, mastication speed A was calculated. Data obtained from the 3-axis accelerometer were analyzed with fast Fourier transfer algorithm and identified as mastication speed B. The vibration of the neck recorded in the accelerometer during rumination was considered as mastication movement. Using Bland-Altman plot analysis, the mean difference between mastication speed A and mastication speed B was 0.041 s/bite, and the 95% limits of agreement ranged from -0.080 to 0.161. Since mastication movement occurred periodically, it was possible to detect the movement using spectrum analysis, as mastication speed B. Although there were some differences between calculated speeds and speeds obtained from spectrum analysis, there was clinical compatibility between mastication speed A and B. This study showed the feasibility of establishing a detection method for mastication speed during rumination, which might provide a basic procedure for studying the purpose of mastication and the variable factors involved.

Dairy cattle behavior classifications based on decision tree learning using 3-axis neck-mounted accelerometers

Demand has been increasing recently for an automated monitoring system of animal behavior as a tool for the management of livestock animals. This study investigated the association between the behavior of dairy cattle and the acceleration data collected using three-axis neck-mounted accelerometers, as well as the feasibility of improving the precision of behavior classifications through machine learning. In total 38 Holstein dairy cows were used, and kept in four different farms. A logger was mounted to each collar to obtain acceleration data for calculating the activity level and variations. At the same time the behavior of the cattle was observed visually. Characteristic acceleration waves were recorded for eating, rumination, and lying, respectively; and the activity level and variations were significantly different among these behaviors (p < 0.01). Decision tree learning was performed on the data set from Farm A and validated its precision; which proved to be 99.2% in cross-validation, and 100% in test data sets from Farms B to D. This study showed that highly precise classifications for eating, rumination, and lying is possible by using decision tree learning to calculate the activity level and variations of cattle based on the data obtained by three-axis accelerometers mounted to a collar.

Quantitative aspects of the ruminating process in giraffes (Giraffa camelopardalis) fed with different diets

Giraffes are ruminants feeding on fresh browse and twigs in the wild, but in zoos, their diet is mainly based on alfalfa hay, grains, and pellets occasionally supplemented by twigs and foliage. These diets, which differ in composition and digestibility, affect the behavior of the animals, tooth wear patterns, and chewing efficiency. We quantified several parameters of the rumination process in ten zoo housed giraffes of different sexes and ages fed either with alfalfa hay, fresh browse, or a combination of both. Chewing during rumination was highly ritualized and specimens showed an even distribution of chewing directions during this process, which prevents uneven tooth wear and use of chewing muscles. During rumination of alfalfa hay, chewing cycles of the giraffes took on average 49 s and included 54 jaw movements compared to 37 s and 42 jaw movements during rumination of browse, respectively. Single jaw movements (measured as basic chewing rates) were on average significantly slower during rumination of alfalfa hay (alfalfa: 1.10 chews per second, browse: 1.17 chews per second) and intercycle times between two chewing cycles took significantly longer (alfalfa: 7.77 s, browse: 7.46 s). Our results clearly indicate that several rumination parameters are influenced by the type of diet.