In vitro study of dual layer mixed matrix hollow fiber membranes for outside-in filtration of human blood plasma.

Hemodialysis mainly removes small water-soluble uremic toxins but cannot effectively remove middle molecules and protein-bound uremic toxins. Besides, the therapy is intermittent leading to fluctuating blood values and fluid status which adversely impacts patients' health. Prolonged hemodialysis (with adequate anticoagulation) could improve the removal of toxins and the development of portable and wearable artificial kidneys could offer more flexibility in the dialysis scheme. This would enhance patients' overall health, autonomy, mobility and flexibility, allowing patients to participate in social and economic life. However, the time that patients' blood is exposed to the dialyzer material is longer during prolonged hemodialysis, and blood clots could obstruct the fiber lumen, resulting in a decrease of the effective membrane surface area available for toxin removal. The outside-in filtration (OIF) mode, wherein blood flows through the inter-fiber space instead of through the fiber lumina, has been applied widely in blood oxygenators to prevent fiber clotting, but not in hemodialysis. In this study, we present for the first time the development of a mixed matrix membrane (MMM) for OIF of human blood plasma. This MMM combines diffusion and adsorption and consists of a polymeric membrane matrix with activated carbon (AC) particles on the inside layer, and a polymeric particle-free layer on the outer fiber layer. Our results show that in vitro MMM fibers for OIF demonstrate superior removal of the protein-bound uremic toxins, indoxyl sulfate and hippuric acid, compared to both earlier MMM fibers designed for inside-out filtration mode and commercial high-flux fibers. STATEMENT OF SIGNIFICANCE: Current hemodialysis therapy cannot effectively remove protein-bound toxins. Prolonged hemodialysis could improve toxin removal. However, during prolonged hemodialysis, blood clots could obstruct the fiber lumen, resulting in decreased effective membrane surface area available for toxin removal. We have prepared, for the first time, dual layer mixed matrix hollow fiber membranes (MMM) for outside-in filtration (OIF). The OIF mode wherein blood would flow through the inter-fiber space instead of through the fiber lumina could prevent fiber clotting. Moreover, the MMMs combine diffusion and adsorption to improve (protein-bound) toxin removal. We believe that the new design of our MMM fibers is an important contribution concerning the development of artificial kidney systems and the improvement of the health and well-being of patients with renal failure.

Invited review: Big Data in precision dairy farming.

Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm, feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V's) and the data analytics area (with categories in analysis methods (supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V's) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision.

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing.

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

The effect of routine hoof trimming on locomotion score, ruminating time, activity, and milk yield of dairy cows.

The objective of this study was to quantify the effect of hoof trimming on cow behavior (ruminating time, activity, and locomotion score) and performance (milk yield) over time. Data were gathered from a commercial dairy farm in Israel where routine hoof trimming is done by a trained hoof trimmer twice per year on the entire herd. In total, 288 cows spread over 6 groups with varying production levels were used for the analysis. Cow behavior was measured continuously with a commercial neck activity logger and a ruminating time logger (HR-Tag, SCR Engineers Ltd., Netanya, Israel). Milk yield was recorded during each milking session with a commercial milk flow sensor (Free Flow, SCR Engineers Ltd.). A trained observer assigned on the spot 5-point locomotion scores during 19 nighttime milking occasions between 22 October 2012 and 4 February 2013. Behavioral and performance data were gathered from 1wk before hoof trimming until 1wk after hoof trimming. A generalized linear mixed model was used to statistically test all main and interactive effects of hoof trimming, parity, lactation stage, and hoof lesion presence on ruminating time, neck activity, milk yield, and locomotion score. The results on locomotion scores show that the proportional distribution of cows in the different locomotion score classes changes significantly after trimming. The proportion of cows with a locomotion score ≥3 increases from 14% before to 34% directly after the hoof trimming. Two months after the trimming, the number of cows with a locomotion score ≥3 reduced to 20%, which was still higher than the baseline values 2wk before the trimming. The neck activity level was significantly reduced 1d after trimming (380±6 bits/d) compared with before trimming (389±6 bits/d). Each one-unit increase in locomotion score reduced cow activity level by 4.488 bits/d. The effect of hoof trimming on ruminating time was affected by an interaction effect with parity. The effect of hoof trimming on locomotion scores was affected by an interaction effect with lactation stage and tended to be affected by interaction effects with hoof lesion presence, indicating that cows with a lesion reacted different to the trimming than cows without a lesion did. The results show that the routine hoof trimming affected dairy cow behavior and performance in this farm.

Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity.

The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm's daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow's performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY=0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4d before diagnosis; the slope coefficient of the daily milk yield 4d before diagnosis; the nighttime to daytime neck activity ratio 6d before diagnosis; the milk yield week difference ratio 4d before diagnosis; the milk yield week difference 4d before diagnosis; the neck activity level during the daytime 7d before diagnosis; the ruminating time during nighttime 6d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well.

Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle.

Currently, diagnosis of lameness at an early stage in dairy cows relies on visual observation by the farmer, which is time consuming and often omitted. Many studies have tried to develop automatic cow lameness detection systems. However, those studies apply thresholds to the whole population to detect whether or not an individual cow is lame. Therefore, the objective of this study was to develop and test an individualized version of the body movement pattern score, which uses back posture to classify lameness into 3 classes, and to compare both the population and the individual approach under farm conditions. In a data set of 223 videos from 90 cows, 76% of cows were correctly classified, with an 83% true positive rate and 22% false positive rate when using the population approach. A new data set, containing 105 videos of 8 cows that had moved through all 3 lameness classes, was used for an ANOVA on the 3 different classes, showing that body movement pattern scores differed significantly among cows. Moreover, the classification accuracy and the true positive rate increased by 10 percentage units up to 91%, and the false positive rate decreased by 4 percentage units down to 6% when based on an individual threshold compared with a population threshold.

Mathematical curves for the description of input and output variables of the daily production process in aviary housing systems for laying hens.

The objectives of this study were 1) to compute appropriate mathematical curves that describe the daily production process by the input variables daily feed consumption, water consumption, ambient temperature, and output variables hen-day egg production, egg weight, second grade eggs, floor eggs, cumulative mortality, body weight, and flock uniformity; and 2) to obtain insights into the daily variations in these variables, in order to support the poultry farmer with an aviary housing system in his daily management. Literature and research data attained from six unmolted flocks that were housed in aviary systems were used to formulate the mathematical curves. The curves were a function of the number of days in the laying period. Curves for cumulative mortality, hen-day egg production, egg weight, body weight, and percentage of floor eggs described individual flock results well (0.72 < R2adj < 1.00). The coefficients of determination for feed consumption, water consumption, flock uniformity, and percentage of second grade eggs were in general low (0.33 < R2adj < 0.54), which implies that the form of the curve differs between flocks. Egg weight, body weight, cumulative mortality, and hen-day egg production had the lowest minimum coefficients of variation (0.8 to 1.9), followed by feed consumption, water consumption, and flock uniformity (2.8 to 3.6). Ambient temperature, percentage floor eggs, and percentage of second grade eggs had the highest minimum coefficients of variation (4.8 to 9.1).

Automatic weighing of individual laying hens in aviary housing systems.

1. This study investigated when and where body weight and flock-uniformity should be determined in an aviary system by using automatic weighing systems. 2. An Individual Poultry Weighing System (IPWS) was developed to record time, duration, location and body weight of visits of individual hens to 4 weighing scales. 3. The number of hens that visited the weighing scales per 3 h period varied from less than 10 during the dark-period to more than 60 during the light-period. 4. The average number of visits per individual hen was 1.4 and the average number of successful weighings per hen was 0.6 during the light-period. 5. Body weight showed a diurnal rhythm: the difference between the maximum body weight at night and the minimum body weight in the morning was 63 g. 6. The location of the scales influenced number of visits, number of weighings, mean body weight, flock-uniformity and duration of visits. 7. Body weight per 3 h period did not differ between hens which were individually recognised and those which were not. 8. Flock-uniformity was 2.6% higher during the light-period if it was based on weighings of identified hen visits. 9. The average duration of the visits to the scales in the middle of the feeding tier during the light-period was 63 s. 10. Of all the hens that visited the scales during a 24 h period, 54% visited them only once. 11. Automatic weighing systems without individual hen recognition can deliver reliable management information on mean body weight and flock-uniformity in aviary systems if the weighing scales are located on the feeding tier in the middle of the house and if they are used during the light-period.

Examination of egg number and egg weight variables and their effects on daily management in aviary systems for laying hens.

1. Characteristics of egg numbers and mean egg weight were examined for their usefulness in the daily management of aviary systems for laying hens. 2. A number of 3238 brown Isabrown/Warren hens were housed in 1 compartment, a separated part of the house where the hens could move around freely, of a tiered-wired-floor aviary system (TWF-system). An automatic egg weighing and counting system (EWACS) was used to count and weigh eggs daily from 2 tiers of laying nests on 1 side of the compartment and the number of eggs for the whole compartment were counted daily by the farmer. Each tier was divided into 16 blocks of 5 individual laying nests. Two adjoining blocks were called a group. To prevent hens from walking along all the laying nests in a tier, partitions were placed on the perches in front of the laying nests, between nest groups 2-3, 4-5, and 6-7. 3. After the first 3 weeks of the laying period, the distribution of egg numbers over the nest groups within a tier became stable. If egg numbers were counted daily from only 1 nest group the coefficient of variation was 23.1%. If the eggs from the whole compartment were counted daily, the coefficient of variation for the number of eggs was 2.8%. The nest group, presence of a partition and tier level influenced the daily number of eggs. 4. The distribution of the mean egg weight over the different nest groups within a tier was stable for the whole laying period. The coefficient of variation of the daily mean egg weight for a nest group was 3.1%. The difference in mean egg weight between nest groups was small, between 0.1 and 0.6 g, and the level of tiers and the presence of partitions between nest groups had no effect on the mean egg weight. 5. It could be concluded that egg numbers could not be estimated reliably by taking samples from a group of laying nests or a tier, but that it was necessary to count all the eggs from a compartment. The daily mean egg weight, however, could be estimated reliably on the basis of a sample of eggs from a nest group or a tier. By using EWACS frequent samples could be taken, which diminished the coefficient of variation so that the reliability of the data increased.