Rapid turnover of sensor data to genetic evaluation for dairy cows in the cloud.

More and more sensor and automation data are available that enable animal breeders to define novel traits. However, sensor and automation data are often frequently measured differently (e.g., milk yield and different milk components are continuously measured during each milking). These differences are challenging animal breeders to define traits and use the most appropriate analytical models for genetic evaluation and breeding values. Traditionally, the process from raw data to breeding value estimations involves several steps: data curation, trait definition, variance component estimation, genetic evaluation, and validation of the estimated breeding values (EBV). All these steps often take many iterations and several research projects to optimize the final genetic evaluations. To make this entire process-from raw data to validated EBV-more efficient, we combined all these steps in a cloud environment that allows for faster processing and a faster data distribution time. We used real data (including 1,782,373,113 daily milk-yield records of 1,120,550 dairy cows) and a real trait (a resilience trait based on the deviations from expected milk yields) to demonstrate the functioning of this cloud environment. The daily milk-yield records were incorporated into our cloud solution, in which we have set up central binary large object storage. Subsequent steps were all performed in the cloud. The data set was preprocessed in approximately 6 h to obtain the resilience indicator for 352,871 cows in the first 3 lactations. Estimation of genetic parameters (heritabilities and genetic correlations) was performed by splitting the data into 5 subsets in ASReml, and prediction of subsequent EBV was performed on the entire data set using MiXBLUP. Together with the validation of breeding values, this process encompassed 16.5 h. By combining the different steps from preprocessing sensor data to genetic evaluation of new traits in one cloud environment, we generated EBV and validation plots in approximately 1 working day. Moreover, our setup is a flexible design and can be adapted easily to test new, longitudinal sensor-driven traits and compare the performance of these new traits to previous ones.

Association of the advanced lung cancer inflammation index (ALI) with immune checkpoint inhibitor efficacy in patients with advanced non-small-cell lung cancer.

BACKGROUND: The advanced lung cancer inflammation index [ALI: body mass index × serum albumin/neutrophil-to-lymphocyte ratio (NLR)] reflects systemic host inflammation, and is easily reproducible. We hypothesized that ALI could assist guidance of non-small-cell lung cancer (NSCLC) treatment with immune checkpoint inhibitors (ICIs). PATIENTS AND METHODS: This retrospective study included 672 stage IV NSCLC patients treated with programmed death-ligand 1 (PD-L1) inhibitors alone or in combination with chemotherapy in 25 centers in Greece and Germany, and a control cohort of 444 stage IV NSCLC patients treated with platinum-based chemotherapy without subsequent targeted or immunotherapy drugs. The association of clinical outcomes with biomarkers was analyzed with Cox regression models, including cross-validation by calculation of the Harrell's C-index. RESULTS: High ALI values (>18) were significantly associated with longer overall survival (OS) for patients receiving ICI monotherapy [hazard ratio (HR) = 0.402, P < 0.0001, n = 460], but not chemo-immunotherapy (HR = 0.624, P = 0.111, n = 212). Similar positive correlations for ALI were observed for objective response rate (36% versus 24%, P = 0.008) and time-on-treatment (HR = 0.52, P < 0.001), in case of ICI monotherapy only. In the control cohort of chemotherapy, the association between ALI and OS was weaker (HR = 0.694, P = 0.0002), and showed a significant interaction with the type of treatment (ICI monotherapy versus chemotherapy, P < 0.0001) upon combined analysis of the two cohorts. In multivariate analysis, ALI had a stronger predictive effect than NLR, PD-L1 tumor proportion score, lung immune prognostic index, and EPSILoN scores. Among patients with PD-L1 tumor proportion score ≥50% receiving first-line ICI monotherapy, a high ALI score >18 identified a subset with longer OS and time-on-treatment (median 35 and 16 months, respectively), similar to these under chemo-immunotherapy. CONCLUSIONS: The ALI score is a powerful prognostic and predictive biomarker for patients with advanced NSCLC treated with PD-L1 inhibitors alone, but not in combination with chemotherapy. Its association with outcomes appears to be stronger than that of other widely used parameters. For PD-L1-high patients, an ALI score >18 could assist the selection of cases that do not need addition of chemotherapy.

Invited review: Toward a common language in data-driven mastitis detection research.

Sensor technologies for mastitis detection have resulted in the collection and availability of a large amount of data. As a result, scientific publications reporting mastitis detection research have become less driven by approaches based on biological assumptions and more by data-driven modeling. Most of these approaches try to predict mastitis events from (combinations of) raw sensor data to which a wide variety of methods are applied originating from machine learning and classical statistical approaches. However, an even wider variety in terminologies is used by researchers for methods that are similar in nature. This makes it difficult for readers from other disciplines to understand the specific methods that are used and how these differ from each other. The aim of this paper was to provide a framework (filtering, transformation, and classification) for describing the different methods applied in sensor data-based clinical mastitis detection research and use this framework to review and categorize the approaches and underlying methods described in the scientific literature on mastitis detection. We identified 40 scientific publications between 1992 and 2020 that applied methods to detect clinical mastitis from sensor data. Based on these publications, we developed and used the framework and categorized these scientific publications into the 2 data processing techniques of filtering and transformation. These data processing techniques make raw data more amendable to be used for the third step in our framework, that of classification, which is used to distinguish between healthy and nonhealthy (mastitis) cows. Most publications (n = 34) used filtering or transformation, or a combination of these 2, for data processing before classification, whereas the remaining publications (n = 6) classified the observations directly from raw data. Concerning classification, applying a simple threshold was the most used method (n = 19 publications). Our work identified that within approaches several different methods and terminologies for similar methods were used. Not all publications provided a clear description of the method used, and therefore it seemed that different methods were used between publications, whereas in fact just a different terminology was used, or the other way around. This paper is intended to serve as a reference for people from various research disciplines who need to collaborate and communicate efficiently about the topic of sensor-based mastitis detection and the methods used in this context. The framework used in this paper can support future research to correctly classify approaches and methods, which can improve the understanding of scientific publication. We encourage future research on sensor-based animal disease detection, including that of mastitis detection, to use a more coherent terminology for methods, and clearly state which technique (e.g., filtering) and approach (e.g., moving average) are used. This paper, therefore, can serve as a starting point and further stimulates the interdisciplinary cooperation in sensor-based mastitis research.

Storing, combining and analysing turkey experimental data in the Big Data era.

With the increasing availability of large amounts of data in the livestock domain, we face the challenge to store, combine and analyse these data efficiently. With this study, we explored the use of a data lake for storing and analysing data to improve scalability and interoperability. Data originated from a 2-day animal experiment in which the gait score of approximately 200 turkeys was determined through visual inspection by an expert. Additionally, inertial measurement units (IMUs), a 3D-video camera and a force plate (FP) were installed to explore the effectiveness of these sensors in automating the visual gait scoring. We deployed a data lake using the IMU and FP data of a single day of that animal experiment. This encompasses data from 84 turkeys for which we preprocessed by performing an 'extract, transform and load' (ETL-) procedure. To test scalability of the ETL-procedure, we simulated increasing volumes of the available data from this animal experiment and computed the 'wall time' (elapsed real time) for converting FP data into comma-separated files and storing these files. With a simulated data set of 30 000 turkeys, the wall time reduced from 1 h to less than 15 min, when 12 cores were used compared to 1 core. This demonstrated the ETL-procedure to be scalable. Subsequently, a machine learning (ML) pipeline was developed to test the potential of a data lake to automatically distinguish between two classses, that is, very bad gait scores v. other scores. In conclusion, we have set up a dedicated customized data lake, loaded data and developed a prediction model via the creation of an ML pipeline. A data lake appears to be a useful tool to face the challenge of storing, combining and analysing increasing volumes of data of varying nature in an effective manner.

Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle.

In this study, we compared multiple logistic regression, a linear method, to naive Bayes and random forest, 2 nonlinear machine-learning methods. We used all 3 methods to predict individual survival to second lactation in dairy heifers. The data set used for prediction contained 6,847 heifers born between January 2012 and June 2013, and had known survival outcomes. Each animal had 50 genomic estimated breeding values available at birth and up to 65 phenotypic variables that accumulated over time. Survival was predicted at 5 moments in life: at birth, at 18 mo, at first calving, at 6 wk after first calving, and at 200 d after first calving. The data sets were randomly split into 70% training and 30% testing sets to evaluate model performance for 20-fold validation. The methods were compared for accuracy, sensitivity, specificity, area under the curve (AUC) value, contrasts between groups for the prediction outcomes, and increase in surviving animals in a practical scenario. At birth and 18 mo, all methods had overlapping performance; no method significantly outperformed the other. At first calving, 6 wk after first calving, and 200 d after first calving, random forest and naive Bayes had overlapping performance, and both machine-learning methods outperformed multiple logistic regression. Overall, naive Bayes has the highest average AUC at all decision points up to 200 d after first calving. Random forest had the highest AUC at 200 d after first calving. All methods obtained similar increases in survival in the practical scenario. Despite this, the methods appeared to predict the survival of individual heifers differently. All methods improved over time, but the changes in mean model outcomes for surviving and non-surviving animals differed by method. Furthermore, the correlations of individual predictions between methods ranged from r = 0.417 to r = 0.700; the lowest correlations were at first calving for all methods. In short, all 3 methods were able to predict survival at a population level, because all methods improved survival in a practical scenario. However, depending on the method used, predictions for individual animals were quite different between methods.

The development of a virtual reality training programme for ophthalmology: repeatability and reproducibility (part of the International Forum for Ophthalmic Simulation Studies).

PURPOSE: To evaluate the variability of performance among novice ophthalmic trainees in a range of repeated tasks using the Eyesi virtual reality (VR) simulator. METHODS: Eighteen subjects undertook three attempts of five cataract specific and generic three-dimensional tasks: continuous curvilinear capsulorhexis, cracking and chopping, cataract navigation, bimanual cataract training, anti-tremor. Scores for each attempt were out of a maximum of 100 points. A non-parametric test was used to analyse the data, where a P-value of <0.05 was considered statistically significant. RESULTS: Highly significant differences were found between the scores achieved in the first attempt and that during the second (P<0.0001) and third (P<0.0001) but not between the second and third attempt (P=0.65). There was no significant variability in the overall score between the users (P=0.1104) or in the difference between their highest and lowest score (P=0.3878). Highly significant differences between tasks were shown both in the overall score (P=0.0001) and in the difference between highest and lowest score (P=0.003). CONCLUSION: This study, which is the first to quantify reproducibility of performance in entry level trainees using a VR tool, demonstrated significant intra-novice variability. The cohort of subjects performed equally overall in the range of tasks (no inter-novice variability) but each showed that performance varies significantly with the complexity of the task when using this high-fidelity instrument.

Sutureless and glue-free conjunctival autograft in pterygium surgery: a case series.

AIMS: Foreign materials used in ocular surface surgery may lead to local complications such as discomfort, scarring, or infection. Plasma-derived products such as fibrin glue may produce possible hypersensitivity reactions whereas the risk of viral transmission remains. We describe a simple method of achieving conjunctival autograft adherence during pterygium surgery avoiding potential complications associated with the use of fibrin glue or sutures. METHODS: After pterygium excision and fashioning of the autologous conjunctival graft, the recipient bed is encouraged to achieve natural haemostasis and relative dessication before graft placement. Excessive haemorrhage in the graft bed is tamponaded. Graft adherence and positioning is examined 20 min after surgery. RESULTS: A total of 15 eyes of 12 patients (mean (SD) age 73.7 (11.2) years), 8 females underwent SGF autologous conjunctival graft post-pterygium excision. Mean graft area was 24(1.5)mm(2). Mean follow-up time was 9.2 (2.2) months. Cosmesis was excellent in all cases and visual acuity improved in one patient. There were no intra- or post-operative complications requiring further treatment. CONCLUSION: This simple technique for pterygium surgery may prevent potential adverse reactions encountered with the use of foreign materials and in this small series provided safe and comparable results to current methods.

Traumatic occipital meningoencephalocele presenting with homonymous hemianopia in a professional driver.

BACKGROUND: This study presents the case of a 32-year-old male who is a chronic headache sufferer. He is a professional driver who presented with homonymous hemianopia secondary to a traumatic occipital meningoencephalocele. This is the first time such an association has been reported. CONCLUSIONS: The complete lack of previous visual symptoms supports the belief of late development of the hemianopia. However, the chronicity and the nature of the patients' headaches could have justified further investigations in the past.

Surgical management of optic disc pit associated maculopathy.

PURPOSE: To report the surgical outcome in seven cases of optic disc pit associated maculopathy. METHODS: This was a retrospective case-note analysis of presenting features, pre- and postoperative visual acuities, surgical procedures, complications, and follow-up. The principal treatment in all the cases was pars plana vitrectomy, posterior hyaloid peel, endolaser to the papillomacular bundle temporal to the disc, and internal tamponade with gas or silicone oil. RESULTS: Of the four male and three female patients, two were children. All the patients had posterior hyaloid peeling during the vitrectomy and endolaser. Six patients had intraocular gas tamponade and one had silicone oil. Four patients needed a second surgical procedure to obtain a satisfactory anatomic and visual outcome. Postoperatively, four patients had an improvement of 2 or more Snellen lines. One patient with a history of multiple surgeries developed high intraocular pressure postoperatively and cataract. The mean follow-up period was 9.1 months. CONCLUSIONS: Serous retinopathy associated with optic disc pit responds well to early vitrectomy, endolaser, and internal tamponade. Silicone oil was effective in one refractory case. Cumulative data are required to define the management of this condition.

Kefir yeast technology: scale-up in SCP production using milk whey.

A three-step process to scale-up kefir biomass production at a semi-industrial scale employing whey is reported. Aerobic fermentations were initially performed at laboratory scales, in 1.5- and 4-L bioreactors, yielding 79 g/L final kefir biomass (0.89 g/g of lactose utilized), in 7 h of fermentation time. The use of whey as carbon source even in solid cultures led to the formation of a granular biomass. These results encouraged scale-up at a semi-industrial-scale pilot plant employing 100- and 3,000-L bioreactors, leading to the development of a process for granular kefir biomass production. The results validated the laboratory-scale experiments and the avoidance of centrifugal separators due to granular biomass formation. Pilot-plant operations showed kefir to be highly resistant to contamination under actual industrial conditions and no serious problems in handling of raw materials and equipment were observed. Economic analysis showed a 20% higher cost of the market price of products, with added value of up to 15.9 x 10(9) within the European Union.

Development of a novel whey beverage by fermentation with kefir granules. Effect of various treatments.

The development of a novel whey-based beverage with acceptable organoleptic properties is reported, where various treatments were studied. Kefir yeast immobilized on delignified cellulosic materials (DCM) or gluten pellets proved to accelerate whey fermentation significantly, with the latter support being not so preferable. Kefir granules seemed to achieve similar fermentation times as DCM. The final pH of the product is suggested to be 4.1 since the profile of the volatile byproducts was higher than other pH values tested. The addition of fructose seemed to be beneficial on the volatile content of the product, although its acceptability as determined by a preference panel was similar to that of the control. Finally, black raisin extract appeared to promote fermentation without any positive effect on the preference of the evaluators.

Industrial scale pilot plant operation by Kefir yeast cells immobilized on delignified cellulosic material for whey treatment.

Kefir yeast cells immobilized on Delignified Cellulosic Materials (DCM) is proposed as a suitable form of biocatalyst for whey treatment. A bioreactor of 11.000L was used in all experiments. 5 repeated batch fermentations were taken place with whey volumes of 3000L and 5000L. Fermentation times achieved were below 15 h and final ethanol concentration was of the level of 2%v/v. Residual sugar was low while conversion rate reached 95%. In a second frame of experiments the immobilized cells bioreactor was operated on a feed-batch process, beginning from 2000L of whey and adding 2000L in every feed. In a third frame of experiments, whey fermentation was performed on a continuous operation for a period of 13 days. The system retained its operational stability, exhibiting relatively high ethanol yield and ethanol productivity. All experiments indicated that the biocatalyst of kefir yeast cells immobilized on delignified cellulosic materials has a carry through property on whey fermentation on industrial scale.

Whey liquid waste of the dairy industry as raw material for potable alcohol production by kefir granules.

Kefir granular biomass was used in the fermentation of sweet whey and proved to be more effective compared to single-cell biomass of kefir yeast. The operational stability of the biocatalyst was assessed by carrying out 20 repeated batch fermentations. Levels of ethanol productivity reached 2.57 g L(-1) h(-1)), whereas the yield was 0.45 g/g. The fermentation time was only 8 h. Mixtures of sweet whey with molasses were fermented at initial densities ranging from 4.2 to 10.2 degrees Be and resulted in ethanol yield factors between 0.36 and 0.48 g of ethanol/g of utilized sugar. Lower degrees Be values led to an increase of percentages of ethyl acetate on total volatiles determined and a reduction of amyl alcohols. The addition of 1% black raisin extract to whey appears to promote whey fermentation, whereas the same was not observed in the case of white sultana extract addition. It was finally established that it is preferable to ferment mixtures of whey-molasses by adding molasses in whey after the completion of whey fermentation.

Effect of carbohydrate substrate on fermentation by kefir yeast supported on delignified cellulosic materials.

The suitability of delignified cellulosic (DC) material supported kefir yeast to ferment raw materials that contain various single carbohydrates, for the production of potable alcohol and alcoholic drinks, is examined in this investigation. Results are reported of fermentations carried out with sucrose, fructose, and glucose in synthetic media. Repeated batch fermentations at various initial sugar concentrations of sucrose, fructose, and glucose were performed at 30 degrees C in the presence of the aforementioned biocatalyst. The results clearly show feasible yields in the range of 0.38-0.41 g/g, alcohol concentrations of 7.6-8.2% v/v, fermentation time of 90-115 h, and conversion of 92-96%. DC material supported kefir fermented 11-fold more rapidly than free cells and 9-fold more rapidly in comparison to kissiris supported kefir. The main volatile byproducts such as amyl alcohols (mixture of 2-methyl-1-butanol and 3-methyl-1-butanol), ethanal, and ethyl acetate were formed in all sugar fermentation products. The formation of 65-110 ppm of ethyl acetate is as high and even higher than that obtained with traditional wine yeasts. The increase of the initial concentration of sugar in the fermentation media resulted in an increase in contents of volatiles. The fine aroma that was obtained in the product of fructose could be attributed to the high percentage of ethyl acetate on total volatiles. The efficiency of DC material supported kefir was the same for the fermentations of individual sugars or a mixture of fructose, sucrose, and glucose. When whey with raisin extracts was fermented, lower yields were obtained but the aroma of the product was even better.