scholarly journals Data-driven approach to using individual cattle weights to estimate mean adult dairy cattle weight

2019 ◽  
Vol 185 (17) ◽  
pp. 540-540 ◽  
Author(s):  
Hannah Schubert ◽  
Sarah Wood ◽  
Kristen Reyher ◽  
Harriet Mills
Keyword(s):  
Dairy Cattle ◽  
Data Driven ◽  
Average Weight ◽  
Evidence Based ◽  
Antimicrobial Use ◽  
Data Driven Approach ◽  
The Uk ◽  
On Farm ◽  
First Time ◽  
Lactating Dairy Cattle

BackgroundKnowledge of accurate weights of cattle is crucial for effective dosing of individual animals and for reporting antimicrobial usage. For the first time, we provide an evidence-based estimate of the average weight of UK dairy cattle to better inform farmers, veterinarians and the scientific community.MethodsData were collected for 2747 lactating dairy cattle from 20 farms in the UK. Data were used to calculate a mean weight for lactating dairy cattle by breed and a UK-specific mean weight. Trends in weight by lactation number and production level were also explored.ResultsMean weight for adult dairy cattle in this study was 617 kg (sd=85.6 kg). Mean weight varied across breeds, with a range of 466 kg (sd=56.0 kg, Jersey) to 636 kg (sd=84.1, Holsteins). When scaled to UK breed proportions, the estimated UK-specific mean weight was 620 kg.ConclusionThis study is the first to calculate a mean weight of adult dairy cattle in the UK based on on-farm data. Overall mean weight was higher than that most often proposed in the literature (600 kg). Evidence-informed weights are crucial as the UK works to better monitor and report metrics to measure antimicrobial use and are useful to farmers and veterinarians to inform dosing decisions.

scholarly journals First data-driven approach to using individual cattle weights to estimate mean adult dairy cattle weight in the UK

2018 ◽  
Author(s):  
Hannah E. Schubert ◽  
Sarah Wood ◽  
Kristen K. Reyher ◽  
Harriet L. Mills
Keyword(s):  
Dairy Cattle ◽  
Production Level ◽  
Average Weight ◽  
Level Data ◽  
Holstein Friesian ◽  
Data Driven Approach ◽  
Automatic Milking Systems ◽  
Common Weight ◽  
The Uk ◽  
On Farm

ABSTRACTIntroductionKnowledge of accurate weights of cattle is crucial for effective dosing of individual animals with medicine and for reporting antimicrobial usage metrics, amongst other uses. The most common weight for dairy cattle presented in current literature is 600 kg, but this is not evidenced by data. For the first time, we provide an evidence-based estimate of the average weight of UK dairy cattle to better inform decisions by farmers, veterinarians and the scientific community.MethodsWe collected data for 2,747 dairy cattle from 20 farms in the UK, 19 using Lely Automatic Milking Systems with weigh floors and 1 using a crush with weigh scales. These data covered farms with different breed types, including Holstein, Friesian, Holstein-Friesian and Jersey, as well as farms with dual purpose breeds and cross-breeds. Data were used to calculate a mean weight for dairy cattle by breed, and a UK-specific mean weight was generated by scaling to UK-specific breed proportions. Trends in weight by lactation number, DIM and production level were also explored using individual cattle-level data.ResultsMean weight for adult dairy cattle included in this study was 617 kg (standard deviation (sd) 85.6 kg). Mean weight varied across breeds, with a range of 466 kg (sd=56.0 kg, Jersey) to 636 kg (sd=84.1, Holsteins). When scaled to UK breed proportions, the estimated mean UK dairy cattle weight was 620 kg. Overall, first-lactation heifers weighed 9% less than cows. Mean weight declined for the first 30 days post-calving, before steadily increasing. For cattle at peak production, mean weight increased with production level.ConclusionsThis study is the first to calculate a mean weight of adult dairy cattle in the UK based on on-farm data. Overall mean weight was higher than that most often proposed in the literature (600 kg). Evidence-informed weights are crucial as the UK works to better monitor and report metrics to monitor antimicrobial use and are useful to farmers and veterinarians to inform dosing decisions.

Formulating Industrial Policy

2019 ◽  
Vol 250 ◽  
pp. R47-R53
Author(s):  
Tim Besley ◽  
Richard Davies
Keyword(s):  
Market Failure ◽  
Data Driven ◽  
Executive Summary ◽  
Structural Weakness ◽  
The Face ◽  
Data Driven Approach ◽  
Economic Contribution ◽  
The Government ◽  
The Uk

Executive SummaryAlongside the challenge of maintaining economic competitiveness in the face of great uncertainty, Brexit brings an opportunity for the government to set out a new industrial strategy. The case for doing so rests on the need to address areas of persistent structural weakness in the UK economy, including low productivity. But it is important that any new industrial strategy be based on appropriately granular data reflecting the real structure of the UK corporate sector: the overwhelmingly preponderant role of services as opposed to manufacturing, for example; the importance of young, fast-growing firms as opposed to SMEs; the relatively high failure rate of companies in the UK; and the relative lack of successful mid-sized firms. Such a data-driven approach might spawn an industrial strategy quite different from the piecemeal programmes of recent years.Internationally, the UK is a laggard in this area, and the recently-created Industrial Strategy Council does not look strong enough to change that position. To move forward, the government needs to make industrial strategy a central plank of economic policy, embedded at the heart of the administration with its own staff and funding, and operations based on a comprehensive review of the economic contribution and potential of various types of firm. Needless to say, it cannot be a substitute for a continuing commitment to competition and markets, or a stalking horse for protectionism: interventions should be justified by carefully-argued market failure arguments, be time-limited, and transparently evaluated.

scholarly journals Genetic Contributions to Multivariate Data-Driven Brain Networks Constructed via Source-Based Morphometry

2020 ◽  
Vol 30 (9) ◽  
pp. 4899-4913
Author(s):  
Amanda L Rodrigue ◽  
Aaron F Alexander-Bloch ◽  
Emma E M Knowles ◽  
Samuel R Mathias ◽  
Josephine Mollon ◽  
...  
Keyword(s):  
Brain Function ◽  
Complex Traits ◽  
Brain Regions ◽  
Data Driven ◽  
Additional Insight ◽  
Genetic Overlap ◽  
Data Driven Approach ◽  
The Uk ◽  
Genetic Contributions ◽  
Insight Into

Abstract Identifying genetic factors underlying neuroanatomical variation has been difficult. Traditional methods have used brain regions from predetermined parcellation schemes as phenotypes for genetic analyses, although these parcellations often do not reflect brain function and/or do not account for covariance between regions. We proposed that network-based phenotypes derived via source-based morphometry (SBM) may provide additional insight into the genetic architecture of neuroanatomy given its data-driven approach and consideration of covariance between voxels. We found that anatomical SBM networks constructed on ~ 20 000 individuals from the UK Biobank were heritable and shared functionally meaningful genetic overlap with each other. We additionally identified 27 unique genetic loci that contributed to one or more SBM networks. Both GWA and genetic correlation results indicated complex patterns of pleiotropy and polygenicity similar to other complex traits. Lastly, we found genetic overlap between a network related to the default mode and schizophrenia, a disorder commonly associated with neuroanatomic alterations.

scholarly journals Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle

2020 ◽  
Vol 171 ◽  
pp. 105286 ◽  
Author(s):  
Mohit Taneja ◽  
John Byabazaire ◽  
Nikita Jalodia ◽  
Alan Davy ◽  
Cristian Olariu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dairy Cattle ◽  
Fog Computing ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts

JAMIA Open ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Fuchiang R Tsui ◽  
Lingyun Shi ◽  
Victor Ruiz ◽  
Neal D Ryan ◽  
Candice Biernesser ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Suicide Attempt ◽  
Language Processing ◽  
Suicide Attempts ◽  
Large Data ◽  
Structured Data ◽  
Data Driven ◽  
Data Driven Approach ◽  
First Time

Abstract Objective Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data. Methods This case-control study included patients aged 10–75 years who were seen between 2007 and 2016 from emergency departments and inpatient units. Cases were first-time suicide attempts from coded diagnosis; controls were randomly selected without suicide attempts regardless of demographics, following a ratio of nine controls per case. Four data-driven ML models were evaluated using 2-year historical EHR data prior to suicide attempt or control index visits, with prediction windows from 7 to 730 days. Patients without any historical notes were excluded. Model evaluation on accuracy and robustness was performed on a blind dataset (30% cohort). Results The study cohort included 45 238 patients (5099 cases, 40 139 controls) comprising 54 651 variables from 5.7 million structured records and 798 665 notes. Using both unstructured and structured data resulted in significantly greater accuracy compared to structured data alone (area-under-the-curve [AUC]: 0.932 vs. 0.901 P < .001). The best-predicting model utilized 1726 variables with AUC = 0.932 (95% CI, 0.922–0.941). The model was robust across multiple prediction windows and subgroups by demographics, points of historical most recent clinical contact, and depression diagnosis history. Conclusions Our large data-driven approach using both structured and unstructured EHR data demonstrated accurate and robust first-time suicide attempt prediction, and has the potential to be deployed across various populations and clinical settings.

scholarly journals A data-driven test for cross-cultural differences in face preferences

2018 ◽  
Author(s):  
Lingshan Zhang ◽  
Iris Jasmin Holzleitner ◽  
Anthony J Lee ◽  
Vanessa Fasolt ◽  
Hongyi Wang ◽  
...  
Keyword(s):  
Cultural Differences ◽  
Principal Components ◽  
Facial Attractiveness ◽  
Cross Cultural ◽  
Data Driven ◽  
Cross Cultural Differences ◽  
Data Driven Approach ◽  
The Uk ◽  
Data Driven Test

Previous research has shown strong cross-cultural agreement in facial attractiveness judgments. However, these studies all used a theory-driven approach in which responses to specific facial characteristics are compared between cultures. This approach is constrained by the predictions that can be derived from existing theories and can therefore bias impressions of the extent of cross-cultural agreement in face preferences. We directly addressed this problem by using a data-driven, rather than theory-driven, approach to compare facial attractiveness judgments made by Chinese-born participants who were resident in China, Chinese-born participants currently resident in the UK, and UK-born and -resident White participants. Analyses of the principal components along which faces naturally varied suggested that Chinese and White UK participants used face information in different ways, at least when judging women’s facial attractiveness. In other words, the data-driven approach used in the current study revealed some cross-cultural differences in face preferences that were not apparent in studies using theory-driven approaches.

scholarly journals From oscillation dip to oscillation valley in atmospheric neutrino experiments

2021 ◽  
Vol 81 (2) ◽  
Author(s):  
Anil Kumar ◽  
Amina Khatun ◽  
Sanjib Kumar Agarwalla ◽  
Amol Dighe
Keyword(s):  
Atmospheric Neutrino ◽  
Simulated Data ◽  
Data Driven ◽  
Muon Energy ◽  
Identification Algorithm ◽  
Statistical Fluctuations ◽  
Data Driven Approach ◽  
Neutrino Experiments ◽  
First Time ◽  
Atmospheric Mass

AbstractAtmospheric neutrino experiments can show the “oscillation dip” feature in data, due to their sensitivity over a large L/E range. In experiments that can distinguish between neutrinos and antineutrinos, like INO, oscillation dips can be observed in both these channels separately. We present the dip-identification algorithm employing a data-driven approach – one that uses the asymmetry in the upward-going and downward-going events, binned in the reconstructed L/E of muons – to demonstrate the dip, which would confirm the oscillation hypothesis. We further propose, for the first time, the identification of an “oscillation valley” in the reconstructed ($$E_\mu $$ E μ ,$$\,\cos \theta _\mu $$ cos θ μ ) plane, feasible for detectors like ICAL having excellent muon energy and direction resolutions. We illustrate how this two-dimensional valley would offer a clear visual representation and test of the L/E dependence, the alignment of the valley quantifying the atmospheric mass-squared difference. Owing to the charge identification capability of the ICAL detector at INO, we always present our results using $$\mu ^{-}$$ μ - and $$\mu ^{+}$$ μ + events separately. Taking into account the statistical fluctuations and systematic errors, and varying oscillation parameters over their currently allowed ranges, we estimate the precision to which atmospheric neutrino oscillation parameters would be determined with the 10-year simulated data at ICAL using our procedure.

Data-Driven and Practice-Based Evidence

2016 ◽  
pp. 295-328 ◽  
Author(s):  
Hamzah Osop ◽  
Tony Sahama
Keyword(s):  
Data Warehouse ◽  
Data Privacy ◽  
Health Data ◽  
Data Driven ◽  
Systematic Research ◽  
Evidence Based ◽  
Patient Health ◽  
Data Driven Approach ◽  
The Right

Decision making is such an integral aspect in health care routine that the ability to make the right decisions at crucial moments can lead to patient health improvements. Evidence-based practice, the paradigm used to make those informed decisions, relies on the use of current best evidence from systematic research such as randomized controlled trials. Limitations of the outcomes from RCT, such as “quantity” and “quality” of evidence generated, has lowered healthcare professionals' confidence in using EBP. An alternate paradigm of Practice-Based Evidence has evolved with the key being evidence drawn from practice settings. Through the use of health information technology, electronic health records capture relevant clinical practice “evidence”. A data-driven approach is proposed to capitalize on the benefits of EHR. The issues of data privacy, security and integrity are diminished by an information accountability concept. Data warehouse architecture completes the data-driven approach by integrating health data from multi-source systems, unique within the healthcare environment.

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