Delayed milk ejection, manifested most often as bimodal milk flow, occurs when the cisternal milk fraction is removed before the alveolar milk reaches the gland cistern. It is thought to be a ...consequence of not meeting cows' physiological needs, due to insufficient premilking teat stimulation, inadequate timing of milking unit attachment, or both. It has been associated with decreased milking efficiency, reduced milk yield, and impaired teat and udder health. Traditionally, portable electronic milk meters have been used to assess the presence of delayed milk ejection in dairy cows. By contrast, incremental milk flow rates from on-farm milk meters and their suitability as a measure to assess delayed milk ejection have not been studied by rigorous methods. The objectives were (1) to describe a protocol for identification of cows with chronically delayed milk ejection (CDME) and (2) to investigate risk factors for CDME using incremental milk flow rates obtained from automated on-farm milk meters. In a retrospective case control study, milk flow data from a 4,300-cow dairy with a thrice-daily milking schedule were obtained over a 1-wk period. Incremental milk flow rates (0–15 s, 15–30 s, 30–60 s, and 60–120 s) were used to identify cows with delayed milk ejection. Cases of CDME were defined as presence of delayed milk ejection at all 21 milking observations. Cows that had no delayed milk ejection at any of the same 21 milking observations were included as controls. A total of 171 cases and 393 controls were included in the study based on these criteria. A logistic regression model was used to evaluate associations of the following risk factors with CDME: parity (1, 2, ≥3), stage of lactation (<100, 101–200, >200 DIM), presence of a nonlactating quarter, milk somatic cell count, average daily milk production, and health and management events. Parity and CDME were associated such that compared with cows in their third or greater lactation, the odds (95% confidence intervals, 95% CI) of CDME were 1.27 (0.71–2.25) for cows in their first and 4.77 (2.47–9.22) for animals in their second lactation. The odds of CDME increased with increasing stage of lactation, with an odds ratio of 0.20 (0.11–0.36) for early and 0.28 (0.15–0.52) for mid-lactation animals, respectively, compared with late lactation cows. A 1-kg increase in average daily milk production was associated with decreased odds of CDME odds ratio (95% CI): 0.89 (0.87–0.92). A lameness event during the study period increased the odds of CDME odds ratio (95% CI): 8.04 (1.20–53.83), as did a vaccination event 1 wk before the study period odds ratio (95% CI): 4.07 (0.99–16.71). This study confirmed associations between CDME and previously reported risk factors and identified several previously less rigorously investigated health and management events that could be associated with CDME. Incremental milk flow rates from individual cows serve as an automated tool to evaluate milk flow dynamics. This information could be used to improve individual premilking udder preparation to meet the animal's physiological requirements, improve teat and udder health, and enhance parlor efficiency.
Over the last few decades, the proliferation of the Internet of Things (IoT) has produced an overwhelming flow of data and services, which has shifted the access control paradigm from a fixed desktop ...environment to dynamic cloud environments. Fog computing is associated with a new access control paradigm to reduce the overhead costs by moving the execution of application logic from the centre of the cloud data sources to the periphery of the IoT-oriented sensor networks. Indeed, accessing information and data resources from a variety of IoT sources has been plagued with inherent problems such as data heterogeneity, privacy, security and computational overheads. This paper presents an extensive survey of security, privacy and access control research, while highlighting several specific concerns in a wide range of contextual conditions (e.g., spatial, temporal and environmental contexts) which are gaining a lot of momentum in the area of industrial sensor and cloud networks. We present different taxonomies, such as contextual conditions and authorization models, based on the key issues in this area and discuss the existing context-sensitive access control approaches to tackle the aforementioned issues. With the aim of reducing administrative and computational overheads in the IoT sensor networks, we propose a new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework, combining the benefits of the cloud, IoT and context-aware computing; and ensuring proper access control and security at the edge of the end-devices. Our goal is not only to control context-sensitive access to data resources in the cloud, but also to move the execution of an application logic from the cloud-level to an intermediary-level where necessary, through adding computational nodes at the edge of the IoT sensor network. A discussion of some open research issues pertaining to context-sensitive access control to data resources is provided, including several real-world case studies. We conclude the paper with an in-depth analysis of the research challenges that have not been adequately addressed in the literature and highlight directions for future work that has not been well aligned with currently available research.
Context:
TSH is the major growth factor for thyrocytes and may have a causative role in thyroid cancer.
Objective:
The objective of the study was to systematically assess the association between ...serum TSH and thyroid cancer.
Data Sources:
The MEDLINE and EMBASE databases were searched using synonyms for TSH and thyroid cancer, supplemented with reference list searches and author contact.
Study Selection:
Prospective cohort, case-control, and cross-sectional studies were identified with TSH the exposure and thyroid cancer the outcome.
Data Extraction:
Three reviewers independently extracted data. Studies reporting odds ratio (OR) for TSH levels and thyroid cancer were analyzed via meta-analysis and generalized least-squares trend estimation for dose-response relationships.
Data Synthesis:
Data extracted from 28 studies included a total of 42,032 subjects and 5,786 thyroid cancer cases. Dose-response spline analysis revealed a nonlinear relationship (P < 0.001). For TSH levels less than 1 mU/liter, the OR for thyroid cancer was 1.72 (1.42, 2.07) per milliunits per liter. However, the relationship changed for TSH levels 1 mU/liter and greater, with the OR thereafter being 1.16 (1.12, 1.21) per milliunits per liter. Studies controlling for autoimmunity reported the lowest OR TSH below 2.5 mU/liter, OR 1.23 (1.02–1.47) per milliunits per liter; TSH 2.5 mU/liter or greater, OR 0.98 (0.89–1.09) per milliunits per liter. Six groups assessed serum TSH in relation to markers of poor thyroid cancer prognosis, with three showing significant positive relationships.
Conclusions:
Higher serum TSH concentration is associated with an increased risk of thyroid cancer. Thyroid autoimmunity may partially explain the association, but further epidemiological assessment is required. Future clinical research should investigate the validity of including serum TSH in diagnostic nomograms, its prognostic importance, and the potential for therapeutic TSH suppression in thyroid cancer prevention.
Various species of the intestinal microbiota have been associated with the development of colorectal cancer
, but it has not been demonstrated that bacteria have a direct role in the occurrence of ...oncogenic mutations. Escherichia coli can carry the pathogenicity island pks, which encodes a set of enzymes that synthesize colibactin
. This compound is believed to alkylate DNA on adenine residues
and induces double-strand breaks in cultured cells
. Here we expose human intestinal organoids to genotoxic pks
E. coli by repeated luminal injection over five months. Whole-genome sequencing of clonal organoids before and after this exposure revealed a distinct mutational signature that was absent from organoids injected with isogenic pks-mutant bacteria. The same mutational signature was detected in a subset of 5,876 human cancer genomes from two independent cohorts, predominantly in colorectal cancer. Our study describes a distinct mutational signature in colorectal cancer and implies that the underlying mutational process results directly from past exposure to bacteria carrying the colibactin-producing pks pathogenicity island.
In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting
security incident patterns
or ...insights from cybersecurity data and building corresponding
data-driven model
, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on
cybersecurity data science
, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the
latest data-driven patterns
for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated
research issues and future directions
. Furthermore, we provide a
machine learning
based
multi-layered framework
for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.
Due to the increasing popularity of recent advanced features and context-awareness in smart mobile phones, the contextual data relevant to users’ diverse activities with their phones are recorded ...through the device logs. Modeling and predicting individual’s smartphone usage based on
contexts
, such as temporal, spatial, or social information, can be used to build various context-aware personalized systems. In order to intelligently assist them, a
machine learning classifier
based usage prediction model for individual users’ is the key. Thus, we aim to analyze the
effectiveness
of various
machine learning classification models
for predicting personalized usage utilizing individual’s phone log data. In our context-aware analysis, we first employ ten classic and well-known machine learning classification techniques, such as ZeroR, Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Adaptive Boosting, Repeated Incremental Pruning to Produce Error Reduction, Ripple Down Rule Learner, and Logistic Regression classifiers. We also present the empirical evaluations of Artificial Neural Network based classification model, which is frequently used in
deep learning
and make comparative analysis in our context-aware study. The effectiveness of these classifier based context-aware models is examined by conducting a range of experiments on the real mobile phone datasets collected from individual users. The overall experimental results and discussions can help both the researchers and applications developers to design and build intelligent context-aware systems for smartphone users.
Determining the species of mycoplasma isolated from culture-positive milk samples is important for understanding the clinical significance of their detection. Between August 2016 and December 2019, ...214,518 milk samples from 2,757 dairy herds were submitted to Quality Milk Production Services (QMPS) at Cornell University for mycoplasma culture. From these samples, 3,728 collected from 204 herds were culture positive. Based on the request of herd managers, owners, or veterinarians, 889 isolates from 98 herds were subjected to molecular identification by PCR and amplicon sequencing. The largest proportion of the identified isolates were from New York State (78.1%), while the others came from the eastern United States (17.8%), Texas (2.0%), and New Mexico (2.1%). As expected, Mycoplasma spp. were the most common (855 isolates, 96.2%) and Acholeplasma spp. accounted for the remainder (34 isolates, 3.8%). Mycoplasma bovis was the most prevalent Mycoplasma species (75.1%), followed by M. bovigenitalium (6.5%), M. canadense (5.9%), M. alkalescens (5%), M. arginini (1.7%), M. californicum (0.1%), and M. primatum (0.1%). A portion of the isolates were confirmed as Mycoplasma spp. other than M. bovis but were not identified at the species level (16 isolates, 1.8%) because further information was not requested by the manager, owner, or veterinarian. Mycoplasma bovis was the only species identified in 59 of the 98 herds. However, more than 1 Mycoplasma sp. was identified in 29 herds, suggesting that herd infection with 2 or more mycoplasmas is not uncommon. Moreover, a Mycoplasma sp. other than M. bovis was the only species identified in 8 herds. From the subset of 889 mycoplasma culture-positive isolates from 98 herds, we determined that over a third of the herds had either more than 1 Mycoplasma sp. or a Mycoplasma sp. other than M. bovis detected in their milk samples. In conclusion, we observed that M. bovis is the most common pathogenic Mycoplasma species found in mastitic milk, but other Mycoplasma species are not uncommon. Our results suggest that it is critical to test milk samples for mycoplasmas using diagnostic tests able to identify both the genus and the species.
Distributed Ledger Technology (DLT) has emerged as one of the most disruptive technologies in the last decade. It promises to change the way people do their business, track their products, and manage ...their personal data. Though the concept of DLT was first implemented in 2009 as Bitcoin, it has gained significant attention only in the past few years. During this time, different DLT enthusiasts and commercial companies have proposed and developed several DLT platforms. These platforms are usually categorized as public vs private, general purpose vs application specific and so on. As a growing number of people are interested to build DLT applications, it is important to understand their underlying architecture and capabilities in order to determine which DLT platform should be leveraged for a specific DLT application. In addition, the platforms need to be evaluated and critically analyzed to assess their applicability, resiliency and sustainability in the long run. In this paper, we have surveyed several leading DLT platforms and evaluated their capabilities based on a number of quantitative and qualitative criteria. The comparative analysis presented in this paper will help the DLT developers and architects to choose the best platform as per their requirement(s).
Although ransomware has been around since the early days of personal computers, its sophistication and aggression have increased substantially over the years. Ransomware, as a type of malware to ...extort ransom payments from victims, has evolved to deliver payloads in different attack vectors and on multiple platforms, and creating repeated disruptions and financial loss to many victims. Many studies have performed ransomware analysis and/or presented detection, defense, or prevention techniques for ransomware. However, because the ransomware landscape has evolved aggressively, many of those studies have become less relevant or even outdated. Previous surveys on anti-ransomware studies have compared the methods and results of the studies they surveyed, but none of those surveys has attempted to critique on the internal or external validity of those studies. In this survey, we first examined the up-to-date concept of ransomware, and listed the inadequacies in current ransomware research. We then proposed a set of unified metrics to evaluate published studies on ransomware mitigation, and applied the metrics to 118 such studies to comprehensively compare and contrast their pros and cons, with the attempt to evaluate their relative strengths and weaknesses. Finally, we forecast the future trends of ransomware evolution, and propose future research directions.
PDF
