Evaluation of camel milk for selected processing related parameters and comparisons with cow and buffalo milk

Cow and buffalo milk and camel milk were analyzed and compared for processing related parameters. The average heat stability of cow, buffalo and camel milk samples analyzed was 1807.4 seconds, 1574.6 seconds and 133.6 seconds respectively at 140 °C. Thus, the heat stability of camel milk was significantly lower than the cow milk and buffalo milk. The average rennet coagulation time (RCT) of cow, buffalo and camel milk was 310.6 seconds, 257.4 seconds and 604.2 seconds respectively. Thus, RCT of camel milk was significantly higher than the cow milk and buffalo milk. The camel, cow and buffalo milk samples showed negative alcohol stability. The rate of acidity was increased propositionally with time in camel milk with no curd formation and weaker body

Order within disorder: the atomic structure of ion-beam sputtered amorphous tantala (a-Ta2O5)

Amorphous tantala (a-Ta2O5) is a technologically important material often used in high-performance coatings. Understanding this material at the atomic level provides a way to further improve performance. This work details extended X-ray absorption fine structure measurements of a-Ta2O5 coatings, where high-quality experimental data and theoretical fits have allowed a detailed interpretation of the nearest-neighbor distributions. It was found that the tantalum atom is surrounded by four shells of atoms in sequence; oxygen, tantalum, oxygen, and tantalum. A discussion is also included on how these models can be interpreted within the context of published crystalline Ta 2O5 and other a-T2O5 studies

Cornea lenticule viability and structural integrity after refractive lenticule extraction (ReLEx) and cryopreservation

Molecular Vision173437-3449MVEP

Ways to increase equity, diversity and inclusion

The eLife Early-Career Advisory Group (ECAG), an international group of early-career researchers committed to improving research culture, calls for radical changes at eLife and other journals to address racism in the scientific community and to make science more diverse and inclusive.Fil: Mehta, Devang. University of Alberta; CanadáFil: Bediako, Yaw. University Of Ghana; GhanaFil: De Winde, Charlotte M.. Colegio Universitario de Londres; Reino UnidoFil: Ebrahimi, Hedyeh. No especifíca;Fil: Fernández, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Ilangovan, Vinodh. University Aarhus; DinamarcaFil: Paz Quezada, Carolina. Universidad Bernardo O'higgins; ChileFil: Riley, Julia L.. Dalhousie University Halifax; CanadáFil: Saladi, Shyam M.. California Institute of Technology; Estados UnidosFil: Tay, Andy. No especifíca;Fil: Weissgerber, Tracey. No especifíca

Mitigating the impact of conference and travel cancellations on researchers’ futures

The need to protect public health during the current COVID-19 pandemic has necessitated conference cancellations on an unprecedented scale. As the scientific community adapts to new working conditions, it is important to recognize that some of our actions may disproportionately affect early-career researchers and scientists from countries with limited research funding. We encourage all conference organizers, funders and institutions who are able to do so to consider how they can mitigate the unintended consequences of conference and travel cancellations and we provide seven recommendations for how this could be achieved. The proposed solutions may also offer long-term benefits for those who normally cannot attend conferences, and thus lead to a more equitable future for generations of researchers

Acumen : an open-source testbed for cyber-physical systems research

Developing Cyber-Physical Systems requires methods and tools to support simulation and verification of hybrid (both continuous and discrete) models. The Acumen modeling and simulation language is an open source testbed for exploring the design space of what rigorousbut- practical next-generation tools can deliver to developers of Cyber- Physical Systems. Like verification tools, a design goal for Acumen is to provide rigorous results. Like simulation tools, it aims to be intuitive, practical, and scalable. However, it is far from evident whether these two goals can be achieved simultaneously. This paper explains the primary design goals for Acumen, the core challenges that must be addressed in order to achieve these goals, the “agile research method” taken by the project, the steps taken to realize these goals, the key lessons learned, and the emerging language design

Effect of Fibrin Glue on the Biomechanical Properties of Human Descemet's Membrane

10.1371/journal.pone.0037456PLoS ONE75

Survey and evaluation of hypertension machine learning research

Background: Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision‐making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results: The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension‐related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real‐time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions: Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption