Enzyme‐assisted aqueous extraction of Kalahari melon seed oil: optimization using response surface methodology

Enzymatic extraction of oil from Kalahari melon seeds was investigated and evaluated by response surface methodology (RSM). Two commercial protease enzyme products were used separately: Neutrase® 0.8 L and Flavourzyme® 1000 L from Novozymes (Bagsvaerd, Denmark). RSM was applied to model and optimize the reaction conditions namely concentration of enzyme (20–50 g kg−1 of seed mass), initial pH of mixture (pH 5–9), incubation temperature (40–60 °C), and incubation time (12–36 h). Well fitting models were successfully established for both enzymes: Neutrase 0.8 L (R 2 = 0.9410) and Flavourzyme 1000 L (R 2 = 0.9574) through multiple linear regressions with backward elimination. Incubation time was the most significant reaction factor on oil yield for both enzymes. The optimal conditions for Neutrase 0.8 L were: an enzyme concentration of 25 g kg−1, an initial pH of 7, a temperature at 58 °C and an incubation time of 31 h with constant shaking at 100 rpm. Centrifuging the mixture at 8,000g for 20 min separated the oil with a recovery of 68.58 ± 3.39%. The optimal conditions for Flavourzyme 1000 L were enzyme concentration of 21 g kg−1, initial pH of 6, temperature at 50 °C and incubation time of 36 h. These optimum conditions yielded a 71.55 ± 1.28% oil recovery

Integration heterogener medizinischer und biologischer Daten in elektronische Patientenakten

The shortage of data for patients with chronic and other diseases and previous medical treatments shows significant weakness in the diagnosis and treatment of patients. Due to the healthcare system insufficiency, patients with comorbidities might not survive the diseases, especially when the disease is novel. The lack of information on patients' genetic disorders, especially when they are unaware of them, also contributes to increased patient deaths. This conveys the necessity to integrate medical and health data with various biological omics and other data, especially in pandemic circumstances. Patients' health data matters are apparent, but they are stored in multiple hospitals and health systems such as electronic health records (EHRs), healthcare institutions, and laboratories. Furthermore, biological data are often not integrated and cannot be used by patients, physicians, and specialists to treat particular diseases. Although the urgent need for healthcare and medical data integration is apparent, personal data protection regulations are severe. They do not allow much progress in the area without implementing security and privacy standards for patient healthcare data. One solution for this issue is setting a personal health record (PHR) as an integrative system for the patient. Many ontological frameworks have been proposed to unify the record formats, but none of them is accepted as a healthcare standard. The efforts toward approving the Health Level Seven (HL7) standards and the common medical coding systems ensure further data integration. Some efforts are made to associate particular diseases with data obtained from external environmental sensors that measure disease-associated data. Using these data, which are called exposome, the increasing symptoms of particular diseases influenced by external factors can be clarified. This paper suggests a cloud-based model for integrating healthcare and medical data from different sources such as EHRs, health information systems, and measurement sensors into the PHR as the first stage toward integrating patient health data. Besides the patients' personal and clinical data, various omics data should be integrated for improved individualized disease prognosis and treatment of the patients. These data are stored in the cloud following the required data security and privacy standards.Der Mangel an Daten über PatientInnen mit chronischen und anderen Krankheiten und medizinischen Vorbehandlungen zeigt eine erhebliche Schwäche bei der Diagnose und Behandlung vieler PatientInnen auf. Aufgrund der Unzulänglichkeit des Gesundheitssystems kann es sein, dass PatientInnen mit Komorbiditäten die Krankheiten nicht überleben, insbesondere wenn es sich um eine neue Krankheit handelt. Der Mangel an Informationen über die genetischen Störungen der PatientInnen, vor allem wenn sie sich derer nicht bewusst sind, trägt ebenfalls zu einer erhöhten PatientInnensterblichkeit bei. Daraus ergibt sich die Notwendigkeit, medizinische und gesundheitliche Daten mit verschiedenen biologischen Omics und anderen Daten zu integrieren, insbesondere unter Pandemiebedingungen. Die Relevanz des Themas der Gesundheitsdaten von PatientInnen ist offensichtlich, aber die Daten werden in verschiedenen Krankenhäusern und Gesundheitssystemen wie der elektronischen Patientenakte (ePA), Gesundheitseinrichtungen und Laboren gespeichert. Darüber hinaus werden biologische Daten oft nicht integriert und können von PatientInnen, ÄrztInnen und SpezialistInnen nicht zur Behandlung bestimmter Krankheiten genutzt werden. Obwohl der dringende Bedarf an der Integration von Gesundheits- und medizinischen Daten offensichtlich ist, sind die Vorschriften zum Schutz personenbezogener Daten streng. Sie lassen keine großen Fortschritte in diesem Bereich zu, ohne dass Sicherheits- und Datenschutzstandards für Gesundheitsdaten von PatientInnen eingeführt werden. Eine Lösung für dieses Problem ist die Einrichtung eines Personal Health Records (PHR) als integratives System für die PatientInnen. Viele ontologische Rahmenwerke wurden vorgeschlagen, um die Datensatzformate zu vereinheitlichen, aber keines von ihnen ist als Standard im Gesundheitswesen anerkannt. Die Bemühungen um die Annahme der Health Level Seven (HL7)-Standards und der gängigen medizinischen Codierungssysteme sorgen für eine weitere Datenintegration. Es gibt Bestrebungen, bestimmte Krankheiten mit Daten in Verbindung zu bringen, die von externen Umweltsensoren gewonnen werden, die krankheitsassoziierte Daten messen. Anhand dieser Daten, die als Exposom bezeichnet werden, können die zunehmenden Symptome bestimmter Krankheiten, die durch externe Faktoren beeinflusst werden, geklärt werden. In diesem Artikel wird ein Cloud-basiertes Modell zur Integration von Gesundheits- und medizinischen Daten aus verschiedenen Quellen wie der ePA, Gesundheitsinformationssystemen und Messsensoren in den PHR als erster Schritt zur Integration von Gesundheitsdaten vorgeschlagen. Neben den persönlichen und klinischen Daten der PatientInnen sollen auch verschiedene Omics-Daten integriert werden, um eine bessere individualisierte Krankheitsprognose und Behandlung der PatinentInnen zu ermöglichen. Diese Daten werden in der Cloud unter Einhaltung der erforderlichen Datensicherheits- und Datenschutzstandards gespeichert

Complexity and health functionality of plant cell wall fibers from fruits and vegetables

The prevalence of lifestyle-related diseases is increasing in developing countries with the causes for death starting to follow the same pattern in the developed world. Lifestyle factors including inadequate dietary intake of fruits and vegetables and over consumption of nutrient-poor processed foods, are considered to be major causal risk factors associated with increased susceptibility to developing certain diseases (Alldrick, 1998; Kiani, 2007). Recent epidemiological evidence confirms a strong association between dietary fiber and reduced all-cause mortality risk, as well as a risk reduction for a number of non-communicable diseases (Chuang et al., 2012). The relationship between dietary fiber and mortality has been described as “convincing observations that call for mechanistic investigations” (Landberg, 2012). In particular, the health protective roles played by dietary fibers of different origin are not well understood. Whilst Hippocrates was the earliest known physician to study the health benefits of fiber derived from grains (Burkitt, 1987), the functionality of fruit and vegetable fiber, especially in association with other compounds such as polyphenols and carotenoids, is an area of more recent interest. Hence the objective of this review is to assess the complexity and health-related functional role of plant cell wall (PCW) fibers from fruits and vegetables with a particular emphasis on interactions between cell walls and phytonutrients

A Novel Interactive Assembly Teaching Aid Using Multi-Template Augmented Reality

[[abstract]]Augmented Reality (AR) technology is a fast growing field in the academics and industry. This paper presents a novel design of an interactive assembly teaching aid based on a multi-template AR system, which consists of three units: a multi-template AR unit, an online three-dimension (3D) model assembly unit, a hand-gesture interaction unit. The design of the multi-template AR unit employs an efficient multi-template pose tracking method to detect and track multiple template images simultaneously. The online 3D model assembly unit is enabled, when the pose of each target template is tracked and computed by the multi-template pose tracking method. This method measures the distance between the two templates to determine the 3D rendering mode of the virtual object. The third unit aims to realize a vision-based human-computer interactive system, which combines the AR rendering system with the real-time hand-gesture recognition method to decide the status of AR rendering of a 3D dynamic animation according to the user’s hand gesture. Based on the feedback of twenty-one users of the AR-based interactive teaching-aid, it can be concluded that the system creates an interactive experiences that engages the users and facilitates them to increase their learning interest significantly. In future, we plan to port the proposed AR system to Android and iOS mobile devices while improving the functionality, interactivity, and entertaining qualities of the teaching aid system.[[notice]]補正完