Computational design of a broad-spectrum multi-epitope vaccine candidate against seven strains of human coronaviruses.
Computational design of a broad-spectrum multi-epitope vaccine candidate against seven strains of human coronaviruses.
复制标题
针对七种人类冠状病毒株的广谱多表位候选疫苗的计算设计。
DOI：
10.1007/978-3-540-89208-3_240
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发表时间：
2022
期刊：
影响因子：
2.8
通讯作者：
Kumar A
中科院分区：
文献类型：
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作者：
Kumar A
十分之一的（英国）人口将在其生命的某个阶段遭受致残性精神障碍。双相情感障碍就是这样一种疾病，其特征是抑郁或躁狂活动的时期穿插着正常的延伸。一些患者能够通过他们的自我意识来控制这种情况，这使他们能够检测到衰弱发作的开始，从而采取有效的行动。这种自我管理可以通过以纸张为基础的过程来实现，尽管最近使用个人数据助理取得了成功。本演讲将介绍个性化环境监测（PAM）概念，该概念旨在通过自动提供和合并与个人活动相关的环境细节和信息来增强此类过程。从本质上讲，PAM项目正在研究可以被粗略地称为“电子”监控的东西，以自动记录“活动签名”，并随后使用这些数据发出警报。我们正在考虑使用的数据类型包括：位置和活动（例如通过GPS和加速度计）；环境（例如温度和光照水平）。正在考虑的其他类型的传感器是无源红外传感器（在家中）；和声音处理记录音频“环境”。这种监测的使用将由患者及其保健团队商定，预计不同的患者将适应不同的传感器包，从而使监测个性化。尽管这种远程监控现在普遍存在，但它在精神疾病治疗中的应用仍处于起步阶段。本文将考虑将其应用于该社区所面临的具体问题以及该项目的目标。此外，还将考虑使用建模来预测稀疏数据中可能出现的问题的影响，并预测其对整个患者路径的影响。
One in ten of the (UK) population will suffer a disabling mental disorder at some stage in their life. Bipolar disorder is one such illness and is characterized by periods of depression or manic activity interspersed with stretches of normality. Some patients are able to manage this condition via their self-awareness that enables them to detect the onset of debilitating episodes and so take effective action. Such self management can be achieved through a paper-based process, although more recently PDAs have been used with success. This presentation will introduce the Personalised Ambient Monitoring (PAM) concept that aims to augment such processes by automatically providing and merging environmental details and information relating to personal activity. Essentially the PAM project is investigating what may be loosely referred to as ‘electronic’ monitoring to automatically record ‘activity signatures’ and subsequently use this data to issue alerts. The types of data that we are considering using includes: location and activity (e.g. via GPS and accelerometers); and environment (e.g. temperature and light levels). Other types of sensor under consideration are passive IR sensors (within the home); and sound processing to log the audio ‘environment’. The use of such monitoring will be agreed between the patient and their health care team and it is anticipated that different patients will be comfortable with different sensor packages, thus personalizing the monitoring. Although such tele-monitoring is now generally common, its use in the treatment of the mentally ill is still in its infancy. This paper will consider the specific problems faced in applying it to this community along with the aims of this project. In addition, the use of modelling to predict the effects of the possible problems of sparse data that is expected, and to predict the effect on the overall patient pathway will be considered.