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Looking for Content Requests for HLS Modern Workplace Fire Away Friday's
As Health and Life Sciences moves forward with a regular rhythm for our Modern Workplace Fire Away Friday's we want to make sure that content we deliver matches your needs. To that end we would love to have you submit your requests here for content in the collaboration area. Looking for Teams best practices? Let us know. Looking for new ways to aggregate and visual data for your org? Let us know. We look forward to your requests and then we will see you online...live!
Understanding the Role of SASSA Grants: A Discussion
How have https://onlinesassastatuscheck.co.za/ Grants impacted poverty alleviation and social welfare in South Africa? Join the conversation to share your insights, experiences, and analysis of the role SASSA plays in supporting vulnerable populations across the country.
"Enhancing Service Delivery at NSFAS through Microsoft Technologies"
By integrating Microsoft technologies, https://applyfornsfas.co.za/ can streamline application processes, enhance communication with applicants, and automate administrative tasks, resulting in improved efficiency, transparency, and service delivery to students in need.
Excel Template
Hi, I would like a suggestion on how to create an excel sheet to monitor sample reception monthly and yearly. I would like to include variables such as total samples received monthly, number of samples tested, number of positive and the district the samples were received from. I am not really a tech guru but I follow instructions. ThanksDevelopment of Health Care Strategy
I have started a new career with Home Health Care organisation that is engaged in: Working for the community of the vulnerable aged and pensioners Enhancing Later life Health of people Fitness for the future In current circumstances of Coronavirus pandemic, my job will be very challenging. It involves developing strategies for avoiding the virus and curing those who are sick due to the virus. The aged are most at risk and need special care and attention, almost like intensive care of patients at home. Dementia care strategy - focus on people living with dementia, people not living with dementia, families and friends of people living with dementia, colleagues and volunteers. Hand Hygiene strategy - ensuring care and safety of every individual from the attacks of micro-organisms, bacteria, chain of infection and the good health of the hands. Diabetes care strategy - focus on people who are suffering from Type 1 Diabetes and Type 2 Diabetes, managing diabetes by medication and blood glucose monitoring, administering and injecting insulin, elderly diabetics, diet and weight management. A check-list of the following procedures may be followed in Hand Hygiene as a matter of great emergency today: - Train and educate everyone in hand washing correctly. Many presentations have been illustrated by government agencies, including posters published by WHO. - Use of soaps for washing and bathing the hands, face, feet and other parts of the skin that are exposed. The infection is spread by physical touch and by coughing and sneezing when droplets touch other people within a very short distance. - Proper hand and face drying. Wet towels and tissues may carry the viral bacteria, they must not be touched and must be put in washing machine and dryer. Hot air drying may be the safe method. - Skin related problems. Care must be taken when treating skin that shows rash, inflammation, burns, sores and pores. Water may attract more bacteria and micro-organisms. An antiseptic soap and cream may be useful. - Frequently unwashed parts of the hands. Water should not be run over or splashed in high volume, as that will leave unwashed and dry parts. Best way to clean is that which shows shinning by rubbing and massaging all over gently. This will ensure that treated water goes beyond the hair and cleans the skin thoroughly. - Home care and hand hygiene. Ensure that the surface of everything is sparkling clean, washed and shinning. Take care of glass, door and window handles, pots and pans and other utensils, wash up all the dishes, cups, saucers, tumblers put in the sink or left on tables and worktops, clean up also the mobile phones, laptops and computer keyboards and screens, chairs, settee and furnishing. - Disposable gloves. Wear them properly and ensure they are fit and new, dispose of them properly and carefully after use so that nobody touches them and they don't touch surface of things. It is better to wash the hands with soap after removing the disposable gloves.
24 hour time slots from a specific time point
Hi! Is there a formula to make 24 hour time slots from a specific time? For example, 3/3/25 @ 0810. The 1st 24 hour box would be (3/3/25 @ 0810 - 3/4/25 @ 0810), 2nd 24 hour box (3/4/25 @ 0810 - 3/5/25 @ 0810), etc. Also, once those 24 hour prefilled dates and times are created for 15 days, is it possible to take a shreadsheet with dates and time entries and place them into the correlating 24 hour time slots from a specific time? For example, if an entry was dated and timed 3/3/25 @ 0935, and 3/4/25 @ 0700, both of those would fall into the 1st 24 hour box and so on. Thank you in advance for saving me hundreds of hours doing this by hand!Did Microsoft make an effort to lift poverty in South Africa?
What specific initiatives has Microsoft undertaken to address poverty in South Africa, and how do these efforts compare with government programs like https://srd-sassa-gov.co.za/ and other social assistance initiatives aimed at alleviating poverty and promoting economic empowerment?AI and Machine Learning Revolutionizing Healthcare
Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the healthcare landscape, bringing about a new era of personalized, efficient, and data-driven care. These technologies are revolutionizing various aspects of healthcare, from diagnosis and treatment to drug discovery and patient management. Diagnosis and Treatment: AI and ML algorithms are being used to analyze medical images, such as X-rays, CT scans, and MRIs, with unprecedented accuracy. This allows for earlier and more accurate diagnosis of diseases like cancer, heart disease, and neurological disorders. Additionally, AI-powered systems can analyze patient data, including medical history, lab results, and genetic information, to predict the risk of developing certain diseases and recommend personalized treatment plans. Drug Discovery and Development: AI and ML are playing a crucial role in accelerating drug discovery and development. These technologies can analyze vast amounts of data to identify potential drug targets and predict the efficacy and safety of new drugs. This can significantly reduce the time and cost of bringing new drugs to market. Patient Management and Monitoring: AI-powered chatbots and virtual assistants are being used to provide patients with 24/7 support and information. These systems can answer patients' questions, schedule appointments, and even monitor their health status remotely. Additionally, AI algorithms can analyze patient data to identify those at risk of complications or readmission, allowing for early intervention and improved outcomes. Administrative Tasks and Workflow Optimization: AI and ML can automate many administrative tasks in healthcare, such as scheduling appointments, processing claims, and managing medical records. This frees up healthcare professionals to focus on providing direct patient care. Additionally, AI-powered systems can analyze data to identify inefficiencies in workflows and suggest improvements, leading to increased efficiency and cost savings. Challenges and Ethical Considerations: Despite the numerous benefits, AI and ML in healthcare also present challenges and ethical considerations. Data privacy and security are critical concerns, as AI systems rely on vast amounts of patient data. Additionally, ensuring fairness and avoiding bias in AI algorithms is crucial to prevent discrimination and ensure equitable access to healthcare. Conclusion: AI and ML are revolutionizing healthcare, offering the potential to improve patient outcomes, reduce costs, and increase efficiency. However, it is important to address the challenges and ethical considerations associated with these technologies to ensure their responsible and equitable implementation. As AI and ML continue to evolve, the future of healthcare promises to be more personalized, data-driven, and accessible than ever before.Introducing Scalable and Enterprise-Grade Genomics Workflows in Azure ML
Genomics workflows are essential in bioinformatics as they help researchers analyse and interpret vast amounts of genomic data. However, creating a consistent and repeatable environment with specialized software and complex dependencies can be challenging, making integration with CI/CD tools difficult, too. Azure Machine Learning (Azure ML) is a cloud-based platform that provides a comprehensive set of tools and services for developing, deploying, and managing machine learning models. Azure ML offers great repeatability and auditability features natively that not many workflow solutions offer. It provides a highly integrated and standardised environment for running workflows, ensuring that each step is executed in a consistent and reproducible manner. This feature is particularly useful for genomics workflows that require the use of multiple tools and software packages of certain versions with specific dependencies. In this blog post, we will show how Azure ML can run genomics workflows efficiently and effectively, in addition to being an end-to-end platform for machine learning model training and deployment. Figure 1 illustrates an example of such a workflow. Figure 1: A sample genomics workflow running in Azure ML, consisting of 3 steps. A reference genome input dataset flows into the indexer step, while the sequence quality step gets its data from a folder of DNA sequences (".fastq" files). Azure ML has comprehensive audit and logging capabilities that track and record every step of the workflow, ensuring traceability and repeatability. One of the critical features of Azure ML to achieve these capabilities is its support for users to be able to specify https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?tabs=cli#create-an-environment for each workflow step, which guarantees consistent environment execution. These environments can be versioned and centrally shared. Workflow steps within pipelines then can refer to a particular environment. Figure 2 shows one such environment, bwa, version "5". As we make modifications in the environment definition, the new version will be registered as "6", however, we will still be able to continue to use older versions. Figure 2: An example Azure ML environment, defining a Docker image containing the BWA bioinformatics software package. This is the 5th version of this environment registered under the name, "bwa". Like environments, Azure ML supports user created pipeline https://learn.microsoft.com/en-us/azure/machine-learning/concept-component that can be centrally registered for reuse in other pipelines, also versioned, and with an audit log of their usage. Runs are logged together with standard out and error streams generated by the underlying processes, automatically. https://learn.microsoft.com/en-gb/azure/machine-learning/how-to-use-mlflow-configure-tracking?tabs=cli%2Cmlflow and adding custom tags to all assets and runs are supported, too. This feature ensures that the results are consistent and reproducible, saving users’ time. An example versioned component is shown in Figure 3. Figure 3: An Azure ML component named "BWA Indexer". It is a self-contained, re-usable, versioned piece of code that does one step in a machine learning pipeline: running the bwa indexer command, in this instance. Versioning is not limited to environments and pipeline components. Another essential feature of Azure ML is its support for versioning all input https://learn.microsoft.com/en-gb/azure/machine-learning/concept-data and genomic data, including overall pipeline input, and as well as intermediate step and final outputs, if needed. This feature enables users to keep track of dataset changes and ensure that the same version is used consistently across different runs of the workflow, or in others. There are many genomics workflow engines which are very good with multiple parallel execution when it comes to processing files in parallel. However, Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-parallel support parallel running both at the file-level (one by one, or 3 files at a time etc) and at the file chunk-level (50 MB of data per process, or 20 KB of text per node etc) where appropriate as supported by the consuming application, enabling the processing of large genomic datasets efficiently across elastic compute clusters that can auto-scale. Pipelines can even also https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-attach-compute-targets#local-computer for test/development phases, and of course support powerful CPU and GPU-based VMs, https://learn.microsoft.com/en-gb/azure/machine-learning/how-to-use-low-priority-batch?tabs=cli or on-demand compute clusters, https://learn.microsoft.com/en-gb/azure/machine-learning/quickstart-spark-jobs?tabs=cli, and other compute targets such as https://learn.microsoft.com/en-gb/azure/machine-learning/how-to-attach-kubernetes-anywhere, making it flexible for different use cases. Azure ML has integrations with https://learn.microsoft.com/en-us/azure/machine-learning/how-to-devops-machine-learning and https://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning?tabs=userlevel for CI/CD, making it easy to deploy and manage genomics workflows in a production environment, which in turn makes GenomicsOps possible. Well established pipelines ready for "production use" can be published, and called on-demand or from other Azure services including the https://learn.microsoft.com/en-us/azure/data-factory/transform-data-machine-learning-service. This means we can create a schedule for running pipelines automatically, or whenever data become available. Thanks to its Python SDK, command line utility (https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-cli?tabs=public), REST-API, and user-friendly UI, it makes it possible to develop pipelines and initiate pipeline runs from any preferred means, also providing easy monitoring and management of workflows. That said, event-based triggers and notifications are also supported. For instance, one can set up an email alert that will be triggered whenever a genomics pipeline finishes execution. As compute and storage are de-coupled, any pipeline input or output stored in an Azure ML datastore or blob storage can also be accessed by Azure ML’s https://learn.microsoft.com/en-gb/azure/machine-learning/quickstart-run-notebooks Notebooks for any upstream or downstream analysis. Azure ML is a managed PaaS service, making it an accessible and easy to set up platform for genomics researchers and bioinformaticians. Additionally, it has a https://learn.microsoft.com/en-gb/azure/machine-learning/how-to-setup-vs-code for local development and has a https://learn.microsoft.com/en-gb/azure/machine-learning/concept-workspace for managing pipeline projects, enabling collaboration, and Azure role-based access control (RBAC). In conclusion, Azure ML comes with advanced security features, including AD authentication, public & private endpoints, subscription-based event triggers, storage backed by the Azure Storage Service that comes with encryption at rest and in transit, and application insights, making it a reliable and already proven enterprise platform that can also be natively used for genomics research. For a more detailed tutorial that shows how to set up and run the example workflow shown in Figure 1, as well as for all the source code for creating the aforementioned sample environments and components, please check out this GitHub repository: https://github.com/truehand/azureml-genomics
