This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Hypoglycemia, or low blood glucose, is an important risk factor for people with type 2 diabetes receiving blood glucose-lowering therapies, such as insulin. It can lead to symptoms that interfere with activities of daily living and can sometimes (though rarely) result in debilitating events, including loss of consciousness. Basal insulins are designed to help maintain stable blood glucose levels throughout the day. Data from randomized clinical trials show that newer, second-generation basal insulin analogs (such as insulin glargine 300 units per mL and insulin degludec) have lower hypoglycemia risk than first- generation basal insulin analogs (such as insulin glargine 100 units per mL and insulin detemir), while providing comparable glycemic control. However, these randomized controlled trials may not truly reflect clinical practice, as they applied strict inclusion and exclusion criteria and were conducted under strict oversight dictated by very specific protocols..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the transplantation of donor derived stem cells and treats a variety of hematologic and non-hematologic disorders. Allo-HSCT patients exhibit changes in their gut microbiota and experience a range of complications post-treatment, including acute graft-versus-host disease (aGvHD). The potential roles or timing of gut microbiota reestablishment and immunological homeostasis after allo-HSCT are not known. It is also not yet known if the microbiota at other body sites plays a role. Recently, researchers ran an integrated host-microbiota analysis of the gut, oral, and nasal microbiota in children undergoing allo-HSCT. The bacterial diversity decreased in all three sites during the first month. Certain microbial taxa were already different in allo-HSCT patients before transplantation compared to healthy children. Onset of acute GvHD after treatment could be predicted from the pre-treatment microbiota composition at all three body sites..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"The foods we eat, our biological makeup, and the microbes that dwell in our gut share a highly complex relationship. Sometimes, for example, our resident microbes can turn the beneficial nutrients and drugs we take into harmful substances. That’s the case for L-carnitine, a nutrient found in red meat and supplements, which certain bacteria metabolize into trimethylamine-N-oxide (TMAO), a compound linked to cardiovascular disease. To understand how different individuals’ microbial makeup might predispose them to harmful TMAO production researchers tested 56 individuals who received carnitine supplementation for 1 month. High-TMAO producers showed lower levels of active TMAO, likely because bacteria had already begun to break down a sizeable portion of ingested carnitine. The team also observed that TMAO productivity could be enhanced by carnitine supplementation..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the “information overload” that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.

This course surveys a variety of reasoning, optimization and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research.
Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.

14. Brave New World: Privacy, Data Sharing and Evidence Based Policy Making

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities in particular are in a unique position at the center of commerce and technology becoming hubs for innovation and practical application of emerging technology. In this rapidly changing 24/7 digitized world, governments are leveraging innovation and technology to become more effective, efficient, transparent and to be able to better plan for and anticipate the needs of its citizens, businesses and community organizations. This class will provide the framework for how cities and communities can become smarter and more accessible with technology and more connected.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Artificial intelligence is making rapid advances in medicine. Already, there are machine learning algorithms that can outperform doctors in some medical fields. There’s only one fairly big problem: experts aren’t quite sure how these algorithms work. While designers know full well what goes into the A-I systems they build and what comes out, the learning part in between is often too complex to comprehend. To their users, machine learning algorithms are effectively black boxes. Now, researchers from the RIKEN Center for Advanced Intelligence Project in Japan are lifting the lid. They’ve developed a deep-learning system that can outperform human experts in predicting whether prostate cancer will reoccur within one year. More importantly, the deep learning system they developed can acquire human-understandable features from unannotated pathology images to offer up critical clues that could help humans make better diagnoses themselves..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Quantum materials are opening up a realm of possibilities in materials research. Among the best known examples are superconductivity and quantum computing. But that’s only the beginning. The same properties that make these materials unique are also enabling researchers to demystify the inner workings of the human brain. So what makes quantum materials well suited for this purpose? Unlike the free-flowing electrons in ordinary conductors or semiconductors, electrons in quantum materials show correlated behavior. That in itself has been the focus of intense physics research. But the upshot for brain research is tunable electronic behavior that can mimic the electronic signaling of neurons and the synapses between them. Most importantly, quantum materials can simulate synaptic plasticity. Plasticity is the biological ability that makes learning and memory formation possible. It’s all about timing. Connections between neurons that fire within a short, milliseconds-long time window grow stronger..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

RAISE (Responsible AI for Social Empowerment and Education) is a new MIT-wide initiative headquartered in the MIT Media Lab and in collaboration with the MIT Schwarzman College of Computing and MIT Open Learning. MIT researchers continually develop curriculum modules and associated teaching materials that are available to all K-12 educators for free under a Creative Commons license.

Introduces the fundamental algorithmic approaches for creating robot systems that can autonomously manipulate physical objects in unstructured environments such as homes and restaurants. Topics include perception (including approaches based on deep learning and approaches based on 3D geometry), planning (robot kinematics and trajectory generation, collision-free motion planning, task-and-motion planning, and planning under uncertainty), as well as dynamics and control (both model-based and learning-based).
Homework assignments will guide students through building a software stack that will enable a robotic arm to autonomously manipulation objects in cluttered scenes (like a kitchen). A final project will allow students to dig deeper into a specific aspect of their choosing. The class has hardware available for ambitious final projects, but will also make heavy use of simulation using cloud resources.

The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus lies on classical machine learning techniques, with a bias towards classification and regression. Other learning paradigms and many recent developments in, for instance, Deep Learning are not addressed or only briefly touched upon.

Biehl argues that having a solid knowledge of the foundations of the field is essential, especially for anyone who wants to explore the world of machine learning with an ambition that goes beyond the application of some software package to some data set. Therefore, The Shallow and the Deep places emphasis on fundamental concepts and theoretical background. This also involves delving into the history and pre-history of neural networks, where the foundations for most of the recent developments were laid. These notes aim to demystify machine learning and neural networks without losing the appreciation for their impressive power and versatility.

Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative of the MIT Schwarzman College of Computing, works to train students and facilitate research to assess the broad challenges and opportunities associated with computing, and improve design, policy, implementation, and impacts.
This site is a resource for SERC pedagogical materials developed for use in MIT courses. SERC brings together cross-disciplinary teams of faculty, researchers, and students to develop original pedagogical materials that meet our goal of training students to practice responsible technology development through incorporation of insights and methods from the humanities and social sciences, including an emphasis on social responsibility.
Materials include the MIT Case Studies Series in Social and Ethical Responsibilities of Computing, original Active Learning Projects, and lecture materials that provide students hands-on practice and training in SERC, together with other resources and tools found useful in education at MIT. Original homework assignments and in-class demonstrations are specially created by multidisciplinary teams, to enable instructors to embed SERC-related material into a wide variety of existing courses.
The aim of SERC is to facilitate the development of responsible “habits of mind and action” for those who create and deploy computing technologies, and fostering the creation of technologies in the public interest.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"The sustainability of the terrestrial ecosystem depends on the stability of its tiniest residents. The terrestrial microbiome controls functions such as organic carbon turnover, nutrient-use efficiency, and productivity, and losing critical keystone functions may cause dramatic shifts in microbiome composition and function. A recent study sought to better understand the relationship between biodiversity and microbiome stability. Researchers inoculated microbial communities differing in phylogenetic diversity into sterilized soil and evaluated the resulting microbiome stability. They found that bacterial communities with higher phylogenetic diversity tended to be more stable throughout a range of pH values. Specialized metabolic functions, including “nitrogen metabolism” and "phosphonate and phosphinate metabolism,” were identified as keystone functions. These critical functions were carried out by specific bacterial taxa, including Nitrospira and Gammatimonas..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Bacteriophages are everywhere, influencing everything from microbial evolution to biogeochemical cycling. Phages, however, remain among the least understood members of complex microbiomes. Do the tools used to identify phages introduce biases? A recent study compared ten of the most widely used bioinformatics tools designed to detect phages from metagenomics data. Overall, tool performance varied substantially in the analysis of different benchmarking datasets. For a set of artificial RefSeq contigs, PPR Meta and VirSorter2 showed the highest performance. Kraken2 showed the highest accuracy for a mock community benchmark. And generally, k-mer tools performed better than similarity- or gene-based tools. The study offers insight into the biases introduced by different tools, offers guidance into which one is best suited for different use cases, and suggests that rather than relying on any one tool, researchers may do well to combine different ones to suit their research needs..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

Robots today move far too conservatively, using control systems that attempt to maintain full control authority at all times. Humans and animals move much more aggressively by routinely executing motions which involve a loss of instantaneous control authority. Controlling nonlinear systems without complete control authority requires methods that can reason about and exploit the natural dynamics of our machines.
This course introduces nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on computational methods. Topics include the nonlinear dynamics of robotic manipulators, applied optimal and robust control and motion planning. Discussions include examples from biology and applications to legged locomotion, compliant manipulation, underwater robots, and flying machines.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"As a precursor to biobased chemicals, biomass holds enormous potential for meeting the needs of the circular economy. To get the most out of biomass, a new study proposes borrowing tools from machine learning. During anaerobic fermentation, biomass fuels the growth and proliferation of various microorganisms. These microbes, in turn, form organic molecules that can be processed into specialty chemicals, but the conditions and microbes most conducive to this process aren’t always known. To find out, researchers examined bioreactors designed to form medium-chain carboxylates from xylan and lactate. As expected, reducing the hydraulic retention time, or the average time soluble compounds reside in a bioreactor, boosted the formation of useful medium-chain carboxylates. The key was to identify the organisms responsible for this boost. For that, the team used machine learning models designed to link the change in hydraulic retention time to distinct sequences of microbial DNA..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Some commercial fish species have both wild and farmed populations. Distinguishing between them is important for identifying escaped fish, managing food safety risk, and setting appropriate market prices. However, for many species, suitable genetic markers haven’t yet been discovered. To provide an alternative, researchers recently tried to use gut microbiome data to differentiate between wild and farmed large yellow croaker. Compared with those of wild croaker, the rectums of farmed croaker had lower microbial diversity (Shannon index values) and bacterial loads, and different microbiome compositions that were distinguishable despite high inter-batch variability. For example, the wild fish microbiome was dominated by _Psychrobacter_ spp., while the farmed fish microbiome was not. The predicted functions of the gut microbes also differed between wild and farmed croaker, presumably because the populations have divergent diets and thus divergent gut environments..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Viruses that infect bacteria and archaea are abundant in human and environmental microbiomes. Their roles in manipulating, killing, and recycling microbes makes them key players in environmental processes and human health and disease, including inflammatory bowel diseases. In spite of their importance, the tools available for analyzing viral genomes are limited. Now, a new tool allows researchers to identify viruses and predict their functions using genomic data. VIBRANT (Virus Identification By iteRative ANnoTation), is the first software to use a hybrid machine learning and protein similarity approach. going beyond traditional limitations to maximize the identification of highly diverse viruses. In validation experiments with reference datasets, VIBRANT recovered higher-quality virus sequences and reduced false identification of non-viral genome fragments compared to other identification programs..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

 The organizations that depend largely on collecting data from various sources or are highly digitized must adopt data security. It is better to fight the risks at the initial stage than to regret the loss of data and face the consequences. If the information can not be kept safe from various attacks then the preference of the organization will decrease eventually. Even if personal information cannot be trusted in the hands of the organization then there will be dissatisfaction among customers. If an organization is unable to keep its customers satisfied then its value can hit rock bottom. Hence, by using Artificial Intelligence and Machine Learning the data security should be made better. These technologies will also help in decreasing the extra effort that has to be put by an organization and its employees.