Genomic Features Of A Bumble Bee Symbiont Reflect Its Host Environment

Here, we report the genome of one gammaproteobacterial member of the gut microbiota, for which we propose the name >Candidatus Schmidhempelia bombi,> that was inadvertently sequenced alongside the genome of its host, the bumble bee, Bombus impatiens. This symbiont is a member of the recently described bacterial order Orbales, which has been collected from the guts of diverse insect species; however, >Ca. Schmidhempelia> has been identified exclusively with bumble bees. Metabolic reconstruction reveals that >Ca. Schmidhempelia> lacks many genes for a functioning NADH dehydrogenase I, all genes for the high-oxygen cytochrome o, and most genes in the tricarboxylic acid (TCA) cycle. >Ca. Schmidhempelia> has retained NADH dehydrogenase II, the low-oxygen specific cytochrome bd, anaerobic nitrate respiration, mixed-acid fermentation pathways, and citrate fermentation, which may be important for survival in low-oxygen or anaerobic environments found in the bee hindgut. Additionally, a type 6 secretion system, a Flp pilus, and many antibiotic/multidrug transporters suggest complex interactions with its host and other gut commensals or pathogens. This genome has signatures of reduction (2.0 megabase pairs) and rearrangement, as previously observed for genomes of host-associated bacteria. A survey of wild and laboratory B. impatiens revealed that >Ca. Schmidhempelia> is present in 90% of individuals and, therefore, may provide benefits to its host.Center for Insect Science (University of Arizona)National Science Foundation NSF 1046153NIH Director's Pioneer 1DP1OD006416-01NIH R01-HG006677Swiss National Science Foundation 140157, 147881Integrative Biolog

A Study of Generative Large Language Model for Medical Research and Healthcare

There is enormous enthusiasm and concerns in using large language models (LLMs) in healthcare, yet current assumptions are all based on general-purpose LLMs such as ChatGPT. This study develops a clinical generative LLM, GatorTronGPT, using 277 billion words of mixed clinical and English text with a GPT-3 architecture of 20 billion parameters. GatorTronGPT improves biomedical natural language processing for medical research. Synthetic NLP models trained using GatorTronGPT generated text outperform NLP models trained using real-world clinical text. Physicians Turing test using 1 (worst) to 9 (best) scale shows that there is no significant difference in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p < 0.001). This study provides insights on the opportunities and challenges of LLMs for medical research and healthcare

New algorithms for optimal portfolio selection

Over the past four thousand years, numerous techniques have been developed and used to address problems in Finance. These techniques include simple arithmetic calculations and probabilistic methods as well as intelligent systems techniques such as neural networks, genetic algorithms, multi-agent systems, and support vector machines. The techniques have been developed to accurately and quickly collect, validate, analyze, and integrate data that change dynamically. The particular problem that we address in this dissertation is the construction of efficient algorithms for the problem of an optimal portfolio selection, that is, algorithms that would accurately and in real time determine the best distribution of wealth among several investment assets to achieve a specific goal. The main goal of making investments is to gain as much wealth as possible, therefore the goal is to maximize the return. However, the problem we face is that the investors do not know in advance what the return of each asset will be, but rather there are some educated predictions about the returns from each asset. These return predictions might turn out to be correct, but on the other hand, they might be completely wrong. Thus, there is a risk associated with each predicted return, and therefore with each asset. One of the goals of an investor is the minimization of risk. However, usually, a higher return is associated with a higher risk. Thus, it is impossible to maximize the return and minimize the risk independently. Moreover, additional characteristics, such as time to maturity, preferred portfolio structure, and reputations of companies that sell investments asset, among others, are also considered before locking money into an asset or a portfolio. This problem leads to the necessity of developing intelligent system techniques to find the best trade off between the return and the risk based on the preferences of an investor. The techniques that are used to solve the problem of optimal portfolio selection all have their drawbacks. To solve these problems, we propose a new approach driven by utility-based multi-criteria decision making setting, which utilizes fuzzy measures and integration over intervals

A Paradox of Altruism: How Caring about Future Generations Can Result in Poverty for Everyone (Game-Theoretic Analysis)

Political and social activists are rightfully concerned about future generations: whenever a country borrows money, or an environmental situation worsens, this means, in effect, that we impose an additional burdens on future generations. There is clearly a conflict between the present generation\u27s actions and interests and the welfare of the future generations. There exists a mathematical toolbox that provides solutions to many well-defined conflict situations: namely, the toolbox of game theory. It therefore seems reasonable to apply game theory techniques to the conflict between the generations. In this paper, we show that we need to be very cautious about this application, because reasonable game theoretic techniques such as Nash bargaining solution can lead to disastrous solutions such as universal poverty. In other words, seemingly reasonable altruism can lead to solutions which are as disastrous as extreme selfishness. The development of appropriate techniques -- techniques which would lead to a reasonable resolution of this inter-generational conflict -- thus remains an important open problem

How to Relate Fuzzy and OWA Estimates

Abstract—In many practical situations, we have several estimates x1,..., xn of the same quantity x, i.e., estimates for which x1 ≈ x, x2 ≈ x,..., and xn ≈ x. It is desirable to combine (fuse) these estimates into a single estimate for x. From the fuzzy viewpoint, a natural way to combine these estimates is: (1) to describe, for each x and for each i, the degree µ≈(xi−x) to which x is close to xi, (2) to use a t-norm (“and”-operation) to combine these degrees into a degree to which x is consistent with all n estimates, and then (3) find the estimate x for which this degree is the largest. Alternatively, we can use computationally simpler OWA (Ordered Weighted Average) to combine the estimates xi. To get better fusion, we must appropriately select the membership function µ≈(x), the t-norm (in the fuzzy case) and the weights (in the OWA case). Since both approaches – when applied properly – lead t