Helicobacter pylori infection and insulin resistance in diabetic and nondiabetic population

Helicobacter pylori (HP) is a common worldwide infection with known gastrointestinal and nongastrointestinal complications. One of the gastrointestinal side effects posed for this organism is its role in diabetes and increased insulin resistance. The aim of this study was to evaluate the association between HP and insulin resistance in type 2 diabetic patients and nondiabetics. This cross-sectional study was carried out from May to December 2013 on 211 diabetic patients referred to diabetes clinic of Shahid Beheshti Hospital of Qom and 218 patients without diabetes. HP was evaluated using serology method and insulin resistance was calculated using HOMA-IR. The prevalence of H. pylori infection was 55.8% and 44.2% in diabetics and nondiabetics (P=0.001). The study population was divided into two HP positive and negative groups. Among nondiabetics, insulin resistance degree was 3.01±2.12 and 2.74±2.18 in HP+ and HP- patients, respectively P=0.704. Oppositely, insulin resistance was significantly higher in diabetic HP+ patients rather than seronegative ones (4.484±2.781 versus 3.160±2.327, P=0.013). In diabetic patients, in addition to higher prevalence of HP, it causes a higher degree of insulin resistance. © 2014 Jamshid Vafaeimanesh et al

Monitoring for a Shift in a Process Covariance Matrix Using the Generalized Variance

The commonly recommended charts for monitoring the mean vector are affected by a shift in the covariance matrix. As in the univariate case, a chart for monitoring for a change in the covariance matrix should be examined first before examining the chart used to monitor for a change in the mean vector. One such chart is the one that plots the generalized sample variance lSl verses the sample number t. We propose to study charts based on the statistics V = l(n - 1) Σ₀-¹ Sl¹/̳p̳ and U = 1n (l (n-1) Σ₀-¹Sl¹/̳p̳), where n is the sample size and Σ₀ is the in-control value of the process covariance matrix Σ. In particular, we will study the Shewhart V and U charts supplemented with runs rules. Also, we examine the methods that are useful in studying the run length properties of the cumulative sum (CUSUM) U charts. Further, we will study the effect that estimating Σ₀ has on the performance of these charts. Guidance will be given for designing the Shewhart charts with runs rules with illustrative examples

Effects of light intensity, leaf temperature, and tree aspect on Discula lesion enlargement rates and acervuli production

Field studies were conducted at Lookout Mountain and Knoxville, Tennessee in 1990-91 to determine the effects of light intensity, leaf temperature, and tree aspect on Discula destructiva acervuli production, and lesion enlargement rates. Higher leaf temperatures were associated with higher light intensities in both sun and shade. Leaves on the east side of trees had higher leaf temperatures and light intensities in the morning. The diameter of the anthracnose lesions increased faster in the spring for both the sunny and shady plots. Most lesions on foliage in the sun had purple-rims whereas necrotic lesions were more prevalent on foliage in the shady plots. Necrotic lesions were more likely than purple-rimmed lesions to contain acervuli and have D. destructiva conidia present when the fungus was allowed to sporulate in moist chambers. The rates of lesion enlargement in shady compared to sunny plots at Lookout Mountain in the second experiment were significantly influenced by light intensity and leaf temperature as determined by covariate analysis

Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments

Most Reinforcement Learning (RL) methods are traditionally studied in an active learning setting, where agents directly interact with their environments, observe action outcomes, and learn through trial and error. However, allowing partially trained agents to interact with real physical systems poses significant challenges, including high costs, safety risks, and the need for constant supervision. Offline RL addresses these cost and safety concerns by leveraging existing datasets and reducing the need for resource-intensive real-time interactions. Nevertheless, a substantial challenge lies in the demand for these datasets to be meticulously annotated with rewards. In this paper, we introduce Optimal Transport Reward (OTR) labelling, an innovative algorithm designed to assign rewards to offline trajectories, using a small number of high-quality expert demonstrations. The core principle of OTR involves employing Optimal Transport (OT) to calculate an optimal alignment between an unlabeled trajectory from the dataset and an expert demonstration. This alignment yields a similarity measure that is effectively interpreted as a reward signal. An offline RL algorithm can then utilize these reward signals to learn a policy. This approach circumvents the need for handcrafted rewards, unlocking the potential to harness vast datasets for policy learning. Leveraging the SurRoL simulation platform tailored for surgical robot learning, we generate datasets and employ them to train policies using the OTR algorithm. By demonstrating the efficacy of OTR in a different domain, we emphasize its versatility and its potential to expedite RL deployment across a wide range of fields.Comment: Preprin

Development and Composition of the Warty Layer in Balsam Fir. I. Development

The deposition and ultrastructure of the warty layer in developing tracheids of balsam fir [Abies balsamea (L.) Mill.] were studied by means of transmission electron microscopy. The wart structure gradually was developed external to the plasma membrane after secondary wall deposition and the greater part of lignification were complete. Warts were synthesized first in the cell corners and pit cavities and then on the remainder of the cell walls. No cytoplasmic organelle was found to be associated specifically with wart formation. After the warty layer was elaborated, the cytoplasm disappeared from the cell, leaving no discernible trace of disorganized residue. The bulk of the wart structure exhibited staining properties similar to those of lignin. However, the basal portions of individual warts were sometimes less darkly stained than the outer portions, indicating possible heterogeneous composition

Development and Composition of the Warty Layer in Balsam Fir. II. Composition

From its response to various chemical, physical, fungal, and enzymatic treatments, it was concluded that the warty layer in balsam fir consisted largely of a ligninlike material that was visibly more resistant to extraction than a large fraction of other lignin in the fiber cell wall. Since the warts were the cell-wall component most accessible to the treatment solutions, it is probable that the material in the warty layer was more concentrated and condensed than lignin in other parts of the wall. Vacuum drying at 105 C appeared to condense the wart structure still further, making it even more resistant to most treatments. Gel filtration indicated that the warty layer was extracted as a high molecular weight material by certain treatments. The warty layer may act as a barrier that slows the penetration of liquids into the cell wall and thereby may cause different rates of delignification for different wood species. The basal component of individual warts and some of the accompanying encrustant on the inner surface of the cell wall were found to contain an amorphous carbohydrate, probably a pentosan or a pectic substance. Attempts at physical isolation of the warts were largely unsuccessful

A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges

In recent years, the development of robotics and artificial intelligence (AI) systems has been nothing short of remarkable. As these systems continue to evolve, they are being utilized in increasingly complex and unstructured environments, such as autonomous driving, aerial robotics, and natural language processing. As a consequence, programming their behaviors manually or defining their behavior through reward functions (as done in reinforcement learning (RL)) has become exceedingly difficult. This is because such environments require a high degree of flexibility and adaptability, making it challenging to specify an optimal set of rules or reward signals that can account for all possible situations. In such environments, learning from an expert's behavior through imitation is often more appealing. This is where imitation learning (IL) comes into play - a process where desired behavior is learned by imitating an expert's behavior, which is provided through demonstrations. This paper aims to provide an introduction to IL and an overview of its underlying assumptions and approaches. It also offers a detailed description of recent advances and emerging areas of research in the field. Additionally, the paper discusses how researchers have addressed common challenges associated with IL and provides potential directions for future research. Overall, the goal of the paper is to provide a comprehensive guide to the growing field of IL in robotics and AI.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl