This study addresses the temperature variation and volatility over 100 years, showing results on a daily, monthly, and annual basis. Our focus is to show how these variations have changed over long periods and have become more volatile over the past 30 years. Using this information, we then developed a comprehensive model, the temperature variation index that examines temperature volatility and provides predictions for future years. This index reveals the biggest impact of climate change is occurring in February and November, while the lowest impact is in the Summer. The index serves as a tool to compare temperature data across different time intervals, enabling data scaling and the identification of differences between data points on a new proposed scale. Data from 1900 to 2021 are examined to analyze temperature distributions and patterns over time. A new mapping technique is introduced, along with algorithms, to illustrate the weather patterns and shifts that have occurred in the past 30 years compared to the last 120 years. This technique provides a visual representation of temperature changes, aiding in the identification of trends and patterns. The results of this study contribute to existing research by offering a detailed analysis of temperature variation and volatility over a long period. The findings enhance understanding and management of climate variability.

There are multiple and complex topics about Alzheimer’s disease (AD). Many of which have been investigated through studies on Cerebrospinal fluid (CSF). In this report, we present a machine learning pipeline for the diagnoses of AD using non-CSF body fluids. Protein samples of Saliva, urine, and serum were collected from 10 control subjects, 10 MCI sufferers and 10 AD patients, and analyzed by a mass spectrometer to generate the data used. Variability in data was attained through feature engineering with statistical methods and data mining with custom routines built on Keras deep learning. The model achieved an average of 90

OBJECTIVE:Continuous glucose monitoring (CGM) parameters may identify individuals at risk for progression to overt type 1 diabetes. We aimed to determine whether CGM metrics provide additional insights into progression to clinical stage 3 type 1 diabetes.RESEARCH DESIGN AND METHODS:One hundred five relatives of individuals in type 1 diabetes probands (median age 16.8 years; 89% non-Hispanic White; 43.8% female) from the TrialNet Pathway to Prevention study underwent 7-day CGM assessments and oral glucose tolerance tests (OGTTs) at 6-month intervals. The baseline data are reported here. Three groups were evaluated: individuals with 1) stage 2 type 1 diabetes (n = 42) with two or more diabetes-related autoantibodies and abnormal OGTT; 2) stage 1 type 1 diabetes (n = 53) with two or more diabetes-related autoantibodies and normal OGTT; and 3) negative test for all diabetes-related autoantibodies and normal OGTT (n = 10).RESULTS:Multiple CGM metrics were associated with progression to stage 3 type 1 diabetes. Specifically, spending ≥5% time with glucose levels ≥140 mg/dL (P = 0.01), ≥8% time with glucose levels ≥140 mg/dL (P = 0.02), ≥5% time with glucose levels ≥160 mg/dL (P = 0.0001), and ≥8% time with glucose levels ≥160 mg/dL (P = 0.02) were all associated with progression to stage 3 disease. Stage 2 participants and those who progressed to stage 3 also exhibited higher mean daytime glucose values; spent more time with glucose values over 120, 140, and 160 mg/dL; and had greater variability.CONCLUSIONS:CGM could aid in the identification of individuals, including those with a normal OGTT, who are likely to rapidly progress to stage 3 type 1 diabetes.

OBJECTIVE Continuous glucose monitoring (CGM) parameters may identify subjects at risk of progressing to overt type 1 diabetes. We aimed to determine whether CGM metrics provides additional insights into progression to clinical Stage 3 type 1 diabetes. RESEARCH DESIGN AND METHODS One hundred and five relatives of type 1 diabetes probands (median age 16.8 years; 89% non-Hispanic White; 43.8% female) from the TrialNet Pathway to Prevention Study underwent 7-day CGM assessments and oral glucose tolerance tests (OGTTs) at 6-month intervals, the baseline data is reported here. Three groups were evaluated: individuals with 1) Stage 2 type 1 diabetes (n=42) with ≥2 diabetes-related autoantibodies and abnormal OGTT; 2) Stage 1 type 1 diabetes (n=53) with ≥2 diabetes-related autoantibodies and normal OGTT; and 3) negative test for all diabetes-related autoantibodies and normal OGTT (n=10). RESULTS Multiple CGM metrics were associated with progression to Stage 3 type 1 diabetes. Specifically, spending ≥5% time with glucose levels ≥140 mg/dL (p = 0.01), ≥8% time ≥140 mg/dL (p=0.02), ≥5% time ≥160 mg/dL (p = 0.0001) and ≥8% of the time spent at glucose levels ≥160 mg/dL (p=0.02) were all associated with progression to Stage 3 disease. Stage 2 participants and those who progressed to Stage 3 also exhibited higher mean day-glucose values, spent more time with glucose values over 120, 140 and 160 mg/dL, and had greater variability. CONCLUSIONS CGM could aid in the identification of subjects, including those with a normal OGTT, who are likely to rapidly progress to Stage 3 type 1 diabetes.

IntroductionWe describe herein a large-scale, multidisciplinary course-based undergraduate research experience program (CRE) developed at Lawrence Technological University (LTU). In our program, all students enrolled in CRE classes participate in authentic research experiences within the framework of the curriculum, eliminating self-selection processes and other barriers to traditional extracurricular research experiences.MethodsSince 2014, we have designed and implemented more than 40 CRE courses in our College of Arts and Sciences involving more than 30 instructors from computer science, mathematics, physics, biology, chemistry, English composition, literature, philosophy, media communication, nursing, and psychology.ResultsAssessment survey data indicates that students who participate in CRE courses have an enhanced attitude towards research and discovery, as well as increased self-efficacy. This intervention is particularly relevant for non-traditional students, such as students who commute and/or have significant work or childcare commitments, who often experience limited access to research activities.DiscussionHerein we highlight the importance of a systemic institutional change that has made this intervention sustainable and likely to outlast the external funding phase. Systemic change can emerge from a combination of conditions, including: (1) developing a critical mass of CRE courses by providing instructors with both incentives and training; (2) developing general principles on which instructors can base their CRE activities; (3) securing and maintaining institutional support to promote policy changes towards a more inclusive institution; and (4) diversifying the range of the intervention, both in terms of initiatives and disciplines involved.

We introduce a novel n-stage vaccination model and corresponding system of differential equations that stratify a population according to their vaccination status. The model is an extension of the classical SIR-type models commonly used for time-course simulations of infectious disease spread and allows for the mitigation effects of vaccination to be uncoupled from other factors, such as changes in social behavior and the prevalence of virus variants. We fit the model to the Virginia Department of Health data on new COVID-19 cases, hospitalizations, and deaths broken down by vaccination status. The model suggests that, from 23 January through 11 September, fully vaccinated individuals were 89.8% less likely to become infected with COVID-19 and that the B.1.617.2 (Delta) variant is 2.08 times more transmissible than previously circulating strains of COVID-19. We project the model trajectories into the future to predict the impact of booster shots.

We introduce a distributed-delay differential equation disease spread model for COVID-19 spread. The model explicitly incorporates the population's time-dependent vaccine uptake and incorporates a gamma-distributed temporary immunity period for both vaccination and previous infection. We validate the model on COVID-19 cases and deaths data from the state of Michigan and use the calibrated model to forecast the spread and impact of the disease under a variety of realistic booster vaccine strategies. The model suggests that the mean immunity duration for individuals after vaccination is 350 days and after a prior infection is 242 days. Simulations suggest that both high population-wide adherence to vaccination mandates and a more-than-annually frequency of booster doses will be required to contain outbreaks in the future.

The TrialNet Pathway to Prevention Study (PtP) has enhanced our understanding of the progression through the stages of T1D identifying individuals at early stages of disease for prevention trials. Individuals with multiple islet AAb and normal OGTT are considered to be at Stage 1 T1D, and those with abnormal OGTT are at Stage 2 T1D. Our aim was to determine whether subtle forms of dysglycemia provide new clues about disease progression (Stage 3). 94 relatives of T1D probands from PtP underwent a 7-day CGM assessment (Dexcom G4 Platinum Professional) Three groups of relatives were evaluated: 1) Relatives at Stage 2 T1D whose 5-year risk for T1D progression is ∼70% (n=33); 2), relatives at Stage 1 T1D whose 5-year risk for T1D progression is ∼ 35% (n=51); and relatives with negative confirmed AAb and normal OGTT (n=10) whose 5-year risk for progression is <1%. CGM variations occur in a significant number of subjects with Stage 1 T1D (Figure) with increased variation in CGM glucose values assessed by mean amplitude of glucose excursion (MAGE) in relatives (n= 4 Stage 0; 31 Stage 1; and 27 Stage 2) at Stage 1 and Stage 2 T1D compared to that of low risk relatives (P = 0.005). Interstitial glucose >140 mg/dL was higher in relatives at Stage 1 and Stage 2 T1D compared to low risk relatives (P = 0.02). In conclusion, CGM metrics may be useful in identifying dysglycemia in at risk relatives with normal OGTT. Disclosure D.M. Wilson: Advisory Panel; Self; Tolerion, Inc. Research Support; Self; Beta Bionics, Dexcom, Inc., Medtronic. P. Nelson: None. D. Scheinker: Advisory Panel; Self; Carta Healthcare. S. Pietropaolo: None. M. Acevedo-Calado: None. M. Ebrahimi: None. A. Steck: None. J.L. Dunne: None. C. Greenbaum: Research Support; Self; Janssen Research & Development. M. Pietropaolo: None. Funding National Institutes of Health

ABSTRACT Environmental antibiotic risk management requires an understanding of how subinhibitory antibiotic concentrations contribute to the spread of resistance. We develop a simple model of competition between sensitive and resistant bacterial strains to predict the minimum selection concentration (MSC), the lowest level of antibiotic at which resistant bacteria are selected. We present an analytical solution for the MSC based on the routinely measured MIC, the selection coefficient ( sc ) that expresses fitness differences between strains, the intrinsic net growth rate, and the shape of the bacterial growth dose-response curve with antibiotic or metal exposure (the Hill coefficient [κ]). We calibrated the model by optimizing the Hill coefficient to fit previously reported experimental growth rate difference data. The model fit varied among nine compound-taxon combinations examined but predicted the experimentally observed MSC/MIC ratio well ( R 2 ≥ 0.95). The shape of the antibiotic response curve varied among compounds (0.7 ≤ κ ≤ 10.5), with the steepest curve being found for the aminoglycosides streptomycin and kanamycin. The model was sensitive to this antibiotic response curve shape and to the sc , indicating the importance of fitness differences between strains for determining the MSC. The MSC can be >1 order of magnitude lower than the MIC, typically by the factor sc κ . This study provides an initial quantitative depiction and a framework for a research agenda to examine the growing evidence of selection for resistant bacterial communities at low environmental antibiotic concentrations.

This chapter provides an overview of the Lambert W function approach. The approach has been developed for analysis and control of linear time-invariant time delay systems with a single known delay. A solution in the time-domain is given in terms of an infinite series, with the important characteristic that truncating the series provides a dominant solution in terms of the rightmost eigenvalues. A solution via the Lambert W function approach is first presented for systems of order one, then extended to higher order systems using the matrix Lambert W function. Free and forced solutions are used to investigate key properties of time-delay systems, such as stability, controllability and observability. Through eigenvalue assignment, feedback controllers and state-observers are designed. All of these can be achieved using the Lambert W function-based framework. The use of the MATLAB-based open source software in the LambertWDDE Toolbox is also introduced using numerical examples.

A new approach to design PI controllers for time delay systems is presented. A time-delay can limit and degrade the achievable performance of the controlled system, and even induce instability. This paper presents a new method, based on the Lambert W function [20], for design of PI feedback controllers as an alternative to the well-known Smith predictor. PI controllers for first-order plants with time-delays are de signed by obtaining the rightmost (i.e., dominant) eigenvalues in the infinite eigenspectrum of time-delay systems, and assigning them to desired positions in the complex plane. The process is possible due to a novel property of the Lambert W function. Using the controllers designed by using the presented approach, system performance can be improved as well as successfully stabilized. Also, sensitivity analysis of the rightmost eigenvalues is conducted to show that robustness compares favorably to the Smith predictor.

Regulatory T-cells (Tregs) are a subset of CD4+ T-cells that have been found to suppress the immune response. During HIV viral infection, Treg activity has been observed to have both beneficial and deleterious effects on patient recovery; however, the extent to which this is regulated is poorly understood. We hypothesize that this dichotomy in behavior is attributed to Treg dynamics changing over the course of infection through the proliferation of an ‘adaptive’ Treg population which targets HIV-specific immune responses. To investigate the role Tregs play in HIV infection, a delay differatial equation model was constructed to examine (1) the possible existence of two distinct Treg populations, normal (nTregs) and adaptive (aTregs), and (2) their respective effects in limiting viral load. Sensitivity analysis was performed to test parameter regimes that show the proportionality of viral load with adaptive regulatory populations and also gave insight into the importance of downregulation of CD4+ cells by normal Tregs on viral loads. Through the inclusion of Treg populations in the model, a diverse array of viral dynamics was found. Specifically, oscillatory and steady state behaviors were both witnessed and it was seen that the model provided a more accurate depiction of the effector cell population as compared with previous models. Through further studies of adaptive and normal Tregs, improved treatments for HIV can be constructed for patients and the viral mechanisms of infection can be further elucidated.

An overview of the recently developed Lambert W function approach for analysis and control of linear time invariant time delay systems (TDS) with a single known delay is provided. A solution via the Lambert W function is first presented for systems of order one, then extended to higher order systems using the matrix Lambert W function. Free and forced solutions, stability, observability, controllability, and observer and controller design via eigenvalue assignment can all be studied based on this framework. The use of the Matlab-based open source software in the LambertW_DDE Toolbox is introduced using examples.

A new design method for proportional-plus integral (PI) and proportional-plus velocity (PV) control for DC motors to regulate angular velocity and position considering time-delays, is presented. Time-delays inherent in plants can arise from signal processing and actuation and can lead to undesired system performance including instability. Thus, time-delays should be considered in designing controllers. In this paper, a method based on the Lambert W function is used to address such problems caused by time-delays. The method enables one to find the rightmost (i.e., dominant) characteristic roots in the infinite eigenspectrum. Then, PI and PV control gains are obtained by assigning the rightmost eigenvalues to desired positions in the complex plane. The assignment can be achieved thanks to a novel property of the Lambert W function. System performance can be improved as well as successfully stabilized. Effectiveness of the presented method is verified through simulations and experiments. Also, sensitivity analysis of the rightmost eigenvalues with respect to delay mismatches is conducted to compare the proposed approach to a prediction-based method.

BACKGROUND Several metrics of glucose variability have been proposed to date, but an integrated approach that provides a complete and consistent assessment of glycemic variation is missing. As a consequence, and because of the tedious coding necessary during quantification, most investigators and clinicians have not yet adopted the use of multiple glucose variability metrics to evaluate glycemic variation. METHODS We compiled the most extensively used statistical techniques and glucose variability metrics, with adjustable hyper- and hypoglycemic limits and metric parameters, to create a user-friendly Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation (CGM-GUIDE©). In addition, we introduce and demonstrate a novel transition density profile that emphasizes the dynamics of transitions between defined glucose states. RESULTS Our combined dashboard of numerical statistics and graphical plots support the task of providing an integrated approach to describing glycemic variability. We integrated existing metrics, such as SD, area under the curve, and mean amplitude of glycemic excursion, with novel metrics such as the slopes across critical transitions and the transition density profile to assess the severity and frequency of glucose transitions per day as they move between critical glycemic zones. CONCLUSIONS By presenting the above-mentioned metrics and graphics in a concise aggregate format, CGM-GUIDE provides an easy to use tool to compare quantitative measures of glucose variability. This tool can be used by researchers and clinicians to develop new algorithms of insulin delivery for patients with diabetes and to better explore the link between glucose variability and chronic diabetes complications.

The objective of this study was to determine whether antigenic determinants localized within the extracellular domain of the neuroendocrine autoantigen tyrosine phosphatase-like protein IA-2 are targets of humoral responses in type 1 diabetes (T1DM). Previous studies indicated that the immunodominant region of IA-2 is localized within its intracellular domain (IA-2ic; amino acids 601-979). We analyzed 333 subjects from the Children's Hospital of Pittsburgh study, 102 of whom progressed to insulin-requiring diabetes (prediabetics). Autoantibodies from these individuals were initially assayed for ICA512bdc (Barbara Davis Center amino acids 257-556; 630-979), IA-2ic (amino acids 601-979), and IA-2 full-length (amino acids 1-979) in addition to islet cell antibody (ICA), glutamic acid decarboxylase, 65-kDa isoform, and insulin autoantibodies. We identified an autoantibody response reactive with the extracellular domain of IA-2 that is associated with very high risk of T1DM progression. Relatives with no detectable autoantibodies against ICA512bdc (or IA-2ic) exhibited antibody responses against the IA-2 full-length peptide (log rank, P = 0.008). This effect was also observed in first-degree relatives who were positive for glutamic acid decarboxylase, 65-kDa isoform (log rank, P = 0.026) or at least two islet autoantibodies but were negative for ICA512bdc (log rank, P = 0.022). Competitive binding experiments and immunoprecipitation of the IA-2 extracellular domain (amino acid residues 26-577) further lend support for the presence of autoantibodies reactive with new antigenic determinants within the extracellular domain of IA-2. In summary, the addition of measurements of autoantibodies reactive with the IA-2 extracellular domain to assays geared to assess the progression of autoimmunity to clinical T1DM may more accurately characterize this risk. This has considerable implications not only for stratifying high diabetes risk but also facilitating the search for pathogenic epitopes to enable the design of peptide-based immunotherapies that may prevent the progression to overt T1DM at its preclinical stages.