AL refers to the axial length, CCT to the central corneal thickness, ACD to the external phakic anterior chamber depth measured from the corneal front apex to the front apex of the crystalline lens, LT to the central thickness of the crystalline lens, R1 and R2 to the corneal radii of curvature for the flat and steep meridians, Rmean to the average of R1 and R2, PIOL to the refractive power of the intraocular lens implant, and SEQ to the spherical equivalent power achieved 5 to 12 weeks after cataract surgery.

The accurate calculation of reaction-free energies (ΔrG°) for diboronic acids and carbohydrates is challenging due to reactant flexibility and strong solute–solvent interactions. In this study, these challenges are addressed with a semiautomatic workflow based on quantum chemistry methods to calculate conformational free energies, generate microsolvated solute structural ensembles, and compute ΔrG°. Workflow parameters were optimized for accuracy and precision while controlling computational costs. We assessed the accuracy by studying three reactions of diboronic acids with glucose and galactose, finding that the conformational entropy contributes significantly (by 3–5 kcal/mol at room temperature). Explicit solvent molecules improve the computed ΔrG° accuracy by about 4 kcal/mol compared to experimental data, though using 13 or more water molecules reduced precision and increased computational overhead. After fine-tuning, the workflow demonstrated remarkable accuracy, with an absolute error of about 2 kcal/mol compared to experimental ΔrG° and an average interquartile range of 2.4 kcal/mol. These results highlight the workflow’s potential for designing and screening tweezer-like ligands with tailored selectivity for various carbohydrates.

📊 Walmart Stock Price Dataset & Exploratory Data Analysis (EDA)

🏢 About Walmart

Walmart Inc. is a multinational retail corporation that operates a chain of hypermarkets, discount department stores, and grocery stores. It is one of the world's largest companies by revenue and a key player in the retail sector. Walmart's stock is actively traded on major stock exchanges, making it an interesting subject for financial analysis.

📌 Dataset Overview

This dataset contains historical stock price data for Walmart, sourced directly from Yahoo Finance using the yfinance Python API. The data covers daily stock prices and includes multiple key financial indicators.

📊 Features Included in the Dataset

• Date 📅 – The trading day recorded.
• Open Price 🟢 – Price at market open.
• High Price 🔼 – Highest price of the day.
• Low Price 🔽 – Lowest price of the day.
• Close Price 🔴 – Price at market close.
• Adjusted Close Price 📉 – Closing price adjusted for splits & dividends.
• Trading Volume 📈 – Total shares traded.
• Dividends 💰 – Cash payments to shareholders.
• Stock Splits 🔄 – Records stock split events.

🔍 Exploratory Data Analysis (EDA) Steps

This notebook performs an extensive EDA to uncover insights into Walmart's stock price trends, volatility, and overall behavior in the stock market. The following analysis steps are included:

1️⃣ Data Preprocessing & Cleaning

• Load data using Pandas
• Handle missing values (if any)
• Check data types and format them properly
• Convert date column into a datetime format

2️⃣ Descriptive Statistics & Summary

• Calculate key statistical measures like mean, median, standard deviation, and interquartile range (IQR)
• Identify stock price trends over time
• Check data distribution and skewness

3️⃣ Data Visualizations

• 📉 Line Plot – Analyze trends in closing prices over time.
• 📦 Box Plot – Detect potential outliers in stock prices.
• 📊 Histogram – Understand the distribution of closing prices.
• 📈 Moving Averages – Use short-term and long-term moving averages to observe stock trends.
• 🔥 Correlation Heatmap – Find relationships between stock market indicators.

4️⃣ Time Series Analysis

• Identify trends and seasonality in the stock price data.
• Calculate daily, weekly, and monthly returns.
• Use rolling windows to analyze moving averages and volatility.

5️⃣ Insights & Conclusions

• How volatile is Walmart’s stock over the given period?
• Does the stock exhibit strong uptrends or downtrends?
• Are there any strong correlations between features?
• What insights can be drawn for investors and traders?

🚀 Use Cases & Applications

This dataset and analysis can be useful for: - 📡 Stock Market Analysis – Evaluating Walmart’s stock price trends and volatility. - 🏦 Investment Research – Assisting traders and investors in making informed decisions. - 🎓 Educational Purposes – Teaching data science and financial analysis using real-world stock data. - 📊 Algorithmic Trading – Developing trading strategies based on historical stock price trends.

📥 Download the dataset and explore Walmart’s stock performance today! 🚀

The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% for the test data.

The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low-frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.

The features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ. These time-domain signals (prefix 't' to denote time) were captured at a constant rate of 50 Hz. Then they were filtered using a median filter and a 3rd order low pass Butterworth filter with a corner frequency of 20 Hz to remove noise. Similarly, the acceleration signal was then separated into the body and gravity acceleration signals (tBodyAcc-XYZ and tGravityAcc-XYZ) using another low pass Butterworth filter with a corner frequency of 0.3 Hz.

Subsequently, the body l linear acceleration and angular velocity were derived in time to obtain Jerk signals (tBodyAccJerk-XYZ and tBodyGyroJerk-XYZ). Also the magnitude of these three-dimensional signals were calculated using the Euclidean norm (tBodyAccMag, tGravityAccMag, tBodyAccJerkMag, tBodyGyroMag, tBodyGyroJerkMag).

Finally a Fast Fourier Transform (FFT) was applied to some of these signals producing fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag. (Note the 'f' to indicate frequency domain signals).

These signals were used to estimate variables of the feature vector for each pattern: '-XYZ' is used to denote 3-axial signals in the X, Y, and Z directions.

tBodyAcc-XYZ tGravityAcc-XYZ tBodyAccJerk-XYZ tBodyGyro-XYZ tBodyGyroJerk-XYZ tBodyAccMag tGravityAccMag tBodyAccJerkMag tBodyGyroMag tBodyGyroJerkMag fBodyAcc-XYZ fBodyAccJerk-XYZ fBodyGyro-XYZ fBodyAccMag fBodyAccJerkMag fBodyGyroMag fBodyGyroJerkMag

The set of variables that were estimated from these signals are:

mean(): Mean value std(): Standard deviation mad(): Median absolute deviation max(): Largest value in array min(): Smallest value in array sma(): Signal magnitude area energy(): Energy measure. Sum of the squares divided by the number of values. iqr(): Interquartile range entropy(): Signal entropy arCoeff(): Autorregresion coefficients with Burg order equal to 4 correlation(): correlation coefficient between two signals maxInds(): index of the frequency component with the largest magnitude meanFreq(): Weighted average of the frequency components to obtain a mean frequency skewness(): skewness of the frequency domain signal kurtosis(): kurtosis of the frequency domain signal bandsEnergy(): Energy of a frequency interval within the 64 bins of the FFT of each window. angle(): Angle between two vectors.

Additional vectors are obtained by averaging the signals in a signal window sample. These are used on the angle() variable:

gravityMean tBodyAccMean tBodyAccJerkMean tBodyGyroMean tBodyGyroJerkMean

This data set consists of the following columns:

1 tBodyAcc-mean()-X 2 tBodyAcc-mean()-Y 3 tBodyAcc-mean()-Z 4 tBodyAcc-std()-X 5 tBodyAcc-std()-Y 6 tBodyAcc-std()-Z 7 tBodyAcc-mad()-X 8 tBodyAcc-mad()-Y 9 tBodyAcc-mad()-Z 10 tBodyAcc-max()-X 11 tBodyAcc-max()-Y 12 tBodyAcc-max()-Z 13 tBodyAcc-min()-X 14 tBodyAcc-min()-Y 15 tBodyAcc-min()-Z 16 tBodyAcc-sma() 17 tBodyAcc-energy()-X 18 tBodyAcc-energy()-Y 19 tBodyAcc-energy()-Z 20 tBodyAcc-iqr()-X 21 tBodyAcc-iqr()-Y 22 tBodyAcc-iqr()-Z 23 tBodyAcc-entropy()-X 24 tBodyAcc-entropy()-Y 25 tBodyAcc-entropy()-Z 26 tBodyAcc-arCoeff()-X,1 27 tBodyAcc-arCoeff()-X,2 28 tBodyAcc-arCoeff()-X,3 29 tBodyAcc-arCoeff()-X,4 30 tBodyAcc-arCoeff()-Y,1 31 tBodyAcc-arCoeff()-Y,2 32 tBodyAcc-arCoeff()-Y,3 33 tBodyAcc-arCoeff()-Y,4 34 tBodyAcc-arCoeff()-Z,1 35 tBodyAcc-arCoeff()-Z,2 36 tBodyAcc-arCoeff()-Z,3 37 tBodyAcc-arCoeff()-Z,4 38 tBodyAcc-correlation()-X,Y 39 tBodyAcc-correlation()-X,Z 40 tBodyAcc-correlation()-Y,Z 41 tGravityAcc-mean()-X 42 tGravit...

Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.

BackgroundRheumatic and musculoskeletal disorders (RMDs) are associated with cardiovascular diseases (CVDs), with hypertension being the most common. We aimed to determine the prevalence of high blood pressure (HBP), awareness, treatment, and blood pressure control among patients with RMDs seen in a Rheumatology clinic in Uganda.MethodsWe conducted a cross-sectional study at the Rheumatology Clinic of Mulago National Referral Hospital (MNRH), Kampala, Uganda. Socio-demographic, clinical characteristics and anthropometric data were collected. Multivariable logistic regression was performed using STATA 16 to determine factors associated with HBP in patients with RMDs.ResultsA total of 100 participants were enrolled. Of these, majority were female (84%, n = 84) with mean age of 52.1 (standard deviation: 13.8) years and median body mass index of 28 kg/m2 (interquartile range (IQR): 24.8 kg/m2–32.9 kg/m2). The prevalence of HBP was 61% (n = 61, 95% CI: 51.5–70.5), with the majority (77%, n = 47, 95% CI: 66.5–87.6) being aware they had HTN. The prevalence of HTN was 47% (n = 47, 37.2–56.8), and none had it under control. Factors independently associated with HBP were age 46-55years (adjusted prevalence ratio (aPR): 2.5, 95% confidence interval (CI): 1.06–5.95), 56–65 years (aPR: 2.6, 95% CI: 1.09–6.15), >65 years (aPR: 2.5, 95% CI: 1.02–6.00), obesity (aPR: 3.7, 95% CI: 1.79–7.52), overweight (aPR: 2.7, 95% CI: 1.29–5.77).ConclusionThere was a high burden of HBP among people with RMDs in Uganda with poor blood pressure control, associated with high BMI and increasing age. There is a need for further assessment of the RMD specific drivers of HBP and meticulous follow up of patients with RMDs.

BackgroundOverweight and obesity are major concerns among people living with HIV (PLWH), particularly those on integrase inhibitors, as they elevate the risk of cardiovascular diseases. However, longitudinal data on the burden and risk factors for overweight/obesity in sub-Saharan Africa (SSA) remain limited. This study aimed to estimate the incidence and identify factors associated with overweight and obesity among PLWH who switched to a dolutegravir (DTG)-based ART regimen at Livingstone University Teaching Hospital.MethodsWe enrolled 249 adults aged ≥18 years living with HIV on ART [non-nucleoside/nucleotide reverse transcriptase inhibitor (NNRTI) n = 174, protease inhibitor (PI) n = 21, and DTG n = 54] with a baseline body mass index (BMI) < 25 kg/m² between April 2019 and May 2020 and conducted a single follow-up assessment between December 2022 and June 2023. Participants were followed for a median of 43 months (interquartile range [IQR]: 42, 44). At follow-up, all participants were on a DTG-based regimen for a median time of 23 months (IQR: 19, 40). Demographic, clinical, and laboratory data were collected using a structured questionnaire. The primary outcome was overweight/obesity, defined as BMI ≥ 25 kg/ m2. Poisson regression with robust standard errors was used to determine risk factors for being overweight and obesity.ResultsThe median age was 44 years (interquartile range (IQR) 36, 51) at baseline, with the majority being female (59.4%, n = 148). Over a total follow-up of 871.5 person-years, 44 incident cases of overweight/obesity occurred, yielding a cumulative incidence of 17.7% (44/249) and an incidence rate of 5.05 per 100 person-years. Factors positively associated with the risk of being overweight/obesity included being married (adjusted incidence rate ratio [aIRR] 2.34; 95% CI 1.24, 4.40), lower baseline CD4 count (aIRR 4.13; 95% CI 1.41, 13.38) and higher waist circumference (WC) values (aIRR 1.07; 95% CI 1.03, 1.11). While older age was associated with a lower risk of overweight/obesity (aIRR 0.97; 95% CI 0.94, 0.99).ConclusionThe burden of overweight/obesity was high, and it was significantly driven by demographic, anthropometric, and immunological factors among our study participants. The findings suggest the importance of implementing targeted screening and management strategies for overweight and obesity, particularly among married individuals with higher WC values. Studies investigating the underlying mechanisms of excessive weight gain among PLWH on an integrase inhibitor-based regimen in resource-limited settings are warranted.

SD refers to the standard deviation, 2.5% quantile and 97.5% quantile to the lower and upper boundary of the 95% confidence interval, and IQR to the interquartile range as the difference between the 75% and the 25% quantile. Formula constant optimisation was performed to minimise the sum of squared prediction errors PE. Situation A) refers to the ‘classical’ formulae with standard nK/nC values, with situation B) the formula constants and nK/nC in the main part of the formula were varied for optimisation, with situation C) the formula constants and nK/nC in the main part of the formula were varied to minimise for PE and the PE trend error over corneal radius, and with situation D) a standard optimisation was performed using the nK/nC value from situation B) derived from the other dataset in terms of a cross-validation.

The association between maternal intrahepatic cholestasis and different offspring neurodevelopmental conditions, stratified by the gestational period of onset of intrahepatic cholestasis.

The association between intrahepatic cholestasis of pregnancy and neurodevelopmental conditions after adjusting for gestational age.

BackgroundHypertension has in the recent past surfaced as one of the conditions that has a significant impact on workforce productivity in emerging economies. Zambia is no different and has in the recent past recorded increasing cases. Despite the impact of hypertension being of great importance in regards to productivity, we have scarcity of data and studies on hypertension-related Productivity-Adjusted Life-Years (PALYs) in Zambia and Africa at large.This study assessed the impact of hypertension on PALYs lost and socioeconomic factors associated with nonadherence to antihypertensive medication (NATAM).MethodsThis was a cross-sectional study of 198 participants from Livingstone University Teaching Hospital and Maramba Clinic situated in Livingstone, Zambia. Structured questionnaires were used to collect data. Productivity index multiplied by years lived was used to calculate PALYs and descriptive statistics were used to summarize sociodemographic, clinical and economic variables. Multivariable logistic regression was used to determine factors associated with NATAM.ResultsThe participants had a median age (interquartile range (IQR)) of 49 years (41, 59) and 60.1% (n = 119) were females while 39.9% (n = 79) were male. Our estimated PALYs lost per person due to hypertension were 0.2 (IQR 0.0, 2.7). Cumulative PALYs value lost due to the burden of hypertension was estimated to be at $871,239.58 in gross domestic product (GDP). The prevalence of NATAM was 48% (n = 95). The factors that were significantly associated with NATAM were age (odds ratio (OR) 0.94; 95% confidence interval (CI) 0.90, 0.98), female sex (OR 2.52; 95%CI 1.18, 5.40), self-employment (OR 2.57; 95%CI 1.02, 6.45) and absenteeism from work (OR 3.60; 95%CI 1.16, 11.22).ConclusionsFindings in our study highlight a high economic loss of PALYs due to hypertension with a potential to impact GDP negatively. We also found that NATAM reduced productivity and income among individuals of working age further impacting PALYs lost due to hypertension. The factors associated with NATAM were age, sex, employment status and absenteeism from work. This study underscores the need for interventions targeting young people, females, self-employed individuals, and absentees at work to improve adherence to antihypertensive drugs in order to reduce PALYs lost due to hypertension.

WHO age-standardized and age-specific multimorbidity and dual long-term conditions combinations prevalence estimates: Malawi, The Gambia and Uganda.

Baseline lifestyle factor prevalence estimates: Malawi and Uganda.

Estimates of the association between sociodemographic and lifestyle factors and multimorbidity: Malawi, The Gambia and Uganda.

BackgroundAs a metric to determine the robustness of trial results, the fragility index (FI) is the number indicating how many patients would be required to reverse the significant results. This study aimed to calculate the FI in randomized controlled trials (RCTs) involving premature.MethodsTrials were included if they had a 1:1 study design, reported statistically significant dichotomous outcomes, and had an explicitly stated sample size or power calculation. The FI was calculated for binary outcomes using Fisher’s exact test, and the FIs of subgroups were compared. Spearman’s correlation was applied to determine correlations between the FI and study characteristics.ResultsFinally, 66 RCTs were included in the analyses. The median FI for these trials was 3.00 (interquartile range [IQR]: 1.00–5.00), with a median fragility quotient of 0.014 (IQR: 0.008–0.028). FI was ≤ 3 in 42 of these 66 RCTs (63.6%), and in 42.4% (28/66) of the studies, the number of patients lost to follow-up was greater than that of the FI. Significant differences were found in the FI among journals (p = 0.011). We observed that FI was associated with the sample size, total number of events, and reported p-values (rs = 0.437, 0.495, and −0.857, respectively; all p < 0.001).ConclusionFor RCTs in the premature population, a median of only three events was needed to change from a “non-event” to “event” to render a significant result non-significant, indicating that the significance may hinge on a small number of events.

Characteristics of the 2,375,856 children, born to 1,308,096 mothers between 1987 and 2010, who were included in the study, stratified by maternal ICP diagnosis.

BackgroundTuberculosis (TB) remains the leading cause of death among people living with HIV (PLHIV). To prevent TB among PLHIV, the Ugandan national guidelines recommend Isoniazid Preventive Therapy (IPT) across differentiated service delivery (DSD) models, an effective way of delivering ART. DSD models include Community Drug Distribution Point (CDDP), Community Client-led ART Delivery (CCLAD), Facility-Based Individual Management (FBIM), Facility-Based Group (FBG), and Fast Track Drug Refill (FTDR). Little is known about the impact of delivering IPT through DSD.MethodsWe reviewed medical records of PLHIV who initiated IPT between June-September 2019 at TASO Soroti (TS), Katakwi Hospital (KH) and Soroti Regional Referral Hospital (SRRH). We defined IPT completion as completing a course of isoniazid within 6–9 months. We utilized a modified Poisson regression to compare IPT completion across DSD models and determine factors associated with IPT completion in each DSD model.ResultsData from 2968 PLHIV were reviewed (SRRH: 50.2%, TS: 25.8%, KH: 24.0%); females: 60.7%; first-line ART: 91.7%; and Integrase Strand Transfer Inhibitor (INSTI)-based regimen: 61.9%. At IPT initiation, the median age and duration on ART were 41.5 (interquartile range [IQR]; 32.3–50.2) and 6.0 (IQR: 3.7–8.6) years, respectively. IPT completion overall was 92.8% (95%CI: 91.8–93.7%); highest in CDDP (98.1%, 95%CI: 95.0–99.3%) and lowest in FBG (85.8%, 95%CI: 79.0–90.7%). Compared to FBIM, IPT completion was significantly higher in CDDP (adjusted rate ratio [aRR] = 1.15, 95%CI: 1.09–1.22) and CCLAD (aRR = 1.09, 95% CI 1.02–1.16). In facility-based models, IPT completion differed between sites (p

IntroductionHuman Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major public health concern globally. Due to advancements in Anti-Retroviral Treatment (ART) therapy, more people with HIV are living longer with about 1.4 million infected people in Uganda. Anxiety disorders are often unrecognized and undetected in older persons living with HIV (PLWH) yet they impair an elderly person’s physical health and decrease the ability to perform daily activities.ObjectiveTo determine the prevalence and factors associated with probable anxiety disorders among elderly PLWH at Mulago Immune Suppression Syndrome (ISS) clinic.MethodsA cross-sectional study was conducted at Mulago ISS clinic among 273 systematically selected participants living with HIV/AIDS on antiretroviral therapy for at least 6 months between April and May 2024. Interviews were conducted using the Generalized Anxiety Disorder 7-item (GAD-7) screening tool to help identify individuals who may be at risk for anxiety disorders and structured questionnaires for socio-demographics, and psychological factors. Drug and clinical factors data were extracted from records, entered into Epidata, and later to STATA version 17 for analysis. Prevalence was reported as a percentage and modified Poisson regression analysis was used to determine the factors associated with anxiety disorders.ResultsWe enrolled 273 participants with a median age (Interquartile range) was 56 (52, 61.5) years. 54.9% were females, 56.8% didn’t have a partner and 53.8% were employed. The prevalence of probable anxiety disorders was 16.8% (95% CI 12.5–21.6). Employment status (aPR- 2.113, 95% CI 1.252–3.567), family history of mental health disorder (aPR-2.041, 95% CI 1.228–3.394), stigma (aPR-2.564, 95% CI 1.544–4.257) and family support (aPR-2.169, 95% CI 1.272–3.699) were significantly associated with having probable anxiety disorders.ConclusionOne in every six elderly persons living with HIV may have a probable anxiety disorder. Being unemployed, having a family history of mental health disorders, having stigma and having inadequate family support were significantly associated with having a probable anxiety disorder. Healthcare workers should provide comprehensive anxiety screening and patient-centered care for elderly persons with HIV. At the same time, the government develops financial empowerment strategies and supports mental health through family groups, and public campaigns to reduce HIV stigma and educate families on effective support.

Baseline socio-demographic factor prevalence estimates: Malawi, The Gambia and Uganda.

Factors associated with IPT completion among PLHIV in each DSD model.