Potential Variables Research Articles

Exploration of financial tax system in the context of big data

Abstract The financial tax system is related to the actual effectiveness of a country’s modernized economic development and has a close relationship with the country’s economic enhancement. To examine the causal effects between potential variables in the tax system, this paper constructs a measurement model of financial tax system variables using Bayesian structural equations. Parameter estimation for SEM is completed by adding prior information to the sample data and calculating the posterior distribution of relevant parameters using Bayes’ theorem. The correlation between financial taxation and economic development is calculated to indicate whether there is consistency in financial taxation on economic efficiency. Cronbach’s coefficient is used to measure the degree of consistency and stability of each part of the financial tax index system. The results show that the impact value of personal tax is 0.71, 0.37 and 4.18 in provinces C, A and B. Promoting economic development in provinces and municipalities with a better degree of economic development is aided by the current personal income tax sharing ratio. Reducing the proportion of indirect taxes while keeping the macro tax burden unchanged results in a gradual increase of the proportion of personal income tax in total tax revenue from 8.7% to 18.7%. The research in this paper further improves the financial tax system and provides an effective method to improve economic efficiency.

Parking choice behavior analysis of rural residents based on latent variable random forest model

ABSTRACT The imbalance of rural parking supply and demand has a great impact on traffic congestion and environmental pollution, which has attracted the attention of many scholars as well as policymakers. However, most of the current research on parking choice mainly focuses on urban business and residential areas, lacks research on rural parking choice behavior, and focuses on the analysis of observable factors, ignoring the internal relationship with potential variables. Based on this, this study considers the heterogeneity of individuals and uses the random forest algorithm to construct a model of rural residents’ willingness to choose parking with both latent and explicit variables, to explore how much and in what ways individual characteristics and parking characteristics affect rural residents’ parking choices, and to explore parking planning programs and strategies that are truly applicable to rural areas. The results of the study suggest that safety and convenience of the parking environment are key factors influencing the parking choice behavior of rural residents, and can greatly improve the predictive accuracy of the parking willingness model. Upon comparison, it is found that the application of the random forest algorithm is also significantly better than the logit model in terms of prediction effect, indicating that there is a nonlinear effect among the factors influencing the parking choice behavior of rural residents and that the random forest model with the addition of latent variables provides a better explanatory ability for the study of the parking choice behavior of rural residents.

Effect of Teleconnection Patterns on the Formation of Potential Ecological Flow Variables in Lowland Rivers

Climate is probably the most important factor affecting river discharge and flow dynamics. Low flows in rivers during the warm period cause stress to aquatic ecosystems and pose a challenge to sustainable water management. Previous research has shown that the average minimum discharge of the 30 driest continuous days, known as Q30, is a suitable measure for ecological flow estimation in Lithuania. This study aims to examine whether large-scale atmospheric processes, so-called teleconnections, can have an impact on Q30 during the warm period. Hydrological data for 1961–2020 from 25 water gauging stations were used to search for hydrological response signals with five selected climate indices (NAO, SCA, POL, EA/WR, and EA). Pearson correlation and Wilcoxon–Mann–Whitney test approaches were applied. The results suggested that the EA/WR and NAO had the strongest influence on Q30 in the studied region during the warm period. The positive phases of the indices tended to cause a greater decrease in Q30 values due to the prevailing easterly edge of the anticyclonic circulation over the studied region determined by the EA/WR and NAO indices, while the negative phases of the mentioned indices caused an increase and greater dispersion of Q30.

Characterization of Angraecum (Angraecinae, Orchidaceae) Plastomes and Utility of Sequence Variability Hotspots.

Angraecum, commonly known as Darwin's orchid, is the largest genus of Angraecinae (Orchidaceae). This genus exhibits a high morphological diversity, making it as a good candidate for macroevolutionary studies. In this study, four complete plastomes of Angraecum were firstly reported and the potential variability hotspots were explored. The plastomes possessed the typical quadripartite structure and ranged from 150,743 to 151,818 base pair (bp), with a guanine-cytosine (GC) content of 36.6-36.9%. The plastomes all contained 120 genes, consisting of 74 protein-coding genes (CDS), 38 transfer RNA (tRNA) genes and 8 ribosomal RNA (rRNA) genes; all ndh genes were pseudogenized or lost. A total of 30 to 46 long repeats and 55 to 63 SSRs were identified. Relative synonymous codon usage (RSCU) analysis indicated a high degree of conservation in codon usage bias. The Ka/Ks ratios of most genes were lower than 1, indicating that they have undergone purifying selection. Based on the ranking of Pi (nucleotide diversity) values, five regions (trnSGCU-trnGGCC, ycf1-trnNGGU, trnNGUU-rpl32, psaC-ndhE and trnSGCU-trnGGCC) and five protein-coding genes (rpl32, rps16, psbK, rps8, and ycf1) were identified. The consistent and robust phylogenetic relationships of Angraecum were established based on a total of 40 plastomes from the Epidendroideae subfamily. The genus Angraecum was strongly supported as a monophyletic group and sister to Aeridinae. Our study provides an ideal system for investigating molecular identification, plastome evolution and DNA barcoding for Angraecum.

Using Machine Learning (XGBoost) to Predict Outcomes After Infrainguinal Bypass for Peripheral Artery Disease.

To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass. Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited. The Vascular Quality Initiative database was used to identify patients who underwent infrainguinal bypass for peripheral artery disease between 2003 and 2023. We identified 97 potential predictor variables from the index hospitalization [68 preoperative (demographic/clinical), 13 intraoperative (procedural), and 16 postoperative (in-hospital course/complications)]. The primary outcome was 1-year major adverse limb event (composite of surgical revision, thrombectomy/thrombolysis, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained 6 ML models using preoperative features. The primary model evaluation metric was the area under the receiver operating characteristic curve (AUROC). The top-performing algorithm was further trained using intraoperative and postoperative features. Model robustness was evaluated using calibration plots and Brier scores. Overall, 59,784 patients underwent infrainguinal bypass, and 15,942 (26.7%) developed 1-year major adverse limb event/death. The best preoperative prediction model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93-0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.61 (0.59-0.63). Our XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs (95% CI's) of 0.94 (0.93-0.95) and 0.96 (0.95-0.97), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.08 (preoperative), 0.07 (intraoperative), and 0.05 (postoperative). ML models can accurately predict outcomes after infrainguinal bypass, outperforming logistic regression.

Could emergency admission plasma D-dimer level predict first pass effect of stent retriever thrombectomy in acute ischemic stroke?

Evidence on plasma biomarkers to identify first pass effect (FPE) in patients with acute ischemic stroke (AIS) with large vessel occlusion (LVO) treated with thrombectomy is limited. To evaluate whether plasma D-dimer could predict FPE. Consecutive patients with LVO who underwent first-line stent retriever thrombectomy at our center between January 2018 and August 2021 were enrolled. Patients were classified into the FPE (modified Thrombolysis in Cerebral Infarction [mTICI] ≥2c) group or non-FPE (mTICI 0-2b) group based on angiographic outcomes. Logistic regression analysis was performed to determine the predictors of FPE. The overall ability of D-dimer levels in predicting FPE was evaluated using receiver operating characteristic (ROC) curves. In total, 313 patients were included; 88 (28.1%) patients achieved FPE. Compared to those with non-FPE, patients with FPE had more diabetes mellitus history, lower D-dimer levels, higher clot burden score, a higher proportion of M1 middle cerebral artery, and a higher proportion of main stem occlusion pattern (P <0.05). After adjusting for potential variables, D-dimer levels (OR=0.81, 95% CI=0.52-0.96), clot burden score (OR=1.76, 95% CI=1.38-2.87), and main stem occlusion pattern (OR=1.85, 95% CI=1.19-2.62) remained independently associated with FPE. Based on the ROC analysis, the D-dimer as a predictor for predicting FPE presented with a specificity of 79%, a negative predictive value of 87%, and an area under the curve of 0.761. Low emergency admission plasma D-dimer level is an independent predictor of FPE in patients with AIS treated with stent retriever thrombectomy.

Measuring and Analyzing the Process Capability of Productivity – An Applied Study in the Al-Tahady Factory for the Production of Filters

This study addressed concerns related to increased percentages of damaged and re-worked production, heightened demand for factory products, and lack of awareness of the approved Sigma (σ) level during manufacturing, and associated deviations in the manufacturing process. The primary research problem was to assess the manufacturing process's stability and capability to consistently produce conical filters that meet required specifications. The study followed a sample-based approach, where twenty samples, each containing four observations, were collected continuously over a period of seven days. For each sample, the mean (X ̅) and range (R) were calculated. The mean X-Double bar of 319.32 and the average range R-bar of 0.848 were obtained through data analysis. The main findings revealed that, on average, the manufacturing process was relatively close to the target value (X-Double bar = 319.32). However, the presence of several data points outside the control limits indicated potential variability in the process. The average range (R-bar = 0.848) highlighted certain variations in the manufacturing process, which might contribute to issues like damaged or re-worked production. The study identified the need for further investigation to determine the root causes of these variations, which could include machine malfunctions, material fluctuations, or operator errors. By addressing these concerns and reducing process variability, the factory can enhance product quality, decrease waste, and improve customer satisfaction. In conclusion, continuous process monitoring and improvement initiatives, such as Six Sigma, are essential for achieving greater process capability in conical filter manufacturing. This research contributes valuable insights into process performance and provides a basis for implementing corrective actions to ensure consistent product quality and meet customer demands.

Correlation of European and U.S. Technology and Financial Stocks during COVID-19 Pandemic

In 2022 stock markets around the world lost some of their value, led by high-growth sectors, sectors that tend to show a significantly higher growth rate than the average market. At the same time the financial sector shows a potentially higher interest rate sensitivity. The prevailing academic opinion is that central bank monetary policy during the COVID-19 pandemic is one key factor for the stock market development globally. This paper discusses the assumption of a significant technology and financial sector correlation for Europe and the United States from 2020 to 2022 based on market capitalization. The objective is fulfilled with a Pearson correlation for representative parts of STOXX Europe 600 and S&amp;P 500. The results confirm significant correlation for the selected sectors and stocks for both regions, indicating potentially general sensitivity for the entire investigation period. Based on this, further research on sectors and potential alternative independent variables is recommended.

COVID-19 vaccination effectiveness in the population of Friuli Venezia Giulia, North-East Italy. Control of bias associated with divergent compliance to policies in a test-negative case-control study

BackgroundVaccine effectiveness (VE) studies consolidate knowledge of real-world effectiveness in different contexts. However, methodological issues may undermine their conclusions: to assess the VE against COVID-19 within the Italian population, a specific threat to validity is related to the consequences of divergent compliance to the Green Pass policy.MethodsTo address this challenge we conducted a test negative case-control (TNCC) study and multiple sensitivity analysis among residents aged ≥ 12 in Friuli Venezia Giulia Region (FVG), North-east Italy, from February 1, 2021 to March 31, 2022. Information regarding 211,437 cases of COVID-19 infection and 845,748 matched controls was obtained from the regional computerized health database. The investigation considered: COVID-19 infection, hospitalization, and death. Multiple conditional logistic regressions adjusted for covariates were performed and VE was estimated as (1-OR COVID-19vaccinated vs. unvaccinated)x100. Mediation analyses were carried out to offset potential collider variables, particularly, the number of swabs performed after the introduction of pandemic restrictions.ResultsFull-cycle VE against infection decreased from 96% (95% CI: 96, 97) in the Alpha period to 43% (95% CI: 42, 45) in the Omicron period. Booster dose raised the protection in Omicron period to 67% (95% CI: 66, 67). Against the evasive Omicron variant, the protection of the booster dose was 87% (95% CI: 83, 90) for hospitalization and 90% (95% CI: 82, 95) for death. The number of swabs performed was included as a covariate in the adjustments, and the mediation analysis confirmed that it was a strong mediator between vaccination and COVID-19-related outcomes.ConclusionsThe study suggests that, under similar TNCC settings, mediation analysis and adjustment for number of diagnostic tests should be included, as an effective approach to the challenge of differential testing behavior that may determine substantial selection bias. This correction allowed us to align with results from other studies that show how full-cycle VE against infection was initially high but decreased over time by variant circulation, counterbalanced by booster dose that raised protection across variants and outcome severity.

The use of tracheostoma humidification by people with total laryngectomy in England and Wales: A cross-sectional survey

BACKGROUND: People with total laryngectomy (PTL) are advised to use a tracheostoma cover to protect their altered airway and optimise pulmonary health. Despite evidence for efficacy, variable adherence rates are reported. OBJECTIVE: To investigate the use of tracheostoma humidification by PTL in England and Wales and explore influencing factors. METHODS: A national multi-centre audit of PTL was completed in response to the Covid-19 pandemic over a six-month period (March to September 2020). This paper reports secondary analysis of data collected on type of humidification used by PTL. Type of humidification was dichotomised as ‘HME’ (closed-system heat moisture exchanger) or ‘non-HME’ (alternative stoma cover or no stoma cover). Univariable analysis was performed to determine the association with several potential explanatory variables including gender, age, living circumstances, distance from treatment centre, communication method and time elapsed since laryngectomy. A backwards selection procedure was used to determine the final model for multiple regression analysis. RESULTS: Data were obtained from 1216 PTL from 26 centres; information on type of tracheostoma humidification used was available for 1097 PTL. Most PTL (69%) used an HME. Following multiple regression analysis, time elapsed since laryngectomy (p = &lt;0.001), living circumstances (p = 0.002) and communication method (p = &lt;0.001) were statistically significant factors in HME use. CONCLUSIONS: In England and Wales most PTL use a closed-system HME, though there is marked variability across centres. HME use is influenced by time elapsed since laryngectomy, living circumstances and communication method.

Do humans categorize hierarchically? The case of sugar perception in milk and milk alternatives

Categories allow humans to anticipate sensory properties of a food before eating it. Placement of foods into different categories has implications for marketing and food product development. Current research in food categorization has focused on card sorting methods using pictures or words, but our understanding of sensory-based food categorization and its potential variability across individuals remains limited. The purpose of this study was to determine if a person’s experiences and habits affect the way they categorize foods in the category of milk and milk alternatives. In this two-part study, focus groups were first conducted with 34 participants to determine how usage and important attributes of milk differed across diet demographic groups. Then, the influence of demographics on categorization was examined using agglomerative free sorting in which 69 panellists sorted 24 distinct milk alternative products and 31 panellists sorted 30 milk and milk alternative products. The results from a DISTATIS analysis revealed that sugar content was a primary factor in categorization across all demographic groups, indicating a potential universal hierarchical categorization process. When removing sugar content as a factor, differences in categorization were related to attitudes towards creaminess, nutrition, price, colour, and usage of milk in drinks. These insights trigger new questions about what other attributes would drive categorization when removing sugar as a factor and how sugar content would affect categorization of solid foods.

Modelling Red-Crowned Parrot (Psittaciformes: Amazona viridigenalis [Cassin, 1853]) distributions in the Rio Grande Valley of Texas using elevation and vegetation indices and their derivatives.

Texas Rio Grande Valley Red-crowned Parrots (Psittaciformes: Amazona viridigenalis [Cassin, 1853]) primarily occupy vegetated urban rather than natural areas. We investigated the utility of raw vegetation indices and their derivatives as well as elevation in modelling the Red-crowned parrot's general use, nest site, and roost site habitat distributions. A feature selection algorithm was employed to create and select an ensemble of fine-scale, top-ranked MaxEnt models from optimally-sized, decorrelated subsets of four to seven of 199 potential variables. Variables were ranked post hoc by frequency of appearance and mean permutation importance in top-ranked models. Our ensemble models accurately predicted the three distributions of interest ([Formula: see text] Area Under the Curve [AUC] = 0.904-0.969). Top-ranked variables for different habitat distribution models included: (a) general use-percent cover of preferred ranges of entropy texture of Normalized Difference Vegetation Index (NDVI) values, entropy and contrast textures of NDVI, and elevation; (b) nest site-entropy textures of NDVI and Green-Blue NDVI, and percent cover of preferred range of entropy texture of NDVI values; (c) roost site-percent cover of preferred ranges of entropy texture of NDVI values, contrast texture of NDVI, and entropy texture of Green-Red Normalized Difference Index. Texas Rio Grande Valley Red-crowned Parrot presence was associated with urban areas with high heterogeneity and randomness in the distribution of vegetation and/or its characteristics (e.g., arrangement, type, structure). Maintaining existing preferred vegetation types and incorporating them into new developments should support the persistence of Red-crowned Parrots in southern Texas.

Predictors of clinical worsening during a discontinuation trial of serotonin reuptake inhibitors for obsessive compulsive disorder

ObjectiveTo explore predictors and moderators of clinical worsening during a double-blind trial in which patients with obsessive-compulsive disorder (OCD) were randomized to either continue or discontinue their Serotonin Reuptake Inhibitor (SRI) medication after achieving wellness from the addition of exposure and response prevention (EX/RP) therapy. MethodThe data came from a double-blind discontinuation trial that included N = 101 participants, 35 of whom were removed from the study due to clinical worsening. We first used LASSO logistic regression to identify which of the 34 potential baseline variables of interest (including demographics, diagnoses, other relevant clinical constructs, and specific genotypes), might moderate or predict this clinical worsening. Then logistic regression was used to examine which of these identified variables were significantly related to later clinical worsening. We verified the validity of our final prediction model using k-fold cross-validation. ResultsThere was one significant predictor of clinical worsening: In both groups, those with more past diagnoses had a greater likelihood of clinical worsening (p = .015). There were several moderators. Rates of clinical worsening were higher in the Discontinuation group compared to the Continuation group for participants who were taking a shorter half-life SRI (p = .044), were female (p = .022), had higher baseline levels of maladaptive metacognitions (p < .001), had fewer sleep problems at baseline (p = .001), and/or had more years of education (p < .001). ConclusionsOur results identified several factors that may predict the development of clinical worsening in OCD patients discontinuing SRI medication following successful EX/RP treatment.

Automated 3D Ultrasound as an Adjunct to Screening Mammography Programs in Dense Breast: Literature Review and Metanalysis.

Purpose: The purpose of this meta-analysis is to investigate the effectiveness of supplementing screening mammography with three-dimensional automated breast ultrasonography (3D ABUS) in improving breast cancer detection rates in asymptomatic women with dense breasts. Materials and Methods: We conducted a thorough review of scientific publications comparing 3D ABUS and mammography. Articles for inclusion were sourced from peer-reviewed journal databases, namely MEDLINE (PubMed) and Scopus, based on an initial screening of their titles and abstracts. To ensure a sufficient sample size for meaningful analysis, only studies evaluating a minimum of 20 patients were retained. Eligibility for evaluation was further limited to articles written in English. Additionally, selected studies were required to have participants aged 18 or above at the time of the study. We analyzed 25 studies published between 2000 and 2021, which included a total of 31,549 women with dense breasts. Among these women, 229 underwent mammography alone, while 347 underwent mammography in combination with 3D ABUS. The average age of the women was 50.86 years (±10 years standard deviation), with a range of 40-56 years. In our efforts to address and reduce bias, we applied a range of statistical analyses. These included assessing study variation through heterogeneity assessment, accounting for potential study variability using a random-effects model, exploring sources of bias via meta-regression analysis, and checking for publication bias through funnel plots and the Egger test. These methods ensured the reliability of our study findings. Results: According to the 25 studies included in this metanalysis, out of the total number of women, 27,495 were diagnosed with breast cancer. Of these, 211 were diagnosed through mammography alone, while an additional 329 women were diagnosed through the combination of full-field digital mammography (FFDSM) and 3D ABUS. This represents an increase of 51.5%. The rate of cancers detected per 1000 women screened was 23.25‱ (95% confidence interval [CI]: 21.20, 25.60; p < 0.001) with mammography alone. In contrast, the addition of 3D ABUS to mammography increased the number of tumors detected to 20.95‱ (95% confidence interval [CI]: 18.50, 23; p < 0.001) per 1000 women screened. Discussion: Even though variability in study results, lack of long-term outcomes, and selection bias may be present, this systematic review and meta-analysis confirms that supplementing mammography with 3D ABUS increases the accuracy of breast cancer detection in women with ACR3 to ACR4 breasts. Our findings suggest that the combination of mammography and 3D ABUS should be considered for screening women with dense breasts. Conclusions: Our research confirms that adding 3D automated breast ultrasound to mammography-only screening in patients with dense breasts (ACR3 and ACR4) significantly (p < 0.05) increases the cancer detection rate.

Developing Integer‐based Risk Scores for Predicting the Risk of Incident Cognitive Impairment in Cognitively Normal Older Adults

AbstractBackgroundWe aimed to developed integer‐based risk scores for predicting incident cognitive impairment up to 10 years in initially cognitively normal older adults.MethodsParticipants were 479 older adults (aged 60 to 90) from longitudinal studies of aging and Alzheimer’s disease from the Knight ADRC at Washington University in St. Louis. The primary outcome was incident cognitive impairment, defined as conversion from Clinical Dementia Rating (CDR®) 0 to &gt;0. The sample was divided into training (60%) and test (40%) sets. Feature‐sets were formed from combinations of four categories of variables: demographics (D), genetics (G), cognitive measures (C), and biomarkers (B) (Details in Figure 1 legend). Utilizing a modified version of the AutoScore‐Survival algorithm, variables were ranked based on variable importance using reduction in predictive accuracy due to replacement with a random permutation value in combination with Random Survival Forests. Variables were iteratively included in constructing risk score models based on coefficients of Cox regression models.ResultsFive risk score models were developed using five feature‐sets. All models retained age, APOE4 status, free recall from the Free and Cued Selective Reminding Test, hippocampal atrophy, and Tau positivity when included as potential variables in the feature‐sets. The risk score derived from the DGCB model had a Harrell’s concordance index (C‐index) of 0.754 and integrated Area Under the ROC Curve (iAUC) of 0.792. The best performing model without biomarkers used the DGC feature‐set and had a C‐index of 0.694 and iAUC of 0.699 while the risk score using the DG feature‐set only had a C‐index of 0.632 and iAUC of 0.647. Participants who had risk scores in the top quartile of the DGCB model risk score had an increased rate of conversion to CDR &gt; 0.ConclusionsWe developed five risk score models for predicting incident cognitive impairment. Risk scores consisted of different sets of measures which differed in ease‐of‐access and costs. Using such simple risk scores that do not include biomakers as clinical decision support tools is practical in the clinical setting. Including biomarkers in risk scores provides slight improvement to classification accuracy, emphasizing their importance in research settings.

Modified SPWM technique for single phase variable voltage nine level inverter for PV system

&lt;span lang="EN-US"&gt;Multilevel inverters are widely popular because they boost the overall staging of the network with low harmonic distortion, less strain, and simply create highest possible development in industrial and electrical vehicle applications. Here a hybrid multilevel inverter having few switches compare to traditional multi-level inverters has been implemented. It has the capability to operate as a 3-level, 5-level, 7-level and 9-level inverter with different magnitudes of output voltage by varying the modulation index without changing of circuit topology and switching sequences. A new variable amplitude phase opposition disposition sinusoidal pulse width modulation (VAPODPWM) technique is proposed to control the inverter. In this concept unsymmetrical carrier wave form, i.e. A single triangular wave is divided into three triangular waves of different magnitude was chosen. it reduces the volt-sec of output due to that it gives less total harmonic distortion (THD) compare to other conventional space pulse width modulation (SPWM) techniques and conventional space vector pulse width modulation (CSVPWM) control techniques. The ease of conversion capability with less count of switches and operation with less THD shows the effectiveness of the converter. The converter is simulated by using MATLAB/Simulink and results are discussed.&lt;/span&gt;

Deep Learning Artificial Intelligence Tool for Automated Radiographic Determination of Posterior Tibial Slope in Patients With ACL Injury.

An increased posterior tibial slope (PTS) corresponds with an increased risk of graft failure after anterior cruciate ligament (ACL) reconstruction (ACLR). Validated methods of manual PTS measurements are subject to potential interobserver variability and can be inefficient on large datasets. To develop a deep learning artificial intelligence technique for automated PTS measurement from standard lateral knee radiographs. It was hypothesized that this deep learning tool would be able to measure the PTS on a high volume of radiographs expeditiously and that these measurements would be similar to previously validated manual measurements. Cohort study (diagnosis); Level of evidence, 2. A deep learning U-Net model was developed on a cohort of 300 postoperative short-leg lateral radiographs from patients who underwent ACLR to segment the tibial shaft, tibial joint surface, and tibial tuberosity. The model was trained via a random split after an 80 to 20 train-validation scheme. Masks for training images were manually segmented, and the model was trained for 400 epochs. An image processing pipeline was then deployed to annotate and measure the PTS using the predicted segmentation masks. Finally, the performance of this combined pipeline was compared with human measurements performed by 2 study personnel using a previously validated manual technique for measuring the PTS on short-leg lateral radiographs on an independent test set consisting of both pre- and postoperative images. The U-Net semantic segmentation model achieved a mean Dice similarity coefficient of 0.885 on the validation cohort. The mean difference between the human-made and computer-vision measurements was 1.92° (σ = 2.81° [P = .24]). Extreme disagreements between the human and machine measurements, as defined by ≥5° differences, occurred <5% of the time. The model was incorporated into a web-based digital application front-end for demonstration purposes, which can measure a single uploaded image in Portable Network Graphics format in a mean time of 5 seconds. We developed an efficient and reliable deep learning computer vision algorithm to automate the PTS measurement on short-leg lateral knee radiographs. This tool, which demonstrated good agreement with human annotations, represents an effective clinical adjunct for measuring the PTS as part of the preoperative assessment of patients with ACL injuries.

Predicting cetacean habitats beyond surveyed regions in Indonesian waters: species distribution model transferability may not always be preferable

Understanding the distribution of cetaceans in Indonesian waters is imperative for their conservation management, however such information is lacking for the country. Our study predicted the species distributions of two cetacean species (common bottlenose dolphin and sperm whale) beyond the surveyed regions in Indonesian Exclusive Economic Zone (EEZ). This was done by using a combination of presence-only data, randomly generated pseudo-absences and environmental predictors variables within the Biomod2 framework in R. Ten potential predictor variables were identified, of which five were selected after correlation tests. Local Random Forest models were built to the extent of four small study regions, and later projected to the whole Indonesian EEZ. The common bottlenose dolphin local models showed preference for areas close to the coast and shallower waters. Sperm whale local predictions were located further into the open waters and at deeper waters. The extrapolated predictions into the Indonesian EEZ, however, showed some unexpected results. The high occurrences for common bottlenose dolphins were not only located close to the islands, but also more into open waters. In contrast, sperm whale distributions have high occurrences near coastal areas and in the vicinity of islands than in the open oceans. This information suggested that the transferability of species distribution models may not always be preferable, because provide low accuracy. Sighting data, choices of variables and model settings influenced the outcome of the extrapolated models. Despite the unpreferable of the extrapolations, the results are still beneficial for cetacean conservation purposes, since the study was able to identify potential habitats in unsurveyed regions.

Tissue Microvessel Density as a Potential Predictive Marker for Vascular Invasion in Colorectal Cancer.

This study aimed to determine whether microvessel density (MVD) in the tumor tissues could be a potential predictive marker for vascular invasion (VI). Surgical specimens of 73 patients with colorectal adenocarcinoma in Phramongkutklao Hospital were analyzed. Tissues of patients receiving preoperative radiation or prior anti-angiogenic therapy were excluded. Tumor MVD was determined using the average number of counted CD34-stained endothelial cells from two selected fields at 200x magnification in each slide. The presence of VI was defined by tumor involvement of endothelial cell-lined spaces. The optimal cut-off value of MVD to predict VI was examined using receiver operating characteristic analysis to assess the area under the curve and accuracy. VI was detected in 17 of 73 specimens (23.3%). Colorectal cancer (CRC) specimens were classified according to MVD as low (61 specimens, 83.6%) and high density (12 specimens, 16.4%). Average MVD was slightly higher in specimens with VI (81.3±9.3) than those without VI (76.3±7.6), but without statistical significance (p = 0.736). The MVD's cut-off value of 60 vessels/200x field provided 88% sensitivity, 40% specificity, and 57.5% accuracy, with the area under the ROC curve of 0.5788. Patients with CRC having MVD of > 60 vessels/200x field were at significantly higher risk of VI than those with CRC having MVD of <60 vessels/200x field (P=0.009, Fisher's exact test). Univariate analysis revealed that MVD, nodal involvement and AJCC tumor stage were associated with the presence of VI (p <0.05). Further multivariate analysis of these three potential variables demonstrated MVD (OR, 11.994; 95% CI, 2.197 to 65.483; p <0.01) and nodal involvement (OR, 10.767; 95% CI, 1.973 to 58.748; p <0.05) as independent prognostic factors associated with VI. Based on our study, MVD immunostaining was an angiogenic marker that potentially be a predictive marker for VI.

Empowering Home Buyers: A Data-Ddriven Approach to Real Estate Pricing in Ames, Iowa

The demand for both housing and investment real estate has significantly increased due to rising urbanization and growing household savings. To address the need for operable real estate pricing models, this study explores potential variables affecting the sale prices of properties in Ames, Iowa, from 2006 to 2010. Utilizing data mining techniques and regression analysis, this study develops a model incorporating 12 independent variables that can be easily obtained during property visits. By offering an easily appliable tool, this research enables potential home buyers to estimate property sale prices, even without extensive expertise in Data Science, Investment, and Economics. The findings demonstrate that analyzing 12 variables directly related to the property itself such as interior finish of the garage, foundation material, and remodeling date can explain approximately 85.2% of the variance in sale prices. Empowering consumers with this knowledge can help reduce the information gap in the real estate market and promote informed decision-making in property purchases.