Statistical Significance Tests Research Articles

Abstract A013: Advanced Gene Signatures for the Diagnosis and Personalized Treatment of Pancreatic Ductal Adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDA) is among the most lethal tumors, with limited treatment options. The absence of reliable screening and diagnostic tools (biomarkers or procedures) significantly contributes to its poor prognosis. Biomarkers that can diagnose PDA, select optimal therapy combinations, and predict the risk of occult metastases are crucial for enhancing treatment outcomes. Previously, we identified methylation signatures capable of predicting outcomes and diagnosing PDA, focusing on genes/proteins with preclinical evidence of influencing key drugs (gemcitabine [Gem], 5-fluorouracil [5FU], oxaliplatin, cisplatin, paclitaxel, and irinotecan) used in treatment. To make these findings more clinically applicable and propose a novel approach for personalized treatment selection between Gem-based (Gem/nab-paclitaxel) and 5FU-based (FOLFIRINOX) combinations, we explored the diagnostic value of genes associated specifically with Gem and 5FU. The objective was to develop a gene signature for diagnosing PDA and aiding in treatment selection. Based on recent literature, we updated our previous gene panels (from 55 to 93 for Gem, and from 20 to 28 for 5FU). We analyzed RNA and gene expression differences between normal (NT) and PDA tissues using TNMplot.com, a web tool that leverages data from the Gene Expression Omnibus (NCBI-GEO), Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and The Genotype-Tissue Expression (GTEx) repositories, employing Mann-Whitney or Kruskal-Wallis tests for statistical significance. For the Gem-specific gene panel, the combined RNA expression of 90 out of 93 genes was significantly higher in PDA compared to NT (2.59, p=6.48e-61), while the combined gene expression of 80 out of 93 genes was also significantly higher in PDA tissues (1.62, p=3.48e-29). For the 5FU-specific gene panel, the combined RNA expression of 27 out of 28 genes was significantly elevated in PDA (2.00, p=5.44e-57), and the combined gene expression of 23 out of 28 genes was significantly higher in PDA tissues (1.21, p=1.44e-7). Our preliminary findings suggest that this approach has the potential to serve as an effective tool for diagnosing PDA and guiding treatment selection. However, these results must be validated through larger prospective studies. Additionally, this application could serve as a screening tool for high-risk populations, such as individuals with potentially malignant lesions like intraductal papillary mucinous neoplasms, first-degree relatives of PDA patients, or those with concerning genetic risks (e.g., BRCA1 or BRCA2 mutations). Citation Format: Harshitha Puram, Deepak Sherpally, Sravan Jeepalyam, Ashish Manne. Advanced Gene Signatures for the Diagnosis and Personalized Treatment of Pancreatic Ductal Adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A013.

A novel hybrid BWO-BiLSTM-ATT framework for accurate offshore wind power prediction

Accurate wind power forecasting is pivotal in facilitating the efficient integration of renewable energy sources into existing power grids. This study proposes a novel hybrid framework, termed BWO-BiLSTM-ATT, which synergistically combines the strengths of a bidirectional long short-term memory (BiLSTM) network, a self-attention mechanism, and the Beluga Whale Optimization (BWO) algorithm. The BiLSTM architecture, with its unique ability to capture bidirectional temporal dependencies, effectively models the intricate dynamics present in wind power time series data. Integrating the self-attention mechanism further enhances the model's performance by discerning and emphasizing the most salient features within the input data. Furthermore, employing the BWO algorithm optimizes the model's hyperparameters, ensuring optimal configuration and enhancing its predictive accuracy and generalization capabilities. The proposed BWO-BiLSTM-ATT framework was rigorously evaluated using real-world data from an offshore wind farm in Yangjiang City, China. Comparative analyses were conducted against several baseline algorithms, including persistence, ARIMA, SVR, and vanilla LSTM models. The results demonstrate the superior performance of the BWO-BiLSTM-ATT framework, achieving higher R-squared and explained variance scores, coupled with lower error metrics such as MSE, RMSE, MedAE, and MAE. Statistical significance tests further corroborated the substantial performance improvements offered by the proposed framework. The combination of advanced deep learning techniques and metaheuristic optimization algorithms embedded in the BWO-BiLSTM-ATT framework provides a robust and accurate solution for wind power forecasting, enabling efficient management and seamless integration of renewable energy resources into existing power grids.

Prevalence and Pattern of Ear Disease Among Primary School Students in Samoa: Preliminary Results from ENT Clinic Outreach to Urban, Rural and Remote Settings.

Objectives: To assess the prevalence and pattern of ear disease among students attending primary school in urban, rural, and remote regions of Samoa in order to develop an evidence-based national school ear and hearing health program. Methods: Retrospective clinical data review of all primary school students seen by the Ear, Nose & Throat Clinic of Samoa during school-based outreach visits from March 2022 to April 2024. A purposefully designed spreadsheet was created to extract the following information from the clinical records: school location (urban, rural, remote), gender (male, female), age (years), age category (0-4, 5-9, 10-14, 15+ years), right ear diagnosis, left ear diagnosis. Data were entered into the SPSS statistical software package for descriptive statistical analysis and association tests of significance. Results: A total of 1491 primary school students were seen for ear health examinations (51.1% male, 48.9% female), with a mean age of 9.1 years (SD = 2.3). Five primary schools were included, representing urban (n = 865), rural (n = 258), and remote (n = 368) settings. Overall, 682 (45.7%) students had normal ear examination results for both ears, 237 (15.9%) had an ear pathology in 1 ear only, and 568 (38.1%) had ear pathology in both ears. Bilateral ear pathology was significantly higher among students who were in the 5 to 9 age group (P < .00001), female (P = .035), and from rural settings (P = .0001). The most common pathology was impacted cerumen (36.8%), followed by otitis media with effusion (7.5%). Overall prevalence of chronic suppurative otitis media (CSOM) was 2.6% (n = 39 students). Following the school visits, no further intervention was required for 1180 students (79%), mostly due to successful cerumen and foreign body removals for 468 students (31.4%). Conclusion: Impacted cerumen is a significant issue among Samoan school-aged students, and future school-based ear health programs should include staff members trained in cerumen removal. CSOM prevalence suggests an avoidable burden of disease that must be addressed.

Prevalence and Predictors of Musculoskeletal Disorders among Bus Drivers Engaged in Long- Distance Transportation in Port Harcourt, Nigeria

Background An increasing number of occupational injuries occur due to work-related musculoskeletal disorders (WRMSDs) in both developing and industrialized countries. These disorders have an impact on workers' health as well as society at large, posing medical, occupational, and economic challenges.  Objectives This research assessed work-related musculoskeletal disorders and its risk factors among Bus drivers for private long-distance Transport Companies in Port Harcourt, Nigeria.  Materials and Methods This study employed a cross-sectional study design. The multi-stage sampling technique was used to sample 338 long-distance bus drivers in the city of Port Harcourt. Participants' demographic data and information regarding work related musculoskeletal disorders were collected using a modified version of the Standardized Nordic Musculoskeletal Survey questionnaire. Descriptive statistics determined prevalence and inferential statistics was used to determine associated risk factors of WRMSD. The p-value of the test of statistical significance was set at ≤0.05. The participants were all male.  Result 58.6% of the research sample had WRMSDs, according to the findings. After logistic regression analysis, only marital status (AOR=5.714, 95% CI for AOR=3.046- 10.721; p=0.001), prolonged sitting of ≥8 hours, (AOR=0.350; 95% CI for AOR=0.166-0.741; p=0.006) and previous history of pain in a previous occupation (AOR=5.714, 95% CI for AOR=3.046-10.721; p=0.001), were significantly associated with the prevalence of WRMSD.  Conclusion WRMSDs are prevalent in private long-distance bus drivers and are strongly correlated with marrital status, prolonged sitting times (more than eight hours), and a history of muscle or joint discomfort in a prior job.

CIRA: Class imbalance resilient adaptive Gaussian process classifier

The problem of class imbalance is pervasive across various real-world applications, resulting in machine learning classifiers exhibiting bias towards majority classes. Algorithm-level balancing approaches adapt the machine learning algorithms to learn from imbalanced datasets while preserving the data’s original distribution. The Gaussian process classifier is a powerful machine learning classification algorithm, however, as with other standard classifiers, its classification performance could be exacerbated by class imbalance. In this work, we propose the Class Imbalance Resilient Adaptive Gaussian process classifier (CIRA), an algorithm-level adaptation of the binary Gaussian process classifier to alleviate the class imbalance. To the best of our knowledge, the proposed algorithm (CIRA) is the first adaptive method for the Gaussian process classifier to handle unbalanced data. The proposed CIRA algorithm consists of two balancing modifications to the original classifier. The first modification balances the posterior mean approximation to learn a more balanced decision boundary between the majority and minority classes. The second modification adopts an asymmetric conditional prediction model to give more emphasis to the minority points during the training process. We conduct extensive experiments and statistical significance tests on forty-two real-world unbalanced datasets. Through the experiments, our proposed CIRA algorithm surpasses six popular data sampling methods with an average of 2.29%, 3.25%, 3.67%, and 1.81% in terms of the Geometric mean, F1-measure, Matthew correlation coefficient, and Area under the receiver operating characteristics curve performance metrics respectively.

Reimagining otitis media diagnosis: A fusion of nested U-Net segmentation with graph theory-inspired feature set

Otitis media (OM) is a common infection or inflammation of the middle ear causing conductive hearing loss that primarily affects children and may delay speech, language, and cognitive development. OM can manifest itself in different forms, and can be diagnosed using (video) otoscopy (visualizing the tympanic membrane) or (video) pneumatic otoscopy and tympanometry. Accurate diagnosis of OM is challenging due to subtle differences in otoscopic features. This research aims to develop an automated computer-aided design (CAD) system to assist clinicians in diagnosing OM using otoscopy images. The ground truths, generated manually and validated by otolaryngologists, are utilized to train the proposed nested U-Net++ model. Ten clinically relevant gray level co-occurrence matrix (GLCM) and morphological features were extracted from the segmented Region of Interest (ROI) and validated for OM classification based on a statistical significance test. These features serve as input for a Graph Neural Network (GNN) model, the base model in our research. An optimized GNN model is proposed after ablation study of the base model. Three datasets, one private dataset, and two public ones have been used, where the private dataset is utilized for both training and testing, and the public datasets are used to test the robustness of the proposed GNN model only. The proposed GNN model obtained the highest accuracy in diagnosing OM: 99.38 %, 93.51 %, and 91.38 % for the private dataset, public dataset1, and public dataset2, respectively. The proposed methodology and results of this research might enhance clinicians' effectiveness in diagnosing OM.

“Oh Grandmother, what big teeth you have!” Incentives to spur scientific research at business schools have been treacherous

Scientific research was introduced in business schools in the mid-1950s to raise the quality of business education as well as to improve actual business practices. Schools expected scientific research to provide a benign and respected path toward improvements, and government support for research and students’ strong interest in studying business brought financial resources and hiring opportunities that made large changes possible. Consequently, hundreds of business schools began urging professors to publish scientific research. Sixty years later, plentiful evidence is showing that scientific research has not brought expected benefits for business operations; on the contrary, business-school research has generated a high percentage of untrustworthy “findings”. This disappointing outcome has six interacting causes: (1) Business schools have expanded by hiring professors who study fundamental topics of economics, psychology, and sociology rather than topics relevant for business practices, so few research findings have been adopted and tested by firms. (2) Instead of evaluating the quality or consequences of research, schools have relied on evaluations by journal editors and reviewers, whose overriding criteria emphasize theory at the expense of practical implications. (3) Editorial evaluations have high error rates because editors and reviewers only know what researchers choose to tell them, and because the complexity of manuscripts causes editors and reviewers to disagree frequently. (4) Schools have incentivized professors by making promotions and job security depend on publishing numerous papers in “prestigious” journals. (5) Many professors have responded to these incentives by engaging in covert practices that make studies unlikely to be supported in replications, and some professors have gone so far as to manufacture fake data. (6) Researchers, editors, and reviewers have largely relied on null-hypothesis statistical significance tests, which are prone to interpret small variations or random errors in data as “significant” findings. Although there are fresh efforts to make research findings more reliable and impactful, the need for further interventions to reform the present knowledge-production system is all too apparent.

Plant size inequality: How to analyse competing spatial dissimilarity Indices?

Worldwide biodiversity loss is perceived as a major threat and is likely to be enforced by ongoing climate change. Continued monitoring of biodiversity can assist in compensating for decreasing biodiversity by goal-oriented conservation management. Plant size diversity, also termed size inequality and size hierarchy, has often been neglected in studies of biodiversity, but plays a crucial role in many ecosystems, e.g. in forests. Several competing diversity indices and other characteristics of spatial plant size inequality have been proposed but to date no protocols or guidelines exist for evaluating their relative merits. In our study, we proposed a broad framework for such an analysis. In order to put this framework to a test, we revisited the dissimilarity coefficient, a somewhat ignored but promising spatial size inequality index published at the end of the 1990s, identified its nearest index competitors and analysed their performances in different mathematical-statistical contexts. We learned that the dissimilarity coefficient is more sensitive in statistical significance tests relating to three very different summary characteristics than its closest competitor, the size differentiation index. In addition the dissimilarity coefficient has a more solid foundation than the differentiation index, since it is based on the well-known coefficient of variation. The dissimilarity coefficient can be recommended in a wide range of plant diversity applications. The principle of the dissimilarity coefficient can also be used to define an effective mark correlation function. We recommend our evaluation framework for use in similar analyses of competing diversity characteristics.

Web-assisted instruction for teaching and learning EFL phonetics to Spanish learners: Effectiveness, perceptions and challenges

Research has established that phonetic training is particularly challenging for learners in EFL contexts. Computer-Assisted Pronunciation (CAPT) resources have proved to enhance learners’ perceptual and productive phonetic capacities. No prior study, however, has evaluated the effectiveness of EPSSML (English Pronunciation for Speakers of Spanish Multimedia Lab) (https://www.usc.gal/multimlab/), a CAPT tool for web-assisted, blended instruction (WABI), while simultaneously examining the students’ and instructors’ perceptions in order to identify the emerging and existing needs. Statistical significance tests applied to course results from 504 Spanish university students supported the effectiveness of EPSSML, which was reported to increase students’ academic achievements and learning motivation, fostering course participation and empowering learners with the necessary capabilities and attitudes to decide not to drop the class. Additionally, questionnaires administered to 127 Post-WABI students showed that they enjoyed and were very interested in EPSSML and WABI. In the case of EFL university instructors, their responses revealed that most of them also rated the tool very highly, featuring specialized open web-sites with free downloadable materials and gamified learning pathways as the “ideal CAPT resources”.

Assessing CMIP models’ ability to detect observed surface warming signals related to climate change

Abstract Assessing if CMIP models can detect observed climate change signals is crucial for evaluating their realism and strengthening confidence in future projections. These signals can only be compared once distinguishable from the background climate “noise”. Build for impact studies but not detection purpose, the time of Emergence (ToE) estimates when this occurs using a fixed signal-to-noise ratio. To more accurately assess models’ ability to reproduce observed climate change signals, we introduce the Time of Detection (ToD), which employs a statistical significance test of the nonlinear trend, accounting for the time series length. ToD and ToE are computed for Sea Surface Temperature (SST) and relative SST (RSST) across four observational and 69 CMIP5/6 historical and SSP585 scenario datasets. ToD precedes ToE and is less sensitive to the chosen threshold. While already detectable in most of the tropics, SST warming in the equatorial Pacific is detectable in ∼90% of models but only 1 out of 4 observational datasets, due to a stronger modelled warming. Because RSST signals are weaker than SST signals, they do not emerge anywhere but are detectable in two regions. The enhanced warming in the western Indian Ocean, detected in ∼45% of models, is not robust in observations when excluding pre-1960 dubious data. Conversely, the subdued South-East Pacific warming detected in observations and ∼80% of models is robust and consistent with a poleward extension of the southern hemisphere Hadley cell. The ToD ability to detect subtle signals allows assessing the realism of the CMIP warming pattern in this region.

Significance testing for cross correlation: A critical examination of correlations between ENSO and GRACE-derived terrestrial water storage variabilities

The cross correlation has a wide range of applications in geophysical fields for measuring linear connections or relationships among physical quantities. Nonetheless, there remains a dearth of comprehensive discourse regarding its statistical significance testing, which is crucial for differentiating meaningful outcomes from those merely stemming from fortuity or pure randomness. Conventional theoretical methods for significance testing, commonly used in statistical analysis tools such as SPSS and MATLAB, are only applicable when dealing with idealized circumstances such as white noise. In discretionary application of these methods to analyze geophysical signals with, say, red noise may result in potentially unjustified conclusions. This study aims to develop a comprehensive approach based on the t-distribution within a rigorous statistical context, aiming to facilitate significance tests of cross correlation for general signals with specified time shifts or ranges. Extensive Monte Carlo experiments substantiate its robustness, thereby paving the way for accurate and expeditious identification of statistically (hence potentially physically) meaningful correlations in general. As an example, we examine critically the previously purported significant correlations between ENSO (El Niño Southern Oscillation) and global terrestrial water storage variations derived from the GRACE (Gravity Recovery and Climate Experiment) satellite mission, demonstrating that they are subject to questioning in the absence of complete significance testing.

Social Vulnerabilities Among Hired Migrant and Seasonal Farmworkers During COVID-19: The Need for Employment-Based Indicators

ABSTRACT Objectives This study aimed to explore farmworkers’ experiences of social vulnerability during the peak period of the COVID-19 pandemic. Methods Telephone surveys of 63 migrant and seasonal farmworkers across three major agricultural areas in Florida were conducted. The survey, designed and disseminated with critical support from a statewide farmworker membership and advocacy organization, included items related to social and occupational precarity and a suite of demographic conditions, including specific employment-based indicators and categories. Data were analyzed with SPSS using a series of statistical significance tests including Pearson’s Chi-Square, Fisher’s Exact, T-tests, and Mann-Whitney U. An open-ended question regarding employment precarity was also analyzed for frequencies of responses pertaining to a set of descriptive categories. Results Survey findings demonstrated a high degree of social vulnerability among the farmworker sample, with notable variation in the type and severity of vulnerability and risk exposure across employment-based indicators and occupational categories. For example, a cross-industry comparison between vegetable field workers and greenhouse nursery workers revealed a disparity in COVID-exposure risk through commuting characteristics, as 43% of vegetable field workers used shared, employer-provided transport, while 68% of nursery workers used personal vehicles. Conclusion While previous research has broadly established the extreme precarity of migrant and seasonal farmworkers during the peak COVID-19 period, the variability of experience, exposure to risk, and social vulnerability between farmworkers representing distinct employment-based indicators and occupational categories demonstrated in this study contributes to widening awareness of the importance of assessing farmworker experiences at a more granular level. In addition to delineating social vulnerability across key demographic categories, cross-industry comparisons between farmworkers revealed significant discrepancies in risk and vulnerability to COVID-19. Future research that further explores this variability may reveal opportunities to improve disaster-relief planning and mitigate social vulnerability in future disaster scenarios. The importance of surveying the vulnerability of worker populations, aside from geographic communities, is highlighted.

Extended dipeptide composition framework for accurate identification of anticancer peptides

The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorporation of feature representation. These advancements improve the predictive accuracy and efficiency of ACP identification. Thus, the effort of this research is to propose and develop an advanced framework based on feature extraction. Thus, to achieve this objective herein we propose an Extended Dipeptide Composition (EDPC) framework. The proposed EDPC framework extends the dipeptide composition by considering the local sequence environment information and reforming the CD-HIT framework to remove noise and redundancy. To measure the accuracy, we have performed several experiments. These experiments were employed using four famous machine learning (ML) algorithms named; Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K Nearest Neighbor (KNN). For comparisons, we have used accuracy, specificity, sensitivity, precision, recall, and F1-Score as evaluation criteria. The reliability of the proposed framework is further evaluated using statistical significance tests. As a result, the proposed EDPC framework exhibited enhanced performance than SAAC and PseAAC, where the SVM model delivered the highest accuracy of 96. 6% and significant enhancements in specificity, sensitivity, precision, and F1-score over multiple datasets. Due to the incorporation of enhanced feature representation and the incorporation of local and global sequence profiles proposed EDPC achieves higher classification performance. The proposed frameworks can deal with noise and also duplicating features. These are accompanied by a wide range of feature representations. Finally, our proposed framework can be used for clinical applications where ACP identification is essential. Future works will include extending to a larger variety of datasets, incorporating tertiary structural information, and using deep learning techniques to improve the proposed EDPC.

Evaluation of the measurement accuracy and uncertainty of a solid-state detector under diagnostic x-ray beam conditions.

An accurate measurement of x-ray beams is expected to reduce the uncertainties associated with estimating radiation risk to patients in clinical settings. To perform assessment tasks based on the readings of a solid-state detector (SSD) using semiconductor technology, the characteristics of the detector should be elucidated. In this study, we evaluated the measurement accuracy of a new SSD under diagnostic x-ray beam conditions in terms of air kerma, tube voltage, and half-value layer (HVL). The performance of the SSD was then compared with those of reference instruments. The tube voltage was varied within the range of 50-120kV in steps of 10kV and the thickness and materials of additional filters were concurrently changed (several combinations were tested). In addition, the dose rate and energy dependence of the SSD were also investigated. These effects were analyzed based on statistical significance tests. Furthermore, the expanded uncertainties in the series of measurements were meticulously calculated. The results showed average relative differences of -3.26±1.33%, 0.44±1.01%, and -2.60±3.31% for air kerma, tube voltage, and HVL, respectively. Furthermore, air kerma did not exhibit any dependence on dose rate and energy, in contrast to tube voltage and HVL measurements. The measurement values of the SSD fall within the acceptable range of uncertainty, highlighting its measurement accuracy and reliability. Furthermore, based on the characteristics elucidated by this study, valuable insights are provided concerning the assurance of appropriate measurement values in clinical settings.

Valuing Outpatients' Perspective on Primary Health Care Services in Greece: A Cross-Sectional Survey on Satisfaction and Personal-Centered Care.

The aims of the study were to identify and analyze the determinants associated with outpatient satisfaction in Greek primary care. This is because there is a general consensus that primary care is the linchpin of effective person-centered care delivery. A cross-sectional survey was conducted with 1012 patients' exit interviews; sociodemographic variables were included in the questionnaire to obtain data on the satisfaction of primary care users with 20 public primary healthcare centers in Athens between June 2019 and April 2021. Statistical analysis was applied to 55 items and eight dimensions of patient satisfaction, namely, arrival and admission, waiting before the appointment, cleanliness of toilets, medical examination and behavior of physician, behavior of nursing staff, laboratories, departure, and contribution of the PHCs. Descriptive analyses and multiple linear regression were used to analyze the factors influencing patient satisfaction through coefficients (β) with 95% confidence intervals and associated tests of statistical significance. Τwo-thirds (74.21%) of this survey's participants ranged from 45 to 74 years of age. More than half of the participants were women (62.15%). The most common reasons for visits were pathological (26.48%), followed by cardiological conditions (9.78%), orthopedics (9.49%), gynecologic conditions (8.70%), and ophthalmologic problems (7.31%). In the center of satisfaction with primary care was the medical care and the behavior of the physician (β = 0.427; p < 0.01), followed by the time during appointment (β = 0.390; p < 0.01). Dimensions like "accessibility and availability, 2.19/5"; "waiting times, 2.89/5"; "infrastructure of facilities (2.04/5) and cleanliness of them, (2/5)"; "laboratories, 2.99/5" and "bureaucracy in the departure, 2.29/5" were crucial for the trust and satisfaction of patients. Overall satisfaction was rated at a moderate level (2.62 ± 0.18) while person-centered care was rated as weak (2.49 ± 0.28). Greece is recommended to increase the sensitivity of the use of the primary health care system by patients as a first contact, continuous, comprehensive, and effective patient- and family-focused care.

Reasons for university dropout in Chile: does student gender play a role?

ABSTRACT Students who drop out of university cite various reasons for their decision. Female enrolment has significantly increased over the past few decades and is now higher than male enrolment. In terms of performance, it is recognised that women perform better than males do, and fewer women drop out of university than men do. However, the relationship between specific reasons for dropping out and students' gender is unknown. This study explores a variety of dropout factors and how they relate to student gender in Chilean universities. The investigation uses information from a survey conducted in 2021 among former students from 11 universities. The survey was applied to 707 students who left their university studies and belonging to the entry cohorts of 2010, 2016 and 2017. Different entry cohorts were considered in order to make a comparative analysis based on the existent data. To ascertain the degree of associativity between reasons for dropouts and the gender of the students, statistical significance tests were utilised in the second step. Subsequently, ten logistic regression models were fitted, the results of which are presented using the odds ratio. The results show that gender has a statistically significant link, mostly with dropouts due to vocational reasons. The findings of this research allow us to reflect on the effectiveness of gender equity actions and policies that favour women.

Pulmonary nodule detection in low dose computed tomography using a medical-to-medical transfer learning approach.

Lung cancer is the second most common cancer and the leading cause of cancer death globally. Low dose computed tomography (LDCT) is the recommended imaging screening tool for the early detection of lung cancer. A fully automated computer-aided detection method for LDCT will greatly improve the existing clinical workflow. Most of the existing methods for lung detection are designed for high-dose CTs (HDCTs), and those methods cannot be directly applied to LDCTs due to domain shifts and inferior quality of LDCT images. In this work, we describe a semi-automated transfer learning-based approach for the early detection of lung nodules using LDCTs. In this work, we developed an algorithm based on the object detection model, you only look once (YOLO) to detect lung nodules. The YOLO model was first trained on CTs, and the pre-trained weights were used as initial weights during the retraining of the model on LDCTs using a medical-to-medical transfer learning approach. The dataset for this study was from a screening trial consisting of LDCTs acquired from 50 biopsy-confirmed lung cancer patients obtained over 3 consecutive years (T1, T2, and T3). About 60 lung cancer patients' HDCTs were obtained from a public dataset. The developed model was evaluated using a hold-out test set comprising 15 patient cases (93 slices with cancerous nodules) using precision, specificity, recall, and F1-score. The evaluation metrics were reported patient-wise on a per-year basis and averaged for 3 years. For comparative analysis, the proposed detection model was trained using pre-trained weights from the COCO dataset as the initial weights. A paired t-test and chi-squared test with an alpha value of 0.05 were used for statistical significance testing. The results were reported by comparing the proposed model developed using HDCT pre-trained weights with COCO pre-trained weights. The former approach versus the latter approach obtained a precision of 0.982 versus 0.93 in detecting cancerous nodules, specificity of 0.923 versus 0.849 in identifying slices with no cancerous nodules, recall of 0.87 versus 0.886, and F1-score of 0.924 versus 0.903. As the nodule progressed, the former approach achieved a precision of 1, specificity of 0.92, and sensitivity of 0.930. The statistical analysis performed in the comparative study resulted in a -value of 0.0054 for precision and a -value of 0.00034 for specificity. In this study, a semi-automated method was developed to detect lung nodules in LDCTs using HDCT pre-trained weights as the initial weights and retraining the model. Further, the results were compared by replacing HDCT pre-trained weights in the above approach with COCO pre-trained weights. The proposed method may identify early lung nodules during the screening program, reduce overdiagnosis and follow-ups due to misdiagnosis in LDCTs, start treatment options in the affected patients, and lower the mortality rate.

Modeling the Cadastral Value of Land Plots of Gardening and Horticultural Non-Profit Partnerships Taking into Account the Influence of Local Factors of the Territory

Concerning the dependence of land taxation on the cadastral or market value of lands in developed and developing countries, the role of land resources in the system of socio-economic development is quite high. World studies show the presence of methodological problems related, among other things, to the composition of price-forming factors of mass assessment. In relation to garden and vegetable garden lands, the issue of taking into account factors related to the immediate environment and soil quality is especially relevant, which is associated with the social justice of land taxation. The current paper aims to determine the influence of factors of the immediate environment and soil fertility on the cadastral value of the lands under consideration by determining which methodological apparatus has been built. Modeling of the specific indicator of cadastral value (SICV) of land plots is performed on the example of five gardening and vegetable gardening partnerships of the Belgorod district, where 79% of the territory is occupied by agricultural lands, which are quite diverse in soil composition, relief, and other studied factors. A linear model of dependence between local factors and the UPCS is proved. The reliability of the model is confirmed by testing for homoscedasticity, autocorrelation, and statistical significance of factors. The results of determining the cadastral value demonstrated an average change of 10%.

247 Prophylactic Use of 3D Intraperitoneal Mesh in the Prevention of Parastomal Hernia in Permanent Colostomies After Rectal Cancer Resection

Abstract Background Parastomal hernia (PSH) is the most common complication of stomas associated with significant morbidity. Prophylactic mesh in preventing PSH is less well studied, and evidence is conflicting. We compare PSH rates in patients in whom 3D intraperitoneal mesh was and was not used prophylactically in a cohort of rectal cancer patients undergoing elective curative resection. Method Retrospective data from the Somerset Database and Electronic Patient Record were collected on patients under a single consultant surgeon between May 2016 and December 2021. Follow up was for a minimum of 2 years (excluding deaths). Appropriate statistical tests of difference significance were performed for proportions of categorical data, and time to hernia occurrence in patients with and without Dynamesh IPST placement. (Fisher’s Exact test; Kaplan Meier) Results From 397 colorectal cancer patients under the care of a single surgeon, 223 had elective surgery with curative intent, of which 63 (28%) underwent total mesorectal excision (TME) (30 TME &amp; ileostomy, 12 TME Hartmann and 21 APER) - 90% laparoscopic. 16 patients had prophylactic mesh used. Demographics and co-morbidities between groups were similar. PSH incidence was 1 (6%) (radiological) vs.7 (41%) (clinical) in the mesh vs. non-mesh group; P=0.039. Post operative complications and pain scores differed only slightly. Time to hernia development did not differ between groups. Conclusions Use of prophylactic mesh during permanent stoma formation in our practice appears safe, and effective in preventing PSH. Further studies may answer the question “Should 3D collared mesh be routinely placed during the formation of permanent stomas to prevent parastomal hernia?”

Transport and mining machinery cabins’ ergonomic evaluation as a path to its redesign

Transport and mining machinery cabins are still not well adapted to their users, while checklists for their evaluation are not common in the literature. This article proposes a new checklist for ergonomic evaluation and tests its universality empirically with a sample of 96 transport and mining machine operators. The objective of the article is two-fold. First, the article checks whether there are anthropometric dimension differences between different machines’ operators. Second, statistical significance testing regarding items in the proposed checklist is performed to check its universality. Significant differences have not been found between anthropometric dimensions of transport and mining machine operators. Group comparisons prove that mining machines have better ergonomics characteristics of the chair, manual controls and vision field. The recommendation for crane designers is to examine mining machines solutions and analyze the possibility of adapting these solutions, due to anthropometric fit. Wide usage of the checklist is recommended.