Exploratory Data Research Articles

Linguistic profiling and career development through the lived experience of service sector employees in Pakistan

PurposeUnderstanding linguistic profiling and its substantial consequences on employee career development is essential in diverse workplaces. This study utilizes Levinson’s eras and career development theories to analyze the complicated relationship between linguistic profiling and biases, which hamper employee career development.Design/methodology/approachThis study used an interpretive methodology and conducted thematic data analysis. It documented lessons learned from diverse viewpoints through semi-structured interviews with 19 participants conducted in November and December 2022.FindingsThe study demonstrates that linguistic discrimination occurs in diverse workplaces. After data exploration, four intriguing themes appeared. The first theme was related to employees who were discriminated against because of various languages. This shows how often language choice affects employees. The second theme examined how linguistic profiling intersected with marginalized groups, increasing discrimination. The third theme, linguistic profiling and career development showed that bias had a huge influence on career progression. The fourth theme emphasizes organizational policies for preventing language discrimination, promoting career growth and inclusive organizations.Originality/valueThis study advances the understanding of linguistic profiling and career development in a multilingual society. In addition, it furthers discourse and provides ways to minimize biases, creating a more inclusive workplace environment.

Importance of Characteristic Features and Their Form for Data Exploration

Importance of Characteristic Features and Their Form for Data Exploration

Exploring the impact of chaos engineering with various user loads on cloud native applications: an exploratory empirical study

AbstractOne of the most popular models that provide computer resources today is cloud computing. Today’s dynamic and successful platforms are created to take advantage of various resources available from service providers. Ensuring the performance and availability of such resources and services is a crucial problem. Any software system may be subject to faults that might propagate to cause failures. Such faults with the potential of contributing to failures are critical because they impair performance and result in a delayed reaction, which is regarded as a dependability problem. To ensure that critical faults can be discovered as soon as possible, the impact of such faults on the system must be tested. The performance and dependability of cloud-native systems are examined in this empirical study using fault injection, one of the chaos engineering techniques. The study explores the impacts and results of injecting various delay times into two cloud-native applications with diverse user numbers. The performance of the applications with various numbers of users is measured in relation to these delays, which accordingly reflects measuring the dependability of those systems. Firstly, the systems’ architecture were identified, and serverless with two Lambda functions and containerised microservices applications were chosen, which depend on utilising and incorporating cloud-native services. Secondly, faults are injected in order to quantify performance attributes such as throughput and latency. The results of several controlled experiments carried out in real-world cloud environments provide exploratory empirical data, which promoted comparisons and statistical analysis that we utilised to identify the behaviour of the application while experiencing stress. Typical results from this investigation include an overall reduction in performance that is embodied in an increase in latency with injecting delays. However, a remarkable result is noticed at a particular delay in which defects and availability problems appear out of nowhere. These findings assist in highlighting the value of using chaos engineering in general and fault injection in particular to assess the dependability of cloud-native applications and to find unpredicted failures that could arise quickly from defects that aren’t supposed to spread and result in dependability issues.

Abstract PS16-01: Comparison of an Atezolizumab monotherapy window followed by Atezolizumab and chemotherapy vs. Atezolizumab and chemotherapy alone in triple negative breast cancer (TNBC) – final analysis of the neoadjuvant neoMono trial

Abstract Background: Improvement of systemic therapy of TNBC still is a medical need. Exploratory data from the neoadjuvant GeparNuevo trial suggested a benefit from an immune checkpoint inhibitor (ICI) monotherapy window in TNBC. The neoMono trial prospectively analyzed whether the addition of a preceding Atezolizumab monotherapy window prior to Atezolizumab and chemotherapy (CTX) improves pCR rates among patients (pts) with early TNBC. In an interim analysis after 100 pCR events, patients with unselected TNBC did not show a significant benefit from an Atezolizumab monotherapy window, while an exploratory analysis suggested a highly clinically relevant benefit among pts with PD-L1 positive TNBC. Here we present the final primary endpoint analysis. Methods: NeoMono is a phase 2 randomized multicenter trial that was planned to recruit a maximum of 458 female and male pts with primary TNBC (defined as ER/PR < 10% and HER2 negative) with tumor stages cT1c – cT4d (cN0 and cN+). As the protocol mandated termination of trial recruitment based on the results of the interim analysis, the final ITT population was limited to 359 pts. PD-L1 status had to be identifiable by central pathology by means of the VENTANA PD-L1 (SP142) assay and was defined by PD-L1 expression on immune cells (IC). Neoadjuvant treatment in both study arms consisted of Atezolizumab 1200 mg every 3 weeks in addition to neoadjuvant CTX (12 x Carboplatin/Paclitaxel q1w followed by 4x Epirubicin/Cyclophosphamide q3w). Combination therapy in arm A was preceded by an Atezolizumab monotherapy window of 840 mg once two weeks prior to initiation of combination therapy, while patients in arm B received no immune monotherapy window. Study goals are to compare the efficacy of neoadjuvant CTX + Atezolizumab with versus without a two-week atezolizumab monotherapy window preceding CTX + ICI (primary endpoint: pCR) and to identify biomarkers for response and resistance through analysis of sequential tissue and liquid biopsies. The neoMono statistical design uses Bayesian posterior probabilities (uniform prior distribution) and logistic regression to analyze the primary endpoint. Results: 180 pts in arm A and 179 in arm B from 34 study sites were included in the final primary endpoint analysis. Demographics and baseline characteristics as well as drug exposure were well-balanced in both arms. Posterior mean pCR rates in the ITT population in study arms A and B were 65.7% (95% high posterior density interval (HPDI): 58.5%, 72.5%) and 69% (62.2%, 75.9%), respectively. In an exploratory analysis stratified by PD-L1 IC status (negative: < 1% versus positive: ≥ 1%), pCR rates in arm A were 91.5% in the PD-L1 IC-positive group and 56.1% in the PD-L1 IC-negative group, the corresponding pCR rates in arm B were 82.2% and 64.5%, respectively. In a multivariate analysis of the ITT population including tumor size, nodal status, tumor grade, age and PD-L1 status, the odds ratio for achieving a pCR was 4.77 (p < 0.001) for PD-L1-positive and 2.36 (p=0.023) for grade 3 tumors. In an exploratory analysis including HER2 status, odds for achieving a pCR were significantly higher for patients with HER2-negative (IHC 0) vs. HER2-low tumors (OR 1.73, p=0.036). No new safety signals were observed. Conclusion: The final primary endpoint analysis of the neoMono trial demonstrated the highest pCR rates ever reported in a phase II/III trial in TNBC. While a significant impact of an ICI monotherapy window on the pCR rate after combination of CTX + ICI in an unselected ITT population could not be demonstrated, neoMono indicates for the first time in a randomized prospective setting that patients with immune active TNBC might derive particular benefit from a preceding ICI monotherapy window. The results of the neoMono trial are mainly justifying the conduction of a confirmative trial in immune active TNBC. However, our results underscore the potential role of pre-therapeutic ICI monotherapy window as part of future therapeutic concepts in TNBC. Citation Format: Hans-Christian Kolberg, Johannes Schumacher, Ramona Erber, Michael Braun, Peter A. Fasching, Eva-Maria Grischke, Christian Schem, Michael P. Lux, Mustafa Deryal, Oliver Hoffmann, Bernhard Heinrich, Georg Kunz, Kristina Lübbe, Petra Krabisch, Arndt Hartmann, Philip Raeth, Sabine Kasimir-Bauer, Cornelia Kolberg-Liedtke. Comparison of an Atezolizumab monotherapy window followed by Atezolizumab and chemotherapy vs. Atezolizumab and chemotherapy alone in triple negative breast cancer (TNBC) – final analysis of the neoadjuvant neoMono trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS16-01.

Enhanced Fault Detection in Bearings Using Machine Learning and Raw Accelerometer Data: A Case Study Using the Case Western Reserve University Dataset

This study introduces a novel approach for fault classification in bearing components utilizing raw accelerometer data. By employing various neural network models, including deep learning architectures, we bypass the traditional preprocessing and feature-extraction stages, streamlining the classification process. Utilizing the Case Western Reserve University (CWRU) bearing dataset, our methodology demonstrates remarkable accuracy, particularly in deep learning networks such as the three variant convolutional neural networks (CNNs), achieving above 98% accuracy across various loading levels, establishing a new benchmark in fault-detection efficiency. Notably, data exploration through principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) provided valuable insights into feature relationships and patterns, aiding in effective fault detection. This research not only proves the efficacy of neural network classifiers in handling raw data but also opens avenues for more straightforward yet effective diagnostic methods in machinery health monitoring. These findings suggest significant potential for real-world applications, offering a faster yet reliable alternative to conventional fault-classification techniques.

Abstract Wrk1-01: Agnostic trials in the field of targeted therapies

Abstract Tumor-agnostic cancer therapy represents a significant shift in the field of precision oncology. It differs from conventional cancer treatments, which typically focus on specific tumor types, such as breast cancer, lung cancer, renal cell cancer, and others. This innovative approach brings hope to cancer patients by recognizing that identical genetic abnormalities can drive cancer in various parts of the body. The approvals of these therapies have introduced alternative treatment options, particularly for patients with advanced cancers and rare malignancies. Currently, six medicines have received US FDA approval for use in patients with solid tumors harboring one of five specific biomarkers. In this presentation, I will delve into the current landscape of these tissue-agnostic cancer therapies, providing a comprehensive exploration of the relevant clinical data that can enhance our understanding of these groundbreaking treatments. Citation Format: V. Subbiah. Agnostic trials in the field of targeted therapies [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr Wrk1-01.

Abstract PO4-16-06: From Real-World Data to Improved Outcomes: Examining the Impact of Trastuzumab Disparities on Pathological Complete Response and Long-Term Survival in HER-2 Positive Breast Cancer Patients within the Brazilian Public Health System

Abstract Introduction: Equitable access to healthcare is a fundamental principle of public health, aiming to ensure that all individuals have the opportunity to receive effective treatments. However, disparities in access to healthcare still persist in many countries, including Brazil. These disparities can have significant impacts on the health and well-being of patients, particularly in the case of breast cancer treatment. Neoadjuvant chemotherapy (NAC) has been increasingly used for the treatment of breast cancer, encompassing conditions ranging from locally advanced tumors to early-stage triple-negative and HER-2 positive tumors. Objectives: The aim of this study is to evaluate the impact of disparities in the use of trastuzumab (Herceptin) within the Brazilian public health system on pathological complete response (pCR), overall survival (OS), and disease-free survival (DFS). We aim to investigate whether variations in access to trastuzumab treatment influence the achievement of pCR and impact long-term survival outcomes in breast cancer patients. Methods: This retrospective, multicenter cohort study included female patients over 18 years of age with a diagnosis of non-metastatic breast cancer undergoing NAC. pCR was defined as the absence of residual invasive or in situ tumor in the breast and axilla. As an exploratory real-world data study, no confirmatory hypothesis was established, thus no corrections for multiple comparisons were necessary. Overall survival (OS) and disease-free survival (DFS) were estimated using the Kaplan-Meier method, calculated at five years. The study was approved by the Research Ethics Committee with registration number CAAE 64633422.4.0000.5463. Results: Between 2011 and 2020, a total of 1,891 patients were included in the study, of whom 454 (22.9%) were HER-2 positive. The pCR rate was 25.7%. Trastuzumab was accessed by 291 (64.1%) patients (p < 0.001). Among those who received trastuzumab, pCR was observed in 105 (36.1%) patients, compared to 12 (7.6%) in those who did not have access to trastuzumab (p < 0.001). OS in HER-2 positive patients was 90.1% with pCR and 64.5% without pCR (log-rank p < 0.001), while DFS was 89.2% with pCR and 68.2% without pCR (log-rank p = 0.003). Evaluating OS and DFS in patients who received trastuzumab versus those who did not, OS with trastuzumab and pCR was 94.3% and without pCR was 89.5% (log-rank p < 0.001); OS without trastuzumab and pCR was 83.3% and without pCR was 58.2% (log-rank p < 0.001). DFS in patients who received trastuzumab and achieved pCR was 94.5% and without pCR was 90.5% (log-rank p = 0.05), whereas without trastuzumab and pCR was 79.9% and without pCR was 62.1% (log-rank p = 0.001). Conclusion: These findings underscore the significant impact of disparities in trastuzumab utilization within the Brazilian public health system on pCR rates and long-term survival outcomes in HER-2 positive breast cancer patients. Access to trastuzumab was associated with higher pCR rates and improved overall and disease-free survival rates. Ensuring equitable access to targeted therapies is crucial for improving treatment response and long-term outcomes in breast cancer patients. Table 1 Clinicopathological characteristics of patients Legend: AJCC, American Joint Committee on Cancer; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; HR hormonal receptor. Table 2 - NAC schemas and correlation with pCR. AC-T, doxorubicin + cyclophosphamide – taxane; AC-TH, doxorubicin + cyclophosphamide – taxane+herceptin. Citation Format: Marcelo Antonini, Andre Mattar, Gabriel Pannain, Fernanda Grace Bauk Richter, Andressa Amorim, Marina Diogenes, Odair Ferraro, Luiz Gebrim, Reginaldo Coelho Lopes, Juliana Real. From Real-World Data to Improved Outcomes: Examining the Impact of Trastuzumab Disparities on Pathological Complete Response and Long-Term Survival in HER-2 Positive Breast Cancer Patients within the Brazilian Public Health System [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-16-06.

Abstract PO4-22-12: Robot-assisted nipple-sparing mastectomy using da Vinci SP single-port system: primary results from the pilot trial RASPIM-01

Abstract Background Robot-assisted breast surgery was developed in 2014 for nipple-sparing mastectomy through a concealable lateral chest wall incision smaller. Although successful experiences have been reported using a multi-arm surgical robotic system, collisions between robotic arms and instruments often result in the discontinuation of console workflow and limit the performance of the surgical robot. Compared to multi-armed robot systems, the SP system is single-armed, equipped with flexible instruments containing multiple joints that may avoid instrument collision, a controllable camera reducing visual dead spots, and a designated non-third-party port that allows single-port entry and full deployment of the instrument. The RASPIM-01 trial (NCT05448963) is a single-armed pilot study assessing the feasibility of robot-assisted nipple-sparing mastectomy (NSM) using a new SP system. Method This study is a pilot trial conducted in a single-arm, non-randomized design with a recruitment of 30 participants. The SP surgical robot is used in NSM through the lateral chest wall incisions. The major inclusion criteria are 1) Breast cancer with preoperative clinical tumor sizes less than 5 cm, with an adequate tumor-skin distance of at least 3mm, and without nipple-areolar involvement in at least 1cm around the nipple by image, 2) Breast cancer up to clinical stage IIIA (T3, N1-2) showing adequate response to neoadjuvant therapy and meeting criteria 1), 3) Germline pathogenic/likely pathogenic BRCA1 or 2 variant carriers with or without a breast cancer diagnosis, requiring unilateral or bilateral therapeutic mastectomy or prophylactic mastectomy. Exclusion criteria include extensive breast skin or nipple involvement such as inflammatory breast cancer, Paget’s disease, nipple discharge associated with malignancy, image suggestive of cancer involvement of the nipple and subareolar tissue, and stage III breast cancer without response to neoadjuvant therapy, and previous radiotherapy of the surgical side breast. The endpoint measurements include Primary endpoint: The ability to complete nipple-sparing mastectomy with an SP system in the per-protocol population without additional assistant port or conversion to open surgery. Statistical analysis includes point estimation with a 95% confidence interval to analyze the mean or proportion of key performance parameters. No interim analysis will be performed due to the limited number intended to recruit. Results The study recruited 30 breast cancer patients and conducted 30 NSMs, while no prophylactic mastectomy case was recruited. The median age was 44 years old, BMI was 22.4 (18.9-30.0) kg/m2. Median tumor size was 3.0 (1.0-14.6) cm, and axillary lymph node metastases were present in 7 cases (23.3%). T2 (13 cases, 43.3%) tumors were the majority, followed by Tis (10 cases, 33.3%), T1 (5 cases, 16.7%), and T3 (2 cases, 6.7%). The anatomical stages of invasive cancer were Ia (16.7%), IIa (26.7%), IIb (16.7%), and IIIa (6.7%), among them 9 (30.3%) received neoadjuvant chemotherapy before surgery. All NSMs met the primary endpoint of successful completion using the SP robot system without conversion to open mastectomy or the addition of an assistant port. Median console time was 108.5 (73-285) min and docking time was 2.5 (1-9) min. Specimen weight was 311.6 (107-838) gm, while 2 (6.7%) cases had their nipple excised due to cancer-involved margin reported from the frozen section. Twenty-six (86.6%) cases received autologous free flap reconstruction, among which 25 (83.3%) cases received DIEP, and 1 (3.3%) received PAP reconstruction. Four cases received implant (2 cases, 6.7%) or tissue expander (3 cases, 6.7%). Conclusion This is the first NSM clinical trial operated with SP surgical robot system and met its primary endpoint showing the feasibility to conduct NSM. Secondary and exploratory endpoints data will be reported after the completion of clinical follow-ups. Citation Format: Wen-Ling Kuo, Chia-Huei Chu, Jung-ju Huang. Robot-assisted nipple-sparing mastectomy using da Vinci SP single-port system: primary results from the pilot trial RASPIM-01 [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-22-12.

Abstract PO2-16-12: Real world evidence of neoadjuvant chemotherapy for breast cancer treatment in a Brazilian multicenter cohort: correlation of pathological complete response and overall survival

Abstract Introduction: Breast cancer is the most common malignancy among women in Brazil and worldwide. Neoadjuvant chemotherapy (NAC) has been increasingly used for the treatment of breast cancer conditions ranging from locally advanced tumors to early-stage triple-negative and HER-2 positive tumors. Nowadays only 5% of breast cancer patients are enrolled in clinical trials. Real-world data is important to evaluate how clinical trial treatments behave in the real world with all the heterogeneous patient characteristics of those treated differently from the homogeneity of clinical trials. Objectives: To evaluate the rates of pathological complete response (pCR) in patients undergoing neoadjuvant chemotherapy (NAC) for the treatment of breast cancer (BC) To examine the differences in pathological complete response (pCR) rates between HER-2 positive breast cancer patients with and without trastuzumab treatment, and between triple-negative breast cancer (TNBC) patients with and without platinum-based treatment, Additionally, we aim to assess the impact of pCR on overall survival (OS) and disease-free survival (DFS) using real-world data (RWD). Methods: This was a retrospective, multicentric cohort study, that included female patients over 18 years of age, with a diagnosis of non-metastatic breast cancer undergoing NAC. As an exploratory real-world data study, no confirmatory hypothesis was established, so no corrections for multiple comparisons were necessary. Overall survival (OS) and disease-free survival (DFS) were estimated by the Kaplan-Meier method calculated at five years. Additionally, we conducted a multivariate analysis to identify significant associations with pathologic complete response (pCR) and overall survival (OS). We employed a forest plot to visually represent these associations Results: From 2011 to 2020, 1,891 patients were included in the study, and 421 (22,3%) achieved pCR (ypT0 ypN0) and considering the presence of residual in situ carcinoma (DCIS) the pCR was seen in 467 patients (23,5) without significant difference (p=0,567) . The pCR rate varied between the subtypes: luminal A 17,0%, luminal B 21,3%, luminal HER2 23,0%, TNBC 22,5% and HER2 28,5% (p = 0,057). The type of neoadjuvant chemotherapy (NAC) regimen was correlated with pathological complete response (pCR). Among HER2-positive patients, those who received Trastuzumab had a significantly higher rate of pCR compared to those who did not receive it (p < 0.0001). Similarly, in patients with TNBC, those who underwent treatment with platinum-based regimens also showed higher rates of pCR (p < 0.0001). There were 694 deaths, and the 5-year OS rate was 62.9%. OS was also grouped according to pCR status, and pCR OS rate was 88,3% and non-pCR 58.1% significant difference observed (p < 0.0001). Five-year DFS of pCR was 92.2% and non-pCR 64.3% (p < 0.0001). Conclusion: this real-world data study evaluated the rates of pathological complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The results revealed varying pCR rates among different breast cancer subtypes, with HER2-positive and triple-negative subtypes showing higher rates. The use of Trastuzumab in HER2-positive patients and platinum-based regimens in triple-negative patients correlated with significantly higher pCR rates. Furthermore, achieving pCR was associated with improved overall survival (OS) and disease-free survival (DFS). These findings emphasize the importance of considering individual tumor characteristics and tailoring treatment strategies for breast cancer patients to optimize outcomes. Clinicopathological characteristics of patients AJCC, American Joint Committee on Cancer; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; TNBC triple negative breast cancer. NAC schemas and correlation with pCR. AC-T, doxorubicin + cyclophosphamide – taxane; AC-TPlatinum, doxorubicin + cyclophosphamide – taxane+carboplatin; AC-TH, doxorubicin + cyclophosphamide – taxane+herceptin. Citation Format: Marcelo Antonini, Andre Mattar, Fernanda Grace Bauk Richter, Gabriel Pannain, Andressa Amorim, Marina Diogenes, Odair Ferraro, Luiz Gebrim, Reginaldo Coelho Lopes, Juliana Real. Real world evidence of neoadjuvant chemotherapy for breast cancer treatment in a Brazilian multicenter cohort: correlation of pathological complete response and overall survival [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-16-12.

Case Study: Performance Analysis throughout the History in Summer and Winter Olympics

The Olympics is the world’s foremost multisport international event that occurs every four years in 2 seasonal editions which are the Summer and Winter Olympics respectively. With more than 200 nations participating in hundreds of events, it is a matter of prestige for any nation and the dream of every sportsperson to win a medal in the Olympics. Despite the massive hard work of the sportspersons, many nations fail to win medals whereas some nations are able to grab most of the medals under the names of their nations. Therefore, an analysis of the history of the Olympics is needed to understand the performances of nations, athletes and events to observe certain patterns occurring throughout the history of Olympics, in order to help the nations to improve themselves. The primary objective of this paper is to analyze the historical data of both the Winter and Summer Olympics under 14 different factors using Exploratory Data Analysis (EDA) techniques and statistical methods to evaluate the performance of nations and athletes as well as the historical evolution of Olympics and correlation of medal win of sports persons with external variables such as GDP and population. The analyses provide accurate insights of the performance of nations in the Olympics throughout history and help sportspersons to analyze their own and the competitor’s performances. In this paper, the data visualization aspect of EDA is used to provide a statistical view of factors that led to the evolution of the Olympic games as well as the performance of nations and sportspersons and helps countries with average and poor performances to improve themselves in upcoming Olympic editions.

Detection of Botnet in IOT Using Machine Learning

The proliferation of Internet of Things (IoT) devices has introduced unprecedented connectivity and convenience but also heightened the vulnerability to botnet attacks. There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This project presents a straightforward approach to detect botnet activity within IoT networks through the utilization of machine learning techniques. By analyzing network traffic patterns and employing unsupervised learning algorithms, we demonstrate an effective method to identify and mitigate botnet threats in IoT environments. By this project we intend to offer a valuable contribution in enhancing the security of IoT ecosystem. Key Word: Internet of Things(IoT),cybersecurity, botnet attacks, machine learning(ML),UNSW-NB15 dataset, exploratory data analysis, XgBoost

Abstract PO4-04-05: HER2 immunohistochemistry (IHC) expression in HER2-positive (HER2-pos) metastatic breast cancer (mBC): clinical and prognostic differences between IHC 2+ and IHC 3+ populations

Abstract Background: The exploration of heterogeneity in mBC has gained significant interest in optimizing therapeutic strategies. While the impact of differential HER2 IHC staining has been extensively studied in HER2-low neoplasms, limited data is available regarding IHC 2+ and IHC 3+ categories in HER2-pos mBC. The aim of our study is to compare, the clinical variables, metastatic patterns, and prognosis between patients classified as IHC 2+ and IHC 3+ in HER2-pos mBC. Methods: Pts with HER2 pos mBC, determined by HER2 IHC staining of 3+ or 2+ with amplification at the in situ hybridization assay, were selected from the GIM14 study database for analysis. Associations among variables were assessed by conducting logistic regression analyses. Additionally, prognostic factors for overall survival (OS) and time to treatment failure (TTF) were evaluated using both uni- and multi-variable Cox regression models. Results: A total of 766 HER2-pos mBC pts were included in the analysis. Among them, 302 (39%) had IHC score of 2+ and 464 (61%) had a IHC score of 3+. Central nervous system (CSN) metastases (mts) were observed in 68 (9%) pts (32% IHC 2+; 68% IHC 3+), lymph node (LN) mts in 330 (43%) pts (46% IHC 2+; 54% IHC 3+), lung (LU) mts in 182 (24%) pts (46% IHC 2+; 54% IHC 3+), liver (LI) mts in 227 (30%) pts (45% IHC 2+, 55% IHC 3+), skin (SK) mts in 68 (9%) pts (28% IHC 2+; 72% IHC 3+) and pleural (PL) mts in 40 (5%) pts (47% IHC 2+, 53% IHC 3+). In univariable (uni) analysis, IHC 3+ compared to IHC 2+ was significantly associated with SK mts (OR 1.76, P=0.043) and less likely to be associated with LI (OR 0.71, P=0.031), LU (OR 0.70, P =0.036), and LN (OR 0.63, P=0.002) mts. In multivariable (multi) analysis, LN mts maintained statistical significance (OR 0.62, P=0.004). In subgroup analysis, among IHC 3+ patients, factors associated with worse OS pts were neo Tx (HR 1.55, P=0.032), adjuvant radiotherapy (HR 1.34, P=0.042), neo ChT (HR 1.63, P=0.002), CNS mts (HR 1.98, P=0.002), and LI mts (HR 1.68, P< 0.0001). In multi analysis, CNS mts (HR 1.73, P=0.028) and LI mts (HR 1.47, P=0.017) remained prognostic for IHC 3+ subgroup. The only factor associated with worse OS in IHC 2+ pts was LI mts (HR 1.67, P=0.005). Multi analysis in the overall HER2-pos population identified neo chemotherapy (ChT) (HR 1.49, P=0.009), CNS mts (HR 1.53, P=0.003) and PL mts (HR 1.50, P=0.036) as negative prognostic factors for OS, while PL mts (HR 1.74, P=0.048) were associated with a shorter time to treatment failure (TTF). Conclusions: Our exploratory data revealed that HER2-pos mBC with a IHC score of 3+ is less likely to be associated with LN mts and with visceral mts. The site of mts has prognostic significance, as CNS mts are associated with worse OS in pts with an IHC score of 3+ pts, while LI mts are associated with worse OS in both IHC 2+ and 3+ subgroups. However, the IHC score itself (2+ or 3+) does not have independent prognostic value. The relationship between HER2 IHC staining and treatment outcomes requires further investigation to better understand its potential impact on clinical practice. Citation Format: Arianna Dri, Eva Blondeaux, Claudia Bighin, Simona Gasparro, Stefania Russo, Lorenzo Foffano, Palma Pugliese, Andrea Fontana, Enrico Cortesi, Antonella Ferzi, Ferdinando Riccardi, Valentina Sini, Luca Boni, Alessandra Fabi, Filippo Montemurro, Michelino De Laurentiis, Grazia Arpino, Lucia Del Mastro, Lorenzo Gerratana, Fabio Puglisi. HER2 immunohistochemistry (IHC) expression in HER2-positive (HER2-pos) metastatic breast cancer (mBC): clinical and prognostic differences between IHC 2+ and IHC 3+ populations [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-04-05.

Abstract PO5-13-08: Impact of systemic inflammation markers in response to neoadjuvant pembrolizumab and chemotherapy in triple-negative breast cancer - a retrospective analysis

Abstract Background: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment landscape for triple-negative breast cancer (TNBC). Systemic inflammation has been linked to cancer development and overall poor outcomes. Routine blood parameters have been investigated as potential inflammatory biomarkers in patients (pts) with various types of cancer, such as the derived NLR (neutrophils/(leucocytes-neutrophils) ratio (dNLR). A high dNLR has been associated with worse survival in several types of cancer. Specifically, it has been linked to lower pathologic complete response (pCR) rates after neoadjuvant chemotherapy (CTx). We investigated the association between systemic inflammation markers (dNLR, LIPI score and platelet-to-lymphocyte ratio [PLR]) and pCR in early TNBC pts treated with CTx + pembrolizumab (Pemb). Methods: Retrospective analysis of pts with early TNBC treated with Pemb + CTx from April 2022 to March 2023. dNLR was estimated from analytical values of peripheral blood collected before treatment (baseline). dNLR was calculated as the ratio of the absolute neutrophil number to the difference between absolute total leukocyte and absolute neutrophil counts. Univariate and multivariate logistic regression analyses were used to explore the association of dNLR with main clinical characteristics and dNLR capability (distributed as a continuous variable, using median cut-off) to predict pCR. Results: The study included 86 TNBC pts with a median age of 51 years (IQR: 42.2 – 56-7). Most pts had stage II disease (64%) and presented grade 3 tumours (n=66; 78.5%). HER2 low tumours represented 27% (n=23) of the cohort. Germline mutations were present in 20 pts (23%) with the following distribution: BRCA1 (n=14), BRCA 2 (n=5) and PALB2 (n=1). The median stromal tumor infiltrating lymphocytes (TILs) was 20% (IQR: 10-30%). The median Ki67 was 70% (IQR:50%-80%). All pts received Pemb. The majority of pts received carboplatin plus paclitaxel, followed by EC90 (n=77 (90%), while 10% (n=9) did not receive anthracyclines. Data concerning surgical outcomes was available in 80 pts. Pathological complete response rate was 61.2% (n=49). Pts achieving pCR presented significantly higher TILs (p< 0.01). Residual cancer burden (RCB) class (n=80) was distributed as follows: 0: 61% (n=49); I: 13% (n=10); II: 20% (n=16); III: 1% (n=1) and non-assigned: 5% (n=4). In pts with germline mutations (n=20), the pCR rate was 85% (n=17). Median dNLR at baseline was 1.30 (interquartile range (IQR): 0.99 – 1.86). High baseline dNLR levels considered a continuous variable was associated with a higher likelihood of achieving a pCR in univariate (OR: 2.59; 95% CI: 1.01–6.6; p-value = 0.046) but not in multivariate (OR: 2.33; 95%; CI: 0.85–6.33; p-value = 0.097) logistic regression analysis (adjusted for stage and histologic grade). High baseline dNLR, defined by the median cut-off, was associated with an increased likelihood of achieving pCR in both univariate (OR: 3.85; 95% CI: 1.47–10.1; p-value = 0.006) and multivariate (OR: 3.72; 95% CI: 1.28–10.8; p-value = 0.016) logistic regression analysis (adjusted for stage and histologic grade). Conclusion: In this real-life cohort of early TNBC pts treated with Pemb and CTx we found similar pCR rates (61.2%) to the ones reported in the KN522 trial. No association between PLR or LIPI score with pCR was evidenced. High baseline dNLR (assessed as a continuous variable or by the median cut-off) was associated with a higher likelihood of pCR in the univariate analysis. In the multivariate analysis, only high baseline dNLR defined by the median cut-off retained an association with pCR after stage and histologic grade adjustment. Despite several limitations, these exploratory data provide initial evidence of a potential association between dNLR values and independent prediction of pCR. Further research to assess the role of dNLR as a predictive marker for pCR in early TNBC pts treated with CTx + immunotherapy is needed. Citation Format: Martina Spotti, Elie Rassy, Alessandro Viansone, Fiona Pham, Layal Rached, Barbara Pistilli, Charles-Antoine Dutertre, Joana Mourato Ribeiro. Impact of systemic inflammation markers in response to neoadjuvant pembrolizumab and chemotherapy in triple-negative breast cancer - a retrospective analysis [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-13-08.

Detection of Malicious URLs using Machine Learning based on Lexical Features

As the digital world evolves, the risk of valuable information being exposed to unauthorized parties is increasing. One common vulnerability is the use of malicious Uniform Resource Locator (URL), which are fraudulent links spread across various platforms such as social media and emails. Traditional methods of identifying these URLs, such as blacklisting and heuristic search, rely heavily on syntax or keyword matching, but struggle to keep up with the evolving tactics of cyber attackers. Hence, this paper proposes a solution for detecting malicious URLs and their types based on lexical features. Lexical features in a URL refer to the components that convey semantic and lexical meaning. These can include domain names, path lengths, special characters, and other elements that can be analyzed for patterns or anomalies. In our proposed method, we use 23 different lexical features that focus on the semantic and lexical meaning of the URLs. An Exploratory Data Analysis (EDA) is used to filter the most important lexical features that effectively contribute to predictions. With these carefully curated features, we address the problem as a multi-classification task, aiming to assess the performance of three distinct classifiers: Random Forest, which currently stands as the domain's best solution and a pure bagging technique, as well as XG Boost and Light GBM, both of which utilize boosting techniques. With the proposed method, we could achieve over 93\% accuracy for all the three classifiers while Random Forest achieving the best performance.

Empowering Global AMR Research Community: Interactive GIS dashboards for AMR data analysis and informed decision-making

Background Antimicrobial Resistance (AMR) is a critical global public health concern, demanding effective tools for research, data analysis, and decision-making. This study proposes a groundbreaking approach to empower the global AMR research community by introducing interactive Geographic Information System (GIS) dashboards. These dashboards aim to facilitate comprehensive data analysis of AMR across multiple countries, providing insights into antimicrobial usage (AMU), resistance patterns, and geographic distribution. Methods The novel approach involves the development of GIS dashboards that integrate and harmonize data from diverse sources, including clinical laboratories, surveillance networks, and public health agencies. Objective 1 focuses on creating a dashboard encompassing all countries, offering comprehensive data analysis capabilities and visualization tools. Objective 2 entails building a focused dashboard specifically for Kenya and Uganda, allowing for comparative analysis of AMR in these regions. Objective 3 involves the generation of a simulated dataset for Kampala, Uganda, addressing data limitations in that specific area. Results The GIS dashboards serve as powerful tools for visualizing and analyzing AMR-related datasets, providing stakeholders with a comprehensive view of the global AMR landscape. These dashboards offer valuable insights into antimicrobial usage, resistance patterns, and geographical distribution. The centralized platform facilitates data exploration and analysis, aiding researchers, policymakers, and healthcare professionals in making informed decisions to combat AMR. Conclusions In conclusion, the study demonstrates that the developed GIS dashboards empower stakeholders by providing valuable insights and informed decision-making capabilities. The dashboards serve as essential tools for addressing the global challenge of AMR, allowing for a deeper understanding of the problem and informing effective strategies. The approach outlined in this study has the potential to significantly contribute to the ongoing efforts to combat AMR on a global scale.

Sexual violence against children and adolescents in Paraná State: geospatial analysis and main socioeconomic indicators

ObjectiveChild sexual violence is a multidimensional problem of many contemporary societies, affecting people of all sexes, social stratum and age groups. Offenses involving children and adolescents are more serious, given their total or partial dependence on parents and caregivers. Information on child sexual violence in Brazil is found in raw form and without detail. The objective was to compare the information with social and economic data in the state of Paraná. MethodsThe authors conducted a retrospective study of secondary data from the Notifiable Diseases Information System (SINAN) on cases of sexual violence involving victims aged 0 to 19 years. Results are presented according to notification characteristics. The authors applied exploratory spatial data analysis to assess spatial autocorrelations and investigated relationships by the ordinary least squares regression model. ResultsBetween 2017 and 2021, there were 13,403 reports of child sexual violence in Paraná State, Brazil. Most victims (82.8%) were female and aged between 10 and 14 years. The majority of sexual violence cases (67.8%) occurred in the home environment. The highest rates on a population basis were observed in the North Central and Greater Curitiba regions, mainly in cities with higher population density and with higher rates of other types of violence. ConclusionThe results provide data that can promote a broader understanding of the distribution of sexual violence and the state and associated variations. It is expected to improve the provision of care for victims of child sexual violence and assist in strategic planning to prevent future offenses.

Lifting the veil off treatment effect heterogeneity

Clinicians often suspect that a treatment effect can vary across individuals. However, they usually lack “evidence-based” guidance regarding potential heterogeneity of treatment effects (HTE). Potentially actionable HTE is rarely discovered in clinical trials and is widely believed (or rationalized) by researchers to be rare. Conventional statistical methods to test for possible HTE are extremely conservative and tend to reinforce this belief. In truth, though, there is no realistic way to know whether a common, or average, effect estimated from a clinical trial is relevant for all, or even most, patients. This absence of evidence, misinterpreted as evidence of absence, may be resulting in sub-optimal treatment for many individuals. We first summarize the historical context in which current statistical methods for randomized controlled trials (RCTs) were developed, focusing on the conceptual and technical limitations that shaped, and restricted, these methods. In particular, we explain how the common-effect assumption came to be virtually unchallenged. Second, we propose a simple graphical method for exploratory data analysis that can provide useful visual evidence of possible HTE. The basic approach is to display the complete distribution of outcome data rather than relying uncritically on simple summary statistics. Modern graphical methods, unavailable when statistical methods were initially formulated a century ago, now render fine-grained interrogation of the data feasible. We propose comparing observed treatment-group data to “pseudo data” engineered to mimic that which would be expected under a particular HTE model, such as the common-effect model. A clear discrepancy between the distributions of the common-effect pseudo data and the actual treatment-effect data provides prima facie evidence of HTE to motivate additional confirmatory investigation. Artificial data are used to illustrate implications of ignoring heterogeneity in practice and how the graphical method can be useful.

Unveiling the Dynamics of Rural Revitalization: From Disorder to Harmony in China’s Production-Life-Ecology Space

This study utilizes provincial panel data from China spanning the period from 2011 to 2020 to assess the coupled and coordinated development of spatial functions related to production, life, and ecology (PLE) in rural areas. The assessment is based on quantifying the spatial function indices for PLE in China’s rural regions. Additionally, it examines the characteristics of their spatial and temporal evolution, spatial correlation, and driving factors. The findings indicate a modest upward trend in the spatial coupling and coordination levels of these functions across rural China, although a significant proportion of provinces still exhibit a near-disordered decline. Exploratory spatial data analysis reveals a geographical disparity, with higher levels of coupled and coordinated development observed in the eastern regions, lower levels in the west, and noticeable spatial clustering. By employing the spatial Durbin model to investigate the determinants of coupling degrees, we discovered that factors such as regional economic development, urbanization, the urban–rural income gap, financial support for agriculture, science and technology investment level, and agricultural structural adjustments significantly influence the spatial coupling of rural PLE functions. Furthermore, using the geographic detector model, the analysis identifies science and technology investment level, economic development, and financial support for agriculture as key drivers influencing the spatial coupling and coordination of these functions. These findings provide valuable reference points for policies and strategies related to rural management.

Optimized random forest model for predicting flexural properties of sustainable composites

AbstractIn an era of technological advancements, the quest for sustainable products has taken a center stage. The utilization of natural fiber reinforced polymer composites has become crucial in the manufacturing of eco‐friendly products due to their low cost, renewability, biodegradability, and comparable properties to synthetic composites. Coir‐based composites have been utilized in roofing, composite panels, furniture, and bio‐based insulation applications, providing enhanced strength and sustainability. In the current investigation, an exploratory data analysis (EDA) was conducted to understand the relationship between the various input variables and the flexural properties of short coir polymer composites (SCPCs). The data analysis revealed that manufacturing process had a significant impact on the flexural properties of SCPCs. Based on the insights gained from the EDA, an optimized Random Forest prediction model was developed to predict the flexural properties. Genetic algorithm approach for hyperparameter optimization led to lower objective loss in contrast to Bayesian optimized Random Forest model. The model's performance was subsequently evaluated through holdout validation, and the outcome demonstrated the model's proficiency in accurately predicting the properties. The developed model can be used as a tool for optimizing the design of SCPCs for specific applications, by predicting the flexural properties of the composites.Highlights Flexural behavior of short coir polymer composites was analyzed and modeled. Manufacturing process has the highest impact on the flexural properties. Random forest model predicted the flexural properties with high accuracy. Genetic algorithm optimized model further enhanced the model performance. The developed framework provides insights into designing biocomposites.

Invest in Idea: Crowd Funding with Predicting Startup Success

Abstract: Crowd funding enables individuals or businesses to raise funds for their projects, ventures, or caused by tapping into a large pool of contributors. It democratizes access to capital, allowing creators to bypass traditional financial institutions and engage directly with their community or target audience. This method fosters innovation, empowers grassroots movements, and facilitates the realization of diverse ideas that might otherwise struggle to secure funding through conventional channels. Utilizing a rich dataset encompassing funding details, milestones, relationships, and geographical data of various startups, we embark on an in- depth analysis involving data preprocessing, feature engineering, and exploratory data analysis to uncover key success determinants. Employing advanced classifiers like LGBM, XG Boost and gradient Boosting, our model undergoes rigorous training and evaluation, with LGBM emerging as the top performer, achieving an accuracy of 90.48%. The analysis underscores the critical role of funding, and investor presence in forecasting startup success. This research not only equips stakeholders with a powerful predictive tool but also highlights significant features influencing startup outcomes, offering a novel perspective on strategic investment planning. Additionally, we developed an interactive frontend platform to complement our predictive model. Utilizing AngularJS, the platform serves as a gateway for startups to register their ventures, providing realtime access to predictive insights. Through intuitive forms, startups enter essential information such as geographical location, funding received, and sector of operation. Upon submission, the data is processed by our backend, which evaluates it against our machine learning model to predict success rates. This integration empowers startups with immediate insights and facilitates data-driven decision-making among investors, thereby fostering a more dynamic and informed startup ecosystem.