How You Split Matters: Data Leakage and Subject Characteristics Studies in Longitudinal Brain MRI Analysis

Deep learning models have revolutionized the field of medical image analysis, offering significant promise for improved diagnostics and patient care. However, their performance can be misleadingly optimistic due to a hidden pitfall called 'data leakage'. In this study, we investigate data leakage in 3D medical imaging, specifically using 3D Convolutional Neural Networks (CNNs) for brain MRI analysis. While 3D CNNs appear less prone to leakage than 2D counterparts, improper data splitting during cross-validation (CV) can still pose issues, especially with longitudinal imaging data containing repeated scans from the same subject. We explore the impact of different data splitting strategies on model performance for longitudinal brain MRI analysis and identify potential data leakage concerns. GradCAM visualization helps reveal shortcuts in CNN models caused by identity confounding, where the model learns to identify subjects along with diagnostic features. Our findings, consistent with prior research, underscore the importance of subject-wise splitting and evaluating our model further on hold-out data from different subjects to ensure the integrity and reliability of deep learning models in medical image analysis.Comment: submitted to MICCAI FAIMI 202

Increasing Diversity: Modeling of Social Capital for Navigating the Science and Health Professions Pipeline

Social capital theory states that resources, both actual and prospective, are inherently linked to networks and relationships that can be used as opportunities. Therefore, a basic tenet of social capital theory is that "relationships matter." In the science and health profession pipeline, strong mentoring relationships and collaborative research networks are critical elements in developing an individual's capacity for navigating the pipeline and for success and advancement in these fields. However, underrepresented minorities are often bereft of social capital because they lack proper mentorships and are often not part of "inner" circles for networking. Additionally, social capital can be leveraged to develop organizational capacity that supports diversity. In this dissertation, social capital theory is examined through the lens of three pipeline initiatives targeting pre-high school, high school, undergraduate, and graduate-level populations. The three initiatives (E-matching, achieving Successful Productive Academic Research Careers, and Mentoring in Medicine) were evaluated and the results are presented here as three related but unique manuscripts. The particular forms of social capital examined are knowledge, mentorship, and networks needed to navigate the pipeline for science and health professions careers. All three initiatives had significant impact on increasing social capital via the social capital indicators of increased knowledge, mentorship, networks, information and resources. Study results suggest that it would be useful to replicate these initiatives on a larger scale to build social capital at earlier levels of the pipeline to enhance diversity in the science and health professions. Additionally, study results suggest that the social capital obtained from brief interactions in short duration initiatives is valuable as a factor in assisting students to navigate the pipeline; therefore this should not be underestimated. Lastly, a logic model framework is provided for measuring social capital for navigating the STEM and health professions pipeline

Ray transfer matrix for a spiral phase plate

We present a ray transfer matrix for a spiral phase plate. Using this matrix, we determine the stability of an optical resonator made of two spiral phase plates, and trace stable ray orbits in the resonator. Our results should be relevant to laser physics, optical micromanipulation, quantum information and optomechanics.Comment: 5 pages, 3 figure

Thrombospondin1 Deficiency Reduces Obesity-Associated Inflammation and Improves Insulin Sensitivity in a Diet-Induced Obese Mouse Model

BACKGROUND: Obesity is prevalent worldwide and is associated with insulin resistance. Advanced studies suggest that obesity-associated low-grade chronic inflammation contributes to the development of insulin resistance and other metabolic complications. Thrombospondin 1 (TSP1) is a multifunctional extracellular matrix protein that is up-regulated in inflamed adipose tissue. A recent study suggests a positive correlation of TSP1 with obesity, adipose inflammation, and insulin resistance. However, the direct effect of TSP1 on obesity and insulin resistance is not known. Therefore, we investigated the role of TSP1 in mediating obesity-associated inflammation and insulin resistance by using TSP1 knockout mice. METHODOLOGY/PRINCIPAL FINDINGS: Male TSP1-/- mice and wild type littermate controls were fed a low-fat (LF) or a high-fat (HF) diet for 16 weeks. Throughout the study, body weight and fat mass increased similarly between the TSP1-/- mice and WT mice under HF feeding conditions, suggesting that TSP1 deficiency does not affect the development of obesity. However, obese TSP1-/- mice had improved glucose tolerance and increased insulin sensitivity compared to the obese wild type mice. Macrophage accumulation and inflammatory cytokine expression in adipose tissue were reduced in obese TSP1-/- mice. Consistent with the local decrease in pro-inflammatory cytokine levels, systemic inflammation was also decreased in the obese TSP1-/- mice. Furthermore, in vitro data demonstrated that TSP1 deficient macrophages had decreased mobility and a reduced inflammatory phenotype. CONCLUSION: TSP1 deficiency did not affect the development of high-fat diet induced obesity. However, TSP1 deficiency reduced macrophage accumulation in adipose tissue and protected against obesity related inflammation and insulin resistance. Our data demonstrate that TSP1 may play an important role in regulating macrophage function and mediating obesity-induced inflammation and insulin resistance. These data suggest that TSP1 may serve as a potential therapeutic target to improve the inflammatory and metabolic complications of obesity

The Existence Of The International Anarcho-Syndicalism Movement Based On Indonesian Law Perspective

Anarcho-syndicalism is an ideology in which workers want to work freely, not bound by rules. Anarcho-syndicalism is an ideology that wants the dissolution of all political power institutions that are narrated to oppress and exploit workers to be replaced by free communities bound by socio-economic interests. This research aims to find out the existence of the anarcho-syndicalist movement based on international law and the readiness of national law in facing the threat of anarcho-syndicalist ideology in Indonesia. The research method used in this research is normative legal research (library research) which has relevance to the research issues studied. Sources and legal materials used are primary, secondary, and tertiary legal materials. Based on the results of the research that has been carried out, it is known that the existence of the anarcho-syndicalism movement in terms of International Law has an impact on the application of the Right to freedom of association for workers/laborers as regulated in ILO (International Labour Organization) Convention No. 87 of 1948 concerning freedom of association and protection of the right to organize. Furthermore, the State has regulated people who join organizations to commit criminal offenses or prohibited organizations will be subject to imprisonment or fines, namely by what is written in the Criminal Code/KUHP (Kitab Undang-Undang Hukum Pidana) Article 261 concerning Participation in Organisations to commit Criminal Offences

Thrombospondin1 Deficiency Reduces Obesity-Associated Inflammation and Improves Insulin Sensitivity in a Diet-Induced Obese Mouse Model

Obesity is prevalent worldwide and is associated with insulin resistance. Advanced studies suggest that obesity-associated low-grade chronic inflammation contributes to the development of insulin resistance and other metabolic complications. Thrombospondin 1 (TSP1) is a multifunctional extracellular matrix protein that is up-regulated in inflamed adipose tissue. A recent study suggests a positive correlation of TSP1 with obesity, adipose inflammation, and insulin resistance. However, the direct effect of TSP1 on obesity and insulin resistance is not known. Therefore, we investigated the role of TSP1 in mediating obesity-associated inflammation and insulin resistance by using TSP1 knockout mice.Male TSP1-/- mice and wild type littermate controls were fed a low-fat (LF) or a high-fat (HF) diet for 16 weeks. Throughout the study, body weight and fat mass increased similarly between the TSP1-/- mice and WT mice under HF feeding conditions, suggesting that TSP1 deficiency does not affect the development of obesity. However, obese TSP1-/- mice had improved glucose tolerance and increased insulin sensitivity compared to the obese wild type mice. Macrophage accumulation and inflammatory cytokine expression in adipose tissue were reduced in obese TSP1-/- mice. Consistent with the local decrease in pro-inflammatory cytokine levels, systemic inflammation was also decreased in the obese TSP1-/- mice. Furthermore, in vitro data demonstrated that TSP1 deficient macrophages had decreased mobility and a reduced inflammatory phenotype.TSP1 deficiency did not affect the development of high-fat diet induced obesity. However, TSP1 deficiency reduced macrophage accumulation in adipose tissue and protected against obesity related inflammation and insulin resistance. Our data demonstrate that TSP1 may play an important role in regulating macrophage function and mediating obesity-induced inflammation and insulin resistance. These data suggest that TSP1 may serve as a potential therapeutic target to improve the inflammatory and metabolic complications of obesity

Efektivitas Alat Tangkap Bubu Berdasarkan Jenis Umpan dan Waktu Perendaman Terhadap Hasil Tangkapan Ikan di Rawa Wasur, Kabupaten Merauke

Penelitian dilaksanakan di salah satu perairan rawa yang berlokasi pada Taman Nasional Wasur, dimana perairan tersebut merupakan lokasi mata pencaharian dari masyarakat sekitar. Adapun tujuan dari penelitian ini yaitu untuk mengetahui efektivitas alat tangkap bubu berdasarkan jenis umpan dan waktu perendaman terhadap hasil tangkapan ikan di Rawa Wasur. Penelitian dilaksanakan pada bulan April hingga Mei 2023, dimana metode yang digunakan yaitu experimental fishing dengan Rancangan Acak Kelompok (RAK) Faktorial dalam upaya memahami bagaimana variasi jenis umpan serta lama waktu perendaman memengaruhi hasil tangkapan ikan. Hasil tangkapan ikan yang didapatkan menunjukkan bahwa jenis umpan semut mendominasi jika dibandingkan dengan jenis umpan yang lain, baik itu pada periode waktu perendaman pagi dan malam hari. Secara total, jenis ikan gabus dan ikan betok merupakan jenis ikan yang paling banyak tertangkap dengan jumlah masing-masing sebanyak 268 dan 210 ekor. Namun demikian, analisis sidik ragam (ANOVA) pada variasi jenis umpan menunjukkan bahwa nilai F hitung < F tabel (2,24 < 4,10), dimana interaksi antara perlakuan tidak memberikan pengaruh terhadap hasil tangkapan. Meskipun demikian, hasil analisis pada perbedaan waktu perendaman memberikan interaksi antara perlakuan dengan rerata jumlah tangkapan yang tidak sama, dengan kata lain memberikan perbedaan yang nyata (7,49 > 4,10). Hal ini menunjukkan bahwasanya alat tangkap bubu lebih efektif digunakan pada waktu malam hari pada perairan yang berjenis rawa dengan spesies target tertentu.Penelitian dilaksanakan di salah satu perairan rawa yang berlokasi pada Taman Nasional Wasur, dimana perairan tersebut merupakan lokasi mata pencaharian dari masyarakat sekitar. Adapun tujuan dari penelitian ini yaitu untuk mengetahui efektivitas alat tangkap bubu berdasarkan jenis umpan dan waktu perendaman terhadap hasil tangkapan ikan di Rawa Wasur. Penelitian dilaksanakan pada bulan April hingga Mei 2023, dimana metode yang digunakan yaitu experimental fishing dengan Rancangan Acak Kelompok (RAK) Faktorial dalam upaya memahami bagaimana variasi jenis umpan serta lama waktu perendaman memengaruhi hasil tangkapan ikan. Hasil tangkapan ikan yang didapatkan menunjukkan bahwa jenis umpan semut mendominasi jika dibandingkan dengan jenis umpan yang lain, baik itu pada periode waktu perendaman pagi dan malam hari. Secara total, jenis ikan gabus dan ikan betok merupakan jenis ikan yang paling banyak tertangkap dengan jumlah masing-masing sebanyak 268 dan 210 ekor. Namun demikian, analisis sidik ragam (ANOVA) pada variasi jenis umpan menunjukkan bahwa nilai F hitung < F tabel (2,24 < 4,10), dimana interaksi antara perlakuan tidak memberikan pengaruh terhadap hasil tangkapan. Meskipun demikian, hasil analisis pada perbedaan waktu perendaman memberikan interaksi antara perlakuan dengan rerata jumlah tangkapan yang tidak sama, dengan kata lain memberikan perbedaan yang nyata (7,49 > 4,10). Hal ini menunjukkan bahwasanya alat tangkap bubu lebih efektif digunakan pada waktu malam hari pada perairan yang berjenis rawa dengan spesies target tertentu

Brain Tumor Classification in MRI Images Using En-CNN

Brain tumors are among the most common diseases of the central nervous system and are harmful. Early diagnosis is essential for patient proper treatment. Radiologists need an automated system to identify brain tumor images successfully. The identification process is often a tedious and error-prone task. Furthermore, brain tumor binary classification is often characterized by malignant and benign because it involves multi-sequence MRI (T1, T2, T1CE, and FLAIR), making radiologist's work quite challenging. Recently, several classification methods based on deep learning are being used to classify brain tumors. Each model's performance is highly dependent on the CNN architecture used. Due to the complexity of the existing CNN architecture, hyperparameter tuning becomes a problem in its application. We propose a CNN method called en-CNN to overcome this problem. This method is based on VGG-16 that consists of seven convolutional networks, four ReLU, and four max-pooling. The proposed method is used to facilitate the hyperparameter tuning. We also proposed a new approach in which the classification of brain tumors is done directly without priorly doing the segmentation process. The new approach consists of the following stages: preprocessing, image augmentation, and applying the en-CNN method. Our new approach is also doing the classification using four MRI sequences of T1, T1CE, T2, and FLAIR. The proposed method delivers accuracy on the MRI multi-sequence BraTS 2018 dataset with an accuracy of 95.5% for T1, 95.5% for T1CE, 94% for T2, and 97% for FLAIR with mini-batch size 128 and epoch 200 using ADAM optimizer. The accuracy was 4% higher than previous research in the same dataset

Tailoring Science Outreach through E-Matching Using a Community-Based Participatory Approach

In an effort to increase science exposure for pre-college (K-12) students and as part of the science education reform agenda, many biomedical research institutions have established university-community partnerships. Typically, these science outreach programs consist of pre-structured, generic exposure for students, with little community engagement. However, the use of a medium that is accessible to both teachers and scientists, electronic web-based matchmaking (E-matching) provides an opportunity for tailored outreach utilizing a community-based participatory approach (CBPA), which involves all stakeholders in the planning and implementation of the science outreach based on the interests of teachers/students and scientists. E-matching is a timely and urgent endeavor that provides a rapid connection for science engagement between teachers/students and experts in an effort to fill the science outreach gap. National Lab Network (formerly National Lab Day), an ongoing initiative to increase science equity and literacy, provides a model for engaging the public in science via an E-matching and hands-on learning approach. We argue that science outreach should be a dynamic endeavor that changes according to the needs of a target school. We will describe a case study of a tailored science outreach activity in which a public school that serves mostly under-represented minority students from disadvantaged backgrounds were E-matched with a university, and subsequently became equal partners in the development of the science outreach plan. In addition, we will show how global science outreach endeavors may utilize a CBPA, like E-matching, to support a pipeline to science among under-represented minority students and students from disadvantaged backgrounds. By merging the CBPA concept with a practical case example, we hope to inform science outreach practices via the lens of a tailored E-matching approach