Efficient segmentation and classification of the tumor using improved encoder-decoder architecture in brain MRI images

Primary diagnosis of brain tumors is crucial to improve treatment outcomes for patient survival. T1-weighted contrast-enhanced images of Magnetic Resonance Imaging (MRI) provide the most anatomically relevant images. But even with many advancements, day by day in the medical field, assessing tumor shape, size, segmentation, and classification is very difficult as manual segmentation of MRI images with high precision and accuracy is indeed a time-consuming and very challenging task. So newer digital methods like deep learning algorithms are used for tumor diagnosis which may lead to far better results. Deep learning algorithms have significantly upgraded the research in the artificial intelligence field and help in better understanding medical images and their further analysis. The work carried out in this paper presents a fully automatic brain tumor segmentation and classification model with encoder-decoder architecture that is an improvisation of traditional UNet architecture achieved by embedding three variants of ResNet like ResNet 50, ResNet 101, and ResNext 50 with proper hyperparameter tuning. Various data augmentation techniques were used to improve the model performance. The overall performance of the model was tested on a publicly available MRI image dataset containing three common types of tumors. The proposed model performed better in comparison to several other deep learning architectures regarding quality parameters including Dice Similarity Coefficient (DSC) and Mean Intersection over Union (Mean IoU) thereby enhancing the tumor analysis

Mapping Activity Area Localization in Functional MRI Imaging with Deep Learning based Automatic Segmented Brain Tumor for Presurgical Tumor Resection Planning

Functional Magnetic Resonance Imaging (fMRI) determines small blood flow variations that arise due to brain activity. fMRI major study is about functional anatomy which determines the area of the brain controlling vital functions such as hand and foot motor movements for both left and right, speech mantra, and speech word activities. For this instinctive localization of activity areas for specific tasks is very important. This paper appropriately describes the fMRI paradigm timeline with a modified fMRI paradigm timeline due to the hemodynamic response function (HRF).   Efficient activity area localization of thirty-three patients for fMRI data acquired from the hospital is achieved with dynamic thresholding. Dynamic thresholding is also effective in removing excess highlighted areas which helps in the reduction in expert efforts and time required to generate the patient report.  The localize activity area is further mapped with deep learning-based automatic segmented brain tumor regions to find overlapping regions. The exact location of the overlapping region is recovered which helps with preoperative counseling and tumor resection planning. All the results are verified and validated by two expert radiologists from the Hospital

Heteroskedastic Geospatial Tracking with Distributed Camera Networks

Visual object tracking has seen significant progress in recent years. However, the vast majority of this work focuses on tracking objects within the image plane of a single camera and ignores the uncertainty associated with predicted object locations. In this work, we focus on the geospatial object tracking problem using data from a distributed camera network. The goal is to predict an object's track in geospatial coordinates along with uncertainty over the object's location while respecting communication constraints that prohibit centralizing raw image data. We present a novel single-object geospatial tracking data set that includes high-accuracy ground truth object locations and video data from a network of four cameras. We present a modeling framework for addressing this task including a novel backbone model and explore how uncertainty calibration and fine-tuning through a differentiable tracker affect performance

BiOX (X = I or Cl?) modified Na-K2Ti6O13 nanostructured materials for efficient degradation of Tetracycline, Acid Black 1 dye and microbial disinfection in wastewater under Blue LED

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Photocatalysis process has emerged as a prompt method for wastewater treatment and microbial disinfection. The development of visible light active (VLA) photocatalysts, especially Blue LED active (BLA) is a challenge task for the current research scenario towards pollutants degradation and real wastewater treatment. Here, we have developed a material which is highly active under Blue LED. BiOX (X = I or Cl) modified Na-K2Ti6O13 with two concentrations of BiOX was fabricated and effectively utilized for Acid Black 1 (AB 1) dye and tetracycline (TCN) degradations under Blue LED. The TCN degradation was also performed under white LED and direct solar light for comparison, and found that Na-K2Ti6O13/BiOX composite was very efficient in Blue LED and white LED than direct solar irradiation. The higher activity of Na-K2Ti6O13/BiOX in Blue LED confirmed by Blue light absorption of Na-K2Ti6O13/BiOX via DRS measurements. The bare Na-K2Ti6O13 is almost no active (≈10 %) under Blue LED, while Na-K2Ti6O13/BiOX showed 99 % degradation under the same condition for AB 1 degradation. The stability of the Na-K2Ti6O13/BiOX was tested against AB 1 dye degradation with multiple runs. The degradation intermediates of AB 1 and TCN were analysed by GC–MS, and suitable degradation pathways were proposed. The Na-K2Ti6O13/BiOX was tested real wastewater treatment via microbial disinfection under Blue LED. The prepared composite could be effectivity used for piolet or industrial scale level effluent treatment

ZFNGenome: A comprehensive resource for locating zinc finger nuclease target sites in model organisms

<p>Abstract</p> <p>Background</p> <p>Zinc Finger Nucleases (ZFNs) have tremendous potential as tools to facilitate genomic modifications, such as precise gene knockouts or gene replacements by homologous recombination. ZFNs can be used to advance both basic research and clinical applications, including gene therapy. Recently, the ability to engineer ZFNs that target any desired genomic DNA sequence with high fidelity has improved significantly with the introduction of rapid, robust, and publicly available techniques for ZFN design such as the Oligomerized Pool ENgineering (OPEN) method. The motivation for this study is to make resources for genome modifications using OPEN-generated ZFNs more accessible to researchers by creating a user-friendly interface that identifies and provides quality scores for all potential ZFN target sites in the complete genomes of several model organisms.</p> <p>Description</p> <p>ZFNGenome is a GBrowse-based tool for identifying and visualizing potential target sites for OPEN-generated ZFNs. ZFNGenome currently includes a total of more than 11.6 million potential ZFN target sites, mapped within the fully sequenced genomes of seven model organisms; <it>S. cerevisiae, C. reinhardtii, A. thaliana</it>, <it>D. melanogaster, D. rerio, C. elegans</it>, and <it>H. sapiens </it>and can be visualized within the flexible GBrowse environment. Additional model organisms will be included in future updates. ZFNGenome provides information about each potential ZFN target site, including its chromosomal location and position relative to transcription initiation site(s). Users can query ZFNGenome using several different criteria (e.g., gene ID, transcript ID, target site sequence). Tracks in ZFNGenome also provide "uniqueness" and ZiFOpT (Zinc Finger OPEN Targeter) "confidence" scores that estimate the likelihood that a chosen ZFN target site will function <it>in vivo</it>. ZFNGenome is dynamically linked to ZiFDB, allowing users access to all available information about zinc finger reagents, such as the effectiveness of a given ZFN in creating double-stranded breaks.</p> <p>Conclusions</p> <p>ZFNGenome provides a user-friendly interface that allows researchers to access resources and information regarding genomic target sites for engineered ZFNs in seven model organisms. This genome-wide database of potential ZFN target sites should greatly facilitate the utilization of ZFNs in both basic and clinical research.</p> <p>ZFNGenome is freely available at: <url>http://bindr.gdcb.iastate.edu/ZFNGenome</url> or at the Zinc Finger Consortium website: <url>http://www.zincfingers.org/</url>.</p

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV