A Cadaveric Case Report of a Double Right Coronary Artery and Its Clinical Implications.

A double right coronary artery is one of the rarest variations of the coronary arterial system. This case report presents a unique variation: a right coronary and an accessory coronary artery arise from a single ostium within the right aortic sinus. The two vessels appeared externally to have a common trunk but were partitioned internally by a carina-like septum. This report underscores the importance of understanding the embryological development of coronary arteries and recognizing potential variations. It discusses the specific variation observed in this case and its clinical implications. The aim is to contribute to the limited literature on this condition and highlight the importance of recognizing and managing such anomalies in clinical procedures.

A Unique Arc of Bühler Variant Connecting the Arteries of the Foregut, Midgut, and Hindgut and Its Surgical Significance.

This case report presents a rare variation of the arc of Bühler (AOB) in a cadaver during the abdominal dissection assignment in the Ross Anatomy Lab at William Carey University College of Osteopathic Medicine. The AOB is a patent anastomotic channel between the celiac trunk and the superior mesenteric artery independent of the gastroduodenal artery and dorsal pancreatic artery. This report describes in detail a complex and extensive branching pattern of a unique AOB variant. Our findings contribute to the limited literature on this condition and emphasize the importance of thorough knowledge of vascular variations to avoid potential complications during surgical procedures.

Hemophilia A subjects with an intron-22 gene inversion mutation show CD4+ T-effector responses to multiple epitopes in FVIII.

Background: Almost half of severe hemophilia A (HA) is caused by an intron 22 inversion mutation (Int22Inv), which disrupts the 26-exon F8 gene. Inverted F8 mRNA exons 1-22 are transcribed, while F8B mRNA, containing F8 exons 23-26, is transcribed from a promoter within intron 22. Neither FVIII activity nor FVIII antigen (cross-reacting material, CRM) are detectable in plasma of patients with an intron-22 inversion. Objectives: To test the hypothesis that (putative) intracellular synthesis of FVIII proteins encoded by inverted F8 and F8B mRNAs confers T-cell tolerance to almost the entire FVIII sequence, and to evaluate the immunogenicity of the region encoded by the F8 exon 22-23 junction sequence. Patients/Methods: Peripheral blood mononuclear cells (PBMCs) from 30 severe or moderate HA subjects (17 with an Int22Inv mutation) were tested by ELISPOT assays to detect cytokine secretion in response to FVIII proteins and peptides and to map immunodominant T-cell epitopes. Potential immunogenicity of FVIII sequences encoded by the F8 exon 22-23 junction region was also tested using peptide-MHCII binding assays. Results: Eight of the Int22Inv subjects showed robust cytokine secretion from PBMCs stimulated with FVIII proteins and/or peptides, consistent with earlier publications from the Conti-Fine group. Peptide ELISPOT assays identified immunogenic regions of FVIII. Specificity for sequences encoded within F8 mRNA exons 1-22 and F8B mRNA was confirmed by staining Int22Inv CD4+ T cells with peptide-loaded HLA-Class II tetramers. FVIII peptides spanning the F8 exon 22-23 junction (encoding M2124-V2125) showed limited binding to MHCII proteins and low immunogenicity, with cytokine secretion from only one Int22Inv subject. Conclusions: PBMCs from multiple subjects with an Int22Inv mutation, with and without a current FVIII inhibitor, responded to FVIII epitopes. Furthermore, the FVIII region encoded by the exon 22-23 junction sequence was not remarkably immunoreactive and is therefore unlikely to contain an immunodominant, promiscuous CD4+ T-cell epitope. Our results indicate that putative intracellular expression of partial FVIII proteins does not confer T-cell tolerance to FVIII regions encoded by inverted F8 mRNA or F8B mRNA.

Medical and economic burden of delirium on hospitalization outcomes of acute respiratory failure: A retrospective national cohort.

Although delirium in patients with acute respiratory failure (ARF) may evolve in any hospital setting, previous studies on the impact of delirium on ARF were restricted to those in the intensive care unit (ICU). The data about the impact of delirium on ARF hospitalizations outside of the ICU is limited. Therefore, we conducted the first national study to examine the effect-magnitude of delirium on ARF in all hospital settings, that is, in the ICU as well as on the general medical floor. We searched the 2016 and 2017 National Inpatient Sample databases for ARF hospitalizations and created "Delirium" and "No delirium" groups. The outcomes of interest were mortality, endotracheal intubation, length of stay (LOS), and hospitalization costs. We also aimed to explore any potential demographic, racial, or healthcare disparities that may be associated with the diagnosis of delirium among ARF patients. Multivariable logistic regression was used to control for demographics and comorbidities. Delirium was present in 12.7% of the sample. Racial disparities among African Americans were also significant. Delirious patients had more comorbidities, higher mortality, and intubation rates (17.5% and 9.2% vs 10.6% and 6.1% in the "No delirium" group [P < .001], respectively). Delirious patients had a longer LOS and higher hospitalization costs (5.9 days and $15,395 USD vs 3.7 days and $9393 USD in "No delirium" [P < .001], respectively). Delirium was associated with worse mortality (adjusted odds ratio 1.49, confidence interval [CI] = 1.41, 1.57), higher intubation rates (adjusted odds ratio 1.46, CI = 1.36, 1.56), prolonged LOS (adjusted mean ratio 1.40, CI = 1.37, 1.42), and increased hospitalization costs (adjusted mean ratio 1.49, CI = 1.46, 1.52). A racial disparity in the diagnosis of delirium among African Americans hospitalized with ARF was noted in our sample. Patients in small, non-teaching hospitals were diagnosed with delirium less frequently compared to large, urban, teaching centers. Delirium predicts worse mortality and morbidity for ARF patients, regardless of bed placement and severity of the respiratory failure.

Securing federated learning with blockchain: a systematic literature review.

Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. FL exploits the concept of collaborative learning and builds privacy-preserving models. Nevertheless, the integral features of FL are fraught with problems, such as the disclosure of private information, the unreliability of uploading model parameters to the server, the communication cost, etc. Blockchain, as a decentralized technology, is able to improve the performance of FL without requiring a centralized server and also solves the above problems. In this paper, a systematic literature review on the integration of Blockchain in federated learning was considered with the analysis of the existing FL problems that can be compensated. Through carefully screening, most relevant studies are included and research questions cover the potential security and privacy attacks in traditional federated learning that can be solved by blockchain as well as the characteristics of Blockchain-based FL. In addition, the latest Blockchain-based approaches to federated learning have been studied in-depth in terms of security and privacy, records and rewards, and verification and accountability. Furthermore, open issues related to the combination of Blockchain and FL are discussed. Finally, future research directions for the robust development of Blockchain-based FL systems are proposed.

Human-Immune-System (HIS) humanized mouse model (DRAGA: HLA-A2.HLA-DR4.Rag1KO.IL-2RγcKO.NOD) for COVID-19.

We report a Human Immune System (HIS)-humanized mouse model ("DRAGA": HLA-A2.HLA-DR4.Rag1KO.IL-2 RγcKO.NOD) for COVID-19 research. DRAGA mice express transgenically HLA-class I and class-II molecules in the mouse thymus to promote human T cell development and human B cell Ig-class switching. When infused with human hematopoietic stem cells from cord blood reconstitute a functional human immune system, as well as human epi/endothelial cells in lung and upper respiratory airways expressing the human ACE2 receptor for SARS-CoV-2. The DRAGA mice were able to sustain SARS-CoV-2 infection for at least 25 days. Infected mice showed replicating virus in the lungs, deteriorating clinical condition, and human-like lung immunopathology including human lymphocyte infiltrates, microthrombi and pulmonary sequelae. Among the intra-alveolar and peri-bronchiolar lymphocyte infiltrates, human lung-resident (CD103+) CD8+ and CD4+ T cells were sequestered in epithelial (CD326+) lung niches and secreted granzyme B and perforin, suggesting anti-viral cytotoxic activity. Infected mice also mounted human IgG antibody responses to SARS-CoV-2 viral proteins. Hence, HIS-DRAGA mice showed unique advantages as a surrogate in vivo human model for studying SARS-CoV-2 immunopathological mechanisms and testing the safety and efficacy of candidate vaccines and therapeutics.

A Human-Immune-System (HIS) humanized mouse model (DRAGA: HLA-A2. HLA-DR4. Rag1 KO.IL-2Rγc KO. NOD) for COVID-19.

We report the first Human Immune System (HIS)-humanized mouse model ("DRAGA": HLA-A2.HLA-DR4.Rag1KO.IL-2RγcKO.NOD) for COVID-19 research. This mouse is reconstituted with human cord blood-derived, HLA-matched hematopoietic stem cells. It engrafts human epi/endothelial cells expressing the human ACE2 receptor for SARS-CoV-2 and TMPRSS2 serine protease co-localized on lung epithelia. HIS-DRAGA mice sustained SARS-CoV-2 infection, showing deteriorated clinical condition, replicating virus in the lungs, and human-like lung immunopathology including T-cell infiltrates, microthrombi and pulmonary sequelae. Among T-cell infiltrates, lung-resident (CD103+) CD8+ T cells were sequestered in epithelial (CD326+) lung niches and secreted granzyme B and perforin, indicating cytotoxic potential. Infected mice also developed antibodies against the SARS-CoV-2 viral proteins. Hence, HIS-DRAGA mice showed unique advantages as a surrogate in vivo human model for studying SARS-CoV-2 immunopathology and for testing the safety and efficacy of candidate vaccines and therapeutics.

A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification.

This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes.

Hemophilia A Inhibitor Subjects Show Unique PBMC Gene Expression Profiles That Include Up-Regulated Innate Immune Modulators.

Formation of pathological anti-FVIII antibodies, or "inhibitors," is the most serious complication of therapeutic FVIII infusions, affecting up to 1/3 of severe Hemophilia A (HA) patients. Inhibitor formation is a classical T-cell dependent adaptive immune response. As such, it requires help from the innate immune system. However, the roles of innate immune cells and mechanisms of inhibitor development vs. immune tolerance, achieved with or without Immune Tolerance Induction (ITI) therapy, are not well-understood. To address these questions, temporal transcriptomics profiling of FVIII-stimulated peripheral blood mononuclear cells (PBMCs) was carried out for HA subjects with and without a current or historic inhibitor using RNA-Seq. PBMCs were isolated from 40 subjects in the following groups: HA with an inhibitor that resolved either following ITI or spontaneously; HA with a current inhibitor; HA with no inhibitor history and non-HA controls. PBMCs were stimulated with 5 nM FVIII and RNA was isolated 4, 16, 24, and 48 h following stimulation. Time-series differential expression analysis was performed and distinct transcriptional signatures were identified for each group, providing clues as to cellular mechanisms leading to or accompanying their disparate anti-FVIII antibody responses. Subjects with a current inhibitor showed differential expression of 56 genes and a clustering analysis identified three major temporal profiles. Interestingly, gene ontology enrichments featured innate immune modulators, including NLRP3, TLR8, IL32, CLEC10A, and COLEC12. NLRP3 and TLR8 are associated with enhanced secretion of the pro-inflammatory cytokines IL-1ß and TNFα, while IL32, which has several isoforms, has been associated with both inflammatory and regulatory immune processes. RNA-Seq results were validated by RT-qPCR, ELISAs, multiplex cytokine analysis, and flow cytometry. The inflammatory status of HA patients suffering from an ongoing inhibitor includes up-regulated innate immune modulators, which may act as ongoing danger signals that influence the responses to, and eventual outcomes of, ITI therapy.

Unifying the seeds auto-generation (SAGE) with knee cartilage segmentation framework: data from the osteoarthritis initiative.

PURPOSE: Manual segmentation is sensitive to operator bias, while semiautomatic random walks segmentation offers an intuitive approach to understand the user knowledge at the expense of large amount of user input. In this paper, we propose a novel random walks seed auto-generation (SAGE) hybrid model that is robust to interobserver error and intensive user intervention. METHODS: Knee image is first oversegmented to produce homogeneous superpixels. Then, a ranking model is developed to rank the superpixels according to their affinities to standard priors, wherein background superpixels would have lower ranking values. Finally, seed labels are generated on the background superpixel using Fuzzy C-Means method. RESULTS: SAGE has achieved better interobserver DSCs of 0.94 ± 0.029 and 0.93 ± 0.035 in healthy and OA knee segmentation, respectively. Good segmentation performance has been reported in femoral (Healthy: 0.94 ± 0.036 and OA: 0.93 ± 0.034), tibial (Healthy: 0.91 ± 0.079 and OA: 0.88 ± 0.095) and patellar (Healthy: 0.88 ± 0.10 and OA: 0.84 ± 0.094) cartilage segmentation. Besides, SAGE has demonstrated greater mean readers' time of 80 ± 19 s and 80 ± 27 s in healthy and OA knee segmentation, respectively. CONCLUSIONS: SAGE enhances the efficiency of segmentation process and attains satisfactory segmentation performance compared to manual and random walks segmentation. Future works should validate SAGE on progressive image data cohort using OA biomarkers.

Proteomics and Network Analyses Reveal Inhibition of Akt-mTOR Signaling in CD4+ T Cells by Mycobacterium tuberculosis Mannose-Capped Lipoarabinomannan.

Mycobacterium tuberculosis (Mtb) cell wall glycolipid mannose-capped lipoarabinomannan (ManLAM) inhibits CD4+ T-cell activation by inhibiting proximal T-cell receptor (TCR) signaling when activated by anti-CD3. To understand the impact of ManLAM on CD4+ T-cell function when both the TCR-CD3 complex and major costimulator CD28 are engaged, we performed label-free quantitative MS and network analysis. Mixed-effect model analysis of peptide intensity identified 149 unique peptides representing 131 proteins that were differentially regulated by ManLAM in anti-CD3- and anti-CD28-activated CD4+ T cells. Crosstalker, a novel network analysis tool identified dysregulated translation, TCA cycle, and RNA metabolism network modules. PCNA, Akt, mTOR, and UBC were found to be bridge node proteins connecting these modules of dysregulated proteins. Altered PCNA expression and cell cycle analysis showed arrest at the G2M phase. Western blot confirmed that ManLAM inhibited Akt and mTOR phosphorylation, and decreased expression of deubiquitinating enzymes Usp9x and Otub1. Decreased NF-κB phosphorylation suggested interference with CD28 signaling through inhibition of the Usp9x-Akt-mTOR pathway. Thus, ManLAM induced global changes in the CD4+ T-cell proteome by affecting Akt-mTOR signaling, resulting in broad functional impairment of CD4+ T-cell activation beyond inhibition of proximal TCR-CD3 signaling.

Toll like Receptor 2 engagement on CD4+ T cells promotes TH9 differentiation and function.

We have recently demonstrated that mycobacterial ligands engage Toll like receptor 2 (TLR2) on CD4+ T cells and up-regulate T-cell receptor (TCR) triggered Th1 responses in vitro and in vivo. To better understand the role of T-cell expressed TLR2 on CD4+ T-cell differentiation and function, we conducted a gene expression analysis of murine naïve CD4+ T-cells stimulated in the presence or absence of TLR2 co-stimulation. Unexpectedly, naïve CD4+ T-cells co-stimulated via TLR2 showed a significant up-regulation of Il9 mRNA compared to cells co-stimulated via CD28. Under TH9 differentiation, we observed up-regulation of TH9 differentiation, evidenced by increases in both percent of IL-9 secreting cells and IL-9 in culture supernatants in the presence of TLR2 agonist both in polyclonal and Ag85B cognate peptide specific stimulations. Under non-polarizing conditions, TLR2 engagement on CD4+ T-cells had minimal effect on IL-9 secretion and TH9 differentiation, likely due to a prominent effect of TLR2 signaling on IFN-γ secretion and TH1 differentiation. We also report that, TLR2 signaling in CD4+ T cells increased expression of transcription factors BATF and PU.1, known to positively regulate TH9 differentiation. These results reveal a novel role of T-cell expressed TLR2 in enhancing the differentiation and function of TH9 T cells.

Investigation of random walks knee cartilage segmentation model using inter-observer reproducibility: Data from the osteoarthritis initiative.

BACKGROUND: Existing knee cartilage segmentation methods have reported several technical drawbacks. In essence, graph cuts remains highly susceptible to image noise despite extended research interest; active shape model is often constraint by the selection of training data while shortest path have demonstrated shortcut problem in the presence of weak boundary, which is a common problem in medical images. OBJECTIVES: The aims of this study is to investigate the capability of random walks as knee cartilage segmentation method. METHODS: Experts would scribble on knee cartilage image to initialize random walks segmentation. Then, reproducibility of the method is assessed against manual segmentation by using Dice Similarity Index. The evaluation consists of normal cartilage and diseased cartilage sections which is divided into whole and single cartilage categories. RESULTS: A total of 15 normal images and 10 osteoarthritic images were included. The results showed that random walks method has demonstrated high reproducibility in both normal cartilage (observer 1: 0.83±0.028 and observer 2: 0.82±0.026) and osteoarthritic cartilage (observer 1: 0.80±0.069 and observer 2: 0.83±0.029). Besides, results from both experts were found to be consistent with each other, suggesting the inter-observer variation is insignificant (Normal: P=0.21; Diseased: P=0.15). CONCLUSION: The proposed segmentation model has overcame technical problems reported by existing semi-automated techniques and demonstrated highly reproducible and consistent results against manual segmentation method.

Novel high throughput pooled shRNA screening identifies NQO1 as a potential drug target for host directed therapy for tuberculosis.

UNLABELLED: Chemical regulation of macrophage function is one key strategy for developing host-directed adjuvant therapies for tuberculosis (TB). A critical step to develop these therapies is the identification and characterization of specific macrophage molecules and pathways with a high potential to serve as drug targets. Using a barcoded lentivirus-based pooled short-hairpin RNA (shRNA) library combined with next generation sequencing, we identified 205 silenced host genes highly enriched in mycobacteria-resistant macrophages. Twenty-one of these "hits" belonged to the oxidoreductase functional category. NAD(P)H: quinone oxidoreductase 1 (NQO1) was the top oxidoreductase "hit". NQO1 expression was increased after mycobacterial infection, and NQO1 knockdown increased macrophage differentiation, NF-κB activation, and the secretion of pro-inflammatory cytokines TNF-α and IL-1ß in response to infection. This suggests that mycobacteria hijacks NQO1 to down-regulate pro-inflammatory and anti-bacterial functions. The competitive inhibitor of NQO1 dicoumarol synergized with rifampin to promote intracellular killing of mycobacteria. Thus, NQO1 is a new host target in mycobacterial infection that could potentially be exploited to increase antibiotic efficacy in vivo. Our findings also suggest that pooled shRNA libraries could be valuable tools for genome-wide screening in the search for novel druggable host targets for adjunctive TB therapies.

SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

Mannose-Capped Lipoarabinomannan from Mycobacterium tuberculosis Induces CD4+ T Cell Anergy via GRAIL.

Mycobacterium tuberculosis cell wall glycolipid, lipoarabinomannan, can inhibit CD4(+) T cell activation by downregulating the phosphorylation of key proximal TCR signaling molecules: Lck, CD3ζ, ZAP70, and LAT. Inhibition of proximal TCR signaling can result in T cell anergy, in which T cells are inactivated following an Ag encounter, yet remain viable and hyporesponsive. We tested whether mannose-capped lipoarabinomannan (LAM)-induced inhibition of CD4(+) T cell activation resulted in CD4(+) T cell anergy. The presence of LAM during primary stimulation of P25 TCR-transgenic murine CD4(+) T cells with M. tuberculosis Ag85B peptide resulted in decreased proliferation and IL-2 production. P25 TCR-transgenic CD4(+) T cells primed in the presence of LAM also exhibited decreased response upon restimulation with Ag85B. The T cell anergic state persisted after the removal of LAM. Hyporesponsiveness to restimulation was not due to apoptosis, generation of Foxp3-positive regulatory T cells, or inhibitory cytokines. Acquisition of the anergic phenotype correlated with upregulation of gene related to anergy in lymphocytes (GRAIL) protein in CD4(+) T cells. Inhibition of human CD4(+) T cell activation by LAM also was associated with increased GRAIL expression. Small interfering RNA-mediated knockdown of GRAIL before LAM treatment abrogated LAM-induced hyporesponsiveness. In addition, exogenous IL-2 reversed defective proliferation by downregulating GRAIL expression. These results demonstrate that LAM upregulates GRAIL to induce anergy in Ag-reactive CD4(+) T cells. Induction of CD4(+) T cell anergy by LAM may represent one mechanism by which M. tuberculosis evades T cell recognition.

Randomized Controlled Trial on the Effect of Channa striatus Extract on Measurement of the Uterus, Pulsatility Index, Resistive Index of Uterine Artery and Superficial Skin Wound Artery in Post Lower Segment Caesarean Section Women.

AIM: To compare the mean of anteroposterior (AP) measurements of the uterus in longitudinal and oblique transverse planes, and the pulsatility index (PI) and resistive index (RI) of the uterine artery and superficial skin wound artery between patients taking Channa striatus and placebo. BACKGROUND: Channa striatus, also known as haruan, is a fresh water snakehead fish consumed in many parts of Southeast Asia. Channa striatus is also normally consumed by women postpartum to promote wound healing as well as to reduce post-operative pain. METHODOLOGY: This study is a randomised, double blind, placebo-controlled study conducted in women after Lower Segment Caesarean Section (LSCS). Subjects were randomised to either a Channa striatus or a placebo group and were given a daily dosage of 500 mg of Channa striatus extract or 500 mg maltodextrin, respectively, for six weeks post LSCS. The anteroposterior measurements of the uterus in the longitudinal and oblique transverse planes, and the pulsatility index (PI) and resistive index (RI) of the uterine and superficial skin wound arteries were assessed using pelvic Gray-scale ultrasound and Doppler ultrasound at baseline (Day 3) and at two weeks, four weeks and six weeks post-operatively. RESULTS: Sixty-six subjects were randomised into the study with 33 in the Channa striatus group and 33 in the placebo group. No significant differences were detected in terms of the pulsatility index (PI) and the resistive index (RI) of the uterine and superficial skin wound arteries between the Channa striatus and placebo groups. However, in the Channa striatus group, the AP measurements of the uterus on the longitudinal and oblique transverse planes were significantly lower compared to the placebo group (p<0.05 and p<0.001, respectively). CONCLUSION: Daily intake of Channa striatus extract results in marked differences compared to placebo in terms of uterine involution and recovery in women post LSCS. TRIAL REGISTRATION: www.isrctn.com 11960786.

Decoding of visual information from human brain activity: A review of fMRI and EEG studies.

Brain is the command center for the body and contains a lot of information which can be extracted by using different non-invasive techniques. Electroencephalography (EEG), Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) are the most common neuroimaging techniques to elicit brain behavior. By using these techniques different activity patterns can be measured within the brain to decode the content of mental processes especially the visual and auditory content. This paper discusses the models and imaging techniques used in visual decoding to investigate the different conditions of brain along with recent advancements in brain decoding. This paper concludes that it's not possible to extract all the information from the brain, however careful experimentation, interpretation and powerful statistical tools can be used with the neuroimaging techniques for better results.

Interactive knee cartilage extraction using efficient segmentation software: data from the osteoarthritis initiative.

In medical image segmentation, manual segmentation is considered both labor- and time-intensive while automated segmentation often fails to segment anatomically intricate structure accordingly. Interactive segmentation can tackle shortcomings reported by previous segmentation approaches through user intervention. To better reflect user intention, development of suitable editing functions is critical. In this paper, we propose an interactive knee cartilage extraction software that covers three important features: intuitiveness, speed, and convenience. The segmentation is performed using multi-label random walks algorithm. Our segmentation software is simple to use, intuitive to normal and osteoarthritic image segmentation and efficient using only two third of manual segmentation's time. Future works will extend this software to three dimensional segmentation and quantitative analysis.

A lightweight distributed framework for computational offloading in mobile cloud computing.

The latest developments in mobile computing technology have enabled intensive applications on the modern Smartphones. However, such applications are still constrained by limitations in processing potentials, storage capacity and battery lifetime of the Smart Mobile Devices (SMDs). Therefore, Mobile Cloud Computing (MCC) leverages the application processing services of computational clouds for mitigating resources limitations in SMDs. Currently, a number of computational offloading frameworks are proposed for MCC wherein the intensive components of the application are outsourced to computational clouds. Nevertheless, such frameworks focus on runtime partitioning of the application for computational offloading, which is time consuming and resources intensive. The resource constraint nature of SMDs require lightweight procedures for leveraging computational clouds. Therefore, this paper presents a lightweight framework which focuses on minimizing additional resources utilization in computational offloading for MCC. The framework employs features of centralized monitoring, high availability and on demand access services of computational clouds for computational offloading. As a result, the turnaround time and execution cost of the application are reduced. The framework is evaluated by testing prototype application in the real MCC environment. The lightweight nature of the proposed framework is validated by employing computational offloading for the proposed framework and the latest existing frameworks. Analysis shows that by employing the proposed framework for computational offloading, the size of data transmission is reduced by 91%, energy consumption cost is minimized by 81% and turnaround time of the application is decreased by 83.5% as compared to the existing offloading frameworks. Hence, the proposed framework minimizes additional resources utilization and therefore offers lightweight solution for computational offloading in MCC.