Implications of Toll-Like Receptor 10 Mutants in Cell Signaling

Our innate immune system serves the important purpose of quickly recognizing microbial pathogens and providing the first line of defense against infection. Toll-Like Receptors (TLRs) are one class of receptors responsible for enacting our bodies’ innate immune responses. Specifically, TLR10 is an extremely under-researched receptor. Some naturally occurring mutations in TLR10 have been linked with a worsening prognosis of rheumatoid arthritis, with previous research uncovering a mutant form of TLR10 that impairs innate immune system-controlled inflammatory responses. However, other research has found that wild-type forms of TLR10 also contribute to pro-inflammatory pathways in healthy cells. To better understand the wild-type form and the naturally occurring mutants, we wish to explore the impacts on cell localization and signaling in different forms of TLR10 in healthy cells. This was done by generating the mutant plasmid, followed by transfection of different forms into HEK cells. Flow cytometry revealed that the mutant forms do not localize to the cell surface at the same level as the wild-type. Similarly, they do not activate the NFkB signaling pathway to the same extent as the wild-type. This helps us to understand how mutations in TLR10 can affect different cell types and further study will elucidate more information

Jean-Pierre Léaud: star of the French cinema

This study is intended to examine the star persona of French actor, Jean-Pierre Léaud. By inquiring into both the character on and off screen I have analysed the career and performances of an actor who has laid a significant mark on contemporary French cinema. Whilst Léaud’s nouvelle vague image has become an enduring symbol within French cinema, little is known about Léaud from written texts alone. Moreover, he has not been readily accepted as a star in the same way as many of his male contemporaries. Using star studies as a theoretical approach, I am exploring the various characters Léaud has incarnated on screen, his performances, and the strong relationships he has forged with various directors throughout his career in seeking to conceptualise his star image. Looking at Léaud the person and Léaud the actor I hope to establish wherein lies the mythology surrounding this unique actor. By looking at stardom and spectatorship theory, I have explored the ways in which we can perceive Léaud as actor and star. The central question to this is: how do subjectivity and spectatorship shape our perceptions of what makes a star? Underlying this question are the various ambiguities and sites of contradiction that make up his star image. In examining such contradictions I have taken Richard Dyer’s Stars and Edgar Morin’s Les Stars as a starting point. This leads to a consideration of Dyer’s formulation of “alternative or subversive types” (Dyer 1979: 52) together with questions of gender representations and sexuality. The types embodied by Léaud’s characters are not idealised males as seen in the star personae of Jean Gabin, Jean-Paul Belmondo, Alain Delon, and Gérard Depardieu among other French stars. Yet even within these more conventional star images, sites of contradiction are present that help qualify the ambiguities presented by Léaud’s star status. What is important here is Dyer’s notion that stars hold the capacity to bring together the ‘ordinary’ within sites of contradiction. This is presented by Léaud’s androgynous figure, the unlikely occupations undertaken by many of his characters, their awkward attempt to seduce women and subsequent unsatisfactory relationships, the concept of the flâneur, and a certain quirky comic side to these characters

Learning emergent models from ab initio\textit{ab initio} many-body calculations

We present a generalized framework, renormalized density matrix downfolding (R-DMD), to derive systematically improvable effective models from ab initio calculations. This framework moves beyond the common role of ab initio calculations as calculating the parameters of a proposed Hamiltonian. Instead, R-DMD provides the capability to decide whether a given effective Hilbert space can be identified from the ab initio data and assess the relative quality of ansatz Hamiltonians. Any method of ab initio solution can be used as a data source, and as the ab initio solutions improve, the resultant model also improves. We demonstrate the framework in an application to the downfolding of a hydrogen chain to a spin model, in which we find the interatomic separations for which a nonperturbative mapping can be made, and compute a renormalized spin model Hamiltonian that quantitatively reproduces the ab initio dynamics.Comment: 7 pages, 5 figures; includes Supplementary Material (11 pages, 8 figures

RescueAlert-an accident detection and rescue mechanism

With the increase of vehicles and cars of different kind and the large movement that occurs every day on the roads it was natural to observe an increase in traffic accidents, but the real dilemma lies in how to make the rescue process efficient. The problem that we want to solve is the response of ambulances towards accidents and the lengthy registration process of patients in hospitals. In the above two scenarios, the manual process of calling the ambulance leads to delay in rescue of patients from an accident and the delay in registration of patient leads to delay in medication or treatment of the patient. We want to make the process more efficient by automating accident detection for increasing the efficiency of the ambulance rescue process and by sending the details of the patient before the patient reaches the hospitals for faster treatment of patients. Along with this, alert messages will be sent to the family or friends of the patients to notify them as soon as an accident is detected

Processing Big Data Applications using MapReduce

The term Big data is one of the important terms now days for every sector. As there are no. of organizations starts generating huge amount of data so there is a need of having some processing as well as storage engine. The term big data a collection of massive, huge  and complex data sets which includes the large quantities of data, social media its analysis, data management as well as real time data. To fetch and extract the use full data or to derive some conclusion from the Big data, analysis of the data in proper manner is required. So, this analysis can be done with different techniques one of them is Hadoop. And more specifically a MapReduce Framework

Highly specific plasmonic biosensors for ultrasensitive microRNA detection in plasma from pancreatic cancer patients

MicroRNAs (miRs) are small noncoding RNAs that regulate mRNA stability and/or translation. Because of their release into the circulation and their remarkable stability, miR levels in plasma and other biological fluids can serve as diagnostic and prognostic disease biomarkers. However, quantifying miRs in the circulation is challenging due to issues with sensitivity and specificity. This Letter describes for the first time the design and characterization of a regenerative, solid-state localized surface plasmon resonance (LSPR) sensor based on highly sensitive nanostructures (gold nanoprisms) that obviates the need for labels or amplification of the miRs. Our direct hybridization approach has enabled the detection of subfemtomolar concentration of miR-X (X = 21 and 10b) in human plasma in pancreatic cancer patients. Our LSPR-based measurements showed that the miR levels measured directly in patient plasma were at least 2-fold higher than following RNA extraction and quantification by reverse transcriptase-polymerase chain reaction. Through LSPR-based measurements we have shown nearly 4-fold higher concentrations of miR-10b than miR-21 in plasma of pancreatic cancer patients. We propose that our highly sensitive and selective detection approach for assaying miRs in plasma can be applied to many cancer types and disease states and should allow a rational approach for testing the utility of miRs as markers for early disease diagnosis and prognosis, which could allow for the design of effective individualized therapeutic approaches

Using the RE-AIM Framework for Implementation of Electronic Patient-Reported Outcomes in Head-Neck Cancer Patients Treated with Radiation Therapy

Patients undergoing radiation therapy for head and neck cancers can experience moderate-severe toxicities. To monitor symptoms, patient-reported outcomes have proven highly effective, but are hard to implement in clinic settings. We performed an implementation study in the head-neck radiation oncology clinic and evaluated this through the RE-AIM framework. The implementation study demonstrated increased PRO utilization and identified potential barriers to implementation and ways to address these.https://openworks.mdanderson.org/radonc24/1004/thumbnail.jp

Implementation of Electronic Patient-Reported Outcomes in Head-Neck Cancer Patients Treated with Radiation Therapy Using the RE-AIM Framework​

Radiation therapy for head and neck cancers can result in many moderate-severe toxicities. Patient-reported outcomes can be used to monitor these symptoms, but are difficult to implement in the clinic. To evaluate the implementation of electronic patient-reported outcomes, a study was conducted in the head and neck radiation oncology clinic. This resulted in increased PRO utilization, along with the identification of barriers to implementation, with next steps to address.https://openworks.mdanderson.org/soar24/1007/thumbnail.jp

Label-Free Nanoplasmonic-Based Short Noncoding RNA Sensing at Attomolar Concentrations Allows for Quantitative and Highly Specific Assay of MicroRNA-10b in Biological Fluids and Circulating Exosomes

MicroRNAs are short noncoding RNAs consisting of 18-25 nucleotides that target specific mRNA moieties for translational repression or degradation, thereby modulating numerous biological processes. Although microRNAs have the ability to behave like oncogenes or tumor suppressors in a cell-autonomous manner, their exact roles following release into the circulation are only now being unraveled and it is important to establish sensitive assays to measure their levels in different compartments in the circulation. Here, an ultrasensitive localized surface plasmon resonance (LSPR)-based microRNA sensor with single nucleotide specificity was developed using chemically synthesized gold nanoprisms attached onto a solid substrate with unprecedented long-term stability and reversibility. The sensor was used to specifically detect microRNA-10b at the attomolar (10(-18) M) concentration in pancreatic cancer cell lines, derived tissue culture media, human plasma, and media and plasma exosomes. In addition, for the first time, our label-free and nondestructive sensing technique was used to quantify microRNA-10b in highly purified exosomes isolated from patients with pancreatic cancer or chronic pancreatitis, and from normal controls. We show that microRNA-10b levels were significantly higher in plasma-derived exosomes from pancreatic ductal adenocarcinoma patients when compared with patients with chronic pancreatitis or normal controls. Our findings suggest that this unique technique can be used to design novel diagnostic strategies for pancreatic and other cancers based on the direct quantitative measurement of plasma and exosome microRNAs, and can be readily extended to other diseases with identifiable microRNA signatures

A policymaker’s guide to understanding youth livelihood aspirations in Myanmar

The International Water Management Institute (IWMI) and WorldFish conducted a study with young people from a fishing community in the Ayeyarwady Delta of Myanmar. The study sought to understand the livelihood aspirations of these young people and how they were connected to eventual livelihood realities