Investigating the mechanism of action of the chemical probe QC6352

Glioblastoma, despite being the most aggressive form of brain cancer, has seen no significant advancement in patient survival rates since the introduction of the Stupp protocol in 2005. The stagnation in therapeutic progress highlights the complex challenges inherent to glioblastoma treatment, namely, a highly heterogeneous tumour population, an immunosuppressive microenvironment, and the restrictive blood-brain barrier. These challenges are compounded by a critical lack of robust data in preclinical studies, leading to suboptimal drug candidates that ultimately fail in clinical trials. In a comprehensive systematic review, we assessed the adherence to expert-recommended practices in the use of chemical probes. Our findings revealed that only 4% of the analysed publications complied with the established guidelines. Improper use of chemical probes results in biased conclusions about the importance of certain proteins in disease models, embedding inaccurate information as the foundation for further drug development studies. The chemical probe QC6352, a known KDM4 inhibitor, was identified through phenotypic screening of a library of epigenetic inhibitors to have potent antiproliferative efficacy in glioblastoma cells. While validating the anti-proliferative efficacy of QC6352 in glioblastoma, discrepancies arose regarding the importance of KDM4 in QC6352’s mechanism of action. By integrating diverse state-of-the-art investigations at the genomic, transcriptomic, and proteomic levels, a phenotype reflecting an activated MAPK pathway was identified and considered to result from the inhibition of PP5 by QC6352. This PhD thesis highlights the critical need to follow best practice guidelines for preclinical mechanistic studies to establish robust findings and eliminate biased and potentially incorrect conclusions. To obtain improved glioblastoma therapies, the foundational research surrounding the novel target must be derived from robust and well-controlled experiments

Hierarchical multithreading: programming model and system software

This paper addresses the underlying sources of performance degradation (e.g. latency, overhead, and starvation) and the difficulties of programmer productivity (e.g. explicit locality management and scheduling, performance tuning, fragmented memory, and synchronous global barriers) to dramatically enhance the broad effectiveness of parallel processing for high end computing. We are developing a hierarchical threaded virtual machine (HTVM) that defines a dynamic, multithreaded execution model and programming model, providing an architecture abstraction for HEC system software and tools development. We are working on a prototype language, LITL-X (pronounced "little-X") for latency intrinsic-tolerant language, which provides the application programmers with a powerful set of semantic constructs to organize parallel computations in a way that hides/manages latency and limits the effects of overhead. This is quite different from locality management, although the intent of both strategies is to minimize the effect of latency on the efficiency of computation. We work on a dynamic compilation and runtime model to achieve efficient LITL-X program execution. Several adaptive optimizations were studied. A methodology of incorporating domain-specific knowledge in program optimization was studied. Finally, we plan to implement our method in an experimental testbed for a HEC architecture and perform a qualitative and quantitative evaluation on selected applications

A Small Fission Power System for NASA Planetary Science Missions

In March 2010, the Decadal Survey Giant Planets Panel (GPP) requested a short-turnaround study to evaluate the feasibility of a small Fission Power System (FPS) for future unspecified National Aeronautics and Space Administration (NASA) science missions. FPS technology was considered a potential option for power levels that might not be achievable with radioisotope power systems. A study plan was generated and a joint NASA and Department of Energy (DOE) study team was formed. The team developed a set of notional requirements that included 1-kW electrical output, 15-year design life, and 2020 launch availability. After completing a short round of concept screening studies, the team selected a single concept for concentrated study and analysis. The selected concept is a solid block uranium-molybdenum reactor core with heat pipe cooling and distributed thermoelectric power converters directly coupled to aluminum radiator fins. This paper presents the preliminary configuration, mass summary, and proposed development program

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Time after Time in Words: Chronology through Language Statistics

Previous research has shown that perceptual relations, social affiliations, and geographical locations can be predicted using distributional semantics. We investigated whether this extends to chronological relations. In several computational studies we demonstrated that the chronological order of days, months, years, and the chronological sequence of historical figures can be predicted using language statistics. In fact, both the leaders of the Soviet Union and the presidents of the United States can be ordered chronologically based on the cooccurrences of their names in language. An experiment also showed that the bigram frequency of US president names predicted the response time of participants in their evaluation of the chronology of these presidents. These findings are explained by the Symbol Interdependency Hypothesis which predicts that as a function of language use, language encodes relations in the world around us. Language users can then use language as a cognitive short-cut for mental representation

Parallex: A study of a new parallel computation model

This paper proposes the study of a new computation model that attempts to address the underlying sources of performance degradation (e.g. latency, overhead, and starvation) and the difficulties of programmer productivity (e.g. explicit locality management and scheduling, performance tuning, fragmented memory, and synchronous global barriers) to dramatically enhance the broad effectiveness of parallel processing for high end computing. In this paper, we present the progress of our research on a parallel programming and execution model- mainly, ParalleX. We describe the functional elements of ParalleX, one such model being explored as part of this project. We also report our progress on the development and study of a subset of ParalleX- the LITL-X at University of Delaware. We then present a novel architecture model- Gilgamesh II- as a ParalleX processing architecture. A design point study of Gilgamesh II and the architecture concept strategy are presented.

Serum amyloid a and risk of death and end-stage renal disease in diabetic kidney disease

AimsTo determine if serum levels of serum amyloid A (SAA) predict death and end-stage renal disease in a cohort of people with diabetic kidney disease.MethodsIn a longitudinal cohort study of 135 participants with type 2 diabetes and diabetic kidney disease, serum samples were assayed for SAA. Censored time-to-event analyses in Cox-proportional hazard models were utilized to assess SAA as a predictor of the primary outcome of death and end-stage renal disease.ResultsParticipants were 73% Mexican-American (99/135) and 55% men (75/135), with a mean±SD age of 57±7.5years. At baseline, participants had hemoglobin A1c of 8.6±2.3%, systolic blood pressure of 153±27mm Hg, body mass index of 31±9kg/m2, median urine-albumin-to-creatinine ratio of 1861mg/g (interquartile range 720-3912mg/g), and estimated glomerular filtration rate of 55.7±22.3ml/min/1.73m2. Over a median duration of follow-up of 3.5years, 44% (60/135) of participants experienced a primary outcome event. The hazards ratio for the primary outcome was 3.03 (95% CI 1.43-6.40, p=0.003) in the highest (>1.0 μg/ml) compared to the lowest (<0.55 μg/ml) SAA tertile in a model adjusted for urine-albumin-to-creatinine ratio, estimated glomerular filtration rate, age, sex, and race/ethnicity. Addition of SAA as a covariate improved the model C-statistic (Δ c=0.017).ConclusionsIn a longitudinal cohort study of participants with type 2 diabetes and DKD, higher levels of serum SAA predicted higher risk of death and ESRD. SAA is a promising targetable biomarker for DKD