Identification of Candidate Biomarker and Drug Targets for Improving Endometrial Cancer Racial Disparities

Racial disparities in incidence and survival exist for many human cancers. Racial disparities are undoubtedly multifactorial and due in part to differences in socioeconomic factors, access to care, and comorbidities. Within the U.S., fundamental causes of health inequalities, including socio-economic factors, insurance status, access to healthcare and screening and treatment biases, are issues that contribute to cancer disparities. Yet even these epidemiologic differences do not fully account for survival disparities, as for nearly every stage, grade and histologic subtype, survival among Black women is significantly lower than their White counterparts. To address this, we sought to investigate the proteomic profiling molecular features of endometrial cancer in order to detect modifiable and targetable elements of endometrial cancer in different racial groups, which could be essential for treatment planning. The majority of proteins identified to be significantly altered among the racial groups and that can be regulated by existing drugs or investigational agents are enzymes that regulate metabolism and protein synthesis. These drugs have the potential to improve the worse outcomes of endometrial cancer patients based on race.     

Synthesis and Biochemical Evaluation of Biotinylated Conjugates of Largazole Analogues: Selective Class I Histone Deacetylase Inhibitors

The synthesis of biotinylated conjugates of synthetic analogues of the potent and selective histone deacetylase (HDAC) inhibitor largazole is reported. The thiazole moiety of the parent compound's cap group was derivatized to allow the chemical conjugation to biotin. The derivatized largazole analogues were assayed across a panel of HDACs 1–9 and retained potent and selective inhibitory activity towards the class I HDAC isoforms. The biotinylated conjugate was further shown to pull down HDACs 1, 2, and 3.     

Self-organizing maps for pattern recognition in design of alloys

A combined experimental–computational methodology for accelerated design of AlNiCo-type permanent magnetic alloys is presented with the objective of simultaneously extremizing several magnetic properties. Chemical concentrations of eight alloying elements were initially generated using a quasi-random number generator so as to achieve a uniform distribution in the design variable space. It was followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic protocol. These experimental data were used to develop meta-models capable of directly relating the chemical composition with desired macroscopic properties of the alloys. These properties were simultaneously optimized to predict chemical compositions that result in improvement of properties. These data were further utilized to discover various correlations within the experimental dataset by using several concepts of artificial intelligence. In this work, an unsupervised neural network known as self-organizing maps was used to discover various patterns reported in the literature. These maps were also used to screen the composition of the next set of alloys to be manufactured and tested in the next iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys. This approach helps significantly reducing the time and the number of alloys needed in the alloy development process.     

Inverted Cylindrical Magnetron Sputtering

The shape of inverted cylindrical magnetrons makes them ideal for coating a variety of optical components having non‐planar geometries such as prisms, gratings and spherical lenses. In addition, combining dual cathode mid frequency unbalanced magnetron sputtering with the cylindrical geometry produces a unique source for ion enhanced deposition on planar substrates located at the ends of the cathode. Using a 330 mm diameter source, thickness uniformity on stationary substrates of ± 0.25 % over a diameter of 160 mm and ± 1.3 % over 200 mm can be achieved. SiO₂ and Ta₂O₅ deposited on Si have indices of refraction at a wavelength 632 nm of 1.47 and 2.13 respectively and dispersion similar to those reported for ion assisted processes. The off‐axis nature of this arrangement makes such sources particularly useful where the acceleration of negative ions produced at the target surface can damage sensitive materials such as OLEDs.     

Architectures for Silicon Nanoelectronics and Beyond

Although nanoelectronics won't replace CMOS for some time, research is needed now to develop the architectures, methods, and tools to maximally leverage nanoscale devices and terascale capacity. Addressing the complementary architectural and system issues involved requires greater collaboration at all levels. The effective use of nanotechnology calls for total system solutions     

Situating natural language understanding within experience-based design

Building useful systems with an ability to understand "real" natural language input has long been an elusive goal for Artificial Intelligence. Well-known problems such as ambiguity, indirectness, and incompleteness of natural language inputs have thwarted efforts to build natural language interfaces to intelligent systems. In this article, we report on our work on a model of understanding natural language design specifications of physical devices such as simple electrical circuits. Our system, called KA, solves the classical problems of ambiguity, incompleteness and indirectness by exploiting the knowledge and problem-solving processes in the situation of designing simple physical devices. In addition, KA acquires its knowledge structures (apart from a basic ontology of devices) from the results of its problem-solving processes. Thus, KA can be bootstrapped to understand design specifications and user feedback about new devices using the knowledge structures it acquired from similar devices designed previously.In this paper, we report on three investigations in the KA project. Our first investigation demonstrates that KA can resolve ambiguities in design specifications as well as infer unarticulated requirements using the ontology, the knowledge structures, and the problem-solving processes provided by its design situation. The second investigation shows that KA's problem-solving capabilities help ascertain the relevance of indirect design specifications, and identify unspecified relations between detailed requirements. The third investigation demonstrates the extensibility of KA's theory of natural language understanding by showing that KA can interpret user feedback as well as design requirements. Our results demonstrate that situating language understanding in problem solving, such as device design in KA, provides effective solutions to unresolved problems in natural language processing.     

Mechanistic Analysis of an Engineered Enzyme that Catalyzes the Formose Reaction

An enzyme that catalyzes the formose reaction, termed “formolase”, was recently engineered through a combination of computational protein design and directed evolution. We have investigated the kinetic role of the computationally designed residues and further characterized the enzyme's product profile. Kinetic studies illustrated that the computationally designed mutations were synergistic in their contributions towards enhancing activity. Mass spectrometry revealed that the engineered enzyme produces two products of the formose reaction—dihydroxyacetone and glycolaldehyde—with the product profile dependent on the formaldehyde concentration. We further explored the effects of this product profile on the thermodynamics and yield of the overall carbon assimilation from the formolase pathway to help guide future efforts to engineer this pathway.     

Electro‐optical transfer function preferences for LCD TVs: The effect of display brightness and surround illumination on preferred gamma

Abstract— Recent commercial liquid‐crystal‐display (LCD) televisions are larger and brighter than traditional televisions, thus impacting the viewing conditions in which they are viewed. These changes in viewing conditions may require different electro‐optical transfer functions (EOTFs) for LCD TVs than those for conventional TVs. Here, the way various EOTFs affect the preferred image quality of test images with changes in brightness and surround illumination conditions are examined. The first method used a gain, offset, and gamma (GOG) function with a range of gamma values, and the second method altered the intrinsic EOTFs. Image preference for the simulated EOTFs was determined using a paired‐comparison experiment for ten images. The first experiment took place in a darkened room at two display luminance levels. The results indicated that a gamma of 1.6 was most preferred overall although more so at a lower screen luminance level. In a second experiment, the procedure was repeated with a dim surround of 10% of the display's white point. With this surround, preference for a gamma value of around 1.6 at both screen luminance levels was more enhanced. These results indicated that image preference for different EOTFs is dependent on display luminance and that this dependence is maintained with a dim surround.