Target Selection Strategy Research Articles

Q-Learning-Based Target Selection for Bearings-Only Autonomous Navigation

This paper presents a Q-learning-based target selection algorithm for spacecraft autonomous navigation using bearing observations of known visible targets. For the considered navigation system, the position and velocity of the spacecraft are estimated using an extended Kalman filter (EKF) with the measurements of inter-satellite line-of-sight (LOS) vectors obtained via an onboard star camera. This paper focuses on the selection of the appropriate target at each observation period for the star camera adaptively, such that the performance of the EKF is enhanced. To derive an effective algorithm, a Q-function is designed to select a proper observation region, while a U-function is introduced to rank the targets in the selected region. Both the Q-function and the U-function are constructed based on the sequence of innovations obtained from the EKF. The efficiency of the Q-learning-based target selection algorithm is illustrated via numerical simulations, which show that the presented algorithm outperforms the traditional target selection strategy based on a Cramer-Rao bound (CRB) in the case that the prior knowledge about the target location is inaccurate.

CAR T-Cells in Multiple Myeloma Are Ready for Prime Time.

The survival of patients with multiple myeloma (MM) has been dramatically improved in the last decade thanks to the incorporation of second-generation proteasome inhibitors (PI), immunomodulatory drugs (IMID), and, more recently, anti-CD38 monoclonal antibodies (MoAb). Nevertheless, still, a major proportion of MM patients will relapse, underscoring the need for new therapies in this disease. Moreover, survival in patients failing the current standard of care regimens (including PI, IMIDs, and anti-CD38 MoAb), which is now defined as triple-class refractory, remains dismal, and new drugs with different mechanism of action are needed. B-cell maturation antigen (BCMA)-targeted therapies and in particular chimeric antigen receptor T cell (CAR T-cell) treatment have emerged as promising platforms to overcome refractoriness to conventional drugs. In this manuscript, we review the current available data regarding CAR T-cell therapy for MM, with a special focus on target selection, clinical results, limitations, and future strategies.

Multi-Omic Based Antigen Discovery for the Immunotherapy of Pediatric Acute T Cell Lymphoblastic Leukemia

Pediatric T-cell acute lymphoblastic leukemia (T-ALL) is a high-risk disease due to treatment related complications and poor prognosis of patients with relapsed disease. Immunotherapy with monoclonal antibodies (MAbs) and/or chimeric antigen receptor (CAR) T-cells for T-ALL is limited by identification of tumor specific target antigens. Differential expression is necessary to prevent on-target/off-tumor toxicities and fratricide of activated T-cells. Targeting multiple antigens can bypass immune escape and result in improved T-cell effector function, since antigen density correlates with T-cell activation. Here we designed a pipeline (Figure 1) to identify unique surface antigens expressed in T-ALL using proteomic and transcriptomic analyses followed by flow cytometry validation, and functional studies with CAR T cells targeting the identified antigens. We generated an Illumina total stranded RNAseq library from healthy donor myeloid and lymphoid cells of bone marrow, peripheral blood and cord blood (N= 116). We compared data to 265 St. Jude pediatric T-ALL samples and against 53 normal tissue expression data from the GTEx (Genotype-Tissue Expression) project. To analyze the T-cell surface proteome, we isolated plasma membrane fractions from 11 samples including healthy T-cells and T-ALL cell lines using a differential centrifugation-based method. The purity of the plasma membrane fraction was confirmed by western blot. Na+/K+ ATPase and GAPDH were used as controls for the plasma membrane and cytosolic fractions respectively. Following plasma membrane enrichment, the membrane proteins were applied for proteomic analysis using an advanced TMT-L/LC-MS/MS pipeline, and the acquired proteomic data were further processed via the JUMP software suite. 997 unique proteins were quantified from the membrane fractions. Integrated analysis the transcriptomic and proteomic datasets showed significant correlation and yielded a list of candidate genes, which were validated by flow cytometry on a panel of T-ALL cell lines (CCRF, RPMI8402, and MOLT3) and resting and activated T-cells from healthy donors. We identified GRP78 as one of the differentially expressed cell surface antigens and further confirmed its expression on additional T-ALL cell lines (KE37, PF382, PEER, CEMC7) and 3 PDX samples. Finally, we generated GRP78-CAR T cells and demonstrate that GRP78-CAR T cells recognize and kill GRP78+ T-ALL cells and have potent antitumor activity in xenograft and PDX models. We have established an unbiased pipeline to identify differentially expressed antigens on the cell surface of T-ALL blasts and created a healthy tissue RNAseq library. The results from our analyses are encouraging and interrogation of our pipeline has yielded differentially expressed immunotherapy targets for the treatment of relapsed refractory T-ALL. Our results highlight the importance of integrated surface proteomics and transcriptomics analysis. Figure 1: Outline of strategy for target selection: Figure Disclosures Hebbar: St. Jude: Patents & Royalties. Epperly:St. Jude: Patents & Royalties. Gottschalk:Inmatics and Tidal: Membership on an entity's Board of Directors or advisory committees; TESSA Therapeutics: Other: research collaboration; Patents and patent applications in the fields of T-cell & Gene therapy for cancer: Patents & Royalties; Merck and ViraCyte: Consultancy. Mullighan:AbbVie, Inc.: Research Funding; Illumina: Consultancy, Honoraria, Speakers Bureau; Pfizer: Honoraria, Research Funding, Speakers Bureau. Velasquez:Rally! Foundation: Membership on an entity's Board of Directors or advisory committees; St. Jude: Patents & Royalties.

Joint Radial Velocity and Direct Imaging Planet Yield Calculations. I. Self-consistent Planet Populations

Abstract Planet yield calculations may be used to inform the target selection strategy and science operations of space observatories. Forthcoming and proposed NASA missions, such as the Wide-Field Infrared Survey Telescope, the Habitable Exoplanet Imaging Mission, and the Large UV/Optical/IR Surveyor, are expected to be equipped with sensitive coronagraphs and/or starshades. We are developing a suite of numerical simulations to quantify the extent to which ground-based radial velocity (RV) surveys could boost the detection efficiency of direct imaging missions. In this paper, we discuss the first step in the process of estimating planet yields: generating synthetic planetary systems consistent with observed occurrence rates from multiple detection methods. In an attempt to self-consistently populate stars with orbiting planets, it is found that naive extrapolation of occurrence rates (mass, semimajor axis) results in an unrealistically large number density of Neptune-mass planets beyond the ice line (a ≳ 5 au), causing dynamic interactions that would destabilize orbits. We impose a stability criterion for multiplanet systems based on mutual Hill radii separation. Considering the influence of compact configurations containing Jovian-mass and Neptune-mass planets results in a marked suppression in the number of terrestrial planets that can exist at large radii. This result has a pronounced impact on planet yield calculations particularly in regions accessible to high-contrast imaging and microlensing. The dynamically compact configurations and occurrence rates that we develop may be incorporated as input into joint RV and direct imaging yield calculations to place meaningful limits on the number of detectable planets with future missions.

Structural Insight into Paramyxovirus and Pneumovirus Entry Inhibition.

Paramyxoviruses and pneumoviruses infect cells through fusion (F) protein-mediated merger of the viral envelope with target membranes. Members of these families include a range of major human and animal pathogens, such as respiratory syncytial virus (RSV), measles virus (MeV), human parainfluenza viruses (HPIVs), and highly pathogenic Nipah virus (NiV). High-resolution F protein structures in both the metastable pre- and the postfusion conformation have been solved for several members of the families and a number of F-targeting entry inhibitors have progressed to advanced development or clinical testing. However, small-molecule RSV entry inhibitors have overall disappointed in clinical trials and viral resistance developed rapidly in experimental settings and patients, raising the question of whether the available structural information may provide a path to counteract viral escape through proactive inhibitor engineering. This article will summarize current mechanistic insight into F-mediated membrane fusion and examine the contribution of structural information to the development of small-molecule F inhibitors. Implications are outlined for future drug target selection and rational drug engineering strategies.

SDSS-IV MaStar: A Large and Comprehensive Empirical Stellar Spectral Library—First Release

Abstract We present the first release of the MaNGA Stellar Library (MaStar), which is a large, well-calibrated, high-quality empirical library covering the wavelength range 3622–10354 Å at a resolving power of R ∼ 1800. The spectra were obtained using the same instrument as used by the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) project, by piggybacking on the Sloan Digital Sky Survey (SDSS-IV)/Apache Point Observatory Galaxy Evolution Experiment 2-N (APOGEE-2N) observations. Compared to previous empirical libraries, the MaStar library will have a higher number of stars and a more comprehensive stellar-parameter coverage, especially of cool dwarfs, low-metallicity stars, and stars with different [α/Fe], achieved by a sophisticated target-selection strategy that takes advantage of stellar-parameter catalogs from the literature. This empirical library will provide a new basis for stellar-population synthesis and is particularly well suited for stellar-population analysis of MaNGA galaxies. The first version of the library contains 8646 high-quality per-visit spectra for 3321 unique stars. Compared to photometry, the relative flux calibration of the library is accurate to 3.9% in g − r, 2.7% in r − i, and 2.2% in i − z. The data are released as part of SDSS Data Release 15. We expect the final release of the library to contain more than 10,000 stars.

Enabling New Articulatory Gestures in Children With Persistent Speech Sound Disorders Using Ultrasound Visual Biofeedback.

Purpose This study evaluated ultrasound visual biofeedback treatment for teaching new articulations to children with a wide variety of speech sound disorders. It was hypothesized that motor-based intervention incorporating ultrasound would lead to rapid acquisition of a range of target lingual gestures with generalization to untreated words. Method Twenty children aged 6-15 years with a range of mild to severe speech disorders affecting a variety of lingual targets enrolled in a case series with replication. Of these, 15 children completed the intervention. All of the children presented with a variety of errors. We therefore employed a target selection strategy to treat the most frequent lingual error. These individual speech targets were treated using ultrasound visual biofeedback as part of ten to twelve 1-hr intervention sessions. The primary outcome measure was percentage of target segments correct in untreated wordlists. Results Six children were treated for velar fronting; 3 children, for postalveolar fronting; 2 children, for backing alveolars to pharyngeal or glottal place; 1 child, for debuccalization (production of all onsets as [h]); 1 child, for vowel merger; and 2 children, for lateralized sibilants. Ten achieved the new articulation in the 1st or 2nd session of intervention, despite no children being readily stimulable for their target articulation before intervention. In terms of generalization, effect sizes for percentage of target segments correct ranged from no effect (5 children), small effect (1 child), medium effect (4 children), and large effect (5 children). Conclusions Ultrasound visual biofeedback can be used to treat a wide range of lingual errors in children with various speech sound disorders, from mild to severe. Visual feedback may be useful for establishing new articulations; however, generalization is more variable.

Generation of an anti-desmoglein 3 antibody without pathogenic activity of pemphigus vulgaris for therapeutic application to squamous cell carcinoma

It is ideal for the target antigen of a cytotoxic therapeutic antibody against cancer to be cancer-specific, but such antigens are rare. Thus an alternative strategy for target selection is necessary. Desmoglein 3 (DSG3) is highly expressed in lung squamous cell carcinoma, while it is well-known that anti-DSG3 antibodies cause pemphigus vulgaris, an autoimmune disease. We evaluated DSG3 as a novel target by selecting an epitope that exerts efficacy against cancer with no pathogenic effects in normal tissues. Pathogenic anti-DSG3 antibodies induce skin blisters by inhibiting the cell–cell interaction in a Ca2+-dependent manner. We screened anti-DSG3 antibodies that bind DGS3 independent of Ca2+ and have high antibody-dependent cell cytotoxicity (ADCC) activity against DSG3-expressing cells. These selected antibodies did not inhibit cell–cell interaction and showed ADCC activity against squamous cell carcinoma cell lines. Furthermore, one of the DSG3 antibodies showed anti-tumour activity in tumour mouse models but did not induce adverse effects such as blister formation in the skin. Thus it was possible to generate an antibody against DSG3 by using an appropriate epitope that retained efficacy with no pathogenicity. This approach of epitope selection may expand the variety of druggable target molecules.

Target Selection Strategies for LC-MS/MS Food Allergen Methods.

The detection and quantitation of allergens as contaminants in foods using MS is challenging largely due to the requirement to detect proteins in complex, mixed, and often processed matrixes. Such methods necessarily rely on the use of proteotypic peptides as indicators of the presence and amount of allergenic foods. These peptides should represent the allergenic food in question in such a way that their use is both sensitive (no false-negatives) and specific (no false-positives). Choosing such peptides to represent food allergens is beset with issues, including, but not limited to, separated ingredients (e.g., casein and whey), extraction difficulties (particularly from thermally processed foods), and incomplete sequence information, as well as the more common issues associated with protein quantitation in biological samples. Here, we review the workflows that have been used to select peptide targets for food allergen detection. We describe the use and limitations of both in silico-based analyses and experimental methods relying on high-resolution MS. The variation in the way in which target selection is performed highlights a lack of standardization, even around the principles describing what the detection method should achieve. A lack of focus on the food matrixes to which the method will be applied is also apparent during the peptide target selection process. It is hoped that highlighting some of these issues will assist in the generation of MS-based allergen detection methods that will encourage uptake and use by the analytical community at large.

Shape models of asteroids based on lightcurve observations with BlueEye600 robotic observatory

We present physical models, i.e. convex shapes, directions of the rotation axis, and sidereal rotation periods, of 18 asteroids out of which 10 are new models and 8 are refined models based on much larger data sets than in previous work. The models were reconstructed by the lightcurve inversion method from archived publicly available lightcurves and our new observations with BlueEye600 robotic observatory. One of the new results is the shape model of asteroid (1663) van den Bos with the rotation period of 749 h, which makes it the slowest rotator with known shape. We describe our strategy for target selection that aims at fast production of new models using the enormous potential of already available photometry stored in public databases. We also briefly describe the control software and scheduler of the robotic observatory and we discuss the importance of building a database of asteroid models for studying asteroid physical properties in collisional families.

A bioinformatics approach for precision medicine off-label drug drug selection among triple negative breast cancer patients.

Cancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment. The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data. In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowledge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selection in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression. By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. Sixty-five drug targets are identified as TNBC treatment targets and 85 candidate drugs are recommended. Many existing TNBC candidate targets, such as Poly (ADP-Ribose) Polymerase 1 (PARP1), Cell division protein kinase 6 (CDK6), epidermal growth factor receptor, etc., were identified. On the other hand, we found some additional targets that are not yet fully investigated in the TNBC, such as Gamma-Glutamyl Hydrolase (GGH), Thymidylate Synthetase (TYMS), Protein Tyrosine Kinase 6 (PTK6), Topoisomerase (DNA) I, Mitochondrial (TOP1MT), Smoothened, Frizzled Class Receptor (SMO), etc. Our additional analysis of target and drug selection strategy is also fully supported by the drug screening data on TNBC cell lines in the Cancer Cell Line Encyclopedia. The proposed bioinformatics approach lays a foundation for cancer precision medicine. It supplies much needed knowledge base for the off-label cancer drug usage in clinics.

Cooperative exploration based on supervisory control of multi-robot systems

When multiple mobile robots cooperatively explore an unknown environment, the advantages of robustness and redundancy are guaranteed. However, available traditional economy approaches for coordination of multi-robot systems (MRS) exploration lack efficient target selection strategy under a few of situations and rely on a perfect communication. In order to overcome the shortages and endow each robot autonomy, a novel coordinated algorithm based on supervisory control of discrete event systems and a variation of the market approach is proposed in this paper. Two kinds of utility and the corresponding calculation schemes which take into account of cooperation between robots and covering the environment in a minimal time, are defined. Different moving target of each robot is determined by maximizing the corresponding utility at the lower level of the proposed hierarchical coordinated architecture. Selection of a moving target assignment strategy, dealing with communication failure, and collision avoidance are modeled as behaviors of each robot at the upper level. The proposed approach distinctly speeds up exploration process and reduces the communication requirement. The validity of our algorithm is verified by computer simulations.

Abstract B49: Investigating kinase inhibitor resistance mechanisms through quantitative network mapping of the human kinase interactome

Abstract The human kinases have become one of the most important classes of drug targets in the pharmaceutical industry. More than 20 drugs targeting one or more kinases have been approved for clinical use in a variety of cancers, including lung, breast, melanoma, colorectal, pancreatic, and prostate cancers. However, patients treated with those kinase inhibitors eventually develop resistance, and their prolong survivals are typically only a few months. So far, no general model has emerged to address the kinase inhibitor resistance. In this study, we constructed a systems biology-based framework to catalogue the human kinome (kinases and their interactions), including 538 kinase genes, in the broader context of the human interactome. Specifically, we constructed three networks: a kinase-substrate interaction network containing 7,346 pairs connecting 379 kinases to 36,576 phosphorylation sites in 1,961 substrates, a protein-protein interaction network (PPIN) containing 92,699 pairs, and an atomic resolution PPIN containing 4,278 pairs. We identified the conserved regulatory phosphorylation motifs (e.g., Ser/Thr-Pro) using a sequence logo analysis. We found the typical anticancer target selection strategy that uses network hubs as drug targets might lead to a high adverse drug reaction risk. Furthermore, we found the distinct network centrality of kinases creates a high anticancer drug resistance risk by feedback or crosstalk mechanisms within cellular networks. This notion is supported by the systematic network and pathway analyses that anticancer drug resistance genes are significantly enriched as hubs and heavily participate in multiple signaling pathways. Collectively, this comprehensive human kinome map sheds light on anticancer drug resistance mechanisms and provides an innovative resource for rational kinase inhibitor design. Note: This abstract was not presented at the conference. Citation Format: Feixiong Cheng, Zhongming Zhao. Investigating kinase inhibitor resistance mechanisms through quantitative network mapping of the human kinase interactome. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Drug Sensitivity and Resistance: Improving Cancer Therapy; Jun 18-21, 2014; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(4 Suppl): Abstract nr B49.

TARGET SELECTION FOR THE LBTI EXOZODI KEY SCIENCE PROGRAM

The Hunt for Observable Signatures of Terrestrial planetary Systems (HOSTS) on the Large Binocular Telescope Interferometer will survey nearby stars for faint emission arising from ~300 K dust (exozodiacal dust), and aims to determine the exozodiacal dust luminosity function. HOSTS results will enable planning for future space telescopes aimed at direct spectroscopy of habitable zone terrestrial planets, as well as greater understanding of the evolution of exozodiacal disks and planetary systems. We lay out here the considerations that lead to the final HOSTS target list. Our target selection strategy maximizes the ability of the survey to constrain the exozodi luminosity function by selecting a combination of stars selected for suitability as targets of future missions and as sensitive exozodi probes. With a survey of approximately 50 stars, we show that HOSTS can enable an understanding of the statistical distribution of warm dust around various types of stars and is robust to the effects of varying levels of survey sensitivity induced by weather conditions.

Birthdate and Outgrowth Timing Predict Cellular Mechanisms of Axon Target Matching in the Developing Visual Pathway

How axons select their appropriate targets in the brain remains poorly understood. Here, we explore the cellular mechanisms of axon target matching in the developing visual system by comparing four transgenic mouse lines, each with a different population of genetically labeled retinal ganglion cells (RGCs) that connect to unique combinations of brain targets. We find that the time when an RGC axon arrives in the brain is correlated with its target selection strategy. Early-born, early-arriving RGC axons initially innervate multiple targets. Subsequently, most of those connections are removed. By contrast, later-born, later-arriving RGC axons are highly accurate in their initial target choices. These data reveal the diversity of cellular mechanisms that mammalian CNS axons use to pick their targets and highlight the key role of birthdate and outgrowth timing in influencing this precision. Timing-based mechanisms may underlie the assembly of the other sensory pathways and complex neural circuitry in the brain.

Burglar Target Selection: A Cross-national Comparison.

Objectives:This study builds on research undertaken by Bernasco and Nieuwbeerta and explores the generalizability of a theoretically derived offender target selection model in three cross-national study regions.Methods:Taking a discrete spatial choice approach, we estimate the impact of both environment- and offender-level factors on residential burglary placement in the Netherlands, the United Kingdom, and Australia. Combining cleared burglary data from all study regions in a single statistical model, we make statistical comparisons between environments.Results:In all three study regions, the likelihood an offender selects an area for burglary is positively influenced by proximity to their home, the proportion of easily accessible targets, and the total number of targets available. Furthermore, in two of the three study regions, juvenile offenders under the legal driving age are significantly more influenced by target proximity than adult offenders. Post hoc tests indicate the magnitudes of these impacts vary significantly between study regions.Conclusions:While burglary target selection strategies are consistent with opportunity-based explanations of offending, the impact of environmental context is significant. As such, the approach undertaken in combining observations from multiple study regions may aid criminology scholars in assessing the generalizability of observed findings across multiple environments.

Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy

The human kinome is gaining importance through its promising cancer therapeutic targets, yet no general model to address the kinase inhibitor resistance has emerged. Here, we constructed a systems biology-based framework to catalogue the human kinome, including 538 kinase genes, in the broader context of the human interactome. Specifically, we constructed three networks: a kinase-substrate interaction network containing 7,346 pairs connecting 379 kinases to 36,576 phosphorylation sites in 1,961 substrates, a protein-protein interaction network (PPIN) containing 92,699 pairs, and an atomic resolution PPIN containing 4,278 pairs. We identified the conserved regulatory phosphorylation motifs (e.g., Ser/Thr-Pro) using a sequence logo analysis. We found the typical anticancer target selection strategy that uses network hubs as drug targets, might lead to a high adverse drug reaction risk. Furthermore, we found the distinct network centrality of kinases creates a high anticancer drug resistance risk by feedback or crosstalk mechanisms within cellular networks. This notion is supported by the systematic network and pathway analyses that anticancer drug resistance genes are significantly enriched as hubs and heavily participate in multiple signaling pathways. Collectively, this comprehensive human kinome interactome map sheds light on anticancer drug resistance mechanisms and provides an innovative resource for rational kinase inhibitor design.

Flux upper limits for 47 AGN observed with H.E.S.S. in 2004−2011

About 40% of the observation time of the High Energy Stereoscopic System (H.E.S.S.) is dedicated to studying active galactic nuclei (AGN), with the aim of increasing the sample of known extragalactic very-high-energy (VHE, E>100 GeV) sources and constraining the physical processes at play in potential emitters. H.E.S.S. observations of AGN, spanning a period from April 2004 to December 2011, are investigated to constrain their gamma-ray fluxes. Only the 47 sources without significant excess detected at the position of the targets are presented. Upper limits on VHE fluxes of the targets were computed and a search for variability was performed on the nightly time scale. For 41 objects, the flux upper limits we derived are the most constraining reported to date. These constraints at VHE are compared with the flux level expected from extrapolations of Fermi-LAT measurements in the two-year catalog of AGN. The H.E.S.S. upper limits are at least a factor of two lower than the extrapolated Fermi-LAT fluxes for 11 objects. Taking into account the attenuation by the extragalactic background light reduces the tension for all but two of them, suggesting intrinsic curvature in the high-energy spectra of these two AGN. Compilation efforts led by current VHE instruments are of critical importance for target-selection strategies before the advent of the Cherenkov Telescope Array, CTA.

Optimizing spectroscopic and photometric galaxy surveys: efficient target selection and survey strategy

The next generation of spectroscopic surveys will have a wealth of photometric data available for use in target selection. Selecting the best targets is likely to be one of the most important hurdles in making these spectroscopic campaigns as successful as possible. Our ability to measure dark energy depends strongly on the types of targets that we are able to select with a given photometric data set. We show in this paper that we will be able to successfully select the targets needed for the next generation of spectroscopic surveys. We also investigate the details of this selection, including optimisation of instrument design and survey strategy in order to measure dark energy. We use color-color selection as well as neural networks to select the best possible emission line galaxies and luminous red galaxies for a cosmological survey. Using the Fisher matrix formalism we forecast the efficiency of each target selection scenario. We show how the dark energy figures of merit change in each target selection regime as a function of target type, survey time, survey density and other survey parameters. We outline the optimal target selection scenarios and survey strategy choices which will be available to the next generation of spectroscopic surveys.

Drug discovery in academic institutions

Although academic science has always provided a fundamental understanding of the biological and clinical basis of disease, the opportunity and imperative for academics to contribute more directly to the discovery of new medicines continues to grow. Embedding medicinal chemists with cancer biologists creates collaborative opportunities for drug discovery and the design and synthesis of chemical biology tool compounds (chemical probes) to better elucidate the role of specific proteins and pathways in biology and disease. Two case studies are presented here: (1) the discovery of inhibitors of mer kinase to treat acute lymphoblastic leukemia in children and (2) the discovery of chemical probes targeting epigenetic regulators. These case studies provide lessons in target selection strategies, the requirement for iterative optimization of lead compounds (useful drugs/probes rarely come directly from a screen), and the value of mutually dependent collaborations between medicinal chemists and cancer biologists.