In conjunction with an enhanced system for Agrobacterium-mediated plant transformation, a new binary bacterial artificial chromosome (BIBAC) vector has been developed that is capable of transferring at least 150 kb of foreign DNA into a plant nuclear genome. The transferred DNA appears to be intact in the majority of transformed tobacco plants analyzed and is faithfully inherited in the progeny. The ability to introduce high molecular weight DNA into plant chromosomes should accelerate gene identification and genetic engineering of plants and may lead to new approaches in studies of genome organization.The ability to stably transfer foreign DNA into plant chromosomes is the foundation of plant genetic engineering. DNA transfer to plants has been accomplished by many methods, including Agrobacterium-mediated transformation, biolistic transformation, and microinjection (1, 2). However, no method for routinely introducing DNA fragments larger than about 25 kb into the plant nuclear genome has yet been demonstrated. Genes with related functions, such as disease resistance genes in plants, have been found in clusters (3). A reliable system for transforming large segments (>100 kb) of DNA into plants would make it feasible to introduce a natural gene cluster or a series of previously unlinked foreign genes into a single locus. Such a group of genes could provide resistance to several different pests or pathogens, or it could constitute an entirely new metabolic pathway for production of a novel biomolecule. The integrated "megalocus" would be inherited as a single Mendelian unit and could easily be incorporated into conventional plant breeding programs. Large insert transformation would make it feasible to study the expression of plant genes or gene clusters in their native genomic context and might eliminate site-dependent gene expression, which can be a serious problem in plant transformation experiments. Finally, such a system may make positional cloning applicable to the isolation of genes that encode complex quantitative traits and allow for the transfer of one or more of these genes to various plant species (4).The construction of large insert libraries in bacterial artificial chromosome (BAC) vectors has been reported for several plants (5-7) and animals (8,9

DNA‐based genetic markers are now widely used by geneticists to locate genes for quantitative traits, and may also serve as a valuable tool for dissecting complex physiological phenomena. Van den Berg et al. (1996a QTL analysis of potato tuberization. Theor Appl Gen 93: 307–316), using restriction fragment length polymorphism (RFLP)‐mapped populations of potato, detected eleven quantitative trait loci (QTLs) for tuberization. Taylor et al. (1992 Expression and sequence analysis of cDNAs induced during the early stages of tuberisation in different organs of the potato plant [Solanum tuberosum L.]. Plant Mol Biol 20: 641–651) have identified one of the genes associated with tuberization as that for the enzyme S‐adenosylmethionine decarboxylase (SAMdc), an enzyme of the polyamine biosynthetic pathway. Chromosomal loci for SAMdc and arginine decarboxylase were established on the potato and tomato chromosomal maps, respectively, by hybridizing cDNA probes for these genes to RFLP digests. The polyamine content of leaves from an RFLP‐mapped potato population was analyzed by fluorescence detection following HPLC, with quantitation using an internal standard. The data were analyzed by the ‘qGene’ statistical program, and QTLs for polyamines were detected on seven chromosomes. At least six QTLs were found for spermine, two for spermidine, and two for putrescine. A spermidine QTL was on chromosome 5 linked to marker TG441, very close to the place where SAMdc mapped. There was some congruence between QTLs for spermine and those previously detected for tuberization and dormancy, but relationships were not consistent.

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The DARPA Ground Truth project sought to evaluate social science by constructing four varied simulated social worlds with hidden causality and unleashed teams of scientists to collect data, discover their causal structure, predict their future, and prescribe policies to create desired outcomes. This large-scale, long-term experiment of in silico social science, about which the ground truth of simulated worlds was known, but not by us, reveals the limits of contemporary quantitative social science methodology. First, problem solving without a shared ontology—in which many world characteristics remain existentially uncertain—poses strong limits to quantitative analysis even when scientists share a common task, and suggests how they could become insurmountable without it. Second, data labels biased the associations our analysts made and assumptions they employed, often away from the simulated causal processes those labels signified, suggesting limits on the degree to which analytic concepts developed in one domain may port to others. Third, the current standard for computational social science publication is a demonstration of novel causes, but this limits the relevance of models to solve problems and propose policies that benefit from the simpler and less surprising answers associated with most important causes, or the combination of all causes. Fourth, most singular quantitative methods applied on their own did not help to solve most analytical challenges, and we explored a range of established and emerging methods, including probabilistic programming, deep neural networks, systems of predictive probabilistic finite state machines, and more to achieve plausible solutions. However, despite these limitations common to the current practice of computational social science, we find on the positive side that even imperfect knowledge can be sufficient to identify robust prediction if a more pluralistic approach is applied. Applying competing approaches by distinct subteams, including at one point the vast TopCoder.com global community of problem solvers, enabled discovery of many aspects of the relevant structure underlying worlds that singular methods could not. Together, these lessons suggest how different a policy-oriented computational social science would be than the computational social science we have inherited. Computational social science that serves policy would need to endure more failure, sustain more diversity, maintain more uncertainty, and allow for more complexity than current institutions support.

Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent, progressive disorder and growing public health concern. To date, no treatments exist for NAFLD. To address this issue, considerable research has been undertaken in pursuit of new NAFLD therapeutics. Development of effective, high-throughput in vitro models are an important aspect of drug discovery. Here, a micropatterned hepatocyte coculture (MPCC) was used to model liver steatosis and NAFLD. The MPCC model (HEPATOPAC) involves cells patterned onto a standard 96-well plate, increasing throughput and allowing the cultures can be handled and imaged like 2D cultures. Treatment of MPCC with free fatty acids (FFA), high glucose and fructose (HGF), or a combination of both induces hepatic steatosis. Additionally, inclusion of Kupffer cells to generate a tri-culture (MPTC) increased lipid loading induced by steatotic media. MPCC treatment with ACC1/ACC2 inhibitors, as either a preventative or reversal agent showed efficacy against FFA induced hepatic steatosis. Drug induced steatosis was also evaluated. Treatment with valproic acid showed steatosis induction in a lean background, which was significantly potentiated in a fatty liver background. Additionally, these media treatments changed expression of fatty liver related genes. Treatment of MPCC with FFA, HGF, or a combination reversibly altered expression of genes involved in fatty acid metabolism, insulin signaling, and lipid transport. These changes were largely consistent with data from other clinical or in vivo fatty liver studies. Together, these data demonstrate that MPCC and MPTC are easy to use long-term functional in vitro models of NAFLD. MPCC and MPTC are able to replicate numerous clinical and in vivo NAFLD observations, allowing their broad utility for compound screening, drug toxicity evaluation and assessment of gene expression changes.