Despite the substantial impacts of nonindigenous plant pests and weeds, relatively little is known about the pathways by which these organisms arrive in the U.S. One source of such information is the Port Information Network (PIN) database, maintained by the U.S. Department of Agriculture, Animal and Plant Health Inspection Service (APHIS) since 1984. The PIN database is comprised of records of pests intercepted by APHIS personnel during inspections of travelers' baggage, cargo, conveyances and related items arriving at U.S. ports of entry and border crossings. Each record typically includes the taxonomic identify of the pest, its country of origin, and information related to the commodity and interception site. We summarized more than 725,000 pest interceptions recorded in PIN from 1984 to 2000 to examine origins, interception sites and modes of transport for nonindigenous insects, mites, mollusks, nematodes, plant pathogens and weeds. Roughly 62% of intercepted pests were associated with baggage, 30% were associated with cargo and 7% were associated with plant propagative material. Pest interceptions occurred most commonly at airports (73%), U.S.-Mexico land border crossings (13%) and marine ports (9%). Insects dominated the database, comprising 73 to 84% of the records annually, with the orders Homoptera, Lepidoptera and Diptera collectively accounting for over 75% of the insect records. Plant pathogens, weeds and mollusks accounted for 13, 7 and 1.5% of all pest records, respectively, while mites and nematodes comprised less than 1% of the records. Pests were intercepted from at least 259 different locations. Common origins included Mexico, Central and South American countries, the Caribbean and Asia. Within specific commodity pathways, richness of the pest taxa generally increased linearly with the number of interceptions. Application of PIN data for statistically robust predictions is limited by nonrandom sampling protocols, but the data provide a valuable historical record of the array of nonindigenous organisms transported to the U.S. through international trade and travel.

Dessau, Shen, and Marshall Reply: In our Letter, we reported on high energy-resolution angle-resolved photoemission (ARPES) data taken from three Bi2212 samples [1]. All of these samples showed a gap anisotropy in the a-b plane which is very much larger than in conventional superconductors. This was the main point of our paper, and precluded the possibility of the isotropic ^wave gap A^ =A^o. We further compared our data to the k-space dependences that one would expect from other more complicated forms of the order parameter, including a simple form for the extended ^--wave gap A^ex -[QOs{kxa)-^cos{kya)] and the d^i^yi wave gap A^ --[cos(kxa) -cos(kya)], as well as other suggestions in the literature such as the mixed symmetry order parameter s + id. The direction of the gap anisotropy Uiat our measurement displayed-maximum near the M point (/r,0) and minimum near the V-XiV) zone diagonal -was consistent with the d^i-yi and s+id gaps, but was inconsistent with the simplest extended ^--wave gap (e.g.

Wheat (Triticum aestivum L.) spike characteristics determine the number of grains produced on each spike and constitute key components of grain yield. Understanding of the genetic basis of spike characteristics in wheat, however, is limited. In this study, genotyping-by-sequencing (GBS) and the iSelect 9K assay were used on a doubled-haploid (DH) soft red winter wheat population that showed a wide range of phenotypic variation for spike traits. A genetic map spanning 2934.1 cM with an average interval length of 3.4 cM was constructed. Quantitative trait loci (QTL) analysis involving additive effects, epistasis (QQ) and QTL  environment (QE), and epistasis  environment (QQE) interactions detected a total of 109 QTL, 13 QE, and 20 QQ interactions in five environments. Spike characteristics were mainly determined by additive effects and were fine-tuned by QQ, QE, and QQE. Major QTL QSl.cz-1A/QFsn.cz-1A explained up to 30.9% of the phenotypic variation for spike length (SL) and fertile spikelet number, QGsp.cz-2B.1 explained up to 15.6% of the phenotypic variation of grain number per spikelet, and QSc.cz-5A.3 explained up to 80.2% of the phenotypic variation for spike compactness. Additionally, QTL for correlated spike characteristics formed QTL clusters on chromosomes 1A, 5A, 2B, 3B, 5B, 1D, and 5D. This study expands the understanding of the genetic basis of spike characteristics in hexaploid wheat. A number of stable QTL detected in this study have potential to be used in marker-assisted selection. Additionally, the genetic map generated in this study could be used to study other traits of economic importance.

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