Electronic spectroscopy of the alkaline-earth halide cluster Ca_2Cl_3

A visible spectrum of the cluster Ca_2Cl_3 was observed from 651 to 630 nm by 1 + 1[prime] resonant multiphoton ionization spectroscopy. Spectra were obtained for each of the four isotopomers: Ca2 35Cl3, Ca2 35Cl2 37Cl, Ca2 35Cl 37Cl2, and Ca2 37Cl3. The spectra were composed of a strong origin band at 15 350.8 cm^(–1) and several very weak vibronic bands. All of the bands were sharp with partially resolved rotational band contours. Density functional calculations predicted three minimum energy isomers. The spectrum was assigned to the 2B2<--X-tilde 2A1 transition of the lowest energy isomer, a planar C2v structure having a ring of two Cl and two Ca atoms and a terminal Cl atom. The ring isomer of Ca_2Cl_3 has the unpaired electron localized on one Ca^(2+) ion to form a Ca^+ chromophore. The two other predicted isomers, a D3h trigonal bipyramid and a C2v planar V-shaped structure, were not consistent with the observations

Tim-3 expression defines a novel population of dysfunctional T cells with highly elevated frequencies in progressive HIV-1 infection

Progressive loss of T cell functionality is a hallmark of chronic infection with human immunodeficiency virus 1 (HIV-1). We have identified a novel population of dysfunctional T cells marked by surface expression of the glycoprotein Tim-3. The frequency of this population was increased in HIV-1–infected individuals to a mean of 49.4 ± SD 12.9% of CD8+ T cells expressing Tim-3 in HIV-1–infected chronic progressors versus 28.5 ± 6.8% in HIV-1–uninfected individuals. Levels of Tim-3 expression on T cells from HIV-1–infected inviduals correlated positively with HIV-1 viral load and CD38 expression and inversely with CD4+ T cell count. In progressive HIV-1 infection, Tim-3 expression was up-regulated on HIV-1–specific CD8+ T cells. Tim-3–expressing T cells failed to produce cytokine or proliferate in response to antigen and exhibited impaired Stat5, Erk1/2, and p38 signaling. Blocking the Tim-3 signaling pathway restored proliferation and enhanced cytokine production in HIV-1–specific T cells. Thus, Tim-3 represents a novel target for the therapeutic reversal of HIV-1–associated T cell dysfunction

Genome-Wide Identification of Calcium-Response Factor (CaRF) Binding Sites Predicts a Role in Regulation of Neuronal Signaling Pathways

Calcium-Response Factor (CaRF) was first identified as a transcription factor based on its affinity for a neuronal-selective calcium-response element (CaRE1) in the gene encoding Brain-Derived Neurotrophic Factor (BDNF). However, because CaRF shares no homology with other transcription factors, its properties and gene targets have remained unknown. Here we show that the DNA binding domain of CaRF has been highly conserved across evolution and that CaRF binds DNA directly in a sequence-specific manner in the absence of other eukaryotic cofactors. Using a binding site selection screen we identify a high-affinity consensus CaRF response element (cCaRE) that shares significant homology with the CaRE1 element of Bdnf. In a genome-wide chromatin immunoprecipitation analysis (ChIP-Seq), we identified 176 sites of CaRF-specific binding (peaks) in neuronal genomic DNA. 128 of these peaks are within 10kB of an annotated gene, and 60 are within 1kB of an annotated transcriptional start site. At least 138 of the CaRF peaks contain a common 10-bp motif with strong statistical similarity to the cCaRE, and we provide evidence predicting that CaRF can bind independently to at least 64.5% of these motifs in vitro. Analysis of this set of putative CaRF targets suggests the enrichment of genes that regulate intracellular signaling cascades. Finally we demonstrate that expression of a subset of these target genes is altered in the cortex of Carf knockout (KO) mice. Together these data strongly support the characterization of CaRF as a unique transcription factor and provide the first insight into the program of CaRF-regulated transcription in neurons

Development and validation of a weather-based model for predicting infection of loquat fruit by Fusicladium eriobotryae

A mechanistic, dynamic model was developed to predict infection of loquat fruit by conidia of Fusicladium eriobotryae, the causal agent of loquat scab. The model simulates scab infection periods and their severity through the sub-processes of spore dispersal, infection, and latency (i.e., the state variables); change from one state to the following one depends on environmental conditions and on processes described by mathematical equations. Equations were developed using published data on F. eriobotryae mycelium growth, conidial germination, infection, and conidial dispersion pattern. The model was then validated by comparing model output with three independent data sets. The model accurately predicts the occurrence and severity of infection periods as well as the progress of loquat scab incidence on fruit (with concordance correlation coefficients .0.95). Model output agreed with expert assessment of the disease severity in seven loquatgrowing seasons. Use of the model for scheduling fungicide applications in loquat orchards may help optimise scab management and reduce fungicide applications.This work was funded by Cooperativa Agricola de Callosa d'En Sarria (Alicante, Spain). Three months' stay of E. Gonzalez-Dominguez at the Universita Cattolica del Sacro Cuore (Piacenza, Italy) was supported by the Programa de Apoyo a la Investigacion y Desarrollo (PAID-00-12) de la Universidad Politecnica de Valencia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.González Domínguez, E.; Armengol Fortí, J.; Rossi, V. (2014). Development and validation of a weather-based model for predicting infection of loquat fruit by Fusicladium eriobotryae. PLoS ONE. 9(9):1-12. https://doi.org/10.1371/journal.pone.0107547S11299Sánchez-Torres, P., Hinarejos, R., & Tuset, J. J. (2009). Characterization and Pathogenicity ofFusicladium eriobotryae, the Fungal Pathogen Responsible for Loquat Scab. Plant Disease, 93(11), 1151-1157. doi:10.1094/pdis-93-11-1151Gladieux, P., Caffier, V., Devaux, M., & Le Cam, B. (2010). Host-specific differentiation among populations of Venturia inaequalis causing scab on apple, pyracantha and loquat. Fungal Genetics and Biology, 47(6), 511-521. doi:10.1016/j.fgb.2009.12.007González-Domínguez, E., Rossi, V., Armengol, J., & García-Jiménez, J. (2013). Effect of Environmental Factors on Mycelial Growth and Conidial Germination ofFusicladium eriobotryae, and the Infection of Loquat Leaves. Plant Disease, 97(10), 1331-1338. doi:10.1094/pdis-02-13-0131-reGonzález-Domínguez, E., Rossi, V., Michereff, S. J., García-Jiménez, J., & Armengol, J. (2014). Dispersal of conidia of Fusicladium eriobotryae and spatial patterns of scab in loquat orchards in Spain. European Journal of Plant Pathology, 139(4), 849-861. doi:10.1007/s10658-014-0439-0Becker, C. M. (1994). Discontinuous Wetting and Survival of Conidia ofVenturia inaequalison Apple Leaves. Phytopathology, 84(4), 372. doi:10.1094/phyto-84-372Hartman, J. R., Parisi, L., & Bautrais, P. (1999). Effect of Leaf Wetness Duration, Temperature, and Conidial Inoculum Dose on Apple Scab Infections. Plant Disease, 83(6), 531-534. doi:10.1094/pdis.1999.83.6.531Holb, I. J., Heijne, B., Withagen, J. C. M., & Jeger, M. J. (2004). Dispersal of Venturia inaequalis Ascospores and Disease Gradients from a Defined Inoculum Source. Journal of Phytopathology, 152(11-12), 639-646. doi:10.1111/j.1439-0434.2004.00910.xRossi, V., Giosue, S., & Bugiani, R. (2003). Influence of Air Temperature on the Release of Ascospores of Venturia inaequalis. Journal of Phytopathology, 151(1), 50-58. doi:10.1046/j.1439-0434.2003.00680.xStensvand, A., Gadoury, D. M., Amundsen, T., Semb, L., & Seem, R. C. (1997). Ascospore Release and Infection of Apple Leaves by Conidia and Ascospores ofVenturia inaequalisat Low Temperatures. Phytopathology, 87(10), 1046-1053. doi:10.1094/phyto.1997.87.10.1046Machardy WE (1996) Apple scab. Biology, epidemiology and management. St. Paul: APS Press. 545.James, J. R. (1982). Environmental Factors Influencing Pseudothecial Development and Ascospore Maturation ofVenturia inaequalis. Phytopathology, 72(8), 1073. doi:10.1094/phyto-72-1073Li, B., Zhao, H., Li, B., & Xu, X.-M. (2003). Effects of temperature, relative humidity and duration of wetness period on germination and infection by conidia of the pear scab pathogen (Venturia nashicola). Plant Pathology, 52(5), 546-552. doi:10.1046/j.1365-3059.2003.00887.xLi, B.-H., Xu, X.-M., Li, J.-T., & Li, B.-D. (2005). Effects of temperature and continuous and interrupted wetness on the infection of pear leaves by conidia of Venturia nashicola. Plant Pathology, 54(3), 357-363. doi:10.1111/j.1365-3059.2005.01207.xUMEMOTO, S. (1990). Dispersion of ascospores and conidia of causal fungus of Japanese pear scab, Venturia nashicola. Japanese Journal of Phytopathology, 56(4), 468-473. doi:10.3186/jjphytopath.56.468Rossi, V., Salinari, F., Pattori, E., Giosuè,, S., & Bugiani, R. (2009). Predicting the Dynamics of Ascospore Maturation ofVenturia pirinaBased on Environmental Factors. Phytopathology, 99(4), 453-461. doi:10.1094/phyto-99-4-0453Spotts, R. A. (1991). Effect of Temperature and Wetness on Infection of Pear byVenturia pirinaand the Relationship Between Preharvest Inoculation and Storage Scab. Plant Disease, 75(12), 1204. doi:10.1094/pd-75-1204Spotts, R. A. (1994). Factors Affecting Maturation and Release of Ascospores ofVenturia pirinain Oregon. Phytopathology, 84(3), 260. doi:10.1094/phyto-84-260Villalta, O., Washington, W. S., Rimmington, G. M., & Taylor, P. A. (2000). Australasian Plant Pathology, 29(4), 255. doi:10.1071/ap00048Villalta, O. N., Washington, W. S., Rimmington, G. M., & Taylor, P. A. (2000). Effects of temperature and leaf wetness duration on infection of pear leaves by Venturia pirina. Australian Journal of Agricultural Research, 51(1), 97. doi:10.1071/ar99068Lan, Z., & Scherm, H. (2003). Moisture Sources in Relation to Conidial Dissemination and Infection byCladosporium carpophilumWithin Peach Canopies. Phytopathology, 93(12), 1581-1586. doi:10.1094/phyto.2003.93.12.1581Lawrence, Jr., E. G. (1982). Environmental Effects on the Development and Dissemination ofCladosporium carpophilumon Peach. Phytopathology, 72(7), 773. doi:10.1094/phyto-72-773Gottwald, T. R. (1985). Influence of Temperature, Leaf Wetness Period, Leaf Age, and Spore Concentration on Infection of Pecan Leaves by Conidia ofCladosporium caryigenum. Phytopathology, 75(2), 190. doi:10.1094/phyto-75-190Latham, A. J. (1982). Effects of Some Weather Factors andFusicladium effusumConidium Dispersal on Pecan Scab Occurrence. Phytopathology, 72(10), 1339. doi:10.1094/phyto-72-1339MARZO, L., FRISULLO, S., LOPS, F., & ROSSI, V. (1993). Possible dissemination of Spilocaea oleagina conidia by insects (Ectopsocus briggsi). EPPO Bulletin, 23(3), 389-391. doi:10.1111/j.1365-2338.1993.tb01341.xLOPS, F., FRISULLO, S., & ROSSI, V. (1993). Studies on the spread of the olive scab pathogen, Spilocaea oleagina. EPPO Bulletin, 23(3), 385-387. doi:10.1111/j.1365-2338.1993.tb01340.xObanor, F. O., Walter, M., Jones, E. E., & Jaspers, M. V. (2007). Effect of temperature, relative humidity, leaf wetness and leaf age on Spilocaea oleagina conidium germination on olive leaves. European Journal of Plant Pathology, 120(3), 211-222. doi:10.1007/s10658-007-9209-6Obanor, F. O., Walter, M., Jones, E. E., & Jaspers, M. V. (2010). Effects of temperature, inoculum concentration, leaf age, and continuous and interrupted wetness on infection of olive plants by Spilocaea oleagina. Plant Pathology, 60(2), 190-199. doi:10.1111/j.1365-3059.2010.02370.xViruega, J. R., Moral, J., Roca, L. F., Navarro, N., & Trapero, A. (2013). Spilocaea oleaginain Olive Groves of Southern Spain: Survival, Inoculum Production, and Dispersal. Plant Disease, 97(12), 1549-1556. doi:10.1094/pdis-12-12-1206-reViruega, J. R., Roca, L. F., Moral, J., & Trapero, A. (2011). Factors Affecting Infection and Disease Development on Olive Leaves Inoculated withFusicladium oleagineum. Plant Disease, 95(9), 1139-1146. doi:10.1094/pdis-02-11-0126Eikemo, H., Gadoury, D. M., Spotts, R. A., Villalta, O., Creemers, P., Seem, R. C., & Stensvand, A. (2011). Evaluation of Six Models to Estimate Ascospore Maturation in Venturia pyrina. Plant Disease, 95(3), 279-284. doi:10.1094/pdis-02-10-0125Li, B.-H., Yang, J.-R., Dong, X.-L., Li, B.-D., & Xu, X.-M. (2007). A dynamic model forecasting infection of pear leaves by conidia of Venturia nashicola and its evaluation in unsprayed orchards. European Journal of Plant Pathology, 118(3), 227-238. doi:10.1007/s10658-007-9138-4Rossi, V., Giosuè, S., & Bugiani, R. (2007). A-scab (Apple-scab), a simulation model for estimating risk of Venturia inaequalis primary infections. EPPO Bulletin, 37(2), 300-308. doi:10.1111/j.1365-2338.2007.01125.xXU, X.-M., BUTT, D. J., & SANTEN, G. (1995). A dynamic model simulating infection of apple leaves by Venturia inaequalis. Plant Pathology, 44(5), 865-876. doi:10.1111/j.1365-3059.1995.tb02746.xRoubal, C., Regis, S., & Nicot, P. C. (2012). Field models for the prediction of leaf infection and latent period ofFusicladium oleagineumon olive based on rain, temperature and relative humidity. Plant Pathology, 62(3), 657-666. doi:10.1111/j.1365-3059.2012.02666.xPayne, A. F., & Smith, D. L. (2012). Development and Evaluation of Two Pecan Scab Prediction Models. Plant Disease, 96(9), 1358-1364. doi:10.1094/pdis-03-11-0202-reTrapman M, Jansonius PJ (2008) Disease management in organic apple orchards is more than applying the right product at the correct time. Ecofruit-13th International Conference on Cultivation Technique and Phytopathological Problems in Organic Fruit-Growing: Proceedings to the Conference from 18th February to 20th February 2008 at Weinsberg/Germany. 16–22.HOLB, I. J., JONG, P. F., & HEIJNE, B. (2003). Efficacy and phytotoxicity of lime sulphur in organic apple production. Annals of Applied Biology, 142(2), 225-233. doi:10.1111/j.1744-7348.2003.tb00245.xGent, D. H., Mahaffee, W. F., McRoberts, N., & Pfender, W. F. (2013). The Use and Role of Predictive Systems in Disease Management. Annual Review of Phytopathology, 51(1), 267-289. doi:10.1146/annurev-phyto-082712-102356Alavanja, M. C. R., Hoppin, J. A., & Kamel, F. (2004). Health Effects of Chronic Pesticide Exposure: Cancer and Neurotoxicity. Annual Review of Public Health, 25(1), 155-197. doi:10.1146/annurev.publhealth.25.101802.123020Brent KJ, Hollomon DW (2007) Fungicide resistance in crop pathogens: How can it be managed? FRAC Monog 2. Fungicide Resistance Action Committee.Shtienberg, D. (2013). Will Decision-Support Systems Be Widely Used for the Management of Plant Diseases? Annual Review of Phytopathology, 51(1), 1-16. doi:10.1146/annurev-phyto-082712-102244Leffelaar P (1993) On Systems Analysis and Simulation of Ecological Processes. Kluwer. London.Rossi V, Giosuè S, Caffi T (2010) Modelling plant diseases for decision making in crop protection. In: Oerke E-C, Gerhards R, Menz G, Sikora RA, editors. Precision Crop Protection-the Challenge and Use of Heterogeneity.Hui, C. (2006). Carrying capacity, population equilibrium, and environment’s maximal load. Ecological Modelling, 192(1-2), 317-320. doi:10.1016/j.ecolmodel.2005.07.001Townsend C, Begon M, Harper J (2008) Essentials of ecology. John Wiley and Sons. New York. 510.Zadoks J, Schein R (1979) Epidemiology and plant disease management. Oxford University Press, New York. 427.Bennett, J. C., Diggle, A., Evans, F., & Renton, M. (2013). Assessing eradication strategies for rain-splashed and wind-dispersed crop diseases. Pest Management Science, 69(8), 955-963. doi:10.1002/ps.3459Ghanbarnia, K., Dilantha Fernando, W. G., & Crow, G. (2009). Developing Rainfall- and Temperature-Based Models to Describe Infection of Canola Under Field Conditions Caused by Pycnidiospores of Leptosphaeria maculans. Phytopathology, 99(7), 879-886. doi:10.1094/phyto-99-7-0879Gilligan, C. A., & van den Bosch, F. (2008). Epidemiological Models for Invasion and Persistence of Pathogens. Annual Review of Phytopathology, 46(1), 385-418. doi:10.1146/annurev.phyto.45.062806.094357Buck, A. L. (1981). New Equations for Computing Vapor Pressure and Enhancement Factor. Journal of Applied Meteorology, 20(12), 1527-1532. doi:10.1175/1520-0450(1981)0202.0.co;2Madden L V, Hughes G, van den Bosch F (2007) The study of plant disease epidemics. APS press. St. Paul. 421.González-Domínguez E, Rodríguez-Reina J, García-Jiménez J, Armengol J (2014) Evaluation of fungicides to control loquat scab caused by Fusicladium eriobotryae. Plant Heal Prog Accepted.De Wolf, E. D., & Isard, S. A. (2007). Disease Cycle Approach to Plant Disease Prediction. Annual Review of Phytopathology, 45(1), 203-220. doi:10.1146/annurev.phyto.44.070505.143329Krause, R. A., & Massie, L. B. (1975). Predictive Systems: Modern Approaches to Disease Control. Annual Review of Phytopathology, 13(1), 31-47. doi:10.1146/annurev.py.13.090175.000335Fourie, P., Schutte, T., Serfontein, S., & Swart, F. (2013). Modeling the Effect of Temperature and Wetness on Guignardia Pseudothecium Maturation and Ascospore Release in Citrus Orchards. Phytopathology, 103(3), 281-292. doi:10.1094/phyto-07-11-0194Gadoury, D. M. (1982). A Model to Estimate the Maturity of Ascospores ofVenturia inaequalis. Phytopathology, 72(7), 901. doi:10.1094/phyto-72-901Holtslag, Q. A., Remphrey, W. R., Fernando, W. G. D., St-Pierre, R. G., & Ash, G. H. B. (2004). The development of a dynamic diseaseforecasting model to controlEntomosporium mespilionAmelanchier alnifolia. Canadian Journal of Plant Pathology, 26(3), 304-313. doi:10.1080/07060660409507148Legler SEE, Caffi T, Rossi V (2013) A Model for the development of Erysiphe necator chasmothecia in vineyards. Plant Pathol. DOI:10.1111/ppa.12145.Luo, Y., & Michailides, T. J. (2001). Risk Analysis for Latent Infection of Prune by Monilinia fructicola in California. Phytopathology, 91(12), 1197-1208. doi:10.1094/phyto.2001.91.12.1197Gadoury, D. M. (1986). Forecasting Ascospore Dose of Venturia inaequalis in Commercial Apple Orchards. Phytopathology, 76(1), 112. doi:10.1094/phyto-76-112Gent, D. H., De Wolf, E., & Pethybridge, S. J. (2011). Perceptions of Risk, Risk Aversion, and Barriers to Adoption of Decision Support Systems and Integrated Pest Management: An Introduction. Phytopathology, 101(6), 640-643. doi:10.1094/phyto-04-10-0124Schut, M., Rodenburg, J., Klerkx, L., van Ast, A., & Bastiaans, L. (2014). Systems approaches to innovation in crop protection. A systematic literature review. Crop Protection, 56, 98-108. doi:10.1016/j.cropro.2013.11.017Mills W, Laplante A (1954) Diseases and insect in the orchard. Cornell Ext Bull 711.GVA (2013) Octubre-Noviembre 2013. Butlletí d’avisos 13.MacHardy, W. E. (1989). A Revision of Mills’s Criteria for Predicting Apple Scab Infection Periods. Phytopathology, 79(3), 304. doi:10.1094/phyto-79-30

EXPERIMENTAL AND THEORETICAL STUDY OF THE BOUND-FREE G2Σ(4p)←X2Π(3p)G^{2}\Sigma (4p) \leftarrow X^{2}\Pi (3p) TRANSITION OF AlAr

Author Institution: A. A. Noyes Laboratory of Chemical Physics, California Institute of Technology; Air Force Research Laboratory, OLAC PLIRKSThe $G^{2}\Sigma (4p)$ state of AlAr has been observed by both 1+1 and 2+1 resonant multiphoton ionization from the $X^{2}\Pi (3p)$ state. The G state is a dissociative state resulting in a broad but asymmetric band peaked at $33920 cm^{-1}$, detectable by action spectroscopy upon one- and two-photon absorption. The atomic Al 3p-4p transition is forbidden, and molecular AlAr transitions between states arising from these states are primarily two-photon in character due to the weak mixing of Al-atom configurations. Simulations of both the one- and two-photon bands using ab initio potential curves and dipole moment functions calculated by MR-Cl methods are in good agreement with observations

PAPER CRIMINALISTICS Optimization of a Statistical Algorithm for Objective Comparison of Toolmarks*

ABSTRACT: Due to historical legal challenges, there is a driving force for the development of objective methods of forensic toolmark identification. This study utilizes an algorithm to separate matching and nonmatching shear cut toolmarks created using fifty sequentially manufactured pliers. Unlike previously analyzed striated screwdriver marks, shear cut marks contain discontinuous groups of striations, posing a more difficult test of algorithm applicability. The algorithm compares correlation between optical 3D toolmark topography data, producing a Wilcoxon rank sum test statistic. Relative magnitude of this metric separates the matching and nonmatching toolmarks. Results show a high degree of statistical separation between matching and nonmatching distributions. Further separation is achieved with optimized input parameters and implementation of a &quot;leash&quot; preventing a previous source of outliers-however complete statistical separation was not achieved. This paper represents further development of objective methods of toolmark identification and further validation of the assumption that toolmarks are identifiably unique. KEYWORDS: forensic science, toolmark, algorithm, statistical comparison, pliers, striae, quasi-striated, shear cutter In recent history, the legitimacy of scientific testimony has been questioned in several court cases-specifically Daubert v. Merrell Dow Pharmaceuticals, Inc. This challenge has had profound implications in the field of firearm and toolmark examination and resulted in many studies conducted to validate the practice of comparative forensic examination. The primary validation needed is the assumption of toolmark examination-every tool has its own unique surface that will leave a unique mark. Screwdriver marks are among the most studied due to their uniform and continuous striae. They have been previously characterized using stylus profilometry and confocal microscopy in various attempts designed for the identification of matching and/ or nonmatching toolmarks (1-4). The results from these types of studies typically show that striae may be successfully objectively compared using a computer algorithm with relatively high accuracy. For example, in a previous study by the authors using a statistical algorithm, marks from fifty sequentially manufactured screwdriver tips were successfully separated between matching and nonmatching pairs to a reasonable degree of accuracy (2). Studies of other tools that produce striations have also been conducted. Pliers are another type of tool that can create a variety of marks. Cassidy, one of the first to study sequentially manufactured pliers (5), found toolmarks produced by plier teethsuch as when a burglar would twist off a door knob to enter a building-to be unique because the broaching process used to manufacture the plier teeth was performed in a direction perpendicular to the striae it would create. This study established the uniqueness of marks created by plier teeth; however the analysis was based on logical reasoning and not backed by mathematical analysis. More recently Bachrach et al. (4) studied tongue and groove pliers marks created on brass pipe, galvanized steel pipe, and lead rope. Test marks were made using a singular tooth from the pliers to create a striated mark. This study found that marks could be compared when made on different material but with less accuracy than marks made on the same material. Petraco et al. (3) has studied striated chisel marks. The test marks created by the chisels were striated but discontinuous, resulting in patches of striations. Unfortunately, the nature of the created marks was too difficult for the employed software to analyze. While regularly striated marks have received the majority of research attention, the extension of mathematically based studies to other forms of toolmarks is also highly desirable. The results discussed in this paper investigate the applicability of the algorithm employed in (2) to quasi-striated marks created by slip-joint pliers. This type of plier mark was chosen for two reasons. First, the type of mark produced, termed a shear cut, presents a more difficult pattern for identification than a fully striated mark. Second, pliers such as these and other tools that produce shear cut marks are routinely used by criminals to steal copper from construction sites. A July 30, 2013 report on CNBC stated that copper theft in the U.S.A. has become a 1 billion dollar industry (http:// www.cnbc.com/id/100917758 Results from an initial investigation on slip-joint pliers were conducted by Grieve (6). These results having shown promise, this paper presents results on shear cut marks made by 50 sequentially manufactured slip-joint pliers. While the initial results revealed the algorithm could correctly separate a large majority of matching and nonmatching pairs, some algorithm parameter values and options that work well for regularly striated marks are not optimal in the present setting. Two distinct deficiencies hindering algorithm operation were noted. The goal of this study was to investigate optimization of the parameters best suited for analysis of marks described as quasi-striated. The first deficiency addressed involved parameters that affect the degree of statistical separation in the results. While separation was seen using the parameters employed for fully striated markings, better results could be obtained by changing the operational parameters of the algorithm. The second deficiency noted was concerning what the authors have termed the &quot;Opposite End Problem&quot;. This problem manifests itself when, in a small number of cases, the algorithm declares a &quot;match&quot; from two data sets which are known to be nonmatching. Observation of the raw data files shows that the opposite ends of the two sets of toolmarks being compared are identified as the matching region. Such a match is physically impossible and results due to the inability of the algorithm to successfully complete the validation procedure, which is integral to the operation of the algorithm, when confronted with similar topography at opposite ends of the data sets. This possibility was first noted during research on regularly striated screwdriver toolmarks (1,2). This study involves complete analysis of fifty pliers using various parameter values and an option that accounts for the &quot;Opposite End&quot; problem. The results of the study, including a brief description of the statistical algorithm, are discussed below. Experimental Methodology Fifty sequentially manufactured slip-joint pliers were obtained from Wilde Tool Co., Inc. It is common knowledge within the field of study that the manufacturing process significantly affects the toolmarks that are created The pliers start as pieces of steel that are hot forged into half blanks. Each half blank was then cold forged once again using the same die for every piece. After forging, the first difference between half-pairs was introduced. Fifty halves were punched to create a small hole, while the other fifty halves received a double-hole punch-allowing future users to better hold a wider dimension range of objects. The gripping teeth and shear surface were next created with a broaching process. Two broaching machines were used in the production of the pliers. Plier halves with the double-hole punch went to one machine, while pliers with a single-hole punch went to the other. During this separation time, the manufacturer stamped the numbers 1-50 on the plier halves, so the correct sequence could be ensured. The broaching process on the shear surface created the characteristic nature that is of interest for this study. After broaching, both halves of each plier were given the same heat treatment and shot peened to strengthen the material and increase the surface hardness. The flat side regions were next polished, and the double-hole punch half was branded with the company logo. The plier half with the double-hole punch and company logo was labeled as the &quot;B&quot; side for every plier pair, and the other side &quot;A&quot;. An overview of pliers from unfinished to finished states is shown in Wire test samples were created using bolt cutters to cut 2&quot; samples from wire spools. The bolt cut ends were marked using a permanent marker so they could not be confused with the plier shear cut surfaces. Diameters of the wire used were 0.1620&quot; for the copper and 0.1875&quot; for the lead. Test marks were made by shear cutting the copper and lead wire. Shear cutters are defined by the Association of Firearm and Toolmark Examiners as &quot;opposed jawed cutters whose cutting blades are offset to pass by each other in the cutting process&quot; (9). As the shear face was used on the pliers to make the samples, by definition the created marks are shear cutting marks. JOURNAL OF FORENSIC SCIENCES exact location used on the pliers to create the toolmarks. Each shear cut was made by placing the sample between the shear surfaces-with the &quot;B&quot; surface always facing downward-marking the sides &quot;A&quot; and &quot;B&quot; corresponding to which plier shear surface would be acting on that section of the wire. Thus, two samples, one &quot;A&quot; and one &quot;B&quot;, were created with each shear cut. The samples were shear cut alternating between copper and lead until ten of each sample type were created. This resulted in 2000 total samples: 1000 samples for both copper and lead with half of each coming from each side of the pliers. For consistency, every sample was made by the author who is a retired forensic examiner. When the wire is mechanically separated, the two surfaces of the shear edges move past each other. The resultant action is therefore a combination of both cutting the surfaces and a shearing action of the edges as they move through the material. The result is two surfaces being created on each half of the separated wire sample, comprising both shear cut and impression markings, roughly at 90°to each other with both being %45°to the long axis of the wire. Only the shear cut surfaces on the &quot;A&quot; and &quot;B&quot; sides of the sample were scanned and analyzed. A schematic showing the process is shown in The scope of this study included only the copper samples, leading to a total sample size of 1000. To obtain the surface data from the samples, each piece was scanned using an Infinite Focus Microsope G3 (Alicona). Scans were completed at 109 magnification with a two micron vertical resolution. An example image obtained using the IFM is shown in An example of the scanned data from the infinite focus microscope prior to and post noise reduction is shown in SPOTTS ET AL. . OBJECTIVE COMPARISON OF TOOLMARKS 305 any remaining spikes and the sides of the cut wire, the authors used a visual painting program to paint over noisy regions. The computer algorithm then interpreted the painted areas as data points to exclude from the analysis. As it is impossible to scan every sample at precisely the same angle relative to the equipment, it is necessary to correct for this sample angle using a process called detrending. To detrend the data, linear least squares were used to fit a plane to the data. To make this process faster and less sensitive to noise, only 80 points were used in the plane fitting. These points were selected in an &quot;X&quot; pattern that evenly covered the majority of the sample surface. Once the plane fitting was obtained, the plane was subtracted from the surface data to remove the global surface angle. When employed in the initial study (6), the comparative algorithm used was discovered to have the same limitation that prevented it from operating effectively in certain instances in (2). For a more complete discussion of the algorithm, the reader is referred to (2). Briefly, the algorithm works in two major steps, the optimization and validation steps. An iterative &quot;search&quot; window of user-determined size (in pixels) is held stationary on Trace 1, while the correlation to a same size window is calculated over the entirety of Trace 2. The window is then shifted one pixel over on Trace 1 and the process is repeated. This is performed until the two regions of best correlation are found. Once the region of highest correlation is found during the optimization step, two shifts are applied and compared-random shifts and rigid shifts. This is the validation step. The size of the &quot;validation&quot; windows that are shifted are user determined. During the rigid shift step, a user-defined window is moved a set distance from the best correlation window on each trace and the correlation at that point is calculated. For the random shift step, the same size window is moved randomly calculated distances from the best correlation window for the two comparison scans and the correlation is again calculated. An example of rigid and random shifts is shown in In the initial investigation (6), outlier data points were observed to stem from the algorithm misidentifying the opposite ends of marks as a positive match. One example of this is shown in As To address this problem, a &quot;leash&quot; was applied to the search window of the original algorithm (2) during the optimization step, the purpose being to limit the comparison distance between profiles. In this case, the comparative correlation is no longer calculated over the entirety of Trace 2 for each iteration of the search window, but only to a certain percentage of the entire distance. The current version of the leash is set as a percentage of the total length of the trace. The leash was set at 80% for this analysis. A Wilcoxon rank Sum test statistic (centered and scaled to have a nominal SD 1) is calculated during the validation step and is what is returned by the algorithm. The T1 statistic is determined by comparing the results of rigid and random shifts. Matching marks should have relatively high correlation after a rigid shift if they are truly similar and lower correlations during random shifts. The magnitude of the T1 statistic is affected by how much the rigid and random shifts differ. High rigid shift correlation and low random shift correlation would result in a high T1 value-indicating a matching pair-while the opposite scenario would result in a low or negative T1 value indicating a nonmatching pair. The reason many shifts are applied is because random chance may allow a few random shift windows to have a high correlation. As more shifts are applied to a matching pair, the probability of observing a small T1 statistic will decrease. As more shifts are applied to a nonmatching pair, the expected JOURNAL OF FORENSIC SCIENCES trend would be average rigid and random shift correlations that become closer in value-resulting in a T1 statistic value near zero. Currently, there is not a definitive T1 value that perfectly defines when a match or nonmatch pair has been confirmed. This is due to the nature of the data-the comparisons are not independent events. Even if a definitive value was created for this study, it would likely not be applicable to other toolmark comparisons due to inherent differences in toolmark variability between different tools. Statements of correct or incorrect &quot;identification&quot; in this paper are qualitative-when the matching pairs consistently have significantly larger T1 values than nonmatching pairs, it is fair to state that the algorithm is correctly separating (&quot;identifying&quot;) the majority of the pairs. A more advanced statistical argument is necessary to truly state whether an individual comparison was correctly identified. Results The data from the fifty sequentially manufactured pliers were compared using three different types of comparisons resulting in three sets of data. All three comparison types were performed using data from both the long and short edges as defined in Set 1: Comparing known matching pairs. Data for Set 1 were created by comparing marks made by the same side of the same pliers. Comparisons were made between marks 2 and 4, as well as marks 6 and 8 for both sides of each plier. An example of the methodology for comparisons in Set 1 is best described in a tabular format; an example of the comparison order through two pliers is shown in Set 3: Comparing known nonmatching pairs. Data for Set 3 were created by comparing marks from the same side of different pliers. Marks 16, 18, and 20 were compared between different pliers for both sides. An example of the methodology for comparisons in Set 3 is shown in Search and validation window sizes of 200 and 100 pixels, respectively, were used as part of the initial analysis. These window sizes had been previously used for successful matching of SPOTTS ET AL. . OBJECTIVE COMPARISON OF TOOLMARKS 307 screwdriver toolmarks (2). The results for all three data sets are shown in Observation of Results from the initial investigation that used the original algorithm are shown in As the quasi-striated marks produced by the plier shear cuts are far less regular than the previous screwdriver marks studied, experiments were conducted to determine the effect window size (i.e., search and validation) may have on the results. The 2:1 window size ratio was maintained for this second round of analysis with the search windows set to 1000, 500, 200, and 100 pixels with corresponding validation window sizes of 500, 250, 100, and 50 pixels. The results of these experiments for the short and long edges are shown in Figs 12, 13, and 14. In some cases during the analysis, the algorithm would not return a result for every comparison. This is because the algorithm does not allow validation windows to overlap. Thus, as larger and larger window sizes are used it becomes more likely that the algorithm will run out of profile length, especially on short edges with large window sizes, and not return a T1 value. For a 2:1 ratio, the algorithm did not return 6 values for the Set 1 short edge, 9 values for the Set 2 long edge, 13 values for the Set 2 short edge, and 19 values for the Set 3 short edge. These numbers should be compared to the total of 3965 data comparisons that did return a result for the 2:1 ratio analysis. The algorithm returned a result more than 98% of the time. With a clear trend in the results due to window size, the effect of size ratio was next analyzed using both 4:1 and 6:1 search to validation window size ratios. Search windows were set to 800, 600, 400, and 200 pixels with corresponding validation window sizes of 200, 150, 100, and 50 pixels used for the 4:1 ratio experiment. Search windows were set to 750, 600, 450, and 300 pixels with corresponding validation window sizes of 125, 150, 75, and 50 pixels for the 6:1 ratio experiment. The results from these analyses are shown in Figs 15-20 for the short and long edges. Observation of SPOTTS ET AL. . OBJECTIVE COMPARISON OF TOOLMARKS 309 Set 2 and 3 comparisons, known nonmatches, are shown in Figs 17-20. Observations for Set 2 and 3 comparisons showed a median value near zero regardless of window size, increasing data spread with increasing window sizes and no clear trend in the number of outliers. In general, long edge comparisons had better results evidenced by the general decrease in data spread. The algorithm did not fail to return any results for the 4:1 and 6:1 ratios. This analysis contained 4012 comparisons for each ratio. Discussion The results presented add further credence to the basic assumption involved in toolmark identification, namely, that all manufactured tools are unique due to the machining processes used in their manufacture. This uniqueness is transferred to toolmarks as the tool is employed. Use of advanced characterization methods and computer algorithms can, to a large degree, allow objective comparison and identification of a series of toolmarks. When the research transitioned from regularly striated to quasi-striated marks, it became apparent that a parameter optimization of the algorithm employed is necessary for different tools. This optimization led to improved results. The algorithm used in this research was optimized to provide better results for the current set of toolmarks by experimentally changing window sizes and utilizing an option that limits errors due to the opposite end problem. While the leash restriction is effective, it should be JOURNAL OF FORENSIC SCIENCES realized that its effectiveness is only made possible by the introduction of contextual knowledge into the analysis. For the plier marks, the nonsymmetric shape of the shear cut makes it easy to determine in which direction the scans should be analyzed. A more symmetric plier mark might be more difficult to orient properly to make use of the leash, involving a trained examiner to ensure the data were obtained correctly. In the most ideal scenario, there would be complete data separation between known matching and nonmatching pairs, giving a clear indication of correlation, with no outliers in the data. Although ideal degree of separation has not been achieved, there is clearly a large majority of correctly identified toolmarks. Close examination of the outlying data points from both edges reveals that for these specific comparisons the algorithm produces a correct result for the vast majority of window combinations used. For example, consider If the underlying hypothesis behind the application of the T1 statistic is that matching pairs will have more correlation than nonmatching pairs, one might assume that this also holds true if one uses more search and validation window combinations. A simple experiment was performed to see the effect of using multiple search and validation window combinations simultane