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Our primary purposes are to determine the genesis and the main source rocks that provide the magnetite on the beaches, as well as the compatible and discriminate elements of the studied magnetite using the multivariate diagram and principal component analysis (PCA). Optical microscopy, X-ray diffraction (XRD) and scanning electron microscopy (SEM) are primarily used to evaluate the purity of magnetite grains, and laser ablation–inductively coupled plasma–mass spectrometry is primarily used to determine the chemical composition of magnetite. In this study, two types of magnetite were distinguished, namely magmatic magnetite and hydrothermal magnetite. To determine the compatible and discriminant variables, three steps were performed. In the first step, the number of variables was reduced using a multivariate diagram. The results revealed that Zn, P, Ca, Cr, Cu, Na, and K, and Fe, Mg, Al, Ti, V, Mn, Co, Sc, Ni, Ga, Zr, Nb, and Sn served as compatible variables in magmatic and hydrothermal magnetite, respectively. In the second step, box-and-whisker plots, hierarchical clustering, and variable importance in projection (VIP) plots were used to identify discriminant variables based on the aforementioned compatible elements. The results indicated that in both hydrothermal and magmatic magnetite, Fe, Al, Ti, Mg, Mn, V, and Zn served as highly discriminant variables, while Zr, Sn, Sc, and Nb served as poorly discriminant variables. Other elements such as Co, Cr, Cu, Ca, Ga, Ni, K, and Na exhibited varying discriminant values. In the third step, the discriminant variables determined in the second step were arranged using the ranking model of PCA for each study sample. In conclusion, multivariate plots, box-and-whisker plots, VIP plots, and PCA are useful tools for determining the compatible and discriminant elements of magnetite. [ABSTRACT FROM AUTHOR]

The geochemistry of magnetite provides constraints on the source(s) of the magnetite and other ore minerals. In this study, we constructed a magnetite distribution map, compared the chemical composition of magnetite present in hard rocks with that present in sand beach and stream deposits to determine magnetite's provenance, investigated relationships among different trace elements, examined variations in the concentrations of trace elements present in magnetite, and identified the type of studied magnetite. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and inductively coupled plasma mass spectrometry/inductively coupled plasma atomic emission spectroscopy was used to analyze samples available in small and large amounts, respectively. The average magnetite compositions of beach and river samples were substantially similar to those of andesite rocks, revealing that the magnetite samples may have originated from surrounding andesite rocks; this finding is consistent with the chemical composition of the samples analyzed using LA-ICP-MS. Some of the andesite rocks found in the study area were the primary source of magnetite aggregations in the beach sands and stream sands near or slightly away from these rocks. High Ti–V magnetites lie within the fields of Fe–Ti–V bearing magnetite and titanomagnetite (ulvöspinel). These titanomagnetites may be undergoing transformation to spinel minerals such as galaxite, gahnite, magnesiochromite, and chromite, which are characterized by the high concentrations of Al, Ti, Mg, Cr, Ni, Mn, and Zn. The positive correlation between Fe and Ti, Cr and Ni, and also the negative correlation between Fe and Mg, Mn and Zn can be attributed to various magnetite phases present in the study area. V, Co, Mo, Nb, Ga, Sn, and Ta exhibited a positive correlation with Fe and a negative correlation with Al. The positive correlation between Fe and these elements indicates the enrichment of these elements in the magnetite series. We observed a negative correlation between Fe and Li, Rb, Ca, Ba, Sr, Cu, Cd, Sb, As, Sc, Y, REE, U, and Th. A positive correlation between Al and these elements indicates the enrichment of these elements in the spinel series. [ABSTRACT FROM AUTHOR]

[Display omitted] • Studied magnetite distribution on Taiwan's eastern and western beaches using magnetite concentrations in beach sands. • Investigated geochemical compatibility and discrimination elements in the magnetite crystal structure. • Unraveled magnetite provenance and genesis to target possible mining locations. This study investigated the compatible and discriminant elements of magnetite in rocks and beach sands on Taiwan's western and eastern shores. Scanning electron microscopy, X-ray diffraction, and optical microscopy were used to evaluate the purity of the studied magnetite grains, while laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-Ms) was used to determine the chemical composition. Multivariate diagrams and principal component analysis (PCA) were also used to better determine Taiwan's magnetite resource and mining potential. Two types of magnetite were identified in this study: primary magmatic and hydrothermal. Three steps were followed to determine the compatible and discriminant variables. In the first step, a multivariate diagram was used to reduce the number of variables. The results revealed that Zn, P, Ca, Cr, Cu, Na, and K, as well as Fe, Mg, Al, Ti, V, Mn, Co, Sc, Ni, Ga, Zr, Nb, and Sn, were compatible variables in magmatic and hydrothermal magnetite, respectively. In the second step, box-and-whisker plots, hierarchical clustering, and variable importance in projection (VIP) plots were used to identify discriminant variables based on the aforementioned compatible elements. The results indicated that Fe, Al, Ti, Mg, Mn, V, and Zn were highly discriminant variables. In contrast, Zr, Sn, Sc, and Nb were low discriminant variables in hydrothermal and magmatic magnetite. Other elements, such as Co, Cr, Cu, Ca, Ga, Ni, K, and Na, exhibited varying discriminant values. The discriminant variables obtained from the second step were arranged using the PCA ranking model for each study sample in the third step. In summary, multivariate diagrams, box-and-whisker plots, VIP plots, and PCA were useful tools for determining magnetite-compatible and discriminant elements. Regarding its resource potential, Taiwanese magnetite was found to be of high grade (>65%), suitable for manufacturing pig, cast, wrought, and steel iron based on its Mn content. Finally, this study highlighted the magnetite resource potential of andesite, migmatite, and serpentinite units exposed at the surface. Exploration and evaluation of these units are strongly recommended. [ABSTRACT FROM AUTHOR]

Purpose: Adenocarcinoma is the most dominant type of lung cancer in never-smoker patients. The risk alleles from genome-wide association studies have small odds ratios and unclear biologic roles. Here we have taken an approach featuring suitable medical actionability to identify alleles with low population frequency but high disease-causing potential., Patients and Methods: Whole-genome sequencing was performed for a family with an unusually high density of lung adenocarcinoma with available DNA from the affected mother, four affected daughters, and one nonaffected son. Candidate risk alleles were confirmed by matrix-assisted laser desorption ionization time of flight mass spectroscopy. Validation was conducted in an external cohort of 1,135 participants without cancer and 1,312 patients with lung adenocarcinoma. Family follow-ups were performed by genotyping the relatives of the original proband and the relatives of the identified risk-allele carriers. Low-dose computed tomography scans of the chest were evaluated for lung abnormalities., Results: YAP1 R331W missense mutation from the original family was identified and validated in the external controls and the cohort with lung adenocarcinoma. The YAP1 mutant-allele carrier frequency was 1.1% in patients with lung adenocarcinoma compared with 0.18% in controls (P = .0095), yielding an odds ratio (adjusted for age, sex, and smoking status) of 5.9. Among the relatives, YAP1-mutant carriers have overwhelmingly higher frequencies of developing lung adenocarcinoma or ground-glass opacity lung lesions than those who do not carry the mutation (10:0 v 1:7; P < .001). YAP1 mutation was shown to increase the colony formation ability and invasion potential of lung cancer cells., Conclusion: These results implicated YAP1 R331W as an allele predisposed for lung adenocarcinoma with high familial penetrance. Low-dose computed tomography scans may be recommended to this subpopulation, which is at high risk for lung cancer, for personalized prevention and health management., (© 2015 by American Society of Clinical Oncology.)