Method and Meaning: Selections from the Gettysburg College Collection

What is art historical study and how it should be carried out are fundamental questions the exhibition Method and Meaning: Selections from the Gettysburg College Collection intends to answer. This student-curated exhibition is an exciting academic endeavor of seven students of art history majors and minors in the Art History Methods course. The seven student curators are Shannon Callahan, Ashlie Cantele, Maura D’Amico, Xiyang Duan, Devin Garnick, Allison Gross and Emily Zbehlik. As part of the class assignment, this exhibition allows the students to explore various art history methods on individual case studies. The selection of the works in the exhibition reflects a wide array of student research interests including an example of 18th century Chinese jade chime stone, jade and bronze replicas of ancient Chinese bronze vessels, a piece of early 20th century Chinese porcelain, oil paintings by Pennsylvania Impressionist painter Fern Coppedge, prints by Salvador Dalí and by German artist Käthe Kollwitz, and an early 20th century wood block print by Japanese artist Kawase Hasui. [excerpt]https://cupola.gettysburg.edu/artcatalogs/1014/thumbnail.jp

Harmonic Elimination and Magnetic Resonance Sounding Signal Extraction Based on Matching Pursuit Algorithm

Magnetic resonance sounding (MRS) is a non-invasive, direct, and quantitative geophysical method for detecting groundwater, and has been widely used in groundwater survey, water resource assessment, and disaster water source forecasting. However, the MRS signal is weak (nV level) and highly susceptible to environmental noise, such as random noise and power-line harmonics, resulting in reduced quality of received data. Achieving reliable extraction of MRS signals under strong noise is difficult. To solve this problem, we propose a matching pursuit algorithm based on sparse decomposition theory for data noise suppression and MRS signal extraction. In accordance with the characteristics of the signal and noise, an oscillating atomic library is constructed as a sparse dictionary to realize signal sparse decomposition. A two-step denoising strategy is proposed to reconstruct the power-line harmonics and then extract the MRS signal. We simulated synthetic data with different signal-to-noise ratios (SNRs), relaxation times, and Larmor frequencies. Our results show that the proposed algorithm can effectively remove power-line harmonics and reduce random noise. SNR is significantly improved by up to 35.6 dB after denoising. The effectiveness and superiority of the proposed algorithm are further verified by the measured data and through comparison with the singular spectrum analysis algorithm and harmonic modeling cancellation algorithm

The primary data and results for selection and phylogenetic analysis

In the manuscript, we have made a selection and phylogenetic analysis for MHC genes(DQB and DRB) in the Endangered Indo-Pacific Humpback Dolphin (Sousa chinensis).FUBAR software and CODENL program were used to test for the evidence of positive selection, and phylogenetic trees were reconstructed using MrBayes 3.1.2. The best-fit evolution model for MHC was selected on the basis of the Akaike Information Criterion(AIC) using MODELTEST 3.7. All the data underlying these analyses was submitted in a zip file(Data for Dryad)

Potential association between exposure to legacy persistent organic pollutants and parasitic body burdens in Indo-Pacific finless porpoises from the Pearl River Estuary, China

A high prevalence of infectious diseases (mostly lungworms) is found in finless porpoises (genus Neophocaena) in the coastal waters of China, which is one of the most dichlorodiphenyltrichloroethane (DDT)- polluted areas worldwide, while its association with contaminant exposure remains undetermined. To address this gap, we investigated blubber levels of polychlorinated diphenyls (PCBs), organochlorine pesticides (OCPs) and polycyclic aromatic hydrocarbons in Indo- Pacific finless porpoises (Neophocaena phocaenoides) stranded in the Pearl River Estuary (PRE) of China. In the post- mortem examinations, lungworms (Halocercus species) were found to be the most common parasites, with a high density observed in lungs and bronchi. Severe infections by nematode parasites were also found in the uterus (Cystidicola species), intestine (Anisakis typica) and muscle (A. typica). For all the pollutant compounds analyzed, only the concentrations of p,p'-DDT, p,p'dichlorodiphenyldichloroethane (DDD) and o,p'-DDD were significantly higher in porpoises died of infectious diseases than in the "healthy" individuals (died from physical trauma). Contrasted accumulation pattern of DDTs and their metabolites was found between animals with different health status. The proportion of p, p'DDT in Sigma DDTs was higher than that of p,p'- dichlorodiphenyldichloroethylene (DDE) in diseased animals, whereas an opposite pattern was shown for "healthy" ones. While this study is the first to describe a significant positive correlation between parasitic diseases and high levels of DDTs in cetaceans, the direction of causality cannot be determined in our data: either a parasitic infection affected the porpoises' ability to metabolize DDTs, resulting in high levels of p,p'-DDT in their blubber, or the pollutant burden rendered them more susceptible to parasitic infection. (c) 2018 Elsevier B.V. All rights reserved

Quantitative Analysis of Soil Cd Content Based on the Fusion of Vis-NIR and XRF Spectral Data in the Impacted Area of a Metallurgical Slag Site in Gejiu, Yunnan

Vis-NIR and XRF spectroscopy are widely used in monitoring heavy metals in soil due to their advantages of being fast, non-destructive, cost-effective, and non-polluting. However, when used individually, XRF and vis-NIR may not meet the accuracy requirements for Cd determination. In this study, we focused on the impact area of a non-ferrous metal smelting slag site in Gejiu City, Yunnan Province, fused the pre-selected vis-NIR and XRF spectra using the Pearson correlation coefficient (PCC), and identified the characteristic spectra using the competitive adaptive reweighted sampling (CARS) method. Based on this, a quantitative model for soil Cd concentration was established using partial least squares regression (PLSR). The results showed that among the four fusion spectral quantitative models constructed, the model combining vis-NIR spectral second-order derivative transformation and XRF spectral first-order derivative transformation (D2(vis-NIR) + D1(XRF)) had the highest coefficient of determination (R2 = 0.9505) and the smallest root mean square error (RMSE = 0.1174). Compared to the estimation models built using vis-NIR and XRF spectra alone, the average computational time of the fusion models was reduced by 68.19% and 63.92%, respectively. This study provides important technical means for real-time and large-scale on-site rapid estimation of Cd content using multi-source spectral fusion

Optimizing the nucleic acid screening strategy to mitigate regional outbreaks of SARS-CoV-2 Omicron variant in China: a modeling study

Abstract Background The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads rapidly and insidiously. Coronavirus disease 2019 (COVID-19) screening is an important means of blocking community transmission in China, but the costs associated with testing are high. Quarantine capacity and medical resources are also threatened. Therefore, we aimed to evaluate different screening strategies to balance outbreak control and consumption of resources. Methods A community network of 2000 people, considering the heterogeneities of household size and age structure, was generated to reflect real contact networks, and a stochastic individual-based dynamic model was used to simulate SARS-CoV-2 transmission and assess different whole-area nucleic acid screening strategies. We designed a total of 87 screening strategies with different sampling methods, frequencies of screening, and timings of screening. The performance of these strategies was comprehensively evaluated by comparing the cumulative infection rates, the number of tests, and the quarantine capacity and consumption of medical resource, which were expressed as medians (95% uncertainty intervals, 95% UIs). Results To implement COVID-19 nucleic acid testing for all people (Full Screening), if the screening frequency was four times/week, the cumulative infection rate could be reduced to 13% (95% UI: 1%, 51%), the miss rate decreased to 2% (95% UI: 0%, 22%), and the quarantine and medical resource consumption was lower than higher-frequency Full Screening or sampling screening. When the frequency of Full Screening increased from five to seven times/week (which resulted in a 2581 increase in the number of tests per positive case), the cumulative infection rate was only reduced by 2%. Screening all people weekly by splitting them equally into seven batches could reduce infection rates by 73% compared to once per week, which was similar to Full Screening four times/week. Full Screening had the highest number of tests per positive case, while the miss rate, number of tests per positive case, and hotel quarantine resource consumption in Household-based Sampling Screening scenarios were lower than Random Sampling Screening. The cumulative infection rate of Household-based Sampling Screening or Random Sampling Screening seven times/week was similar to that of Full Screening four times/week. Conclusions If hotel quarantine, hospital and shelter hospital capacity are seriously insufficient, to stop the spread of the virus as early as possible, high-frequency Full Screening would be necessary, but intermediate testing frequency may be more cost-effective in non-extreme situations. Screening in batches is recommended if the testing capacity is low. Household-based Sampling Screening is potentially a promising strategy to implement. Graphical Abstrac