A Direct-Dated Ceramic AMS Sequence from the Gaspereau Lake Reservoir Site Complex, Maine–Maritimes Region, Northeastern North America

ABSTRACTThe Gaspereau Lake Reservoir Site Complex in Nova Scotia, Canada, yielded a large ceramic assemblage that permitted the first fine-grained analysis of ceramic change in the region at the Middle–Late Woodland Transition from ca. 1550 BP to ca. 1150 BP. The aim of this study was to refine the standard regional chronology first proposed by researchers J B Petersen and D Sanger. To do this, ceramics were directly dated using accelerator mass spectrometry (AMS), and the assemblage was categorized and analyzed to identify clusters of attributes. Ten AMS dates were acquired on carbonized food residue on the interiors of pottery and yielded the largest continuous ceramic sequence in the Maritime Provinces of Canada. This sequence was used to infer a change in manufacturing practices between the Middle (2150–1300 BP) and Late (1300–500 BP) Woodland periods and to propose five new subperiods between 1650 BP and 950 BP. Increasing incidence of coil breaks and temper percentage from the Middle to the Late Woodland were found to be chronologically sensitive. The analysis showed that, at Gaspereau Lake, a gradual shift from finely decorated and manufactured pottery to expediently made pottery suggests that pottery was made in larger numbers to support large-scale gatherings.

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Shifting priorities apparent in Middle and Late Woodland ceramics from Nova Scotia

North American Archaeologist ◽

10.1177/0197693118806070 ◽

2018 ◽

Vol 39 (4) ◽

pp. 260-291 ◽

Cited By ~ 2

Author(s):

Cora A Woolsey

Keyword(s):

Nova Scotia ◽

Large Scale ◽

Late Woodland ◽

Woodland Period ◽

Statistical Techniques ◽

Ceramic Assemblage ◽

Late Woodland Period

In the Maine–Maritimes Region, the Late Woodland (1350–500 BP) Period is thought to have been accompanied by a decrease in ceramic quality because of less-skilled potters. Although ceramics made during the Late Woodland tend to physically degrade easier than earlier ceramics because of coarser pastes and less well-joined coils, the reasons for the change in manufacturing practices have not been explored. Using the ceramic assemblage from the Gaspereau Lake Reservoir Site Complex in King’s County, Nova Scotia, Canada, this study used simple statistical techniques to suggest that potters increasingly used more expedient manufacture through time. These practices would have enabled potters to turn out pots under tighter deadlines to support large-scale gatherings that probably became more prevalent during the Late Woodland Period.

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A proximity biotinylation map of a human cell

10.1101/796391 ◽

2019 ◽

Cited By ~ 25

Author(s):

Christopher D. Go ◽

James D.R. Knight ◽

Archita Rajasekharan ◽

Bhavisha Rathod ◽

Geoffrey G. Hesketh ◽

...

Keyword(s):

Mass Spectrometry ◽

Human Cell ◽

Large Scale ◽

Mitochondrial Dynamics ◽

Hek293 Cells ◽

Fine Grained ◽

Cellular Processes ◽

Biochemical Fractionation ◽

Cellular Compartments ◽

Better Than

Compartmentalization is an essential characteristic of eukaryotic cells, ensuring that cellular processes are partitioned to defined subcellular locations. High throughput microscopy1 and biochemical fractionation coupled with mass spectrometry2-6 have helped to define the proteomes of multiple organelles and macromolecular structures. However, many compartments have remained refractory to such methods, partly due to lysis and purification artefacts and poor subcompartment resolution. Recently developed proximity-dependent biotinylation approaches such as BioID and APEX provide an alternative avenue for defining the composition of cellular compartments in living cells (e.g. 7-10). Here we report an extensive BioID-based proximity map of a human cell, comprising 192 markers from 32 different compartments that identifies 35,902 unique high confidence proximity interactions and localizes 4,145 proteins expressed in HEK293 cells. The recall of our localization predictions is on par with or better than previous large-scale mass spectrometry and microscopy approaches, but with higher localization specificity. In addition to assigning compartment and subcompartment localization for many previously unlocalized proteins, our data contain fine-grained localization information that, for example, allowed us to identify proteins with novel roles in mitochondrial dynamics. As a community resource, we have created humancellmap.org, a website that allows exploration of our data in detail, and aids with the analysis of BioID experiments.

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AMS Radiocarbon Dates for Pleistocene Fauna from the American Northeast

Radiocarbon ◽

10.2458/azu_rc.57.18220 ◽

2015 ◽

Vol 57 (1) ◽

pp. 189-192 ◽

Cited By ~ 2

Author(s):

Matthew T Boulanger ◽

Gregory D Lattanzi ◽

David C Parris ◽

Michael J O'Brien ◽

R Lee Lyman

Keyword(s):

Mass Spectrometry ◽

Radiocarbon Dating ◽

Extraction Methods ◽

Museum Collections ◽

Faunal Remains ◽

Radiocarbon Dates ◽

Carbon Contamination ◽

Northeastern North America ◽

Botanical Remains ◽

Ams Dates

Northeastern North America has produced an incredible number of late Pleistocene faunal remains; however, many of these were discovered and excavated prior to the development of radiocarbon dating. Moreover, many of the 14C dates that do exist for such specimens were assayed prior to the development of purified collagen extraction methods, were performed on botanical remains of unspecified association with the faunal remains, or were accepted without concerns of young-carbon contamination from museum preservatives. Here, we present a set of high-precision accelerator mass spectrometry (AMS) dates obtained on Pleistocene faunal specimens from Connecticut, New Jersey, and Pennsylvania. Our data contain both newly discovered specimens and specimens that have resided in museum collections for over a century.

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Algorithm of combining chromatography mass spectrometry-untargeted profiling and multivariate analysis for identification of marker-substances in samples of complex composition

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2020-86-7-12-19 ◽

2020 ◽

Vol 86 (7) ◽

pp. 12-19

Author(s):

I. V. Plyushchenko ◽

D. G. Shakhmatov ◽

I. A. Rodin

Keyword(s):

Mass Spectrometry ◽

Multivariate Analysis ◽

Large Scale ◽

Complex Composition ◽

Unified Protocol ◽

Chromatography Mass Spectrometry ◽

Marker Substances ◽

Selection Testing ◽

Untargeted Profiling

A viral development of statistical data processing, computing capabilities, chromatography-mass spectrometry, and omics technologies (technologies based on the achievements of genomics, transcriptomics, proteomics, metabolomics) in recent decades has not led to formation of a unified protocol for untargeted profiling. Systematic errors reduce the reproducibility and reliability of the obtained results, and at the same time hinder consolidation and analysis of data gained in large-scale multi-day experiments. We propose an algorithm for conducting omics profiling to identify potential markers in the samples of complex composition and present the case study of urine samples obtained from different clinical groups of patients. Profiling was carried out by the method of liquid chromatography mass spectrometry. The markers were selected using methods of multivariate analysis including machine learning and feature selection. Testing of the approach was performed using an independent dataset by clustering and projection on principal components.

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Mass Spectrometry Techniques: Principles and Practices for Quantitative Proteomics

Current Protein and Peptide Science ◽

10.2174/1389203721666200921153513 ◽

2020 ◽

Vol 21 ◽

Author(s):

Rocco J. Rotello ◽

Timothy D. Veenstra

Keyword(s):

Mass Spectrometry ◽

Quantitative Proteomics ◽

Protein Identification ◽

Dynamic Nature ◽

Complete Coverage ◽

The Past ◽

Proteome Level ◽

A Cell ◽

Quantitative Changes ◽

Made In

: In the current omics-age of research, major developments have been made in technologies that attempt to survey the entire repertoire of genes, transcripts, proteins, and metabolites present within a cell. While genomics has led to a dramatic increase in our understanding of such things as disease morphology and how organisms respond to medications, it is critical to obtain information at the proteome level since proteins carry out most of the functions within the cell. The primary tool for obtaining proteome-wide information on proteins within the cell is mass spectrometry (MS). While it has historically been associated with the protein identification, developments over the past couple of decades have made MS a robust technology for protein quantitation as well. Identifying quantitative changes in proteomes is complicated by its dynamic nature and the inability of any technique to guarantee complete coverage of every protein within a proteome sample. Fortunately, the combined development of sample preparation and MS methods have made it capable to quantitatively compare many thousands of proteins obtained from cells and organisms.

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Strategic resources and smallholder performance at the bottom of the pyramid

International Food and Agribusiness Management Review ◽

10.22434/ifamr2018.0111 ◽

2019 ◽

Vol 22 (3) ◽

pp. 365-380 ◽

Cited By ~ 1

Author(s):

Matthias Olthaar ◽

Wilfred Dolfsma ◽

Clemens Lutz ◽

Florian Noseleit

Keyword(s):

Large Scale ◽

Business Environment ◽

Agricultural Economics ◽

Local Market ◽

Bottom Of The Pyramid ◽

Fine Grained ◽

Strategic Resources ◽

Relative Contribution ◽

The Relationship ◽

Resource Based Theory

In a competitive business environment at the Bottom of the Pyramid smallholders supplying global value chains may be thought to be at the whims of downstream large-scale players and local market forces, leaving no room for strategic entrepreneurial behavior. In such a context we test the relationship between the use of strategic resources and firm performance. We adopt the Resource Based Theory and show that seemingly homogenous smallholders deploy resources differently and, consequently, some do outperform others. We argue that the ‘resource-based theory’ results in a more fine-grained understanding of smallholder performance than approaches generally applied in agricultural economics. We develop a mixed-method approach that allows one to pinpoint relevant, industry-specific resources, and allows for empirical identification of the relative contribution of each resource to competitive advantage. The results show that proper use of quality labor, storage facilities, time of selling, and availability of animals are key capabilities.

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Inverse Infiltration Modeling of Dike Covers Made of Dredged Material Using PEST and AMALGAM

Geosciences ◽

10.3390/geosciences11020041 ◽

2021 ◽

Vol 11 (2) ◽

pp. 41

Author(s):

Tim Jurisch ◽

Stefan Cantré ◽

Fokke Saathoff

Keyword(s):

Large Scale ◽

Measurement Data ◽

Fine Grained ◽

Dredged Materials ◽

Measurement Results ◽

Large Scale Field ◽

Eigenvalue Ratio ◽

Ill Posed ◽

Materials Used ◽

Installation Technology

A variety of studies recently proved the applicability of different dried, fine-grained dredged materials as replacement material for erosion-resistant sea dike covers. In Rostock, Germany, a large-scale field experiment was conducted, in which different dredged materials were tested with regard to installation technology, stability, turf development, infiltration, and erosion resistance. The infiltration experiments to study the development of a seepage line in the dike body showed unexpected measurement results. Due to the high complexity of the problem, standard geo-hydraulic models proved to be unable to analyze these results. Therefore, different methods of inverse infiltration modeling were applied, such as the parameter estimation tool (PEST) and the AMALGAM algorithm. In the paper, the two approaches are compared and discussed. A sensitivity analysis proved the presumption of a non-linear model behavior for the infiltration problem and the Eigenvalue ratio indicates that the dike infiltration is an ill-posed problem. Although this complicates the inverse modeling (e.g., termination in local minima), parameter sets close to an optimum were found with both the PEST and the AMALGAM algorithms. Together with the field measurement data, this information supports the rating of the effective material properties of the applied dredged materials used as dike cover material.

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Synthesis of Metal Organic Frameworks by Ball-Milling

Crystals ◽

10.3390/cryst11010015 ◽

2020 ◽

Vol 11 (1) ◽

pp. 15

Author(s):

Cheng-An Tao ◽

Jian-Fang Wang

Keyword(s):

Ball Milling ◽

Large Scale ◽

Raw Materials ◽

Metal Organic Frameworks ◽

Research Topic ◽

Mass Ratio ◽

Time Consumption ◽

Metal Organic ◽

Short Time ◽

Made In

Metal-organic frameworks (MOFs) have been used in adsorption, separation, catalysis, sensing, photo/electro/magnetics, and biomedical fields because of their unique periodic pore structure and excellent properties and have become a hot research topic in recent years. Ball milling is a method of small pollution, short time-consumption, and large-scale synthesis of MOFs. In recent years, many important advances have been made. In this paper, the influencing factors of MOFs synthesized by grinding were reviewed systematically from four aspects: auxiliary additives, metal sources, organic linkers, and reaction specific conditions (such as frequency, reaction time, and mass ratio of ball and raw materials). The prospect for the future development of the synthesis of MOFs by grinding was proposed.

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ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

International Journal of Computer Vision ◽

10.1007/s11263-021-01477-5 ◽

2021 ◽

Author(s):

Anil S. Baslamisli ◽

Partha Das ◽

Hoang-An Le ◽

Sezer Karaoglu ◽

Theo Gevers

Keyword(s):

Neural Network ◽

Large Scale ◽

State Of The Art ◽

Image Decomposition ◽

Natural Environments ◽

Decomposition Algorithms ◽

Ambient Light ◽

Fine Grained ◽

Large Scale Dataset ◽

Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

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Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

Remote Sensing ◽

10.3390/rs13163065 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3065

Author(s):

Libo Wang ◽

Rui Li ◽

Dongzhi Wang ◽

Chenxi Duan ◽

Teng Wang ◽

...

Keyword(s):

Large Scale ◽

Texture Features ◽

Semantic Segmentation ◽

Autonomous Driving ◽

Research Field ◽

Learning Approaches ◽

Fine Grained ◽

Urban Scene ◽

Fine Resolution ◽

With Memory

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

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