How have retail sales patterns changed across rural America? A case study of Nebraska

Retailing is an important sector of any economy at all geographic levels. Majority of the times, metropolitan areas serve as retail centers for larger geographic areas as the volume of retail activity generated is certainly an important metric to those places. In this paper, using Nebraska as a case study, we analyze the retailing patterns across the state at different geographical scales. For the analysis, we use Pull Factor (PF) as the primary unit of measurement of retail strength. PF measures the relative market share of retailing by a specific geographic area over a specific time period. Results show that population and major highway tended to be the largest factors that affects retailing and the corollary pull factor for county analysis. Less than one-fifth of Nebraska’s counties recorded a 2015 retail pull factor of greater than one, indicating they were trade-capture counties. Interstate 80 ran through half of these counties, providing them opportunity to capture retail trade from travelers, as well as providing greater ease of transportation for customers from nearby counties. The Great Recession had heterogeneous effect on different size communities. Smaller communities often serve a local agricultural economy; the relative robustness of agriculture seems to have spared them from the full brunt of the national recession. In contrast, the largest population class experienced almost no decrease in their pull factor – one possible reason being that higher-cost retail goods and services tend to be concentrated in those centers. An analysis of top retail performers was done to see how these towns/cities levied additional local taxes permitted in Nebraska in addition to state sales tax. We found that all but one top retail performing town employing the additional tax which meant some tax shift from community residents to non- residents who purchase taxable goods and services from that community. On average rural county residents need to spend relatively more on motor vehicle purchases than their metropolitan counterparts, which can leave less disposable income for other retail activity given no drastic differences in median household income levels across the state. We found that higher (lower) the purchase index for motor vehicles, the lower (higher) the county retail pull factor tends to be for other taxable sales activity because of the aforementioned reason. Finally, retail volume over the past quarter century continues to evolve towards the urban and larger population centers of the state driven by both the supply and demand sides of the retail sector. We find substantial evidence that the larger cities of the state command a dominant retail role

How have retail sales patterns changed across rural America? A case study of Nebraska

This study investigates retailing activity and trends at different spatial scales for the last quarter of a century, from 1990 to 2015, for Nebraska using data from the Nebraska Department of Revenue. The primary unit of measurement used to assess the retail strength was Pull Factor. The Pull Factor (PF), is widely used to identify and measure leakage and/or capture of retail trade across political boundaries as well as identifying trends over time. Retailing is an important sector of any economy at all geographic levels and is watched carefully as an indicator of overall economic performance. For 2015 total taxable retail sales for the state was over 23 billion nominal dollars (slightly more than 13 percent of State Gross Product) for Nebraska. Results showed that population was the single largest factor that affected retailing activity. An analysis of top retail performers based on population class showed that all but one town employing the tax shift implications, by levying a local sales tax under the Local Option Revenue Act (applicable to cities) or Nebraska Revenue Statue 13-319 to 13-325 (applicable to counties), associated with their being trade-capture municipalities. This study also found that the higher (lower) the purchase index for motor vehicles, the lower (higher) the county retail pull factor for other taxable sales activity. This was because on average rural county residents spent relatively more on motor vehicle purchases than their metropolitan county cousins, which left less disposable income for other retail activity given no drastic differences in median household income levels across the state. Rising unemployment and income stagnation, which reduced buying power and uncertainty among consumers, during the most recent recession years, slowed the growth of the retail sector significantly between the 2005 and 2010 period relative to both the pre-and post-time periods for the state. Recession consequences did not appear to be uniform across the town/city size classes of Nebraska communities. The smallest class of towns, less than 500 people, saw an increase in retail, most likely because retailing services are almost entirely inelastic goods and services that people need whatever the economic climate and the individual’s economic condition. The metropolitan areas however saw a slight increase in retail dollar volume between 2005 and 2010 while their share of the state’s total retail sales declined slightly

Market Facilitation Program: Impact on Nebraska Corn and Soybean Producers

In July 2018, President Trump imposed a first round of 25-percent tariffs on Chinese electronics and high-tech equipment including automobiles, computer hard drives, and LEDs. The tariffs were imposed on roughly $34 billion worth of imported goods. In August 2018 additional 25-percent tariffs were imposed on$16 billion worth of Chinese exports to the United States and in September tariffs on $200 billion worth of Chinese exports to the United States were added. (Bradsher, 2018). The Trump Administration has also imposed automobile, steel and aluminum tariffs on imports from Canada, the European Union and other countries. In response to the first two sets of tariffs, China placed retaliatory tariffs on$60 billion dollars of imports from the United States matching the value of the goods subjected to U.S. tariffs. According to Bradsher (2018), Chinese imports from the United States are so much smaller than U.S. imports from China that the Chinese government was unable to match the magnitude ($200 billion) of the latest round of U.S. tariffs, applying tariffs only to an additional$60 billion worth of U.S. goods. The Chinese tariffs target sensitive U.S. sectors including several agricultural industries. In the initial round of retaliation, for example, U.S. soybean exports to China—which account for more than 50 percent of total U.S. soybean exports—were hit with 25 percent tariffs. Swanson et al. (2018) reported predictions that the tariff would cause the average annual 2018 soybean price to fall from an expected $9.70 per bushel if no tariff were imposed to$8.85 per bushel

DNMT Inhibitors Increase Methylation in the Cancer Genome

DNA methyltransferase inhibitors (DNMTi) decitabine and azacytidine are approved therapies for myelodysplastic syndrome and acute myeloid leukemia, and their combinations with other anticancer agents are being tested as therapeutic options for multiple solid cancers such as colon, ovarian, and lung cancer. However, the current therapeutic challenges of DNMTis include development of resistance, severe side effects and no or partial treatment responses, as observed in more than half of the patients. Therefore, there is a critical need to better understand the mechanisms of action of these drugs. In order to discover molecular targets of DNMTi therapy, we identified 638 novel CpGs with an increased methylation in response to decitabine treatment in HCT116 cell lines and validated the findings in multiple cancer types (e.g., bladder, ovarian, breast, and lymphoma) cell lines, bone marrow mononuclear cells from primary leukemia patients, as well as peripheral blood mononuclear cells and ascites from platinum resistance epithelial ovarian cancer patients. Azacytidine treatment also increased methylation of these CpGs in colon, ovarian, breast, and lymphoma cancer cell lines. Methylation at 166 identified CpGs strongly correlated (vertical bar r vertical bar >= 0.80) with corresponding gene expression in HCT116 cell line. Differences in methylation at some of the identified CpGs and expression changes of the corresponding genes was observed in TCGA colon cancer tissue as compared to adjacent healthy tissue. Our analysis revealed that hypermethylated CpGs are involved in cancer cell proliferation and apoptosis by P53 and olfactory receptor pathways, hence influencing DNMTi responses. In conclusion, we showed hypermethylation of CpGs as a novel mechanism of action for DNMTi agents and identified 638 hypermethylated molecular targets (CpGs) common to decitabine and azacytidine therapy. These novel results suggest that hypermethylation of CpGs should be considered when predicting the DNMTi responses and side effects in cancer patients.Peer reviewe

High-throughput screening for drug discovery targeting the cancer cell-microenvironment interactions in hematological cancers

Introduction The interactions between leukemic blasts and cells within the bone marrow environment affect oncogenesis, cancer stem cell survival, as well as drug resistance in hematological cancers. The importance of this interaction is increasingly being recognized as a potentially important target for future drug discoveries and developments. Recent innovations in the high throughput drug screening-related technologies, novel ex-vivo disease-models, and freely available machine-learning algorithms are advancing the drug discovery process by targeting earlier undruggable proteins, complex pathways, as well as physical interactions (e.g. leukemic cell-bone microenvironment interaction). Area covered In this review, the authors discuss the recent methodological advancements and existing challenges to target specialized hematopoietic niches within the bone marrow during leukemia and suggest how such methods can be used to identify drugs targeting leukemic cell-bone microenvironment interactions. Expert opinion The recent development in cell-cell communication scoring technology and culture conditions can speed up the drug discovery by targeting the cell-microenvironment interaction. However, to accelerate this process, collecting clinical-relevant patient tissues, developing culture model systems, and implementing computational algorithms, especially trained to predict drugs and their combination targeting the cancer cell-bone microenvironment interaction are needed.Peer reviewe

Comparison of artificial inoculation methods for studying pathogenesis of Alternaria brassicae (Berk.) Sacc on Brassica juncea (L.) Czern. (Indian mustard)

Establishment of disease by artificial inoculation is essential for studies of various aspects of plant pathology. Keeping this in mind, five inoculation methods were compared namely spraying, infiltration, wounding, spore suspension drop and spore suspension drop along with agarose method. The findings of the present study suggest that out of five inoculation methods used, spore suspension drop along with agarose inoculation method was most ideal as this fixed the inoculum on the target site. The mean value of number of initial disease lesions in drop plus agarose method was highest in all of the time intervals of observation namely 312.2, 484.2, 664.2, 734.2 and 799.2 at 24, 48, 72, 96 and 120 h after pathogen inoculation compared to other methods respectively. Statistical analysis software (SAS) was used to find out the significant comparison among the different methods. The spore suspension drop along with agarose method has the advantage of being accurate and precise, and it was also easy to handle the inoculated plants.Keywords: Artificial inoculation, Brassica, Alternaria, pathogenicity, screeningAfrican Journal of BiotechnologyVol. 12(18), pp. 2422-242

Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data

Identification of cell populations often relies on manual annotation of cell clusters using established marker genes. However, the selection of marker genes is a time-consuming process that may lead to sub-optimal annotations as the markers must be informative of both the individual cell clusters and various cell types present in the sample. Here, we developed a computational platform, ScType, which enables a fully-automated and ultra-fast cell-type identification based solely on a given scRNA-seq data, along with a comprehensive cell marker database as background information. Using six scRNA-seq datasets from various human and mouse tissues, we show how ScType provides unbiased and accurate cell type annotations by guaranteeing the specificity of positive and negative marker genes across cell clusters and cell types. We also demonstrate how ScType distinguishes between healthy and malignant cell populations, based on single-cell calling of single-nucleotide variants, making it a versatile tool for anticancer applications. The widely applicable method is deployed both as an interactive web-tool (https://sctype.app ), and as an open-source R-package.Peer reviewe

SynergyFinder 2.0 : visual analytics of multi-drug combination synergies

SynergyFinder (https://synergyfinder.fimm.fi) is a stand-alone web-application for interactive analysis and visualization of drug combination screening data. Since its first release in 2017, SynergyFinder has become a widely used web-tool both for the discovery of novel synergistic drug combinations in pre-clinical model systems (e.g. cell lines or primary patient-derived cells), and for better understanding of mechanisms of combination treatment efficacy or resistance. Here, we describe the latest version of SynergyFinder (release 2.0), which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements. A number of additional improvements were also implemented based on the user requests, including new visualization and export options, updated user interface, as well as enhanced stability and performance of the web-tool. With these improvements, SynergyFinder 2.0 is expected to greatly extend its potential applications in various areas of multi-drug combinatorial screening and precision medicine.Peer reviewe

SynergyFinder 3.0 : an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples

SynergyFinder (https://synergyfinderfimm.fi) is a free web-application for interactive analysis and visualization of multi-drug combination response data. Since its first release in 2017, SynergyFinder has become a popular tool for multi-dose combination data analytics, partly because the development of its functionality and graphical interface has been driven by a diverse user community, including both chemical biologists and computational scientists. Here, we describe the latest upgrade of this community-effort, SynergyFinder release 3.0, introducing a number of novel features that support interactive multisample analysis of combination synergy, a novel consensus synergy score that combines multiple synergy scoring models, and an improved outlier detection functionality that eliminates false positive results, along with many other post-analysis options such as weighting of synergy by drug concentrations and distinguishing between different modes of synergy (potency and efficacy). Based on user requests, several additional improvements were also implemented, including new data visualizations and export options for multi-drug combinations. With these improvements, SynergyFinder 3.0 supports robust identification of consistent combinatorial synergies for multi-drug combinatorial discovery and clinical translation. [GRAPHICS] .Peer reviewe

Removal of Arsenic (III) and Chromium (VI) from The Water Using Phytoremediation and Bioremediation Techniques

Advancement in science and technologies parallel to industrial revolution has opened new vistas to exploit the inherent traits of natural resources including green plants and microorganisms to overcome the damage to the environment by pollutants. The present work was aimed to develop the phytoremediation potential of the aquatic plant Eichhornia crassipes for arsenic (III) and chromium (VI) from water. The accumulation, relative growth and bio-concentration factor of plant on treatment with different concentrations of arsenic(III) and chromium(VI) solution significantly increased (P<0.05) with the passage of time. Plants treated with 0.100 mg/L arsenic (III) accumulated the highest concentration of arsenite in roots (7.20 mg kg-1, dry weight) and shoots (32.1 mg kg-1, dry weight); while those treated with 4.0 mg/L of chromium (VI) accumulated the highest concentration of hexavalent chromium in roots (1320 mg/kg, dry weight) and shoots (260 mg/kg, dry weight) after 15 days. The plant biomass was characterized by SEM, EDX, FTIR and XRD techniques. Microwave-assisted extraction efficiency is investigated for extraction of arsenic from plant materials by comparison of the results by three extractant solutions: (i) 10% (v/v) tetramethylammonium hydroxide (TMAH) (ii) Deionized water and (iii) Modified protein extracting solution at different temperature and times. Extraction of chromium ions was carried by same procedure from plant materials using three extractant solutions: (i) 0.02 M ethylenediaminetetraacetic acid (EDTA), (ii) Deionized water and (iii) HCl solution at different temperature and times. Chromatograms are obtained for arsenic and chromium species in plant shoot biomass by using HPLC-ICP-MS. The biosorption of arsenic (III) and chromium (VI) from water is studied by living cells of Bacillus cereus biomass as bioremediation. Bacillus cereus biomass is characterized, using SEM-EDX, AFM and FTIR. Dependence of biosorption was studied with variation of various parameters to achieve the optimum condition. The maximum biosorption capacity of living cells of Bacillus cereus for arsenic (III) and chromium (VI) was found to be 32.42 mg/g and 39.06 mg/g at pH 7.5, at optimum conditions of contact time of 30 min, biomass dosage of 6 g/L, and temperature of 30 ± 2°C. Biosorption data of arsenic (III) chromium (VI) are fitted to linearly transformed Langmuir isotherm and pseudo-second-order model with R2 (correlation vi vii coefficient) > 0.99. Thermodynamic parameters reveal the endothermic, spontaneous, and feasible nature of sorption process of arsenic (III) chromium (VI) onto Bacillus cereus biomass. The arsenic (III) and chromium (VI) ions are desorbed from Bacillus cereus using both 1M HCl and 1M HNO3. The biosorption data of both arsenic (III) and chromium (VI) ions collected from laboratory scale experimental set up is used to train a back propagation (BP) learning algorithm having 4-7-1 architecture. The model uses tangent sigmoid transfer function at input to hidden layer whereas a linear transfer function is used at output layer. The removal of chromium (VI) from aqueous solutions by activated carbon prepared from the Eichhornia crassipes root biomass. The maximum removal capacity of activated carbon was found to be 36.34 mg/g for chromium (VI), at pH 4.5, contact time of 30 min, biomass dosage of 7 g/L, and temperature of 25 ± 2 °C. The adsorption mechanisms of chromium (VI) ions onto activated carbon prepared from the Eichhornia crassipes root biomass are also evaluated in terms of thermodynamics, equilibrium isotherm and kinetics studies. Column studies are also performed to know the breakthrough point with an initial concentration of 10 mg/L