Hawaii Macadamia Nut Company

Owners of the Hawaii Macadamia Nut Company (HMNC) are facing an expansion opportunity. A land owner has property available that would enable the HMNC to expand its acreage and revenue by about 20%. To fully consider this opportunity the owners must decide 1) whether the expansion is strategically and financially viable, 2) how to raise capital to finance the expansion, and 3) whether they have the skills to manage the company's growth during expansion. This is a case study describing a real company facing a real opportunity in Hawaii. The names of the company and its principals have been disguised

Hawaii Macadamia Nut Company- A Case Study

Owners of the Hawaii Macadamia Nut Company (HMNC) are facing an expansion opportunity. A land owner has preperty available that would enable the HMNC to expand its acreage and revenue by about 20%. To fully consider this opportunity the owners must decide 1)whether the expansion is strategically and financially viable, 2)how to raise capital to finance the expansion, and 3)whether they have the skills to manage the company\u27s growth during expansion. This is a case study describing a real company facing a real opportunity in Hawaii. The names of the company and its principals have been disguised

Functional brain network architecture supporting the learning of social networks in humans

Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. From the behavioral data in both tasks, we found that learners were sensitive to the community structure of the networks, as evidenced by a slower reaction time on trials transitioning between clusters than on trials transitioning within a cluster. From the neuroimaging data collected during the social network learning task, we observed that the functional connectivity of the hippocampus and temporoparietal junction was significantly greater when transitioning between clusters than when transitioning within a cluster. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions during the social task than during the non-social task. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies

Routine activities and proactive police activity: a macro-scale analysis of police searches in London and New York City

This paper explored how city-level changes in routine activities were associated with changes in frequencies of police searches using six years of police records from the London Metropolitan Police Service and the New York City Police Department. Routine activities were operationalised through selecting events that potentially impacted on (a) the street population, (b) the frequency of crime or (c) the level of police activity. OLS regression results indicated that routine activity variables (e.g. day of the week, periods of high demand for police service) can explain a large proportion of the variance in search frequency throughout the year. A complex set of results emerged, revealing cross-national dissimilarities and the differential impact of certain activities (e.g. public holidays). Importantly, temporal frequencies in searches are not reducible to associations between searches and recorded street crime, nor changes in on-street population. Based on the routine activity approach, a theoretical police-action model is proposed

Nitrate Biogeochemistry and Reactive Transport in California Groundwater: LDRD Final Report

Nitrate is the number one drinking water contaminant in the United States. It is pervasive in surface and groundwater systems,and its principal anthropogenic sources have increased dramatically in the last 50 years. In California alone, one third of the public drinking-water wells has been lost since 1988 and nitrate contamination is the most common reason for abandonment. Effective nitrate management in groundwater is complicated by uncertainties related to multiple point and non-point sources, hydrogeologic complexity, geochemical reactivity, and quantification of denitrification processes. In this paper, we review an integrated experimental and simulation-based framework being developed to study the fate of nitrate in a 25 km-long groundwater subbasin south of San Jose, California, a historically agricultural area now undergoing rapid urbanization with increasing demands for groundwater. The modeling approach is driven by a need to integrate new and archival data that support the hypothesis that nitrate fate and transport at the basin scale is intricately related to hydrostratigraphic complexity, variability of flow paths and groundwater residence times, microbial activity, and multiple geochemical reaction mechanisms. This study synthesizes these disparate and multi-scale data into a three-dimensional and highly resolved reactive transport modeling framework

Trichostatin A enhances acetylation as well as protein stability of ERα through induction of p300 protein

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Abstract Introduction Trichostatin A (TSA) is a well-characterized histone deacetylase (HDAC) inhibitor. TSA modifies the balance between HDAC and histone acetyltransferase activities that is important in chromatin remodeling and gene expression. Although several previous studies have demonstrated the role of TSA in regulation of estrogen receptor alpha (ERα), the precise mechanism by which TSA affects ERα activity remains unclear. Methods Transient transfection was performed using the Welfect-EX™Plus procedure. The mRNA expression was determined using RT-PCR. Protein expression and interaction were determined by western blotting and immunoprecipitation. The transfection of siRNAs was performed using the Oligofectamine™ reagent procedure. Results TSA treatment increased acetylation of ERα in a dose-dependent manner. The TSA-induced acetylation of ERα was accompanied by an increased stability of ERα protein. Interestingly, TSA also increased the acetylation and the stability of p300 protein. Overexpression of p300 induced acetylation and stability of ERα by blocking ubiquitination. Knockdown of p300 by RNA interference decreased acetylation as well as the protein level of ERα, indicating that p300 mediated the TSA-induced stabilization of ERα. Conclusions We report that TSA enhanced acetylation as well as the stability of the ERα protein by modulating stability of p300. These results may provide the molecular basis for pharmacological functions of HDAC inhibitors in the treatment of human breast cancer

Large-Scale Gene Disruption in Magnaporthe oryzae Identifies MC69, a Secreted Protein Required for Infection by Monocot and Dicot Fungal Pathogens

To search for virulence effector genes of the rice blast fungus, Magnaporthe oryzae, we carried out a large-scale targeted disruption of genes for 78 putative secreted proteins that are expressed during the early stages of infection of M. oryzae. Disruption of the majority of genes did not affect growth, conidiation, or pathogenicity of M. oryzae. One exception was the gene MC69. The mc69 mutant showed a severe reduction in blast symptoms on rice and barley, indicating the importance of MC69 for pathogenicity of M. oryzae. The mc69 mutant did not exhibit changes in saprophytic growth and conidiation. Microscopic analysis of infection behavior in the mc69 mutant revealed that MC69 is dispensable for appressorium formation. However, mc69 mutant failed to develop invasive hyphae after appressorium formation in rice leaf sheath, indicating a critical role of MC69 in interaction with host plants. MC69 encodes a hypothetical 54 amino acids protein with a signal peptide. Live-cell imaging suggested that fluorescently labeled MC69 was not translocated into rice cytoplasm. Site-directed mutagenesis of two conserved cysteine residues (Cys36 and Cys46) in the mature MC69 impaired function of MC69 without affecting its secretion, suggesting the importance of the disulfide bond in MC69 pathogenicity function. Furthermore, deletion of the MC69 orthologous gene reduced pathogenicity of the cucumber anthracnose fungus Colletotrichum orbiculare on both cucumber and Nicotiana benthamiana leaves. We conclude that MC69 is a secreted pathogenicity protein commonly required for infection of two different plant pathogenic fungi, M. oryzae and C. orbiculare pathogenic on monocot and dicot plants, respectively

How Might Crime-Scripts Be Used to Support the Understanding and Policing of Cloud Crime?

Crime scripts are becoming an increasingly popular method for understanding crime by turning a crime from a static event into a process, whereby every phase of the crime is scripted. It is based on the work relating to cognitive scripts and rational-choice theory. With the exponential growth of cyber-crime, and more specifically cloud-crime, policing/law enforcement agencies are struggling with the amount of reported cyber-crime. This paper argues that crime scripts are the most effective way forward in terms of helping understand the behaviour of the criminal during the crime itself. They act as a common language between different stakeholders, focusing attention and resources on the key phases of a crime. More importantly, they shine a light on the psychological element of a crime over the more technical cyber-related elements. The paper concludes with an example of what a cloud-crime script might look like, asking future research to better understand: (i) cloud criminal fantasy development; (ii) the online cultures around cloud crime; (iii) how the idea of digital-drift affects crime scripts, and; (iv) to improve on the work by Ekblom and Gill in improving crime scripts

Disease Gene Characterization through Large-Scale Co-Expression Analysis

In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET).Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.UGET is an invaluable resource for a geneticist that permits the rapid inclusion of expression criteria from one to hundreds of genes in genomic intervals linked to disease. By using thousands of arrays UGET annotates and prioritizes genes better than other tools especially with rare tissue disorders or complex multi-tissue biological processes. This information can be critical in prioritization of candidate genes for sequence analysis