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In this article the authors propose an appropriate commodities benchmark for pension funds. Commodity investment, like traditional investment, requires suitable benchmarks for performance measurement and attribution. Investing in commodities as an asset class has increased dramatically as a result of financialization. It is typically conducted by pension funds through the futures market. The tools to implement and benchmark the success or failure of such an investment strategy are still in their infancy. The authors argue that factor model indexes (FMIs) are a viable alternative to existing production-based indexes that make use of futures contracts. Using principal component analysis, they identify five factors related to grains, meats, industrial metals, energy, and precious metals. They suggest that FMI benchmarks constructed using such an approach would allow commodity investors to better measure their investment objectives. Key Findings ▪ Proposed commodity factor model index (FMI) derived from principal components for use by institutional investors. ▪ The FMI addresses the issue that the market capitalization concept does not make sense when applied to commodities. ▪ The FMI is a viable alternative to existing production-based indexes that make use of futures contracts.

Elucidating brain cell type specific gene expression patterns is critical towards a better understanding of how cell-cell communications may influence brain functions and dysfunctions. We set out to compare and contrast five human and murine cell type-specific transcriptome-wide RNA expression data sets that were generated within the past several years. We defined three measures of brain cell type-relative expression including specificity, enrichment, and absolute expression and identified corresponding consensus brain cell "signatures," which were well conserved across data sets. We validated that the relative expression of top cell type markers are associated with proxies for cell type proportions in bulk RNA expression data from postmortem human brain samples. We further validated novel marker genes using an orthogonal ATAC-seq dataset. We performed multiscale coexpression network analysis of the single cell data sets and identified robust cell-specific gene modules. To facilitate the use of the cell type-specific genes for cell type proportion estimation and deconvolution from bulk brain gene expression data, we developed an R package, BRETIGEA. In summary, we identified a set of novel brain cell consensus signatures and robust networks from the integration of multiple datasets and therefore transcend limitations related to technical issues characteristic of each individual study.

Analyzing governance structure is considered important when designing development interventions in the value chain. The objective of this paper was to explore the governance structure (GS) in cauliflower and tomato value chain, specifically the types of GS followed by the farmers and factors behind these choices. The two-stage random sampling procedure was used to collect data from 100 farmers whereas 60 traders were surveyed purposively for this study. Multinomial logit model was used to analyze the factors affecting farmer’s choice of GS. Trader’s perceptions regarding internal and external governance as well as good governance indicators were assessed by utilizing non-parametric test. Cauliflower and tomato farmers were involved in practicing spot market, relational, farmer’s association and outgrowing scheme-based GS. The results showed that the probability to choose relational, outgrowing scheme and farmer’s association-based GS were significantly affected by education of farmers, distance to the nearest market, access to extension services, access to price information, trust commitment, transaction specific investment, preferences of diversification, farm size, transactional uncertainty, and access to producer’s cooperative. Wilcoxon-Mann-Whitney-U test established trader’s dissimilation towards governance indicators. The study suggests that smallholder farmers need to be well organized and coordination mechanisms among all stakeholders should be increased.

Genetic and genomic studies have advanced our knowledge of inherited Parkinson’s disease (PD), however, the etiology and pathophysiology of idiopathic PD remain unclear. Herein, we perform a meta-analysis of 8 PD postmortem brain transcriptome studies by employing a multiscale network biology approach to delineate the gene-gene regulatory structures in the substantia nigra and determine key regulators of the PD transcriptomic networks. We identify STMN2, which encodes a stathmin family protein and is down-regulated in PD brains, as a key regulator functionally connected to known PD risk genes. Our network analysis predicts a function of human STMN2 in synaptic trafficking, which is validated in Stmn2-knockdown mouse dopaminergic neurons. Stmn2 reduction in the mouse midbrain causes dopaminergic neuron degeneration, phosphorylated α-synuclein elevation, and locomotor deficits. Our integrative analysis not only begins to elucidate the global landscape of PD transcriptomic networks but also pinpoints potential key regulators of PD pathogenic pathways., Parkinson’s disease (PD) is characterized by neurodegeneration associated with loss of dopaminergic (DA) neurons and deposition of Lewy bodies. Here, Wang et al. use co-expression network analysis to pinpoint disease pathways and propose reduced expression of STMN2 as a cause of presynaptic function loss in PD.

Understanding the transcriptional changes that are engaged in stress resilience may reveal novel antidepressant targets. Here we use gene co-expression analysis of RNA-sequencing data from brains of resilient mice to identify a gene network that is unique to resilience. Zfp189, which encodes a previously unstudied zinc finger protein, is the highest-ranked key driver gene in the network, and overexpression of Zfp189 in prefrontal cortical neurons preferentially activates this network and promotes behavioral resilience. The transcription factor CREB is a predicted upstream regulator of this network and binds to the Zfp189 promoter. To probe CREB-Zfp189 interactions, we employ CRISPR-mediated locus-specific transcriptional reprogramming to direct CREB or G9a (a repressive histone methyltransferase) to the Zfp189 promoter in prefrontal cortex neurons. Induction of Zfp189 with site-specific CREB is pro-resilient, whereas suppressing Zfp189 expression with G9a increases susceptibility. These findings reveal an essential role for Zfp189 and CREB-Zfp189 interactions in mediating a central transcriptional network of resilience.

Barges on the Mississippi River system are traded via a forward market. We find that risk premiums to use this market vary by season and in terms of which party bares the cost of forward contracting. During the summer season barge operators pay a risk premium. Yet during the rest of the year, elevator operators pay a premium to lock in rates.

The CIF NOLA “river market” represents an important but opaque forward market that serves Gulf exporters and elevators. CIF NOLA bids function similarly to traditional forward contracts; however, like a futures market, firms can offset their forward contractual obligations by offsetting positions in a liquid off-exchange paper market. Analysis shows grain sellers pay a risk premium for fall harvest delivery contracts. However, outside of fall harvest, contract liquidity, coupled with a good institutional balance of long and short market participants, mostly removes the pricing bias commonly found in farmer forward contracting in corn and soybeans.

Genetic factors are known to contribute to Late Onset Alzheimer’s disease (LOAD) but their contribution to pathophysiology, specially to prodomic phases accessible to therapeutic approaches are far to be understood. To translate genetic risk of Alzheimer's disease (AD) into mechanistic insight, we generated transgenic mouse lines that express a ~195 kbp human BAC that includes only BIN1, a gene associated to LOAD. This model gives a modest BIN1 overexpression, dependent of the number of BAC copies. At 6 months of age, we detected impaired entorhinal cortex (EC)-hippocampal pathways with specific impairments in EC-dentate gyrus synaptic long-term potentiation, dendritic spines of granular cells and recognition episodic memory. Structural changes were quantified using MRI. Their whole-brain functional impact were analyzed using resting state fMRI with a hypoconnectivity centered on entorhinal cortex. These early phenotype defects independent of any changes in A-beta can be instrumental in the search for new AD drug targets.

A genome-wide survival analysis of 14,406 Alzheimer's disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and 14 novel loci associated with age at onset. Linkage disequilibrium score regression of 220 cell types implicated the regulation of myeloid gene expression in AD risk. The minor allele of rs1057233 (G), within the previously reported CELF1 AD risk locus, showed association with delayed AD onset and lower expression of SPI1 in monocytes and macrophages. SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function. AD heritability was enriched within the PU.1 cistrome, implicating a myeloid PU.1 target gene network in AD. Finally, experimentally altered PU.1 levels affected the expression of mouse orthologs of many AD risk genes and the phagocytic activity of mouse microglial cells. Our results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.

Background Perfusing fixatives through the cerebrovascular system is the gold standard approach in animals to prepare brain tissue for spatial biomolecular profiling, circuit tracing, and ultrastructural studies such as connectomics. Translating these discoveries to humans requires examination of postmortem autopsy brain tissue. Yet banked brain tissue is routinely prepared using immersion fixation, which is a significant barrier to optimal preservation of tissue architecture. The challenges involved in adopting perfusion fixation in brain banks and the extent to which it improves histology quality are not well defined. Methodology We searched four databases to identify studies that have performed perfusion fixation in human brain tissue and screened the references of the eligible studies to identify further studies. From the included studies, we extracted data about the methods that they used, as well as any data comparing perfusion fixation to immersion fixation. The protocol was preregistered at the Open Science Framework: https://osf.io/cv3ys/. Results We screened 4489 abstracts, 214 full-text publications, and identified 35 studies that met our inclusion criteria, which collectively reported on the perfusion fixation of 558 human brains. We identified a wide variety of approaches to perfusion fixation, including perfusion fixation of the brain in situ and ex situ, perfusion fixation through different sets of blood vessels, and perfusion fixation with different washout solutions, fixatives, perfusion pressures, and postfixation tissue processing methods. Through a qualitative synthesis of data comparing the outcomes of perfusion and immersion fixation, we found moderate confidence evidence showing that perfusion fixation results in equal or greater subjective histology quality compared to immersion fixation of relatively large volumes of brain tissue, in an equal or shorter amount of time. Conclusions This manuscript serves as a resource for investigators interested in building upon the methods and results of previous research in designing their own perfusion fixation studies in human brains or other large animal brains. We also suggest several future research directions, such as comparing the in situ and ex situ approaches to perfusion fixation, studying the efficacy of different washout solutions, and elucidating the types of brain donors in which perfusion fixation is likely to result in higher fixation quality than immersion fixation. Electronic supplementary material The online version of this article (10.1186/s40478-019-0799-y) contains supplementary material, which is available to authorized users.

Innate immunity is associated with Alzheimer’s disease1, but the influence of immune activation on the production of amyloid-β is unknown2,3. Here we identify interferon-induced transmembrane protein 3 (IFITM3) as a γ-secretase modulatory protein, and establish a mechanism by which inflammation affects the generation of amyloid-β. Inflammatory cytokines induce the expression of IFITM3 in neurons and astrocytes, which binds to γ-secretase and upregulates its activity, thereby increasing the production of amyloid-β. The expression of IFITM3 is increased with ageing and in mouse models that express familial Alzheimer’s disease genes. Furthermore, knockout of IFITM3 reduces γ-secretase activity and the formation of amyloid plaques in a transgenic mouse model (5xFAD) of early amyloid deposition. IFITM3 protein is upregulated in tissue samples from a subset of patients with late-onset Alzheimer’s disease that exhibit higher γ-secretase activity. The amount of IFITM3 in the γ-secretase complex has a strong and positive correlation with γ-secretase activity in samples from patients with late-onset Alzheimer’s disease. These findings reveal a mechanism in which γ-secretase is modulated by neuroinflammation via IFITM3 and the risk of Alzheimer’s disease is thereby increased. The IFITM3 innate immunity protein directly binds presenilin near the active site and upregulates γ-secretase activity and the production of amyloid-β, and IFITM3 is upregulated in patients with late-onset Alzheimer’s disease.

Commodity futures markets play an important role, through risk management and price discovery, in helping firms make sustainable production and marketing decisions. An important related issue is how pricing signals between futures exchanges impact traders&rsquo, risk. We address this issue by shedding light on risk transmission between the most mature (U.S.) and the fastest growing (Chinese) commodity futures markets. Gaining greater insight of risk transmission between these key markets is vitally important to firms engaged in the efficient and sustainable trade of commodities needed to feed the world. We examine the risk transmission between Chinese and U.S. agricultural futures markets for soybean, corn, and sugar with a Copula based conditional value at risk (CoVaR) approach. We find significant upside, and to a lesser extent downside risk transmission, between Chinese and U.S. markets. We confirm the dominant pricing role of U.S. agricultural futures markets while acknowledging the increasing price discovery role performed by Chinese markets. Our results highlight that soybean markets exhibit greater risk transmission than sugar and corn markets. We argue that our findings may be explained by Chinese government policy intervention, and by the large role played by U.S. firms in the underlying cash commodity markets&ndash, both in terms of production and trade.

There is a widely held belief among public consumers that rice prices are manipulated in Bangladesh. This manipulation may have led to price asymmetry in the vertical chain of Bangladesh rice markets. This paper is an attempt to investigate the existence of asymmetry between wholesale and retail rice prices in Bangladesh. Maximum likelihood estimation (MLE) based cointegration test was applied to determine long-run equilibrium relationship. We examine whether the wholesale market dominates the retail market—in terms of price discovery and price leadership—or vice versa. Finally, we analyze whether the wholesale-retail price relationship is asymmetric with respect to price increases and price decreases. To test the asymmetric price transmission we used the asymmetric error correction-EG approach. Our results show that wholesale and retail prices are cointegrated, and wholesale market plays a leadership role in determining retail prices, which is in line with industrial organization theory. Our results confirm the fear and concerns of consumers about the existence of price asymmetry. [JEL Classification: Q110; Q113].

Late Onset Alzheimer Disease (LOAD) is the most common form of dementia and one of the most challenging diseases of modern society. Understanding the preclinical stages of AD that begins in the brain at least 2 to 3 decades before evidence of episodic memory defects in patients is pivotal for the design of successful approaches to delay or reverse the transition from normal brain function to cognitive impairments. Our working hypothesis is that LOAD genetic risk factors can be sufficient to generate early phenotypical changes before any changes in either Abeta or Tau. To test this hypothesis, we generated an hBIN1 mouse model based on the human BIN1 gene overexpression that we found in post-mortem brain samples from LOAD patients. BIN1 is the second important risk factor for AD, following the APOE gene. We identified co-deregulated gene repertoires common to both 7-week mouse hippocampus sub-regions and post-mortem brain samples from LOAD patients, demonstrating the validity of this hBIN1 model. We evidenced an early phenotype of neurodegeneration starting at 3 months with structural impairment fiber pathways quantified by high resolution (17.2T) (MRI DTI) and related functional impacts. We found structural changes in entorhinal cortex-dentate gyrus (EC-DG) pathway known to be the earliest brain region impacted in LOAD. Similarly, the function of this pathway was impaired both in vitro and in vivo, with the changes in spine density and dendritic simplification of DG neurons, impaired EC-DG long-term potentiation (LTP) and behavioral deficits linked to object recognition episodic memory. As expected for a neurodegenerative model, we evidenced the progression of dysfunction at the morphological, functional and behavioral levels with age. Structural spreading involved impairment of fibers in somatosensory and temporal associative cortexes at month 15. Functional and behavioral spreading was characterized by impact on pattern separation of spatial episodic memory. Moreover, this neurodegeneration occurred without any detectable changes in Abeta 1-42 and tau. Overall, these data show the possibility to identify a repertoire of molecular changes occurring both in patients and in hBIN1 mice and whose further manipulation can be instrumental to rescue or delay episodic memory defects.

SummaryStress resilience involves numerous brain-wide transcriptional changes. Determining the organization and orchestration of these transcriptional events may reveal novel antidepressant targets, but this remains unexplored. Here, we characterize the resilient transcriptome with co-expression analysis and identify a single transcriptionally-active uniquely-resilient gene network. Zfp189, a previously unstudied zinc finger protein, is the top network key driver and its overexpression in prefrontal cortical (PFC) neurons preferentially activates this network, alters neuronal activity and promotes behavioral resilience. CREB, which binds Zfp189, is the top upstream regulator of this network. To probe CREB-Zfp189 interactions as a network regulatory mechanism, we employ CRISPR-mediated locus-specific transcriptional reprogramming to direct CREB selectively to the Zfp189 promoter. This single molecular interaction in PFC neurons recapitulates the pro-resilient Zfp189-dependent downstream effects on gene network activity, electrophysiology and behavior. These findings reveal an essential role for Zfp189 and a CREB-Zfp189 regulatory axis in mediating a central transcriptional network of resilience.

Elucidating brain cell type specific gene expression patterns is critical towards a better understanding of how cell-cell communications may influence brain functions and dysfunctions. We set out to compare and contrast five human and murine cell type-specific transcriptome-wide RNA expression data sets that were generated within the past several years. We defined three measures of brain cell type-relative expression including specificity, enrichment, and absolute expression and identified corresponding consensus brain cell “signatures,” which were well conserved across data sets. We validated that the relative expression of top cell type markers are associated with proxies for cell type proportions in bulk RNA expression data from postmortem human brain samples. We further validated novel marker genes using an orthogonal ATAC-seq dataset. We performed multiscale coexpression network analysis of the single cell data sets and identified robust cell-specific gene modules. To facilitate the use of the cell type-specific genes for cell type proportion estimation and deconvolution from bulk brain gene expression data, we developed an R package, BRETIGEA. In summary, we identified a set of novel brain cell consensus signatures and robust networks from the integration of multiple datasets and therefore transcend limitations related to technical issues characteristic of each individual study.

Background Oligodendrocytes (OLs) and myelin are critical for normal brain function and have been implicated in neurodegeneration. Several lines of evidence including neuroimaging and neuropathological data suggest that Alzheimer’s disease (AD) may be associated with dysmyelination and a breakdown of OL-axon communication. Methods In order to understand this phenomenon on a molecular level, we systematically interrogated OL-enriched gene networks constructed from large-scale genomic, transcriptomic and proteomic data obtained from human AD postmortem brain samples. We then validated these networks using gene expression datasets generated from mice with ablation of major gene expression nodes identified in our AD-dysregulated networks. Results The robust OL gene coexpression networks that we identified were highly enriched for genes associated with AD risk variants, such as BIN1 and demonstrated strong dysregulation in AD. We further corroborated the structure of the corresponding gene causal networks using datasets generated from the brain of mice with ablation of key network drivers, such as UGT8, CNP and PLP1, which were identified from human AD brain data. Further, we found that mice with genetic ablations of Cnp mimicked aspects of myelin and mitochondrial gene expression dysregulation seen in brain samples from patients with AD, including decreased protein expression of BIN1 and GOT2. Conclusions This study provides a molecular blueprint of the dysregulation of gene expression networks of OL in AD and identifies key OL- and myelination-related genes and networks that are highly associated with AD. Electronic supplementary material The online version of this article (10.1186/s13024-017-0219-3) contains supplementary material, which is available to authorized users.

We extract low- and high-frequency volatility from China’s Shanghai gold futures market using an asymmetric Spline-GARCH (ASP-GARCH) model. We then regress monthly low-frequency volatility on selected monthly macroeconomic indicators to study the impact of macroeconomy on gold futures market and to test for excess volatility. Our main result is volatility in China’s Shanghai gold futures market resulting from both macroeconomic fluctuations and investor behaviour. Chinese Consumer Price Index Volatility and US dollar volatility are the two main determinants of low-frequency gold volatility. We also find significant evidence of excess volatility, which can in part be explained in terms of loss-aversive investor behaviour.

A genome-wide survival analysis of 14,406 Alzheimer’s disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and fourteen novel loci associated with age at onset. LD score regression of 220 cell types implicated regulation of myeloid gene expression in AD risk. In particular, the minor allele of rs1057233 (G), within the previously reported CELF1 AD risk locus, showed association with delayed AD onset and lower expression of SPI1 in monocytes and macrophages. SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function. AD heritability is enriched within the PU.1 cistrome, implicating a myeloid PU.1 target gene network in AD. Finally, experimentally altered PU.1 levels affect the expression of mouse orthologs of many AD risk genes and the phagocytic activity of mouse microglial cells. Our results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.

This paper offers a holistic study on the complex relationships between crude oil, corn and ethanol during a turbulent period between 2006 and end of 2011. Through a holistic mapping of the current market situation and a contextual analytical design we show that there exists a strong relationship between crude oil and corn markets on one side, and crude oil and ethanol on the other. However, the price relationship between corn and ethanol was revealed to be less straightforward, and is driven by the US government fuel policy. Furthermore the study indicates that corn markets have became more prone to volatility due to ethanol production, especially when the demand for corn is high and/or the crude oil prices are high enough to create a competitive market for ethanol.

Purpose – Pricing densities implied from options on live cattle futures show a persistent and negative skew. The purpose is to examine whether the skew can be explained, in part, by peso-type problems. Design/methodology/approach – Two announcements of bovine spongiform encephalopathy (BSE) provide a natural setting within which to examine the validity of the peso-problem explanation. These announcements represent the first documented cases of BSE in North America. Prior to the announcements, the potential for BSE would have been known by market participants as the disease had been found among cattle in the British Isles, Europe and Asia. The paper uses options market data to compute implied moments of the pricing distribution for live cattle futures. The paper then analyzes these moments around BSE announcements. Findings – The first Canadian BSE announcement impacted the mean and volatility but not the implied skew. Later in the year, BSE was found in a US cow and the paper finds a statistically significant change in the implied skew. The distribution showed a pronounced leftward skew prior to the US announcement but was nearly symmetric during the days afterwards. This finding is consistent with the market having priced the possibility of a BSE discovery into deep out-of-the-money put options. Originality/value – Peso problems have been documented in other financial markets. The results are important because they suggest that they may also be important to agricultural markets and that agricultural options markets do account for low probability but highly important events.

The landscape of drug abuse is shifting. Traditional means of characterizing these changes, such as national surveys or voluntary reporting by frontline clinicians, can miss changes in usage the emergence of novel drugs. Delays in detecting novel drug usage patterns make it difficult to evaluate public policy aimed at altering drug abuse. Increasingly, newer methods to inform frontline providers to recognize symptoms associated with novel drugs or methods of administration are needed. The growth of social networks may address this need. The objective of this manuscript is to introduce tools for using data from social networks to characterize drug abuse. We outline a structured approach to analyze social media in order to capture emerging trends in drug abuse by applying powerful methods from artificial intelligence, computational linguistics, graph theory, and agent-based modeling. First, we describe how to obtain data from social networks such as Twitter using publicly available automated programmatic interfaces. Then, we discuss how to use artificial intelligence techniques to extract content useful for purposes of toxicovigilance. This filtered content can be employed to generate real-time maps of drug usage across geographical regions. Beyond describing the real-time epidemiology of drug abuse, techniques from computational linguistics can uncover ways that drug discussions differ from other online conversations. Next, graph theory can elucidate the structure of networks discussing drug abuse, helping us learn what online interactions promote drug abuse and whether these interactions differ among drugs. Finally, agent-based modeling relates online interactions to psychological archetypes, providing a link between epidemiology and behavior. An analysis of social media discussions about drug abuse patterns with computational linguistics, graph theory, and agent-based modeling permits the real-time monitoring and characterization of trends of drugs of abuse. These tools provide a powerful complement to existing methods of toxicovigilance.

Background Dissecting the regulatory relationships between genes is a critical step towards building accurate predictive models of biological systems. A powerful approach towards this end is to systematically study the differences in correlation between gene pairs in more than one distinct condition. Results In this study we develop an R package, DGCA (for Differential Gene Correlation Analysis), which offers a suite of tools for computing and analyzing differential correlations between gene pairs across multiple conditions. To minimize parametric assumptions, DGCA computes empirical p-values via permutation testing. To understand differential correlations at a systems level, DGCA performs higher-order analyses such as measuring the average difference in correlation and multiscale clustering analysis of differential correlation networks. Through a simulation study, we show that the straightforward z-score based method that DGCA employs significantly outperforms the existing alternative methods for calculating differential correlation. Application of DGCA to the TCGA RNA-seq data in breast cancer not only identifies key changes in the regulatory relationships between TP53 and PTEN and their target genes in the presence of inactivating mutations, but also reveals an immune-related differential correlation module that is specific to triple negative breast cancer (TNBC). Conclusions DGCA is an R package for systematically assessing the difference in gene-gene regulatory relationships under different conditions. This user-friendly, effective, and comprehensive software tool will greatly facilitate the application of differential correlation analysis in many biological studies and thus will help identification of novel signaling pathways, biomarkers, and targets in complex biological systems and diseases. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0349-1) contains supplementary material, which is available to authorized users.

Background Alzheimer’s disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. However, despite extensive clinical and genomic studies, the molecular basis of AD development and progression remains elusive. Methods To elucidate molecular systems associated with AD, we developed a large scale gene expression dataset from 1053 postmortem brain samples across 19 cortical regions of 125 individuals with a severity spectrum of dementia and neuropathology of AD. We excluded brain specimens that evidenced neuropathology other than that characteristic of AD. For the first time, we performed a pan-cortical brain region genomic analysis, characterizing the gene expression changes associated with a measure of dementia severity and multiple measures of the severity of neuropathological lesions associated with AD (neuritic plaques and neurofibrillary tangles) and constructing region-specific co-expression networks. We rank-ordered 44,692 gene probesets, 1558 co-expressed gene modules and 19 brain regions based upon their association with the disease traits. Results The neurobiological pathways identified through these analyses included actin cytoskeleton, axon guidance, and nervous system development. Using public human brain single-cell RNA-sequencing data, we computed brain cell type-specific marker genes for human and determined that many of the abnormally expressed gene signatures and network modules were specific to oligodendrocytes, astrocytes, and neurons. Analysis based on disease severity suggested that: many of the gene expression changes, including those of oligodendrocytes, occurred early in the progression of disease, making them potential translational/treatment development targets and unlikely to be mere bystander result of degeneration; several modules were closely linked to cognitive compromise with lesser association with traditional measures of neuropathology. The brain regional analyses identified temporal lobe gyri as sites associated with the greatest and earliest gene expression abnormalities. Conclusions This transcriptomic network analysis of 19 brain regions provides a comprehensive assessment of the critical molecular pathways associated with AD pathology and offers new insights into molecular mechanisms underlying selective regional vulnerability to AD at different stages of the progression of cognitive compromise and development of the canonical neuropathological lesions of AD. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0355-3) contains supplementary material, which is available to authorized users.

Recent studies have indicated that innate immune signalling molecules are involved in late-onset Alzheimer's disease (LOAD) risk. Amyloid beta (Aβ) accumulates in AD brain, and has been proposed to act as a trigger of innate immune responses. Caspase-4 is an important part of the innate immune response. We recently characterized transgenic mice carrying human CASP4, and observed that the mice manifested profound innate immune responses to lipopolysaccharide (LPS). Since these inflammatory processes are important in the aetiology of AD, we have now analysed the correlation of expression of caspase-4 in human brain with AD risk genes, and studied caspase-4 effects on AD-related phenotypes in APPswe/PS1deltaE9 (APP/PS1) mice. We observed that the expression of caspase-4 was strongly correlated with AD risk genes including TYROBP, TREM2, CR1, PSEN1, MS4A4A and MS4A6A in LOAD brains. Caspase-4 expression was upregulated in CASP4/APP/PS1 mice in a region-specific manner, including hippocampus and prefrontal cortex. In APP/PS1 mice, caspase-4 expression led to impairments in the reversal phase of a Barnes maze task and in hippocampal synaptic plasticity, without affecting soluble or aggregated Aβ levels. Caspase-4 was expressed predominantly in microglial cells, and in the presence of CASP4, more microglia were clustered around amyloid plaques. Furthermore, our data indicated that caspase-4 modulates microglial cells in a manner that increases proinflammatory processes. We propose that microglial caspase-4 expression contributes to the cognitive impairments in AD, and that further study of caspase-4 will enhance our understanding of AD pathogenesis and may lead to novel therapeutic targets in AD.

PA-U2, an engineered anthrax protective antigen that is activated by urokinase was combined with wild-type lethal factor in the treatment of Colo205 colon adenocarcinoma in vitro and B16-BL6 mouse melanoma in vitro and in vivo. This therapy was also tested in combination with the small molecule paclitaxel, based on prior reports suggesting synergy between ERK1/2 inhibition and chemotherapeutics. Colo205 was sensitive to PA-U2/LF while B16-BL6 was not. For the combination treatment of B16-BL6, paclitaxel showed a dose response in vitro, but cells remained resistant to PA-U2/LF even in the presence of paclitaxel. In vivo, each therapy slowed tumor progression, and an additive effect between the two was observed. Since LF targets tumor vasculature while paclitaxel is an anti-mitotic, it is possible the agents were acting against different cells in the stroma, precluding a synergistic effect. The engineered anthrax toxin PA-U2/LF warrants further development and testing, possibly in combination with an anti-angiogenesis therapy such as sunitinib or sorafinib.

The article examines the dynamic relationship between the world and the domestic market price of rice for Bangladesh given agricultural trade liberalization. A Johansen multivariate cointegration test was used, followed by an error correction model. Results show that there exists a long-run unidirectional equilibrium relationship, meaning that the domestic prices adjust to the world prices but not vice versa. Our results highlight the dependence of the Bangladeshi rice market on the world rice market and underline the need for adequate policies which specifically address the issue of food security when world prices are very high. The goal of such policies should be to dampen or reduce domestic price volatility induced by the world market.

This study created an experimental design with which students can empirically assess their risk behavior with respect to exam grades within an expected utility framework. Specifically, the authors analyzed students’ risk preferences associated with taking exams and earning a “risky” unknown grade versus not taking exams and instead obtaining a “sure” grade. Students have grade-choice decisions in nonhypothetical situations that impact their actual exam grades. Estimates indicate that the more risk-averse a student is, the more willing he or she is to accept a lower certain grade and not take an exam than to run the risk of actually taking it. We believe that this experimental setup and its binding results make it an easy but effective way of teaching the obtuse concept of risk aversion.

Even though significant attempts have appeared in literature, the current perception of co-movement of commodity prices appear inadequate and static. In particular we focus on price movements between crude oil futures and a series of agricultural commodities and gold futures. A comparative framework is applied to identify changes in relationships through time and various cointegration methodologies and causality tests are employed. Our results indicate that co-movement is a dynamic concept and that some economic and policy development may change the relationship between commodities. Furthermore we show that biofuel policy buffers the co-movement of crude oil and corn futures until the crude oil prices surpass a certain threshold.