Haiti: coffee and mango production in a changing climate

Coffee and mango contribute significantly to Haiti’s agricultural gross domestic product and export revenues. Generating income valued at US$11 million in 2011, mango has become one of the country’s most important export commodities. In contrast, coffee exports steadily declined from$7 million to $1 million between 2000 and 2010, even though demand for high-quality Haitian coffee has actually increased on the global market. A recent study conducted by the International Center for Tropical Agriculture (CIAT) and Catholic Relief Services (CRS) revealed that future changes in temperature and rainfall patterns will have significant effects on the suitability of coffee and mango for production in Haiti. While mango will continue to be highly viable, coffee will become considerably less suitable for production at lower elevations. Changing climatic conditions could also lower quality and yields in current coffee-producing regions, such as Plaisance and Dondon in the North and Beaumont in the Southwest. To cope with the challenges that coffee and mango growers are likely to face, it will be important to promote the diversification of agricultural systems, introduce improved coffee varieties, offer financial incentives to adopt sustainable land use practices, build capacity among smallholders, and foster the sharing of expertise

Machine learning predicts new anti-CRISPR proteins

Abstract The increasing use of CRISPR–Cas9 in medicine, agriculture, and synthetic biology has accelerated the drive to discover new CRISPR–Cas inhibitors as potential mechanisms of control for gene editing applications. Many anti-CRISPRs have been found that inhibit the CRISPR–Cas adaptive immune system. However, comparing all currently known anti-CRISPRs does not reveal a shared set of properties for facile bioinformatic identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method to aid direct identification of new potential anti-CRISPRs using only protein sequence information. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking. We then applied AcRanker to predict candidate anti-CRISPRs from predicted prophage regions within self-targeting bacterial genomes and discovered two previously unknown anti-CRISPRs: AcrllA20 (ML1) and AcrIIA21 (ML8). We show that AcrIIA20 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9). We also show that AcrIIA21 inhibits SpyCas9, Streptococcus aureus Cas9 (SauCas9) and SinCas9 with low potency. The addition of AcRanker to the anti-CRISPR discovery toolkit allows researchers to directly rank potential anti-CRISPR candidate genes for increased speed in testing and validation of new anti-CRISPRs. A web server implementation for AcRanker is available online at http://acranker.pythonanywhere.com/

Synthese von Bausteinen für potentielle Medikamente

Synthese, Reinigung und Analyse (LC-MS und NMR) von Zwischenprodukten auf dem Weg zu einem Wirkstoff-Kandidaten wurden durchgeführt

Molecular mechanism of cargo recognition and handover by the mammalian signal recognition particle

Co-translational protein targeting to membranes by the signal recognition particle (SRP) is a universally conserved pathway from bacteria to humans. In mammals, SRP and its receptor (SR) have many additional RNA features and protein components compared to the bacterial system, which were recently shown to play regulatory roles. Due to its complexity, the mammalian SRP targeting process is mechanistically not well understood. In particular, it is not clear how SRP recognizes translating ribosomes with exposed signal sequences and how the GTPase activity of SRP and SR is regulated. Here, we present electron cryo-microscopy structures of SRP and SRP·SR in complex with the translating ribosome. The structures reveal the specific molecular interactions between SRP and the emerging signal sequence and the elements that regulate GTPase activity of SRP·SR. Our results suggest the molecular mechanism of how eukaryote-specific elements regulate the early and late stages of SRP-dependent protein targeting.ISSN:2666-3864ISSN:2211-124

TLR4 signaling improves PD-1 blockade therapy during chronic viral infection.

CD8 T cells are necessary for the elimination of intracellular pathogens, but during chronic viral infections, CD8 T cells become exhausted and unable to control the persistent infection. Programmed cell death-1 (PD-1) blockade therapies have been shown to improve CD8 T cell responses during chronic viral infections. These therapies have been licensed to treat cancers in humans, but they have not yet been licensed to treat chronic viral infections because limited benefit is seen in pre-clinical animal models of chronic infection. In the present study, we investigated whether TLR4 triggering could improve PD-1 therapy during a chronic viral infection. Using the model of chronic lymphocytic choriomeningitis virus (LCMV) infection in mice, we show that TLR4 triggering with sublethal doses of lipopolysaccharide (LPS) followed by PD-1 blockade results in superior improvement in circulating virus-specific CD8 T cell responses, relative to PD-1 blockade alone. Moreover, we show that the synergy between LPS and PD-1 blockade is dependent on B7 costimulation and mediated by a dendritic cell (DC) intrinsic mechanism. Systemic LPS administration may have safety concerns, motivating us to devise a safer regimen. We show that ex vivo activation of DCs with LPS, followed by adoptive DC transfer, results in a similar potentiation of PD-1 therapy without inducing wasting disease. In summary, our data demonstrate a previously unidentified role for LPS/TLR4 signaling in modulating the host response to PD-1 therapy. These findings may be important for developing novel checkpoint therapies against chronic viral infection

Machine learning predicts new anti-CRISPR proteins.

The increasing use of CRISPR-Cas9 in medicine, agriculture, and synthetic biology has accelerated the drive to discover new CRISPR-Cas inhibitors as potential mechanisms of control for gene editing applications. Many anti-CRISPRs have been found that inhibit the CRISPR-Cas adaptive immune system. However, comparing all currently known anti-CRISPRs does not reveal a shared set of properties for facile bioinformatic identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method to aid direct identification of new potential anti-CRISPRs using only protein sequence information. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking. We then applied AcRanker to predict candidate anti-CRISPRs from predicted prophage regions within self-targeting bacterial genomes and discovered two previously unknown anti-CRISPRs: AcrllA20 (ML1) and AcrIIA21 (ML8). We show that AcrIIA20 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9). We also show that AcrIIA21 inhibits SpyCas9, Streptococcus aureus Cas9 (SauCas9) and SinCas9 with low potency. The addition of AcRanker to the anti-CRISPR discovery toolkit allows researchers to directly rank potential anti-CRISPR candidate genes for increased speed in testing and validation of new anti-CRISPRs. A web server implementation for AcRanker is available online at http://acranker.pythonanywhere.com/

Local Perceptions and Responses to Climate Change and Variability: The Case of Laikipia District, Kenya

Agricultural policies in Kenya aim to improve farmers’ livelihoods. With projected climate change, these policies are short of mechanisms that promote farmers’ adaptation. As a result, smallholders are confronted with a variety of challenges including climate change, which hinders their agricultural production. Local knowledge can be instrumental in assisting smallholders to cope with climate change and variability. In this paper, we present empirical evidence that demonstrates local knowledge, perceptions and adaptations to climate change and variability amongst smallholders of Laikipia district of Kenya. A Palmer Drought Severity Index (PDSI) calculated for one station is compared with smallholders’ perceptions. Data was collected using qualitative and quantitative methods in Umande and Muhonia sub-locations. Qualitative data included 46 transcripts from focus group discussions and key informant interviews. Quantitative data is derived from 206 interviewees. We analyzed qualitative and quantitative data using Atlas-ti and SPSS respectively. According to smallholders’ perceptions, climatic variability is increasingly changing. Local perceptions include decreasing rainfalls, increasing temperatures, increasing frosts and increasing hunger. The PDSI shows a trend towards severe droughts in the last four decades, which is in accordance with farmers’ perceptions. Smallholders use a combination of coping and adaptation strategies to respond to variability, including, among others, diversification of crop varieties, migration and sale of livestock. Significant relationships exist between drought perceptions and some adaptations such as migration and sale of livestock. Farmers have an in-depth knowledge of climatic variability, which they use to inform their coping and adaptation strategies. Knowledge of climatic perceptions and adaptations are vital entry points for decision makers and policy makers to learn how and where to enhance the adaptive capacity of smallholders in rainy and drought periods