STRUCTURING INSTITUTIONS TO SHARE LOCAL WEATHER RISK GLOBALLY

This paper envisions the national weather index as an efficient instrument to hedge the agricultural risk. The theoretical framework is established based on the partition of risk and the cost minimization. The Morocco case was applied and the result shows that the risk can be reduced to a larger extent.Resource /Energy Economics and Policy,

Optimal Inter-Period Weighting of Cumulative Indices for Weather-Based Contingent Claims

Weather-based contingent claims typically rely on a cumulative index of the weather variable. Frequently, the index is weighted to reflect the importance of timing in a weather-production relationship. This article reviews alternative optimization methods and apply criteria for selecting among them to obtain an optimal and robust distribution of weights.Resource /Energy Economics and Policy,

HEDGING CROP RISK WITH WEATHER INDEX AND INDIVIDUAL CROP INSURANCE

This paper provides a theoretical analysis for the optimal portfolio of weather index and individual crop insurance in farm level under mean-variance framework and stresses the impacts of risk aversion level, transaction cost, and basis risk. An empirical application of corn farms in Todd county of Kentucky is applied to.Risk and Uncertainty,

Generation of Transplantable Beta Cells for Patient-Specific Cell Therapy

Islet cell transplantation offers a potential cure for type 1 diabetes, but it is challenged by insufficient donor tissue and side effects of current immunosuppressive drugs. Therefore, alternative sources of insulin-producing cells and isletfriendly immunosuppression are required to increase the efficiency and safety of this procedure. Beta cells can be transdifferentiated from precursors or another heterologous (non-beta-cell) source. Recent advances in beta cell regeneration from somatic cells such as fibroblasts could circumvent the usage of immunosuppressive drugs. Therefore, generation of patient-specific beta cells provides the potential of an evolutionary treatment for patients with diabetes

The complete mitochondrial genome of Pontederia crassipes: using HiFi reads to investigate genome recombination and gene transfer from chloroplast genome

Water hyacinth (Pontederia crassipes Mart.) is a monocotyledonous aquatic plant renowned for its rapid growth, extensive proliferation, biological invasiveness, and ecological resilience to variations in pH, nutrients, and temperature. The International Union for Conservation of Nature (IUCN) has listed P. crassipes among the top 100 invasive species. However, comprehensive genomic information, particularly concerning its mitochondrial genome (mitogenome), remains surprisingly limited. In this study, the complete mitogenome of P. crassipes was analyzed using bioinformatics approaches. The mitogenome is 399,263 bp long and contains 38 protein-coding genes (PCGs), 24 tRNA genes, and 3 rRNA genes. Sequence analysis revealed that the complete mitogenome of the species contains 3,289 dispersed repeats, and 765 RNA editing sites in protein-coding genes. The P. crassipes mitogenome possessed un-conserved structures, including extensive sequence transfer between its chloroplasts and mitochondria. Our study on the mitogenome of P. crassipes offers critical insights into its evolutionary patterns and phylogenetic relationships with related taxa. This research enhances our understanding of this invasive species, known for its significant biomass and rapid overgrowth in aquatic environments

Exploratory application of a cannulation model in recently weaned pigs to monitor longitudinal changes in the enteric microbiome across varied porcine reproductive and respiratory syndrome virus (PRRSV) infection status

The enteric microbiome and its possible modulation to improve feed conversion or vaccine efficacy is gaining more attention in pigs. Weaning pigs from their dam on top of many routine procedures is stressful. A better understanding of the impact of this process on the microbiome may be important to improve pig production. The objective of this study was to develop a weaner pig cannulation model allowing ileum content collection from the same pig over time for 16S rRNA sequencing under different porcine reproductive and respiratory syndrome virus (PRRSV) infection status. Methods: Fifteen 3-week-old pigs underwent abdominal surgery and were fitted with an ileum cannula and ileum contents were collected over time. In this pilot study, treatment groups included a NEG-CONTROL group (no vaccination, no PRRSV challenge), a POS-CONTROL group (no vaccination, PRRSV challenge), a VAC-PRRSV group (vaccinated, PRRSV challenged), a VAC-PRO-PRRSV group (supplemented with probiotics, vaccinated, challenged with PRRSV) and a VAC-ANTI-PRRSV group (antibiotic administration, vaccinated, PRRSV challenged). We assessed the microbiome over time, measured anti-PRRSV serum antibodies, PRRSV load in serum and nasal samples, and severity of lung lesions. Results: Vaccination was protective against PRRSV challenge, irrespective of other treatments. All vaccinated pigs mounted an immune response to PRRSV within 1 week after vaccination. A discernible impact of treatment on the diversity, structure, and taxonomic abundance of enteric microbiome among the groups was not observed. Instead, significant influences on ileum microbiome were observed in relation to time and treatment. Discussion: The cannulation model described in this pilot study has the potential to be useful to study the impact of weaning, vaccination, disease challenge, and antimicrobial administration on the enteric microbiome and its impact on pig health and production. Remarkably, despite the cannulation procedures, all vaccinated pigs exhibited robust immune responses and remained protected against PRRSV challenge, as evidenced by development of anti-PRRSV serum antibodies and viral shedding data

Learned Point Cloud Geometry Compression

This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a.k.a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE). In our approach, PCG is first voxelized, scaled and partitioned into non-overlapped 3D cubes, which is then fed into stacked 3D convolutions for compact latent feature and hyperprior generation. Hyperpriors are used to improve the conditional probability modeling of latent features. A weighted binary cross-entropy (WBCE) loss is applied in training while an adaptive thresholding is used in inference to remove unnecessary voxels and reduce the distortion. Objectively, our method exceeds the geometry-based point cloud compression (G-PCC) algorithm standardized by well-known Moving Picture Experts Group (MPEG) with a significant performance margin, e.g., at least 60% BD-Rate (Bjontegaard Delta Rate) gains, using common test datasets. Subjectively, our method has presented better visual quality with smoother surface reconstruction and appealing details, in comparison to all existing MPEG standard compliant PCC methods. Our method requires about 2.5MB parameters in total, which is a fairly small size for practical implementation, even on embedded platform. Additional ablation studies analyze a variety of aspects (e.g., cube size, kernels, etc) to explore the application potentials of our learned-PCGC.Comment: 13 page

Generation of Transplantable Beta Cells for Patient-Specific Cell Therapy

Islet cell transplantation offers a potential cure for type 1 diabetes, but it is challenged by insufficient donor tissue and side effects of current immunosuppressive drugs. Therefore, alternative sources of insulin-producing cells and isletfriendly immunosuppression are required to increase the efficiency and safety of this procedure. Beta cells can be transdifferentiated from precursors or another heterologous (non-beta-cell) source. Recent advances in beta cell regeneration from somatic cells such as fibroblasts could circumvent the usage of immunosuppressive drugs. Therefore, generation of patient-specific beta cells provides the potential of an evolutionary treatment for patients with diabetes