Peer-grouping is used in many sectors for organisational learning, policy implementation, and benchmarking. Clustering provides a statistical, data-driven method for constructing meaningful peer groups, but peer groups must be compatible with business constraints such as size and stability considerations. Additionally, statistical peer groups are constructed from many different variables, and can be difficult to understand, especially for non-statistical audiences. We developed methodology to apply business constraints to clustering solutions and allow the decision-maker to choose the balance between statistical goodness-of-fit and conformity to business constraints. Several tools were utilised to identify complex distinguishing features in peer groups, and a number of visualisations are developed to explain high-dimensional clusters for non-statistical audiences. In a case study where peer group size was required to be small (≤ 100 members), we applied constrained clustering to a noisy high-dimensional data-set over two subsequent years, ensuring that the clusters were sufficiently stable between years. Our approach not only satisfied clustering constraints on the test data, but maintained an almost monotonic negative relationship between goodness-of-fit and stability between subsequent years. We demonstrated in the context of the case study how distinguishing features between clusters can be communicated clearly to different stakeholders with substantial and limited statistical knowledge.Savannah tsetse flies avoid flying toward tsetse fly-refractory waterbuck (Kobus defassa) mediated by a repellent blend of volatile compounds in their body odor comprised of δ-octalactone, geranyl acetone, phenols (guaiacol and carvacrol), and homologues of carboxylic acids (C5-C10) and 2-alkanones (C8-C13). However, although the blends of carboxylic acids and that of 2-alkanones contributed incrementally to the repellency of the waterbuck odor to savannah tsetse flies, some waterbuck constituents (particularly, nonanoic acid and 2-nonanone) showed significant attractive properties. In another study, increasing the ring size of δ-octalactone from six to seven membered ring changed the activity of the resulting molecule (ε-nonalactone) on the savannah tsetse flies from repellency to attraction. In the present study, we first compared the effect of blending ε-nonalactone, nonanoic acid and 2-nonanone in 11 binary and 111 ternary combination on responses of Glossina pallidipes and Glossina morsitans morsitans tsal for downstream deployment in bait technologies for more effective control of G. pallidipes, G. m. morsitans, and perhaps other savannah tsetse fly species, in ‘pull’ and ‘pull-push’ tactics.Collaborative, One Health approaches support governments to effectively prevent, detect and respond to emerging health challenges, such as zoonotic diseases, that arise at the human-animal-environmental interfaces. To overcome these challenges, operational and outcome-oriented tools that enable animal health and human health services to work specifically on their collaboration are required. While international capacity and assessment frameworks such as the IHR-MEF (International Health Regulations-Monitoring and Evaluation Framework) and the OIE PVS (Performance of Veterinary Services) Pathway exist, a tool and process that could assess and strengthen the interactions between human and animal health sectors was needed. Through a series of six phased pilots, the IHR-PVS National Bridging Workshop (NBW) method was developed and refined. The NBW process gathers human and animal health stakeholders and follows seven sessions, scheduled across three days. ABT-199 concentration The outputs from each session build towards the next one, following a structured process that goes from gap identification to joint planning of corrective measures. The NBW process allows human and animal health sector representatives to jointly identify actions that support collaboration while advancing evaluation goals identified through the IHR-MEF and the OIE PVS Pathway. By integrating sector-specific and collaborative goals, the NBWs help countries in creating a realistic, concrete and practical joint road map for enhanced compliance to international standards as well as strengthened preparedness and response for health security at the human-animal interface.Over the past decade, Ulva compressa, a cosmopolitan green algal species, has been identified as a component of green tides in the Yellow Sea, China. In the present study, we sequenced and annotated the complete chloroplast genome of U. compressa (alpha-numeric code RD9023) and focused on the assessment of genome length, homology, gene order and direction, intron size, selection strength, and substitution rate. We compared the chloroplast genome with the mitogenome. The generated phylogenetic tree was analyzed based on single and aligned genes in the chloroplast genome of Ulva compared to mitogenome genes to detect evolutionary trends. U. compressa and U. mutabilis chloroplast genomes had similar gene queues, with individual genes exhibiting high homology levels. Chloroplast genomes were clustered together in the entire phylogenetic tree and shared several forward/palindromic/tandem repetitions, similar to those in U. prolifera and U. linza. However, U. fasciata and U. ohnoi were more divergent, especially ins in the region.Keeping an eye on pedestrians as they navigate through a scene, surveillance cameras are everywhere. With this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails recognizing attributes such as age-group, clothing style, accessories, footwear style etc. This multi-label problem is extremely challenging even for human observers and has rightly garnered attention from the computer vision community. Towards a solution to this problem, in this paper, we adopt trainable Gabor wavelets (TGW) layers and cascade them with a convolution neural network (CNN). Whereas other researchers are using fixed Gabor filters with the CNN, the proposed layers are learnable and adapt to the dataset for a better recognition. We propose a two-branch neural network where mixed layers, a combination of the TGW and convolutional layers, make up the building block of our deep neural network. We test our method on twoo challenging publicly available datasets and compare our results with state of the art.