Identification Of Functional Regions Research Articles

A Multi-Feature Fusion Method for Urban Functional Regions Identification: A Case Study of Xi’an, China

Research on the identification of urban functional regions is of great significance for the understanding of urban structure, spatial planning, resource allocation, and promoting sustainable urban development. However, achieving high-precision urban functional region recognition has always been a research challenge in this field. For this purpose, this paper proposes an urban functional region identification method called ASOE (activity–scene–object–economy), which integrates the features from multi-source data to perceive the spatial differentiation of urban human and geographic elements. First, we utilize VGG16 (Visual Geometry Group 16) to extract high-level semantic features from the remote sensing images with 1.2 m spatial resolution. Then, using scraped building footprints, we extract building object features such as area, perimeter, and structural ratios. Socioeconomic features and population activity features are extracted from Point of Interest (POI) and Weibo data, respectively. Finally, integrating the aforementioned features and using the Random Forest method for classification, the identification results of urban functional regions in the main urban area of Xi’an are obtained. After comparing with the actual land use map, our method achieves an identification accuracy of 91.74%, which is higher than other comparative methods, making it effectively identify four typical urban functional regions in the main urban area of Xi’an (e.g., residential regions, industrial regions, commercial regions, and public regions). The research indicates that the method of fusing multi-source data can fully leverage the advantages of big data, achieving high-precision identification of urban functional regions.

Functional dissection of human mitotic genes using CRISPR-Cas9 tiling screens.

The identity of human protein-coding genes is well known, yet our in-depth knowledge of their molecular functions and domain architecture remains limited by shortcomings in homology-based predictions and experimental approaches focused on whole-gene depletion. To bridge this knowledge gap, we developed a method that leverages CRISPR-Cas9-induced mutations across protein-coding genes for the a priori identification of functional regions at the sequence level. As a test case, we applied this method to 48 human mitotic genes, revealing hundreds of regions required for cell proliferation, including domains that were experimentally characterized, ones that were predicted based on homology, and novel ones. We validated screen outcomes for 15 regions, including amino acids 387-402 of Mad1, which were previously uncharacterized but contribute to Mad1 kinetochore localization and chromosome segregation fidelity. Altogether, we demonstrate that CRISPR-Cas9-based tiling mutagenesis identifies key functional domains in protein-coding genes de novo, which elucidates separation of function mutants and allows functional annotation across the human proteome.

DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility

Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcription factors (TFs) and regulatory elements to achieve interpretable annotations of chromatin accessibility across diverse cellular contexts. To solve this problem, we propose DeepCAGE, a deep learning framework that integrates sequence information and binding statuses of TFs, for the accurate prediction of chromatin accessible regions at a genome-wide scale in a variety of cell types. DeepCAGE takes advantage of a densely connected deep convolutional neural network architecture to automatically learn sequence signatures of known chromatin accessible regions and then incorporates such features with expression levels and binding activities of human core TFs to predict novel chromatin accessible regions. In a series of systematic comparisons with existing methods, DeepCAGE exhibits superior performance in not only the classification but also the regression of chromatin accessibility signals. In a detailed analysis of TF activities, DeepCAGE successfully extracts novel binding motifs and measures the contribution of a TF to the regulation with respect to a specific locus in a certain cell type. When applied to whole-genome sequencing data analysis, our method successfully prioritizes putative deleterious variants underlying a human complex trait and thus provides insights into the understanding of disease-associated genetic variants. DeepCAGE can be downloaded from https://github.com/kimmo1019/DeepCAGE.

Fine-Grained Urban Functional Region Identification via Mobile App Usage Data

Understanding fine-grained urban function for different regions is essential for both city managers and residents in terms of strategy design, tourism recommendation, business site selection, etc. A huge amount of data from the mobile network in the past several years provides the possibility for fine-grained urban function identification since it provides the opportunity to extract useful information about urban functions. However, challenges remain: (i) there is no prior knowledge about the existence of App usage patterns relating to urban functional regions; (ii) collected data are very noisy and data from different cellular towers have different noise levels. Therefore, it is difficult to extract unique patterns to identify urban functional regions. This article proposes a fine-grained urban functional region identification system, which utilizes mobile App usage data from cellular towers. To address challenge (i), we first extract three key variables for each cellular tower, App number, user number, and traffic. Then, we design a Davies–Bouldin index (DBI)-based filtering method to automatically select the most distinguishable features for multiclassification. To address challenge (ii), we first reduce cellular tower level noise by designing a clustering-based method to select the most representative cellular tower data. The data from these cellular towers share similar patterns for the same urban functional region and different patterns between different urban functional regions. Then, we reduce feature level noise by designing a Fourier transform-based method to reconstruct the features with several key frequency components, which preserves the most important information and removes the unnecessary noise. We evaluate our system and selected features with three representative supervised learning models, all of which achieve more than 95 % classification accuracy.

Structural insights into the inhibition of bacterial RecA by naphthalene polysulfonated compounds.

SummaryAs a promising target for alternative antimicrobials, bacterial recombinase A (RecA) protein has attracted much attention for its roles in antibiotic-driven SOS response and mutagenesis. Naphthalene polysulfonated compounds (NPS) such as suramin have previously been explored as antibiotic adjuvants targeting RecA, although the underlying structural bases for RecA-ligand interactions remain obscure. Based on our in silico predictions and documented activity of NPS in vitro, we conclude that the analyzed NPS likely interact with Tyr103 (Y103) and other key residues in the ATPase activity center (pocket A). For validation, we generated recombinant RecA proteins (wild-type versus Y103 mutant) to determine the binding affinities for RecA protein interactions with suramin and underexamined NPS in isothermal titration calorimetry. The corresponding dissociation constants (Kd) ranged from 11.5 to 18.8 μM, and Y103 was experimentally shown to be critical to RecA-NPS interactions.

Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments.

The rapid development of urbanization has increased traffic pressure and made the identification of urban functional regions a popular research topic. Some studies have used point of interest (POI) data and smart card data (SCD) to conduct subway station classifications; however, the unity of both the model and the dataset limits the prediction results. This paper not only uses SCD and POI data, but also adds Online to Offline (OTO) e-commerce platform data, an application that provides customers with information about different businesses, like the location, the score, the comments, and so on. In this paper, these data are combined to and used to analyze each subway station, considering the diversity of data, and obtain a passenger flow feature map of different stations, the number of different types of POIs within 800 m, and the situation of surrounding OTO stores. This paper proposes a two-stage framework, to identify the functional region of subway stations. In the passenger flow stage, the SCD feature is extracted and converted to a feature map, and a ResNet model is used to get the output of stage 1. In the built environment stage, the POI and OTO features are extracted, and a deep neural network with stacked autoencoders (SAE–DNN) model is used to get the output of stage 2. Finally, the outputs of the two stages are connected and a SoftMax function is used to make the final identification of functional region. We performed experimental testing, and our experimental results show that the framework exhibits good performance and has a certain reference value in the planning of subway stations and their surroundings, contributing to the construction of smart cities.

Co-localization between Sequence Constraint and Epigenomic Information Improves Interpretation of Whole-Genome Sequencing Data

The identification of functional regions in the noncoding human genome is difficult but critical in order to gain understanding of the role noncoding variation plays in gene regulation in human health and disease. We describe here a co-localization approach that aims to identify constrained sequences that co-localize with tissue- or cell-type-specific regulatory regions, and we show that the resulting score is particularly well suited for the identification of rare regulatory variants. For 127 tissues and cell types in the ENCODE/Roadmap Epigenomics Project, we provide catalogs of putative tissue- or cell-type-specific regulatory regions under sequence constraint. We use the newly developed co-localization score for brain tissues to score de novo mutations in whole genomes from 1,902 individuals affected with autism spectrum disorder (ASD) and their unaffected siblings in the Simons Simplex Collection. We show that noncoding de novo mutations near genes co-expressed in midfetal brain with high confidence ASD risk genes, and near FMRP gene targets are more likely to be in co-localized regions if they occur in ASD probands versus in their unaffected siblings. We also observed a similar enrichment for mutations near lincRNAs, previously shown to co-express with ASD risk genes. Additionally, we provide strong evidence that prioritized denovo mutations in autism probands point to a small set of well-known ASD genes, the disruption of which produces relevant mouse phenotypes such as abnormal social investigation and abnormal discrimination/associative learning, unlike the de novo mutations in unaffected siblings. The genome-wide co-localization results are available online.

An Evolutionary Trace method defines functionally important bases and sites common to RNA families.

Functional non-coding (fnc)RNAs are nucleotide sequences of varied lengths, structures, and mechanisms that ubiquitously influence gene expression and translation, genome stability and dynamics, and human health and disease. Here, to shed light on their functional determinants, we seek to exploit the evolutionary record of variation and divergence read from sequence comparisons. The approach follows the phylogenetic Evolutionary Trace (ET) paradigm, first developed and extensively validated on proteins. We assigned a relative rank of importance to every base in a study of 1070 functional RNAs, including the ribosome, and observed evolutionary patterns strikingly similar to those seen in proteins, namely, (1) the top-ranked bases clustered in secondary and tertiary structures. (2) In turn, these clusters mapped functional regions for catalysis, binding proteins and drugs, post-transcriptional modification, and deleterious mutations. (3) Moreover, the quantitative quality of these clusters correlated with the identification of functional regions. (4) As a result of this correlation, smoother structural distributions of evolutionary important nucleotides improved functional site predictions. Thus, in practice, phylogenetic analysis can broadly identify functional determinants in RNA sequences and functional sites in RNA structures, and reveal details on the basis of RNA molecular functions. As example of application, we report several previously undocumented and potentially functional ET nucleotide clusters in the ribosome. This work is broadly relevant to studies of structure-function in ribonucleic acids. Additionally, this generalization of ET shows that evolutionary constraints among sequence, structure, and function are similar in structured RNA and proteins. RNA ET is currently available as part of the ET command-line package, and will be available as a web-server.

Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data

Along with the rapid development of China’s economy as well as the continuing urbanization, the internal spatial and functional structures of cities within this country are also gradually changing and restructuring. The study of functional region identification of a city is of great significance to the city’s functional cognition, spatial planning, economic development, human livability, and so forth. Backed by the emerging urban Big Data, and taking the traffic community as the smallest research unit, a method is proposed to identify urban functional regions by combining floating car track data with point of interest (POI) data recorded on an electronic map. It provides a new perspective for the study of urban functional region identification. Firstly, the main functional regions of the city studied are identified through clustering analysis according to the passenger’s spatial-temporal travel characteristics derived from the floating car data. Secondly, the fine-grained identification of the functional region attributes of the traffic communities is achieved using the label information from POI data. Finally, the AND-OR operation is performed on the recognition results derived by the clustering algorithm and the Delphi method, to obtain the identification of urban functional regions. This approach is verified by applying it to the main urban zone within Chengdu’s Third Ring Road. The results show that: (1) There are fewer single functional regions and more mixed functional regions in the main urban zone of Chengdu, and the distribution of the functional regions are roughly concentric centering in the city center. (2) Using the traffic community as a research unit, combined with dynamic human activity trajectory data and static urban interest point data, complex urban functional regions can be effectively identified.

Online identification of functional regions in deep brain stimulation based on an unsupervised random forest with feature selection

Objective. The identification of functional regions, in particular the subthalamic nucleus, through microelectrode recording (MER) is the key step in deep brain stimulation (DBS). To eliminate variability in a neurosurgeon’s judgment, this study presents an online identification method for identifying functional regions along the electrode trajectory. Approach. Functional regions can be identified through offline clustering and online identification based on the unsupervised random forest (RF) algorithm. We took 106 features from MER and the estimated anatomical distance to target as the dataset to train the RF model. To improve the prediction performance and reduce the computation time, a wrapper feature selection (FS) method was added into the algorithm. The method contains feature ranking based on out-of-bag error or silhouette index and feature subset search based on the roulette selection algorithm. Main results. To evaluate the optimization effect of the FS method on the unsupervised RF algorithm, we compared the results of the algorithm with or without FS on the DBS dataset. In addition, the optimization effect of FS on the computation time is evaluated. The results show that for offline clustering, the accuracy obtained with the selected features is higher than that obtained with all features, and the running time decreased from 259.7 s to 60.8 s in the iteration of the FS. The accuracy in online identification improved from 76.19% to 92.08% through FS. In addition, the functional region online identification time is 41.5 ms, which can meet the requirements of DBS surgery. Significance. In conclusion, using the FS method can improve the accuracy and reduce the computation time of the online identification of functional regions. In addition, the online identification method can provide valuable assistance for both neurosurgeons and stereotactic surgery robots in guiding implantation of the electrode in real time.

Automatic feature group combination selection method based on GA for the functional regions clustering in DBS

Background and ObjectiveThe functional regions clustering through microelectrode recording (MER) is a critical step in deep brain stimulation (DBS) surgery. The localization of the optimal target highly relies on the neurosurgeon's empirical assessment of the neurophysiological signal. This work presents an unsupervised clustering algorithm to get the optimal cluster result of the functional regions along the electrode trajectory. MethodsThe dataset consists of the MERs obtained from the routine bilateral DBS for PD patients. Several features have been extracted from MER and divided into groups based on the type of neurophysiological signal. We selected single feature groups rather than all features as the input samples of each division of the divisive hierarchical clustering (DHC) algorithm. And the optimal cluster result has been achieved through a feature group combination selection (FGS) method based on genetic algorithm (GA). To measure the performance of this method, we compared the accuracy and validation indexes of three methods, including DHC only, DHC with GA-based FGS and DHC with GA-based feature selection (FS) in other studies, on the universal and DBS datasets. ResultsResults show that the DHC with GA-based FGS achieved the optimal cluster result compared with other methods. The three borders of the STN can be identified from the cluster result. The dorsoventral sizes of the STN and dorsal STN are 4.45 mm and 2.02 mm. In addition, the features extracted from the multiunit activity, background unit activity and local field potential are found to be the most representative feature groups to identify the dorsal, d-v and ventral borders of the STN, respectively. ConclusionsOur clustering algorithm showed a reliable performance of the automatic identification of functional regions in DBS. The findings can provide valuable assistance for both neurosurgeons and stereotactic surgical robots in DBS surgery.

Locally Weighted Adjustable Parameter-Based LPVG in the Identification of Functional Regions

Mobile users’ online traffic records are a type of data that are closely related to human activities. To explore the differences in lifestyles of Internet users, online records of base stations expressed as time series need to be classified. However, due to the circadian rhythms, such time series have the same trend and small-scale local differences. Traditional similarity measurement using original time series is difficult to adequately capture local features of time series. By mapping the time series into visibility graph, the similarity measurement in the graph domain is obtained preserving the dynamic structure and local features to the greatest extent. In this paper, a locally weighted adjustable parameter-based limited penetrable visibility graph (LWAPLPVG) is proposed to further improve the noise resistance of visibility graph and local identification of limited penetrable visibility graph. We get APLPVG by modifying the visibility criteria, which has noise resistance while preserving local features as well. Simple measurement, such as degree sequence in graph domain, is extracted and proved, which is able to amplify small-scale differences. By considering the circadian rhythms of cyclical and trend time series, APLPVG is weighted locally. We use a real dataset of usage detail records (UDRs) to verify that LWAPLPVG can better improve the identification of time series of functional regions with noise resistance.

Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs

The actual functions of a region may not reflect the intent of the original zoning scheme from planners. To identify the actual urban functional regions, numerous methods have been proposed with computational advancement. Specifically, remote sensing by image recognition, geodemographic classification, social sensing with big data and geo-text mining techniques have been widely applied. Points-of-interest (POIs) are one of the most common open-access data type used to extract information pertaining to functional zones. However, previous works have either lost sight or did not make full use of the spatial interactions that can be extracted from POIs due to model limitations in the context of geographical space. In this research, we introduced an approach that detects functional regions at the scale of a neighborhood area (NA) by combining POI data and a simplified Place2vec model, which is theorized from the first law of geography. First, the POI-based spatial context is constructed by using the nearest neighbor approach. Then, we can increase the number of training tuples (tcenter,tcontext) based on the weight derived from the distance between the POI tcenter and POI tcontext. Next, high-dimensional characteristic vectors of the POIs are extracted by using the skip-gram training framework. By summarizing the POI vectors at the NA level, we employ a K-means clustering model to cluster the functional regions. Compared with other probabilistic topic models (PTMs) and Word2vec, the Place2vec-based approach obtained the highest mean reciprocal rank value (MRR-SWP=0.356, MRR-SLC=0.401, MRR-SJC=0.433, and MRR-SLin=0.421) in terms of similarity capturing performance and functional region identification accuracy (OA=0.7424). The research has important implications to urban planning and governance.

Discovering Patterns From Sequences Using Pattern-Directed Aligned Pattern Clustering.

Functional region identification is of fundamental importance for protein sequences analysis. Such knowledge provides better scientific understanding and could assist drug discovery. Up-to-date, domain annotation is one approach, but it needs to leverage existing databases. For de novo discovery, motif discovery locates and aligns locally homologous sub-sequences to obtain a position-weight matrix (PWM), which is a fixed-length representation model, whereas protein functional region size varies. It thus requires computational expensive exhaustive search to obtain a PWM with width of optimal range. This paper presents a new method known as pattern-directed aligned pattern clustering (PD-APCn) to discover and align patterns in conserved protein functional regions. It adopts aligned pattern cluster (APC) with patterns of variable length and strong support to direct the incremental APC expansion. It allows substitution and frame-shift mutations until a robust termination condition is reached. The concept of breakpoint gap is introduced to identify spots of mutations, such as substitution and frame shifts. Experiments on synthetic data sets with different sizes and noise levels showed that PD-APCn outperforms MEME with much higher recall and Fmeasure and computational speed 665 times faster that MEME. When applying to Cytochrome C and Ubiquitin families, it found all key binding sites within the APCs.

Using Spatial Semantics and Interactions to Identify Urban Functional Regions

The spatial structures of cities have changed dramatically with rapid socio-economic development in ways that are not well understood. To support urban structural analysis and rational planning, we propose a framework to identify urban functional regions and quantitatively explore the intensity of the interactions between them, thus increasing the understanding of urban structures. A method for the identification of functional regions via spatial semantics is proposed, which involves two steps: (1) the study area is classified into three types of functional regions using taxi origin/destination (O/D) flows; and (2) the spatial semantics for the three types of functional regions are demonstrated based on point-of-interest (POI) categories. To validate the existence of urban functional regions, we explored the intensity of interactions quantitatively between them. A case study using POI data and taxi trajectory data from Beijing validates the proposed framework. The results show that the proposed framework can be used to identify urban functional regions and promotes an enhanced understanding of urban structures.

Identification of functional regions of the HrpZPsg protein from Pseudomonas savastanoi pv. glycinea that induce disease resistance and enhance growth in plants

Harpin HrpZ from the plant-pathogen Pseudomonas spp. elicits the hypersensitive response (HR), pathogen defense responses, and enhances growth in plants. To identify regions of HrpZ related to these bioactivities, we constructed 11 mutants of HrpZPsgS1, a 346-amino-acid harpin protein from P. savastanoi pv. glycinea S1. Results showed that proteins HrpZ74–204 and HrpZ1–194 could not induce macroscopic HR but could elicit microscopic HR in tobacco. The HR elicitation activity of mutant proteins with other C-terminal deletions in HrpZPsgS1, such as HrpZ1–102, HrpZ△195–238, HrpZ△241–248, HrpZ△254–298, and HrpZ△290–313, was reduced. The activity of the remaining mutants, other than HrpZ200–346, which lacks part of the N-terminus, was similar to wild-type. These results indicate that the C-terminus is indispensable for HR elicitation, and that parts of the N-terminus play a regulatory role. Also, mutants HrpZΔ89–124 and HrpZΔ254–298 enhanced growth in rice more than wild-type HrpZPsgS1. These mutants were also more effective at inducing resistance to Xanthomonas oryzae pv. oryzae in rice and to Tobacco Mosaic Virus (TMV) in tobacco. qRT-PCR assays showed that HrpZΔ89–124 and HrpZΔ254–298 induced higher levels of expression in genes related to HR, pathogen defense, and growth. Therefore, the modified proteins HrpZΔ89–124 and HrpZΔ254–298 may have potential for development as protein-type biocontrol agents.

Identification of functional regions in the Rhodospirillum rubrum L-asparaginase by site-directed mutagenesis.

Site-directed mutagenesis of Rhodospirillum rubrum L-asparaginase (RrA) was performed in order to identify sites of the protein molecule important for its therapeutic and physico-chemical properties. Ten multipoint mutant genes were obtained, and five recombinant RrA variants were expressed in E. coli BL21(DE3) cells and isolated as functionally active highly purified proteins. Protein purification was performed using Q-Sepharose and DEAE-Toyopearl chromatography. Overall yield of the active enzymes was 70-80 %, their specific activity at pH 7.4 and 37 °C varied of 140-210 U/mg. L-Glutaminase activity did not exceed 0.01 % of L-asparaginase activity. All RrA mutants showed maximum enzyme activity at pH 9.3-9.5 and 53-58 °C. Km and Vmax values for L-asparagine were evaluated for all mutants. Mutations G86P, D88H, M90K (RrAH), G121L, D123A (RrАI) caused the loss of enzyme activity and confirmed the importance of these sites in the implementation of catalytic functions. Removal of four residues from C-terminal area of the enzyme (RrAK) resulted in the enzyme instability. Mutations D60K, F61L(RrАD), and R118H, G120R(RrАJ) led to the improvement of kinetic parameters and enzyme stabilization. Substitutions E149R, V150P (RrАB) improved antineoplastic and cytotoxic activity of the RrA. A64V, E67K substitutions, especially in combination with E149R, V150P (RrАE), considerably destabilized recombinant enzyme.

Delimitation of Functional Regions Using a p-Regions Problem Approach

Various spatial data analyses have been used for the identification of functional regions. Functional regions are identified by grouping many areal units into fewer clusters to classify the areal units in terms of similar properties, as well as to constrain the spatial contiguity of the areal units in each cluster. This article proposes a spatial optimization model, called the p-functional regions problem, to solve a regionalization problem by considering geographic flows. The magnitude of geographic flows, such as journey-to-work, is widely considered a good indicator of functional relationships between areas so that regionalization models incorporating various criteria, such as the maximum intraregion flows or the total inflows from other units, may be used to identify the p regions. We also propose an analytical target reduction approach to enhance the model tractability in generating optimal solutions to large problems and to demonstrate the effectiveness of the optimization model using journey-to-work data from Seoul (South Korea) and South Carolina (the United States).

Detecting genomic regions associated with a disease using variability functions and Adjusted Rand Index

BackgroundThe identification of functional regions contained in a given multiple sequence alignment constitutes one of the major challenges of comparative genomics. Several studies have focused on the identification of conserved regions and motifs. However, most of existing methods ignore the relationship between the functional genomic regions and the external evidence associated with the considered group of species (e.g., carcinogenicity of Human Papilloma Virus). In the past, we have proposed a method that takes into account the prior knowledge on an external evidence (e.g., carcinogenicity or invasivity of the considered organisms) and identifies genomic regions related to a specific disease.Results and conclusionWe present a new algorithm for detecting genomic regions that may be associated with a disease. Two new variability functions and a bipartition optimization procedure are described. We validate and weigh our results using the Adjusted Rand Index (ARI), and thus assess to what extent the selected regions are related to carcinogenicity, invasivity, or any other species classification, given as input. The predictive power of different hit region detection functions was assessed on synthetic and real data. Our simulation results suggest that there is no a single function that provides the best results in all practical situations (e.g., monophyletic or polyphyletic evolution, and positive or negative selection), and that at least three different functions might be useful. The proposed hit region identification functions that do not benefit from the prior knowledge (i.e., carcinogenicity or invasivity of the involved organisms) can provide equivalent results than the existing functions that take advantage of such a prior knowledge. Using the new algorithm, we examined the Neisseria meningitidis FrpB gene product for invasivity and immunologic activity, and human papilloma virus (HPV) E6 oncoprotein for carcinogenicity, and confirmed some well-known molecular features, including surface exposed loops for N. meningitidis and PDZ domain for HPV.

Correlation of nasal morphology to air-conditioning and clearance function

Nasal morphology plays an important functional role in the maintenance of upper airway health. Identification of functional regions, based on morphological attributes, assists in correlating location to primary purpose. The effects of morphological variation on heat and water mass transport in congested and patent nasal airways were investigated by examining nasal cross-sectional MRI images from 8 healthy subjects. This research confirms the previous identification of functional air-conditioning regions within the nose. The first is the anterior region where the morphology prevents over-stressing of tissue heat and fluid supply near the nares. The second is the mid region where low flow velocity favours olfaction and particle deposition. The third is the posterior region which demonstrates an increase in heat and water mass flux coefficients to compensate for rising air humidity and temperature. Factors identified within the congested airway that favour enhanced mucocillary clearance were also identified.