Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE.

Nitrogen dioxide (NO2) is the most active pollutant gas emitted in the industrial era and is highly correlated with human activities. Tracking NO2 emissions and predicting their concentrations represent important steps toward controlling pollution and setting rules to protect people's health indoors, such as in factories, and in outdoor environments. The concentration of NO2 was affected by the COVID-19 lockdown period and decreased because of restrictions on outdoor activities. In this study, the concentration of NO2 was predicted at 14 ground stations in the United Arab Emirates (UAE) during December 2020 based on training over a full time period of two years (2019-2020). Statistical and machine learning models, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), long short-term memory (LSTM), and nonlinear autoregressive neural network (NAR-NN), are used with both open- and closed-loop architectures. The mean absolute percentage error (MAPE) was used to evaluate the performance of the models, and the results ranged from "very good" (MAPE of 8.64% at the Liwa station with the closed loop) to "acceptable" (MAPE of 42.45% at the Khadejah School station with the open loop). The results show that the predictions based on the open loop are generally better than those based on the closed loop because they yield statistically significantly lower MAPE values. For both loop types, we selected stations exhibiting the lowest, medium, and highest MAPE values as representative cases. In addition, we demonstrated that the MAPE value is highly correlated with the relative standard deviation of NO2 concentration values.

A study of nitrogen dioxide (NO2) periodicity over the United Arab Emirates using wavelet analysis.

NO2 and nitric oxide (NO) are the most reactive gases in the atmosphere. The interaction of NOx molecules with oxygen, water and other chemicals leads to the formation of acid rain. The presence of NO2 in the air affects human health and forms a photochemical smog. In this study, we utilize wavelet analysis, namely, the Morlet wavelet, which is a type of continuous wavelet transform, to conduct a spectral analysis of the periodicity of nitrogen dioxide (NO2). The study is conducted using data from 14 weather stations located in diverse geographic areas of the United Arab Emirates (UAE) over a period of two years (2019 and 2020). We explain and relate the significance of human activities to the concentration level of NO2, particularly considering the effect of the COVID-19 lockdown to the periodicity of NO2. The results show that NO2 concentrations in desert areas such as Liwa and Al Quaa were unaffected by the lockdown period (April-July 2020) resulting from the COVID-19 pandemic. The other stations in the urban areas of Abu Dhabi city, Al Dhafra and Al Ain, showed a reduction in NO2 during the lockdown. NO2 is more highly concentrated during winter seasons than during other seasons. The periodicity of NO2 lasted from a few days up to 16 days in most regions. However, some stations located in the Al Dhafra region, such as Al Ruwais and the Gayathi School stations, exhibited a longer period of more than 32 days with a 0.05 significance test. In the Abu Dhabi region, NO2 lasted between 64 and 128 days at the Al Mafraq station. The correlation between the NO2 concentration across several ground stations was studied using wavelet coherence.

Towards a Virtual Fencing System: Training Domestic Sheep Using Audio Stimuli.

Fencing in livestock management is essential for location and movement control yet with conventional methods to require close labour supervision, leading to increased costs and reduced flexibility. Consequently, virtual fencing systems (VF) have recently gained noticeable attention as an effective method for the maintenance and control of restricted areas for animals. Existing systems to control animal movement use audio followed by controversial electric shocks which are prohibited in various countries. Accordingly, the present work has investigated the sole application of audio signals in training and managing animal behaviour. Audio cues in the range of 125-17 kHz were used to prohibit the entrance of seven Hebridean ewes from a restricted area with a feed bowl. Two trials were performed over the period of a year which were video recorded. Sound signals were activated when the animal approached a feed bowl and a restricted area with no feed bowl present. Results from both trials demonstrated that white noise and sounds in the frequency ranges of 125-440 Hz to 10-17 kHz successfully discouraged animals from entering a specific area with an overall success rate of 89.88% (white noise: 92.28%, 10-14 kHz: 89.13%, 15-17 kHz: 88.48%, 125-440 Hz: 88.44%). The study demonstrated that unaided audio stimuli were effective at managing virtual fencing for sheep.

Transfer learning: a friendly introduction.

Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.

SLiM on Diet: finding short linear motifs on domain interaction interfaces in Protein Data Bank.

MOTIVATION: An important class of protein interactions involves the binding of a protein's domain to a short linear motif (SLiM) on its interacting partner. Extracting such motifs, either experimentally or computationally, is challenging because of their weak binding and high degree of degeneracy. Recent rapid increase of available protein structures provides an excellent opportunity to study SLiMs directly from their 3D structures. RESULTS: Using domain interface extraction (Diet), we characterized 452 distinct SLiMs from the Protein Data Bank (PDB), of which 155 are validated in varying degrees-40 have literature validation, 54 are supported by at least one domain-peptide structural instance, and another 61 have overrepresentation in high-throughput PPI data. We further observed that the lacklustre coverage of existing computational SLiM detection methods could be due to the common assumption that most SLiMs occur outside globular domain regions. 198 of 452 SLiM that we reported are actually found on domain-domain interface; some of them are implicated in autoimmune and neurodegenerative diseases. We suggest that these SLiMs would be useful for designing inhibitors against the pathogenic protein complexes underlying these diseases. Our findings show that 3D structure-based SLiM detection algorithms can provide a more complete coverage of SLiM-mediated protein interactions than current sequence-based approaches.

COVID-19 Pandemic Outbreak in the Subcontinent: A Data Driven Analysis.

Human civilization is experiencing a critical situation that presents itself for a new coronavirus disease 2019 (COVID-19). This virus emerged in late December 2019 in Wuhan city, Hubei, China. The grim fact of COVID-19 is, it is highly contagious in nature, therefore, spreads rapidly all over the world and causes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Responding to the severity of COVID-19 research community directs the attention to the analysis of COVID-19, to diminish its antagonistic impact towards society. Numerous studies claim that the subcontinent, i.e., Bangladesh, India, and Pakistan, could remain in the worst affected region by the COVID-19. In order to prevent the spread of COVID-19, it is important to predict the trend of COVID-19 beforehand the planning of effective control strategies. Fundamentally, the idea is to dependably estimate the reproduction number to judge the spread rate of COVID-19 in a particular region. Consequently, this paper uses publicly available epidemiological data of Bangladesh, India, and Pakistan to estimate the reproduction numbers. More specifically, we use various models (for example, susceptible infection recovery (SIR), exponential growth (EG), sequential Bayesian (SB), maximum likelihood (ML) and time dependent (TD)) to estimate the reproduction numbers and observe the model fitness in the corresponding data set. Experimental results show that the reproduction numbers produced by these models are greater than 1.2 (approximately) indicates that COVID-19 is gradually spreading in the subcontinent.

PPiClust: efficient clustering of 3D protein-protein interaction interfaces.

The biological mechanisms through which proteins interact with one another are best revealed by studying the structural interfaces between interacting proteins. Protein-protein interfaces can be extracted from three-dimensional (3D) structural data of protein complexes and then clustered to derive biological insights. However, conventional protein interface clustering methods lack computational scalability and statistical support. In this work, we present a new method named "PPiClust" to systematically encode, cluster, and analyze similar 3D interface patterns in protein complexes efficiently. Experimental results showed that our method is effective in discovering visually consistent and statistically significant clusters of interfaces, and at the same time sufficiently time-efficient to be performed on a single computer. The interface clusters are also useful for uncovering the structural basis of protein interactions. Analysis of the resulting interface clusters revealed groups of structurally diverse proteins having similar interface patterns. We also found, in some of the interface clusters, the presence of well-known linear binding motifs which were noncontiguous in the primary sequences. These results suggest that PPiClust can discover not only statistically significant, but also biologically significant, protein interface clusters from protein complex structural data.

BSAlign: a rapid graph-based algorithm for detecting ligand-binding sites in protein structures.

Detection of ligand-binding sites in protein structures is a crucial task in structural bioinformatics, and has applications in important areas like drug discovery. Given the knowledge of the site in a particular protein structure that binds to a specific ligand, we can search for similar sites in the other protein structures that the same ligand is likely to bind. In this paper, we propose a new method named "BSAlign" (Binding Site Aligner) for rapid detection of potential binding site(s) in the target protein(s) that is/are similar to the query protein's ligand-binding site. We represent both the binding site and the protein structure as graphs, and employ a subgraph isomorphism algorithm to detect the similarities of the binding sites in a very time-efficient manner. Preliminary experimental results show that the proposed BSAlign binding site detection method is about 14 times faster than a well-known method called SiteEngine, while offering the same level of accuracy. Both BSAlign and SiteEngine achieve 60% search accuracy in finding adenine-binding sites from a data set of 126 proteins. The proposed method can be a useful contribution towards speed-critical applications such as drug discovery in which a large number of proteins are needed to be processed. The program is available for download at: http://www1.i2r.a-star.edu.sg/~azeyar/BSAlign/.

Rapid retrieval of protein structures from databases.

As protein databases continue to grow in size, exhaustive search methods that compare a query structure against every database structure can no longer provide satisfactory performance. Instead, the filter-and-refine paradigm offers an efficient alternative to database search without compromising the accuracy of the answers. In this paradigm, protein structures are represented in an abstract form. During querying, based on the abstract representations, the filtering phase prunes away dissimilar structures quickly so that only a small collection of promising structures are examined using a detailed structure alignment technique in the refinement phase. This article reviews mainly techniques developed for the filtering phase.

Mining super-secondary structure motifs from 3d protein structures: a sequence order independent approach.

Super-Secondary structure elements (super-SSEs) are the structurally conserved ensembles of secondary structure elements (SSEs) within a protein. They are of great biological interest. In this work, we present a method to formally represent and mine the sequence order independent super-SSE motifs that occur repeatedly in large data sets of protein structures. We represent a protein structure as a graph, and mine the common cliques from a set of protein graphs in order to find the motifs. We mine two categories of super-SSE motifs: the generic motifs that occur frequently across the entire database of protein structures, and the fold-preferential motifs that are concentrated in particular protein fold types. From the experimental data set of 600 proteins belonging to 15 large SCOP Folds, we have discovered 21 generic motifs and 75 fold-preferential motifs that are both statistically significant and biologically relevant. A number of the discovered motifs (both generic and fold-preferential) resemble the well-known super-SSE motifs in the literature such as beta hairpins, Greek keys, zinc fingers, etc. Some of the discovered motifs are of novel shapes that have not been documented yet. Our method is time-efficient where it can discover all the motifs across the 600 proteins in less than 14 minutes on a standalone PC. The discovered motifs are reported in our project webpage: http://www1.i2r.a-star.edu.sg/~azeyar/SuperSSE/

MatAlign: precise protein structure comparison by matrix alignment.

We propose a detailed protein structure alignment method named "MatAlign". It is a two-step algorithm. Firstly, we represent 3D protein structures as 2D distance matrices, and align these matrices by means of dynamic programming in order to find the initially aligned residue pairs. Secondly, we refine the initial alignment iteratively into the optimal one according to an objective scoring function. We compare our method against DALI and CE, which are among the most accurate and the most widely used of the existing structural comparison tools. On the benchmark set of 68 protein structure pairs by Fischer et al., MatAlign provides better alignment results, according to four different criteria, than both DALI and CE in a majority of cases. MatAlign also performs as well in structural database search as DALI does, and much better than CE does. MatAlign is about two to three times faster than DALI, and has about the same speed as CE. The software and the supplementary information for this paper are available at http://xena1.ddns.comp.nus.edu.sg/~genesis/MatAlign/.

Automatic 3D protein structure classification without structural alignment.

In this paper, we present a new scheme named ProtClass for automatic classification of three-dimensional (3D) protein structures. It is a dedicated and unified multiclass classification scheme. Neither detailed structural alignment nor multiple binary classifications are required in this scheme. We adopt a nearest neighbor-based classification strategy. We use a filter-and-refine scheme. In the first step, we filter out the improbable answers using the precalculated parameters from the training data. In the second, we perform a relatively more detailed nearest neighbor search on the remaining answers. We use very concise and effective encoding schemes of the 3D protein structures in both steps. We compare our proposed method against two other dedicated protein structure classification schemes, namely SGM and CPMine. The experimental results show that ProtClass is slightly better in accuracy than SGM and much faster. In comparison with CPMine, ProtClass is much more accurate, while their running times are about the same. We also compare ProtClass against a structural alignment-based classification scheme named DALI, which is found to be more accurate, but extremely slow. The software is available upon request from the authors. The supplementary information on ProtClass method can be found at: http://xena1.ddns.comp.nus.edu.sg/ approximately genesis/PClass.htm.

Rapid 3D protein structure database searching using information retrieval techniques.

MOTIVATION: As the sizes of three-dimensional (3D) protein structure databases are growing rapidly nowadays, exhaustive database searching, in which a 3D query structure is compared to each and every structure in the database, becomes inefficient. We propose a rapid 3D protein structure retrieval system named 'ProtDex2', in which we adopt the techniques used in information retrieval systems in order to perform rapid database searching without having access to every 3D structure in the database. The retrieval process is based on the inverted-file index constructed on the feature vectors of the relationships between the secondary structure elements (SSEs) of all the 3D protein structures in the database. ProtDex2 is a significant improvement, both in terms of speed and accuracy, upon its predecessor system, ProtDex. RESULTS: The experimental results show that ProtDex2 is very much faster than two well-known protein structure comparison methods, DALI and CE, yet not sacrificing on the accuracy of the comparison. When comparing with a similar SSE-based method, namely TopScan, ProtDex2 is much faster with comparable degree of accuracy. AVAILABILITY: The software is available at: http://xena1.ddns.comp.nus.edu.sg/~genesis/PD2.htm