Droplet Morphology-Based Wettability Tuning and Design of Fog Harvesting Mesh to Minimize Mesh-Clogging.

Fog harvesting relies on intercepting atmospheric or industrial fog by placing a porous obstacle, for example, a mesh and collecting the deposited water. In the face of global water scarcity, such fog harvesting has emerged as a viable alternative source of potable water. Typical fog harvesting meshes suffer from poor collection efficiency due to aerodynamic bypassing of the oncoming fog stream and poor collection of the deposited water from the mesh. One pestering challenge in this context is the frequent clogging up of mesh pores by the deposited fog water, which not only yields low drainage efficiency but also generates high aerodynamic resistance to the oncoming fog stream, thereby negatively impacting the fog collection efficiency. Minimizing the clogging is possible by rendering the mesh fibers superhydrophobic, but that entails other detrimental effects like premature dripping and flow-induced re-entrainment of water droplets into the fog stream from the mesh fiber. Herein, we improvise on traditional interweaved metal mesh designs by defining critical parameters, viz., mesh pitch, shade coefficient, and fiber wettability, and deducing their optimal values from numerically and experimentally observed morphology of collected fog water droplets under various operating scenarios. We extend our investigations over a varying range of mesh-wettability, including superhydrophilic and hydrophobic fibers, and go on to find optimal shade coefficients which would theoretically render clog-proof fog harvesting meshes. The aerodynamic, deposition, and overall collection efficiencies are characterized. Hydrophobic meshes with square pores, having fiber diameters smaller than the capillary length scale of water, and an optimal shade coefficient are found to be the most effective design of such clog-proof meshes.

Patient Independent Interictal Epileptiform Discharge Detection.

Epilepsy is a highly prevalent brain condition with many serious complications arising from it. The majority of patients which present to a clinic and undergo electroencephalogram (EEG) monitoring would be unlikely to experience seizures during the examination period, thus the presence of interictal epileptiform discharges (IEDs) become effective markers for the diagnosis of epilepsy. Furthermore, IED shapes and patterns are highly variable across individuals, yet trained experts are still able to identify them through EEG recordings - meaning that commonalities exist across IEDs that an algorithm can be trained on to detect and generalise to the larger population. This research proposes an IED detection system for the binary classification of epilepsy using scalp EEG recordings. The proposed system features an ensemble based deep learning method to boost the performance of a residual convolutional neural network, and a bidirectional long short-term memory network. This is implemented using raw EEG data, sourced from Temple University Hospital's EEG Epilepsy Corpus, and is found to outperform the current state of the art model for IED detection across the same dataset. The achieved accuracy and Area Under Curve (AUC) of 94.92% and 97.45% demonstrates the effectiveness of an ensemble method, and that IED detection can be achieved with high performance using raw scalp EEG data, thus showing promise for the proposed approach in clinical settings.

Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM.

Neurological disorders have an extreme impact on global health, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is crucial for the diagnosis and management of these disorders. However, manual evaluation of EEG recordings is not only time-consuming but also requires specialized skills. This problem is exacerbated by the scarcity of trained neurologists in the healthcare sector, especially in low- and middle-income countries. These factors emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, there is a great interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of two distinct paths, WaveNet-Long Short-Term Memory (LSTM) and LSTM, for the automatic detection of abnormal raw EEG data. Through multiple ablation experiments, we demonstrated the effectiveness and importance of all parts of our proposed model. The performance of our proposed model was evaluated using TUH abnormal EEG Corpus V.2.0.0. (TUAB) and achieved a high classification accuracy of 88.76%, which is higher than in the existing state-of-the-art research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUEP, without any hyperparameter tuning or adjustment. The obtained accuracy was 97.45% for the classification between normal and abnormal EEG recordings, confirming the robustness of our proposed model.

Prevention of inorganic arsenic induced squamous cell carcinoma of the skin in Swiss albino mice by black tea through epigenetic modulation.

Consumption of inorganic Arsenic (iAs) above the safe level may lead to many diseases including cancers of skin. It is known that carcinogenicity of iAs is mediated through generation of excessive reactive oxygen species and polyphenols present in black tea extract (BTE) ameliorate the deleterious effect. Epigenetics also plays vital roles in carcinogenesis. The aim of this paper is to study the influence of iAs on epigenetics and the modulatory effect of BTE. Male Swiss albino mice were divided into three groups, (i) control, (ii) iAs-administered and (iii) iAs + BTE administered. Group (ii) developed invasive squamous cell carcinoma (SCC) of the skin after 330 days, while only hyperplasic and dysplastic changes were observed in group (iii). Expression levels of histone methylation, acetylation marks and several histone methylases, demethylases and acetylases due to iAs were studied; most aberrant expression levels due to iAs were modulated by BTE. JARID1B, a histone demethylase implicated as one of the markers in SCC and a therapeutic target gets upregulated by iAs, but is not influenced by BTE. However, SCC is prevented by BTE. Upregulation of JARID1B by iAs represses H3K4me3; BTE upregulates H3K4me3 without influencing JARID1B expression level. It is known that theaflavin compounds in BTE are transported to the nucleus and interact with histone proteins. in-silico findings in this paper hint that theaflavin compounds present in BTE are very good inhibitors of JARID1B and BTE inhibits its demethylating activity. BTE reverses the epigenetic alterations caused by iAs, thus aids in prevention of SCC.

Deep learning models for RNA secondary structure prediction (probably) do not generalize across families.

MOTIVATION: The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive results for intra-family predictions but seldom address the much more difficult (and practical) inter-family problem. RESULTS: We demonstrate that it is nearly trivial with convolutional neural networks to generate pseudo-free energy changes, modelled after structure mapping data that improve the accuracy of structure prediction for intra-family cases. We propose a more rigorous method for inter-family cross-validation that can be used to assess the performance of learning-based models. Using this method, we further demonstrate that intra-family performance is insufficient proof of generalization despite the widespread assumption in the literature and provide strong evidence that many existing learning-based models have not generalized inter-family. AVAILABILITY AND IMPLEMENTATION: Source code and data are available at https://github.com/marcellszi/dl-rna. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An agent-based model of spread of a pandemic with validation using COVID-19 data from New York State.

We introduce a novel agent based model where each agent carries an effective viral load that captures the instantaneous state of infection of the agent. We simulate the spread of a pandemic and subsequently validate it by using publicly available COVID-19 data. Our simulation tracks the temporal evolution of a virtual city or community of agents in terms of contracting infection, recovering asymptomatically, or getting hospitalized. The virtual community is divided into family groups with 2-6 individuals in each group. Agents interact with other agents in virtual public places like at grocery stores, on public transportation and in offices. We initially seed the virtual community with a very small number of infected individuals and then monitor the disease spread and hospitalization over a period of fifty days, which is a reasonable time-frame for the initial spread of a pandemic. An uninfected or asymptomatic agent is randomly selected from a random family group in each simulation step for visiting a random public space. Subsequently, an uninfected agent contracts infection if the public place is occupied by other infected agents. We have calibrated our simulation rounds according to the size of the population of the virtual community for simulating realistic exposure of agents to a contagion. Our simulation results are consistent with the publicly available hospitalization and ICU patient data from three distinct regions of varying sizes in New York state. Our model can predict the trend in epidemic spread and hospitalization from a set of simple parameters and could be potentially useful in predicting the disease evolution based on available data and observations about public behavior. Our simulations suggest that relaxing the social distancing measures may increase the hospitalization numbers by some 30% or more.

Determining parameters for non-linear models of multi-loop free energy change.

MOTIVATION: Predicting the secondary structure of RNA is a fundamental task in bioinformatics. Algorithms that predict secondary structure given only the primary sequence, and a model to evaluate the quality of a structure, are an integral part of this. These algorithms have been updated as our model of RNA thermodynamics changed and expanded. An exception to this has been the treatment of multi-loops. Although more advanced models of multi-loop free energy change have been suggested, a simple, linear model has been used since the 1980s. However, recently, new dynamic programing algorithms for secondary structure prediction that could incorporate these models were presented. Unfortunately, these models appear to have lower accuracy for secondary structure prediction. RESULTS: We apply linear regression and a new parameter optimization algorithm to find better parameters for the existing linear model and advanced non-linear multi-loop models. These include the Jacobson-Stockmayer and Aalberts & Nandagopal models. We find that the current linear model parameters may be near optimal for the linear model, and that no advanced model performs better than the existing linear model parameters even after parameter optimization. AVAILABILITY AND IMPLEMENTATION: Source code and data is available at https://github.com/maxhwardg/advanced_multiloops. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Stable target opinion through power-law bias in information exchange.

We study a model of binary decision making when a certain population of agents is initially seeded with two different opinions, "+" and "-," with fractions p_{1} and p_{2}, respectively, p_{1}+p_{2}=1. Individuals can reverse their initial opinion only once based on this information exchange. We study this model on a completely connected network, where any pair of agents can exchange information, and a two-dimensional square lattice with periodic boundary conditions, where information exchange is possible only between the nearest neighbors. We propose a model in which each agent maintains two counters of opposite opinions and accepts opinions of other agents with a power-law bias until a threshold is reached, when they fix their final opinion. Our model is inspired by the study of negativity bias and positive-negative asymmetry, which has been known in the psychology literature for a long time. Our model can achieve a stable intermediate mix of positive and negative opinions in a population. In particular, we show that it is possible to achieve close to any fraction p_{3}, 0≤p_{3}≤1, of "-" opinion starting from an initial fraction p_{1} of "-" opinion by applying a bias through adjusting the power-law exponent of p_{3}.

Advanced multi-loop algorithms for RNA secondary structure prediction reveal that the simplest model is best.

Algorithmic prediction of RNA secondary structure has been an area of active inquiry since the 1970s. Despite many innovations since then, our best techniques are not yet perfect. The workhorses of the RNA secondary structure prediction engine are recursions first described by Zuker and Stiegler in 1981. These have well understood caveats; a notable flaw is the ad-hoc treatment of multi-loops, also called helical-junctions, that persists today. While several advanced models for multi-loops have been proposed, it seems to have been assumed that incorporating them into the recursions would lead to intractability, and so no algorithms for these models exist. Some of these models include the classical model based on Jacobson-Stockmayer polymer theory, and another by Aalberts and Nadagopal that incorporates two-length-scale polymer physics. We have realized practical, tractable algorithms for each of these models. However, after implementing these algorithms, we found that no advanced model was better than the original, ad-hoc model used for multi-loops. While this is unexpected, it supports the praxis of the current model.

Double transition in a model of oscillating percolation.

Two distinct transition points have been observed in a problem of lattice percolation studied using a system of pulsating disks. Sites on a regular lattice are occupied by circular disks whose radii vary sinusoidally within [0,R_{0}] starting from a random distribution of phase angles. A lattice bond is said to be connected when its two end disks overlap with each other. Depending on the difference of the phase angles of these disks, a bond may be termed as dead or live. While a dead bond can never be connected, a live bond is connected at least once in a complete time period. Two different time scales can be associated with such a system, leading to two transition points. Namely, a percolation transition occurs at R_{0c}=0.908(5) when a spanning cluster of connected bonds emerges in the system. Here, information propagates across the system instantly, i.e., with infinite speed. Secondly, there exists another transition point R_{0}^{*}=0.5907(3) where the giant cluster of live bonds spans the lattice. In this case the information takes finite time to propagate across the system through the dynamical evolution of finite-size clusters. This passage time diverges as R_{0}→R_{0}^{*} from above. Both the transitions exhibit the critical behavior of ordinary percolation transition. The entire scenario is robust with respect to the distribution of frequencies of the individual disks. This study may be relevant in the context of wireless sensor networks.

Patient monitoring with Google Glass: a pilot study of a novel monitoring technology.

BACKGROUND: Head-mounted devices (HMDs) are of significant interest for applications within medicine, including in anesthesia for patient monitoring. Previous devices trialed in anesthesia for this purpose were often bulky, involved cable tethers, or were otherwise ergonomically infeasible. Google Glass is a modern HMD that is lightweight and solves many of the issues identified with previous HMDs. AIM: To examine the acceptance of Google Glass as a patient monitoring device in a pediatric anesthesia context at Princess Margaret Hospital for Children, Perth, Australia. METHODS: We developed a custom-designed software solution for integrating Google Glass into the anesthesia environment, which enabled the device user to continuously view patient monitoring parameters transmitted wirelessly from the anesthesia workstation. RESULTS: A total of 40 anesthetists were included in the study. Each anesthetist used the device for the duration of a theater list. We found 90% of anesthetists trialing the device agreed that it was comfortable to wear, 86% agreed the device was easy to read, and 82.5% agreed the device was not distracting. In 75% of cases, anesthetists reported unprompted that they were comfortable using the device in theater. Anesthetists reported that they would use the device again in 76% of cases, and indicated that they would recommend the device to a colleague in 58% of cases. CONCLUSION: Given the pilot nature of this study, we consider these results highly favorable. Anesthetists readily accepted Google Glass in the anesthetic environment, with further enhancements to device software, rather than hardware, now being the barrier to adoption. There are a number of applications for HMDs in pediatric anesthesia.

Predicting pseudoknotted structures across two RNA sequences.

MOTIVATION: Laboratory RNA structure determination is demanding and costly and thus, computational structure prediction is an important task. Single sequence methods for RNA secondary structure prediction are limited by the accuracy of the underlying folding model, if a structure is supported by a family of evolutionarily related sequences, one can be more confident that the prediction is accurate. RNA pseudoknots are functional elements, which have highly conserved structures. However, few comparative structure prediction methods can handle pseudoknots due to the computational complexity. RESULTS: A comparative pseudoknot prediction method called DotKnot-PW is introduced based on structural comparison of secondary structure elements and H-type pseudoknot candidates. DotKnot-PW outperforms other methods from the literature on a hand-curated test set of RNA structures with experimental support. AVAILABILITY: DotKnot-PW and the RNA structure test set are available at the web site http://dotknot.csse.uwa.edu.au/pw. CONTACT: janaspe@csse.uwa.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Heuristic RNA pseudoknot prediction including intramolecular kissing hairpins.

Pseudoknots are an essential feature of RNA tertiary structures. Simple H-type pseudoknots have been studied extensively in terms of biological functions, computational prediction, and energy models. Intramolecular kissing hairpins are a more complex and biologically important type of pseudoknot in which two hairpin loops form base pairs. They are hard to predict using free energy minimization due to high computational requirements. Heuristic methods that allow arbitrary pseudoknots strongly depend on the quality of energy parameters, which are not yet available for complex pseudoknots. We present an extension of the heuristic pseudoknot prediction algorithm DotKnot, which covers H-type pseudoknots and intramolecular kissing hairpins. Our framework allows for easy integration of advanced H-type pseudoknot energy models. For a test set of RNA sequences containing kissing hairpins and other types of pseudoknot structures, DotKnot outperforms competing methods from the literature. DotKnot is available as a web server under http://dotknot.csse.uwa.edu.au.

DotKnot: pseudoknot prediction using the probability dot plot under a refined energy model.

RNA pseudoknots are functional structure elements with key roles in viral and cellular processes. Prediction of a pseudoknotted minimum free energy structure is an NP-complete problem. Practical algorithms for RNA structure prediction including restricted classes of pseudoknots suffer from high runtime and poor accuracy for longer sequences. A heuristic approach is to search for promising pseudoknot candidates in a sequence and verify those. Afterwards, the detected pseudoknots can be further analysed using bioinformatics or laboratory techniques. We present a novel pseudoknot detection method called DotKnot that extracts stem regions from the secondary structure probability dot plot and assembles pseudoknot candidates in a constructive fashion. We evaluate pseudoknot free energies using novel parameters, which have recently become available. We show that the conventional probability dot plot makes a wide class of pseudoknots including those with bulged stems manageable in an explicit fashion. The energy parameters now become the limiting factor in pseudoknot prediction. DotKnot is an efficient method for long sequences, which finds pseudoknots with higher accuracy compared to other known prediction algorithms. DotKnot is accessible as a web server at http://dotknot.csse.uwa.edu.au.

Experimental and numerical characterization of magnetophoretic separation for MEMS-based biosensor applications.

Magnetophoretic isolation of biochemical and organic entities in a microfluidic environment is a popular tool for a wide range of bioMEMS applications, including biosensors. An experimental and numerical analysis of magnetophoretic capture of magnetic microspheres in a microfluidic channel under the influence of an external field is investigated. For a given microfluidic geometry, the operating conditions for marginal capture is found to be interrelated in such a manner that a unique critical capture parameter [Pi(crit) = ((Iota(crit)a))(2)/Q(eta)], that is proportional to the ratio of the magnetic force to viscous force, can be identified. Influences of the flow rate, magnetic field and other parameters on the particle trajectories in the microfluidic channel are investigated both numerically and through bright-field imaging under a microscope. Like the event of critical capture, particle trajectories are also found to be guided by a similar parameter, pi. Magnetophoretic capture efficiency of the device is also evaluated as a function of a nondimensional number [Pi(*) = chiP(2)a(2) / (U(null)etah(5)], when both numerical and experimental results are found to agree reasonably well. Results of this investigation can be applied for the selection of the operating parameters and for prediction of device performance of practical microfluidic separators.

KnotSeeker: heuristic pseudoknot detection in long RNA sequences.

Pseudoknots are folded structures in RNA molecules that perform essential functions as part of cellular transcription machinery and regulatory processes. The prediction of these structures in RNA molecules has important implications in antiviral drug design. It has been shown that the prediction of pseudoknots is an NP-complete problem. Practical structure prediction algorithms based on free energy minimization employ a restricted problem class and dynamic programming. However, these algorithms are computationally very expensive, and their accuracy deteriorates if the input sequence containing the pseudoknot is too long. Heuristic methods can be more efficient, but do not guarantee an optimal solution in regards to the minimum free energy model. We present KnotSeeker, a new heuristic algorithm for the detection of pseudoknots in RNA sequences as a preliminary step for structure prediction. Our method uses a hybrid sequence matching and free energy minimization approach to perform a screening of the primary sequence. We select short sequence fragments as possible candidates that may contain pseudoknots and verify them by using an existing dynamic programming algorithm and a minimum weight independent set calculation. KnotSeeker is significantly more accurate in detecting pseudoknots compared to other common methods as reported in the literature. It is very efficient and therefore a practical tool, especially for long sequences. The algorithm has been implemented in Python and it also uses C/C++ code from several other known techniques. The code is available from http://www.csse.uwa.edu.au/~datta/pseudoknot.

A new technique for rendering complex portals.

In this paper, we identify a general paradigm for portal-based rendering and present an image-space algorithm for rendering complex portals. Our general paradigm is an abstraction of portal-based rendering that is independent of scene geometry. It provides a framework for flexible and dynamic scene composition by connecting cells with transformative portals. Our rendering algorithm maintains a visible volume in image-space and uses fragment culling to discard fragments outside of this volume. We discuss our implementation in OpenGL and present results that show it provides correct rendering of complex portals at interactive rates on current hardware. We believe that our work will be useful in many applications that require a means of creating dynamic and meaningful visual connections between different sets of data.

Multiple sequence alignment in parallel on a workstation cluster.

SUMMARY: Multiple sequence alignment is the NP-hard problem of aligning three or more DNA or amino acid sequences in an optimal way so as to match as many characters as possible from the set of sequences. The popular sequence alignment program ClustalW uses the classical method of approximating a sequence alignment, by first computing a distance matrix and then constructing a guide tree to show the evolutionary relationship of the sequences. We show that parallelizing the ClustalW algorithm can result in significant speedup. We used a cluster of workstations using C and message passing interface for our implementation. Experimental results show that speedup of over 5.5 on six processors is obtainable for most inputs. AVAILABILITY: The software is available upon request from the second author.