Predictive modelling of arsenate (As(V)) adsorption onto surface-engineered magnetite nanoparticles

Background: Since adsorption is a complex process, numerous models and theories have been devised to gain general understanding of its underlying mechanisms. The interaction between the adsorbates and adsorbents can be identified via modelling of the adsorption data with different adsorption isotherms as well as kinetic models. Many studies are also focused on developing predictive modelling techniques to facilitate accurate prediction of future adsorption trends. Methods: In this study, a predictive model was developed based on a multiple linear regression technique using existing data of As(V) adsorption onto several coated and uncoated magnetite samples. To understand the mechanisms and interactions involved, the data was first modelled using either Temkin or Freundlich linear isotherms. The predicted value is a single data point extension from the training data set. Subsequently, the predicted outcome and the experimental values were compared using multiple error functions to assess the predictive model’s performance. Results: In addition, certain values were compared to that obtained from the literature, and the results were found to have low error margins. Conclusion: To further gauge the effectiveness of the proposed model in accurately predicting future adsorption trends, it should be further tested on different adsorbent and adsorbate combinations.

Boundary layer state detection using piezoelectric sensors

Two piezoelectric series bimorph sensors were embedded below the skin of a NACA 0012 symmetrical airfoil to detect the local state of the boundary layer during wind tunnel testing. Small vanes piercing the airfoil skin were glued onto the bimorphs providing a mechanical coupling to the local mechanical force fluctuations imparted by the local unsteady boundary layer flow. The state of the boundary layer at the sensor sites was varied by changing the angle of attack. The objective of this work was to establish the ability of this sensor concept to accurately distinguish among typical boundary layer states such as attached laminar flow, turbulent flow and separated flow. The output of the sensor was compared to concurrent time-resolved particle image velocimetry measurements, which served as a validation technique. Using the developed sensor response envelope, a single data point time series of the piezo electrical signal was proven to be sufficient to accurately detect the boundary layer state on classical airfoils in the low Reynolds number regime. In projected future applications, single or arrays of bimorph sensors can be used to map the boundary layer of more complex or morphing shape airfoils. The fast response of the sensor can in principle be utilised in closed-loop flow control systems, aimed at drag reduction or lift enhancement.

Improved Constrained k-Means Algorithm for Clustering with Domain Knowledge

Witnessing the tremendous development of machine learning technology, emerging machine learning applications impose challenges of using domain knowledge to improve the accuracy of clustering provided that clustering suffers a compromising accuracy rate despite its advantage of fast procession. In this paper, we model domain knowledge (i.e., background knowledge or side information), respecting some applications as must-link and cannot-link sets, for the sake of collaborating with k-means for better accuracy. We first propose an algorithm for constrained k-means, considering only must-links. The key idea is to consider a set of data points constrained by the must-links as a single data point with a weight equal to the weight sum of the constrained points. Then, for clustering the data points set with cannot-link, we employ minimum-weight matching to assign the data points to the existing clusters. At last, we carried out a numerical simulation to evaluate the proposed algorithms against the UCI datasets, demonstrating that our method outperforms the previous algorithms for constrained k-means as well as the traditional k-means regarding the clustering accuracy rate although with a slightly compromised practical runtime.

Computational analysis of B cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences

Analyzing B cell receptor (BCR) repertoires is immensely useful in evaluating one’s immunological status. Conventionally, repertoire analysis methods have focused on comprehensive assessments of clonal compositions, including V(D)J segment usage, nucleotide insertions/deletions, and amino acid distributions. Here, we introduce a novel computational approach that applies deep-learning-based protein embedding techniques to analyze BCR repertoires. By selecting the most frequently occurring BCR sequences in a given repertoire and computing the sum of the vector representations of these sequences, we represent an entire repertoire as a 100-dimensional vector and eventually as a single data point in vector space. We demonstrate that this new approach enables us to not only accurately cluster BCR repertoires of coronavirus disease 2019 (COVID-19) patients and healthy subjects but also efficiently track minute changes in immune status over time as patients undergo treatment. Furthermore, using the distributed representations, we successfully trained an XGBoost classification model that achieved a mean accuracy rate of over 87% given a repertoire of CDR3 sequences.

Analysis of B-cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences

Analyzing B-cell receptor (BCR) repertoires is immensely useful in evaluating one's immunological status. Conventionally,repertoire analysis methods have focused on comprehensive assessment of clonal compositions, including V(D)J segment usage, nucleotide insertion/deletion, and amino acid distribution. Here, we introduce a novel computational approach that applies deep-learning based protein embedding techniques to analyze BCR repertoires. By selecting the most frequently occurring BCR sequences in a given repertoire and computing the sum of the vector representations of these sequences, we represent an entire repertoire as a 100-dimensional vector and eventually as a single data point in vector space. We demonstrate that our new approach enables us to not only accurately cluster repertoires of COVID-19 patients and healthy subjects, but also efficiently track minute changes in immunity conditions as patients undergo a course of treatment over time. Furthermore, using the distributed representations, we successfully trained an XGBoost classification model that achieved over 87% mean accuracy rate given a repertoire of CDR3 sequences.

Assessment of Evidential Value Requires More Than a Single Data Point

A comment to Sakaluk (2020) pointing out that sex research has too many single study papers and we should thrive for a norm of multi-study investigations.

Effortful listening under the microscope: examining relations between pupillometric and subjective markers of effort and tiredness from listening.

Effort during listening is commonly measured using the task-evoked pupil response (TEPR); a pupillometric marker of physiological arousal. However, studies to date report no association between TEPR and perceived effort. One possible reason for this is the way in which self-report effort measures are typically administered, namely as a single data point collected at the end of a testing session. Another possible reason is that TEPR might relate more closely to the experience of tiredness from listening than to effort per se. To examine these possibilities, we conducted two pre-registered experiments that recorded subjective ratings of effort and tiredness from listening at multiple time points and examined their co-variance with TEPR over the course of listening tasks varying in levels of acoustic and attentional demand. In both experiments, we showed a within-subject association between TEPR and tiredness from listening, but no association between TEPR and effort. The data also suggest that the effect of task difficulty on the experience of tiredness from listening may go undetected using the traditional approach of collecting a single data point at the end of a listening block. Finally, this study demonstrates the utility of a novel correlation analysis technique (‘rmcorr’), which can be used to overcome statistical power constraints commonly found in the literature. Teasing apart the subjective and physiological mechanisms that underpin effortful listening is a crucial step towards addressing these difficulties in older and/or hearing-impaired individuals.