Biochemical, biophysical, and functional characterization of bacterially expressed and refolded receptor binding domain of Plasmodium vivax duffy-binding

Invasion of erythrocytes by malaria parasites is mediated by specific molecular interactions. Plasmodium vivax is completely dependent on interaction with the Duffy blood group antigen to invade human erythrocytes. The P. vivax Duffy-binding protein, which binds the Duffy antigen during invasion, belongs to a family of erythrocyte-binding proteins that also includesPlasmodium falciparum sialic acid binding protein andPlasmodium knowlesi Duffy binding protein. The receptor binding domains of these proteins lie in a conserved, N-terminal, cysteine-rich region, region II, found in each of these proteins. Here, we have expressed P. vivax region II (PvRII), the P. vivax Duffy binding domain, in Escherichia coli. Recombinant PvRII is incorrectly folded and accumulates in inclusion bodies. We have developed methods to refold and purify recombinant PvRII in its functional conformation. Biochemical, biophysical, and functional characterization confirms that recombinant PvRII is pure, homogeneous, and functionally active in that it binds Duffy-positive human erythrocytes with specificity. Refolded PvRII is highly immunogenic and elicits high titer antibodies that can inhibit binding of P. vivax Duffy-binding protein to erythrocytes, providing support for its development as a vaccine candidate forP. vivax malaria. Development of methods to produce functionally active recombinant PvRII is an important step for structural studies as well as vaccine development

Fuzzy Distance Measure Based Affinity Propagation Clustering

Affinity Propagation (AP) is an effective algorithm that find exemplars repeatedly exchange real valued messages between pairs of data points. AP uses the similarity between data points to calculate the messages. Hence, the construction of similarity is essential in the AP algorithm. A common choice for similarity is the negative Euclidean distance. However, due to the simplicity of Euclidean distance, it cannot capture the real structure of data. Furthermore, Euclidean distance is sensitive to noise and outliers such that the performance of the AP might be degraded. Therefore, researchers have intended to utilize different similarity measures to analyse the performance of AP. nonetheless, there is still a room to enhance the performance of AP clustering. A clustering method called fuzzy based Affinity propagation (F-AP) is proposed, which is based on a fuzzy similarity measure. Experiments shows the efficiency of the proposed F-AP, experiments is performed on UCI dataset. Results shows a promising improvement on AP

State-Of-The-Art In Image Clustering Based On Affinity Propagation

Proclivity spread (AP) is a productive unsupervised grouping technique, which display a quick execution speed and discover bunches in a low mistake rate. AP calculation takes as info a similitude network that comprise of genuine esteemed likenesses between information focuses. The strategy iteratively trades genuine esteemed messages between sets of information focuses until a decent arrangement of models developed. The development of the comparability network dependent on the Euclidean separation is a significant stage during the time spent AP. Appropriately, the conventional Euclidean separation which is the summation of the pixel-wise force contrasts perform beneath normal when connected for picture grouping, as it endures of being reasonable to exceptions and even to little misshapening in pictures. Studies should be done on different methodologies from existing investigations especially in the field of picture grouping with different datasets. In this way, a sensible picture closeness metric will be researched to suite with datasets in the picture clustering field. As an end, changing the comparability lattice will prompt a superior clustering results

DBGC:Dimension-Based Generic Convolution Block for Object Recognition

The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs

Quasi-Optical Terahertz Microfluidic Devices for Chemical Sensing and Imaging

We first review the development of a frequency domain quasi-optical terahertz (THz) chemical sensing and imaging platform consisting of a quartz-based microfluidic subsystem in our previous work. We then report the application of this platform to sensing and characterizing of several selected liquid chemical samples from 570–630 GHz. THz sensing of chemical mixtures including isopropylalcohol-water (IPA-H₂O) mixtures and acetonitrile-water (ACN-H₂O) mixtures have been successfully demonstrated and the results have shown completely different hydrogen bond dynamics detected in different mixture systems. In addition, the developed platform has been applied to study molecule diffusion at the interface between adjacent liquids in the multi-stream laminar flow inside the microfluidic subsystem. The reported THz microfluidic platform promises real-time and label-free chemical/biological sensing and imaging with extremely broad bandwidth, high spectral resolution, and high spatial resolution.Keywords: laminar flow, terahertz, chemical sensing and imaging, molecule diffusion, frequency domain, microfluidic, quasi-optical, label fre

Non-Timber Forest Products Collection Affects Education of Children in Forest Proximate Communities in Northeastern Pakistan

Non-timber forest products (NTFPs) are crucial in driving the economy of communities living inside or around forests. The scarcity of business and employment opportunities often push the forest proximate communities to tap a range of NTFPs for earning their livelihoods. In many forest-based communities around the world, children are actively involved in NTFPs collection, which is likely to affect the socioeconomic paradigms of these children. We aim to investigate how the NTFP collection venture affects the education of the children involved in the forest proximate communities of Azad Jammu and Kashmir (AJK), Pakistan. A stratified sampling followed by a series of focus group discussions and one-to-one interviews were carried out to collect information on collection behaviour, patterns, income generation, and other socioeconomic variables. We used a binary logistic regression model to explain children’s state of attending schools using a range of socioeconomic variables. The empirical evidence showed that 42% of the NTFP-collecting children were not going to school, and nearly two-thirds were working in unfavourable working environments. The regression model showed that the role and behaviour of contractors, along with factors like household conditions, were important factors in employing children for long working hours. The study has implications for reforming policies regarding the nexus of income generation and education in the forest-based communities

Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review

Sepsis continues to be recognized as a significant global health challenge across all ages and is characterized by a complex pathophysiology. In this scoping review, PRISMA-ScR guidelines were adhered to, and a transcriptomic methodology was adopted, with the protocol registered on the Open Science Framework. We hypothesized that gene expression analysis could provide a foundation for establishing a clinical research framework for sepsis. A comprehensive search of the PubMed database was conducted with a particular focus on original research and systematic reviews of transcriptomic sepsis studies published between 2012 and 2022. Both coding and non-coding gene expression studies have been included in this review. An effort was made to enhance the understanding of sepsis at the mRNA gene expression level by applying a systems biology approach through transcriptomic analysis. Seven crucial components related to sepsis research were addressed in this study: endotyping (n = 64), biomarker (n = 409), definition (n = 0), diagnosis (n = 1098), progression (n = 124), severity (n = 451), and benchmark (n = 62). These components were classified into two groups, with one focusing on Biomarkers and Endotypes and the other oriented towards clinical aspects. Our review of the selected studies revealed a compelling association between gene transcripts and clinical sepsis, reinforcing the proposed research framework. Nevertheless, challenges have arisen from the lack of consensus in the sepsis terminology employed in research studies and the absence of a comprehensive definition of sepsis. There is a gap in the alignment between the notion of sepsis as a clinical phenomenon and that of laboratory indicators. It is potentially responsible for the variable number of patients within each category. Ideally, future studies should incorporate a transcriptomic perspective. The integration of transcriptomic data with clinical endpoints holds significant potential for advancing sepsis research, facilitating a consensus-driven approach, and enabling the precision management of sepsis