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Various biophysical techniques have been extensively employed to study protein aggregation due to its significance. Traditionally, these methods detect aggregation at micrometer length scales and micromolar concentrations. However, unlike in vitro, protein aggregation typically occurs at nanomolar concentrations in vivo. Here, using fluorescence correlation spectroscopy (FCS), we captured bromelain aggregation at concentrations as low as ~20 nM, surpassing the detection limit of traditional methods like thioflavin T fluorescence, scattering, and fluorescence microscopy by more than one order of magnitude. Moreover, using thioflavin T fluorescence‐based FCS, we have detected larger aggregates at higher bromelain concentrations, which is undetectable in FCS otherwise. Importantly, our study reveals inherent heterogeneity in bromelain aggregation, inaccessible to ensemble‐averaged techniques. The presented report may provide a platform for the characterization of premature aggregates at very low protein concentrations, which are thought to be functionally significant species in protein aggregation‐induced diseases. [ABSTRACT FROM AUTHOR]

Several global health incidents and evidences show the increasing likelihood of pandemics (large‐scale outbreaks of infectious disease), which has adversely affected all aspects of human lives. It is essential to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data‐sources to deliver insights for enhancing preparedness to combat the pandemic. Specifically, human mobility, travel history, and other transport statistics have significantly impact on the spread of any infectious disease. This article proposes a spatio‐temporal knowledge mining framework, named STOPPAGE, to model the impact of human mobility and other contextual information over the large geographic areas in different temporal scales. The framework has two key modules: (i) spatio‐temporal data and computing infrastructure using fog/edge based architecture; and (ii) spatio‐temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. We created a pandemic‐knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hotspot zones. Further, we provide necessary support in home‐health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real‐life datasets related to COVID‐19 in India illustrate the efficacy of the proposed methods. STOPPAGE outperforms the existing works and baseline methods in terms of accuracy by ≈$$ \approx $$(18–21)% in predicting hotspots and reduces the power consumption of the smartphone significantly. The scalability study yields that the STOPPAGE framework is flexible enough to analyze a huge amount of spatio‐temporal datasets and reduces the delay in predicting health status compared to the existing studies. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has a pivotal role in developing intelligent and computational solutions to facilitate varied real‐life applications. To execute high‐end computations and data analytics, IoT and cloud‐based solutions play the most significant role. However, frequent communication with long distant cloud servers is not a delay‐aware and energy‐efficient solution while providing time‐critical applications such as healthcare. This article explores the possibilities and opportunities of integrating cloud technology with fog and edge‐based computing to provide healthcare services to users in exigency. Here, we propose an end‐to‐end framework named RESCUE (enabling green healthcare services using integrated iot‐edge‐fog‐cloud computing environments), consisting efficient spatio‐temporal data analytics module for efficient information sharing, spatio‐temporal data analysis to predict the path for users to reach the destination (healthcare center or relief camps) with minimum delay in the time of exigency (say, natural disaster). This module analyzes the collected information through crowd‐sourcing and assists the user by extracting optimal path postdisaster when many regions are nonreachable. Our work is different from the existing literature in varied aspects: it analyses the context and semantics by augmenting real‐time volunteered geographical information (VGI) and refines it. Furthermore, the novel path prediction module incorporates such VGI instances and predicts routes in emergencies avoiding all possible risks. Also, the design of development of a latency‐aware, power‐aware data‐driven analytics system helps to resolve any spatio‐temporal query more efficiently compared to the existing works for any time‐critical application. The experimental and simulation results outperform the baselines in terms of accuracy, delay, and power consumption. [ABSTRACT FROM AUTHOR]

The interactions between proteins and their specific DNA sequences are the basis of many cellular processes. Hence, developing methods to build an atomic level picture of these interactions helps improve our understanding of key cellular mechanisms. CueR is an Escherichia coli copper‐sensing transcription regulator. The inhibition of copper‐sensing transcription regulators can kill pathogens, without harming the host. Several spectroscopic studies and crystallographic data have suggested that changes in the conformation of both the DNA and the protein control transcription. However, due to the inadequate resolution of these methods, the exact number of active conformations of CueR has not been determined. Resolving the structure of CueR in its active state is highly important for the development of specific inhibitors. Herein, the potential of double‐histidine (dHis)‐based CuII spin labeling for the identification of various conformational states of CueR during transcription is shown. [ABSTRACT FROM AUTHOR]

Manganese (Mn) ranks twelfth among the most exuberant metal present in the earth's crust and finds its imperative application in the manufacturing steel, chemical, tannery, glass, and battery industries. Solubilisation of Mn can be performed by several bacterial strains which are useful in developing environmental friendly solutions for mining activities. The present investigation aims to isolate and characterize Mn solubilising bacteria from low grade ores from Sanindipur Manganese mine of Sundargh district in Odisha state of India. Four morphologically distinct bacterial strains showing visible growth on Mn supplemented plates were isolated. Mn solubilising ability of the bacterial strains was assessed by visualizing the lightening of the medium appearing around the growing colonies. Three isolates were gram negative and rod shaped while the remaining one was gram positive, coccobacilli. Molecular identification of the isolates was carried out by 16S rRNA sequencing and the bacterial isolates were taxonomically classified as Bacillus anthrasis MSB 2, Acinetobacter sp. MSB 5, Lysinibacillus sp. MSB 11, and Bacillus sp. MMR-1 using BLAST algorithm. The sequences were deposited in NCBI GenBank with the accession number KP635223, KP635224, KP635225 and JQ936966, respectively. Manganese solubilisation efficiency of 40, 96, 97.5 and 48.5% were achieved by MMR-1, MSB 2, MSB 5 and MSB 11 respectively. The efficiency of Mn solubilisation is suggested with the help of a pH variation study. The results are discussed in relation to the possible mechanisms involved in Manganese solubilisation efficiency of bacterial isolates. [ABSTRACT FROM AUTHOR]

CuII‐NTA‐Spinmarkierung des Transkriptionsregulators CueR, eines Kupfer erkennenden Proteins aus E. coli, ermöglichte die hochaufgelöste Detektion seiner verschiedenen Konformationszustände, wie S. Ruthstein et al. in ihrer Zuschrift auf S. 3085 demonstrieren. Zwei der Zustände, Aktivator 1 und 2, repräsentieren CueR in seiner Bindung an DNA und CuI‐Ionen. Aus den berechneten Strukturen lässt sich schließen, dass einer der beiden Zustände an der DNA stärker komprimiert ist und der Übergang zwischen den beiden Zuständen von der CuI‐Konzentration in der Bakterienzelle abhängt. [ABSTRACT FROM AUTHOR]

CuII‐‐NTA spin labeling of the transcription regulator CueR, an E. coli copper‐sensing protein, enabled the high‐resolution detection of its various conformational states, as described by S. Ruthstein and co‐workers in their Communication on page 3053 ff. Two of the conformations, activators 1 and 2, represent CueR as it binds DNA and CuI ions. The computed structures suggest that one is more compressed on the DNA than the other and the transition between those states depends on CuI concentration in the bacterial cell. [ABSTRACT FROM AUTHOR]