1,365 research outputs found
Programmed exosome fusion for energy generation in living cells
No full text© 2021, The Author(s), under exclusive licence to Springer Nature Limited.Biological membrane-enclosed organelles are fascinating examples of spatially confined nanoreactors for biocatalytic transformations such as cascade reactions involving multiple enzymes; however, the fabrication of their synthetic mimics remains a considerable challenge. Here we demonstrate supramolecular chemistry-based bridging of two membranes leading to controlled fusion of exosomes that act as nanoreactors for effective biocatalytic cascades, with prolonged functionality inside of living cells. Exosome membrane proteins were chemically engineered with a catechol moiety to drive fusion by supramolecular complexation to bridge the membranes. This strategy successfully encapsulated multiple enzymes and assembled the minimal electron transport chain in the plasma membrane, leading to tuneable, enhanced catalytic cascade activity capable of ATP synthesis inside of tissue spheroids. This nanoreactor was functional for many hours after uptake into living cells, showed successful penetration into tissue spheroids and repaired the damaged region by supplying ATP, all of which represent an advance in the mimicking of nature’s own organelles. [Figure not available: see fulltext.].11Nsciescopu
A fidget spinner for the point-of-care diagnosis of urinary tract infection
No full text© 2020, The Author(s), under exclusive licence to Springer Nature Limited.The point-of-care detection of pathogens in biological samples in resource-limited settings should be inexpensive, rapid, portable, simple and accurate. Here, we describe a custom-made fidget spinner that rapidly concentrates pathogens in 1-ml samples of undiluted urine by more than 100-fold for the on-device colorimetric detection of bacterial load and pathogen identification. In Tiruchirappalli, India, the device enabled the on-site detection of infection with the naked eye within 50 min in urine samples from 39 patients suspected of having a urinary tract infection. We also show that, in 30 clinical samples of urinary tract infection, the device can be used to perform an antimicrobial susceptibility test for the antimicrobial drugs ciprofloxacin and cefazolin within 120 min. The fidget spinner could be used in low-resource settings as an inexpensive handheld point-of-care device for the rapid concentration and detection of pathogens in urine sample
Bilateral congenital lacrimal fistulas in an adult as part of ectrodactyly-ectodermal dysplasia-clefting syndrome: A rare anomaly
No full textEctrodactyly-ectodermal dysplasia and clefting syndrome or "Lobster claw" deformity is a rare congenital anomaly that affects tissues of ectodermal and mesodermal origin. Nasolacrimal duct (NLD) obstruction with or without atresia of lacrimal passage is a common finding of such a syndrome. The authors report here even a rarer presentation of the syndrome which manifested as bilateral NLD obstruction and lacrimal fistula along with cleft lip and palate, syndactyly affecting all four limbs, mild mental retardation, otitis media, and sinusitis. Lacrimal duct obstruction and fistula were managed successfully with endoscopic dacryocystorhinostomy (DCR) which is a good alternative to lacrimal probing or open DCR in such a case
Classical and quantum machine learning applications in spintronics
No full textIn this article we demonstrate the applications of classical and quantum
machine learning in quantum transport and spintronics. With the help of a
two-terminal device with magnetic impurity we show how machine learning
algorithms can predict the highly non-linear nature of conductance as well as
the non-equilibrium spin response function for any random magnetic
configuration. By mapping this quantum mechanical problem onto a classification
problem, we are able to obtain much higher accuracy beyond the linear response
regime compared to the prediction obtained with conventional regression
methods. We finally describe the applicability of quantum machine learning
which has the capability to handle a significantly large configuration space.
Our approach is applicable for solid state devices as well as for molecular
systems. These outcomes are crucial in predicting the behavior of large-scale
systems where a quantum mechanical calculation is computationally challenging
and therefore would play a crucial role in designing nano devices.Comment: 9 pages, 8 figure
Evaluating and Predicting the Risk of Algal Blooms in a Freshwater Lake through a 4-Dimensional Approach: A Case Study on Lake Mitchell
No full textExcessive algal growth in freshwater lakes can negatively impact ecosystems, recreation, and human health. Though algae are a natural part of freshwater ecosystems, elevated nutrient loading from anthropogenic and natural sources can lead to algal blooms. Both algae and blue-green algae (BGA) are responsible for algal blooms; however, BGA (cyanobacteria) is more dangerous. The first objective of this research was to prepare a conceptual model to understand how various environmental variables affect algae. This conceptual model was used to choose the environmental variables that help increase or decrease algae in the water environment. The second objective was to develop empirical equations to identify how the environmental variables are helping algal increase or decrease. Lake Mitchell, near Mitchell, SD, was chosen as a case study to collect the data of the environmental variables. Along with the total algae (Total algae = Chlorophyll-a + Phycocyanin), five variables: (1) conductivity, (2) temperature, (3) fluorescent dissolved organic matter, (4) ammonium, and (5) dissolved oxygen, were collected. Algae concentrations can change temporally, vertically within the water column, and spatially across lakes and thus, a four-dimensional approach was used to accurately quantify alga
The direct and interactive effects of job insecurity and job embeddedness on unethical pro-organizational behavior
No full text
Purpose
The purpose of this paper is to empirically examine both the direct effects and the interactive effects of job insecurity and job embeddedness on unethical pro-organizational behavior.
Design/methodology/approach
Data were collected, using established scales, from employees of different Indian organizations. In all, 346 responses were collected. The data were analyzed using a stepwise multiple regression technique.
Findings
The results of the analysis reveal that both job insecurity and job embeddedness are positively linked to unethical pro-organizational behavior. Further, the relationship between job insecurity and unethical pro-organizational behavior is moderated by job embeddedness.
Research limitations/implications
The study’s results indicate that managers should be aware that employees who run the risk of losing their jobs might be inclined to perform pro-organizational behavior that could be unethical. Intrinsically, such acts could be detrimental to the organization’s long-term health and therefore managers should be vigilant and timely in discouraging this behavior.
Originality/value
Unethical pro-organizational behavior as a means used by employees to combat job insecurity has not previously been addressed by researchers. Thus, this study contributes to the literature through its empirical examination of the role of job insecurity and job embeddedness as factors influencing unethical pro-organizational behavior.
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Expanding Dimensions Of Test Management For Object Oriented Software Author – Sumit Kumar
No full textThe role of Test Management in the software industry has not remained the same. Few years back, testing was rarely present in the checklist of Project Manager. The strategic elements of business have guided software industry towards the realization of impact of quality in this industry. The growing complexity of today’s application, combined with increased pressure and skyrocketing costs of application failure and downtime have catapulted the need for a process, which can be effectively used for achieving desired quality level. Evolution of Test Management as a process has provided the solution to the industry. Ground rules have been laid down under Test Management process and organizations are trying to synchronize this process with their business goals by selecting the most suitable Test Model. But, in this race for Quality, an important aspect of testing has been ignored. Present Test Management process has been identified and established at syntactic level where emphasis is on testing the code. The growing need of Quality in busines
Redox-Responsive Nanocapsules for the Spatiotemporal Release of Miltefosine in Lysosome: Protection against Leishmania
No full textLeishmaniasis, a vector-borne disease, is caused by intracellular parasite Leishmania donovani. Unlike most intracellular pathogens, Leishmania donovani are lodged in parasitophorous vacuoles and replicate within the phagolysosomes in macrophages. Effective vaccines against this disease are still under development, while the efficacy of the available drugs is being questioned owing to the toxicity for nonspecific distribution in human physiology and the reported drug-resistance developed by Leishmania donovani. Thus, a stimuli-responsive nanocarrier that allows specific localization and release of the drug in the lysosome has been highly sought after for addressing two crucial issues, lower drug toxicity and a higher drug efficacy. We report here a unique lysosome targeting polymeric nanocapsules, formed via inverse mini-emulsion technique, for stimuli-responsive release of the drug miltefosine in the lysosome of macrophage RAW 264.7 cell line. A benign polymeric backbone, with a disulfide bonding susceptible to an oxidative cleavage, is utilized for the organelle-specific release of miltefosine. Oxidative rupture of the disulfide bond is induced by intracellular glutathione (GSH) as an endogenous stimulus. Such a stimuli-responsive release of the drug miltefosine in the lysosome of macrophage RAW 264.7 cell line over a few hours helped in achieving an improved drug efficacy by 200 times as compared to pure miltefosine. Such a drug formulation could contribute to a new line of treatment for leishmaniasis.A. Das acknowledges SERB (India) Grants (CRG/2020/000492 and JCB/2017/000004) and DBT Grant (BT/PR22251/NNT/28/1274/2017) for supporting this research. N. Mukherjee acknowledges SERB (India) Grant PDF/2016/001437 and K. Das acknowledges the grant EMR/2015/001674 for supporting this research. Financial support from DST (DST/INSPIRE/03/2017/002477) is acknowledged by R.T. This manuscript bears CSMCRI registration no 7/2021.Pramanik, SK (corresponding author), CSIR Cent Salt & Marine Chem Res Inst, Bhavnagar 364002, Gujarat, India.
Mukherjee, N (corresponding author), CSIR Indian Inst Chem Biol, Canc Biol & Inflammatory Disorder Div, Kolkata 700032, India.
Chattopadhy, S (corresponding author), BITS Pilani, Pilani 403726, Goa, India.
Das, A (corresponding author), Indian Inst Sci Educ & Res Kolkata, Mohanpur 741246, W Bengal, India.
[email protected]; [email protected]; [email protected]
Exploring exotic configurations with anomalous features with deep learning: Application of classical and quantum-classical hybrid anomaly detection
No full textWe present the application of classical and quantum-classical hybrid anomaly detection schemes to explore exotic configurations with anomalous features. We consider the Anderson model as a prototype, where we define two types of anomalies—a high conductance in the presence of strong impurity and a low conductance in the presence of weak impurity—as a function of random impurity distribution. Such anomalous outcome constitutes an imperceptible fraction of the data set and is not a part of the training process. These exotic configurations, which can be a source of rich new physics, usually remain elusive to conventional classification or regression methods and can be tracked only with a suitable anomaly detection scheme. We also present a systematic study of the performance of the classical and the quantum-classical hybrid anomaly detection method and show that the inclusion of a quantum circuit significantly enhances the performance of anomaly detection, which we quantify with suitable performance metrics. Our approach is quite generic in nature and can be used for any system that relies on a large number of parameters to find their new configurations, which can hold exotic new features
Classical and quantum machine learning applications in spintronics
No full textIn this article we demonstrate the applications of classical and quantum machine learning in quantum transport and spintronics. With the help of a two-terminal device with magnetic impurities we show how machine learning algorithms can predict the highly non-linear nature of conductance as well as the non-equilibrium spin response function for any random magnetic configuration. By mapping this quantum mechanical problem onto a classification problem, we are able to obtain much higher accuracy beyond the linear response regime compared to the prediction obtained with conventional regression methods. We finally describe the applicability of quantum machine learning which has the capability to handle a significantly large configuration space. Our approach is applicable for solid state devices as well as for molecular systems. These outcomes are crucial in predicting the behavior of large-scale systems where a quantum mechanical calculation is computationally challenging and therefore would play a crucial role in designing nanodevices
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