Design and development of a tunnel magnetoresistance (TMR) magnetometer for LISA

Gravitational waves are a prediction of Einstein’s General Relativity recently detect ed by the on - ground laser interferometers LIGO. LISA (Laser Interferometer Space Antenna) is an ESA mission with expected launch in 2034 aiming to detect gravitational radiation by putting three satellites in heliocentric orbit separated 1 million km one f rom each other, forming a triangle. The Gravitational Astronomy group at the Institut de Ciències de l’Espai (IEEC - CSIC) has provided the Data and Diagnostics Subsystems of LISA Pathfinder, a precursor mission launched in December 2015 that successfully pr oved the key technologies to reach the purest free - fall in space to the date, i.e. down to the sub - femto - g [1] . Our group is currently developing the techniques required for future gravitational wave detectors in space. A particular interesting challenge is the m agnetic diagnostic subsystem [2] which requires to reach sensitivities below 10nT/sqrt(Hz) down to the very stable measuring bandwidth of 0.1 mHz. On top of that, the sensors need to be located close to the free - falling test mass --- a condition that can not be achieved with fluxgate sensors. For that reason, o ur group already start ed the development of a magnetic diagnostic subsystem by means of an Anisotropic Magn etoresistors (AMR) [3] . This would allow several improvements when compared to fluxgate sensors: (i) a more compact design, allowing a more of these sensors to be distributed in the spacecraft improving the spatial resolution of t he magnetic field mapping; (ii) low magnetic and thermal back - action enabling a closer location to the TM and (iii) low noise performance in the LISA band down to 0.1 mHz. The current work would be to study an improved design using a new available sensor technology, the so called Tunnel Magnetoresistance (TMR) which would allow and increased sensitivity when compared with the AMRs. This will require the re - design of the signal conditioning circuits for the sensors and the noise and thermal stability chara cterization of the new design in the frequency range of LISA (0.1 mHz to 0.1 Hz)

A Gale-Shapley View of Unique Stable Marriages

Stable marriage of a two-sided market with unit demand is a classic problem that arises in many real-world scenarios. In addition, a unique stable marriage in this market simplifies a host of downstream desiderata. In this paper, we explore a new set of sufficient conditions for unique stable matching (USM) under this setup. Unlike other approaches that also address this question using the structure of preference profiles, we use an algorithmic viewpoint and investigate if this question can be answered using the lens of the deferred acceptance (DA) algorithm (Gale and Shapley, 1962). Our results yield a set of sufficient conditions for USM (viz., MaxProp and MaxRou) and show that these are disjoint from the previously known sufficiency conditions like sequential preference and no crossing. We also provide a characterization of MaxProp that makes it efficiently verifiable, and shows the gap between MaxProp and the entire USM class. These results give a more detailed view of the sub-structures of the USM class.Comment: 18 pages, 1 figur

Synergistic impact of nanomaterials and plant probiotics in agriculture: A tale of two-way strategy for long-term sustainability

Modern agriculture is primarily focused on the massive production of cereals and other food-based crops in a sustainable manner in order to fulfill the food demands of an ever-increasing global population. However, intensive agricultural practices, rampant use of agrochemicals, and other environmental factors result in soil fertility degradation, environmental pollution, disruption of soil biodiversity, pest resistance, and a decline in crop yields. Thus, experts are shifting their focus to other eco-friendly and safer methods of fertilization in order to ensure agricultural sustainability. Indeed, the importance of plant growth-promoting microorganisms, also determined as “plant probiotics (PPs),” has gained widespread recognition, and their usage as biofertilizers is being actively promoted as a means of mitigating the harmful effects of agrochemicals. As bio-elicitors, PPs promote plant growth and colonize soil or plant tissues when administered in soil, seeds, or plant surface and are used as an alternative means to avoid heavy use of agrochemicals. In the past few years, the use of nanotechnology has also brought a revolution in agriculture due to the application of various nanomaterials (NMs) or nano-based fertilizers to increase crop productivity. Given the beneficial properties of PPs and NMs, these two can be used in tandem to maximize benefits. However, the use of combinations of NMs and PPs, or their synergistic use, is in its infancy but has exhibited better crop-modulating effects in terms of improvement in crop productivity, mitigation of environmental stress (drought, salinity, etc.), restoration of soil fertility, and strengthening of the bioeconomy. In addition, a proper assessment of nanomaterials is necessary before their application, and a safer dose of NMs should be applicable without showing any toxic impact on the environment and soil microbial communities. The combo of NMs and PPs can also be encapsulated within a suitable carrier, and this method aids in the controlled and targeted delivery of entrapped components and also increases the shelf life of PPs. However, this review highlights the functional annotation of the combined impact of NMs and PPs on sustainable agricultural production in an eco-friendly manner

Dynamics of Hot QCD Matter -- Current Status and Developments

The discovery and characterization of hot and dense QCD matter, known as Quark Gluon Plasma (QGP), remains the most international collaborative effort and synergy between theorists and experimentalists in modern nuclear physics to date. The experimentalists around the world not only collect an unprecedented amount of data in heavy-ion collisions, at Relativistic Heavy Ion Collider (RHIC), at Brookhaven National Laboratory (BNL) in New York, USA, and the Large Hadron Collider (LHC), at CERN in Geneva, Switzerland but also analyze these data to unravel the mystery of this new phase of matter that filled a few microseconds old universe, just after the Big Bang. In the meantime, advancements in theoretical works and computing capability extend our wisdom about the hot-dense QCD matter and its dynamics through mathematical equations. The exchange of ideas between experimentalists and theoreticians is crucial for the progress of our knowledge. The motivation of this first conference named "HOT QCD Matter 2022" is to bring the community together to have a discourse on this topic. In this article, there are 36 sections discussing various topics in the field of relativistic heavy-ion collisions and related phenomena that cover a snapshot of the current experimental observations and theoretical progress. This article begins with the theoretical overview of relativistic spin-hydrodynamics in the presence of the external magnetic field, followed by the Lattice QCD results on heavy quarks in QGP, and finally, it ends with an overview of experiment results.Comment: Compilation of the contributions (148 pages) as presented in the `Hot QCD Matter 2022 conference', held from May 12 to 14, 2022, jointly organized by IIT Goa & Goa University, Goa, Indi

HIV-1 gp120 Induces Expression of IL-6 through a Nuclear Factor-Kappa B-Dependent Mechanism: Suppression by gp120 Specific Small Interfering RNA

In addition to its role in virus entry, HIV-1 gp120 has also been implicated in HIV-associated neurocognitive disorders. However, the mechanism(s) responsible for gp120-mediated neuroinflammation remain undefined. In view of increased levels of IL-6 in HIV-positive individuals with neurological manifestations, we sought to address whether gp120 is involved in IL-6 over-expression in astrocytes. Transfection of a human astrocyte cell line with a plasmid encoding gp120 resulted in increased expression of IL-6 at the levels of mRNA and protein by 51.3±2.1 and 11.6±2.2 fold respectively; this effect of gp120 on IL-6 expression was also demonstrated using primary human fetal astrocytes. A similar effect on IL-6 expression was observed when primary astrocytes were treated with gp120 protein derived from different strains of X4 and R5 tropic HIV-1. The induction of IL-6 could be abrogated by use of gp120-specific siRNA. Furthermore, this study showed that the NF-κB pathway is involved in gp120-mediated IL-6 over-expression, as IKK-2 and IKKβ inhibitors inhibited IL-6 expression by 56.5% and 60.8%, respectively. These results were also confirmed through the use of NF-κB specific siRNA. We also showed that gp120 could increase the phosphorylation of IκBα. Furthermore, gp120 transfection in the SVGA cells increased translocation of NF-κB from cytoplasm to nucleus. These results demonstrate that HIV-1 gp120-mediated over-expression of IL-6 in astrocytes is one mechanism responsible for neuroinflammation in HIV-infected individuals and this is mediated by the NF-κB pathway

Design and development of a tunnel magnetoresistance (TMR) magnetometer for LISA

Gravitational waves are a prediction of Einstein’s General Relativity recently detect ed by the on - ground laser interferometers LIGO. LISA (Laser Interferometer Space Antenna) is an ESA mission with expected launch in 2034 aiming to detect gravitational radiation by putting three satellites in heliocentric orbit separated 1 million km one f rom each other, forming a triangle. The Gravitational Astronomy group at the Institut de Ciències de l’Espai (IEEC - CSIC) has provided the Data and Diagnostics Subsystems of LISA Pathfinder, a precursor mission launched in December 2015 that successfully pr oved the key technologies to reach the purest free - fall in space to the date, i.e. down to the sub - femto - g [1] . Our group is currently developing the techniques required for future gravitational wave detectors in space. A particular interesting challenge is the m agnetic diagnostic subsystem [2] which requires to reach sensitivities below 10nT/sqrt(Hz) down to the very stable measuring bandwidth of 0.1 mHz. On top of that, the sensors need to be located close to the free - falling test mass --- a condition that can not be achieved with fluxgate sensors. For that reason, o ur group already start ed the development of a magnetic diagnostic subsystem by means of an Anisotropic Magn etoresistors (AMR) [3] . This would allow several improvements when compared to fluxgate sensors: (i) a more compact design, allowing a more of these sensors to be distributed in the spacecraft improving the spatial resolution of t he magnetic field mapping; (ii) low magnetic and thermal back - action enabling a closer location to the TM and (iii) low noise performance in the LISA band down to 0.1 mHz. The current work would be to study an improved design using a new available sensor technology, the so called Tunnel Magnetoresistance (TMR) which would allow and increased sensitivity when compared with the AMRs. This will require the re - design of the signal conditioning circuits for the sensors and the noise and thermal stability chara cterization of the new design in the frequency range of LISA (0.1 mHz to 0.1 Hz)

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%

Correlation of corrected QT interval with quantitative cardiac troponin-I levels and its prognostic role in Non-ST-elevation myocardial infarction

Non-ST-elevation Myocardial Infarction (NSTEMI) subgroup of ACS has wide variability in patient prognosis. Risk stratification in NSTE-ACS is essential for deciding about early management. Corrected QT interval estimation is one tool which has utility in bedside risk stratification. Whether it differentiates NSTEMI patients into different risk groups is the contention of this study. To assess (1) correlation between maximum corrected QT interval (QTc) and cardiac Troponin I (cTnI) levels; (2) if prolonged corrected QT interval is an independent predictor of higher MACE in NSTEMI patients. We prospectively studied 301 NSTEMI patients. cTnI level and QTc were measured at 0, 12, 24 and 48h post-admission. Patients were followed for 30days post-discharge for incidence of major adverse cardiac events (MACE) defined as composite of cardiac death, non-fatal MI and urgent revascularization. We assessed correlation between cTnI level and maximum QTc value. Regression analysis was performed to identify independent predictors of MACE. We found a strong positive linear correlation between maximum QTc interval and cTnI level with a correlation coefficient of 0.637 (p468ms predicted poor prognosis in form of MACE with 72% sensitivity and 61% specificity. Multivariate analysis revealed that after adjusting for different prognostic variables, TIMI score>2 and QTc>468ms, were the only independent predictors of MACE. QTc-max interval has a strong positive linear correlation with cTnI level. Prolonged QTc has utility as an independent high risk predictor in NSTEMI population

Śodhana: An Ayurvedic process for detoxification and modification of therapeutic activities of poisonous medicinal plants

Ayurveda involves the use of drugs obtained from plants, animals, and mineral origin. All the three sources of drugs can be divided under poisonous and nonpoisonous category. There are various crude drugs, which generally possess unwanted impurities and toxic substances, which can lead to harmful health problems. Many authors have reported that not all medicinal plants are safe to use since they can bear many toxic and harmful phytoconstituents in them. Śodhana (detoxification/purification) is the process, which involves the conversion of any poisonous drug into beneficial, nonpoisonous/nontoxic ones. Vatsanābha (Aconitum species), Semecarpus anacardium, Strychnos nux-vomica, Acorus calamus, Abrus precatorius etc., are some of the interesting examples of toxic plants, which are still used in the Indian system of medicine. Aconite, bhilawanols, strychnine, β-asarone, abrin are some of the toxic components present in these plants and are relatively toxic in nature. Śodhana process involves the purification as well as reduction in the levels of toxic principles which sometimes results in an enhanced therapeutic efficacy. The present review is designed to extensively discuss and understand the scientific basis of the alternative use of toxic plants as a medicine after their purification process