New normality axioms and decompositions of normality

Generalizations of normality, called (weakly) (functionally) θ-normal spaces, are introduced and studied. This leads to decompositions of normality. It turns out that every paracompact space is θ-normal. Moreover, every Lindelof space as well as every almost compact space is weakly θ-normal. Preservation of θ-normality and its variants under mappings is studied. This in turn strengthens several known results pertaining to normality

New normality axioms and decompositions of normality

Generalizations of normality, called (weakly) (functionally) θ-normal spaces, are introduced and studied. This leads to decompositions of normality. It turns out that every paracompact space is θ-normal. Moreover, every Lindelof space as well as every almost compact space is weakly θ-normal. Preservation of θ-normality and its variants under mappings is studied. This in turn strengthens several known results pertaining to normality

Magneto-optical Kerr Effect Studies of Square Artificial Spin Ice

We report a magneto-optical Kerr effect study of the collective magnetic response of artificial square spin ice, a lithographically-defined array of single-domain ferromagnetic islands. We find that the anisotropic inter-island interactions lead to a non-monotonic angular dependence of the array coercive field. Comparisons with micromagnetic simulations indicate that the two perpendicular sublattices exhibit distinct responses to island edge roughness, which clearly influence the magnetization reversal process. Furthermore, such comparisons demonstrate that disorder associated with roughness in the island edges plays a hitherto unrecognized but essential role in the collective behavior of these systems.Comment: Physical Review B, Rapid Communications (in press

Competitive Priorities and Strategic Consensus in Emerging Economies: Evidence from India

Purpose – The purpose of this paper is to understand the competitive priorities of manufacturers in India, and examine the level of agreement or strategic consensus between senior executives and manufacturing managers on manufacturing competitive priorities in light of the prevalent culture. Design/methodology/approach – Survey data collected from 156 respondents from 78 manufacturing units based on a national sample in India are used to test the hypotheses using the paired samples t‐tests and multivariate analysis of variance. Findings – A relatively high emphasis by both levels of managers on quality, compared to the other three competitive priorities, is noteworthy and consistent with the global trends. The emphasis on delivery is a close second. Differences in competitive priorities exist across managerial levels in India despite the high power distance and low individualism. Research limitations/implications – The effect of ownership as private or public company was examined and no significant differences found, but data could not be collected on the ownership structure such as wholly owned domestic firms, foreign subsidiaries, or joint ventures. and whether a firm is a supplier to a multinational company. It may also be noted that a majority of the manufacturing companies in this paper came from three industries – chemicals, fabricated metals, and electronic and electrical equipment – and, hence, the findings of the paper might have been unduly influenced by the prevalent practices in these industries. Practical implications – The paper informs global managers and firms seeking to outsource to, or invest in, India that the Indian managers place significantly high emphasis on quality and delivery, but not as much on product variety or ability to make frequent changes to product design and production volume. The managers in India need to take note of prevailing differences in managerial priorities and efforts need to be made such that the priorities are aligned and manufacturing strategy may be unified and coordinated. Originality/value – In the Indian context, this is the first study that deployed multiple respondents to understand the manufacturing competitive priorities, and also the first to examine strategic consensus in operations strategy

Fungicidal effect of volatile oils from Eucalyptus citriodora and its major constituent citronellal

A study was undertaken to explore the effect of volatile oils fromEucalyptus citriodora and its major constituent citronellal against two well-known rice pathogens, Rhizoctonia solani and Helminthosporium oryzae. The radial growth and dry weight of both the test fungi were drastically reduced in response to the volatile oils. A complete inhibition of R. solani and H. oryzae was observed at 10 and 20 ppm, respectively. Citronellal alone was found to be more effective than eucalypt oils. Based on the study, it was concluded that eucalypt volatile oils have potential for the suppression of phytopathogenic fungi

Evidence for a Semisolid Phase State of Aerosols and Droplets Relevant to the Airborne and Surface Survival of Pathogens

The phase state of respiratory aerosols and droplets has been linked to the humidity-dependent survival of pathogens such as SARS-CoV-2. To inform strategies to mitigate the spread of infectious disease, it is thus necessary to understand the humidity-dependent phase changes associated with the particles in which pathogens are suspended. Here, we study phase changes of levitated aerosols and droplets composed of model respiratory compounds (salt and protein) and growth media (organic-inorganic mixtures commonly used in studies of pathogen survival) with decreasing relative humidity (RH). Efflorescence was suppressed in many particle compositions and thus unlikely to fully account for the humidity-dependent survival of viruses. Rather, we identify organic-based, semisolid phase states that form under equilibrium conditions at intermediate RH (45 to 80%). A higher-protein content causes particles to exist in a semisolid state under a wider range of RH conditions. Diffusion and, thus, disinfection kinetics are expected to be inhibited in these semisolid states. These observations suggest that organic-based, semisolid states are an important consideration to account for the recovery of virus viability at low RH observed in previous studies. We propose a mechanism in which the semisolid phase shields pathogens from inactivation by hindering the diffusion of solutes. This suggests that the exogenous lifetime of pathogens will depend, in part, on the organic composition of the carrier respiratory particle and thus its origin in the respiratory tract. Furthermore, this work highlights the importance of accounting for spatial heterogeneities and time-dependent changes in the properties of aerosols and droplets undergoing evaporation in studies of pathogen viability

Algorithmic assessment of cellular senescence in experimental and clinical specimens

The development of genetic tools allowed for the validation of the pro-aging and pro-disease functions of senescent cells in vivo. These discoveries prompted the development of senotherapies—pharmaceutical interventions aimed at interfering with the detrimental effect of senescent cells—that are now entering the clinical stage. However, unequivocal identification and examination of cellular senescence remains highly difficult because of the lack of universal and specific markers. Here, to overcome the limitation of measuring individual markers, we describe a detailed two-phase algorithmic assessment to quantify various senescence-associated parameters in the same specimen. In the first phase, we combine the measurement of lysosomal and proliferative features with the expression of general senescence-associated genes to validate the presence of senescent cells. In the second phase we measure the levels of pro-inflammatory markers for specification of the type of senescence. The protocol can help graduate-level basic scientists to improve the characterization of senescence-associated phenotypes and the identification of specific senescent subtypes. Moreover, it can serve as an important tool for the clinical validation of the role of senescent cells and the effectiveness of anti-senescence therapies

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13 Submissions were made by three free-modelling methods which combine the predictions of three neural networks. All three systems were guided by predictions of distances between pairs of residues produced by a neural network. Two systems assembled fragments produced by a generative neural network, one using scores from a network trained to regress GDT_TS. The third system shows that simple gradient descent on a properly constructed potential is able to perform on-par with more expensive traditional search techniques and without requiring domain segmentation. In the CASP13 free-modelling assessors' ranking by summed z-scores, this system scored highest with 68.3 vs 48.2 for the next closest group. (An average GDT_TS of 61.4.) The system produced high-accuracy structures (with GDT_TS scores of 70 or higher) for 11 out of 43 free-modelling domains. Despite not explicitly using template information, the results in the template category were comparable to the best performing template-based methods

Improved protein structure prediction using potentials from deep learning

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7