Nearest neighbor vector analysis of sdss dr5 galaxy distribution

We present the Nearest Neighbor Distance (NND) analysis of SDSS DR5 galaxies. We give NND results for observed, mock and random sample, and discuss the differences. We find that the observed sample gives us a significantly stronger aggregation characteristic than the random samples. Moreover, we investigate the direction of NND and find that the direction has close relation with the size of the NND for the observed sample.Comment: Natural Science, Vol.5, No.1 in January 201

The geomicrobiology of CO2 geosequestration: a focused review on prokaryotic community responses to field-scale CO2 injection

Our primary research paper (Mu et al., 2014) demonstrated selective changes to a deep subsurface prokaryotic community as a result of CO2 stress. Analyzing geochemical and microbial 16S rRNA gene profiles, we evaluated how in situ prokaryotic communities responded to increased CO2 and the presence of trace organic compounds, and related temporal shifts in phylogeny to changes in metabolic potential. In this focused review, we extend upon our previous discussion to present analysis of taxonomic unit co-occurrence profiles from the same field experiment, to attempt to describe dynamic community behavior within the deep subsurface. Understanding the physiology of the subsurface microbial biosphere, including how key functional groups integrate into the community, will be critical to determining the fate of injected CO2. For example, community-wide network analyses may provide insights to whether microbes cooperatively produce biofilm biomass, and/or biomineralize the CO2, and hence, induce changes to formation porosity or changes in electron flow. Furthermore, we discuss potential impacts to the feasibility of subsurface CO2 storage of selectively enriching for particular metabolic functions (e.g., methanogenesis) as a result of CO2 injection

Author Correction: Applying federated learning to combat food fraud in food supply chains:Applying federated learning to combat food fraud in food supply chains (npj Science of Food, (2023), 7, 1, (46), 10.1038/s41538-023-00220-3)

“In this article the affiliation details for Anand Gavai, Yamine Bouzembrak, Hans J. P. Marvin were incorrectly given as ‘Anand Gavai1, Yamine Bouzembrak2, Hans J. P. Marvin9 ‘, but should have been ‘Anand Gavai1,2, Yamine Bouzembrak2,3, Hans J. P. Marvin2,9’. The original article has been corrected.”</p

Applying federated learning to combat food fraud in food supply chains

Ensuring safe and healthy food is a big challenge due to the complexity of food supply chains and their vulnerability to many internal and external factors, including food fraud. Recent research has shown that Artificial Intelligence (AI) based algorithms, in particularly data driven Bayesian Network (BN) models, are very suitable as a tool to predict future food fraud and hence allowing food producers to take proper actions to avoid that such problems occur. Such models become even more powerful when data can be used from all actors in the supply chain, but data sharing is hampered by different interests, data security and data privacy. Federated learning (FL) may circumvent these issues as demonstrated in various areas of the life sciences. In this research, we demonstrate the potential of the FL technology for food fraud using a data driven BN, integrating data from different data owners without the data leaving the database of the data owners. To this end, a framework was constructed consisting of three geographically different data stations hosting different datasets on food fraud. Using this framework, a BN algorithm was implemented that was trained on the data of different data stations while the data remained at its physical location abiding by privacy principles. We demonstrated the applicability of the federated BN in food fraud and anticipate that such framework may support stakeholders in the food supply chain for better decision-making regarding food fraud control while still preserving the privacy and confidentiality nature of these data

Light intensity-induced phase transitions in graphene oxide doped polyvinylidene fluoride

The coupling of light with low-frequency functionalities of dielectrics and liquid crystals and an ability to turn “on” and “off” the pyro-, piezo-, or ferro- electric properties of materials on demand by optical means leads to fascinating science and device applications. Moreover, to achieve all-optical control in nano-circuits, the coupling of the light with mechanical degrees of freedom is highly desirable and has been elusive until recently. In this work, we report on the light intensity-induced structural phase transitions in graphene oxide doped piezoelectric polyvinylidene fluoride (PVDF) film observed by micro-Raman spectroscopy. Increasing the laser power results in a steady transformation of the Raman spectrum featured piezoelectric phase to one of non-piezoelectric structure. This effect is accompanied by volumetric change of a PVDF unit cell by a factor of two, useful for a photostriction materials application. Furthermore, we observed the reversible switching of α and phases as a function of the light intensity (laser power between 5.7–31.3 mW). This opens up a new route for multi-functionality control where strain, piezoelectric constants and polarization can be modified by light

Linking micellar structures to hydrogelation for salt-triggered dipeptide gelators

Some functionalised dipeptides can form hydrogels when salts are added to solutions at high pH. We have used surface tension, conductivity, rheology, optical, confocal and scanning electron microscopy, 1H NMR and UV-Vis spectroscopy measurements to characterise fully the phase behaviour of solutions of one specific gelator, 2NapFF, at 25 °C at pH 10.5. We show that this specific naphthalene–dipeptide undergoes structural transformations as the concentration is increased, initially forming spherical micelles, then worm-like micelles, followed by association of these worm-like micelles. On addition of a calcium salt, gels are generally formed as long as worm-like micelles are initially present in solution, although there are structural re-organisations that occur at lower concentrations, allowing gelation at lower than expected concentration. Using IR and SANS, we show the differences between the structures present in the solution and hydrogel phases

Staphylococcus aureus small colony variants impair host immunity by activating host cell glycolysis and inducing necroptosis

Staphylococcus aureus small colony variants (SCVs) are frequently associated with chronic infection, yet they lack expression of many virulence determinants associated with the pathogenicity of wild-type strains. We found that both wild-type S. aureus and a ΔhemB SCV prototype potently activate glycolysis in host cells. Glycolysis and the generation of mitochondrial reactive oxygen species were sufficient to induce necroptosis, a caspase-independent mechanism of host cell death that failed to eradicate S. aureus and instead promoted ΔhemB SCV pathogenicity. To support ongoing glycolytic activity, the ΔhemB SCV induced over a 100-fold increase in the expression of fumC, which encodes an enzyme that catalyses the degradatin of fumarate, an inhibitor of glycolysis. Consistent with fumC-dependent depletion of local fumarate, the ΔhemB SCV failed to elicit trained immunity and protection from a secondary infectious challenge in the skin. The reliance of the S. aureus SCV population on glycolysis accounts for much of its role in the pathogenesis of S. aureus skin infection

Emerging roles for hyaluronidase in cancer metastasis and therapy

Hyaluronidases are a family of five human enzymes that have been differentially implicated in the progression of many solid tumor types, both clinically and in functional studies. Advances in the past five years have clarified many apparent contradictions, (1) by demonstrating that specific hyaluronidases have alternative substrates to hyaluronan (HA) or do not exhibit any enzymatic activity, (2) that high molecular weight HA polymers elicit signaling effects that are opposite those of the hyaluronidase-digested HA oligomers, and (3) that it is actually the combined overexpression of HA synthesizing enzymes with hyaluronidases that confers tumorigenic potential. This review examines the literature supporting these conclusions and discusses novel mechanisms by which hyaluronidases impact invasive tumor cell processes. In addition, a detailed structural and functional comparison of the hyaluronidases is presented with insights into substrate selectivity and potential for therapeutic targeting. Finally, technological advances in targeting hyaluronidase for tumor imaging and cancer therapy are summarized