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1. Recent Advances in Supramolecular Affinity Separations: Affinity Chromatography and Related Methods

   https://doi.org/10.1201/9781003223405-1  

   Woolfork, A.G.; Iftekhar, S.; Ovbude, S.; Suh, K.; Sharmeen, S.; Kyei, I.; Jones, J.; Hage, D.S. (January 2021, Advances in chromatography)

   Grinberg, Nelu; Carr, Peter W. (Ed.)

   Affinity chromatography is a technique that uses a stationary phase based on the supramolecular interactions that occur in biological systems or mimics of these systems. This method has long been a popular tool for the isolation, measurement, and characterization of specific targets in complex samples. This review discusses the basic concepts of this method and examines recent developments in affinity chromatography and related supramolecular separation methods. Topics that are examined include advances that have occurred in the types of supports, approaches to immobilization, and binding agents that are employed in this method. New developments in the applications of affinity chromatography are also summarized, including an overview on the use of this method for biochemical purification, sample preparation or analysis, chiral separations, and biointeraction studies. 

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2. Learning metrics for persistence-based summaries and applications for graph classification

   Zhao, Qi; Wang, Yusu (November 2019, 33rd Conf. Neural Information Processing Systems (NeuRIPS))

   Recently a new feature representation framework based on a topological tool called persistent homology (and its persistence diagram summary) has gained much momentum. A series of methods have been developed to map a persistence diagram to a vector representation so as to facilitate the downstream use of machine learning tools. In these approaches, the importance (weight) of different persistence features are usually pre-set. However often in practice, the choice of the weight-functions hould depend on the nature of the specific data at hand. It is thus highly desirable to learn a best weight-function (and thus metric for persistence diagrams) from labelled data. We study this problem and develop a new weighted kernel, called WKPI, for persistence summaries, as well as an optimization framework to learn the weight (and thus kernel). We apply the learned kernel to the challenging task of graph classification, and show that our WKPI-based classification framework obtains similar or (sometimes significantly) better results than the best results from a range of previous graph classification frameworks on benchmark datasets. 

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3. A scale-wise analysis of intermittent momentum transport in dense canopy flows

   https://doi.org/10.1017/jfm.2022.414  

   Chowdhuri, Subharthi; Ghannam, Khaled; Banerjee, Tirtha (July 2022, Journal of Fluid Mechanics)

   We investigate the intermittent dynamics of momentum transport and its underlying time scales in the near-wall region of the neutrally stratified atmospheric boundary layer in the presence of a vegetation canopy. This is achieved through an empirical analysis of the persistence time scales (periods between successive zero-crossings) of momentum flux events, and their connection to the ejection–sweep cycle. Using high-frequency measurements from the GoAmazon campaign, spanning multiple heights within and above a dense canopy, the analysis suggests that, when the persistence time scales ( $$t_p$$ ) of momentum flux events from four different quadrants are separately normalized by $$\varGamma _{w}$$ (integral time scale of the vertical velocity), their distributions $$P(t_p/\varGamma _{w})$$ remain height-invariant. This result points to a persistent memory imposed by canopy-induced coherent structures, and to their role as an efficient momentum-transporting mechanism between the canopy airspace and the region immediately above. Moreover, $$P(t_p/\varGamma _{w})$$ exhibits a power-law scaling at times $$t_{p}<\varGamma _{w}$$ , with an exponential tail appearing for $$t_{p} \geq \varGamma _{w}$$ . By separating the flux events based on $$t_p$$ , we discover that around 80 % of the momentum is transported through the long-lived events ( $$t_{p} \geq \varGamma _{w}$$ ) at heights immediately above the canopy, while the short-lived ones ( $$t_{p} < \varGamma _{w}$$ ) only contribute marginally ( $$\approx 20\,\%$$ ). To explain the role of instantaneous flux amplitudes in momentum transport, we compare the measurements with newly developed surrogate data and establish that the range of time scales involved with amplitude variations in the fluxes tends to increase as one transitions from within to above the canopy. 

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4. Jet fragmentation transverse momentum distributions in pp and p-Pb collisions at $$ \sqrt{s} $$, $$ \sqrt{s_{\mathrm{NN}}} $$ = 5.02 TeV

   https://doi.org/10.1007/JHEP09(2021)211  

   Acharya, S.; Adamová, D.; Adler, A.; Adolfsson, J.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, S.; Ahn, S. U.; et al (September 2021, Journal of High Energy Physics)

   A bstract Jet fragmentation transverse momentum ( j T ) distributions are measured in proton-proton (pp) and proton-lead (p-Pb) collisions at $$ \sqrt{s_{\mathrm{NN}}} $$ s NN = 5 . 02 TeV with the ALICE experiment at the LHC. Jets are reconstructed with the ALICE tracking detectors and electromagnetic calorimeter using the anti- k T algorithm with resolution parameter R = 0 . 4 in the pseudorapidity range |η| < 0 . 25. The j T values are calculated for charged particles inside a fixed cone with a radius R = 0 . 4 around the reconstructed jet axis. The measured j T distributions are compared with a variety of parton-shower models. Herwig and P ythia 8 based models describe the data well for the higher j T region, while they underestimate the lower j T region. The j T distributions are further characterised by fitting them with a function composed of an inverse gamma function for higher j T values (called the “wide component”), related to the perturbative component of the fragmentation process, and with a Gaussian for lower j T values (called the “narrow component”), predominantly connected to the hadronisation process. The width of the Gaussian has only a weak dependence on jet transverse momentum, while that of the inverse gamma function increases with increasing jet transverse momentum. For the narrow component, the measured trends are successfully described by all models except for Herwig. For the wide component, Herwig and PYTHIA 8 based models slightly underestimate the data for the higher jet transverse momentum region. These measurements set constraints on models of jet fragmentation and hadronisation. 

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5. Endemic, cosmopolitan, and generalist taxa and their habitat affinities within a coastal marine microbiome

   https://doi.org/10.1038/s41598-024-69991-3  

   James, Chase_C; Allen, Andrew_E; Lampe, Robert_H; Rabines, Ariel; Barton, Andrew_D (September 2024, Scientific Reports)

   Abstract The relative prevalence of endemic and cosmopolitan biogeographic ranges in marine microbes, and the factors that shape these patterns, are not well known. Using prokaryotic and eukaryotic amplicon sequence data spanning 445 near-surface samples in the Southern California Current region from 2014 to 2020, we quantified the proportion of taxa exhibiting endemic, cosmopolitan, and generalist distributions in this region. Using in-situ data on temperature, salinity, and nitrogen, we categorized oceanic habitats that were internally consistent but whose location varied over time. In this context, we defined cosmopolitan taxa as those that appeared in all regional habitats and endemics as taxa that only appeared in one habitat. Generalists were defined as taxa occupying more than one but not all habitats. We also quantified each taxon’s habitat affinity, defined as habitats where taxa were significantly more abundant than expected. Approximately 20% of taxa exhibited endemic ranges, while around 30% exhibited cosmopolitan ranges. Most microbial taxa (50.3%) were generalists. Many of these taxa had no habitat affinity (> 70%) and were relatively rare. Our results for this region show that, like terrestrial systems and for metazoans, cosmopolitan and endemic biogeographies are common, but with the addition of a large number of taxa that are rare and randomly distributed. 

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