LEADER 06497nam 2201873z- 450 001 9910619464003321 005 20231214133033.0 010 $a3-0365-5265-0 035 $a(CKB)5670000000391634 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/93252 035 $a(EXLCZ)995670000000391634 100 $a20202210d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMicroplastics Degradation and Characterization 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (400 p.) 311 $a3-0365-5266-9 330 $aIn the last decade, issues related to pollution from microplastics in all environmental compartments and the associated health and environmental risks have been the focus of intense social, media, and political attention worldwide. The assessment, quantification, and study of the degradation processes of plastic debris in the ecosystem and its interaction with biota have been and are still the focus of intense multidisciplinary research. Plastic particles in the range from 1 to 5 mm and those in the sub-micrometer range are commonly denoted as microplastics and nanoplastics, respectively. Microplastics (MPs) are being recognized as nearly ubiquitous pollutants in water bodies, but their actual concentration, distribution, and effects on natural waters, sediments, and biota are still largely unknown. Contamination by microplastics of agricultural soil and other environmental areas is also becoming a matter of concern. Sampling, separation, detection, characterization and evaluating the degradation pathways of micro- and nano-plastic pollutants dispersed in the environment is a challenging and critical goal to understand their distribution, fate, and the related hazards for ecosystems. Given the interest in this topic, this Special Issue, entitled ?Microplastics Degradation and Characterization?, is concerned with the latest developments in the study of microplastics. 606 $aMathematics & science$2bicssc 606 $aChemistry$2bicssc 606 $aQuantum & theoretical chemistry$2bicssc 610 $aPEEK 610 $aSIRM 610 $adamage mechanisms 610 $aGISAXS 610 $airradiation 610 $amicro and nanoplastics 610 $afreshwater 610 $asludge 610 $aoptical detection 610 $aportable devices 610 $ain situ detection 610 $amicroplastics 610 $amarine sediment 610 $apet 610 $anylon 6 610 $anylon 6,6 610 $areversed-phase HPLC 610 $apolyolefin 610 $apolystyrene 610 $aPyr-GC/MS 610 $apolymer degradation 610 $amicroparticles 610 $aPLA 610 $aPBS 610 $aenzymes 610 $aspecificity 610 $athermal profile 610 $aactivation energy 610 $awastewater 610 $aRaman spectroscopy 610 $alaser speckle pattern 610 $atransmittance 610 $asedimentation 610 $aHDPE 610 $amicrobeads 610 $aphotocatalysis 610 $ascavengers 610 $aC,N-TiO2 610 $aremediation 610 $ananotechnology 610 $aplastic pollution 610 $avisible light photodegradation 610 $amicroplastic 610 $aratiometric detection 610 $ano-wash fluorescent probe 610 $aimaging 610 $aone-pot reaction 610 $awater remediation 610 $ananoplastic 610 $aartificial ageing 610 $apolyolefins 610 $apolyethylene terephthalate 610 $amicroplastic fiber 610 $awashing textile 610 $adrying textile 610 $apolyester yarn types 610 $amicroplastic extraction 610 $aoil extraction 610 $adensity separation 610 $aGC?MS 610 $amass spectrometry identification 610 $aplastic polymers 610 $apolyethylene 610 $aterrestrial 610 $asoil 610 $apolymers 610 $ageotechnics 610 $alandfills 610 $ageosynthetics 610 $aGCL 610 $aclay liner 610 $ahydraulic conductivity 610 $aplastics 610 $aanthropogenic activities 610 $aquantification 610 $amarine 610 $amulti-parametric platform 610 $abioplastics 610 $amarine environment 610 $aspectroscopy 610 $aresin pellets 610 $ananoplastics 610 $amicroplastic detection and identification 610 $amicroplastic quantification 610 $afood packaging 610 $aparticle release 610 $aplastic consumption 610 $aecotoxicity assessment 610 $asize influence 610 $aconcentration influence 610 $amicroplastic pellets 610 $aweathering 610 $adegradation 610 $aYellowness Index 610 $aFourier transform infrared spectroscopy 610 $apersistent organic pollutants 610 $aoxidative digestion 610 $aFenton?s reagent 610 $avirgin 610 $aaged 610 $aSEM 610 $aFTIR 610 $aPAHs 610 $asurface water 610 $achemical composition 610 $aHo Chi Minh City 610 $acement mortars 610 $amunicipal incinerated bottom ash 610 $aPET pellets 610 $ahydrogel 610 $apotassium and sodium polyacrylate 610 $aswelling 610 $aphysicochemical changes in the water 610 $apolymeric nanoparticles 610 $aPortugal 610 $aresin 610 $apharmaceutical 610 $aPVC 610 $apaint 610 $awastewater treatment plant 610 $aSouth China Sea 610 $apollution 610 $aPy-GC/MS 610 $afragmentation and degradation 610 $amechanism 615 7$aMathematics & science 615 7$aChemistry 615 7$aQuantum & theoretical chemistry 700 $aLa Nasa$b Jacopo$4edt$01319285 702 $aLa Nasa$b Jacopo$4oth 906 $aBOOK 912 $a9910619464003321 996 $aMicroplastics Degradation and Characterization$93033699 997 $aUNINA LEADER 04909nam 22006615 450 001 9910299779803321 005 20250717140310.0 010 $a94-6239-097-5 024 7 $a10.2991/978-94-6239-097-3 035 $a(CKB)3710000000379768 035 $a(EBL)3108725 035 $a(SSID)ssj0001465355 035 $a(PQKBManifestationID)11896953 035 $a(PQKBTitleCode)TC0001465355 035 $a(PQKBWorkID)11471541 035 $a(PQKB)10656877 035 $a(DE-He213)978-94-6239-097-3 035 $a(MiAaPQ)EBC3108725 035 $a(PPN)184893534 035 $a(EXLCZ)993710000000379768 100 $a20150323d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAnalysis and Enumeration $eAlgorithms for Biological Graphs /$fby Andrea Marino 205 $a1st ed. 2015. 210 1$aParis :$cAtlantis Press :$cImprint: Atlantis Press,$d2015. 215 $a1 online resource (158 p.) 225 1 $aAtlantis Studies in Computing,$x2212-8565 ;$v6 300 $aDescription based upon print version of record. 311 08$a94-6239-096-7 320 $aIncludes bibliographical references. 327 $aIntroduction -- Enumeration Algorithms -- An Application: Biological Graph Analysis -- Telling Stories: Enumerating maximal directed acyclic graphs with constrained set of sources and targets -- Enumerating bubbles: listing pairs of vertex disjoint paths -- Enumerating Cycles and (s,t)-Paths in Undirected Graphs -- Enumerating Diametral and Radial vertices and computing Diameter and Radius of a graph -- Conclusions. 330 $aIn this work we plan to revise the main techniques for enumeration algorithms and to show four examples of enumeration algorithms that can be applied to efficiently deal with some biological problems modelled by using biological networks: enumerating central and peripheral nodes of a network, enumerating stories, enumerating paths or cycles, and enumerating bubbles. Notice that the corresponding computational problems we define are of more general interest and our results hold in the case of arbitrary graphs. Enumerating all the most and less central vertices in a network according to their eccentricity is an example of an enumeration problem whose solutions are polynomial and can be listed in polynomial time, very often in linear or almost linear time in practice. Enumerating stories, i.e. all maximal directed acyclic subgraphs of a graph G whose sources and targets belong to a predefined subset of the vertices, is on the other hand an example of an enumeration problem with an exponential number of solutions, that can be solved by using a non trivial brute-force approach. Given a metabolic network, each individual story should explain how some interesting metabolites are derived from some others through a chain of reactions, by keeping all alternative pathways between sources and targets. Enumerating cycles or paths in an undirected graph, such as a protein-protein interaction undirected network, is an example of an enumeration problem in which all the solutions can be listed through an optimal algorithm, i.e. the time required to list all the solutions is dominated by the time to read the graph plus the time required to print all of them. By extending this result to directed graphs, it would be possible to deal more efficiently with feedback loops and signed paths analysis in signed or interaction directed graphs, such as gene regulatory networks. Finally, enumerating mouths or bubbles with a source s in a directed graph, that is enumerating all the two vertex-disjoint directed paths between the source s and all the possible targets, is an example of an enumeration problem in which all the solutions can be listed through a linear delay algorithm, meaning that the delay between any two consecutive solutions is linear, by turning the problem into a constrained cycle enumeration problem. Such patterns, in a de Bruijn graph representation of the reads obtained by sequencing, are related to polymorphisms in DNA- or RNA-seq data. 410 0$aAtlantis Studies in Computing,$x2212-8565 ;$v6 606 $aAlgorithms 606 $aData mining 606 $aBioinformatics 606 $aAlgorithms 606 $aData Mining and Knowledge Discovery 606 $aComputational and Systems Biology 615 0$aAlgorithms. 615 0$aData mining. 615 0$aBioinformatics. 615 14$aAlgorithms. 615 24$aData Mining and Knowledge Discovery. 615 24$aComputational and Systems Biology. 676 $a570.15118 700 $aMarino$b Andrea$4aut$4http://id.loc.gov/vocabulary/relators/aut$0222755 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299779803321 996 $aAnalysis and enumeration$91522919 997 $aUNINA