Unpuzzling friunavirus-host interactions one piece at a time: phage recognizes Acinetobacter pittii via a new K38 capsule depolymerase

Acinetobacter pittii is a species that belong to the Acinetobacter calcoaceticus-baumannii complex, increasingly recognized as major nosocomial bacterial pathogens, often associated with multiple drug-resistances. The capsule surrounding the bacteria represents a main virulence factor, helping cells avoid phage predation and host immunity. Accordingly, a better understanding of the phage infection mechanisms is required to efficiently develop phage therapy against Acinetobacter of different capsular types. Here, we report the isolation of the novel A. pittii-infecting Fri1-like phage vB_Api_3043-K38 (3043-K38) of the Podoviridae morphotype, from sewage samples. Its 41,580 bp linear double-stranded DNA genome harbours 53 open reading frames and 302 bp of terminal repeats. We show that all studied Acinetobacter Fri1-like viruses have highly similar genomes, which differentiate only at the genes coding for tailspike, likely to adapt to different host receptors. The isolated phage 3043-K38 specifically recognizes an untapped Acinetobacter K38 capsule type via a novel tailspike with K38 depolymerase activity. The recombinant K38 depolymerase region of the tailspike (center-end region) forms a thermostable trimer, and quickly degrades capsules. When the K38 depolymerase is applied to the cells, it makes them resistant to phage predation. Interestingly, while K38 depolymerase treatments do not synergize with antibiotics, it makes bacterial cells highly susceptible to the host serum complement. In summary, we characterized a novel phage-encoded K38 depolymerase, which not only advances our understanding of phage-host interactions, but could also be further explored as a new antibacterial agent against drug-resistant Acinetobacter.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit. This project has been also funded by a Research Grant 2020 of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) to H.O.info:eu-repo/semantics/publishedVersio

Explaining Binary Classifiers - Will Acute Pancreatitis be the End of You?

Machine learning techniques have demonstrated remarkable performance inmany fields but an understanding of the model's inner decision-making process isstill often lacking. To remedy this, techniques have been developed to explainmachine learning models and their predictions, constituting the field of explainability.In this report, an explainability method is proposed that describes the behavior of anarbitrary binary classification model in carefully selected regions of the data spaceinferred from analysis of an existing data set. As a case study, this method wasapplied to a model predicting hospital mortality for acute pancreatitis patients. Thecase study showed that seemingly good explanations could be extracted using themethod, although it is limited by a lack of guarantee that the explanations provide anexhaustive description of the model's behavior. The main results were presented as acollection of tables describing the model's local behavior for five different patientcohorts. From these tables both global and local trends were identified, showing theutility of the method. Even though no comprehensive description is assured by ourmethod, a partial characterization is still valuable to potentially detect unforeseenbehavior or to extract new domain knowledge from a model.Maskininlärningsmetoder har uppvisat anmärkningsvärd prestanda inommånga tillämpningsområden men en förståelse för modellernas inre beslutsfattandesaknas fortfarande ofta. För att förbättra detta har tekniker utvecklats som förklararmaskininlärningsmodeller och deras prediktioner. Detta utgör området förklarbarhet. Idenna rapport föreslås en förklarbarhetsmetod som beskriver en godtycklig binärklassificeringsmodell i noggrant utvalda områden i datarummet, där områdena ärhärledda från analys av ett existerande datamängd. Som en fallstudie tillämpasmetoden på en modell som predicerar sjukhusdödlighet hos patienter med akutpankreatit. Fallstudien visade att till synes goda förklaringar kunde extraheras genomden föreslagna metoden men att den begränsades av brist på försäkran attförklaringen ger en fullständig bild av modellens beteende. Huvudresultatenpresenterades som en samling tabeller som beskrev modellens lokala beteende förfem olika patientgrupper. Från dessa tabeller kunde globala och lokala trenderidentifieras, vilket påvisade nyttan av metoden. Trots att ingen fullständig förklaringgaranteras av metoden kan fortfarande en delvis förklaring vara användbar för attupptäcka oväntat beteende eller för att utvinna ny domänkunskap från en modell.Kandidatexjobb i elektroteknik 2023, KTH, Stockhol

Explaining Binary Classifiers - Will Acute Pancreatitis be the End of You?

Machine learning techniques have demonstrated remarkable performance inmany fields but an understanding of the model's inner decision-making process isstill often lacking. To remedy this, techniques have been developed to explainmachine learning models and their predictions, constituting the field of explainability.In this report, an explainability method is proposed that describes the behavior of anarbitrary binary classification model in carefully selected regions of the data spaceinferred from analysis of an existing data set. As a case study, this method wasapplied to a model predicting hospital mortality for acute pancreatitis patients. Thecase study showed that seemingly good explanations could be extracted using themethod, although it is limited by a lack of guarantee that the explanations provide anexhaustive description of the model's behavior. The main results were presented as acollection of tables describing the model's local behavior for five different patientcohorts. From these tables both global and local trends were identified, showing theutility of the method. Even though no comprehensive description is assured by ourmethod, a partial characterization is still valuable to potentially detect unforeseenbehavior or to extract new domain knowledge from a model.Maskininlärningsmetoder har uppvisat anmärkningsvärd prestanda inommånga tillämpningsområden men en förståelse för modellernas inre beslutsfattandesaknas fortfarande ofta. För att förbättra detta har tekniker utvecklats som förklararmaskininlärningsmodeller och deras prediktioner. Detta utgör området förklarbarhet. Idenna rapport föreslås en förklarbarhetsmetod som beskriver en godtycklig binärklassificeringsmodell i noggrant utvalda områden i datarummet, där områdena ärhärledda från analys av ett existerande datamängd. Som en fallstudie tillämpasmetoden på en modell som predicerar sjukhusdödlighet hos patienter med akutpankreatit. Fallstudien visade att till synes goda förklaringar kunde extraheras genomden föreslagna metoden men att den begränsades av brist på försäkran attförklaringen ger en fullständig bild av modellens beteende. Huvudresultatenpresenterades som en samling tabeller som beskrev modellens lokala beteende förfem olika patientgrupper. Från dessa tabeller kunde globala och lokala trenderidentifieras, vilket påvisade nyttan av metoden. Trots att ingen fullständig förklaringgaranteras av metoden kan fortfarande en delvis förklaring vara användbar för attupptäcka oväntat beteende eller för att utvinna ny domänkunskap från en modell.Kandidatexjobb i elektroteknik 2023, KTH, Stockhol

Transnational migration, changing care arrangements and left-behind children's responses in South-east Asia

The authors are grateful to the Wellcome Trust, UK, for funding the CHAMPSEA project [GR079946/B/06/Z and GR079946/Z/06/Z], Asia Research Institute for funding the conference ‘Inter-Asia Roundtable 2010 – Transnational Migration and Children in Asian Contexts' where this paper was first presented and Singapore Ministry of Education Academic Research Fund Tier 1 (R-109-000-156-112) for supporting the work behind the publication of this paper.Recent increases in the volume of labour migration from South-east Asia – and in particular the feminisation of these movements – suggest that millions of children are growing up in transnational families, separated from their migrant parents. Drawing on both quantitative and qualitative data collected in Indonesia, the Philippines, Thailand and Vietnam, the study seeks to elucidate care arrangements for left-behind children and to understand the ways in which children respond to shifts in intimate family relations brought about by (re)configurations of their care. Our findings emphasise that children, through strategies of resistance, resilience and reworking, are conscious social actors and agents of their own development, albeit within constrained situations resulting from their parents’ migration.Publisher PDFPeer reviewe

Sri Lankan female domestic workers overseas - The impact on their children

Sri Lanka, along with the Philippines and Indonesia, is a major source of migrant domestic workers. There has been little investigation into the impacts of the absence of women on their families and communities left behind. Contract migrant labor in Asia usually means leaving the family behind for two years or even longer. This paper firstly demonstrates how Sri Lankan women are increasingly becoming part of a global care chain. It draws on a survey and qualitative work among families and communities left behind by these migrant workers to explore the impacts on families and children. It examines the ways in which mothers seek to overcome the consequences of their absence on their families and children. A number of policy recommendations are made to ameliorate the negative impacts of the absence of Sri Lankan migrant domestic workers.Graeme Hugo and Swarna Ukwattahttp://www.smc.org.ph/apmj/apmj_details.php?id=104

Transnational families and the family nexus:perspectives of Indonesian and Filipino children left behind by migrant parent(s)

As a significant supplier of labour migrants, Southeast Asia presents itself as an important site for the study of children in transnational families who are growing up separated from at least one migrant parent and sometimes cared for by ‘other mothers’. Through the often-neglected voices of left-behind children, this paper investigates the impact of parental migration and the resulting reconfiguration of care arrangements on the subjective well-being of migrants’ children in two Southeast Asian countries, Indonesia and the Philippines. We theorise the child’s position in the transnational family nexus through the framework of the ‘care triangle’, representing interactions between three subject groups – ‘left-behind’ children, non-migrant parents/other carers, and migrant parent/s. Using both quantitative (from 1,010 households) and qualitative (from 32 children) data from a study of Child Health and Migrant Parents in South-East Asia (CHAMPSEA), we examine relationships within the caring spaces of both home and transnational spaces. The interrogation of different dimensions of care reveals the importance of contact with parents (both migrant and non-migrant) to subjective child well-being, and the diversity of experiences and intimacies among children in the two study countries.PostprintPeer reviewe

Securing a better living environment for left-behind children: Implications and challenges for policies

10.1177/011719681302200306Asian and Pacific Migration Journal223421-44