Succinylation of potato (Ipomoea batatas) starch: effect of varying ethanol concentration on pasting, structural, morphological, and functional properties

The shortcomings arising from usage of native starches in various industrial processes have demanded the need for modification in order to enhance its functionality. This study investigated the impact of ethanol concentration on pasting, structural, morphological, and functional properties of succinylated potato starch. Some granules of the potato starch sample also appeared to be multilobed and fractured. The Fourier transform -1 infrared spectroscopy (FT-IR) study revealed the presence of carbonyl group (1747 cm-1 ) in the starch chains as an additional functional group after succinylation. The statistical analysis of the study data revealed a significant (p < 0.05) decrease in protein, fat, fibre, ash, and reduction in moisture contents after modification. Analysis with visco-analyzer showed reduction in pasting parameters after modification. Modification of native potato starch with ethanol improved the functionality of the starch by imparting some additional physicochemical and functional properties. These improved properties upon modification, therefore, suggest some potential favorable qualities for special applications

Growing up to belong transnationally : parent perceptions on identity formation among Latvian emigrant children in England

As a result of the wide availability of social media, cheap flights and free intra-EU movement it has become considerably easier to maintain links with the country of origin than it was only a generation ago. Therefore, the language and identity formation among children of recent migrants might be significantly different from the experiences of children of the previous generations. The aim of this paper is to examine the perceptions of parents on the formation of national and transnational identity among the ‘1.5 generation migrant children’ – the children born in Latvia but growing up in England and the factors affecting them. In particular, this article seeks to understand whether 1.5 generation migrant children from Latvia construct strong transnational identities by maintaining equally strong ties with their country of origin and mother tongue and, at the same time, intensively creating networks, learning and using the language of the new home country. The results of 16 semi-structured in-depth interviews with the parents of these children reveal that the 1.5 generation Latvian migrants are on a path of becoming English-dominant bilinguals. So far there is little evidence of the development of a strong transnational identity among 1.5 generation migrant children from Latvia. Instead, this study observed a tendency towards an active integration and assimilation into the new host country facilitated by their parents or occurring despite their parents’ efforts to maintain ties with Latvia. These findings suggest that rather than the national identity of the country of origin being supplemented with a new additional national identity – that of the country of settlement – the identity of the country of origin becomes dominated by it instead

AfriQA:Cross-lingual Open-Retrieval Question Answering for African Languages

African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems -- those that retrieve answer content from other languages while serving people in their native language -- offer a means of filling this gap. To this end, we create AfriQA, the first cross-lingual QA dataset with a focus on African languages. AfriQA includes 12,000+ XOR QA examples across 10 African languages. While previous datasets have focused primarily on languages where cross-lingual QA augments coverage from the target language, AfriQA focuses on languages where cross-lingual answer content is the only high-coverage source of answer content. Because of this, we argue that African languages are one of the most important and realistic use cases for XOR QA. Our experiments demonstrate the poor performance of automatic translation and multilingual retrieval methods. Overall, AfriQA proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

The Case for Translanguaging in Black Immigrant Literacies

Black immigrant youth in the United States tend to be considered a new model minority because of the perception that they perform academically better than their African American peers. Yet, Black immigrant youth face challenges with literacy performance that often go unnoticed by teachers, which amplifies the invisibility of their literacies. I assert that nuances presented in the literacies of Black immigrant youth as they become Black, become immigrants, and become speakers of racialized Englishes, necessitate the use of a theoretical lens that allows researchers to foreground race and mediate the linguistic and cultural expectations of these youth. To demonstrate, I propose the lens of translanguaging premised on an integrated model of multilingualism. This perspective provides a basis for foregrounding racialized language to facilitate an ideological understanding surrounding how Black immigrant youth leverage Englishes in their individual linguistic repertoires (i.e., I-languages) to enact literacies. At the same time, it provides an opportunity to understand how these youth engage the often-imposed structural rules of shared grammars within and across these Englishes (i.e., E-languages) in ways that influence these literacies. By presenting Black Caribbean immigrant youth’s literacies as a unique example to foreground racialized language via translanguaging for examining the Englishes of bidialectal youth, I encourage researchers to empower youth to navigate theoretical tensions that remain central to translanguaging research. More broadly, I invite researchers to think beyond dichotomies while clarifying what it means for youth of color from varied backgrounds to leverage their literacies and thrive

AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages

African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems -- those that retrieve answer content from other languages while serving people in their native language -- offer a means of filling this gap. To this end, we create AfriQA, the first cross-lingual QA dataset with a focus on African languages. AfriQA includes 12,000+ XOR QA examples across 10 African languages. While previous datasets have focused primarily on languages where cross-lingual QA augments coverage from the target language, AfriQA focuses on languages where cross-lingual answer content is the only high-coverage source of answer content. Because of this, we argue that African languages are one of the most important and realistic use cases for XOR QA. Our experiments demonstrate the poor performance of automatic translation and multilingual retrieval methods. Overall, AfriQA proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology