Come to the dark side! The role of functional traits in shaping dark diversity patterns of south-eastern European hoverflies

1. Dark diversity represents the set of species that can potentially inhabit a given area under particular ecological conditions, but are currently 'missing' from a site. This concept allows characterisation of the mechanisms determining why species are sometimes absent from an area that seems ecologically suitable for them. 2. The aim of this study was to determine the dark diversity of hoverflies in south-eastern Europe and to discuss the role of different functional traits that might increase the likelihood of species contributing to dark diversity. Based on expert opinion, the Syrph the Net database and known occurrences of species, the study estimated species pools, and observed and dark diversities within each of 11 defined vegetation types for 564 hoverfly species registered in south-eastern Europe. To detect the most important functional traits contributing to species being in dark diversity across different vegetation types, a random forest algorithm and respective statistics for variable importance were used. 3. The highest dark diversity was found for southwest Balkan sub-Mediterranean mixed oak forest type, whereas the lowest was in Mediterranean mixed forest type. Three larval feeding modes (saproxylic, and phytophagous on bulbs or roots) were found to be most important for determining the probability of a species contributing to hoverfly dark diversity, based on univariate correlations and random forest analysis. 4. This study shows that studying dark diversity might provide important insights into what drives community assembly in south-eastern European hoverflies, especially its missing components, and contributes to more precise conservation prioritisation of both hoverfly species and their habitats.Peer reviewe

MAIT cells are imprinted by the microbiota in early life and promote tissue repair

How early-life colonization and subsequent exposure to the microbiota affect long-term tissue immunity remains poorly understood. Here, we show that the development of mucosal-associated invariant T (MAIT) cells relies on a specific temporal window, after which MAIT cell development is permanently impaired. This imprinting depends on early-life exposure to defined microbes that synthesize riboflavin-derived antigens. In adults, cutaneous MAIT cells are a dominant population of interleukin-17A (IL-17A)-producing lymphocytes, which display a distinct transcriptional signature and can subsequently respond to skin commensals in an IL-1-, IL-18-, and antigen-dependent manner. Consequently, local activation of cutaneous MAIT cells promotes wound healing. Together, our work uncovers a privileged interaction between defined members of the microbiota and MAIT cells, which sequentially controls both tissue-imprinting and subsequent responses to injury

Chronic neuropsychiatric sequelae of SARS‐CoV‐2: Protocol and methods from the Alzheimer's Association Global Consortium

Introduction Coronavirus disease 2019 (COVID-19) has caused >3.5 million deaths worldwide and affected >160 million people. At least twice as many have been infected but remained asymptomatic or minimally symptomatic. COVID-19 includes central nervous system manifestations mediated by inflammation and cerebrovascular, anoxic, and/or viral neurotoxicity mechanisms. More than one third of patients with COVID-19 develop neurologic problems during the acute phase of the illness, including loss of sense of smell or taste, seizures, and stroke. Damage or functional changes to the brain may result in chronic sequelae. The risk of incident cognitive and neuropsychiatric complications appears independent from the severity of the original pulmonary illness. It behooves the scientific and medical community to attempt to understand the molecular and/or systemic factors linking COVID-19 to neurologic illness, both short and long term. Methods This article describes what is known so far in terms of links among COVID-19, the brain, neurological symptoms, and Alzheimer's disease (AD) and related dementias. We focus on risk factors and possible molecular, inflammatory, and viral mechanisms underlying neurological injury. We also provide a comprehensive description of the Alzheimer's Association Consortium on Chronic Neuropsychiatric Sequelae of SARS-CoV-2 infection (CNS SC2) harmonized methodology to address these questions using a worldwide network of researchers and institutions. Results Successful harmonization of designs and methods was achieved through a consensus process initially fragmented by specific interest groups (epidemiology, clinical assessments, cognitive evaluation, biomarkers, and neuroimaging). Conclusions from subcommittees were presented to the whole group and discussed extensively. Presently data collection is ongoing at 19 sites in 12 countries representing Asia, Africa, the Americas, and Europe. Discussion The Alzheimer's Association Global Consortium harmonized methodology is proposed as a model to study long-term neurocognitive sequelae of SARS-CoV-2 infection

History of clinical transplantation

How transplantation came to be a clinical discipline can be pieced together by perusing two volumes of reminiscences collected by Paul I. Terasaki in 1991-1992 from many of the persons who were directly involved. One volume was devoted to the discovery of the major histocompatibility complex (MHC), with particular reference to the human leukocyte antigens (HLAs) that are widely used today for tissue matching.1 The other focused on milestones in the development of clinical transplantation.2 All the contributions described in both volumes can be traced back in one way or other to the demonstration in the mid-1940s by Peter Brian Medawar that the rejection of allografts is an immunological phenomenon.3,4 © 2008 Springer New York

Supporting Tutorial Feedback to Student Help Requests and Errors in Symbolic Differentiation

Abstract. The provision of intelligent, user-adaptive, and effective feedback requires human tutors to exploit their expert knowledge about the domain of instruction, and to diagnose students ’ actions through a potentially huge space of possible solutions and misconceptions. Designers and developers of intelligent tutoring systems strive to simulate good human tutors, and to replicate their reasoning and diagnosis capabilities as well as their pedagogical expertise. This is a huge undertaking because it requires an adequate acquisition, formalisation, and operationalisation of material that supports reasoning, diagnosis, and natural interaction with the learner. In this paper, we describe SLOPERT, a glass-box reasoner and diagnoser for symbolic differentiation. Its expert task model, which is enriched with buggy rules, has been informed by an analysis of human-human tutorial dialogues. SLOPERT can provide natural step-by-step solutions for any given problem as well as diagnosis support for typical student errors. SLOPERT’s capabilities thus support the generation of natural problem-solving hints and scaffolding help.

A comparison of decision-theoretic, fixed-policy and random tutorial action selection

Abstract. DT Tutor (DT), an ITS that uses decision theory to select tutorial actions, was compared with both a Fixed-Policy Tutor (FT) and a Random Tutor (RT). The tutors were identical except for the method they used to select tutorial actions: FT employed a common fixed policy while RT selected randomly from relevant actions. This was the first comparison of a decision-theoretic tutor with a non-trivial competitor (FT). In a two-phase study, first DT’s probabilities were learned from a training set of student interactions with RT. Then a panel of judges rated the actions that RT took along with the actions that DT and FT would have taken in identical situations. DT was rated higher than RT and also higher than FT both overall and for all subsets of scenarios except help requests, for which DT’s and FT’s ratings were equivalent.

Scaffolding vs. Hints in the Assistment System

Abstract. Razzaq et al, 2005 reported that the Assistment system was causing students to learn at the computer but we were not sure if that was simply due to students getting practice or more due to the "intelligent tutoring " that we created and force students to do if they get an item wrong. Our survey indicated that some students found being forced to do scaffolding sometimes frustrating. We were not sure if all of the time we invested into these "fancy" scaffolding questions was worth it. We conducted a simple experiment to see if students learned on a set of 4 items, if they were given the scaffolds compared with just being given hints that tried to TELL them the same information that the scaffolding questions tried to ASK from them. Our results show that students that were given the scaffolds performed better although the results were not always statistically significant.