Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning approaches to identify core and dispensable genes in pangenomes

A gene in a given taxonomic group is either present in every individual (core), or absent in at least a single individual (dispensable). Previous pangenomic studies have identified certain functional differences between core and dispensable genes. However, identifying if a gene belongs to the core or dispensable portion of the genome requires the construction of a pangenome, which involves sequencing the genomes of many individuals. Here we aim to leverage the previously characterized core and dispensable gene content for two grass species (Brachypodium distachyon and Oryza sativa) to construct a machine learning model capable of accurately classifying genes as core or dispensable using only a single annotated reference genome. Such a model may mitigate the need for pangenome construction, an expensive hurdle especially in orphan crops which often lack the adequate genomic resources.
Uncharted biosynthetic potential of the ocean microbiome

Microbes are phylogenetically and metabolically diverse. Yet capturing this diversity, assigning functions to host organisms and exploring the biosynthetic potential in natural environments remains challenging. We reconstructed >25,000 draft genomes, including from >2,500 uncharacterized species, from globally-distributed ocean microbial communities, and combined them with ∼10,000 genomes from cultivated and single cells. Mining this resource revealed ∼40,000 putative biosynthetic gene clusters (BGCs), many from unknown phylogenetic groups. Among these, we discovered Candidatus Eudoremicrobiaceae as one of the most biosynthetically diverse microbes detected to date. Discrete transcriptional states structuring natural populations were associated with a potentially niche-partitioning role for BGC products. Together with the characterization of the first Eudoremicrobiaceae natural product, this study demonstrates how microbiomics enables prospecting for candidate bioactive compounds in underexplored microbes and environments.
Explicit and implicit depth-cue integration: Evidence of systematic biases with real objects

In a previous series of experiments using virtual stimuli, we found evidence that 3D shape estimation agrees to a superadditivity rule of depth-cue combination. According to this rule, adding depth cues leads to greater perceived depth magnitudes and, in principle, to depth overestimation. The mechanism underlying the superadditivity effect can be fully accounted for by a normative theory of cue integration, through the adaptation of a model of cue integration termed the Intrinsic Constraint (IC) model. As for its nature, it remains unclear whether superadditivity is a byproduct of the artificial nature of virtual environments, causing explicit reasoning to infiltrate behavior and inflate the depth judgments when a scene is richer in depth cues, or the genuine output of the process of depth-cue integration. In the present study, we addressed this question by testing whether the IC model’s prediction of superadditivity generalizes beyond VR environments to real world situations. We asked participants to judge the perceived 3D shape of cardboard prisms through a matching task. To assay the potential influence of explicit control over those perceptual estimates, we also asked participants to reach and hold the same objects with their fingertips and we analyzed the in-flight grip size during the reaching. Using physical objects ensured that all visual information was fully consistent with the stimuli’s 3D structure without computer-generated artifacts. We designed a novel technique to carefully control binocular and monocular 3D cues independently from one another, allowing to add or remove depth information from the scene seamlessly. Even with real objects, participants exhibited a clear superadditivity effect in both explicit and implicit tasks. Furthermore, the magnitude of this effect was accurately predicted by the IC model. These results confirm that superadditivity is an inherent feature of depth estimation.
Turning movements in real life capture subtle longitudinal and preataxic changes in cerebellar ataxia

OBJECTIVES Clinical and regulatory acceptance of upcoming molecular treatments in degenerative ataxias might greatly benefit from ecologically valid endpoints which capture change in ataxia severity in patients’ real life. This longitudinal study aimed to unravel quantitative motor biomarkers in degenerative ataxias in real life turning movements which are sensitive for changes both longitudinally and at the preataxic stage. METHODS Combined cross-sectional (n=30) and longitudinal (n=14, 1-year interval) observational study in degenerative cerebellar disease (including 8 pre-ataxic mutation carriers) compared to 23 healthy controls. Turning movements were assessed by three body-worn inertial sensors in three conditions: (1) instructed laboratory assessment, (2) supervised free walking, and (3) unsupervised real-life movements. RESULTS Measures which quantified dynamic balance during turning – lateral velocity change (LVC) and outward acceleration –, but not general turning measures such as speed, allowed differentiating ataxic against healthy subjects in real life with high effect size (δ=0.68), with LVC also differentiating preataxic against healthy subjects (δ=0.53). LVC was highly correlated with clinical ataxia severity (SARA score, effect size ρ=0.79) and subjective balance confidence (ABC score, ρ=0.66). Moreover, LVC in real life – but not general turning measures, gait measures, or the SARA score – allowed detecting significant longitudinal change in one-year follow-up with high effect size (rprb=0.66). CONCLUSIONS Measures of turning allow to capture specific changes of dynamic balance in degenerative ataxia in real life, with high sensitivity to longitudinal differences in ataxia severity and to the preataxic stage. They thus present promising ecologically valid motor biomarkers for capturing change in real life, even in the highly treatment-relevant early stages of degenerative cerebellar disease.
Factors Associated with Emerging and Re-emerging of SARS-CoV-2 Variants

Global spread of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has triggered unprecedented scientific efforts, as well as containment and treatment measures. Despite these efforts, SARS-CoV-2 infections remain unmanageable in some parts of the world. Due to inherent mutability of RNA viruses, it is not surprising that the SARS-CoV-2 genome has been continuously evolving since its emergence. Recently, four functionally distinct variants, B.1. 1.7, B.1. 351, P.1 and CAL.20C, have been identified, and they appear to more infectious and transmissible than the original (Wuhan-Hu-1) virus. Here we provide evidence based upon a combination of bioinformatics and structural approaches that can explain the higher infectivity of the new variants. Our results show that the greater infectivity of SARS-CoV-2 than SARS-CoV can be attributed to a combination of several factors, including alternate receptors. Additionally, we show that new SARS-CoV-2 variants emerged in the background of D614G in Spike protein and P323L in RNA polymerase. The correlation analyses showed that all mutations in specific variants did not evolve simultaneously. Instead, some mutations evolved most likely to compensate for the viral fitness.
EFFECT OF FAT EMULSIONS ON ERYTHROCYTE RIGIDITY

Emulsions of soybean o i l a t a b i l i s e d by egg l e c i t h i n (1.2%) have been used successfully a s a source of ca lo r ies i n parenteral nu t r i t ion . The clearance of the emulsions from the c i rcu la t ion and t h e i r subsequent t i s s u e deposition and metabolism have been studied extensively. However, l i t t l e is known about the e f fec t s of f a t emulsions on the microcirculation (Kroeger e t a 1 1970). Blood flow i n the human c i rcu la t ion is p u l s a t i l e and i t follows t h a t the rheological properties of blood i n time-varying flows a re of the greates t physiological significance. The v i scoe las t i c i ty of blood is strongly dependent upon the mechanical properties of erythrocytes, i n pa r t i cu la r t h e i r e l a s t i c deformation.
NHE6-Depletion Corrects ApoE4-Mediated Synaptic Impairments and Reduces Amyloid Plaque Load

Apolipoprotein E4 (ApoE4) is the most important and prevalent risk factor for late-onset Alzheimer’s disease (AD). The isoelectric point of ApoE4 matches the pH of the early endosome (EE), causing its delayed dissociation from ApoE receptors and hence impaired endolysosomal trafficking, disruption of synaptic homeostasis and reduced amyloid clearance. We have shown that enhancing endosomal acidification by inhibiting the EE-specific sodium-hydrogen exchanger NHE6 restores vesicular trafficking and normalizes synaptic homeostasis. Remarkably and unexpectedly, loss of NHE6 effectively suppressed amyloid deposition even in the absence of ApoE4, suggesting that accelerated acidification of early endosomes caused by the absence of NHE6 occludes the effect of ApoE on amyloid plaque formation. NHE6 suppression or inhibition may thus be a universal, ApoE-independent approach to prevent amyloid buildup in the brain. These findings suggest a novel therapeutic approach for the prevention of AD by which partial NHE6 inhibition reverses the ApoE4 induced endolysosomal trafficking defect and reduces amyloid.
Modelling nonlinear dendritic processing of facilitation in a dragonfly target-tracking neuron

Dragonflies are highly skilled and successful aerial predators that are even capable of selectively attending to one target within a swarm. Detection and tracking prey is likely to be driven by small target motion detector (STMD) neurons identified from several insect groups. Prior work has shown that dragonfly STMD responses are facilitated by targets moving on a continuous path, enhancing the response gain at the present and predicted future location of targets. In this study, we combined detailed morphological data with computational modelling to test whether a combination of dendritic morphology combined with the nonlinear properties of NMDA receptors could explain these observations. We developed a hybrid neuronal model of neurons within the dragonfly optic lobe, which integrates numerical and morphological components. The model was able to generate potent facilitation for targets moving on continuous trajectories, including a localized spotlight of maximal sensitivity close to the last seen target location, as also measured during in vivo recordings. The model did not, however, include a mechanism capable of producing a traveling or spreading wave of facilitation. Our data support a strong role for the high dendritic density seen in the dragonfly neuron in enhancing non-linear facilitation. An alternative model based on morphology of an unrelated type of motion processing neuron from a dipteran fly required more than 3 times higher synaptic gain in order to elicit similar levels of facilitation, despite having only 20% fewer synapses. Our data supports a potential role for NMDA receptors in target tracking and also demonstrates the feasibility of combining biologically plausible dendritic computations with more abstract computational models for basic processing as used in earlier studies.
From Suppliers to Complementors: Motivational Factors for Joining Industrial Internet of Things Platform Ecosystems

Spurred by the internet of things, industry firms are increasingly establishing platforms that animate an ecosystem of external actors to provide complementary offerings. But why do independent firms decide to join these ecosystems and to become complementors? The goal of this study is to disentangle their motivational factors in the context of the industrial internet of things. A theoretical framework is developed a priori based on the knowledge-based view of the firm and complementary logics. The framework is empirically explored using a case study design. Our results indicate that financial, technology, and knowledge gains positively influence the decision of complementors to join the ecosystem. Yet, our interviews reveal relative differences in motivations based on complementors’ uncertainty. Our findings contribute to the research on joining nascent digital platform ecosystems from a complementor per-spective and the growing stream of research on industrial internet of things platforms.
Individual Privacy Empowerment: Exploring the trade-offs between Information Sensitivity and Compensation

To provide personalized services and remain competitive, many online companies depend on individual disclosure of personal information. An emerging common theme, in the quest for privacy solutions, is the idea to empower individuals to control the management of their personal information. This study proposes a third-option design that seeks to empower users when signing up for an online service. We also measure individual privacy empowerment in a 2*2 experimental design study (reward/utility-limit mechanism to high/low sensitivity information context) using the proposed third-option design. Results from the multigroup analysis indicate that respondents prefer a reward mechanism over a utility-limit mechanism when asked to disclose less sensitive data. However, the utility-limit mechanism is preferred in the highly sensitive group indicating that a simple linear relationship does not exist between monetary rewards and information sensitivity. Theoretical and practical implications are discussed.
Does Exposure to Shared Solutions Lead to Better Outcomes? An Empirical Investigation in Online Crowdsourcing Contests

Crowdsourcing contests provide an effective way to elicit novel ideas and creative solutions from collective intelligence. A key design feature of crowdsourcing contests is the competition between contest participants to complete a specific task with financial awards to the winner(s). In recent years, some crowdsourcing contest platforms provide options to contest participants for solution sharing during the competition. This study intends to evaluate the influence of exposure to shared solutions on different stakeholders, including the team, and the requester. Our study employs a multiple-level panel data from a large online crowdsourcing platform, Kaggle.com, to examine these effects. For teams, exposure to shared solutions helps new entrant teams to jump-start and help teams to achieve better performance in the subsequent submissions, and the teams’ skill level negatively moderates these positive effects. For requesters, allowing solution sharing has both benefits and costs in terms of improving the best performance of the crowd. We highlight the theoretical implications of the study and provide practical suggestions for crowdsourcing contest platforms to help them decide whether to allow solution sharing during the competition.
Conceptualizing and Analyzing Business Ecosystem Service Offerings

Business ecosystems are dynamic structures of various actors who co-create value. By combining complementary and substitute services, these actors create integrated offerings. This paper proposes a conceptual model which supports the analysis of ecosystems by decomposing the offering into distinct modules. Each module represents a beneficiary-provider duality with a specific value proposition and activities to be performed. It further describes, how different service modules contribute and may change the network-level purpose. The research follows design science and was facilitated in a consortium setup to integrate practitioners’ insights. The paper contributes by developing design principles for a service configuration model, identifying relevant activities to describe service creation, providing a logic to configure distinct services into a whole, and introducing the concept of substitutes into the discussion.
Coping with Organizational Information Technology Events: A Perceived Organizational Support and Relationship-Focused Coping Perspective

While in recent years there is an increasing interest in researching individual technology acceptance and use based on the stress-coping perspective, relatively little attention has been paid to social-contextual factors as the antecedents of individual coping strategies and coping outcomes. The research proposal is intended to address this limitation. Specifically, drawing on coping theory, organizational support theory, and research on organizational conflict management, a model on individual use of technology that incorporate perceived organizational support (POS) and positive/negative relationship-focused coping strategies is proposed. POS as a key coping resource during stress is expected to influence employees’ primary and secondary appraisals of an IT event, which in turn affect their choices of positive/negative relationship-focused coping strategies, and the resulting technology use or nonuse. By considering the social aspect of coping, the proposed research is expected to bring additional insights to researchers and practitioners. coping – lead IS
What Makes Consumer Perception of Online Review Helpfulness: Synthesizing the Past to Guide Future Research

A growing body of academic research has aimed to investigate the helpfulness of online customer reviews (OCRs) given their prevalence and the need to better understand their appraisal mechanisms. However, past studies have applied varied methods and reported conflicting findings. This study aims to improve the understanding of the contributors to OCR helpfulness by synthesizing past studies on the topic. Based on a systematic literature review, a summary of the precursors to OCR helpfulness is provided. We decipher both the consistent and conflicting results and discuss the possible explanations for these mixed findings. By summarizing past studies, the review also points out possible directions for future research.
The Ecosystem of Machine Learning Methods

Machine learning (ML) is a rapidly evolving field and plays an important role in today’s data-driven business environment. Many digital innovations in domains as diverse as healthcare, banking, energy, and retail are powered and enabled by ML. Examples include search engines, recommendation systems, pattern recognition, computer vision, and natural language processing. A key element in ML innovation is the advancement of the underlying methods, which specify how machines should algorithmically process, derive patterns, and learn from data for a given decisioning task. The speed at which this is happening is exponential, with researchers leveraging and building upon existing building blocks as well as introducing entirely new methods. Given the speed, scale, and complexity, understanding this complex evolving ML method space can be challenging. What methods are core and peripheral to ML? Which methods span task areas? How are ML methods evolving? In this exploratory research paper, I address these questions by (1) framing the ML method space and (2) visualizing the evolving structure of the ML methods ecosystem. The results reveal several foundational ML building blocks, different coupling levels between ML areas, and variable speeds of evolution. The study also provides insights into how digital innovation evolves at an algorithmic level. I discuss the implications of the findings and describe opportunities for future ML ecosystem-focused research.
Using a Physical Activity Application to Promote Physical Activity Levels Among Aged People: A Follow-Up Study

The share and life expectancy of the ageing population are increasing. However, aged people are threatened by insufficient physical activity. Therefore, finding ways to support people to live a physically active life in older age is imperative. Digital wellness technologies represent a potential solution, but in order for such technologies to be effective, research is needed to gain a better understanding on their use among aged people. To address this need, this study investigated whether the use of a physical activity application can promote physical activity among aged people. The physical activity levels were measured at three different time points: before taking the application into use, after four months of use, and after 12 months of use. The results show a modest increase in the physical activity levels. When examining physical activity categories (based on the IPAQ-E), a participant rather shifted to a higher than to a lower physical activity category. Overall, the changes were more substantial after 12 months than after four months of use. The results suggest that physical activity applications used in everyday life have potential in promoting physical activity levels among aged people.
Towards an Intra- and Interorganizational Perspective: Objectives and Areas of Activity of Digital Innovation Units

Incumbent firms increasingly strive to embrace digital innovation, often via implementing dedicated digital innovation units (DIUs). As seizing the rapid and various digital innovation-related market movements may be overwhelming for an individual DIU, collaborations within ecosystems are perceived as crucial for continuously recognizing business opportunities and threats. Although this is a growing field of interest in recent research, insights into the objectives of DIUs and the consequent activities for effectively handling digital innovation are yet scarce. We address this issue by synthesizing 28 cases on DIUs through a qualitative meta-analysis. The analysis revealed that while DIUs enforce an intraorganizational cultural and overarching organizational design change, they also impose an interorganizational perspective with customeroriented digital expertise and innovation, as well as cultivation of digital innovation ecosystems. Thus, we contribute to the existing DIU research by clarifying these objectives and extending them to achieve a conscious interorganizational perspective with accompanying activities.
Deep learning object detection as an assistance system for complex image labeling tasks

Object detection via deep learning has many promising areas of application. However, robustness and accuracy of fully automated systems are often insufficient for practical use. Integrating results from Artificial Intelligence (AI) and human intelligence in collaborative settings might bridge the gap between efficiency and accuracy. This study proves increased efficiency when supporting human intelligence through AI without negative impact on effectiveness in a finegrained car scratch image labeling task. Based on the confirmed benefits of AI with human intelligence in the loop approaches, this contribution discusses potential practical application scenarios and envisions the implementation of assistance systems supported by
Whose Advice Counts More - Man or Machine? An Experimental Investigation of AI-based Advice Utilization

Due to advances in Artificial Intelligence (AI), it is possible to provide advisory services without human advisors. Derived from judge-advisor system literature, we examined differences in the advice utilization depending on whether it is given by an AI-based or human advisor and the similarity of the advice and their own estimation. Drawing on task-technology fit we investigated the relationship between task, advisor and advice utilization. In study A we measured the actual advice utilization within a guessing game and in study B we measured the perceived task-advisor fit for this game. The findings show that compared to human advisors, judges utilize advices of AI-based advisors more when the advice is similar to their own estimation. When the advice is very different to their estimation, the advices are used equally. Concluding, we investigated AI-based advice utilization and presented insights for professionals providing AI-based advisory services.