Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of a Vancomycin Dose-Optimization Protocol in Neonates: Impact on Vancomycin Exposure, Biological Parameters, and Clinical Outcomes

Vancomycin dosing used in neonates results frequently in insufficient concentrations. A vancomycin dose-optimization protocol consisting of an individualization of loading and maintenance doses (administered during continuous infusion) through a previously validated pharmacokinetic model was implemented in our center. ABSTRACT Vancomycin dosing used in neonates results frequently in insufficient concentrations. A vancomycin dose-optimization protocol consisting of an individualization of loading and maintenance doses (administered during continuous infusion) through a previously validated pharmacokinetic model was implemented in our center. This monocenter retrospective study aimed to compare vancomycin average concentration (Cavg) in the therapeutic range (15 to 25 mg/L) and biological and clinical parameters before and after implementation of this protocol. A total of 60 and 59 courses of vancomycin treatment in 45 and 49 patients were analyzed in groups before and after implementation, respectively. Initial vancomycin Cavg were more frequently in the therapeutic range in the group after implementation (74.6% versus 28.3%, P < 0.001), with 1.6-fold higher Cavg (20.3 [17.0–22.2] mg/L versus 12.9 [11.3–17.0] mg/L, P < 0.001). Considering all Cavg during longitudinal therapeutic drug monitoring (TDM), the frequency of therapeutic Cavg was higher in the group after implementation (74.8% [n = 103] versus 31% [n = 116], P < 0.001). The dose optimization protocol was also associated with a reduced time to obtain a negative blood culture (P < 0.001) and fewer antibiotic switches (P = 0.025), without increasing the frequency of nephrotoxicity. Clinical outcomes also appeared to be improved, with less periventricular leukomalacia (P = 0.021), trended toward less respiratory instability (P = 0.15) and a shorter duration of vasoactive drug use (P = 0.18) for neonates receiving personalized doses of vancomycin. This personalized vancomycin dose protocol improves vancomycin exposure in neonates, with good safety, and suggests an improvement in biological and clinical outcomes.
The Effect of the Second Messenger c-di-GMP on Bacterial Chemotaxis in Escherichia coli

The ubiquitous bacterial second messenger c-di-GMP was known to regulate chemotaxis in many bacterial species, but its effect on E. coli chemotaxis was unclear. Here we studied the effect of elevated c-di-GMP levels on chemotaxis in E. coli. ABSTRACT c-di-GMP is a ubiquitous bacterial second messenger that plays a central regulatory role in diverse biological processes. c-di-GMP was known to regulate chemotaxis in multiple bacterial species, but its effect on Escherichia coli chemotaxis remained unclear. As an effector of c-di-GMP in E. coli, YcgR when bound with c-di-GMP interacts with the flagellar motor to reduce its speed and its probability of rotating clockwise (CW bias). Here, we found that a significant fraction of the c-di-GMP::YcgR dynamically exchange between the motor and the cytosol. Through fluorescent measurements, we found that there was no competitive binding between the chemotaxis response regulator CheY-P and c-di-GMP::YcgR to the motor. To test the influence of elevated c-di-GMP levels on the chemotaxis pathway, we measured the chemotactic responses of E. coli cells using a FRET assay, finding that elevated c-di-GMP levels had no effect on the upstream part of chemotaxis pathway down to the level of CheY-P concentration. This suggested that the possible effect of elevated c-di-GMP levels on chemotactic motion was through regulation of motor speed and CW bias. Using stochastic simulations of chemotactic swimming, we showed that the effects of reducing motor speed and decreasing CW bias on chemotactic drift velocity are compensating for each other, resulting in minimal effect of elevated c-di-GMP levels on E. coli chemotaxis. Therefore, elevated c-di-GMP levels promote the transition from motile to sedentary forms of bacterial life by reducing the bacterial swimming speed and CW bias, while still maintaining a nearly intact chemotaxis capability in E. coli. IMPORTANCE The ubiquitous bacterial second messenger c-di-GMP was known to regulate chemotaxis in many bacterial species, but its effect on E. coli chemotaxis was unclear. Here we studied the effect of elevated c-di-GMP levels on chemotaxis in E. coli. We found that the binding of c-di-GMP::YcgR (its effector) and the chemotaxis response regulator CheY-P to the flagellar motor are noncompetitive, and elevated c-di-GMP levels do not affect the upstream part of the chemotaxis pathway down to the level of CheY-P concentration. Elevated c-di-GMP levels exert direct effects on the flagellar motor by reducing its speed and CW bias, but the resulting effects on chemotaxis performance are compensating for each other. Our findings here showed that elevated c-di-GMP levels maintain a nearly intact chemotaxis capability when promoting the transition from motile to sedentary forms of bacterial life in E. coli.
Generating Simple Directed Social Network Graphs for Information Spreading

Online social networks are a dominant medium in everyday life to stay in contact with friends and to share information. In Twitter, users can connect with other users by following them, who in turn can follow back. In recent years, researchers studied several properties of social networks and designed random graph models to describe them. Many of these approaches either focus on the generation of undirected graphs or on the creation of directed graphs without modeling the dependencies between reciprocal (i.e., two directed edges of opposite direction between two nodes) and directed edges. We propose an approach to generate directed social network graphs that creates reciprocal and directed edges and considers the correlation between the respective degree sequences. Our model relies on crawled directed graphs in Twitter, on which information w.r. t. a topic is exchanged or disseminated. While these graphs exhibit a high clustering coefficient and small average distances between random node pairs (which is typical in real-world networks), their degree sequences seem to follow a χ2-distribution rather than power law. To achieve high clustering coefficients, we apply an edge rewiring procedure that preserves the node degrees. We compare the crawled and the created graphs, and simulate certain algorithms for information dissemination and epidemic spreading on them. The results show that the created graphs exhibit very similar topological and algorithmic properties as the real-world graphs, providing evidence that they can be used as surrogates in social network analysis. Furthermore, our model is highly scalable, which enables us to create graphs of arbitrary size with almost the same properties as the corresponding real-world networks.
Lightning Fast and Space Efficient k-clique Counting

K-clique counting is a fundamental problem in network analysis which has attracted much attention in recent years. Computing the count of k-cliques in a graph for a large k (e.g., k = 8) is often intractable as the number of k-cliques increases exponentially w.r. t. (with respect to) k. Existing exact k-clique counting algorithms are often hard to handle large dense graphs, while sampling-based solutions either require a huge number of samples or consume very high storage space to achieve a satisfactory accuracy. To overcome these limitations, we propose a new framework to estimate the number of k-cliques which integrates both the exact k-clique counting technique and two novel color-based sampling techniques. The key insight of our framework is that we only apply the exact algorithm to compute the k-clique counts in the sparse regions of a graph, and use the proposed sampling-based techniques to estimate the number of k-cliques in the dense regions of the graph. Specifically, we develop two novel dynamic programming based k-color set sampling techniques to efficiently estimate the k-clique counts, where a k-color set contains k nodes with k different colors. Since a k-color set is often a good approximation of a k-clique in the dense regions of a graph, our sampling-based solutions are extremely efficient and accurate. Moreover, the proposed sampling techniques are space efficient which use near-linear space w.r. t. graph size. We conduct extensive experiments to evaluate our algorithms using 8 real-life graphs. The results show that our best algorithm is at least one order of magnitude faster than the state-of-the-art sampling-based solutions (with the same relative error 0.1%) and can be up to three orders of magnitude faster than the state-of-the-art exact algorithm on large graphs.
Will You Accept the AI Recommendation? Predicting Human Behavior in AI-Assisted Decision Making

Internet users make numerous decisions online on a daily basis. With the rapid advances in AI recently, AI-assisted decision making—in which an AI model provides decision recommendations and confidence, while the humans make the final decisions—has emerged as a new paradigm of human-AI collaboration. In this paper, we aim at obtaining a quantitative understanding of whether and when would human decision makers adopt the AI model’s recommendations. We define a space of human behavior models by decomposing the human decision maker’s cognitive process in each decision-making task into two components: the utility component (i.e., evaluate the utility of different actions) and the selection component (i.e., select an action to take), and we perform a systematic search in the model space to identify the model that fits real-world human behavior data the best. Our results highlight that in AI-assisted decision making, human decision makers’ utility evaluation and action selection are influenced by their own judgement and confidence on the decision-making task. Further, human decision makers exhibit a tendency to distort the decision confidence in utility evaluations. Finally, we also analyze the differences in humans’ adoption behavior of AI recommendations as the stakes of the decisions vary.
Efficient and Effective Similarity Search over Bipartite Graphs

Similarity search over a bipartite graph aims to retrieve from the graph the nodes that are similar to each other, which finds applications in various fields such as online advertising, recommender systems etc. Existing similarity measures either (i) overlook the unique properties of bipartite graphs, or (ii) fail to capture high-order information between nodes accurately, leading to suboptimal result quality. Recently, Hidden Personalized PageRank (HPP) is applied to this problem and found to be more effective compared with prior similarity measures. However, existing solutions for HPP computation incur significant computational costs, rendering it inefficient especially on large graphs. In this paper, we first identify an inherent drawback of HPP and overcome it by proposing bidirectional HPP (BHPP). Then, we formulate similarity search over bipartite graphs as the problem of approximate BHPP computation, and present an efficient solution Approx-BHPP. Specifically, Approx-BHPP offers rigorous theoretical accuracy guarantees with optimal computational complexity by combining deterministic graph traversal with matrix operations in an optimized and non-trivial way. Moreover, our solution achieves significant gain in practical efficiency due to several carefully-designed optimizations. Extensive experiments, comparing BHPP against 8 existing similarity measures over 7 real bipartite graphs, demonstrate the effectiveness of BHPP on query rewriting and item recommendation. Moreover, Approx-BHPP outperforms baseline solutions often by up to orders of magnitude in terms of computational time on both small and large datasets.
Measuring Annotator Agreement Generally across Complex Structured, Multi-object, and Free-text Annotation Tasks

When annotators label data, a key metric for quality assurance is inter-annotator agreement (IAA): the extent to which annotators agree on their labels. Though many IAA measures exist for simple categorical and ordinal labeling tasks, relatively little work has considered more complex labeling tasks, such as structured, multi-object, and free-text annotations. Krippendorff’s α, best known for use with simpler labeling tasks, does have a distance-based formulation with broader applicability, but little work has studied its efficacy and consistency across complex annotation tasks. We investigate the design and evaluation of IAA measures for complex annotation tasks, with evaluation spanning seven diverse tasks: image bounding boxes, image keypoints, text sequence tagging, ranked lists, free text translations, numeric vectors, and syntax trees. We identify the difficulty of interpretability and the complexity of choosing a distance function as key obstacles in applying Krippendorff’s α generally across these tasks. We propose two novel, more interpretable measures, showing they yield more consistent IAA measures across tasks and annotation distance functions.
Early Identification of Depression Severity Levels on Reddit Using Ordinal Classification

User-generated text on social media is a promising avenue for public health surveillance and has been actively explored for its feasibility in the early identification of depression. Existing methods in the identification of depression have shown promising results; however, these methods were all focused on treating the identification as a binary classification problem. To date, there has been little effort towards identifying users’ depression severity level and disregard the inherent ordinal nature across these fine-grain levels. This paper aims to make early identification of depression severity levels on social media data. To accomplish this, we built a new dataset based on the inherent ordinal nature over depression severity levels using clinical depression standards on Reddit posts. The posts were classified into 4 depression severity levels covering the clinical depression standards on social media. Accordingly, we reformulate the early identification of depression as an ordinal classification task over clinical depression standards such as Beck’s Depression Inventory and the Depressive Disorder Annotation scheme to identify depression severity levels. With these, we propose a hierarchical attention method optimized to factor in the increasing depression severity levels through a soft probability distribution. We experimented using two datasets (a public dataset having more than one post from each user and our built dataset with a single user post) using real-world Reddit posts that have been classified according to questionnaires built by clinical experts and demonstrated that our method outperforms state-of-the-art models. Finally, we conclude by analyzing the minimum number of posts required to identify depression severity level followed by a discussion of empirical and practical considerations of our study.
To Trust or Not To Trust: How a Conversational Interface Affects Trust in a Decision Support System

Trust is an important component of human-AI relationships and plays a major role in shaping the reliance of users on online algorithmic decision support systems. With recent advances in natural language processing, text and voice-based conversational interfaces have provided users with new ways of interacting with such systems. Despite the growing applications of conversational user interfaces (CUIs), little is currently understood about the suitability of such interfaces for decision support and how CUIs inspire trust among humans engaging with decision support systems. In this work, we aim to address this gap and answer the following question: to what extent can a conversational interface build user trust in decision support systems in comparison to a conventional graphical user interface? To this end, we built a text-based conversational interface, and a conventional web-based graphical user interface. These served as the means for users to interact with an online decision support system to help them find housing, given a fixed set of constraints. To understand how the accuracy of the decision support system moderates user behavior and trust across the two interfaces, we considered an accurate and inaccurate system. We carried out a 2 × 2 between-subjects study (N = 240) on the Prolific crowdsourcing platform. Our findings show that the conversational interface was significantly more effective in building user trust and satisfaction in the online housing recommendation system when compared to the conventional web interface. Our results highlight the potential impact of conversational interfaces for trust development in decision support systems.
Studying Learning in the Worldwide Classroom Research into edX's First MOOC.

Abstract “Circuits and Electronics” (6.002x), which began in March 2012, was the ﬁrst MOOC developed by edX, the consortium led by MIT and Harvard. Over 155,000 students initially registered for 6.002x, which was composed of video lectures, interactive problems, online laboratories, and a discussion forum. As the course ended in June 2012, researchers began to analyze the rich sources of data it generated. This article describes both the ﬁrst stage of this research, which examined the students’ use of resources by time spent on each, and a second stage that is producing an in-depth picture of who the 6.002x students were, how their own background and capabilities related to their achievement and persistence, and how their interactions with 6.002x’s curricular and pedagogical components contributed to their level of success in the course. Studying Learning in the Worldwide ClassroomResearch into edX’s First MOOC F rom the launch of edX, the joint venture between MIT and Harvard to create and disseminate massive online open courses (MOOCs), the leaders of both institutions have emphasized that research into learning will be one of the initiative’s core missions. As numerous articles in both the academic and popular press have pointed out, the ability of MOOCs to generate a tremendous amount of data opens up considerable opportunities for educational research. edX and Coursera, which together claim almost four and a half million enrollees, have developed platforms that track students’ every click as they use instructional resources, complete assessments, and engage in social interactions. These data have the potential to help researchers identify, at a ﬁner resolution than ever before, what contributes to students’ learning and what hampers their success. The challenge for the research and assessment communities is to determine which questions should be asked and in what priority. How can we set ourselves on a path that will produce useful short-term results while providing a foundation upon which to build? What is economically feasible? What is politically possible? How can research into MOOCs contribute to an understanding of on-campus learning? What do stakeholders—faculty, developers, government agencies, foundations, and, most importantly, students—need in order to realize the potential of digital learning, generally, and massive open online courses, speciﬁcally?
A phased chromosome-level genome and full mitochondrial sequence for the dikaryotic myrtle rust pathogen, Austropuccinia psidii

The fungal plant pathogen Austropuccinia psidii is spreading globally and causing myrtle rust disease symptoms on plants in the family Myrtaceae. A. psidii is dikaryotic, with two nuclei that do not exchange genetic material during the dominant phase of its life-cycle. Phased and scaffolded genome resources for rust fungi are important for understanding heterozygosity, mechanisms of pathogenicity, pathogen population structure and for determining the likelihood of disease spread. We have assembled a chromosome-level phased genome for the pandemic biotype of A. psidii and, for the first time, show that each nucleus contains 18 chromosomes, in line with other distantly related rust fungi. We show synteny between the two haplo-phased genomes and provide a new tool, ChromSyn, that enables efficient comparisons between chromosomes based on conserved genes. Our genome resource includes a fully assembled and circularised mitochondrial sequence for the pandemic biotype.
Cluster-Based Inference for Memory-Based Cognition

This paper proposes a model of hippocampal-based category learning using a cluster-based inference framework which produces two systems (i) an extendable cluster-based memory module (CMM) that provides representations of learning episodes with strong pattern separation, and supports online decision making during that learning, (ii) cluster-based task modules (CTMs) which consolidate clusters learnt by CMM to support higher-accuracy decision making in the mid-term. Learning in CMMs optimises the joint probability of stimuli and category labels, whereas learning in CTMs optimises the probability of categories given stimuli. The latter system learns from the former via a process of “cluster consolidation”. We apply the model to data from a behavioral learning task and find that, as well as improving categorisation performance, cluster consolidation decreases recognition scores for old cues but increases them for novel cues. This model-based perspective explains forgetting and false memory effects as serving future categorisation performance. The paper also expresses a view that models of memorybased cognition should provide human-level performance on complex categorisation tasks, and do so with minimal labelled data. In working toward this goal we therefore compared the accuracy of CMM- and CTM-based decision making to standard Softmax Regression approaches on benchmark machine learning datasets. This produced mixed results. We found some significant improvements of CMM over Softmax Regression and of CTM over CMM. Overall, our framework reconciles complementary learning systems theory with more recent findings from cognitive neuroscience of generative replay and hippocampal-based generalisation.