Trace Data Research Articles

Loss of Htr1B Leads to a Failure in the Neonate Autoresuscitation Reflex

Sudden Infant Death Syndrome (SIDS) is the leading cause of death in infants under the age of 1, with an unknown etiology, that takes the lives of 3,400 infants per year in the US alone. Interestingly, SIDS cases show males predominance (60%), however this sexual disparity in is not fully understood. SIDS is believed to follow a triple risk hypothesis model where the infant has an innate vulnerability, is at a critical developmental period, and is challenged with an exogenous stressor. A leading hypothesis indicates many SIDS cases are mechanistically driven by a failure in the autoresuscitation reflex. The autoresuscitation reflex is a series of gasps that occur to revitalize normal cardiorespiratory function when an infant falls into a hypoxic coma, characterized by an apnea and bradycardia, due to severe hypoxic conditions (i.e., rebreathing while sleeping in face down or with the face blocked). Although the cause of failure in the autoresuscitation reflex for SIDS remains unknown, post-mortem studies have found abnormalities related to the central serotonin (5-HT) neurotransmitter system, including several 5-HT receptors (5-HTr’s). Follow up studies in animal models show that 5-HT system perturbations result in a significant survival decrease in neonatal animals during anoxic (3% CO2/balance N2) gas challenges that test the autoresuscitation function, including pharmacological receptor manipulations. Furthermore, adult animal studies report that 5-HT receptor stimulation alters various respiratory parameters (i.e., tidal volume, respiratory rate, gasping). What remains to be understood is the cellular and molecular mechanisms through which 5-HT plays a role in the autoresuscitation reflex and how those mechanism may be disrupted in SIDS. We hypothesize that Htr1B is required for successful autoresuscitation. To test this hypothesis, we utilized an automated closed loop robotic platform for neonate cardio-respiratory measurements ( Looper) to assay P7 Htr1B loss of function mice for their abilities to survive repeated anoxic gas challenges testing the autoresuscitation reflex. Using Looper, we find that the loss of Htr1B decreases survival to repeated anoxic challenges compared to wild type (WT) controls in both male and female mice. Additionally, a subset of male heterozygous Htr1B mice survive fewer challenges compared to WT mice, whereas others survive a similar number of challenges as WT mice; a phenotype not observed in female mice. Our data supports the hypothesis that modulations to the serotonin system via Htr1B is important in eliciting the autoresuscitation response. Future studies aim to investigate additional respiratory parameters from respiratory trace data, in addition to recovery parameters, and functionally mapping out where in the brainstem and what neuronal populations require the expression of this receptor for the autoresuscitation reflex. R01 HL161142. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

The Role of OPRM1 in the Neonatal Autoresuscitation Reflex and Sudden Infant Death Syndrome

Opioid receptors are widely expressed throughout the brain and integral in nociception as well as homeostatic regulation of autonomic processes including respiration through modulation of respiratory rhythmogenesis and chemosensory responses. Perturbations to homeostatic mu opioid receptor (MOR) signaling via opiate overdose results in respiratory depression, aberrant breathing, and often death. With the rise of opioid use over the last two decades, many infants are being diagnosed with Neonatal Abstinence Syndrome (NAS), a congenital cardio-respiratory disorder resulting from prenatal exposure to opioids. Infants born with NAS have a fourfold increased risk for Sudden Infant Death Syndrome (SIDS). SIDS and related Sudden Unexpected Infant Death takes the lives of approximately 3,400 infants annually under the age of one and its etiology remains mostly unknown. Currently SIDS is explained via the triple risk model that proposes that SIDS occurs in a vulnerable infant during a critical developmental period when triggered by an external stressor, converging on the failure of the neonate autoresuscitation reflex in most cases. Recently a study identified an association between a mutation in the OPRM1 gene encoding MORs and SIDS cases. However, how this gene and perturbations in its native function modulates respiratory regulation are poorly understood. We hypothesize that native OPRM1 function is integral to protective neonate respiratory reflexes and that perturbation of native OPRM1 signaling negatively affects the neonatal autoresuscitation reflex. Male and female OPRM1 loss of function mice were assayed for their autoresuscitation reflex on our novel testbed, Looper. Mice were exposed to repeated bouts of anoxia and allowed to autoresuscitate until death occurred. Preliminary data suggest that loss of OPRM1 function results in a higher mortality rate on the autoresuscitation assay with OPRM1 KO mice surviving significantly less anoxic challenges compared to wild type controls. No sex differences were observed between male and female survival for all genotype groups. Results indicate that perturbation of the native function of OPRM1 produces a vulnerability to autoresuscitation dysfunction and mitigated survival. Our data demonstrate the first functional assessment of OPRM1 in neonatal cardiovascular function and autoresuscitation and reveal an important risk factor that may be implicated in perpetuating SIDS and NAS phenotypes. Additional analyses of distinct parameters from respiratory trace data for OPRM1 loss of function mice will aid in a deeper, mechanistic understanding of features implicated in autoresuscitation dysfunction. Future studies aim to characterize specific splice variants of OPRM1, known to produce neomorphic function and model the SIDS associated VUS, to further characterize the implication of OPRM1 and its variants in congenital respiratory diseases. Funding sources NIH/NHLBI R01HLN161142-01 and R01HL130249-07. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Remodeling of Ventricular Catecholaminergic Axons Following Chronic Intermittent Hypoxia in Mice

Chronic intermittent hypoxia (CIH) is a widely employed model for sleep apnea. The imbalance of autonomic control may contribute to CIH-induced cardiovascular diseases. Previously, we demonstrated that CIH increases sympathetic innervation of the atria. In this study, we determined whether CIH remodels peripheral cardiac tyrosine hydroxylase (TH, a marker for sympathetic efferent axons) innervation in the whole ventricles. The two challenges here were: 1) labeling the entire TH-IR axon innervation in the full thickness (~500 μm) of whole flat-mount ventricles. 2) Quantifying the topographical distribution of TH-IR axons through the thick ventricular walls at the single axon/varicosity scale. In this study, C57BL/6J mice (male, 2 months old, n=7/group) were exposed to room air (RA, 21% O2) or CIH (alternating between 21% and 5.7% O2 every 6 minutes for 10 hours/day) for 8-10 weeks. We prepared flat-mounts of the whole ventricles and septum and processed them with immunohistochemical labeling for TH. Using the Zeiss M2 Imager, confocal microscope, Zeiss Arivis Vision 4D, and Neurolucida system, we traced and digitized the TH-IR axons. We found that: 1) In RA and CIH mice, several large TH-immunoreactive (TH-IR) bundles from the base area entered the ventricles and septum. These large bundles branched into numerous smaller bundles as they traveled downward toward the apex and finally developed into fine varicose axons and terminals in the epicardial and myocardial layers. 2) Using Vision 4D, we were able to accurately detect, trace, and digitize all TH-IR axons in the ventricle walls with high accuracy. 3) CIH increased overall TH-IR axon density and network complexity of the whole ventricles and septum. 3) Abnormal CIH TH-IR axon terminal structures were frequently seen. 4) We geometrically and precisely registered the tracing data into a scaffold map for presentation and comparison in 3D. Altogether, CIH increased sympathetic innervation of the heart which may contribute to the augmented sympathetic drive. Our success in mapping data in the 3D heart scaffold will establish a heart-brain connectome/atlas. Supported by NIH 1 U01 NS113867-01 NIH R15 1R15HL137143-01A1, 2R01HL137832-05 and Carl Zeiss. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Butyrylcholinesterase Is Not Required for the Neonatal Autoresuscitation Reflex

Sudden Infant Death Syndrome (SIDS) is the leading cause of infant death under the age of one year of age, taking the lives of more than 1,400 infants annually, and comprising approximately 41% of all Sudden Unexpected Infant Death (SUID) cases. SIDS etiology remains vastly unknown but is best explained via a triple risk model that proposes SIDS occurs in a vulnerable infant during a critical developmental period when triggered by an external stressor. In SIDS it is hypothesized that there is first a failure in arousal followed by failure in the protective autoresuscitation reflex; a defense mechanism for an infant that is in a hypoxic coma, that promotes deep gasping to attempt to restart cardiovascular function. Many SIDS case studies reveal associations with genetic variants and abnormal biomarkers; however, it is unclear whether these variants are causal. Recently a case study identified a broad association between reduced blood BChE activity and SIDS cases, yet the functional relationship between BChE and respiratory regulation remains to be investigated. We aimed to functionally test the relationship between BChE and the neonatal autoresuscitation reflex utilizing a rapid pipeline for precision genetic modeling and high-throughput functional testing developed by the Ray Lab. BChE loss of function mice, generated through targeted genetic editing, were tested for neonatal autoresuscitation on our automated closed loop phenotyping platform, Looper. Postnatal day 7-8 mice were exposed to 3% CO2 and 97% N2 stimuli to induce a state of hypoxia resulting in detection of bradycardia, initial hyperventilation, hypoventilation, and apnea followed by restoration of room air conditions to facilitate the autoresuscitation reflex and cardio-respiratory recovery. Male and female mice were exposed to repeated anoxic gas challenges and allowed to autoresuscitate until death occurred. Data suggest that loss of BChE function does not affect neonatal autoresuscitation. BChE knockout mice and heterozygous mice survived similar amounts of anoxic challenges as wild type controls. No sex differences were observed between the number of anoxic challenges survived in males and females for all genotypes. Our data demonstrate the first functional assessment of BChE in neonatal cardio-respiratory regulation and the autoresuscitation response and highlight that BChE is not required for successful neonatal autoresuscitation. Additional analyses of respiratory parameters from respiratory trace data for BChE loss of function mice will aid in a further understanding if BChE loss of function is implicated in other aspects respiratory regulation. Funding: Precision Core funding U54 OD030165. NIH/NHLBI R01HLN161142-01 and R01HL130249-07. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Diverse and asymmetric patterns of single-neuron projectome in regulating interhemispheric connectivity

The corpus callosum, historically considered primarily for homotopic connections, supports many heterotopic connections, indicating complex interhemispheric connectivity. Understanding this complexity is crucial yet challenging due to diverse cell-specific wiring patterns. Here, we utilized public AAV bulk tracing and single-neuron tracing data to delineate the anatomical connection patterns of mouse brains and conducted wide-field calcium imaging to assess functional connectivity across various brain states in male mice. The single-neuron data uncovered complex and dense interconnected patterns, particularly for interhemispheric-heterotopic connections. We proposed a metric “heterogeneity” to quantify the complexity of the connection patterns. Computational modeling of these patterns suggested that the heterogeneity of upstream projections impacted downstream homotopic functional connectivity. Furthermore, higher heterogeneity observed in interhemispheric-heterotopic projections would cause lower strength but higher stability in functional connectivity than their intrahemispheric counterparts. These findings were corroborated by our wide-field functional imaging data, underscoring the important role of heterotopic-projection heterogeneity in interhemispheric communication.

Determinants of willingness to donate data from social media platforms

ABSTRACT Social media data donation through data download packages (DDPs) is a promising new way of collecting individual-level digital trace data with informed consent. Nevertheless, given the novelty of this approach, little is known about whether and how people would share their data with researchers, although this could seriously affect selection bias and thus, the outer validity of the results. To study the determinants of data-sharing and help future data donation studies with detecting the conditions, under which the willingness is the highest, we pre-registered two vignette experiments and embedded them in two online surveys conducted in Hungary and the US. In hypothetical requests for donating social media data via DDPs, we manipulated the amount of the monetary incentives (1), the presence or lack of non-monetary incentives (2), the number of requested platforms (3), the estimated upload/download time (4), and the type of requested data (5). The results revealed that data-sharing attitude is strongly subject to the parameters of the actual study, how the request is framed, and some respondent characteristics. Monetary incentives increased willingness to participate in both countries, while other effects were not consistent between the two countries.

Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: Simulation-Driven Approach.

Accurate and responsive epidemiological simulations of epidemic outbreaks inform decision-making to mitigate the impact of pandemics. These simulations must be grounded in quantities derived from measurements, among which the parameters associated with contacts between individuals are notoriously difficult to estimate. Digital contact tracing data, such as those provided by Bluetooth beaconing or GPS colocating, can provide more precise measures of contact than traditional methods based on direct observation or self-reporting. Both measurement modalities have shortcomings and are prone to false positives or negatives, as unmeasured environmental influences bias the data. We aim to compare GPS colocated versus Bluetooth beacon-derived proximity contact data for their impacts on transmission models' results under community and types of diseases. We examined the contact patterns derived from 3 data sets collected in 2016, with participants comprising students and staff from the University of Saskatchewan in Canada. Each of these 3 data sets used both Bluetooth beaconing and GPS localization on smartphones running the Ethica Data (Avicenna Research) app to collect sensor data about every 5 minutes over a month. We compared the structure of contact networks inferred from proximity contact data collected with the modalities of GPS colocating and Bluetooth beaconing. We assessed the impact of sensing modalities on the simulation results of transmission models informed by proximate contacts derived from sensing data. Specifically, we compared the incidence number, attack rate, and individual infection risks across simulation results of agent-based susceptible-exposed-infectious-removed transmission models of 4 different contagious diseases. We have demonstrated their differences with violin plots, 2-tailed t tests, and Kullback-Leibler divergence. Both network structure analyses show visually salient differences in proximity contact data collected between GPS colocating and Bluetooth beaconing, regardless of the underlying population. Significant differences were found for the estimated attack rate based on distance threshold, measurement modality, and simulated disease. This finding demonstrates that the sensor modality used to trace contact can have a significant impact on the expected propagation of a disease through a population. The violin plots of attack rate and Kullback-Leibler divergence of individual infection risks demonstrated discernible differences for different sensing modalities, regardless of the underlying population and diseases. The results of the t tests on attack rate between different sensing modalities were mostly significant (P<.001). We show that the contact networks generated from these 2 measurement modalities are different and generate significantly different attack rates across multiple data sets and pathogens. While both modalities offer higher-resolution portraits of contact behavior than is possible with most traditional contact measures, the differential impact of measurement modality on the simulation outcome cannot be ignored and must be addressed in studies only using a single measure of contact in the future.

The quantification of everythingThe Ordinal Society Marion Fourcade and Kieran Healy Harvard University Press, 2024. 384 pp.

Digital data traces about our lives and preferences are pervasive and increasingly commodified.

Students’ active cognitive engagement with instructional videos predicts STEM learning

The efficacy of well-designed instructional videos for STEM learning is largely reliant on how actively students cognitively engage with them. Students' ability to actively engage with videos likely depends upon individual characteristics like their prior knowledge. In this study, we investigated how digital trace data could be used as indicators of students' cognitive engagement with instructional videos, how such engagement predicted learning, and how prior knowledge moderated that relationship. One hundred twenty-eight biology undergraduate students learned with a series of instructional videos and took a biology unit exam one week later. We conducted sequence mining on the digital events of students' video-watching behaviors to capture the most commonly occurring sequences. Twenty-six sequences emerged and were aggregated into four groups indicative of cognitive engagement: repeated scrubbing, speed watching, extended scrubbing, and rewinding. Results indicated more active engagement via speed watching and rewinding behaviors positively predicted unit exam scores, but only for students with lower prior knowledge. These findings suggest that the ways students cognitively engage with videos predict how they will learn from them, that these relations are dependent upon their prior knowledge, and that researchers can measure students’ cognitive engagement with instructional videos via mining digital log data. This research emphasizes the importance of active cognitive engagement with video interface tools and the need for students to accurately calibrate their learning behaviors in relation to their prior knowledge when learning from videos.

Abstract NG08: Dissecting and quantifying pancreatic cancer plasticity using single-cell multiomics, lineage tracing and functional genomics reveals novel mediators of therapy resistance

Abstract NG08: Dissecting and quantifying pancreatic cancer plasticity using single-cell multiomics, lineage tracing and functional genomics reveals novel mediators of therapy resistance

The landslide traces inventory in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau: a case study of Jianzha County, China

The upper reaches of the Yellow River in China, influenced by erosion of the Yellow River and tectonic activities, are prone to landslides. Therefore, it is necessary to investigate the existing landslide traces. Based on visual interpretation on high-resolution satellite images and terrain data, supplemented and validated by existing landslide records, this paper prepared the most complete and detailed landslide traces inventory in Jianzha County, Huangnan Tibetan Autonomous Prefecture, Qinghai Province, to date. The results indicate that within the study area of 1714 km2, there are at least 713 landslide traces, ranging in scale from 3,556 m2 to 11.13 km2, with a total area of 134.46 km2. The total landslide area excluding the overlap area is 126.30 km2. The overall landslide point density and area density in the study area are 0.42 km-2 and 7.37% respectively. The maximum point density and maximum area density of landslide traces in the area are as high as 5.69 km-2 and 98.0% respectively. The landslides are primarily distributed in the relatively low-elevation northeastern part of Jianzha County, characterized mainly by large-scale loess landslides, with 14 landslides exceeding 1×106 m2. This inventory not only supplements the landslide trace data in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau, but also provides an important basis for subsequent landslide risk zoning, response to climate change, and landscape evolution. Additionally, it holds significant reference value for compiling landslide inventories in similar geological environments.

Estimating the reproduction number and transmission heterogeneity from the size distribution of clusters of identical pathogen sequences

Quantifying transmission intensity and heterogeneity is crucial to ascertain the threat posed by infectious diseases and inform the design of interventions. Methods that jointly estimate the reproduction number R and the dispersion parameter k have however mainly remained limited to the analysis of epidemiological clusters or contact tracing data, whose collection often proves difficult. Here, we show that clusters of identical sequences are imprinted by the pathogen offspring distribution, and we derive an analytical formula for the distribution of the size of these clusters. We develop and evaluate an inference framework to jointly estimate the reproduction number and the dispersion parameter from the size distribution of clusters of identical sequences. We then illustrate its application across a range of epidemiological situations. Finally, we develop a hypothesis testing framework relying on clusters of identical sequences to determine whether a given pathogen genetic subpopulation is associated with increased or reduced transmissibility. Our work provides tools to estimate the reproduction number and transmission heterogeneity from pathogen sequences without building a phylogenetic tree, thus making it easily scalable to large pathogen genome datasets.

Dynamic encoding of temperature in the central circadian circuit coordinates physiological activities

The circadian clock regulates animal physiological activities. How temperature reorganizes circadian-dependent physiological activities remains elusive. Here, using in-vivo two-photon imaging with the temperature control device, we investigated the response of the Drosophila central circadian circuit to temperature variation and identified that DN1as serves as the most sensitive temperature-sensing neurons. The circadian clock gate DN1a’s diurnal temperature response. Trans-synaptic tracing, connectome analysis, and functional imaging data reveal that DN1as bidirectionally targets two circadian neuronal subsets: activity-related E cells and sleep-promoting DN3s. Specifically, behavioral data demonstrate that the DN1a-E cell circuit modulates the evening locomotion peak in response to cold temperature, while the DN1a-DN3 circuit controls the warm temperature-induced nocturnal sleep reduction. Our findings systematically and comprehensively illustrate how the central circadian circuit dynamically integrates temperature and light signals to effectively coordinate wakefulness and sleep at different times of the day, shedding light on the conserved neural mechanisms underlying temperature-regulated circadian physiology in animals.

The Interplay Between Individual Mobility, Health Risk, and Economic Choice: A Holistic Model for COVID-19 Policy Intervention

This paper was motivated by the need to simultaneously address two competing policy objectives during the course of the COVID pandemic: namely, the public health objective, which required people to be less mobile, and the economic objective, which aimed to ensure that the economy was not adversely affected by the constraints imposed by the first objective. To realize these objectives, we developed a data-informed approach to model human mobility, health risk, and economic activity jointly. This approach computes equilibrium between epidemic models of public health and economic activity under policy interventions that could be used to change people’s mobility behavior. Our approach is distinctive in its capacity to assemble proprietary data sets from public and private sectors at the individual and the zip code levels, which heretofore had not been used together. These data enabled customization of the population-level epidemic models widely used in public health (e.g., the SIR model) with individual-level data traces of mobility behaviors for assessment of public health risks. The outputs of the proposed model enabled parameterization of economic choice models of individuals’ economic decision-making. Various policy interventions and their capacities to shift the equilibrium between economic activity and public health were investigated in this study. Whereas the data-informed joint modeling approach was developed and tested in the pandemic context, it is generalizable for the evaluation of any counterfactual policy interventions. History: Olivia R. Liu Sheng and W. Nick Street served as senior editors for this article. Data Ethics &amp; Reproducibility Note: The code capsule is available on Code Ocean at https://doi.org/10.24433/CO.4390192.v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2023.0013 ).

VISUALIZATION OF MICROSERVICES-BASED INFORMATION SYSTEM ARCHITECTURES USING OPENTELEMETRY DATA

Within the framework of the presented article, the relevance and necessity of solving the problem of visualizing the architecture of information systems based on microservices is substantiated. This task is especially significant both for the process of developing new systems and for optimizing the operation of existing ones. To provide comprehensive and balanced up-to-date information about the structure and interaction of microservices, an approach is required that is focused on automatically collecting and processing information about the relationships between microservices and their internal structure for the purpose of subsequent visualization of architectural diagrams. The authors found that specialized software trace logs provide sufficient information for visualizing the architecture. In their article, the authors took a closer look at tracing issues in the context of OpenTelemetry, an open-source project designed specifically for collecting telemetry data from software and then analyzing it. The authors proposed the C4 model as a means for visualizing architecture. The C4 model is a relatively new approach to modeling software systems, which involves the development and representation of context, containers, components, code and their relationships. The main task that the authors set for themselves is to automate the process of creating C4 model diagrams based on data received from OpenTelemetry. This approach will significantly simplify the process of understanding the structure and dynamics of interaction between microservices within the system. The key result of the research is the development of algorithms for collecting and converting trace data to create C4 diagrams, as well as the creation of appropriate software that implements these algorithms.

Ontological scenario model for learners with disabilities in a recommender framework based on assessment analytics

Technology Enhanced Learning Environments (TELE) have attracted many learners to acquire knowledge and skills at their own pace. But the majority of these environments are not accessible for all categories of learners including learners with disabilities. In fact, some environments may provide content during the learning process that does not meet the profiles of every type of disability. Much research has been developed in the area of personalizing e-learning for people with disabilities. The use of assessment analytics, on the other hand, remains largely unexploited despite its great informative potential, which is elated to assessment data generated by online learning environment. Our proposal focuses on the design of a scenario model for Assessment Analytics to develop a recommendation framework for learners with disabilities. This framework is conceived to retrieve and select relevant learning and assessment resources to learners with disabilities based on their preferences, accessibility needs, and assessment trace data in the context of online learning.

Estimating Asymptomatic and Symptomatic Transmission of the COVID-19 First Few Cases in Selenge Province, Mongolia.

Following the first locally transmitted case in Sukhbaatar soum, Selenge Province, we aimed to investigate the ultimate scale of the epidemic in the scenario of uninterrupted transmission. This was a prospective case study following the locally modified WHO FFX cases generic protocol. A rapid response team collected data from November 14 to 29, 2020. We created a stochastic process to draw many transmission chains from this greater distribution to better understand and make inferences regarding the outbreak under investigation. The majority of the cases involved household transmissions (35, 52.2%), work transmissions (20, 29.9%), index (5, 7.5%), same apartment transmissions (2, 3.0%), school transmissions (2, 3.0%), and random contacts between individuals transmissions (1, 1.5%). The posterior means of the basic reproduction number of both the asymptomatic cases and the presymptomatic cases (1.35 [95% CrI 0.88-1.86] and 1.29 [95% CrI 0.67-2.10], respectively) were lower than that of the symptomatic cases (2.00 [95% Crl 1.38-2.76]). Our study highlights the heterogeneity of COVID-19 transmission across different symptom statuses and underscores the importance of early identification and isolation of symptomatic cases in disease control. Our approach, which combines detailed contact tracing data with advanced statistical methods, can be applied to other infectious diseases, facilitating a more nuanced understanding of disease transmission dynamics.

Addressing Digital Transformation in Universities

In facing digital transformation challenges, universities need to set up their data governance strategies. They include effective solutions to trace and value data about key assets (such as researchers, publications, courses, research projects) scattered across multiple legacy IT systems. As part of an overall solution to deal with the unavoidable data fragmentation and diversity, we provide the complete code of a simple and very efficient framework that can be employed by universities to develop their own knowledge graph, offering a comprehensive picture of the strategic data of the university, such that it can be consistently exploited by different digital services.

Learning-Augmented Online Algorithm for Two-Level Ski-Rental Problem

In this paper, we study the two-level ski-rental problem, where a user needs to fulfill a sequence of demands for multiple items by choosing one of the three payment options: paying for the on-demand usage (i.e., rent), buying individual items (i.e., single purchase), and buying all the items (i.e., combo purchase). Without knowing future demands, the user aims to minimize the total cost (i.e., the sum of the rental, single purchase, and combo purchase costs) by balancing the trade-off between the expensive upfront costs (for purchase) and the potential future expenses (for rent). We first design a robust online algorithm (RDTSR) that offers a worst-case performance guarantee. While online algorithms are robust against the worst-case scenarios, they are often overly cautious and thus suffer a poor average performance in typical scenarios. On the other hand, Machine Learning (ML) algorithms typically show promising average performance in various applications but lack worst-case performance guarantees. To harness the benefits of both methods, we develop a learning-augmented algorithm (LADTSR) by integrating ML predictions into the robust online algorithm, which outperforms the robust online algorithm under accurate predictions while ensuring worst-case performance guarantees even when predictions are inaccurate. Finally, we conduct numerical experiments on both synthetic and real-world trace data to corroborate the effectiveness of our approach.

Deriving life-course residential histories in brain bank cohorts: A feasibility study.

The exposome is theorized to interact with biological mechanisms to influence risk for Alzheimer's disease but is not well-integrated into existing Alzheimer's Disease Research Center (ADRC) brain bank data collection. We apply public data tracing, an iterative, dual abstraction and validation process rooted in rigorous historic archival methods, to develop life-course residential histories for 1254 ADRC decedents. The median percentage of the life course with an address is 78.1% (IQR 24.9); 56.5% of the sample has an address for at least 75% of their life course. Archivists had 89.7% agreement at the address level. This method matched current residential survey methodology 97.4% on average. This novel method demonstrates feasibility, reproducibility, and rigor for historic data collection. To our knowledge, this is the first study to show that public data tracing methods for brain bank decedent residential history development can be used to better integrate the social exposome with biobank specimens. Public data tracing compares favorably to survey-based residential history. Public data tracing is feasible and reproducible between archivists. Archivists achieved 89.7% agreement at the address level. This method identifies residences for nearly 80% of life-years, on average. This novel method enables brain banks to add social characterizations.