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Rapid Evaluation of Vaccine Booster Effectiveness against SARS-CoV-2 Variants
No full textAs the COVID-19 pandemic continues, countries around the world are switching toward vaccinations and boosters to combat the pandemic. However, waning immunity against SARS-CoV-2 wild-type (WT) and variants have been widely reported. Booster vaccinations have shown to be able to increase immunological protection against new variants; however, the protection observed appears to decrease quickly over time suggesting a second booster shot may be appropriate. Moreover, heterogeneity and waning of the immune response at the individual level was observed suggesting a more personalized vaccination approach should be considered. To evaluate such a personalized strategy, it is important to have the ability to rapidly evaluate the level of neutralizing antibody (nAbs) response against variants at the individual level and ideally at a point of care setting. Here, we applied the recently developed cellulose pulled-down virus neutralization test (cpVNT) to rapidly assess individual nAb levels to WT and variants of concerns in response to booster vaccination. Our findings confirmed significant heterogeneity of nAb responses against a panel of SARS-CoV-2 variants, and indicated a strong increase in nAb response against variants of concern (VOCs) upon booster vaccination. For instance, the nAb response against current predominant omicron variant was observed with medians of 88.1% (n = 6, 95% CI = 73.2% to 96.2%) within 1-month postbooster and 70.7% (n = 22, 95% CI = 66.4% to 81.8%) 3 months postbooster. Our data show a point of care (POC) test focusing on nAb response levels against VOCs can guide decisions on the potential need for booster vaccinations at individual level. Importantly, it also suggests the current booster vaccines only give a transient protective response against some VOC and new more targeted formulations of a booster vaccine against specific VOC may need to be developed in the future.
IMPORTANCE Vaccination against SARS-CoV-2 induces protection through production of neutralization antibodies (nAb). The level of nAb is a major indicator of immunity against SARS-CoV-2 infection. We developed a rapid point-of-care test that can monitor the nAb level from a drop of finger stick blood. Here, we have implemented the test to monitor individual nAb level against wild-type and variants of SARS-CoV-2 at various time points of vaccination, including post-second-dose vaccination and postbooster vaccination. Huge diversity of nAb levels were observed among individuals as well as increment in nAb levels especially against Omicron variant after booster vaccination. This study evaluated the performance of this point-of-care test for personalized nAb response tracking. It verifies the potential of using a rapid nAb test to guide future vaccination regimens at both the individual and population level
ReMirrorFugue: Examining the Emotional Experience of Presence and (Illusory) Communications Across Time
No full textCHI ’25, Yokohama, JapanThis paper examines how strategies for simulating social presence across distance can evoke a sense of presence and facilitate illusory interactions across time. We conducted a mixed-methods study with 28 participants, exploring their emotional experience of interacting with decade-old recorded piano performances on MirrorFugue—a player piano enhanced with life-sized projections of the pianist’s hands and body, creating the illusion of a virtual reflection playing the instrument. Data were collected via wearable sensors, questionnaires, and interviews.
Results showed that participants felt a strong presence of past pianists, with some experiencing the illusion of two-way communication and an overall increase in connection. The emotional experience was significantly influenced by the participant’s relationship with the recorded pianist and the pianist’s vital status. These findings suggest that telepresence technologies can foster connections with the past, offering spaces for memory recall, self-reflection, and a sense of “time travel.
CURENet: combining unified representations for efficient chronic disease prediction
No full textElectronic health records (EHRs) are designed to synthesize diverse data types, including unstructured clinical notes, structured lab tests, and time-series visit data. Physicians draw on these multimodal and temporal sources of EHR data to form a comprehensive view of a patient’s health, which is crucial for informed therapeutic decision-making. Yet, most predictive models fail to fully capture the interactions, redundancies, and temporal patterns across multiple data modalities, often focusing on a single data type or overlooking these complexities. In this paper, we present CURENet, a multimodal model (Combining Unified Representations for Efficient chronic disease prediction) that integrates unstructured clinical notes, lab tests, and patients’ time-series data by utilizing large language models (LLMs) for clinical text processing and textual lab tests, as well as transformer encoders for longitudinal sequential visits. Curenet has been capable of capturing the intricate interaction between different forms of clinical data and creating a more reliable predictive model for chronic illnesses. We evaluated CURENet using the public MIMIC-III and private FEMH datasets, where it achieved over 94% accuracy in predicting the top 10 chronic conditions in a multi-label framework. Our findings highlight the potential of multimodal EHR integration to enhance clinical decision-making and improve patient outcomes
Causality - Exploiting Multi-Modal Data
No full textKDD '25, August 3–7, 2025, Toronto, ON, CanadaMassive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. Representation learning has become a key driver of deep learning applications, since it allows learning latent spaces that capture important properties of the data without requiring any supervised annotations. While representation learning has been hugely successful in predictive tasks, it can fail miserably in causal tasks including predicting the effect of an intervention. This calls for a marriage between representation learning and causal inference. An exciting opportunity in this regard stems from the growing availability of multi-modal and interventional data (in medicine, advertisement, education, etc.). However, these datasets are still miniscule compared to the action spaces of interest in these applications (e.g. interventions can take on continuous values like the dose of a drug or can be combinatorial as in combinatorial drug therapies). In this talk, we will present a statistical and computational framework for causal representation learning from multi-modal data and its application towards optimal intervention design
CUPID, the Cuore upgrade with particle identification
No full textCUPID, the CUORE Upgrade with Particle Identification, is a next-generation experiment to search for neutrinoless double beta decay ( 0 ν β β ) and other rare events using enriched Li 2 100 MoO 4 scintillating bolometers. It will be hosted by the CUORE cryostat located at the Laboratori Nazionali del Gran Sasso in Italy. The main physics goal of CUPID is to search for 0 ν β β of 100 Mo with a discovery sensitivity covering the full neutrino mass regime in the inverted ordering scenario, as well as the portion of the normal ordering regime with lightest neutrino mass larger than 10 meV. With a conservative background index of 10 - 4 cts / ( keV · kg · yr ) , 240 kg isotope mass, 5 keV FWHM energy resolution at 3 MeV and 10 live-years of data taking, CUPID will have a 90% C.L. half-life exclusion sensitivity of 1.8 · 10 27 yr, corresponding to an effective Majorana neutrino mass ( m β β ) sensitivity of 9–15 meV, and a 3 σ discovery sensitivity of 1 · 10 27 yr, corresponding to an m β β range of 12–21 meV
Within-Subtype HIV-1 Polymorphisms and Their Impacts on Intact Proviral DNA Assay (IPDA) for Viral Reservoir Quantification
No full textThe Intact Proviral DNA Assay (IPDA) is widely used to quantify genome-intact HIV proviruses in people living with HIV, but viral sequence diversity has been observed to cause assay failures due to primer/probe mismatches. Adapted for subtype C, IPDA-BC is a modified version of the IPDA validated on South African HIV-1 subtype C. India is also impacted by subtype C, but IPDA performance within-subtype across geographical regions is not well studied. We analyzed Indian (IN) and South African (ZA) subtype C sequences in silico, hypothesizing that IPDA-BC may underperform with IN viruses. Primer/probe binding was predicted using three increasingly stringent nucleotide mismatch criteria, whose sensitivity and specificity were evaluated against experimental IPDA outcomes. Phylogenetic analyses confirmed that IN and ZA subtype C sequences form distinct clusters with significant compartmentalization (p < 0.003). Across criteria, up to 6–10% decreases in primer/probe binding were observed in IN versus ZA, with the env forward primer being the most affected. These criteria showed low sensitivity (18–53%) and variable specificity (67–100%) in predicting experimental outcomes. In conclusion, even within subtype, HIV-1 variation across geographical regions may impact IPDA performance, underscoring the need for improved predictive models to guide assay design for global HIV cure research
Surrogate-Assisted Adaptive Experimentation for Fused Filament Fabrication Process Optimization
No full textFused Filament Fabrication (FFF) is an advanced manufacturing process that requires precise control of multiple parameters, including nozzle temperature, print speed, and layer height. Due to the complexity of this high-dimensional process design space, experimental evaluations are often constrained. A key challenge in FFF is understanding how these parameters influence print quality and identifying optimal process conditions efficiently. This study addresses this challenge by developing a physics-based thermal model for FFF, implemented using a graphics processing unit-accelerated finite element method. The model is calibrated and validated against experimental thermal data for printing polylactic acid (PLA). It is then used to investigate the effects of nozzle temperature, print speed, bed temperature, and layer thickness on print quality by developing a cooling rate metric. A series of simulations is conducted within the process window using the physics-based model, and the resulting data are analyzed with SHapley Additive exPlanations to understand the influence of process parameters on print quality. The results indicate that layer height is the most critical factor affecting the quality of tensile samples. To enhance process optimization, a surrogate model is trained and optimized using data generated from the physics-based model, enabling the identification of an optimal processing window for PLA. By combining physics-based and data-driven modeling, this approach accelerates thermal prediction in the FFF process, facilitating the study of high-dimensional design spaces and the optimization of material-specific printing parameters. The proposed methodology provides a scalable framework for improving the efficiency and quality of extrusion-based additive manufacturing processes, demonstrating its potential for broader applications in process optimization
A Write-Optimized Distributed B+Tree Index on Disaggregated Memory
No full textIf it were possible to scale memory independently from compute, it would be feasible to dynamically adjust the amount of memory based on the workload. It would further enable better resource utilization. Consider a dynamic workload regarding the number of queries but with very strict response time requirements, which can only be met, if data is kept in-memory. In this case, the separation of compute and memory would enable to scale the compute with the number of queries while keeping all the data constantly in-memory. This design principle is already used by services such as Google, which keeps the entire web-index in-memory
Exploring Visual and Haptic Feedback Systems on User Performance with a Hand-Held Robot
No full textWhile robotic systems allow users to maintain accuracy in high-precision environments, achieving intuitive control is challenging without real-time feedback. Haptic feedback, which communicates otherwise unfelt sensations through vibrations, is widely used in consumer technologies such as video games and smartphones. However, in contexts where knowing the precise force applied by the robot is critical—such as medical procedures or hazardous environments—haptic cues alone may provide insufficient resolution, increasing user workload. Visual feedback, by contrast, is more commonly used and offers greater versatility and precision.
This study compared the impact of visual feedback (a color-changing LED light strip) and haptic feedback (vibrations in a controller) on user performance in a “fragile object” manipulation task. Nine participants completed the task under four feedback conditions: no feedback, visual feedback, haptic feedback, and combined visual-haptic feedback. Subjective ratings showed that most participants preferred modalities that included visual cues, citing lower perceived workload and clearer force awareness. However, despite some participants reporting minimal benefit from haptics, performance metrics revealed that for others, haptic feedback meaningfully supported task success.
These findings suggest that while simple visual indicators, such as green-yellow-orange-red light strips, provide accessible and interpretable force feedback, the integration of haptic cues can enhance performance by offering complementary real-time force information. Future designs may benefit from refining both modalities to balance intuitiveness, resolution, and user comfort, especially in applications requiring precise force modulation.S.B
Data-Driven Modeling and Real-Time Optimal Control of Continuous Manufacturing Processes
No full textWhen faced with complex disturbances, continuous manufacturing processes require robust control and adaptability to maintain product quality and operational efficiency. Although advanced control strategies such as linear quadratic regulator, model predictive control, and adaptive control have demonstrated strong performance, many industrial processes still rely predominantly on classical proportional-integral-derivative (PID) controllers because of their simplicity, ease of implementation, and sufficient results.
This thesis investigates the effectiveness of data-driven modeling techniques in capturing system dynamics more accurately than traditional physics-based models. It further examines using a high-fidelity digital twin, constructed from experimental data via linear system identification and nonlinear deep learning (NARX) approaches, to optimize PID controller parameters through simulation-based gradient descent methods.
A comprehensive experimental platform was developed to collect synchronized sensor and video data from a roll-to-roll continuous manufacturing system, specifically targeting disturbance scenarios that cause process interruptions. The digital twin created from these data was validated against physical experiments and shown to outperform conventional physics-based models when predicting the system’s dynamic response to disturbance inputs.
Optimal control of the system was explored by implementing a virtual PID controller that closely replicates the physical controller. Optimal gain settings were identified through simulation and applied to the physical manufacturing process. The experimental results showed a significant reduction in the mean squared error and the maximum web deviation. These results demonstrate the substantial potential of digital twin-driven, data-centric control approaches in enhancing resilience, efficiency, and adaptability in manufacturing processes. This research also lays the foundation for the future development of real-time, adaptive, and autonomous control strategies in industrial applications.S.M