Author: "A Shrestha" - Searchworks@Jio Institute Digital Library Search Results

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201. Ankle, knee, and elbow arthrography: 2022 survey of Society of Skeletal Radiology members

Author: Shrestha, Roman, Haug, Logan P., Kransdorf, Mark J., and Fox, Michael G.
Published: 2024
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202. Fracture of an aberrant os styloideum: a unique case report

Author: Kunc, Vojtech, Shrestha, Shilu, and Benes, Michal
Published: 2024
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203. Environmental sampling for typhoidal Salmonellas in household and surface waters in Nepal identifies potential transmission pathways.

Author: LeBoa, Christopher, Shrestha, Sneha, Shakya, Jivan, Naga, Shiva, Shrestha, Sony, Shakya, Mudita, Yu, Alexander, Shrestha, Rajeev, Vaidya, Krista, Katuwal, Nishan, Bogoch, Isaac, Uzzell, Christopher, Garrett, Denise, Luby, Stephen, Andrews, Jason, Tamrakar, Dipesh, and Aiemjoy, Kristen
Subjects: Humans, Typhoid Fever, Drinking Water, Nepal, Salmonella typhi, Salmonella paratyphi A
Abstract: INTRODUCTION: Salmonella Typhi and Salmonella Paratyphi, fecal-oral transmitted bacterium, have temporally and geographically heterogeneous pathways of transmission. Previous work in Kathmandu, Nepal implicated stone waterspouts as a dominant transmission pathway after 77% of samples tested positive for Salmonella Typhi and 70% for Salmonella Paratyphi. Due to a falling water table, these spouts no longer provide drinking water, but typhoid fever persists, and the question of the diseases dominant pathway of transmission remains unanswered. METHODS: We used environmental surveillance to detect Salmonella Typhi and Salmonella Paratyphi A DNA from potential sources of transmission. We collected 370, 1L drinking water samples from a population-based random sample of households in the Kathmandu and Kavre Districts of Nepal between February and October 2019. Between November 2019 and July 2021, we collected 380, 50mL river water samples from 19 sentinel sites on a monthly interval along the rivers leading through the Kathmandu and Kavre Districts. We processed drinking water samples using a single qPCR and processed river water samples using differential centrifugation and qPCR at 0 and after 16 hours of liquid culture enrichment. A 3-cycle threshold (Ct) decrease of Salmonella Typhi or Salmonella Paratyphi, pre- and post-enrichment, was used as evidence of growth. We also performed structured observations of human-environment interactions to understand pathways of potential exposure. RESULTS: Among 370 drinking water samples, Salmonella Typhi was detected in 7 samples (1.8%) and Salmonella Paratyphi A was detected in 4 (1.0%) samples. Among 380 river water samples, Salmonella Typhi was detected in 171 (45%) and Salmonella Paratyphi A was detected in 152 (42%) samples. Samples located upstream of the Kathmandu city center were positive for Salmonella Typhi 12% of the time while samples from locations in and downstream were positive 58% and 67% of the time respectively. Individuals were observed bathing, washing clothes, and washing vegetables in the rivers. IMPLICATIONS: These results suggest that drinking water was not the dominant pathway of transmission of Salmonella Typhi and Salmonella Paratyphi A in the Kathmandu Valley in 2019. The high degree of river water contamination and its use for washing vegetables raises the possibility that river systems represent an important source of typhoid exposure in Kathmandu.
Published: 2023

204. Clinical Risk Prediction Using Language Models: Benefits And Considerations

Author: Acharya, Angeela, Shrestha, Sulabh, Chen, Anyi, Conte, Joseph, Avramovic, Sanja, Sikdar, Siddhartha, Anastasopoulos, Antonios, and Das, Sanmay
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: The utilization of Electronic Health Records (EHRs) for clinical risk prediction is on the rise. However, strict privacy regulations limit access to comprehensive health records, making it challenging to apply standard machine learning algorithms in practical real-world scenarios. Previous research has addressed this data limitation by incorporating medical ontologies and employing transfer learning methods. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks. Unlike applying LMs to unstructured EHR data such as clinical notes, this study focuses on using textual descriptions within structured EHR to make predictions exclusively based on that information. We extensively compare against previous approaches across various data types and sizes. We find that employing LMs to represent structured EHRs, such as diagnostic histories, leads to improved or at least comparable performance in diverse risk prediction tasks. Furthermore, LM-based approaches offer numerous advantages, including few-shot learning, the capability to handle previously unseen medical concepts, and adaptability to various medical vocabularies. Nevertheless, we underscore, through various experiments, the importance of being cautious when employing such models, as concerns regarding the reliability of LMs persist., Comment: 12 pages, 6 figures, 4 tables
Published: 2023

205. Labeling Indoor Scenes with Fusion of Out-of-the-Box Perception Models

Author: Li, Yimeng, Rajabi, Navid, Shrestha, Sulabh, Reza, Md Alimoor, and Kosecka, Jana
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language, Computer Science - Robotics
Abstract: The image annotation stage is a critical and often the most time-consuming part required for training and evaluating object detection and semantic segmentation models. Deployment of the existing models in novel environments often requires detecting novel semantic classes not present in the training data. Furthermore, indoor scenes contain significant viewpoint variations, which need to be handled properly by trained perception models. We propose to leverage the recent advancements in state-of-the-art models for bottom-up segmentation (SAM), object detection (Detic), and semantic segmentation (MaskFormer), all trained on large-scale datasets. We aim to develop a cost-effective labeling approach to obtain pseudo-labels for semantic segmentation and object instance detection in indoor environments, with the ultimate goal of facilitating the training of lightweight models for various downstream tasks. We also propose a multi-view labeling fusion stage, which considers the setting where multiple views of the scenes are available and can be used to identify and rectify single-view inconsistencies. We demonstrate the effectiveness of the proposed approach on the Active Vision dataset and the ADE20K dataset. We evaluate the quality of our labeling process by comparing it with human annotations. Also, we demonstrate the effectiveness of the obtained labels in downstream tasks such as object goal navigation and part discovery. In the context of object goal navigation, we depict enhanced performance using this fusion approach compared to a zero-shot baseline that utilizes large monolithic vision-language pre-trained models.
Published: 2023

206. Character-Level Bangla Text-to-IPA Transcription Using Transformer Architecture with Sequence Alignment

Author: Hasan, Jakir, Datta, Shrestha, and Debnath, Ameya
Subjects: Computer Science - Computation and Language
Abstract: The International Phonetic Alphabet (IPA) is indispensable in language learning and understanding, aiding users in accurate pronunciation and comprehension. Additionally, it plays a pivotal role in speech therapy, linguistic research, accurate transliteration, and the development of text-to-speech systems, making it an essential tool across diverse fields. Bangla being 7th as one of the widely used languages, gives rise to the need for IPA in its domain. Its IPA mapping is too diverse to be captured manually giving the need for Artificial Intelligence and Machine Learning in this field. In this study, we have utilized a transformer-based sequence-to-sequence model at the letter and symbol level to get the IPA of each Bangla word as the variation of IPA in association of different words is almost null. Our transformer model only consisted of 8.5 million parameters with only a single decoder and encoder layer. Additionally, to handle the punctuation marks and the occurrence of foreign languages in the text, we have utilized manual mapping as the model won't be able to learn to separate them from Bangla words while decreasing our required computational resources. Finally, maintaining the relative position of the sentence component IPAs and generation of the combined IPA has led us to achieve the top position with a word error rate of 0.10582 in the public ranking of DataVerse Challenge - ITVerse 2023 (https://www.kaggle.com/competitions/dataverse_2023/)., Comment: Achieved top position with a word error rate of 0.10582 in the public ranking of DataVerse Challenge - ITVerse 2023 (link: https://www.kaggle.com/competitions/dataverse_2023/). All codes can be found on the respective competition webpage
Published: 2023

207. Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality

Author: Shrestha, Ajay Kumar, Khan, Faijan Ahamad, Shaikh, Mohammed Afaan, Jaberzadeh, Amir, and Geng, Jason
Subjects: Computer Science - Machine Learning
Abstract: The paper presents an innovative approach to address the challenges of scalability and reliability in Distributed Federated Learning by leveraging the integration of blockchain technology. The paper focuses on enhancing the trustworthiness of participating nodes through a trust penalization mechanism while also enabling asynchronous functionality for efficient and robust model updates. By combining Semi-Decentralized Federated Learning with Blockchain (SDFL-B), the proposed system aims to create a fair, secure and transparent environment for collaborative machine learning without compromising data privacy. The research presents a comprehensive system architecture, methodologies, experimental results, and discussions that demonstrate the advantages of this novel approach in fostering scalable and reliable SDFL-B systems., Comment: To appear in 2023 IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference (IEEE UEMCON)
Published: 2023

208. Student Certificate Sharing System Using Blockchain and NFTs

Author: Khati, Prakhyat, Shrestha, Ajay Kumar, and Vassileva, Julita
Subjects: Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: In this paper, we propose a certificate sharing system based on blockchain that gives students authority and control over their academic certificates. Our strategy involves developing blockchain-based NFT certifications that can be shared with institutions or employers using blockchain addresses. Students may access the data created by each individual institute in a single platform, filter the view of the relevant courses according to their requirements, and mint their certificate metadata as NFTs. This method provides accountability of access, comprehensive records that are permanently maintained in IPFS, and verifiable provenance for creating, distributing, and accessing certificates. It also makes it possible to share certificates more safely and efficiently. By incorporating trust factors through data provenance, our system provides a countermeasure against issues such as fake and duplicate certificates. It addresses the challenge of the traditional certificate verification processes, which are lengthy manual process. With this system, students can manage and validate their academic credentials from multiple institutions in one location while ensuring authenticity and confidentiality using digital signatures and hashing for data protection against unauthorized access. Overall, our suggested system ensures data safety, accountability, and confidentiality while offering a novel approach to certificate distribution.
Published: 2023

209. A Survey on Watching Social Issue Videos among YouTube and TikTok Users

Author: Niu, Shuo, Shrestha, Dilasha, Ghimire, Abhisan, and Lu, Zhicong
Subjects: Computer Science - Human-Computer Interaction, Computer Science - Social and Information Networks, J.4
Abstract: The openness and influence of video-sharing platforms (VSPs) such as YouTube and TikTok attracted creators to share videos on various social issues. Although social issue videos (SIVs) affect public opinions and breed misinformation, how VSP users obtain information and interact with SIVs is under-explored. This work surveyed 659 YouTube and 127 TikTok users to understand the motives for consuming SIVs on VSPs. We found that VSP users are primarily motivated by the information and entertainment gratifications to use the platform. VSP users use SIVs for information-seeking purposes and find YouTube and TikTok convenient to interact with SIVs. VSP users moderately watch SIVs for entertainment and inactively engage in social interactions. SIV consumption is associated with information and socialization gratifications of the platform. VSP users appreciate the diversity of information and opinions but would also do their own research and are concerned about the misinformation and echo chamber problems.
Published: 2023

210. Adversarial sample generation and training using geometric masks for accurate and resilient license plate character recognition

Author: Shrestha, Bishal, Khakurel, Griwan, Simkhada, Kritika, and Adhikari, Badri
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Reading dirty license plates accurately in moving vehicles is challenging for automatic license plate recognition systems. Moreover, license plates are often intentionally tampered with a malicious intent to avoid police apprehension. Usually, such groups and individuals know how to fool the existing recognition systems by making minor unnoticeable plate changes. Designing and developing deep learning methods resilient to such real-world 'attack' practices remains an active research problem. As a solution, this work develops a resilient method to recognize license plate characters. Extracting 1057 character images from 160 Nepalese vehicles, as the first step, we trained several standard deep convolutional neural networks to obtain 99.5% character classification accuracy. On adversarial images generated to simulate malicious tampering, however, our model's accuracy dropped to 25%. Next, we enriched our dataset by generating and adding geometrically masked images, retrained our models, and investigated the models' predictions. The proposed approach of training with generated adversarial images helped our adversarial attack-aware license plate character recognition (AA-LPCR) model achieves an accuracy of 99.7%. This near-perfect accuracy demonstrates that the proposed idea of random geometric masking is highly effective for improving the accuracy of license plate recognition models. Furthermore, by performing interpretability studies to understand why our models work, we identify and highlight attack-prone regions in the input character images. In sum, although Nepal's embossed license plate detection systems are vulnerable to malicious attacks, our findings suggest that these systems can be upgraded to close to 100% resilience.
Published: 2023

211. SN 2022jox: An extraordinarily ordinary Type II SN with Flash Spectroscopy

Author: Andrews, Jennifer E., Pearson, Jeniveve, Hosseinzadeh, Griffin, Bostroem, K. Azalee, Dong, Yize, Shrestha, Manisha, Jencson, Jacob E., Sand, David J., Valenti, S., Hoang, Emily, Janzen, Daryl, Lundquist, M. J., Meza, Nicolas, Wyatt, Samuel, Jha, Saurabh W., Simpson, Chris, Farah, Joseph, Gonzalez, Estefania Padilla, Howell, D. Andrew, McCully, Curtis, Newsome, Megan, Pellegrino, Craig, and Terreran, Giacomo
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: We present high cadence optical and ultraviolet observations of the Type II supernova (SN), SN 2022jox which exhibits early spectroscopic high ionization flash features of \ion{H}{1}, \ion{He}{2}, \ion{C}{4}, and \ion{N}{4} that disappear within the first few days after explosion. SN 2022jox was discovered by the Distance Less than 40 Mpc (DLT40) survey $\sim$0.75 days after explosion with followup spectra and UV photometry obtained within minutes of discovery. The SN reached a peak brightness of M$_V \sim$ $-$17.3 mag, and has an estimated $^{56}$Ni mass of 0.04 M$_{\odot}$, typical values for normal Type II SNe. The modeling of the early lightcurve and the strong flash signatures present in the optical spectra indicate interaction with circumstellar material (CSM) created from a progenitor with a mass loss rate of $\dot{M} \sim 10^{-3}-10^{-2}\ M_\odot\ \mathrm{yr}^{-1}$. There may also be some indication of late-time CSM interaction in the form of an emission line blueward of H$\alpha$ seen in spectra around 200 days. The mass-loss rate is much higher than the values typically associated with quiescent mass loss from red supergiants, the known progenitors of Type II SNe, but is comparable to inferred values from similar core collapse SNe with flash features, suggesting an eruptive event or a superwind in the progenitor in the months or years before explosion., Comment: ApJ, accepted 2024 Feb 14
Published: 2023

212. End-to-End Learning of Behavioural Inputs for Autonomous Driving in Dense Traffic

Author: Shrestha, Jatan, Idoko, Simon, Sharma, Basant, and Singh, Arun Kumar
Subjects: Computer Science - Robotics
Abstract: Trajectory sampling in the Frenet(road-aligned) frame, is one of the most popular methods for motion planning of autonomous vehicles. It operates by sampling a set of behavioural inputs, such as lane offset and forward speed, before solving a trajectory optimization problem conditioned on the sampled inputs. The sampling is handcrafted based on simple heuristics, does not adapt to driving scenarios, and is oblivious to the capabilities of downstream trajectory planners. In this paper, we propose an end-to-end learning of behavioural input distribution from expert demonstrations or in a self-supervised manner. Our core novelty lies in embedding a custom differentiable trajectory optimizer as a layer in neural networks, allowing us to update behavioural inputs by considering the optimizer's feedback. Moreover, our end-to-end approach also ensures that the learned behavioural inputs aid the convergence of the optimizer. We improve the state-of-the-art in the following aspects. First, we show that learned behavioural inputs substantially decrease collision rate while improving driving efficiency over handcrafted approaches. Second, our approach outperforms model predictive control methods based on sampling-based optimization., Comment: Accepted to IROS 2023. arXiv admin note: text overlap with arXiv:2212.02224
Published: 2023

213. Theoretical Analysis of the Radio Map Estimation Problem

Author: Romero, Daniel, Ha, Tien Ngoc, Shrestha, Raju, and Franceschetti, Massimo
Subjects: Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract: Radio maps provide radio frequency metrics, such as the received signal strength, at every location of a geographic area. These maps, which are estimated using a set of measurements collected at multiple positions, find a wide range of applications in wireless communications, including the prediction of coverage holes, network planning, resource allocation, and path planning for mobile robots. Although a vast number of estimators have been proposed, the theoretical understanding of the radio map estimation (RME) problem has not been addressed. The present work aims at filling this gap along two directions. First, the complexity of the set of radio map functions is quantified by means of lower and upper bounds on their spatial variability, which offers valuable insight into the required spatial distribution of measurements and the estimators that can be used. Second, the reconstruction error for power maps in free space is upper bounded for three conventional spatial interpolators. The proximity coefficient, which is a decreasing function of the distance from the transmitters to the mapped region, is proposed to quantify the complexity of the RME problem. Numerical experiments assess the tightness of the obtained bounds and the validity of the main takeaways in complex environments.
Published: 2023

214. Radio Map Estimation: Empirical Validation and Analysis

Author: Shrestha, Raju, Ha, Tien Ngoc, Viet, Pham Q., and Romero, Daniel
Subjects: Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Physics - Applied Physics
Abstract: Radio maps quantify magnitudes such as the received signal strength at every location of a geographical region. Although the estimation of radio maps has attracted widespread interest, the vast majority of works rely on simulated data and, therefore, cannot establish the effectiveness and relative performance of existing algorithms in practice. To fill this gap, this paper presents the first comprehensive and rigorous study of radio map estimation (RME) in the real world. The main features of the RME problem are analyzed and the capabilities of existing estimators are compared using large measurement datasets collected in this work. By studying four performance metrics, recent theoretical findings are empirically corroborated and a large number of conclusions are drawn. Remarkably, the estimation error is seen to be reasonably small even with few measurements, which establishes the viability of RME in practice. Besides, from extensive comparisons, it is concluded that estimators based on deep neural networks necessitate large volumes of training data to exhibit a significant advantage over more traditional methods. Combining both types of schemes is seen to result in a novel estimator that features the best performance in most situations. The acquired datasets are made publicly available to enable further studies., Comment: 13 pages, Journal version, submitted to the IEEE Transactions on Wireless Communications
Published: 2023

215. JWST MIRI/MRS Observations and Spectral Models of the Under-luminous Type Ia Supernova 2022xkq

Author: DerKacy, J. M., Ashall, C., Hoeflich, P., Baron, E., Shahbandeh, M., Shappee, B. J., Andrews, J., Baade, D., Balangan, E. F, Bostroem, K. A., Brown, P. J., Burns, C. R., Burrow, A., Cikota, A., de Jaeger, T., Do, A., Dong, Y., Dominguez, I., Fox, O., Galbany, L., Hoang, E. T., Hsiao, E. Y., Janzen, D., Jencson, J. E., Krisciunas, K., Kumar, S., Lu, J., Lundquist, M., Evans, T. B. Mera, Maund, J. R., Mazzali, P., Medler, K., Retamal, N. E. Meza, Morrell, N., Patat, F., Pearson, J., Phillips, M. M., Shrestha, M., Stangl, S., Stevens, C. P., Stritzinger, M. D., Suntzeff, N. B., Telesco, C. M., Tucker, M. A., Valenti, S., Wang, L., and Yang, Y.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: We present a JWST mid-infrared spectrum of the under-luminous Type Ia Supernova (SN Ia) 2022xkq, obtained with the medium-resolution spectrometer on the Mid-Infrared Instrument (MIRI) $\sim130$ days post-explosion. We identify the first MIR lines beyond 14 $\mu$m in SN Ia observations. We find features unique to under-luminous SNe Ia, including: isolated emission of stable Ni, strong blends of [Ti II], and large ratios of singly ionized to doubly ionized species in both [Ar] and [Co]. Comparisons to normal-luminosity SNe Ia spectra at similar phases show a tentative trend between the width of the [Co III] 11.888 $\mu$m feature and the SN light curve shape. Using non-LTE-multi-dimensional radiation hydro simulations and the observed electron capture elements we constrain the mass of the exploding white dwarf. The best-fitting model shows that SN 2022xkq is consistent with an off-center delayed-detonation explosion of a near-Chandrasekhar mass WD (M$_{\rm ej}$ $\approx 1.37$ M$_{\odot}$) of high-central density ($\rho_c \geq 2.0\times10^{9}$ g cm$^{-3}$) seen equator on, which produced M($^{56}$Ni) $= 0.324$ M$_{\odot}$ and M($^{58}$Ni) $\geq 0.06$ M$_{\odot}$. The observed line widths are consistent with the overall abundance distribution; and the narrow stable Ni lines indicate little to no mixing in the central regions, favoring central ignition of sub-sonic carbon burning followed by an off-center DDT beginning at a single point. Additional observations may further constrain the physics revealing the presence of additional species including Cr and Mn. Our work demonstrates the power of using the full coverage of MIRI in combination with detailed modeling to elucidate the physics of SNe Ia at a level not previously possible., Comment: 31 pages, 18 figures, accepted to ApJ; updated to accepted version
Published: 2023

216. SAGUARO: Time-domain Infrastructure for the Fourth Gravitational-wave Observing Run and Beyond

Author: Hosseinzadeh, Griffin, Paterson, Kerry, Rastinejad, Jillian C., Shrestha, Manisha, Daly, Philip N., Lundquist, Michael J., Sand, David J., Fong, Wen-fai, Bostroem, K. Azalee, Hall, Saarah, Wyatt, Samuel D., Gibbs, Alex R., Christensen, Eric, Lindstrom, William, Nation, Jonathan, Chatelain, Joseph, and McCully, Curtis
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present upgraded infrastructure for Searches after Gravitational Waves Using ARizona Observatories (SAGUARO) during LIGO, Virgo, and KAGRA's fourth gravitational-wave (GW) observing run (O4). These upgrades implement many of the lessons we learned after a comprehensive analysis of potential electromagnetic counterparts to the GWs discovered during the previous observing run. We have developed a new web-based target and observation manager (TOM) that allows us to coordinate sky surveys, vet potential counterparts, and trigger follow-up observations from one centralized portal. The TOM includes software that aggregates all publicly available information on the light curves and possible host galaxies of targets, allowing us to rule out potential contaminants like active galactic nuclei, variable stars, solar-system objects, and preexisting supernovae, as well as to assess the viability of any plausible counterparts. We have also upgraded our image-subtraction pipeline by assembling deeper reference images and training a new neural network-based real-bogus classifier. These infrastructure upgrades will aid coordination by enabling the prompt reporting of observations, discoveries, and analysis to the GW follow-up community, and put SAGUARO in an advantageous position to discover kilonovae in the remainder of O4 and beyond. Many elements of our open-source software stack have broad utility beyond multimessenger astronomy, and will be particularly relevant in the "big data" era of transient discoveries by the Vera C. Rubin Observatory., Comment: updated to match accepted version
Published: 2023
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217. MAGIC detection of GRB 201216C at $z=1.1$

Author: Abe, H., Abe, S., Acciari, V. A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Artero, M., Asano, K., Baack, D., Babić, A., Baquero, A., de Almeida, U. Barres, Barrio, J. A., Batković, I., Baxter, J., González, J. Becerra, Bednarek, W., Bernardini, E., Bernete, J., Berti, A., Besenrieder, J., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošnjak, Ž., Burelli, I., Busetto, G., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Ceribella, G., Chai, Y., Cifuentes, A., Cikota, S., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Amico, G., D'Elia, V., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., Del Popolo, A., Delfino, M., Delgado, J., Mendez, C. Delgado, Depaoli, D., Di Pierro, F., Di Venere, L., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Elsaesser, D., Emery, G., Escudero, J., Fariña, L., Fattorini, A., Foffano, L., Font, L., Fukami, S., Fukazawa, Y., López, R. J. García, Garczarczyk, M., Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinović, N., Grau, R., Green, D., Green, J. G., Hadasch, D., Hahn, A., Hassan, T., Heckmann, L., Herrera, J., Hrupec, D., Hütten, M., Imazawa, R., Inada, T., Iotov, R., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kouch, P. M., Kubo, H., Kushida, J., Lezáun, M. Láinez, Lamastra, A., Leone, F., Lindfors, E., Linhoff, L., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Lyard, E., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Maneva, G., Mang, N., Manganaro, M., Mangano, S., Mannheim, K., Mariotti, M., Martínez, M., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Mićanović, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., González, M. Molero, Molina, E., Mondal, H. A., Moralejo, A., Morcuende, D., Nanci, C., Nava, L., Neustroev, V., Rosillo, M. Nievas, Nigro, C., Nikolić, L., Nilsson, K., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Ohtani, Y., Okumura, A., Otero-Santos, J., Paiano, S., Palatiello, M., Paneque, D., Paoletti, R., Paredes, J. M., Pavletić, L., Pavlović, D., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Priyadarshi, C., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Satalecka, K., Saturni, F. G., Schleicher, B., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schweizer, T., Sciaccaluga, A., Sitarek, J., Sliusar, V., Sobczynska, D., Spolon, A., Stamerra, A., Strišković, J., Strom, D., Strzys, M., Suda, Y., Suutarinen, S., Tajima, H., Takahashi, M., Takeishi, R., Tavecchio, F., Temnikov, P., Terauchi, K., Terzić, T., Teshima, M., Tosti, L., Truzzi, S., Tutone, A., Ubach, S., van Scherpenberg, J., Acosta, M. Vazquez, Ventura, S., Verguilov, V., Viale, I., Vigorito, C. F., Vitale, V., Vovk, I., Walter, R., Will, M., Yamamoto, T., Gomboc, A., Jordana-Mitjans, N., Melandri, A., Mundell, C. G., Shrestha, M., and Steele, I. A.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Gamma-ray bursts (GRBs) are explosive transient events occurring at cosmological distances, releasing a large amount of energy as electromagnetic radiation over several energy bands. We report the detection of the long GRB~201216C by the MAGIC telescopes. The source is located at $z=1.1$ and thus it is the farthest one detected at very high energies. The emission above \SI{70}{\GeV} of GRB~201216C is modelled together with multi-wavelength data within a synchrotron and synchrotron-self Compton (SSC) scenario. We find that SSC can explain the broadband data well from the optical to the very-high-energy band. For the late-time radio data, a different component is needed to account for the observed emission. Differently from previous GRBs detected in the very-high-energy range, the model for GRB~201216C strongly favors a wind-like medium. The model parameters have values similar to those found in past studies of the afterglows of GRBs detected up to GeV energies., Comment: 13 pages, 6 figures, 2 tables. Accepted for publication in Monthly Notices of the Royal Astronomical Society
Published: 2023
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218. A Knowledge Graph-Based Search Engine for Robustly Finding Doctors and Locations in the Healthcare Domain

Author: Kejriwal, Mayank, Haidarian, Hamid, Chiu, Min-Hsueh, Xiang, Andy, Shrestha, Deep, and Javed, Faizan
Subjects: Computer Science - Artificial Intelligence, Computer Science - Databases, Computer Science - Information Retrieval
Abstract: Efficiently finding doctors and locations is an important search problem for patients in the healthcare domain, for which traditional information retrieval methods tend not to work optimally. In the last ten years, knowledge graphs (KGs) have emerged as a powerful way to combine the benefits of gleaning insights from semi-structured data using semantic modeling, natural language processing techniques like information extraction, and robust querying using structured query languages like SPARQL and Cypher. In this short paper, we present a KG-based search engine architecture for robustly finding doctors and locations in the healthcare domain. Early results demonstrate that our approach can lead to significantly higher coverage for complex queries without degrading quality., Comment: Presented as an applied data science poster in KDD 2023
Published: 2023

219. Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation

Author: Pokhrel, Sandesh, Bhandari, Sanjay, Vazquez, Eduard, Shrestha, Yash Raj, and Bhattarai, Binod
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Coronary Artery Diseases (CADs) although preventable, are one of the leading causes of death and disability. Diagnosis of these diseases is often difficult and resource intensive. Angiographic imaging segmentation of the arteries has evolved as a tool of assistance that helps clinicians make an accurate diagnosis. However, due to the limited amount of data and the difficulty in curating a dataset, the task of segmentation has proven challenging. In this study, we introduce the use of pseudo-labels to address the issue of limited data in the angiographic dataset to enhance the performance of the baseline YOLO model. Unlike existing data augmentation techniques that improve the model constrained to a fixed dataset, we introduce the use of pseudo-labels generated on a dataset of separate related task to diversify and improve model performance. This method increases the baseline F1 score by 9% in the validation data set and by 3% in the test data set., Comment: arXiv admin note: text overlap with arXiv:2310.04749
Published: 2023

220. ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis

Author: Pokhrel, Sandesh, Bhandari, Sanjay, Vazquez, Eduard, Shrestha, Yash Raj, and Bhattarai, Binod
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: Coronary Artery Diseases although preventable are one of the leading cause of mortality worldwide. Due to the onerous nature of diagnosis, tackling CADs has proved challenging. This study addresses the automation of resource-intensive and time-consuming process of manually detecting stenotic lesions in coronary arteries in X-ray coronary angiography images. To overcome this challenge, we employ a specialized Convnext-V2 backbone based Mask RCNN model pre-trained for instance segmentation tasks. Our empirical findings affirm that the proposed model exhibits commendable performance in identifying stenotic lesions. Notably, our approach achieves a substantial F1 score of 0.5353 in this demanding task, underscoring its effectiveness in streamlining this intensive process.
Published: 2023

221. VTON-IT: Virtual Try-On using Image Translation

Author: Adhikari, Santosh, Bhusal, Bishnu, Ghimire, Prashant, and Shrestha, Anil
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Virtual Try-On (trying clothes virtually) is a promising application of the Generative Adversarial Network (GAN). However, it is an arduous task to transfer the desired clothing item onto the corresponding regions of a human body because of varying body size, pose, and occlusions like hair and overlapped clothes. In this paper, we try to produce photo-realistic translated images through semantic segmentation and a generative adversarial architecture-based image translation network. We present a novel image-based Virtual Try-On application VTON-IT that takes an RGB image, segments desired body part, and overlays target cloth over the segmented body region. Most state-of-the-art GAN-based Virtual Try-On applications produce unaligned pixelated synthesis images on real-life test images. However, our approach generates high-resolution natural images with detailed textures on such variant images.
Published: 2023

222. Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraints

Author: Fang, Chuan, Dong, Yuan, Luo, Kunming, Hu, Xiaotao, Shrestha, Rakesh, and Tan, Ping
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Text-driven 3D indoor scene generation is useful for gaming, the film industry, and AR/VR applications. However, existing methods cannot faithfully capture the room layout, nor do they allow flexible editing of individual objects in the room. To address these problems, we present Ctrl-Room, which can generate convincing 3D rooms with designer-style layouts and high-fidelity textures from just a text prompt. Moreover, Ctrl-Room enables versatile interactive editing operations such as resizing or moving individual furniture items. Our key insight is to separate the modeling of layouts and appearance. Our proposed method consists of two stages: a Layout Generation Stage and an Appearance Generation Stage. The Layout Generation Stage trains a text-conditional diffusion model to learn the layout distribution with our holistic scene code parameterization. Next, the Appearance Generation Stage employs a fine-tuned ControlNet to produce a vivid panoramic image of the room guided by the 3D scene layout and text prompt. We thus achieve a high-quality 3D room generation with convincing layouts and lively textures. Benefiting from the scene code parameterization, we can easily edit the generated room model through our mask-guided editing module, without expensive edit-specific training. Extensive experiments on the Structured3D dataset demonstrate that our method outperforms existing methods in producing more reasonable, view-consistent, and editable 3D rooms from natural language prompts.
Published: 2023

223. Efficient Video and Audio processing with Loihi 2

Author: Shrestha, Sumit Bam, Timcheck, Jonathan, Frady, Paxon, Campos-Macias, Leobardo, and Davies, Mike
Subjects: Computer Science - Neural and Evolutionary Computing, Computer Science - Emerging Technologies
Abstract: Loihi 2 is an asynchronous, brain-inspired research processor that generalizes several fundamental elements of neuromorphic architecture, such as stateful neuron models communicating with event-driven spikes, in order to address limitations of the first generation Loihi. Here we explore and characterize some of these generalizations, such as sigma-delta encapsulation, resonate-and-fire neurons, and integer-valued spikes, as applied to standard video, audio, and signal processing tasks. We find that these new neuromorphic approaches can provide orders of magnitude gains in combined efficiency and latency (energy-delay-product) for feed-forward and convolutional neural networks applied to video, audio denoising, and spectral transforms compared to state-of-the-art solutions., Comment: 5 pages, 3 figures
Published: 2023

224. Electronic properties of kagome metal ScV6Sn6 using high field torque magnetometry

Author: Shrestha, Keshav, Regmi, Binod, Pokharel, Ganesh, Kim, Seong-Gon, Wilson, Stephen D., Graf, David E., Magar, Birendra A., Phillips, Cole, and Nguyen, Thinh
Subjects: Condensed Matter - Strongly Correlated Electrons
Abstract: This work presents electronic properties of the kagome metal ScV6Sn6 using de Haas-van Alphen (dHvA) oscillations and density functional theory (DFT) calculations. The torque signal with the applied fields up to 43 T shows clear dHvA oscillations with six major frequencies, five of them are below 400 T (low frequencies) and one is nearly 2800 T (high frequency). The Berry phase calculated using the Landau level fan diagram near the quantum limit is approximately {\pi}, which suggests the non-trivial band topology in ScV6Sn6. To explain the experimental data, we computed the electronic band structure and Fermi surface using DFT in both the pristine and charge density wave (CDW) phases. Our results confirm that the CDW phase is energetically favorable, and the Fermi surface undergoes a severe reconstruction in the CDW state. Furthermore, the angular dependence of the dHvA frequencies are consistent with the DFT calculations. The detailed electronic properties presented here are invaluable for understanding the electronic structure and CDWorder in ScV6Sn6, as well as in other vanadium-based kagome systems., Comment: 13 pages, 4 figures
Published: 2023

225. Evidence of weak circumstellar medium interaction in the Type II SN 2023axu

Author: Shrestha, Manisha, Pearson, Jeniveve, Wyatt, Samuel, Sand, David J., Hosseinzadeh, Griffin, Bostroem, K. Azalee, Andrews, Jennifer E., Dong, Yize, Hoang, Emily, Janzen, Daryl, Jencson, Jacob E., Lundquist, M. J., Mehta, Darshana, Retamal, 4 Nicolas Meza, Valenti, Stefano, Rastinejad, Jillian C., Daly, Phil, Porter, Dallan, Hinz, Joannah, Self, Skyler, Weiner, Benjamin, Williams, Grant G., Hiramatsu, Daichi, Howell, D. Andrew, McCully, Curtis, Gonzalez, Estefania Padilla, Pellegrino, Craig, Terreran, Giacomo, Newsome, Megan, Farah, Joseph, Itagaki, Koichi, Jha, Saurabh W., Kwok, Lindsey, Smith, Nathan, Schwab, Michaela, Rho, Jeonghee, and Yang, Yi
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: We present high-cadence photometric and spectroscopic observations of SN~2023axu, a classical Type II supernova with an absolute $V$-band peak magnitude of $-16.5 \pm 0.1$ mag. SN~2023axu was discovered by the Distance Less Than 40 Mpc (DLT40) survey within 1 day of the last non-detection in the nearby galaxy NGC 2283 at 13.7 Mpc. We modeled the early light curve using a recently updated shock cooling model that includes the effects of line blanketing and found the explosion epoch to be MJD 59971.48 $\pm$ 0.03 and the probable progenitor to be a red supergiant with a radius of 417 $\pm$ 28 $R_\odot$. The shock cooling model cannot match the rise of observed data in the $r$ and $i$ bands and underpredicts the overall UV data which points to possible interaction with circumstellar material. This interpretation is further supported by spectral behavior. We see a ledge feature around 4600 \AA\ in the very early spectra (+1.1 and +1.5 days after the explosion) which can be a sign of circumstellar interaction. The signs of circumstellar material are further bolstered by the presence of absorption features blueward of H$\alpha$ and H$\beta$ at day $>$40 which is also generally attributed to circumstellar interaction. Our analysis shows the need for high-cadence early photometric and spectroscopic data to decipher the mass-loss history of the progenitor., Comment: 18 pages, 12 figures, to be submitted to the AAS Journals
Published: 2023

226. Development of a Feeding Assistive Robot Using a Six Degree of Freedom Robotic Arm

Author: Emu, Md Esharuzzaman, Biswas, Samarjith, and Shrestha, Rajendra
Subjects: Computer Science - Robotics, 15A22
Abstract: This project introduces a Feeding Assistive Robot tailored to individuals with physical disabilities, including those with limited arm function or hand control. The core component is a precise 6-degree freedom robotic arm, operated seamlessly through voice commands. Integration of an Arduino-based Braccio Arm, a distance sensor, and Bluetooth module enables voice-controlled movements. The primary goal is to empower users to independently select and consume meals, whether at a dining table or in bed. The system's adaptability, responsiveness, and versatility in serving three different food items mark a significant advancement in enhancing the quality of life for individuals with physical challenges, promoting autonomy in daily activities., Comment: 5 pages, 6 figures
Published: 2023

227. Strong Carbon Features and a Red Early Color in the Underluminous Type Ia SN 2022xkq

Author: Pearson, Jeniveve, Sand, David J., Lundqvist, Peter, Galbany, Lluís, Andrews, Jennifer E., Bostroem, K. Azalee, Dong, Yize, Hoang, Emily, Hosseinzadeh, Griffin, Janzen, Daryl, Jencson, Jacob E., Lundquist, Michael J., Mehta, Darshana, Retamal, Nicolás Meza, Shrestha, Manisha, Valenti, Stefano, Wyatt, Samuel, Anderson, Joseph P., Ashall, Chris, Auchettl, Katie, Baron, Eddie, Blondin, Stéphane, Burns, Christopher R., Cai, Yongzhi, Chen, Ting-Wan, Chomiuk, Laura, Coulter, David A., Cross, Dane, Davis, Kyle W., de Jaeger, Thomas, DerKacy, James M., Desai, Dhvanil D., Dimitriadis, Georgios, Do, Aaron, Farah, Joseph R., Foley, Ryan J., Gromadzki, Mariusz, Gutiérrez, Claudia P., Haislip, Joshua, Hernández, Jonay I. González, Hinkle, Jason T., Hoogendam, Willem B., Howell, D. Andrew, Hoeflich, Peter, Hsiao, Eric, Huber, Mark E., Jha, Saurabh W., Palau, Cristina Jiménez, Kilpatrick, Charles D., Kouprianov, Vladimir, Kumar, Sahana, Kwok, Lindsey A., Larison, Conor, LeBaron, Natalie, Saux, Xavier Le, Lu, Jing, McCully, Curtis, Evans, Tycho Mera, Milne, Peter, Modjaz, Maryam, Morrell, Nidia, Müller-Bravo, Tomás E., Newsome, Megan, Nicholl, Matt, Gonzalez, Estefania Padilla, Payne, Anna V., Pellegrino, Craig, Phan, Kim, Pineda-García, Jonathan, Piro, Anthony L., Piscarreta, Lara, Polin, Abigail, Reichart, Daniel E., Rojas-Bravo, César, Ryder, Stuart D., Salmaso, Irene, Schwab, Michaela, Shahbandeh, Melissa, Shappee, Benjamin J., Siebert, Matthew R., Smith, Nathan, Strader, Jay, Taggart, Kirsty, Terreran, Giacomo, Tinyanont, Samaporn, Tucker, M. A., Valerin, Giorgio, and Young, D. R.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: We present optical, infrared, ultraviolet, and radio observations of SN 2022xkq, an underluminous fast-declining type Ia supernova (SN Ia) in NGC 1784 ($\mathrm{D}\approx31$ Mpc), from $<1$ to 180 days after explosion. The high-cadence observations of SN 2022xkq, a photometrically transitional and spectroscopically 91bg-like SN Ia, cover the first days and weeks following explosion which are critical to distinguishing between explosion scenarios. The early light curve of SN 2022xkq has a red early color and exhibits a flux excess which is more prominent in redder bands; this is the first time such a feature has been seen in a transitional/91bg-like SN Ia. We also present 92 optical and 19 near-infrared (NIR) spectra, beginning 0.4 days after explosion in the optical and 2.6 days after explosion in the NIR. SN 2022xkq exhibits a long-lived C I 1.0693 $\mu$m feature which persists until 5 days post-maximum. We also detect C II $\lambda$6580 in the pre-maximum optical spectra. These lines are evidence for unburnt carbon that is difficult to reconcile with the double detonation of a sub-Chandrasekhar mass white dwarf. No existing explosion model can fully explain the photometric and spectroscopic dataset of SN 2022xkq, but the considerable breadth of the observations is ideal for furthering our understanding of the processes which produce faint SNe Ia., Comment: 38 pages, 16 figures, accepted for publication in ApJ, the figure 15 input models and synthetic spectra are now available at https://zenodo.org/record/8379254
Published: 2023

228. Characterizing the Rapid Hydrogen Disappearance in SN2022crv: Evidence of a Continuum between Type Ib and IIb Supernova Properties

Author: Dong, Yize, Valenti, Stefano, Ashall, Chris, Williamson, Marc, Sand, David J., Van Dyk, Schuyler D., Filippenko, Alexei V., Jha, Saurabh W., Lundquist, Michael, Modjaz, Maryam, Andrews, Jennifer E., Jencson, Jacob E., Hosseinzadeh, Griffin, Pearson, Jeniveve, Kwok, Lindsey A., Boland, Teresa, Hsiao, Eric Y., Smith, Nathan, Elias-Rosa, Nancy, Srivastav, Shubham, Smartt, Stephen, Fulton, Michael, Zheng, WeiKang, Brink, Thomas G., Shahbandeh, Melissa, Bostroem, K. Azalee, Hoang, Emily, Janzen, Daryl, Mehta, Darshana, Meza, Nicolas, Shrestha, Manisha, Wyatt, Samuel, Auchettl, Katie, Burns, Christopher R., Farah, Joseph, Galbany, L., Gonzalez, Estefania Padilla, Haislip, Joshua, Hinkle, Jason T., Howell, D. Andrew, De Jaeger, Thomas, Kouprianov, Vladimir, Kumar, Sahana, Lu, Jing, McCully, Curtis, Moran, Shane, Morrell, Nidia, Newsome, Megan, Pellegrino, Craig, Polin, Abigail, Reichart, Daniel E., Shappee, B. J., Stritzinger, Maximilian D., Terreran, Giacomo, and Tucker, M. A.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: We present optical and near-infrared observations of SN~2022crv, a stripped envelope supernova in NGC~3054, discovered within 12 hrs of explosion by the Distance Less Than 40 Mpc Survey. We suggest SN~2022crv is a transitional object on the continuum between SNe Ib and SNe IIb. A high-velocity hydrogen feature ($\sim$$-$20,000 -- $-$16,000 $\rm km\,s^{-1}$) was conspicuous in SN~2022crv at early phases, and then quickly disappeared around maximum light. By comparing with hydrodynamic modeling, we find that a hydrogen envelope of $\sim 10^{-3}$ \msun{} can reproduce the behaviour of the hydrogen feature observed in SN~2022crv. The early light curve of SN~2022crv did not show envelope cooling emission, implying that SN~2022crv had a compact progenitor with extremely low amount of hydrogen. The analysis of the nebular spectra shows that SN~2022crv is consistent with the explosion of a He star with a final mass of $\sim$4.5 -- 5.6 \msun{} that has evolved from a $\sim$16 -- 22 \msun{} zero-age main sequence star in a binary system with about 1.0 -- 1.7 \msun{} of oxygen finally synthesized in the core. The high metallicity at the supernova site indicates that the progenitor experienced a strong stellar wind mass loss. In order to retain a small amount of residual hydrogen at such a high metallicity, the initial orbital separation of the binary system is likely larger than $\sim$1000~$\rm R_{\odot}$. The near-infrared spectra of SN~2022crv show a unique absorption feature on the blue side of He I line at $\sim$1.005~$\mu$m. This is the first time that such a feature has been observed in a Type Ib/IIb, and could be due to \ion{Sr}{2}. Further detailed modelling on SN~2022crv can shed light on the progenitor and the origin of the mysterious absorption feature in the near infrared., Comment: accepted for publication in ApJ
Published: 2023
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229. Large Language Models for Difficulty Estimation of Foreign Language Content with Application to Language Learning

Author: Vlachos, Michalis, Lungu, Mircea, Shrestha, Yash Raj, and David, Johannes-Rudolf
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: We use large language models to aid learners enhance proficiency in a foreign language. This is accomplished by identifying content on topics that the user is interested in, and that closely align with the learner's proficiency level in that foreign language. Our work centers on French content, but our approach is readily transferable to other languages. Our solution offers several distinctive characteristics that differentiate it from existing language-learning solutions, such as, a) the discovery of content across topics that the learner cares about, thus increasing motivation, b) a more precise estimation of the linguistic difficulty of the content than traditional readability measures, and c) the availability of both textual and video-based content. The linguistic complexity of video content is derived from the video captions. It is our aspiration that such technology will enable learners to remain engaged in the language-learning process by continuously adapting the topics and the difficulty of the content to align with the learners' evolving interests and learning objectives.
Published: 2023

230. Multilevel determinants of antiretroviral therapy initiation and retention in the test-and-treat era of Nepal: a qualitative study

Author: Shrestha, Archana, Poudel, Lisasha, Shrestha, Soniya, Jha, Niharika, Kuikel, Bihari Sharan, Shakya, Prakash, Kunwar, Rajya Shree, Pandey, Lok Raj, KC, Man Bahadur, Wilson, Erin C., and Deuba, Keshab
Published: 2024
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231. Molecular characterization of influenza virus circulating in Nepal in the year 2019

Author: Mehta, Rachana, Jha, Bimalesh Kumar, Awal, Balkrishna, Sah, Ranjit, Shrestha, Lilee, Sherpa, Chhoting, Shrestha, Smriti, and Jha, Runa
Published: 2024
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232. The overlap of accessory virulence factors and multidrug resistance among clinical and surveillance Klebsiella pneumoniae isolates from a neonatal intensive care unit in Nepal: a single-centre experience in a resource-limited setting

Author: Shrestha, Raj Kumar, Shrestha, Dhruba, Kunwar, Ajaya Jang, Thapa, Sandeep, Shrestha, Nipun, Dhoubhadel, Bhim Gopal, and Parry, Christopher M.
Published: 2024
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233. Software Performance of the ATLAS Track Reconstruction for LHC Run 3

Author: Aad, G., Abbott, B., Abeling, K., Abicht, N. J., Abidi, S. H., Aboulhorma, A., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B. S., Bourdarios, C. Adam, Adamczyk, L., Adamek, L., Addepalli, S. V., Addison, M. J., Adelman, J., Adiguzel, A., Adye, T., Affolder, A. A., Afik, Y., Agaras, M. N., Agarwala, J., Aggarwal, A., Agheorghiesei, C., Ahmad, A., Ahmadov, F., Ahmed, W. S., Ahuja, S., Ai, X., Aielli, G., Aikot, A., Tamlihat, M. Ait, Aitbenchikh, B., Aizenberg, I., Akbiyik, M., Åkesson, T. P. A., Akimov, A. V., Akiyama, D., Akolkar, N. N., Khoury, K. Al, Alberghi, G. L., Albert, J., Albicocco, P., Albouy, G. L., Alderweireldt, S., Aleksa, M., Aleksandrov, I. N., Alexa, C., Alexopoulos, T., Alfonsi, F., Algren, M., Alhroob, M., Ali, B., Ali, H. M. J., Ali, S., Alibocus, S. W., Aliev, M., Alimonti, G., Alkakhi, W., Allaire, C., Allbrooke, B. M. M., Allen, J. F., Flores, C. A. Allendes, Allport, P. P., Aloisio, A., Alonso, F., Alpigiani, C., Estevez, M. Alvarez, Fernandez, A. Alvarez, Cardoso, M. Alves, Alviggi, M. G., Aly, M., Coutinho, Y. Amaral, Ambler, A., Amelung, C., Amerl, M., Ames, C. G., Amidei, D., Santos, S. P. Amor Dos, Amos, K. R., Ananiev, V., Anastopoulos, C., Andeen, T., Anders, J. K., Andrean, S. Y., Andreazza, A., Angelidakis, S., Angerami, A., Anisenkov, A. V., Annovi, A., Antel, C., Anthony, M. T., Antipov, E., Antonelli, M., Anulli, F., Aoki, M., Aoki, T., Pozo, J. A. Aparisi, Aparo, M. A., Bella, L. Aperio, Appelt, C., Apyan, A., Aranzabal, N., Val, S. J. Arbiol, Arcangeletti, C., Arce, A. T. H., Arena, E., Arguin, J-F., Argyropoulos, S., Arling, J.-H., Arnaez, O., Arnold, H., Artoni, G., Asada, H., Asai, K., Asai, S., Asbah, N. A., Assahsah, J., Assamagan, K., Astalos, R., Atashi, S., Atkin, R. J., Atkinson, M., Atmani, H., Atmasiddha, P. A., Augsten, K., Auricchio, S., Auriol, A. D., Austrup, V. A., Avolio, G., Axiotis, K., Azuelos, G., Babal, D., Bachacou, H., Bachas, K., Bachiu, A., Backman, F., Badea, A., Bagnaia, P., Bahmani, M., Bailey, A. J., Bailey, V. R., Baines, J. T., Baines, L., Baker, O. K., Bakos, E., Gupta, D. Bakshi, Balakrishnan, V., Balasubramanian, R., Baldin, E. M., Balek, P., Ballabene, E., Balli, F., Baltes, L. M., Balunas, W. K., Balz, J., Banas, E., Bandieramonte, M., Bandyopadhyay, A., Bansal, S., Barak, L., Barakat, M., Barberio, E. L., Barberis, D., Barbero, M., Barel, M. Z., Barends, K. N., Barillari, T., Barisits, M-S., Barklow, T., Baron, P., Moreno, D. A. Baron, Baroncelli, A., Barone, G., Barr, A. J., Barr, J. D., Navarro, L. Barranco, Barreiro, F., da Costa, J. Barreiro Guimarães, Barron, U., Teixeira, M. G. Barros, Barsov, S., Bartels, F., Bartoldus, R., Barton, A. E., Bartos, P., Basan, A., Baselga, M., Bassalat, A., Basso, M. J., Basson, C. R., Bates, R. L., Batlamous, S., Batley, J. R., Batool, B., Battaglia, M., Battulga, D., Bauce, M., Bauer, M., Bauer, P., Hurrell, L. T. Bazzano, Beacham, J. B., Beau, T., Beaucamp, J. Y., Beauchemin, P. H., Becherer, F., Bechtle, P., Beck, H. P., Becker, K., Beddall, A. J., Bednyakov, V. A., Bee, C. P., Beemster, L. J., Beermann, T. A., Begalli, M., Begel, M., Behera, A., Behr, J. K., Beirer, J. F., Beisiegel, F., Belfkir, M., Bella, G., Bellagamba, L., Bellerive, A., Bellos, P., Beloborodov, K., Benchekroun, D., Bendebba, F., Benhammou, Y., Benoit, M., Bensinger, J. R., Bentvelsen, S., Beresford, L., Beretta, M., Kuutmann, E. Bergeaas, Berger, N., Bergmann, B., Beringer, J., Bernardi, G., Bernius, C., Bernlochner, F. U., Bernon, F., Berry, T., Berta, P., Berthold, A., Bertram, I. A., Bethke, S., Betti, A., Bevan, A. J., Bhalla, N. K., Bhamjee, M., Bhatta, S., Bhattacharya, D. S., Bhattarai, P., Bhopatkar, V. S., Bi, R., Bianchi, R. M., Bianco, G., Biebel, O., Bielski, R., Biglietti, M., Bindi, M., Bingul, A., Bini, C., Biondini, A., Birch-sykes, C. J., Bird, G. A., Birman, M., Biros, M., Biryukov, S., Bisanz, T., Bisceglie, E., Biswal, J. P., Biswas, D., Bitadze, A., Bjørke, K., Bloch, I., Blocker, C., Blue, A., Blumenschein, U., Blumenthal, J., Bobbink, G. J., Bobrovnikov, V. S., Boehler, M., Boehm, B., Bogavac, D., Bogdanchikov, A. G., Bohm, C., Boisvert, V., Bokan, P., Bold, T., Bomben, M., Bona, M., Boonekamp, M., Booth, C. D., Borbély, A. G., Bordulev, I. S., Borecka-Bielska, H. M., Borissov, G., Bortoletto, D., Boscherini, D., Bosman, M., Sola, J. D. Bossio, Bouaouda, K., Bouchhar, N., Boudreau, J., Bouhova-Thacker, E. V., Boumediene, D., Bouquet, R., Boveia, A., Boyd, J., Boye, D., Boyko, I. R., Bracinik, J., Brahimi, N., Brandt, G., Brandt, O., Braren, F., Brau, B., Brau, J. E., Brener, R., Brenner, L., Brenner, R., Bressler, S., Britton, D., Britzger, D., Brock, I., Brooijmans, G., Brooks, W. 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E., Vaslin, L., Vasquez, G. A., Vasyukov, A., Vazeille, F., Schroeder, T. Vazquez, Veatch, J., Vecchio, V., Veen, M. J., Veliscek, I., Veloce, L. M., Veloso, F., Veneziano, S., Ventura, A., Gonzalez, S. Ventura, Verbytskyi, A., Verducci, M., Vergis, C., De Araujo, M. Verissimo, Verkerke, W., Vermeulen, J. C., Vernieri, C., Vessella, M., Vetterli, M. C., Vgenopoulos, A., Maira, N. Viaux, Vickey, T., Boeriu, O. E. Vickey, Viehhauser, G. H. A., Vigani, L., Villa, M., Perez, M. Villaplana, Villhauer, E. M., Vilucchi, E., Vincter, M. G., Virdee, G. S., Vishwakarma, A., Visibile, A., Vittori, C., Vivarelli, I., Voevodina, E., Vogel, F., Vokac, P., Volkotrub, Yu., Ahnen, J. Von, Toerne, E. Von, Vormwald, B., Vorobel, V., Vorobev, K., Vos, M., Voss, K., Vossebeld, J. H., Vozak, M., Vozdecky, L., Vranjes, N., Milosavljevic, M. Vranjes, Vreeswijk, M., Vuillermet, R., Vujinovic, O., Vukotic, I., Wada, S., Wagner, C., Wagner, J. M., Wagner, W., Wahdan, S., Wahlberg, H., Wakida, M., Walder, J., Walker, R., Walkowiak, W., Wall, A., Wamorkar, T., Wang, A. Z., Wang, C., Wang, C., Wang, H., Wang, J., Wang, R.-J., Wang, R., Wang, R., Wang, S. M., Wang, S., Wang, T., Wang, W. T., Wang, W., Wang, X., Wang, X., Wang, X., Wang, Y., Wang, Y., Wang, Z., Wang, Z., Wang, Z., Warburton, A., Ward, R. J., Warrack, N., Watson, A. T., Watson, H., Watson, M. F., Watton, E., Watts, G., Waugh, B. M., Weber, C., Weber, H. A., Weber, M. S., Weber, S. M., Wei, C., Wei, Y., Weidberg, A. R., Weik, E. J., Weingarten, J., Weirich, M., Weiser, C., Wells, C. J., Wenaus, T., Wendland, B., Wengler, T., Wenke, N. S., Wermes, N., Wessels, M., Wharton, A. M., White, A. S., White, A., White, M. J., Whiteson, D., Wickremasinghe, L., Wiedenmann, W., Wiel, C., Wielers, M., Wiglesworth, C., Wilbern, D. J., Wilkens, H. G., Williams, D. M., Williams, H. H., Williams, S., Willocq, S., Wilson, B. J., Windischhofer, P. J., Winkel, F. I., Winklmeier, F., Winter, B. T., Winter, J. K., Wittgen, M., Wobisch, M., Wolffs, Z., Wollrath, J., Wolter, M. W., Wolters, H., Wongel, A. F., Worm, S. D., Wosiek, B. K., Woźniak, K. W., Wozniewski, S., Wraight, K., Wu, C., Wu, J., Wu, M., Wu, M., Wu, S. L., Wu, X., Wu, Y., Wu, Z., Wuerzinger, J., Wyatt, T. R., Wynne, B. M., Xella, S., Xia, L., Xia, M., Xiang, J., Xie, M., Xie, X., Xin, S., Xiong, A., Xiong, J., Xu, D., Xu, H., Xu, L., Xu, R., Xu, T., Xu, Y., Xu, Z., Xu, Z., Xu, Z., Yabsley, B., Yacoob, S., Yamaguchi, Y., Yamashita, E., Yamauchi, H., Yamazaki, T., Yamazaki, Y., Yan, J., Yan, S., Yan, Z., Yang, H. J., Yang, H. T., Yang, S., Yang, T., Yang, X., Yang, X., Yang, Y., Yang, Y., Yang, Z., Yao, W-M., Yap, Y. C., Ye, H., Ye, H., Ye, J., Ye, S., Ye, X., Yeh, Y., Yeletskikh, I., Yeo, B. K., Yexley, M. R., Yin, P., Yorita, K., Younas, S., Young, C. J. S., Young, C., Yu, C., Yu, Y., Yuan, M., Yuan, R., Yue, L., Zaazoua, M., Zabinski, B., Zaid, E., Zakareishvili, T., Zakharchuk, N., Zambito, S., Saa, J. A. Zamora, Zang, J., Zanzi, D., Zaplatilek, O., Zeitnitz, C., Zeng, H., Zeng, J. C., Zenger, Jr, D. T., Zenin, O., Ženiš, T., Zenz, S., Zerradi, S., Zerwas, D., Zhai, M., Zhang, B., Zhang, D. F., Zhang, J., Zhang, J., Zhang, K., Zhang, L., Zhang, P., Zhang, R., Zhang, S., Zhang, T., Zhang, X., Zhang, X., Zhang, Y., Zhang, Y., Zhang, Y., Zhang, Z., Zhang, Z., Zhao, H., Zhao, P., Zhao, T., Zhao, Y., Zhao, Z., Zhemchugov, A., Zheng, J., Zheng, K., Zheng, X., Zheng, Z., Zhong, D., Zhou, B., Zhou, H., Zhou, N., Zhou, Y., Zhu, C. G., Zhu, J., Zhu, Y., Zhu, Y., Zhuang, X., Zhukov, K., Zhulanov, V., Zimine, N. I., Zinsser, J., Ziolkowski, M., Živković, L., Zoccoli, A., Zoch, K., Zorbas, T. G., Zormpa, O., Zou, W., and Zwalinski, L.
Published: 2024
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234. Prognostic evaluation of quick sequential organ failure assessment score in ICU patients with sepsis across different income settings

Author: Li, Andrew, Ling, Lowell, Qin, Hanyu, Arabi, Yaseen M., Myatra, Sheila Nainan, Egi, Moritoki, Kim, Je Hyeong, Nor, Mohd Basri Mat, Son, Do Ngoc, Fang, Wen-Feng, Wahyuprajitno, Bambang, Hashmi, Madiha, Faruq, Mohammad Omar, Patjanasoontorn, Boonsong, Al Bahrani, Maher Jaffer, Shrestha, Babu Raja, Shrestha, Ujma, Nafees, Khalid Mahmood Khan, Sann, Kyi Kyi, Palo, Jose Emmanuel M., Mendsaikhan, Naranpurev, Konkayev, Aidos, Detleuxay, Khamsay, Chan, Yiong Huak, Du, Bin, Divatia, Jigeeshu Vasishtha, Koh, Younsuck, and Phua, Jason
Published: 2024
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235. Comparing additionality of tuberculosis cases using GeneXpert or smear-based active TB case-finding strategies among social contacts of index cases in Nepal

Author: Gurung, Suman Chandra, Dixit, Kritika, Paudel, Rajan, Sah, Manoj Kumar, Pandit, Ram Narayan, Aryal, Tara Prasad, Khatiwada, Shikha Upadhyay, Majhi, Govind, Dhital, Raghu, Paudel, Puskar Raj, Shrestha, Gyanendra, Rai, Bhola, Budhathoki, Gangaram, Khanal, Mukti, Mishra, Gokul, Levy, Jens, Van de Rest, Job, Thapa, Anchal, Ramsay, Andrew, Squire, Stephen Bertel, Lonnroth, Knut, Basnyat, Buddha, and Caws, Maxine
Published: 2023

236. Integrative digital tools to strengthen data management for antimicrobial resistance surveillance in the 'one health' domain in Nepal

Author: Yadav, Santosh Kumar, Shrestha, Lilee, Acharya, Jyoti, Gompo, Tulsi Ram, Chapagain, Sharmila, and Jha, Runa
Published: 2023

237. Contributors

Author: Aby, Elizabeth, primary, Ahuja, Vineet, additional, Alizzi, Mohammed, additional, Amin, Pravin, additional, Amin, Vinay, additional, Anugwom, Chimaobi M., additional, Arab, Juan Pablo, additional, Arrese, Marco, additional, Balderramo, Domingo, additional, Bane, Abate, additional, Pereira, Luciano Beltrão, additional, Boecker, Joerg, additional, Oldhafer, Karl J., additional, Brunetti, Enrico, additional, Cabada, Miguel Mauricio, additional, Cainelli, Francesca, additional, Clemente, Wanessa Trindade, additional, Currie, Bart J., additional, Debes, Jose, additional, Desalegn, Hailemichael, additional, Diaz, Luis Antonio, additional, Eapen, C.E., additional, Eickhoff, Axel, additional, Flanagan, Siobhan M., additional, Ford, James, additional, Goel, Ashish, additional, Gordeuk, Victor R., additional, Gotuzzo, Eduardo, additional, Idalsoaga, Francisco, additional, Ijeoma, Ifeorah M., additional, Kauffman, Carol A., additional, K.C., Mandip, additional, Lake, John, additional, Leventhal, Thomas M., additional, Linder, Kathleen, additional, Manciulli, Tommaso, additional, Martel, Mariana, additional, Mattos, Ângelo Z., additional, Musa, Yusuf, additional, Norton, Robert, additional, Oldhafer, Karl Jürgen, additional, Owoseni, Opeyemi, additional, Pena, Francisco Guilherme Cancela, additional, Pereira, Leila Moreira Beltrão, additional, Poovorawan, Kittiyod, additional, Qi, Xinshun, additional, Roberts, Lewis R., additional, Saichua, Prasert, additional, Seid, Amir Sultan, additional, Sharma, Vishal, additional, Shrestha, Ananta, additional, Singal, Ashwani K., additional, Sivabalan, Pirathaban, additional, de Barros Lima, Leila Maria Soares Tojal, additional, Sonderup, Mark W., additional, Spearman, C. Wendy, additional, Sripa, Banchob, additional, Tappata, Manaswita, additional, Teschke, Rolf, additional, Genderen, Perry J.J. van, additional, Vento, Sandro, additional, Xuan, Tran Dang, additional, and Yousif Sr, Mirghani Abd El Rahman, additional
Published: 2025
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238. Hepatitis E virus

Author: KC, Mandip, primary and Shrestha, Ananta, additional
Published: 2025
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239. Estimating the Cost-Effectiveness of HIV Self-Testing in the United States Using Net Benefit Regression.

Author: Islam, Md, Shrestha, Ram, Hoch, Jeffrey, and Farnham, Paul
Subjects: Male, Humans, United States, Cost-Effectiveness Analysis, Cost-Benefit Analysis, Self-Testing, HIV Infections, HIV Testing
Abstract: BACKGROUND: Cost-effectiveness analysis of HIV self-testing using patient-level data from a randomized clinical trial can inform HIV prevention funding decisions. Cost-effectiveness analysis using net-benefit regression addresses the sampling uncertainty in the trial data and the variability of policymakers willingness to pay (WTP). METHODS: We used published data from a 12-month longitudinal randomized clinical trial that enrolled 2665 men who had sex with men randomly assigned to the self-testing arm (participants receiving self-test kits) and control arm (participants receiving standard-of-care), and the self-testing arm identified 48 additional new HIV cases. We used net-benefit regression to investigate the cost-effectiveness of an HIV self-testing intervention, which compared the incremental cost per new HIV diagnosis with policymakers WTP thresholds. We addressed the uncertainties in estimating the incremental cost and the policymakers WTP per new diagnosis through the incremental net-benefit (INB) regression and cost-effectiveness acceptability curve (CEAC) analyses. RESULTS: From the health care providers perspective, the INB analysis showed a positive net benefit of HIV self-testing compared with standard-of-care when policymakers WTP per new HIV diagnosis was $9365 (95% confidence interval: $5700 to $25,500) or higher. The CEAC showed that the probability of HIV self-testing being cost-effective compared with standard-of-care was 58% and >99% at a WTP of $10 000 and $50 000 per new HIV diagnosis, respectively. CONCLUSION: The INB and CEAC analyses suggest that HIV self-testing has the potential to be cost-effective for relatively low values of policymakers WTP.
Published: 2024

240. Cost-effectiveness of Low-complexity Screening Tests in Community-based Case-finding for Tuberculosis

Author: Brümmer, Lukas E, Thompson, Ryan R, Malhotra, Akash, Shrestha, Sourya, Kendall, Emily A, Andrews, Jason R, Phillips, Patrick, Nahid, Payam, Cattamanchi, Adithya, Marx, Florian M, Denkinger, Claudia M, and Dowdy, David W
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Cost Effectiveness Research, HIV/AIDS, Tuberculosis, Rare Diseases, Clinical Research, Health Services, Comparative Effectiveness Research, Prevention, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Infection, Good Health and Well Being, Humans, Cost-Benefit Analysis, South Africa, Health Care Costs, Sputum, Sensitivity and Specificity, tuberculosis, diagnostics, screening, cost-effectiveness, mathematical modelling, Biological Sciences, Medical and Health Sciences, Microbiology, Clinical sciences
Abstract: IntroductionIn high-burden settings, low-complexity screening tests for tuberculosis (TB) could expand the reach of community-based case-finding efforts. The potential costs and cost-effectiveness of approaches incorporating these tests are poorly understood.MethodsWe developed a microsimulation model assessing 3 approaches to community-based case-finding in hypothetical populations (India-, South Africa-, The Philippines-, Uganda-, and Vietnam-like settings) with TB prevalence 4 times that of national estimates: (1) screening with a point-of-care C-reactive protein (CRP) test, (2) screening with a more sensitive "Hypothetical Screening test" (95% sensitive for Xpert Ultra-positive TB, 70% specificity; equipment/labor costs similar to Xpert Ultra, but using a $2 cartridge) followed by sputum Xpert Ultra if positive, or (3) testing all individuals with sputum Xpert Ultra. Costs are expressed in 2023 US dollars and include treatment costs.ResultsUniversal Xpert Ultra was estimated to cost a mean $4.0 million (95% uncertainty range: $3.5 to $4.6 million) and avert 3200 (2600 to 3900) TB-related disability-adjusted life years (DALYs) per 100 000 people screened ($670 [The Philippines] to $2000 [Vietnam] per DALY averted). CRP was projected to cost $550 (The Philippines) to $1500 (Vietnam) per DALY averted but with 44% fewer DALYs averted. The Hypothetical Screening test showed minimal benefit compared to universal Xpert Ultra, but if specificity were improved to 95% and per-test cost to $4.5 (all-inclusive), this strategy could cost $390 (The Philippines) to $940 (Vietnam) per DALY averted.ConclusionsScreening tests can meaningfully improve the cost-effectiveness of community-based case-finding for TB but only if they are sensitive, specific, and inexpensive.
Published: 2024

241. Top-down and bottom-up microbiome engineering approaches to enable biomanufacturing from waste biomass.

Author: Lyu, Xuejiao, Nuhu, Mujaheed, Candry, Pieter, Wolfanger, Jenna, Betenbaugh, Michael, Saldivar, Alexis, Zuniga, Cristal, Wang, Ying, and Shrestha, Shilva
Subjects: Biomanufacturing, Biomass, Microbial community, Synthetic microbial consortia, Value-added products, Biomass, Microbial Consortia, Microbiota, Metabolic Engineering, Biotechnology, Metabolic Networks and Pathways, Bacteria
Abstract: UNLABELLED: Growing environmental concerns and the need to adopt a circular economy have highlighted the importance of waste valorization for resource recovery. Microbial consortia-enabled biotechnologies have made significant developments in the biomanufacturing of valuable resources from waste biomass that serve as suitable alternatives to petrochemical-derived products. These microbial consortia-based processes are designed following a top-down or bottom-up engineering approach. The top-down approach is a classical method that uses environmental variables to selectively steer an existing microbial consortium to achieve a target function. While high-throughput sequencing has enabled microbial community characterization, the major challenge is to disentangle complex microbial interactions and manipulate the structure and function accordingly. The bottom-up approach uses prior knowledge of the metabolic pathway and possible interactions among consortium partners to design and engineer synthetic microbial consortia. This strategy offers some control over the composition and function of the consortium for targeted bioprocesses, but challenges remain in optimal assembly methods and long-term stability. In this review, we present the recent advancements, challenges, and opportunities for further improvement using top-down and bottom-up approaches for microbiome engineering. As the bottom-up approach is relatively a new concept for waste valorization, this review explores the assembly and design of synthetic microbial consortia, ecological engineering principles to optimize microbial consortia, and metabolic engineering approaches for efficient conversion. Integration of top-down and bottom-up approaches along with developments in metabolic modeling to predict and optimize consortia function are also highlighted. ONE-SENTENCE SUMMARY: This review highlights the microbial consortia-driven waste valorization for biomanufacturing through top-down and bottom-up design approaches and describes strategies, tools, and unexplored opportunities to optimize the design and stability of such consortia.
Published: 2024

242. No-tillage, surface residue retention, and cover crops improved San Joaquin Valley soil health in the long term

Author: Mitchell, Jeffrey P., Cappellazzi, Shannon B., Schmidt, Rad, Chiartas, Jessica, Shrestha, Anil, Reicosky, Don, Ferris, Howard, Zhang, Xioake, Ghezzehei, Teamrat, Araya, Samuel, Kelly, Courtland, Fonte, Steven J., Light, Sarah E., Liles, Garrett, Willey, Tom, Roy, Robert, Bottens, Monte, Crum, Cary, Horwath, William R., Koch, Geoffrey M., and Scow, Kate M.
Abstract: A long-term annual crop study in Five Points, California, shows that the combined use of no-tillage, surface residue retention, and cover crops improves soil health compared to conventional practices common to the region. Several chemical, biological, and physical soil health indicators were improved when these practices were combined. Our data suggest that farmers stand to gain multiple synergistic benefits from the integrated use of these practices by increasing soil structural stability, water infiltration and storage, and agroecosystem biodiversity, and improving the efficiencies of the carbon, nitrogen, and water cycles of their production systems.
Published: 2024

243. Large-scale annotated dataset for cochlear hair cell detection and classification

Author: Buswinka, Christopher J, Rosenberg, David B, Simikyan, Rubina G, Osgood, Richard T, Fernandez, Katharine, Nitta, Hidetomi, Hayashi, Yushi, Liberman, Leslie W, Nguyen, Emily, Yildiz, Erdem, Kim, Jinkyung, Jarysta, Amandine, Renauld, Justine, Wesson, Ella, Wang, Haobing, Thapa, Punam, Bordiga, Pierrick, McMurtry, Noah, Llamas, Juan, Kitcher, Siân R, López-Porras, Ana I, Cui, Runjia, Behnammanesh, Ghazaleh, Bird, Jonathan E, Ballesteros, Angela, Vélez-Ortega, A Catalina, Edge, Albert SB, Deans, Michael R, Gnedeva, Ksenia, Shrestha, Brikha R, Manor, Uri, Zhao, Bo, Ricci, Anthony J, Tarchini, Basile, Basch, Martín L, Stepanyan, Ruben, Landegger, Lukas D, Rutherford, Mark A, Liberman, M Charles, Walters, Bradley J, Kros, Corné J, Richardson, Guy P, Cunningham, Lisa L, and Indzhykulian, Artur A
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Bioengineering, Prevention, Neurosciences, Ear, Animals, Mice, Guinea Pigs, Humans, Rats, Swine, Cochlea, Hair Cells, Auditory, Microscopy, Fluorescence, Machine Learning
Abstract: Our sense of hearing is mediated by cochlear hair cells, of which there are two types organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains 5-15 thousand terminally differentiated hair cells, and their survival is essential for hearing as they do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. Machine learning can be used to automate the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, rat, guinea pig, pig, primate, and human cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 107,000 hair cells which have been identified and annotated as either inner or outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair-cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to give other hearing research groups the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.
Published: 2024

244. DNA-protein quasi-mapping for rapid differential gene expression analysis in non-model organisms

Author: Santiago, Kyle Christian L. and Shrestha, Anish M. S.
Published: 2024
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245. A “Prime and Expand” strategy using the multifunctional fusion proteins to generate memory-like NK cells for cell therapy

Author: Shrestha, Niraj, Dee, Michael J., Chaturvedi, Pallavi, Leclerc, Gilles M., Mathyer, Mary, Dufour, Celeste, Arthur, Laura, Becker-Hapak, Michelle, Foster, Mark, McClain, Ethan, Pena, Natalia Valderrama, Kage, Karen, Zhu, Xiaoyun, George, Varghese, Liu, Bai, Egan, Jack, Echeverri, Christian, Wang, Meng, You, Lijing, Kong, Lin, Li, Liying, Berrien-Elliott, Melissa M., Cooper, Matthew L., Fehniger, Todd A., Rhode, Peter R., and Wong, Hing C.
Published: 2024
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246. Causal associations between psoriasis and chronic respiratory disease: a mendelian randomization study

Author: Dong, Peixin, Liu, Baomo, Xu, Xiongye, Su, Yan, Hu, Yu, Shrestha, Ashish, and Zhou, Yanbin
Published: 2024
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247. The Illegal Trade in Otter Pelts in Nepal

Author: Savage, Melissa and Shrestha, Mohan
Published: 2023

248. Implementation of a Trauma and Violence Informed Care Elective to Supplement Early Medical Education

Author: Trisha Agarwal, Anima Shrestha, Natasha Garamani, and Rachael Williams
Abstract: Interpersonal violence (IPV) recognition is vital to patient-centered care. Previous educational efforts have incorporated IPV into undergraduate medical education in the form of standardized patient encounters and IPV screening and awareness, but no semester-long elective that offers an intersectional approach to trauma-informed care and incorporates local advocacy groups and resources currently exists. The aim of this elective was to address the gap in IPV education in current medical school curricula and equip first-year medical students with the skills and knowledge of trauma-informed approaches in healthcare settings. We implemented a Trauma and Violence Informed Care (TVIC) elective for first-year medical students at the University of Colorado School of Medicine during Fall 2022. Seventeen first-year medical students enrolled in the elective. In this 13-week program, students met twice weekly for 1 hour sessions consisting of lectures, group discussions and role-playing activities. Learning objectives addressed IPV, intersectionality, mandatory reporting, survivors' rights and resources, and vicarious trauma. To assess course impact, surveys with Likert scale and free-response questions were distributed. Responses were analyzed using paired T-tests and content analysis. All students completed the surveys. There was significant difference (p < 0.05) in student comfort and knowledge for the following topics: Skills & Abilities, Intersectionality, Resources, System Based Processes, and Course Logistics. A TVIC elective positively impacts medical student comfort and skills around IPV patient care. More educational efforts are needed to address this curricular gap.
Published: 2024
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249. Bridging the Gap between Community Schools and Rural Communities in Nepal Using Participatory Action Research

Author: Salpa Shrestha and Megh Raj Dangal
Abstract: This paper explores the engagement of parents with out-of-school children through communitybased participatory action research in a rural community in Nepal. This study addresses the connection gap between local communities and community schools, which has resulted in consequences such as inconsistent attendance among students and low educational expectations among parents. By investigating the processes of formulating an action plan by a parent-led action group and analysing its execution, the research aimed to understand how participatory action research can foster a stronger bond between community schools and parents, thereby enhancing parental involvement in children's education. The study draws on Mezirow's transformative learning theory, incorporating concepts from Habermas's public sphere and Freire's notion of conscientization. It specifically focuses on the action group's monthly meetings held over nine months and the collaborative outcomes that resulted. By emphasising targeted interventions, collaboration and a departure from deficit-focused approaches, the findings propose effective strategies for bridging the gap between community schools and rural communities in Nepal.
Published: 2024
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250. Contextualizing the Racial Gradient in COVID-19 Outcomes: Narratives From HBCU Students

Author: Anna K. Lee, Jeannette Wade, Stephanie Teixeira-Poit, Dextiny McCain, Christopher Doss, Smriti Shrestha, and Adrienne T. Aiken-Morgan
Abstract: COVID-19 spread across the nation with Black Americans experiencing twice of the prevalence of deaths than White Americans. Black American college students are facing a unique set of biopsychosocial costs including less retention and poorer mental health. Therefore, the purpose of this study was to examine how Historically Black College or University (HBCU) students contextualize COVID-19. Interviews were conducted with 19 participants and lasted 40-60 minutes. They discussed topics including: their COVID-19 knowledge, precautionary measures, and barriers and promoters of school success were covered. Data were coded through semi-open coding and discussed among the research team. Responses were summarized by eight themes: emotional responses, colorblind rhetoric, lack of healthcare, essential work, distrust for the medical field, barriers to precautions like supply shortages and environmental factors, and poor baseline health. These findings may be used to develop interventions that moderate the impact of COVID-19 and future pandemics on mental health.
Published: 2024
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