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1. Data-Driven Gradient Optimization for Field Emission Management in a Superconducting Radio-Frequency Linac

Author

Goldenberg, Steven, Ahammed, Kawser, Carpenter, Adam, Li, Jiang, Suleiman, Riad, and Tennant, Chris

Subjects
Physics - Accelerator Physics, Computer Science - Machine Learning
Abstract

Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings., Comment: 14 pages, 6 figures, 10 tables

Published
2024

2. Many Flavors of Edit Distance

Author

Bhattacharya, Sudatta, Dey, Sanjana, Goldenberg, Elazar, and Koucký, Michal

Subjects
Computer Science - Data Structures and Algorithms
Abstract

Several measures exist for string similarity, including notable ones like the edit distance and the indel distance. The former measures the count of insertions, deletions, and substitutions required to transform one string into another, while the latter specifically quantifies the number of insertions and deletions. Many algorithmic solutions explicitly address one of these measures, and frequently techniques applicable to one can also be adapted to work with the other. In this paper, we investigate whether there exists a standardized approach for applying results from one setting to another. Specifically, we demonstrate the capability to reduce questions regarding string similarity over arbitrary alphabets to equivalent questions over a binary alphabet. Furthermore, we illustrate how to transform questions concerning indel distance into equivalent questions based on edit distance. This complements an earlier result of Tiskin (2007) which addresses the inverse direction.

Published
2024

3. Streamlining Conformal Information Retrieval via Score Refinement

Author

Intrator, Yotam, Kelner, Ori, Cohen, Regev, Goldenberg, Roman, Rivlin, Ehud, and Freedman, Daniel

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Information retrieval (IR) methods, like retrieval augmented generation, are fundamental to modern applications but often lack statistical guarantees. Conformal prediction addresses this by retrieving sets guaranteed to include relevant information, yet existing approaches produce large-sized sets, incurring high computational costs and slow response times. In this work, we introduce a score refinement method that applies a simple monotone transformation to retrieval scores, leading to significantly smaller conformal sets while maintaining their statistical guarantees. Experiments on various BEIR benchmarks validate the effectiveness of our approach in producing compact sets containing relevant information., Comment: 6 pages

Published
2024

4. Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data

Author

Nagaraj, Sujay, Goodwin, Andrew J., Lopushanskyy, Dmytro, Eytan, Danny, Greer, Robert W., Goodfellow, Sebastian D., Assadi, Azadeh, Jayarajan, Anand, Goldenberg, Anna, and Mazwi, Mjaye L.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Central Venous Lines (C-Lines) and Arterial Lines (A-Lines) are routinely used in the Critical Care Unit (CCU) for blood sampling, medication administration, and high-frequency blood pressure measurement. Judiciously accessing these lines is important, as over-utilization is associated with significant in-hospital morbidity and mortality. Documenting the frequency of line-access is an important step in reducing these adverse outcomes. Unfortunately, the current gold-standard for documentation is manual and subject to error, omission, and bias. The high-frequency blood pressure waveform data from sensors in these lines are often noisy and full of artifacts. Standard approaches in signal processing remove noise artifacts before meaningful analysis. However, from bedside observations, we characterized a distinct artifact that occurs during each instance of C-Line or A-Line use. These artifacts are buried amongst physiological waveform and extraneous noise. We focus on Machine Learning (ML) models that can detect these artifacts from waveform data in real-time - finding needles in needle stacks, in order to automate the documentation of line-access. We built and evaluated ML classifiers running in real-time at a major children's hospital to achieve this goal. We demonstrate the utility of these tools for reducing documentation burden, increasing available information for bedside clinicians, and informing unit-level initiatives to improve patient safety., Comment: Machine Learning For Health Care 2024 (MLHC)

Published
2024

5. A mixed methods study to inform fatal overdose prevention in San Diego, California: perspectives from people who use drugs

Author

Bailey, Katie, Schecter, Arielle, H. Eger, William, "CJ" Justin Valasek, Chad, A. Strathdee, Steffanie, Knox, Amy, Harvey-Vera, Alicia, F. Vera, Carlos, M. Goldenberg, Shira, Robertson Bazzi, Angela, and J. Davidson, Peter

Subjects
Naloxone, overdose prevention, people who use drugs, harm reduction
Abstract

Background: In the United States, community overdose education and naloxone distribution (OEND) programs have demonstrated efficacy in reducing opioid-related mortality. OEND programs have expanded across San Diego County, California, but differential naloxone accessibility among people who use drugs (PWUD) has not been assessed. We examined factors that shape individual naloxone accessibility in San Diego.Methods: We employed a convergent parallel mixed methods design using surveys (n=194) and qualitative interviews (n=20). Ordinal logistic regression examined factors associated with personal naloxone accessibility (i.e., the frequency with which participants could access naloxone within five minutes, categorized as never, sometimes, or always). Qualitative interviews explored participant perceptions of naloxone accessibility and whether and how they maintained naloxone. We organized multilevel findings into a modified social-ecological model.Results: In quantitative and qualitative samples, participants were majority male (72% and 70% respectively), non-White race/ethnicity (55% and 75%), with an average age around 42 years. In the quantitative sample, 24% never had personally accessible naloxone, 52% sometimes did, and 24% always did. Factors independently associated with greater personal naloxone accessibility were female gender (Adjusted Odds Ratio [AdjOR]: 2.51, 95% Confidence Interval [CI]: 1.31–4.85), monthly income

Published
2024

7. First combined tuning on transverse kinematic imbalance data with and without pion production constraints

Author

Li, Weijun, Roda, Marco, Tena-Vidal, Julia, Andreopoulos, Costas, Lu, Xianguo, Ashkenazi, Adi, Barrow, Joshua, Dytman, Steven, Gallagher, Hugh, Soto, Alfonso Andres Garcia, Gardiner, Steven, Goldenberg, Matan, Hatcher, Robert, Hen, Or, Kakorin, Igor D., Kuzmin, Konstantin S., Meregalia, Anselmo, Naumov, Vadim A., Papadopoulou, Afroditi, Perdue, Gabriel, Plows, Komninos-John, Sportes, Alon, Steinberg, Noah, Syrotenko, Vladyslav, Wolcott, Jeremy, and Yan, Qiyu

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology, Nuclear Experiment
Abstract

We present the first combined tuning, using GENIE, of four transverse kinematic imbalance measurements of neutrino-hydrocarbon scattering, both with and without pion final states, from the T2K and MINERvA experiments. As a proof of concept, we have simultaneously tuned the initial state and final-state interaction models (SF-CFG and hA, respectively), producing a new effective model that more accurately describes the data., Comment: 18 pages, 17 figures, accepted version in PRD

Published
2024

9. Early-Onset Osteoporosis: Molecular Analysis in Large Cohort and Focus on the PLS3 Gene

Author

Mancini, Maxence, Chapurlat, Roland, Isidor, Bertrand, Desjonqueres, Marine, Couture, Guillaume, Guggenbuhl, Pascal, Coutant, Régis, El Chehadeh, Salima, Fradin, Mélanie, Frazier, Aline, Goldenberg, Alice, Guillot, Pascaline, Koumakis, Eugénie, Mehsen-Cêtre, Nadia, Rossi, Massimiliano, Schaefer, Élise, Sigaudy, Sabine, Porquet-Bordes, Valérie, Fontanges, Élisabeth, Letard, Pauline, Edouard, Thomas, Javier, Rose-Marie, Cohen-Solal, Martine, Funck-Brentano, Thomas, and Collet, Corinne

Published
2024
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15. Predicting Generalization of AI Colonoscopy Models to Unseen Data

Author

Shor, Joel, McNeil, Carson, Intrator, Yotam, Ledsam, Joseph R, Yamano, Hiro-o, Tsurumaru, Daisuke, Kayama, Hiroki, Hamabe, Atsushi, Ando, Koji, Ota, Mitsuhiko, Ogino, Haruei, Nakase, Hiroshi, Kobayashi, Kaho, Miyo, Masaaki, Oki, Eiji, Takemasa, Ichiro, Rivlin, Ehud, and Goldenberg, Roman

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computers and Society
Abstract

$\textbf{Background}$: Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on unseen data require expensive and time-intensive labels. $\textbf{Methods}$: We use a "Masked Siamese Network" (MSN) to identify novel phenomena in unseen data and predict polyp detector performance. MSN is trained to predict masked out regions of polyp images, without any labels. We test MSN's ability to be trained on data only from Israel and detect unseen techniques, narrow-band imaging (NBI) and chromendoscoy (CE), on colonoscopes from Japan (354 videos, 128 hours). We also test MSN's ability to predict performance of Computer Aided Detection (CADe) of polyps on colonoscopies from both countries, even though MSN is not trained on data from Japan. $\textbf{Results}$: MSN correctly identifies NBI and CE as less similar to Israel whitelight than Japan whitelight (bootstrapped z-test, |z| > 496, p < 10^-8 for both) using the label-free Frechet distance. MSN detects NBI with 99% accuracy, predicts CE better than our heuristic (90% vs 79% accuracy) despite being trained only on whitelight, and is the only method that is robust to noisy labels. MSN predicts CADe polyp detector performance on in-domain Israel and out-of-domain Japan colonoscopies (r=0.79, 0.37 respectively). With few examples of Japan detector performance to train on, MSN prediction of Japan performance improves (r=0.56). $\textbf{Conclusion}$: Our technique can identify distribution shifts in clinical data and can predict CADe detector performance on unseen data, without labels. Our self-supervised approach can aid in detecting when data in practice is different from training, such as between hospitals or data has meaningfully shifted from training. MSN has potential for application to medical image domains beyond colonoscopy.

Published
2024

16. Breaking the Language Barrier: Can Direct Inference Outperform Pre-Translation in Multilingual LLM Applications?

Author

Intrator, Yotam, Halfon, Matan, Goldenberg, Roman, Tsarfaty, Reut, Eyal, Matan, Rivlin, Ehud, Matias, Yossi, and Aizenberg, Natalia

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Large language models hold significant promise in multilingual applications. However, inherent biases stemming from predominantly English-centric pre-training have led to the widespread practice of pre-translation, i.e., translating non-English inputs to English before inference, leading to complexity and information loss. This study re-evaluates the need for pre-translation in the context of PaLM2 models (Anil et al., 2023), which have been established as highly performant in multilingual tasks. We offer a comprehensive investigation across 108 languages and 6 diverse benchmarks, including open-end generative tasks, which were excluded from previous similar studies. Our findings challenge the pre-translation paradigm established in prior research, highlighting the advantages of direct inference in PaLM2. Specifically, PaLM2-L consistently outperforms pre-translation in 94 out of 108 languages. These findings pave the way for more efficient and effective multilingual applications, alleviating the limitations associated with pre-translation and unlocking linguistic authenticity.

Published
2024

17. Measurement Scheduling for ICU Patients with Offline Reinforcement Learning

Author

Ji, Zongliang, Goldenberg, Anna, and Krishnan, Rahul G.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Scheduling laboratory tests for ICU patients presents a significant challenge. Studies show that 20-40% of lab tests ordered in the ICU are redundant and could be eliminated without compromising patient safety. Prior work has leveraged offline reinforcement learning (Offline-RL) to find optimal policies for ordering lab tests based on patient information. However, new ICU patient datasets have since been released, and various advancements have been made in Offline-RL methods. In this study, we first introduce a preprocessing pipeline for the newly-released MIMIC-IV dataset geared toward time-series tasks. We then explore the efficacy of state-of-the-art Offline-RL methods in identifying better policies for ICU patient lab test scheduling. Besides assessing methodological performance, we also discuss the overall suitability and practicality of using Offline-RL frameworks for scheduling laboratory tests in ICU settings., Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 11 pages

Published
2024

18. Learning from Time Series under Temporal Label Noise

Author

Nagaraj, Sujay, Gerych, Walter, Tonekaboni, Sana, Goldenberg, Anna, Ustun, Berk, and Hartvigsen, Thomas

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Many sequential classification tasks are affected by label noise that varies over time. Such noise can cause label quality to improve, worsen, or periodically change over time. We first propose and formalize temporal label noise, an unstudied problem for sequential classification of time series. In this setting, multiple labels are recorded in sequence while being corrupted by a time-dependent noise function. We first demonstrate the importance of modelling the temporal nature of the label noise function and how existing methods will consistently underperform. We then propose methods that can train noise-tolerant classifiers by estimating the temporal label noise function directly from data. We show that our methods lead to state-of-the-art performance in the presence of diverse temporal label noise functions using real and synthetic data.

Published
2024

19. Explicit Good Codes Approaching Distance 1 in Ulam Metric

Author

Goldenberg, Elazar, Habib, Mursalin, and S, Karthik C.

Subjects
Computer Science - Information Theory, Computer Science - Computational Complexity, Computer Science - Data Structures and Algorithms, Mathematics - Combinatorics
Abstract

The Ulam distance of two permutations on $[n]$ is $n$ minus the length of their longest common subsequence. In this paper, we show that for every $\varepsilon>0$, there exists some $\alpha>0$, and an infinite set $\Gamma\subseteq \mathbb{N}$, such that for all $n\in\Gamma$, there is an explicit set $C_n$ of $(n!)^{\alpha}$ many permutations on $[n]$, such that every pair of permutations in $C_n$ has pairwise Ulam distance at least $(1-\varepsilon)\cdot n$. Moreover, we can compute the $i^{\text{th}}$ permutation in $C_n$ in poly$(n)$ time and can also decode in poly$(n)$ time, a permutation $\pi$ on $[n]$ to its closest permutation $\pi^*$ in $C_n$, if the Ulam distance of $\pi$ and $\pi^*$ is less than $ \frac{(1-\varepsilon)\cdot n}{4} $. Previously, it was implicitly known by combining works of Goldreich and Wigderson [Israel Journal of Mathematics'23] and Farnoud, Skachek, and Milenkovic [IEEE Transactions on Information Theory'13] in a black-box manner, that it is possible to explicitly construct $(n!)^{\Omega(1)}$ many permutations on $[n]$, such that every pair of them have pairwise Ulam distance at least $\frac{n}{6}\cdot (1-\varepsilon)$, for any $\varepsilon>0$, and the bound on the distance can be improved to $\frac{n}{4}\cdot (1-\varepsilon)$ if the construction of Goldreich and Wigderson is directly analyzed in the Ulam metric.

Published
2024

20. The association between depression and alcohol use among pregnant adults in the USA.

Author

Chapman, Madison, Goldenberg, Shira, and Bandoli, Gretchen

Subjects
Depression, Fetal alcohol spectrum disorders, Mental health, Prenatal alcohol use, Humans, Female, Pregnancy, Adult, Alcohol Drinking, Young Adult, United States, Prevalence, Adolescent, Pregnant Women, Depressive Disorder, Major, Depression, Pregnancy Complications, Logistic Models
Abstract

BACKGROUND: The prevalence of alcohol use among pregnant women aged 18-44 years old increased in recent years. The influence of mental health issues on an individuals likelihood to use alcohol during pregnancy has not been thoroughly investigated. This study will examine the association between experiencing a major depressive episode (MDE) in the past year and past-month alcohol use among pregnant women using the 2011-2020 National Survey on Drug Use and Health (NSDUH). METHODS: Pregnant women between the ages of 18 and 44 years old were included in the study for analysis. Multivariable logistic regression analysis was used to examine the association between past-year MDE and past-month alcohol use adjusting for age, race/ethnicity, marital status, and employment status. Additional logistic regression analysis was performed to investigate whether this relationship differed by trimester of pregnancy. RESULTS: A total of 6745 participants were included in the analytic sample. The prevalence of past-year MDE and past-month alcohol use was 7.67% and 9.15% respectively. Logistic regression analysis showed past-year MDE was significantly associated with past-month alcohol use in pregnant women adjusting for age, race/ethnicity, marital status, and employment status (aOR = 1.96; 95% CI, 1.34-2.87). This relationship became stronger in second and third trimesters of pregnancy. CONCLUSIONS: This study showed a positive association between MDE and past-month alcohol use among pregnant women, with strongest effect estimates in the third trimester. These findings may inform approaches for improved screening guidelines and health education for individuals who may be at higher risk of prenatal alcohol use.

Published
2024

24. The unreasonable effectiveness of AI CADe polyp detectors to generalize to new countries

Author

Shor, Joel, Yamano, Hiro-o, Tsurumaru, Daisuke, Intrator, Yotami, Kayama, Hiroki, Ledsam, Joe, Hamabe, Atsushi, Ando, Koji, Ota, Mitsuhiko, Ogino, Haruei, Nakase, Hiroshi, Kobayashi, Kaho, Oki, Eiji, Goldenberg, Roman, Rivlin, Ehud, and Takemasa, Ichiro

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computers and Society
Abstract

$\textbf{Background and aims}$: Artificial Intelligence (AI) Computer-Aided Detection (CADe) is commonly used for polyp detection, but data seen in clinical settings can differ from model training. Few studies evaluate how well CADe detectors perform on colonoscopies from countries not seen during training, and none are able to evaluate performance without collecting expensive and time-intensive labels. $\textbf{Methods}$: We trained a CADe polyp detector on Israeli colonoscopy videos (5004 videos, 1106 hours) and evaluated on Japanese videos (354 videos, 128 hours) by measuring the True Positive Rate (TPR) versus false alarms per minute (FAPM). We introduce a colonoscopy dissimilarity measure called "MAsked mediCal Embedding Distance" (MACE) to quantify differences between colonoscopies, without labels. We evaluated CADe on all Japan videos and on those with the highest MACE. $\textbf{Results}$: MACE correctly quantifies that narrow-band imaging (NBI) and chromoendoscopy (CE) frames are less similar to Israel data than Japan whitelight (bootstrapped z-test, |z| > 690, p < $10^{-8}$ for both). Despite differences in the data, CADe performance on Japan colonoscopies was non-inferior to Israel ones without additional training (TPR at 0.5 FAPM: 0.957 and 0.972 for Israel and Japan; TPR at 1.0 FAPM: 0.972 and 0.989 for Israel and Japan; superiority test t > 45.2, p < $10^{-8}$). Despite not being trained on NBI or CE, TPR on those subsets were non-inferior to Japan overall (non-inferiority test t > 47.3, p < $10^{-8}$, $\delta$ = 1.5% for both). $\textbf{Conclusion}$: Differences that prevent CADe detectors from performing well in non-medical settings do not degrade the performance of our AI CADe polyp detector when applied to data from a new country. MACE can help medical AI models internationalize by identifying the most "dissimilar" data on which to evaluate models.

Published
2023

25. A brief instrument measuring the water, sanitation and hygiene domain of menstrual health among women who inject drugs

Author

Calderón-Villarreal, Alhelí, Portillo, Lourdes Johanna Avelar, Abramovitz, Daniela, Goldenberg, Shira, Flanigan, Shawn, Quintana, Penelope JE, Harvey-Vera, Alicia, Vera, Carlos F, Rangel, Gudelia, Strathdee, Steffanie A, and Kayser, Georgia L

Subjects
Biomedical and Clinical Sciences, Public Health, Health Sciences, Clinical Research, Behavioral and Social Science, Prevention, Clean Water and Sanitation, Good Health and Well Being, Humans, Female, Adult, Hygiene, Sanitation, Menstruation, Substance Abuse, Intravenous, Surveys and Questionnaires, Middle Aged, Psychometrics, Water, Cross-Sectional Studies, Young Adult, General Science & Technology
Abstract

BackgroundDomains of adequate menstrual health (MH) include access to water, sanitation, and hygiene (WASH). People who menstruate with social disadvantages-such as homelessness or drug injection practices-often face barriers to WASH access. However, validated instruments to measure MH are limited among marginalized populations, and available instruments involve lengthy surveys. We developed and evaluated psychometric properties of a novel 'MH WASH Domain Scale-12' among people who menstruate and who inject drugs in the Tijuana-San Diego region and identified correlates of MH access using this scale.MethodsWe constructed a MH-scale based on access to twelve WASH-related items: (1) menstrual products, (2) body hygiene (bathing per week), (3) water sources for bathing, (4) improved, (5) non-shared, (6) available, (7) private, (8) nearby, (9) and safe sanitation facilities, (10) availability of soap, (11) water source for handwashing, and (12) handwashing facilities with soap/water. Variables were dichotomized and summed, with scores ranging from 0-12 points and higher scores indicating better MH access. We assessed the scale's reliability and construct and content validity using data from a binational cross-sectional study. The sample included people who inject drugs (PWID) who had ever menstruated in their lifetime and were 18+ during 2020-2021. MH-WASH items were described, and the scale was further used as an outcome variable to identify correlates.ResultsAmong 125 (124 cis-female and 1 trans-male) PWID that reported menstruating, our 'MH WASH Domain Scale-12' was reliable (Cronbach's alpha = 0.81, McDonald's Omega total = 0.83) and valid. We identified two sub-domains: Factor-1 included items describing 'WASH availability' and Factor-2 contained items related to 'WASH security'-encompassing physical and biological safety. Scale scores were significantly lower among participants experiencing unsheltered homelessness compared to participants experiencing sheltered homelessness or living in permanent housing.ConclusionWe constructed and validated a novel and reliable scale to measure MH-related WASH access that can be used to assess MH among marginalized populations in English- and Spanish-speaking contexts. Using this scale we identified disparities in MH-WASH access among PWID and who menstruate in the US-Mexico border region.

Published
2024

26. Water, sanitation, and hygiene access among people who inject drugs in Tijuana and San Diego in 2020–2021: a cross-sectional study

Author

Calderón-Villarreal, Alhelí, Avelar Portillo, Lourdes Johanna, Abramovitz, Daniela, Goldenberg, Shira, Flanigan, Shawn, Quintana, Penelope JE, Harvey-Vera, Alicia, Vera, Carlos F, Rangel, Gudelia, Strathdee, Steffanie A, and Kayser, Georgia L

Subjects
Public Health, Health Sciences, Human Society, Prevention, Infectious Diseases, Clean Water and Sanitation, Humans, Cross-Sectional Studies, Sanitation, Female, Male, Adult, Hygiene, California, Substance Abuse, Intravenous, Middle Aged, Mexico, Water Supply, Drinking Water, Young Adult, WASH, WASH insecurity, PWID, US-Mexico border, Homelessness, Health inequalities, Public Health and Health Services, Sociology, Health services and systems, Public health, Policy and administration
Abstract

BackgroundWater, sanitation, and hygiene (WASH) access is critical to public health and human dignity. People who inject drugs (PWID) experience stigma and structural violence that may limit WASH access. Few studies have assessed WASH access, insecurity, and inequities among PWID. We describe WASH access, social and geographic inequalities, and factors associated with WASH insecurity among PWID in the Tijuana-San Diego metropolitan area.MethodsIn this cross-sectional binational study, we interviewed PWID (age 18+) in 2020-2021 about WASH access and insecurity. City of residence (Tijuana/San Diego) and housing status were considered as independent variables to describe key WASH access outcomes and to assess as factors associated with WASH insecurity outcomes. Measures of association between outcomes and independent variables were assessed using log modified-Poisson regression models adjusting for covariates.ResultsOf 586 PWID (202 Tijuana; 384 San Diego), 89% reported basic access to drinking water, 38% had basic hand hygiene, 28% basic sanitation, and 46% access to bathing, and 38% reported recent open defecation. Participants residing in Tijuana reported significantly higher insecurity in accessing basic drinking water (aRR: 1.68, 95%CI: 1.02-2.76), basic hygiene (aRR: 1.45, 95%CI: 1.28-1.64), and bathing (aRR: 1.21, 95%CI: 1.06-1.39) than those living in San Diego. Participants experiencing unsheltered homelessness experienced significantly higher insecurity in accessing basic drinking water (aRR: 2.03, 95%CI: 1.07-3.86), basic sanitation (aRR: 1.68, 95%CI: 1.48, 1.92), bathing (aRR: 1.84, 95%CI: 1.52-2.22), and improved water sources for cleaning wounds (aRR: 3.12, 95%CI: 1.55-6.29) and for preparing drugs (aRR: 2.58, 95%CI: 1.36-4.89) than participants living in permanent housing.ConclusionWASH access among PWID in the Tijuana-San Diego metropolitan area was low by international standards and lower than the national averages in both countries. Homelessness was significantly associated with WASH insecurity in this population. Concentrated efforts are needed to guarantee continuously available WASH services for PWID-especially those who are unsheltered.

Published
2024

27. Failure rates of nonoperative management of low-grade splenic injuries with active extravasation: an Eastern Association for the Surgery of Trauma multicenter study.

Author

Spoor, Kristen, Cull, John, Otaibi, Banan, Hazelton, Joshua, Chipko, John, Reynolds, Jessica, Fugate, Sam, Pederson, Claire, Zier, Linda, Jacobson, Lewis, Williams, Jamie, Easterday, Thomas, Byerly, Saskya, Mentzer, Caleb, Hawke, Edward, Cullinane, Daniel, Ontengco, Julianne, Bugaev, Nikolay, LeClair, Madison, Udekwu, Pascal, Josephs, Cooper, Noorbaksh, Matthew, Babowice, James, Velopulos, Catherine, Urban, Shane, Goldenberg, Anna, Ghobrial, Gaby, Pickering, John, Quarfordt, Steven, Aunchman, Alia, LaRiccia, Aimee, Spalding, Chance, Catalano, Richard, Basham, Jordan, Edmundson, Philip, Tay, Erika, Norwood, Scott, Meadows, Katelyn, Wong, Yee, Hardman, Claire, and Nahmias, Jeffry

Subjects
abdominal injuries, spleen, treatment outcome
Abstract

OBJECTIVES: There is little evidence guiding the management of grade I-II traumatic splenic injuries with contrast blush (CB). We aimed to analyze the failure rate of nonoperative management (NOM) of grade I-II splenic injuries with CB in hemodynamically stable patients. METHODS: A multicenter, retrospective cohort study examining all grade I-II splenic injuries with CB was performed at 21 institutions from January 1, 2014, to October 31, 2019. Patients >18 years old with grade I or II splenic injury due to blunt trauma with CB on CT were included. The primary outcome was the failure of NOM requiring angioembolization/operation. We determined the failure rate of NOM for grade I versus grade II splenic injuries. We then performed bivariate comparisons of patients who failed NOM with those who did not. RESULTS: A total of 145 patients were included. Median Injury Severity Score was 17. The combined rate of failure for grade I-II injuries was 20.0%. There was no statistical difference in failure of NOM between grade I and II injuries with CB (18.2% vs 21.1%, p>0.05). Patients who failed NOM had an increased median hospital length of stay (p=0.024) and increased need for blood transfusion (p=0.004) and massive transfusion (p=0.030). Five patients (3.4%) died and 96 (66.2%) were discharged home, with no differences between those who failed and those who did not fail NOM (both p>0.05). CONCLUSION: NOM of grade I-II splenic injuries with CB fails in 20% of patients. LEVEL OF EVIDENCE: IV.

Published
2024

30. Author Correction: The Hydronephrosis Severity Index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone

Author

Erdman, Lauren, Rickard, Mandy, Drysdale, Erik, Skreta, Marta, Hua, Stanley Bryan, Sheth, Kunj, Alvarez, Daniel, Velaer, Kyla N., Chua, Michael E., Dos Santos, Joana, Keefe, Daniel, Rosenblum, Norman D., Bonnett, Megan A., Weaver, John, Xiang, Alice, Fan, Yong, Viteri, Bernarda, Cooper, Christopher S., Tasian, Gregory E., Lorenzo, Armando J., and Goldenberg, Anna

Published
2024
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31. The International Climate Psychology Collaboration: Climate change-related data collected from 63 countries

Author

Doell, Kimberly C., Todorova, Boryana, Vlasceanu, Madalina, Bak Coleman, Joseph B., Pronizius, Ekaterina, Schumann, Philipp, Azevedo, Flavio, Patel, Yash, Berkebile-Wineberg, Michael M., Brick, Cameron, Lange, Florian, Grayson, Samantha J., Pei, Yifei, Chakroff, Alek, van den Broek, Karlijn L., Lamm, Claus, Vlasceanu, Denisa, Constantino, Sara M., Rathje, Steve, Goldwert, Danielle, Fang, Ke, Aglioti, Salvatore Maria, Alfano, Mark, Alvarado-Yepez, Andy J., Andersen, Angélica, Anseel, Frederik, Apps, Matthew A. J., Asadli, Chillar, Awuor, Fonda Jane, Basaglia, Piero, Bélanger, Jocelyn J., Berger, Sebastian, Bertin, Paul, Białek, Michał, Bialobrzeska, Olga, Blaya-Burgo, Michelle, Bleize, Daniëlle N. M., Bø, Simen, Boecker, Lea, Boggio, Paulo S., Borau, Sylvie, Borau, Sylvie, Bos, Björn, Bouguettaya, Ayoub, Brauer, Markus, Brik, Tymofii, Briker, Roman, Brosch, Tobias, Buchel, Ondrej, Buonauro, Daniel, Butalia, Radhika, Carvacho, Héctor, Chamberlain, Sarah A. E., Chan, Hang-Yee, Chow, Dawn, Chung, Dongil, Cian, Luca, Cohen-Eick, Noa, Contreras-Huerta, Luis Sebastian, Contu, Davide, Cristea, Vladimir, Cutler, Jo, D’Ottone, Silvana, De keersmaecker, Jonas, Delcourt, Sarah, Delouvée, Sylvain, Diel, Kathi, Douglas, Benjamin D., Drupp, Moritz A., Dubey, Shreya, Ekmanis, Jānis, Elbaek, Christian T., Elsherif, Mahmoud, Engelhard, Iris M., Escher, Yannik A., Etienne, Tom W., Farage, Laura, Farias, Ana Rita, Feuerriegel, Stefan, Findor, Andrej, Freira, Lucia, Friese, Malte, Gains, Neil Philip, Gallyamova, Albina, Geiger, Sandra J., Genschow, Oliver, Gjoneska, Biljana, Gkinopoulos, Theofilos, Goldberg, Beth, Goldenberg, Amit, Gradidge, Sarah, Grassini, Simone, Gray, Kurt, Grelle, Sonja, Griffin, Siobhán M., Grigoryan, Lusine, Grigoryan, Ani, Grigoryev, Dmitry, Gruber, June, Guilaran, Johnrev, Hadar, Britt, Hahnel, Ulf J. J., Halperin, Eran, Harvey, Annelie J., Haugestad, Christian A. P., Herman, Aleksandra M., Hershfield, Hal E., Himichi, Toshiyuki, Hine, Donald W., Hofmann, Wilhelm, Howe, Lauren, Huaman-Chulluncuy, Enma T., Huang, Guanxiong, Ishii, Tatsunori, Ito, Ayahito, Jia, Fanli, Jost, John T., Jovanović, Veljko, Jurgiel, Dominika, Kácha, Ondřej, Kankaanpää, Reeta, Kantorowicz, Jaroslaw, Kantorowicz-Reznichenko, Elena, Mintz, Keren Kaplan, Kaya, Ilker, Kaya, Ozgur, Khachatryan, Narine, Klas, Anna, Klein, Colin, Klöckner, Christian A., Koppel, Lina, Kosachenko, Alexandra I., Kothe, Emily J., Krebs, Ruth, Krosch, Amy R., Krouwel, Andre P. M., Kyrychenko, Yara, Lagomarsino, Maria, Cunningham, Julia Lee, Lees, Jeffrey, Leung, Tak Yan, Levy, Neil, Lockwood, Patricia L., Longoni, Chiara, Ortega, Alberto López, Loschelder, David D., Lu, Jackson G., Luo, Yu, Luomba, Joseph, Lutz, Annika E., Majer, Johann M., Markowitz, Ezra, Marsh, Abigail A., Mascarenhas, Karen Louise, Mbilingi, Bwambale, Mbungu, Winfred, McHugh, Cillian, Meijers, Marijn H. C., Mercier, Hugo, Mhagama, Fenant Laurent, Michalaki, Katerina, Mikus, Nace, Milliron, Sarah G., Mitkidis, Panagiotis, Monge-Rodríguez, Fredy S., Mora, Youri L., Morais, Michael J., Moreau, David, Motoki, Kosuke, Moyano, Manuel, Mus, Mathilde, Navajas, Joaquin, Nguyen, Tam Luong, Nguyen, Dung Minh, Nguyen, Trieu, Niemi, Laura, Nijssen, Sari R. R., Nilsonne, Gustav, Nitschke, Jonas P., Nockur, Laila, Okura, Ritah, Öner, Sezin, Özdoğru, Asil Ali, Palumbo, Helena, Panagopoulos, Costas, Panasiti, Maria Serena, Pärnamets, Philip, Paruzel-Czachura, Mariola, Pavlov, Yuri G., Payán-Gómez, César, Pearson, Adam R., da Costa, Leonor Pereira, Petrowsky, Hannes M., Pfattheicher, Stefan, Pham, Nhat Tan, Ponizovskiy, Vladimir, Pretus, Clara, Rêgo, Gabriel G., Reimann, Ritsaart, Rhoads, Shawn A., Riano-Moreno, Julian, Richter, Isabell, Röer, Jan Philipp, Rosa-Sullivan, Jahred, Ross, Robert M., Sabherwal, Anandita, Saito, Toshiki, Sarrasin, Oriane, Say, Nicolas, Schmid, Katharina, Schmitt, Michael T., Schoenegger, Philipp, Scholz, Christin, Schug, Mariah G., Schulreich, Stefan, Shreedhar, Ganga, Shuman, Eric, Sivan, Smadar, Sjåstad, Hallgeir, Soliman, Meikel, Soud, Katia, Spampatti, Tobia, Sparkman, Gregg, Spasovski, Ognen, Stanley, Samantha K., Stern, Jessica A., Strahm, Noel, Suko, Yasushi, Sul, Sunhae, Syropoulos, Stylianos, Taylor, Neil C., Tedaldi, Elisa, Tinghög, Gustav, Huynh, Luu Duc Toan, Travaglino, Giovanni Antonio, Tsakiris, Manos, Tüter, İlayda, Tyrala, Michael, Uluğ, Özden Melis, Urbanek, Arkadiusz, Valko, Danila, van der Linden, Sander, van Schie, Kevin, van Stekelenburg, Aart, Vanags, Edmunds, Västfjäll, Daniel, Vesely, Stepan, Vintr, Jáchym, Vranka, Marek, Wanguche, Patrick Otuo, Willer, Robb, Wojcik, Adrian Dominik, Xu, Rachel, Yadav, Anjali, Zawisza, Magdalena, Zhao, Xian, Zhao, Jiaying, Żuk, Dawid, and Van Bavel, Jay J.

Published
2024
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32. The Hydronephrosis Severity Index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone

Author

Erdman, Lauren, Rickard, Mandy, Drysdale, Erik, Skreta, Marta, Hua, Stanley Bryan, Sheth, Kunj, Alvarez, Daniel, Velaer, Kyla N., Chua, Michael E., Dos Santos, Joana, Keefe, Daniel, Rosenblum, Norman D., Bonnett, Megan A., Weaver, John, Xiang, Alice, Fan, Yong, Viteri, Bernarda, Cooper, Christopher S., Tasian, Gregory E., Lorenzo, Armando J., and Goldenberg, Anna

Published
2024
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33. Task-based functional neural correlates of social cognition across autism and schizophrenia spectrum disorders

Author

Oliver, Lindsay D., Moxon-Emre, Iska, Hawco, Colin, Dickie, Erin W., Dakli, Arla, Lyon, Rachael E., Szatmari, Peter, Haltigan, John D., Goldenberg, Anna, Rashidi, Ayesha G., Tan, Vinh, Secara, Maria T., Desarkar, Pushpal, Foussias, George, Buchanan, Robert W., Malhotra, Anil K., Lai, Meng-Chuan, Voineskos, Aristotle N., and Ameis, Stephanie H.

Published
2024
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40. Author Correction: Safety analyses from the phase 3 ASCENT trial of sacituzumab govitecan in metastatic triple-negative breast cancer

Author

Rugo, Hope S., Tolaney, Sara M., Loirat, Delphine, Punie, Kevin, Bardia, Aditya, Hurvitz, Sara A., O’Shaughnessy, Joyce, Cortés, Javier, Diéras, Véronique, Carey, Lisa A., Gianni, Luca, Piccart, Martine J., Loibl, Sibylle, Goldenberg, David M., Hong, Quan, Olivo, Martin, Itri, Loretta M., and Kalinsky, Kevin

Published
2024
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45. Predicting the generalization of computer aided detection (CADe) models for colonoscopy

Author

Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, and Roman Goldenberg

Subjects
Image foundational model, Self-supervision, Endoscopy, Computer-aided detection (CADe) system, Artificial intelligence, Computational linguistics. Natural language processing, P98-98.5, Electronic computers. Computer science, QA75.5-76.95
Abstract

Abstract Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on unseen data require expensive and time-intensive labels. We show that a "Masked Siamese Network" (MSN), trained to predict masked out regions of polyp images without labels, can predict the performance of Computer Aided Detection (CADe) of polyps on colonoscopies, without labels. This holds on Japanese colonoscopies even when MSN is only trained on Israeli colonoscopies, which differ in scoping hardware, endoscope software, screening guidelines, bowel preparation, patient demographics, and the use of techniques such as narrow-band imaging (NBI) and chromoendoscopy (CE). Since our technique uses neither colonoscopy-specific information nor labels, it has the potential to apply to more medical imaging domains.

Published
2024
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46. Using biodiversity databases to assess vascular plant diversity in Protected Areas: a case study of Parque Estadual do Forno Grande, Brazil

Author

Thuane Bochorny, Guilherme Antar, Igor Azevedo, Fernando Bianchi Junior, Tatiana Carrijo, Valquiria Dutra, André Fontana, Claudio Fraga, Samuele Gerace, Leandro Giacomin, André Gil, Renato Goldenberg, Diego Gonzaga, Gustavo Heiden, Ingrid Koch, Ludovic Kollmann, Paulo Labiak, Duane Lima, Gabriel Marcusso, Pedro Moraes, Filipe Torres-Leite, Pedro Viana, and Rafaela Forzza

Subjects
Atlantic Forest, conservation, Catálogo de Plantas, Biology (General), QH301-705.5
Abstract

The Parque Estadual do Forno Grande is a fully protected area in the southern Espírito Santo State, Brazil. It belongs to the Atlantic Forest domain, with predominantly dense, ombrophilous, seasonal semi-deciduous forests and herbaceous/shrubby vegetation on rock outcrops. The area is recognised as highly important for conservation, designated as a priority biological area for protecting the Atlantic Forest's biodiversity. Although the importance of Protected Areas in conserving the Atlantic Forest biodiversity is unquestionable, it is crucial to understand the floristic patterns within these regions to develop effective conservation strategies. We utilised national online databases to compile species lists containing relevant information about their biodiversity. The updated list of vascular plants recorded in the Parque Estadual do Forno Grande is available in the “Catálogo de Plantas das Unidades de Conservação do Brasil” and it is presented here with further information on richness, endemism and conservation status.The Parque Estadual do Forno Grande harbours 958 species of vascular plants, of which 79.2% are angiosperms, 18.4% are ferns and 2.4% are lycophytes. Amongst these species, 44% are endemic to the Atlantic Forest. There are 58 threatened species, of which six are Critically Endangered, 39 are Endangered and 13 are Vulnerable. Amongst the threatened species, 51 are endemic to the Atlantic Forest. The number of records and the species richness in this area are notably high for Atlantic Forest standards. Our findings suggest that floristic inventories of Brazilian Protected Areas are a key contribution to the general perception of how much we still do not know about our flora. It also highlights the necessity of supporting floristic surveys in poorly-known areas, especially those remaining as forest remnants.

Published
2024
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47. Age, sex, and cell type-resolved hypothalamic gene expression across the pubertal transition in mice

Author

Dustin J. Sokolowski, Huayun Hou, Kyoko E. Yuki, Anna Roy, Cadia Chan, Wendy Choi, Mariela Faykoo-Martinez, Matt Hudson, Christina Corre, Liis Uusküla-Reimand, Anna Goldenberg, Mark R. Palmert, and Michael D. Wilson

Subjects
Medicine, Physiology, QP1-981
Abstract

Abstract Background The hypothalamus plays a central role in regulating puberty. However, our knowledge of the postnatal gene regulatory networks that control the pubertal transition in males and females is incomplete. Here, we investigate the age-, sex- and cell-type-specific gene regulation in the hypothalamus across the pubertal transition. Methods We used RNA-seq to profile hypothalamic gene expression in male and female mice at five time points spanning the onset of puberty (postnatal days (PD) 12, 22, 27, 32, and 37). By combining this data with hypothalamic single nuclei RNA-seq data from pre- and postpubertal mice, we assigned gene expression changes to their most likely cell types of origin. In our colony, pubertal onset occurs earlier in male mice, allowing us to focus on genes whose expression is dynamic across ages and offset between sexes, and to explore the bases of sex effects. Results Our age-by-sex pattern of expression enriched for biological pathways involved hormone production, neuronal activation, and glial maturation. Additionally, we inferred a robust expansion of oligodendrocytes precursor cells into mature oligodendrocytes spanning the prepubertal (PD12) to peri-pubertal (PD27) timepoints. Using spatial transcriptomic data from postpubertal mice, we observed the lateral hypothalamic area and zona incerta were the most oligodendrocyte-rich regions and that these cells expressed genes known to be involved in pubertal regulation. Conclusion Together, by incorporating multiple biological timepoints and using sex as a variable, we identified gene and cell-type changes that may participate in orchestrating the pubertal transition and provided a resource for future studies of postnatal hypothalamic gene regulation.

Published
2024
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48. Early life stages of fish under ocean alkalinity enhancement in coastal plankton communities

Author

S. U. Goldenberg, U. Riebesell, D. Brüggemann, G. Börner, M. Sswat, A. Folkvord, M. Couret, S. Spjelkavik, N. Sánchez, C. Jaspers, and M. Moyano

Subjects
Ecology, QH540-549.5, Life, QH501-531, Geology, QE1-996.5
Abstract

Ocean alkalinity enhancement (OAE) stands as a promising carbon dioxide removal technology. Yet, this solution to climate change entails shifts in environmental drivers with unknown consequences for marine fish that are critical to ecosystem health and food security. Fish and their supporting food webs may be stressed by the novel carbonate chemistry or the nutrients contained in the deployed minerals. With a mesocosm experiment on natural plankton communities, we studied early life stages of fish under alkalinity (+600 µmol kg−1) and silicate (+75 µmol L−1) addition. Larvae and young juveniles of temperate coastal species, including Atlantic herring (Clupea harengus) and cod (Gadus morhua), were exposed to direct physiological and indirect food-web-mediated effects of OAE for 49 d. Neither in the shorter nor in the longer term did we find an impairment of fish growth and survival. Alkalization even led to an increase in fish biomass. This resistance to OAE was despite using non-CO2-equilibrated deployment that induces more severe perturbations in carbonate chemistry (ΔpH =+0.7, pCO2=75 µatm) compared to alternative scenarios. Overall, our community-level study suggests that some fish populations, including key fisheries' species, may be resilient to the water chemistry changes under OAE. Whilst these results give cause for optimism regarding the large-scale application of OAE, other life history stages (embryos) and habitats (open ocean) may prove more vulnerable.

Published
2024
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49. The International Climate Psychology Collaboration: Climate change-related data collected from 63 countries

Author

Kimberly C. Doell, Boryana Todorova, Madalina Vlasceanu, Joseph B. Bak Coleman, Ekaterina Pronizius, Philipp Schumann, Flavio Azevedo, Yash Patel, Michael M. Berkebile-Wineberg, Cameron Brick, Florian Lange, Samantha J. Grayson, Yifei Pei, Alek Chakroff, Karlijn L. van den Broek, Claus Lamm, Denisa Vlasceanu, Sara M. Constantino, Steve Rathje, Danielle Goldwert, Ke Fang, Salvatore Maria Aglioti, Mark Alfano, Andy J. Alvarado-Yepez, Angélica Andersen, Frederik Anseel, Matthew A. J. Apps, Chillar Asadli, Fonda Jane Awuor, Piero Basaglia, Jocelyn J. Bélanger, Sebastian Berger, Paul Bertin, Michał Białek, Olga Bialobrzeska, Michelle Blaya-Burgo, Daniëlle N. M. Bleize, Simen Bø, Lea Boecker, Paulo S. Boggio, Sylvie Borau, Björn Bos, Ayoub Bouguettaya, Markus Brauer, Tymofii Brik, Roman Briker, Tobias Brosch, Ondrej Buchel, Daniel Buonauro, Radhika Butalia, Héctor Carvacho, Sarah A. E. Chamberlain, Hang-Yee Chan, Dawn Chow, Dongil Chung, Luca Cian, Noa Cohen-Eick, Luis Sebastian Contreras-Huerta, Davide Contu, Vladimir Cristea, Jo Cutler, Silvana D’Ottone, Jonas De keersmaecker, Sarah Delcourt, Sylvain Delouvée, Kathi Diel, Benjamin D. Douglas, Moritz A. Drupp, Shreya Dubey, Jānis Ekmanis, Christian T. Elbaek, Mahmoud Elsherif, Iris M. Engelhard, Yannik A. Escher, Tom W. Etienne, Laura Farage, Ana Rita Farias, Stefan Feuerriegel, Andrej Findor, Lucia Freira, Malte Friese, Neil Philip Gains, Albina Gallyamova, Sandra J. Geiger, Oliver Genschow, Biljana Gjoneska, Theofilos Gkinopoulos, Beth Goldberg, Amit Goldenberg, Sarah Gradidge, Simone Grassini, Kurt Gray, Sonja Grelle, Siobhán M. Griffin, Lusine Grigoryan, Ani Grigoryan, Dmitry Grigoryev, June Gruber, Johnrev Guilaran, Britt Hadar, Ulf J. J. Hahnel, Eran Halperin, Annelie J. Harvey, Christian A. P. Haugestad, Aleksandra M. Herman, Hal E. Hershfield, Toshiyuki Himichi, Donald W. Hine, Wilhelm Hofmann, Lauren Howe, Enma T. Huaman-Chulluncuy, Guanxiong Huang, Tatsunori Ishii, Ayahito Ito, Fanli Jia, John T. Jost, Veljko Jovanović, Dominika Jurgiel, Ondřej Kácha, Reeta Kankaanpää, Jaroslaw Kantorowicz, Elena Kantorowicz-Reznichenko, Keren Kaplan Mintz, Ilker Kaya, Ozgur Kaya, Narine Khachatryan, Anna Klas, Colin Klein, Christian A. Klöckner, Lina Koppel, Alexandra I. Kosachenko, Emily J. Kothe, Ruth Krebs, Amy R. Krosch, Andre P. M. Krouwel, Yara Kyrychenko, Maria Lagomarsino, Julia Lee Cunningham, Jeffrey Lees, Tak Yan Leung, Neil Levy, Patricia L. Lockwood, Chiara Longoni, Alberto López Ortega, David D. Loschelder, Jackson G. Lu, Yu Luo, Joseph Luomba, Annika E. Lutz, Johann M. Majer, Ezra Markowitz, Abigail A. Marsh, Karen Louise Mascarenhas, Bwambale Mbilingi, Winfred Mbungu, Cillian McHugh, Marijn H. C. Meijers, Hugo Mercier, Fenant Laurent Mhagama, Katerina Michalaki, Nace Mikus, Sarah G. Milliron, Panagiotis Mitkidis, Fredy S. Monge-Rodríguez, Youri L. Mora, Michael J. Morais, David Moreau, Kosuke Motoki, Manuel Moyano, Mathilde Mus, Joaquin Navajas, Tam Luong Nguyen, Dung Minh Nguyen, Trieu Nguyen, Laura Niemi, Sari R. R. Nijssen, Gustav Nilsonne, Jonas P. Nitschke, Laila Nockur, Ritah Okura, Sezin Öner, Asil Ali Özdoğru, Helena Palumbo, Costas Panagopoulos, Maria Serena Panasiti, Philip Pärnamets, Mariola Paruzel-Czachura, Yuri G. Pavlov, César Payán-Gómez, Adam R. Pearson, Leonor Pereira da Costa, Hannes M. Petrowsky, Stefan Pfattheicher, Nhat Tan Pham, Vladimir Ponizovskiy, Clara Pretus, Gabriel G. Rêgo, Ritsaart Reimann, Shawn A. Rhoads, Julian Riano-Moreno, Isabell Richter, Jan Philipp Röer, Jahred Rosa-Sullivan, Robert M. Ross, Anandita Sabherwal, Toshiki Saito, Oriane Sarrasin, Nicolas Say, Katharina Schmid, Michael T. Schmitt, Philipp Schoenegger, Christin Scholz, Mariah G. Schug, Stefan Schulreich, Ganga Shreedhar, Eric Shuman, Smadar Sivan, Hallgeir Sjåstad, Meikel Soliman, Katia Soud, Tobia Spampatti, Gregg Sparkman, Ognen Spasovski, Samantha K. Stanley, Jessica A. Stern, Noel Strahm, Yasushi Suko, Sunhae Sul, Stylianos Syropoulos, Neil C. Taylor, Elisa Tedaldi, Gustav Tinghög, Luu Duc Toan Huynh, Giovanni Antonio Travaglino, Manos Tsakiris, İlayda Tüter, Michael Tyrala, Özden Melis Uluğ, Arkadiusz Urbanek, Danila Valko, Sander van der Linden, Kevin van Schie, Aart van Stekelenburg, Edmunds Vanags, Daniel Västfjäll, Stepan Vesely, Jáchym Vintr, Marek Vranka, Patrick Otuo Wanguche, Robb Willer, Adrian Dominik Wojcik, Rachel Xu, Anjali Yadav, Magdalena Zawisza, Xian Zhao, Jiaying Zhao, Dawid Żuk, and Jay J. Van Bavel

Subjects
Science
Abstract

Abstract Climate change is currently one of humanity’s greatest threats. To help scholars understand the psychology of climate change, we conducted an online quasi-experimental survey on 59,508 participants from 63 countries (collected between July 2022 and July 2023). In a between-subjects design, we tested 11 interventions designed to promote climate change mitigation across four outcomes: climate change belief, support for climate policies, willingness to share information on social media, and performance on an effortful pro-environmental behavioural task. Participants also reported their demographic information (e.g., age, gender) and several other independent variables (e.g., political orientation, perceptions about the scientific consensus). In the no-intervention control group, we also measured important additional variables, such as environmentalist identity and trust in climate science. We report the collaboration procedure, study design, raw and cleaned data, all survey materials, relevant analysis scripts, and data visualisations. This dataset can be used to further the understanding of psychological, demographic, and national-level factors related to individual-level climate action and how these differ across countries.

Published
2024
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50. The Hydronephrosis Severity Index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone

Author

Lauren Erdman, Mandy Rickard, Erik Drysdale, Marta Skreta, Stanley Bryan Hua, Kunj Sheth, Daniel Alvarez, Kyla N. Velaer, Michael E. Chua, Joana Dos Santos, Daniel Keefe, Norman D. Rosenblum, Megan A. Bonnett, John Weaver, Alice Xiang, Yong Fan, Bernarda Viteri, Christopher S. Cooper, Gregory E. Tasian, Armando J. Lorenzo, and Anna Goldenberg

Subjects
Medicine, Science
Abstract

Abstract Antenatal hydronephrosis (HN) impacts up to 5% of pregnancies and requires close, frequent follow-up monitoring to determine who may benefit from surgical intervention. To create an automated HN Severity Index (HSI) that helps guide clinical decision-making directly from renal ultrasound images. We applied a deep learning model to paediatric renal ultrasound images to predict the need for surgical intervention based on the HSI. The model was developed and studied at four large quaternary free-standing paediatric hospitals in North America. We evaluated the degree to which HSI corresponded with surgical intervention at each hospital using area under the receiver-operator curve, area under the precision-recall curve, sensitivity, and specificity. HSI predicted subsequent surgical intervention with > 90% AUROC, > 90% sensitivity, and > 70% specificity in a test set of 202 patients from the same institution. At three external institutions, HSI corresponded with AUROCs ≥ 90%, sensitivities ≥ 80%, and specificities > 50%. It is possible to automatically and reliably assess HN severity directly from a single ultrasound. The HSI stratifies low- and high-risk HN patients thus helping to triage low-risk patients while maintaining very high sensitivity to surgical cases. HN severity can be predicted from a single patient ultrasound using a novel image-based artificial intelligence system.

Published
2024
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