bluuebunny/crossref_metadata_2025_split huggingface dataset:
NovelHYLS1variants associated with Joubert syndrome suggest potential genotype-phenotype correlates
Authors: Gana Simone , D’Abrusco Fulvio , Nicotra Roberta , Ghiberti Chiara , Catalano Guido , Rognone Elisa , Pichiecchio Anna , Signorini Sabrina , Valente Enza Maria 
Published: December 2024

Joubert syndrome (JS) is an inherited neurodevelopmental ciliopathy with wide clinical and genetic heterogeneity, whose paradigmatic sign is a peculiar cerebellar and brainstem malformation known as the ‘molar tooth sign’. Recessive pathogenic variants in theHYLS1gene are associated with hydrolethalus syndrome (HLS), a severe disorder characterised by multiple developmental defects leading to intrauterine or perinatal death. However,HYLS1biallelic variants were also reported in three individuals with JS.
Here, we report a fourth patient with a purely neurological JS carrying two compound heterozygous missense variants in theHYLS1gene. Notably, while all patients with lethal HLS had both variants falling within the highly conserved HYLS-1 Box, the four patients with milder JS phenotype featured at least one variant external to this evolutionary conserved domain, suggesting a possible correlation between the mutation site and the severity of the phenotype.
Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
Authors: Pasche Olivier C. , Wider Jonathan , Zhang Zhongwei , Zscheischler Jakob , Engelke Sebastian 
Published: December 2024

Abstract
The forecast accuracy of machine learning (ML) weather prediction models is improving rapidly, leading many to speak of a “second revolution in weather forecasting.” With numerous methods being developed and limited physical guarantees offered by ML models, there is a critical need for a comprehensive evaluation of these emerging techniques. While this need has been partly fulfilled by benchmark datasets, they provide little information on rare and impactful extreme events or on compound impact metrics, for which model accuracy might degrade due to misrepresented dependencies between variables. To address these issues, we compare ML weather prediction models (GraphCast, Pangu-Weather, and FourCastNet) and ECMWF’s high-resolution forecast system (HRES) in three case studies: the 2021 Pacific Northwest heatwave, the 2023 South Asian humid heatwave, and the North American winter storm in 2021. We find that ML weather prediction models locally achieve similar accuracy to HRES on the record-shattering Pacific Northwest heatwave but underperform when aggregated over space and time. However, they forecast the compound winter storm substantially better. We also highlight structural differences in how the errors of HRES and the ML models build up to that event. The ML forecasts lack important variables for a detailed assessment of the health risks of the 2023 humid heatwave. Using a possible substitute variable, prediction errors show spatial patterns with the highest danger levels over Bangladesh being underestimated by the ML models. Generally, case study–driven, impact-centric evaluation can complement existing research, increase public trust, and aid in developing reliable ML weather prediction models.

Significance Statement
With the performance of machine learning–based weather forecasting models improving rapidly, thorough analyses are needed to ensure that their forecasts are accurate and reliable before deploying them in operational settings. Existing evaluations often reduce forecast performance to a few metrics, potentially obscuring rare but systematic errors. This is especially problematic for high-impact extreme events, which, by definition, are rare in the data but often substantially affect society. In a detailed analysis of three extreme events, we observe that, although machine learning (ML) models generally outperform the best physics-based numerical weather prediction (NWP) model on benchmark datasets, they do not consistently do so for the studied extreme events or compound impact metrics and lack some impact-relevant variables.
Spectral Latent Heating Retrieval for the Midlatitudes Using GPM DPR. Part II: Development and Consistency Check of the Retrieval Algorithm
Authors: Hamada Atsushi , Yokoyama Chie , Tsuji Hiroki , Ikuta Yasutaka , Shige Shoichi , Yamaji Moeka , Kubota Takuji , Takayabu Yukari N. 
Published: December 2024

Abstract
A new algorithm for estimating the latent heating profile for precipitation in the extratropics was developed to extend the current spectral latent heating (SLH) algorithm that applies only to the tropics. The new algorithm incorporates normalized relative height into the heating estimation for deep stratiform precipitation. The normalized relative height successfully determines the cloud-base heights, above and below which latent heating and cooling occur, from the precipitation profile and adapts well to quite diverse precipitation and related heating in the extratropics. Another important improvement is the detection of multiple precipitation layers in the precipitation profiles that are not implemented in the algorithm for the tropics, further ensuring a reasonable estimation of the heating profile for diverse precipitation in the extratropics. The retrieval algorithm was evaluated from several perspectives. A self-consistency check using the numerical simulation data to construct the lookup tables confirmed that our algorithm can reproduce the true heating profiles well. Application to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR)-measured precipitation further confirmed that our algorithm works well, especially in capturing the variability of cloud-base heights for deep stratiform precipitation. A zonal-mean meridional cross section of the heating, including both tropics and extratropics up to 65° latitude, is described for the first time for the SLH product. The estimated distribution of heating in the extratropics corresponds well to midlatitude storm-track activity.
Validation of the Ensemble Generalized Analog Regression Downscaling (En-GARD) Model to Downscale Near-Surface Wind Speed for the Northeast United States
Authors: Zaman Tasnim , Gutmann Ethan , Wang Guiling , Astitha Marina 
Published: December 2024

Abstract
Wind, often overlooked in climate change impact assessments, is now a crucial atmospheric variable due to our imminent reliance on renewable energy sources like wind energy. Therefore, establishing a wind speed database in higher temporal and spatial resolution is of great importance to assess wind power in historical periods and future projections. This study focuses on enhancing wind data representation and reliability of statistically downscaled model outputs by refining grid spacing from coarser to a desirable finer scale. Employing the Ensemble Generalized Analog Regression Downscaling (En-GARD) technique, we downscaled near-surface wind speed across a northeast (NE) U.S. domain that combines onshore and offshore wind farm areas. We tested multiple atmospheric variable combinations and identified near-surface wind speed (wind speed at 10 m), longitudinal and latitudinal wind at 10 m, and atmospheric temperature at 2 m, as the set that effectively guides the selection of analog days for the analog regression downscaling approach. Validation involved using ERA5 atmospheric reanalysis products (31 km) and high-resolution (4 km) Weather Research and Forecasting (WRF) Model simulations. Downscaling was conducted using a leave-one-year-out approach over 12 years (2001–12). Statistical analysis of the downscaled output over this period demonstrated a significant correlation and low error metrics (less than 20% for normalized RMSE and 16% for normalized MAE), indicating the reliability of the method and paving the way for its future application to downscale climate projection outputs.

Significance Statement
Wind speed has become a significant atmospheric variable due to our society’s imminent reliance on renewable energy resources like wind power. Often overlooked in climate change assessments, wind speed at a local scale over wind farm locations plays a crucial role in assessing and planning wind power resources in a future climate. We are providing the successful validation of a statistical downscaling methodology for near-surface wind speed at 4-km grid spacing using reanalysis data. The validity of the methodology gives confidence to downscale climate projection models and assess changes in wind speed for future climate scenarios in a follow-up study.
Toward a Generalized Understanding of the Role of Convective Available Potential Energy (CAPE) Removal and Entrainment Rate in Convective Parameterizations on the Diurnal Cycle of Tropical Precipitation
Authors: Murata Akihiko , Fukui Shin , Nosaka Masaya , Ushiyama Tomoki , Acierto Ralph Allen 
Published: December 2024

Abstract
The diurnal rainfall cycle is a key factor affecting tropical precipitation variability. However, the diurnal cycle is not sufficiently represented in numerical models, which can be associated with convective parameterization in the models. This study presents the sensitivity of simulated precipitation over Java Island, Indonesia, to adjust two parameters in parameterization schemes. These two parameters are related to convective available potential energy (CAPE) removal and entrainment of environmental air into cumulus convection. Using a climate model, we conducted sensitivity experiments by perturbing the parameters in two plume-type convective parameterization schemes, Kain–Fritsch and Grell. Our results show a delayed phase of the diurnal cycle of the simulated precipitation over land, although this phase delay was not found over the oceanic areas. Sensitivity tests demonstrated that the phase delay of precipitation over land depends on the values of these two parameters. The timing of precipitation was improved by enhancing the degree of elimination of the vertical instability and reducing the entrainment rate. Further analyses by dividing simulated precipitation into parameterized and nonparameterized precipitation demonstrate that enhancing the degree of elimination of vertical instability can produce adequate amount of precipitation and that the reduced entrainment rate can increase the cloud top, improving precipitation timing. These results apply to both the convective parameterization schemes. Therefore, the results obtained in this study can be generalized to a range of convective parameterizations using the plume model.
Deep Learning Enhanced Wind Speed and Direction Forecasting for Airport Regions
Authors: Alves Décio , Mendonça Fábio , Mostafa Sheikh Shanawaz , Morgado-Dias Fernando 
Published: December 2024

Abstract
This work introduces a deep learning architecture tailored for accurate wind speed and direction forecasting for airports using a grid-based input. Moving beyond the limitations of conventional forecasting methods, which struggle with rapid and localized atmospheric changes and demand substantial computational power, this study positions a machine learning approach as a superior alternative for wind nowcasting. By employing a comprehensive dataset covering 75 years, the proposed model distinguishes itself by achieving a mean absolute error of 1.26 m s−1 for wind speed and 16.18° for wind direction in the examined location, Madeira International Airport area. The model’s robustness was further validated using transfer learning across 10 global airport areas, each with diverse atmospheric conditions, resulting in consistent accuracy. Additionally, a precision forecast focused specifically on the runway was conducted, and its performance was found to be consistent with the broader spatial area forecasts. These results underscore the potential of machine learning in wind prediction for aviation, highlighting a promising pathway for future advancements in weather forecasting technology.

Significance Statement
This study introduces a machine learning model that demonstrates the potential for improving spatial grid and runway wind forecasts in aviation. The model achieves a mean absolute error of 1.26 m s−1 for wind speed and 16.18° for wind direction within a region measuring 10° in both latitude and longitude. It also reduces computational demands and enhances processing speed, generating 6-h ahead forecasts in just 6 s.
Efficacy and Safety of LigaSure in Breast Surgery With Axillary Lymph Node Dissection in Patients With Breast Cancer: A Systematic Review and Meta-Analysis
Authors: Imran Muhammad , Ali Mansab , Umar Tungki Pratama , Ali Shujaat , Khalil Saba , Irfan Hamza , Muhammad Aiman , Javed Amna , Shahzadi Fatima , Ara Seemab , Usman Amir , Khan Ubaid 
Published: December 2024

Background Conventional axillary lymph node dissection (ALND) is associated with significant post-operative morbidity in patients undergoing breast surgery due to increased lymphatic leakage. LigaSure, an electrothermal bipolar vessel sealing system, provides better closure of the leakage. This study aims to compare the efficacy and safety of LigaSure against conventional techniques in patients with breast cancer underwent ALND and breast surgery. Methods We conducted a comprehensive search across the databases to identify relevant studies. The search results were imported into Covidence for article eligibility screening, and all relevant outcomes data were synthesized using odd ratios (ORs) or standardized mean differences (SMDs) with 95% confidence intervals (CIs) in meta-analysis models using RevMan 5.4. Results Twelve studies with the total of 895 patients (LigaSure = 441; Conventional technique = 454) with breast cancer underwent breast surgery with ALND were included. LigaSure was associated with significantly lower post-operative drain volume (SMD: −0.39, 95% CI [-0.53, −0.24], P < 0.00001), shortened duration of drain (SMD: −0.51, 95% CI [-0.67, −0.34], P < 0.00001), and reduced hospital stay length (SMD: −0.57, 95% CI [-0.96, −0.18], P = 0.004) compared to conventional techniques. However, no difference observed in total operation time, intra-operative blood loss, seroma related outcomes and post-operative complications between the two groups. Conclusion LigaSure significantly reduced the lymphorrhea, duration of drain and hospital stay, however, it did not prove be effective in seroma-related outcomes.
Lived experiences of intimate partner violence survivors during pregnancy and who had spontaneous abortions: A phenomenological study to inform survivor-centered strategies
Authors: Gufue Zenawi Hagos , Hailu Helen Teweldebrhan , Atsbaha Abadi Hailay , Kiros Berhe Mengesha , Tsegay Etsay Weldekidan , Mengesha Meresa Berwo 
Published: December 2024

Background: Previous studies quantified that women who experienced intimate partner violence (IPV) had a higher risk of spontaneous abortion than women who did not experience IPV. However, there is limited evidence documenting the experiences of women in Northern Ethiopia who have been subjected to partner violence and who had spontaneous abortions. This evidence is critical for policymakers looking to improve women’s access to maternity care. Objectives: This study aimed to explore the lived experiences of IPV survivors during pregnancy who had spontaneous abortions in Northern Ethiopia. Design: A phenomenological qualitative study design was used to explore the lived experiences of survivors of IPV who had spontaneous abortions. Methods: A total of 16 interviews were conducted between April 23 and June 5, 2020. Eight in-depth interviews and eight key informant interviews were conducted with eligible mothers who received the required service, Adigrat General Hospital service providers, zonal women’s affairs experts, and legal professionals. The purposive sampling technique was used to select study participants, and the sample size was determined using the information saturation principle. The data were analyzed through open coding and thematic content analysis. Result: The themes that emerged in this study included opinions toward IPV, mothers’ and experts’ experiences of partner support during pregnancy, attitudes toward IPV, and understanding of spontaneous abortion. Physical, emotional, sexual, and economic pressure were identified as the most common forms of IPV. Participants in the study believed that physical violence and stress were associated with spontaneous abortion. They also mentioned various strategies for preventing IPV, such as providing job opportunities for women, punishing perpetrators, and encouraging mutual tolerance. Conclusion: This study found that spontaneous abortion is linked to any type of IPV, including physical IPV during pregnancy. IPV is a community-supported event that necessitates strategies and legal frameworks to prevent and mitigate its effects.
Use of a weight indexed umbilical artery systolic diastolic ratio to predict the risk for adverse outcomes among growth restricted fetuses
Authors: Russell Ruby , Tessier Katelyn , Contag Stephen 
Published: December 2024

Background and Objectives: To evaluate weight-indexed umbilical systolic diastolic ratio (UASDR) to discriminate risk for adverse outcomes among growth restricted fetuses (FGR). Design and Setting: Retrospective study using prenatal ultrasound data and neonatal outcome data. Two primary outcomes: admission to intensive care and a composite outcome of severe neonatal morbidity. We included births among individuals with a singleton pregnancy at 24–40 weeks gestation without other indications for preterm delivery. We calculated the percentile of the measured standard UASDR, and the same value indexed to the EFW (iUASDR). Results: 296 pregnancies met inclusion criteria. Forty-seven percent required NICU admission and 31% developed a component of the composite outcome. The sensitivity of the iUASDR increased at lower birthweight percentiles. The positive predictive value of the standard UASDR was higher among fetuses with EFW <5th % and <10th % for NICU admission and composite outcome ((EFW <5th %) 0.82 (95% CI: 0.71, 0.91) and 0.60 (95% CI: 0.48, 0.72) and ((EFW <10th %) 0.81 (95% CI: 0.70, 0.89) and 0.58 (95% CI: 0.45, 0.69) compared with indexed values. Discussion: Data does not support use of the iUASDR to improve the test characteristics of the UASDR. Despite a moderate increase in specificity, the positive predictive value was low. UASDR non-indexed or standard values in conjunction with clinical findings and severity of FGR perform best.
Effect of neonatal intensive care unit design on infants with Neonatal Opioid Withdrawal Syndrome
Authors: Nyman Katherine , Decker Elyse , Ronan Kristin , Gopalakrishnan Mathangi , El-Metwally Dina 
Published: December 2024

Background/Objective Neonatal Opioid Withdrawal Syndrome (NOWS) requires advanced and often prolonged medical care in the neonatal intensive care unit (NICU). We examined the effect of NICU design on treatment and NICU length of stay (NICU-LOS) for infants with NOWS. Methods A retrospective chart review of eligible infants with NOWS was conducted from 2013 to 2018. We collected and analyzed data on demographics, medication, and NICU-LOS characteristics. A multivariable linear regression on log-transformed NICU-LOS was performed for comparison between the open-bay design (OBD) for 2013–2015 (August) and the new single-family-room (SFR) NICU for September 2015–2018. Results There were 118 neonates analyzed in OBD and 138 neonates in the SFR. Among infants in the SFR NICU, 86% received pharmacotherapy, compared to 76% of infants in the OBD ( p = 0.059). The breastfeeding rate was 26% in the SFR and 6% in the OBD ( p =< 0.001 ). Infants in the SFR NICU had 1.3 times longer mean NICU-LOS ( p = 0.0163) than infants in OBD after adjusting for covariates including small for gestational age. The median (1Q, 3Q) cumulative doses of morphine (mg/kg) among the SFR and OBD groups were 6.4 (2.3,17.3) and 8.7 (3.2,19.3) p: NS, respectively. Conclusions The SFR NICU was associated with longer NICU-LOS for infants with NOWS despite higher rates of breastfeeding and no difference in morphine dosage. While providing more privacy and possible improved patient satisfaction, there may be unintended consequences of the SFR design.
Effect of vasopressin infusion in refractory neonatal shock
Authors: Thakur Anup , Dangi Sachin , Gehlawat Rohit , Kler Neelam , Garg Pankaj , Fursule Anurag 
Published: December 2024

To evaluate the effect of vasopressin infusion in refractory neonatal shock. Method This was a cohort study involving retrospective review of medical records of all neonates who received intravenous vasopressin infusion for at least 6 h for treatment of refractory shock in a level 3B neonatal intensive care unit of northern India between Jan 2016 to May 2021. Outcome measures assessed were systolic, diastolic, and mean blood pressure, heart rate, urine output, serum sodium level, inotrope score, and oxygenation index at 6, 12, 24, 48, and 72 h of vasopressin therapy. Outcome measures were compared before (T1) and 48 h (T2) after initiation of vasopressin. Results A total of 32 neonates received vasopressin. The mean (SD) systolic, diastolic, and mean blood pressure improved significantly between T1 and T2 from 54 (9) to 64 (11) mm Hg (MD (95% CI) = 10.3 (4.2–16.6), p = 0.003), 36 (8) to 42 (10) mm Hg (MD (95% CI) = 6.2 (1.3–11.3), p = 0.01), and 43 (8) to 51 (10) mm Hg (MD (95% CI) = 8.5 (2.3–14.6), p = 0.01), respectively. Median (IQR) urine output improved from 1.25 (0–3.2) to 2.1 (1.6–7) ml/kg/hr, p = 0.004. There was a significant fall in serum sodium from 139 (9) to 134 (8) mEq/l, p = 0.01. Out of 32 infants, 19 (59.4%) survived, while 6 (18.8 %) were transferred out and 7 (21.8%) died. Conclusion Vasopressin improved hemodynamic parameters in refractory neonatal shock and can be used as a rescue therapy. Vasopressin therapy is associated with a fall in serum sodium.
Breastfeeding initiation according to the severity of Class 3 obesity
Authors: Cordero Leandro , Stenger Michael R , Landon Mark B , Needleman Bradley J , Noria Sabrena , Nankervis Craig A 
Published: December 2024

Background: Class 3 obesity is composed of morbid (BMI 40–49 kg/m2) and extreme (BMI ≥ 50 kg/m2) subgroups. Adverse perinatal outcomes have been associated with obesity; however, data on breastfeeding (BF) initiation for women in either group remains limited. Objective: To compare BF initiation rates (exclusive or partial BF) and related comorbidities of 890 women with morbid and 890 with extreme obesity matched by race, parity, and year of delivery. Methods: Retrospective cohort study of women who delivered singletons at ≥ 34 weeks gestation (2013–2021). Those who had bariatric surgery or infants with major malformations were excluded. Results: Both groups were similar in: primiparity (38%), age (29y), white race (58%), African American (36%), current (10 vs 12%) and former (25 vs 27%) smokers, gestational hypertension (15 vs 16%), polycystic ovary syndrome (5 vs 7%), gastroesophageal reflux disease (10 vs 10%), and anemia (17 vs 17%). Women in the extreme group had a higher prevalence of gestational (17 vs 12%) and pregestational diabetes (12 vs 6%), chronic hypertension (41 vs 17%), severe preeclampsia (18 vs 12%), obstructive sleep apnea (12 vs 3%), asthma (22 vs 16%), and cesarean deliveries (62 vs 44%). Intention to BF (64 vs 71%), exclusive BF (23 vs 34%), and BF initiation (57 vs 64%) rates were lower in the extreme obesity group at discharge. Conclusion: Higher frequency of comorbidities in the extreme obesity group highlights the need for antenatal, intrapartum, and postpartum targeted interventions if the benefits of BF to mothers and infants are to be realized.