RNA methyltransferase SPOUT1/CENP-32 links mitotic spindle organization with the neurodevelopmental disorder SpADMiSS by Avinash V. Dharmadhikari, Maria Alba Abad, Sheraz Khan, Reza Maroofian, Tristan T. Sands, Farid Ullah, Itaru Samejima, Yanwen Shen, Martin A. Wear, Kiara E. Moore, Elena Kondakova, Natalia Mitina, Theres Schaub, Grace K. Lee, Christine H. Umandap, Sara M. Berger, Alejandro D. Iglesias, Bernt Popp, Rami Abou Jamra, Heinz Gabriel, Stefan Rentas, Alyssa L. Rippert, Christopher Gray, Kosuke Izumi, Laura K. Conlin, Daniel C. Koboldt, Theresa Mihalic Mosher, Scott E. Hickey, Dara V. F. Albert, Haley Norwood, Amy Feldman Lewanda, Hongzheng Dai, Pengfei Liu, Tadahiro Mitani, Dana Marafi, Hatice Koçak Eker, Davut Pehlivan, Jennifer E. Posey, Natalie C. Lippa, Natalie Vena, Erin L. Heinzen, David B. Goldstein, Cyril Mignot, Jean-Madeleine de Sainte Agathe, Nouriya Abbas Al-Sannaa, Mina Zamani, Saeid Sadeghian, Reza Azizimalamiri, Tahere Seifia, Maha S. Zaki, Ghada M. H. Abdel-Salam, Mohamed S. Abdel-Hamid, Lama Alabdi, Fowzan Sami Alkuraya, Heba Dawoud, Aya Lofty, Peter Bauer, Giovanni Zifarelli, Erum Afzal, Faisal Zafar, Stephanie Efthymiou, Daniel Gossett, Meghan C. Towne, Raey Yeneabat, Belen Perez-Duenas, Ana Cazurro-Gutierrez, Edgard Verdura, Veronica Cantarin-Extremera, Ana do Vale Marques, Aleksandra Helwak, David Tollervey, Sandeep N. Wontakal, Vimla S. Aggarwal, Jill A. Rosenfeld, Victor Tarabykin, Shinya Ohta, James R. Lupski, Henry Houlden, William C. Earnshaw, Erica E. Davis, A. Arockia Jeyaprakash, Jun Liao

“…Thus, SPOUT1/CENP-32 pathogenic variants cause an autosomal recessive neurodevelopmental disorder: SpADMiSS (SPOUT1 Associated Development delay Microcephaly Seizures Short stature) underpinned by mitotic spindle organization defects and consequent chromosome segregation errors.…”
Get full text

Cross-border impact of agricultural fertilizer use: Environmental spillovers and healthcare costs in European countries by Aleksandra Kuzior, Stanislav Vasylishyn, Vladyslav Hrei, Maksym Huzenko, Olena Akymenko, Oleksii Solohub, Tetiana Vasylieva

“…In contrast, the spatial error model best explained unsafe drinking water outcomes, highlighting the role of unobserved, spatially correlated environmental pressures. …”
Get full text

A Bayesian-Optimized Surrogate Model Integrating Deep Learning Algorithms for Correcting PurpleAir Sensor Measurements by Masrur Ahmed, Jing Kong, Ningbo Jiang, Hiep Nguyen Duc, Praveen Puppala, Merched Azzi, Matthew Riley, Xavier Barthelemy

“…BaySurcls reduced root mean square error (RMSE) by an average of 20% in collocated scenarios, with reductions of up to 25% in highvariation sites. …”
Get full text

Considering water-temperature synergistic factors improves simulations of stomatal conductance models under plastic film mulching by Cheng Li, Yunxin Zhang, Jingui Wang, Hao Feng, Renyou Zhang, Wenxin Zhang, Kadambot H.M. Siddique

“…Specifically, for the BWB model, the -Ta, -Tc, and -Tc&T modifications decreased root mean square error (RMSE) by 11.5–33.3 %, 19.2–50.6 %, and 29.5–56.7 %, respectively. …”
Get full text

HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures by M. Rus, M. Rus, H. Mihanović, M. Ličer, M. Ličer, M. Kristan

“…Results show that HIDRA3 outperforms HIDRA2 and the Mediterranean basin Nucleus for European Modelling of the Ocean (NEMO) setup of the Copernicus Marine Environment Monitoring Service (CMEMS) by <span class="inline-formula">∼</span> 15 % and <span class="inline-formula">∼</span> 13 % mean absolute error (MAE) reductions at high SSH values, creating a solid new state of the art. …”
Get full text

Optimized step size control within the Rosenbrock solvers for stiff chemical ordinary differential equation systems in KPP version 2.2.3_rs4 by R. Dreger, T. Kirfel, T. Kirfel, A. Pozzer, S. Rosanka, R. Sander, D. Taraborrelli, D. Taraborrelli

“…Our analysis indicates that the local error, which is the key factor for the step size selection, is often overestimated, leading to very small substeps. …”
Get full text

A Framework for Autonomous UAV Navigation Based on Monocular Depth Estimation by Jonas Gaigalas, Linas Perkauskas, Henrikas Gricius, Tomas Kanapickas, Andrius Kriščiūnas

“…In this study, fine-tuned models using synthetic RGB and depth image data were used for each environment, demonstrating a noticeable improvement in depth estimation accuracy, with reductions in Mean Absolute Percentage Error (MAPE) from 120.45% to 33.41% in AirSimNH, from 70.09% to 8.04% in Blocks, and from 121.94% to 32.86% in MSBuild2018. …”
Get full text

Gait stability prediction through synthetic time-series and vision-based data by Mauricio C. Cordeiro, Ciaran O. Cathain, Ciaran O. Cathain, Ciaran O. Cathain, Vitor B. Nascimento, Thiago B. Rodrigues

“…The model trained exclusively on synthetic data (TSTR) outperformed the model trained on real data (TRTR), with error reductions (RMSE decreased by 56.3%, MAE by 58.2%, and MSE by 80.9%) and improved variance explanation (R2 increase of 31.2%). …”
Get full text

Reliability and responsiveness of a tissue hardness meter and algometer for measuring tissue hardness and pressure pain threshold in upper trapezius myofascial trigger points by Soukmisai Somphithak, Uraiwan Chatchawan, Atipong Pimdee, Wiraphong Sucharit

“…Both distribution-based methods (mean difference, effect size (ES), standardized response mean (SRM), standard error of measurement (SEM), and minimal detectable change at 95% confidence (MDC95)) and anchor-based methods (minimal clinically important difference (MCID) and area under the curve (AUC)) were utilized in the analysis. …”
Get full text

Identification and analysis of factors affecting the technology transfer process in the power train of automotive (Case study : Iran Khodro Industrial Group) by ٍEbrahim Doostzadeh, Abbas Toloie Ashlaghi, Manochehr Manteghi, Reza Radfard

“…Additionally, the reduction of five other gaps including gearbox vibration, gearbox durability, axle durability, engine noise and gearbox noise requires special attention to the infrastructure factor. …”
Get full text

Quantitative determination of blended proportions in tobacco formulations using near-infrared spectroscopy and transfer learning by Qinlin Xiao, Qinlin Xiao, Ruifang Gu, Li Li, Jing Wen, Xixiang Zhang, Yi Shen, Yang Liu, Lan Xiao, Qinqin Tang, Jun Yang, Yong He, Juan Yang

“…The results show that TCA-PLSR achieved substantial reductions in prediction error in most transfer tasks involving large discrepancies in feature distributions. …”
Get full text

Chronotherapeutic intervention targeting emotion regulation brain circuitry, symptoms, and suicide risk in adolescents and young adults with bipolar disorder: a pilot randomised tr... by Jihoon A Kim, Anjali Sankar, Rebecca Marks, Erin Carrubba, Bernadette Lecza, Susan Quatrano, Linda Spencer, R. Todd Constable, Brian Pittman, Eli R Lebowitz, Wendy K Silverman, Holly A Swartz, Hilary P Blumberg

“…Nineteen BE-SMART-DR and 16 BE-SMART-ER participants completed the intervention, with 11 and 13, respectively, having pre-intervention and post-intervention functional MRI data.Findings In addition to demonstrating feasibility, only BE-SMART-DR showed pre-treatment to post-treatment improvements in DR regularity (Cohen’s d=0.55; 95% CI [0.06, 1.03]), associated with reductions in left amygdala responses to emotional face stimuli (pFWE (family-wise error)-SVC (small volume correction)&lt;0.05), difficulties in emotion regulation (d=0.75; 95% CI [0.23, 1.25]) and suicide risk (d=0.65; 95% CI [0.15, 1.14]). …”
Get full text

Design of a Micro-Plant Factory Using a Validated CFD Model by Xinxin Chen, Tengyuan Hou, Shulin Liu, Yongxiu Guo, Jianping Hu, Gaoming Xu, Guoxin Ma, Wei Liu

“…The accuracy of the CFD model for the micro-plant factory was validated with normalized root mean square error (NMSE) for cultivation layer heights of 250 mm, 300 mm, and 350 mm. …”
Get full text

NitroNet – a machine learning model for the prediction of tropospheric NO₂ profiles from TROPOMI observations by L. Kuhn, L. Kuhn, S. Beirle, S. Osipov, S. Osipov, A. Pozzer, T. Wagner, T. Wagner

“…In comparison to TROPOMI satellite data, NitroNet even shows significantly lower errors and stronger correlation than a direct comparison with WRF-Chem numerical results. …”
Get full text

Changes in air pollutant emissions in China during two clean-air action periods derived from the newly developed Inversed Emission Inventory for Chinese Air Quality (CAQIEI) by L. Kong, L. Kong, X. Tang, X. Tang, Z. Wang, Z. Wang, Z. Wang, J. Zhu, J. Zhu, J. Li, H. Wu, H. Wu, Q. Wu, H. Chen, H. Chen, L. Zhu, W. Wang, B. Liu, Q. Wang, D. Chen, Y. Pan, Y. Pan, J. Li, J. Li, L. Wu, L. Wu, G. R. Carmichael

“…The calculated root-mean-square errors (RMSEs) (0.3 mg m<span class="inline-formula">⁻³</span> for CO and 9.4–21.1 <span class="inline-formula">µg m³</span> for other species, on the monthly scale) and correlation coefficients (0.76–0.94) were also improved from the a priori simulations, demonstrating good performance of the data assimilation system. …”
Get full text

Virtual-Vector-Based Predictive Torque Control for Six-Phase IM With Reduced Computational Burden and Copper Losses by Osvaldo Gonzalez, Jesus Doval-Gandoy, Magno Ayala, Jorge Rodas, Paola Maidana, Christian Medina, Carlos Romero, Larizza Delorme, Raul Gregor, Ricardo Maciel

“…Experimental validation includes a detailed comparison of torque and flux behavior, (<inline-formula><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula>) current mean square error, and total harmonic distortion of the fundamental stator currents, demonstrating the effectiveness of the proposed control approach under various operating conditions. …”
Get full text

Projected future changes in extreme climate indices affecting rice production in China using a multi-model ensemble of CMIP6 projections by Xinmin Chen, Dengpan Xiao, Dengpan Xiao, Yongqing Qi, Yongqing Qi, Zexu Shi, Huizi Bai, Yang Lu, Yang Lu, Man Zhang, Man Zhang, Peipei Pan, Peipei Pan, Dandan Ren, Xiaomeng Yin, Xiaomeng Yin, Renjie Li, Renjie Li

“…The results indicate that the multi-model ensemble constructed via the Independence Weighted Mean method (IWM) significantly outperformed both the arithmetic mean method (AM) and individual GCMs in replicating observed trends of 11 ECIs during the historical period (1981–2014), with notable reductions in root mean square error (RMSE) for certain indices. …”
Get full text

Impact of frailty in older people on health care demand: simulation modelling of population dynamics to inform service planning by Bronagh Walsh, Carole Fogg, Tracey England, Sally Brailsford, Paul Roderick, Scott Harris, Simon Fraser, Andrew Clegg, Simon de Lusignan, Shihua Zhu, Francesca Lambert, Abigail Barkham, Harnish Patel, Vivienne Windle

“…The system dynamics (SD) model has been extensively validated against summary descriptive data from the RCGP RSC cohort (with a 6.9% error) and externally against a similar data set from SAIL (9.3% error) before being scaled up (using ONS estimates for the number of people entering the 50 + population and those turning 65, 75 or 85 in a given year) to consider how frailty incidence and prevalence at a national population level could be represented over the period of the cohort study (2006–17) and 10 years into the future. …”
Get full text

The use of modern military technologies in the light of the law of weapons and the law of targeting by Behzad Seyfi

“…The government's response to technology and changes in technology is a kind of trial and error. In some fields of international law, including the development of the law of the seas, international space law, and the concept of sovereignty, technological changes often occur very quickly at first, and sometimes after a long delay in a long period of time with the gradual acceptance of the government procedure or A custom is created after long negotiations. …”
Get full text

Effects of Lacticaseibacillus paracasei K56 on perceived stress among pregraduate students: a double-blind, randomized, placebo-controlled trial by Yiran Guan, Ruixin Zhu, Wen Zhao, Langrun Wang, Li You, Zhaozhong Zeng, Qiuyue Jiang, Zeyang Zhu, Jiayu Gou, Qi Zhang, Jie Guo, Keji Li, Liang Zhao, Yixuan Li, Pengjie Wang, Bing Fang, Weilian Hung, Jian He, Liwei Zhang, Ran Wang, Jingjing He

“…Pre- and post-treatment serum biomarkers, gut microbiota composition and metabolites were also detected.ResultsThere was no difference in changes of PSS-10 scores from baseline to 2 weeks between the K56 groups and the placebo [mean (standard error): −1.68 (0.48) vs. -0.39 (0.46), p = 0.055]. …”
Get full text