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The TaNHLP1-TaRACK1A module regulates tillering via abscisic acid signaling in wheat

Emergency risk dispatch for integrated electricity and heating system subjected to hurricane event

Liver MRI proton density fat fraction inference from contrast enhanced CT images using deep learning: A proof-of-concept study

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease worldwide, affecting over 30% of the global general population. Its progressive nature and association with other chronic diseases makes early diagnosis important. MRI Proton Density Fat Fraction (PDFF) is the most accurate noninvasive method for quantitatively assessing liver fat but is expensive and has limited availability; accurately quantifying liver fat from more accessible and affordable imaging could potentially improve patient care. This proof-of-concept study explores the feasibility of inferring liver MRI-PDFF values from contrast-enhanced computed tomography (CECT) using deep learning. In this retrospective, cross-sectional study, we analyzed data from living liver donor candidates who had concurrent CECT and MRI-PDFF as part of their pre-surgical workup between April 2021 and October 2022. Manual MRI-PDFF analysis was performed following a standard of clinical care protocol and used as ground truth. After liver segmentation and registration, a deep neural network (DNN) with 3D U-Net architecture was trained using CECT images as single channel input and the concurrent MRI-PDFF images as single channel output. We evaluated performance using mean absolute error (MAE) and root mean squared error (RMSE), and mean errors (defined as the mean difference of results of comparator groups), with 95% confidence intervals (CIs). We used Kappa statistics and Bland-Altman plots to assess agreement between DNN-predicted PDFF and ground truth steatosis grades and PDFF values, respectively. The final study cohort was of 94 patients, mean PDFF = 3.8%, range 0.2–22.3%. When comparing ground truth to segmented reference (MRI-PDFF), our model had an MAE of 0.56, an RMSE of 0.77, and a mean error of 0.06 (−1.75,1.86); when comparing medians of the predicted and reference MRI-PDFF images, our model had an MAE, an RMSE, and a mean error of 2.94, 4.27, and 1.28 (−4.58,7.14), respectively. We found substantial agreement between categorical steatosis grades obtained from DNN-predicted and clinical ground truth PDFF (kappa = 0.75). While its ability to infer exact MRI-PDFF values from CECT images was limited, categorical classification of fat fraction at lower grades was robust, outperforming other prior attempted methods.

Robust organic radical cations with near-unity absorption across solar spectrum

Densimetry of diluted aqueous salt solutions and molecular dynamics simulations identify temperature-dependent differences between the hydration of anions and cations

Abstract This study aims to analyze the temperature-dependent hydration of diluted ionic solutions. Three monovalent anions (Cl-, Br-, and I-), three monovalent cations (Li+, Na+, and K+), and one bivalent ion each (SO4 2- and Mg2+, respectively) were chosen as models. The partial molar volumes of all possible two-component salts (i.e., LiCl, NaCl, KCl, LiBr, NaBr, KBr. LiI, NaI, KI, MgCl2, MgBr2, MgI2, Li2SO4, Na2SO4, K2SO4, and MgSO4) were determined in water at low solute concentrations (10− 3 to 3·10− 2 mol/kg) in the 20 ÷ 40 °C temperature range. The density analysis was based on the first-order (linear) approximation of the density-molality relation corrected for the Debye-Hückel slope for volumes. No additional sophisticated corrections were applied. For all salts except the bivalent-bivalent MgSO4, the partial molar volume is positive and generally increases with temperature much more than bulk water. The temperature-dependent partial molar volumes of particular ions were determined globally, assuming the composition-dependent additive contribution to the partial molar volume of the salt. The qualitative differences between anions and cations were identified, reflecting their divergent electrostatic contributions to solute-solvent interactions. Similar nonlinear trends were observed in molecular dynamics simulations of the solvated separate ions at 10 ÷ 50 °C. The observed differences between anions and cations should be attributed to principal water properties, specifically the electron density distribution, which interferes with the packing of asymmetric water molecules around the ions of interest.

Investigating university English as a foreign language instructors’ implementations in teaching integral listening with speaking

Employing integral instruction of listening and speaking, and understanding their roles is significant for effective language teaching, developing learners’ spoken and written proficiencies, and improving their achievement and motivation. However, there is limited prior research on EFL-integrated listening and speaking in public universities in Ethiopia. Investigating the integral teaching/learning of listening and speaking remains a research problem. Accordingly, the study aims to examine University English EFL instructors’ implementations in teaching integral listening and speaking employing a concurrent mixed-methods design. A total of 252 sample respondents were involved in data collection. Comprehensive and systematic sampling techniques were used to select respondents using a 5-point Likert scale. Through purposive sampling, 12 instructors were identified for qualitative data collection employing semi-structured observations and interviews. Quantitative data analysis employed descriptive and inferential statistics run by SPSS version 20. Qualitative data were thematically analyzed utilizing NVIVO 12 Pro. The findings revealed a remarkable mismatch between EFL instructors’ reported practice and the implementation of integral teaching of listening and speaking. Students’ questionnaire data confirmed the mismatch between the results of the instructors’ practice and implementation. The main themes predicted integral practice included using real-life listening materials, appropriate application of listening phases, and using the language features appropriately. The variance was indicated by the effect size with an eta-squared value of 75%. Qualitative findings supported that EFL teachers frequently relied on non-authentic listening materials. The study implied that EFL program development and curriculum reviews incorporate key factors influencing integral instruction of listening and speaking in the present study. Finally, the study provided recommendations for EFL teachers and the contexts beyond the present settings.

Coniontins, lipopetaibiotics active against Candida auris identified from a microbial natural product fractionation library

Tunable oxygen vacancy diffusion and electronic conduction through strain engineering in PZT films

Effect of individualized PEEP on lung ultrasound score and optic nerve sheath diameter in elderly patients undergoing laparoscopic rectal cancer surgery: A randomized controlled trial

Objective Positive end-expiratory pressure (PEEP) is widely used during surgery, but its effects on lung and brain protection remain debated. This study aimed to evaluate the impact of individualized PEEP on lung ultrasound score (LUS) and optic nerve sheath diameter (ONSD) in elderly patients undergoing laparoscopic rectal cancer surgery. Methods Forty-six patients aged 60–79 years undergoing laparoscopic rectal tumour resection between June 2022 and December 2022 were randomized into two groups: Group E (individualized PEEP guided by driving pressure) and Group C (control group, PEEP = 5 cm H2O). LUS was assessed 30 minutes postoperatively. ONSD was measured at 5 minutes before anesthesia induction (T0), 5 minutes after tracheal tube insertion (T1), 5 and 60 minutes after Trendelenburg positioning (T2, T3), and 30 minutes postoperatively (T4). Arterial oxygen index (OI) and arterial partial pressure of carbon dioxide (PaCO2) were recorded post-intubation and pre-extubation. Postoperative pulmonary and neurological complications were followed up. Results Postoperative LUS was significantly lower in Group E than in Group C (P < 0.05). OI was significantly higher in Group E before extubation (P < 0.05). There were no significant differences in ONSD between groups. Within each group, ONSD values at T2 and T3 were significantly higher than those at T0 (P < 0.01). No significant differences were observed in the incidence of postoperative complications between the two groups. Conclusions During laparoscopic radical resection for rectal cancer, individualized PEEP reduces LUS scores, improves oxygenation, and does not increase ONSD values compared to fixed PEEP. Trial registration Chinese Clinical Trial Registry: ChiCTR2200060434.

Pupil size modulation drives retinal activity in mice and shapes human perception

Abstract Retinal adaptation is assisted by the pupil, with pupil contraction and dilation thought to prevent global light changes from triggering neuronal activity in the retina. However, we find that pupillary constriction from increased light, the pupillary light reflex (PLR), can drive strong responses in retinal ganglion cells (RGCs) in vivo in mice. The PLR drives neural activity in all RGC types, and pupil-driven activity is relayed to the visual cortex. Furthermore, the consensual PLR allows one eye to respond to luminance changes presented to the other eye, leading to a binocular response and modulation during low-amplitude luminance changes. To test if pupil-induced activity is consciously perceived, we performed psychophysics on human volunteers, finding a perceptual dimming consistent with PLR-induced responses in mice. Our findings thus uncover that pupillary dynamics can directly induce visual activity that is consciously detectable, suggesting an active role for the pupil in encoding perceived ambient luminance.

Sensitivity analysis of digital twin model for energy community PV system

Augmented secretary bird optimization algorithm for wireless sensor network deployment and engineering problem

This study develops an enhanced Secretary Bird Optimization Algorithm (ASBOA) based on the original Secretary Bird Optimization Algorithm (SBOA), aiming to further improve the solution accuracy and convergence speed for wireless sensor network (WSN) deployment and engineering optimization problems. Firstly, a differential collaborative search mechanism is introduced in the exploration phase to reduce the risk of the algorithm falling into local optima. Additionally, an optimal boundary control mechanism is employed to prevent ineffective exploration and enhance convergence speed. Simultaneously, an information retention control mechanism is utilized to update the population. This mechanism ensures that individuals that fail to update have a certain probability of being retained in the next generation population, while guaranteeing that the current global optimal solution remains unchanged, thereby accelerating the algorithm’s convergence. The ASBOA algorithm was evaluated using the CEC2017 and CEC2022 benchmark test functions and compared with other algorithms (such as PSO, GWO, DBO, and CPO). The results show that in the CEC2017 30-dimensional case, ASBOA performed best on 23 out of 30 functions; in the CEC2017 100-dimensional case, ASBOA performed best on 26 out of 30 functions; and in the CEC2022 20-dimensional case, it performed best on 9 out of 12 functions. Furthermore, the convergence curves and boxplot results indicate that ASBOA has faster convergence speed and robustness. Finally, ASBOA was applied to WSN problems and three engineering design problems (three-bar truss, tension/compression spring, and cantilever beam design). In the engineering problems, ASBOA consistently outperformed competing methods, while in the WSN deployment scenario, it achieved a coverage rate of 88.32%, an improvement of 1.12% over the standard SBOA. These results demonstrate that the proposed ASBOA has strong overall performance and significant potential in solving complex optimization problems. Although ASBOA performs well in these problems, its performance in high-dimensional multimodal problems and complex constrained optimization is unstable, and the introduced strategies add some complexity. Additionally, different parameter settings may lead to varying results, and the sensitivity of different problems to these parameters can also differ. It is necessary to adjust the settings according to the specific problem at hand in order to further refine and achieve a more stable version.

Iceberg-like pyramids in industrially textured silicon enabled 33% efficient perovskite-silicon tandem solar cells

Impedance shaping based stabilization control method for DC Micro-grid Feed-forward compensation

Abstract The ports of the bi-directional converter exhibit negative impedance characteristics when the energy storage unit of a DC microgrid is operating in charging mode. This can decrease the system’s stability margin, potentially leading to oscillation instability. To address this issue, we propose a feed-forward compensation control method based on impedance shaping. This involves designing a transfer function within the current feed-forward loop of the energy storage converter to ensure that the current reference value follows changes in the bus voltage. Selecting an appropriate time constant ensures that the port impedance of the energy storage unit exhibits a positive resistive characteristic close to the oscillation frequency while retaining a negative impedance characteristic in the low-frequency range. This ensures the stability of system operation. MATLAB/Simulink simulations and RT-LAB semi-physical platform testing have verified that the proposed control method effectively enhances the system’s stability margin, ensuring stable operation and minimal steady-state voltage deviation.

RETRACTED: LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans

Lung diseases (LGDs) are related to an extensive range of lung disorders, including pneumonia (PNEUM), lung cancer (LC), tuberculosis (TB), and COVID-19 etc. The diagnosis of LGDs is performed by using different medical imaging such as X-rays, CT scans, and MRI. However, LGDs contain similar symptoms such as fever, cough, and sore throat, making it challenging for radiologists to classify these LGDs. If LGDs are not diagnosed at their initial phase, they may produce severe complications or even death. An automated classifier is required for the classification of LGDs. Thus, this study aims to propose a novel model named lung diseases classification network (LGD_Net) based on the combination of a capsule network (CapsNet) with the extreme learning machine (ELM) for the classification of five different LGDs such as PNEUM, LC, TB, COVID-19 omicron (COO), and normal (NOR) using CT scans. The LGD_Net model is trained and tested on the five publicly available benchmark datasets. The datasets contain an imbalanced distribution of images; therefore, a borderline SMOTE (BL_SMT) approach is applied to handle this problem. Additionally, the affine transformation methods are used to enhance LGD datasets. The performance of the LGD_Net is compared with four CNN-based baseline models such as Vgg-19 (D 1 ), ResNet-101 (D 2 ), Inception-v3 (D 3 ), and DenseNet-169 (D 4 ). The LGD_Net model achieves an accuracy of 99.71% in classifying LGDs using CT scans. While the other models such as D 1 , D 2 , D 3 , and D 4 attains an accuracy of 91.21%, 94.39%, 93.96%, and 93.82%, respectively. The findings demonstrate that the LGD_Net model works significantly as compared to D 1 , D 2 , D 3 , and D 4 as well as state-of-the-art (SOTA). Thus, this study concludes that the LGD_Net model provides significant assistance to radiologists in classifying several LGDs.

Dynamically urethra-adapted and obligations-oriented trilayer hydrogels integrate scarless urethral repair

Forecasting impacts of climate change on barking deer distribution in Pakistan

Correction: Recombinant cyclin B-Cdk1-Suc1 capable of multi-site mitotic phosphorylation in vitro

Healthcare-seeking intentions of middle-aged and elderly individuals with critical diseases: an expanded TPB model in the post-pandemic era

Emergency temporary standards and COVID-19 trends among Oregon farmworkers

Background During the COVID-19 pandemic, migrant and seasonal farmworkers were deemed essential due to their central roles in US agricultural operations. However, employer-provided housing and transportation conditions increased their risks of SARS-CoV-2 exposure, and some states implemented emergency temporary standards (ETSs) at the insistence of farmworker advocates. Despite numerous studies examining the effectiveness of policy interventions (e.g., workplace closures) for mitigating SARS-CoV-2 transmission, limited research has specifically examined the effectiveness of interventions aimed at protecting farmworkers from COVID-19. Methods We used an interrupted time series analysis to estimate how two ETSs and one executive order issued in Oregon impacted COVID-19 trends from March 1, 2020, to February 27, 2021, for the overall population and among agricultural labor groups in Oregon. Results Our models show that the ETS and executive order, which specifically targeted farmworker housing, transportation, and worksites, did not demonstrate any significant effects on the numbers of COVID-19 cases or associated deaths. However, the other ETS, which targeted all workplaces, was associated with statistically significant decreases in COVID-19 cases among the general population (−142.36214, p-value<0.0001), producers (−1.67128, p-value = 0.0009), hired workers (−2.39413, p-value = 0.0014), unpaid workers (−1.01572, p-value = 0.0003), and migrant workers (−0.60017, p-value = 0.0166). None of the three policy changes were found to have any statistically significant impacts on the numbers of COVID-19–associated deaths. Conclusions The ETS targeting all workplaces was more effective for reducing COVID-19 transmission than the ETS or executive order specifically targeting farmworkers, indicating that the design, communication, and implementation of ETSs targeting farmworkers should be re-evaluated.