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Small-molecule hole-transporting materials (HTMs) containing heteroatoms provide enhanced interface contact and complete passivation effects, which increase the performance of perovskite solar cells (PSC). In this study, we present the synthesis and analysis of two small molecule HTMs, CYH20 and CYH23, based on a novel thienoimidazole-pyridine acceptor core for dopant-free and efficient inverted (p–i–n) PSCs. The two HTMs are structurally similar, except for the end units, 4-(1-cyanovinyl)benzonitrile (acceptor) and 2,2-bis(4-methoxyphenyl)vinyl (donor) moieties. As a result, CYH20 is classed as a donor–acceptor–acceptor (D–A–A) HTM and CYH23 as a donor–acceptor–donor (D–A–D) HTM. These new HTMs have favorable optoelectronic and thermal properties and suitable morphologies, as demonstrated by scanning electron microscopy, photoluminescence (PL) time-resolved PL, and space charge limited current studies. Among both, the D–A–D configured HTM CYH23-based PSC has a high open-circuit voltage of 1.08 V and a high short circuit current density of 21.78 mA cm−2, resulting in the highest power conversion efficiency of 18.77%. The superior performance of CYH23 over CYH20 (17.14%) is attributed to its excellent morphology and charge transfer properties. [ABSTRACT FROM AUTHOR]

Xinglou Chengqi decoction (XLCQD) is a Chinese formula that offers benefits in ischemic stroke. However, the underlying mechanism of the effects of XLCQD‐mediated anti‐ischemic stroke effects remains obscure. This study investigates the ferroptosis mechanism of XLCQD against cerebral ischemia/reperfusion (I/R) injury using rat models of middle cerebral artery occlusion/reperfusion (MCAO/R). Ferroptosis differs from traditional cell death pathways and is linked to oxidative stress‐induced lipid peroxidation and glutathione (GSH) depletion, which is essential to the development of ischemic stroke. In this study, it is shown that XLCQD improves brain infarction, neurological dysfunction, and histopathological changes caused by MCAO/R exposure, and improving I/R‐induced oxidative damage through inhibition of ferroptosis via (Solute Carrier Family 7 Member 11) SLC7A11/ (glutathione peroxidase 4) GPX4 pathway. Interestingly, it is found that XLCQD‐mediated protection in I/R is reversed by the silence of SLC7A11. XLCQD intervention significantly promotes GSH content and suppresses Reactive Oxygen Species(ROS), iron accumulation, as well as Malondialdehyde (MDA) generation, are markedly abrogated when SLC7A11 is knockdown by SLC7A11‐shRNA transfection, indicating that SLC7A11 is the main target of XLCQD to further trigger intracellular events. In conclusion, XLCQD attenuates in vivo cerebral I/R injury by reducing ferroptosis via the SLC7A11/GPX4 pathway. [ABSTRACT FROM AUTHOR]

Simple Summary: The oriental fruit fly, Bactrocera dorsalis, pupates in the soil, and the depth at which it pupates influences its susceptibility to natural enemies, which plays a key role in biological control programs. This laboratory study examined the relationship between temperature and pupation depth, showing that even small changes in temperature lead to significant variations in pupation depth. This result suggests that temperature may affect interactions between B. dorsalis and its natural enemies in ways that were previously overlooked. The oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae), is a notable agricultural pest that undergoes pupation in the soil. Mortality risk from predation and parasitism decreases as the depth of the pupal location increases from the ground surface, with a one-centimetre increase in depth causing a significant change. Soil properties, such as moisture and hardness, influence pupation depth, but the effect of temperature has not been fully tested. This laboratory study examined whether a biologically important variation in pupation depth (e.g., one centimetre) is caused by naturally experienced temperature variations (20 to 35 °C) in B. dorsalis. The temperature–pupation depth relationship revealed a unimodal pattern, with the deepest pupation occurring at intermediate temperature levels and shallower pupation at the two extreme temperature ranges. Strong quantitative effects were observed, with the highest mean pupation depth of 40.8 mm at 27.5 °C and the lowest mean pupation depth of 15 mm at 35 °C. The observed quantitative effect suggests that temperature can strongly affect pupal mortality from predators and parasitoids by influencing pupation depth. Future studies that reveal the ability of biological control agents to forage underground for pupae at different temperatures are awaited, as this is key information for evaluating the effectiveness of these agents. [ABSTRACT FROM AUTHOR]

Background: Pompe disease is a rare metabolic myopathy caused by the lack or deficiency of the lysosomal acid alpha-glucosidase, resulting in skeletal muscle weakness and cardiomyopathy. The disease varies by onset age and genetic mutations and is categorized into infantile-onset and late-onset Pompe disease. Respiratory muscle weakness may persist regardless enzyme replacement therapy. This systemic review and meta-analysis aim to assess the effect of respiratory muscle training (RMT) on respiratory muscle strength, functional endurance, and pulmonary function in patient with Pompe disease. Methods: PubMed, EMBASE, and Cochrane databases were searched up until Aug 2024. Studies examining the therapeutic effects of RMT in patients with Pompe disease were included. Outcome measures included the change in maximal inspiratory pressure (MIP), maximal expiratory pressure (MEP), six-minute walking test (6MWT), pulmonary function before after RMT, quality of life and adverse events. Results: The meta-analysis consisted of 5 single-arm studies, including 31 patients in total. Regarding inspiratory muscle strength, RMT has significantly improving MIP (8.71 cmH2O; 95% CI, 6.23–11.19, p < 0.001) and MEP (12.15 cmH2O; 95% CI, 10.55–13.74, p < 0.001) in both types of Pompe disease. However, no significant change regarding 6MWT. No serious adverse events were reported. Conclusions: Our meta-analysis revealed that RMT may increase inspiratory muscle and expiratory muscle strength, but may not have an effect on 6MWT in patients with Pompe disease. RMT has potential to be integrated into the cardioplulmonary rehabilitation for patients with Pompe disease. Further large randomized controlled trials are needed to verify the efficacy and safety of RMT in patients with Pompe disease. [ABSTRACT FROM AUTHOR]

Purpose: Online adaptive radiotherapy relies on a high degree of automation to enable rapid planning procedures. The Varian Ethos intelligent optimization engine (IOE) was originally designed for conventional treatments so it is crucial to provide clear guidance for lung SAbR plans. This study investigates using the Ethos IOE together with adaptive‐specific optimization tuning structures we designed and templated within Ethos to mitigate inter‐planner variability in meeting RTOG metrics for both online‐adaptive and offline SAbR plans. Methods: We developed a planning strategy to automate the generation of tuning structures and optimization. This was validated by retrospective analysis of 35 lung SAbR cases (total 105 fractions) treated on Ethos. The effectiveness of our planning strategy was evaluated by comparing plan quality with‐and‐without auto‐generated tuning structures. Internal target volume (ITV) contour was compared between that drawn from CT simulation and from cone‐beam CT (CBCT) at time of treatment to verify CBCT image quality and treatment effectiveness. Planning strategy robustness for lung SAbR was quantified by frequency of plans meeting reference plan RTOG constraints. Results: Our planning strategy creates a gradient within the ITV with maximum dose in the core and improves intermediate dose conformality on average by 2%. ITV size showed no significant difference between those contoured from CT simulation and first fraction, and also trended towards decreasing over course of treatment. Compared to non‐adaptive plans, adaptive plans better meet reference plan goals (37% vs. 100% PTV coverage compliance, for scheduled and adapted plans) while improving plan quality (improved GI (gradient index) by 3.8%, CI (conformity index) by 1.7%). Conclusion: We developed a robust and readily shareable planning strategy for the treatment of adaptive lung SAbR on the Ethos system. We validated that automatic online plan re‐optimization along with the formulated adaptive tuning structures can ensure consistent plan quality. With the proposed planning strategy, highly ablative treatments are feasible on Ethos. [ABSTRACT FROM AUTHOR]

Background: In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatment. APBI can be delivered through various modalities including Cobalt‐60‐based systems and linear accelerators with C‐arm, O‐ring, or robotic arm design. Each modality possesses distinct features, such as beam energy or the degrees of freedom in treatment planning, which influence their respective dose distributions. These modality‐specific considerations emphasize the need for a quantitative approach in determining the optimal dose delivery modality on a patient‐specific basis. However, manually generating treatment plans for each modality across every patient is time‐consuming and clinically impractical. Purpose: We aim to develop an efficient and personalized approach for determining the optimal RT modality for APBI by training predictive models using two different deep learning‐based convolutional neural networks. The baseline network performs a single‐task (ST), predicting dose for a single modality. Our proposed multi‐task (MT) network, which is capable of leveraging shared information among different tasks, can concurrently predict dose distributions for various RT modalities. Utilizing patient‐specific input data, such as a patient's computed tomography (CT) scan and treatment protocol dosimetric goals, the MT model predicts patient‐specific dose distributions across all trained modalities. These dose distributions provide patients and clinicians quantitative insights, facilitating informed and personalized modality comparison prior to treatment planning. Methods: The dataset, comprising 28 APBI patients and their 92 treatment plans, was partitioned into training, validation, and test subsets. Eight patients were dedicated to the test subset, leaving 68 treatment plans across 20 patients to divide between the training and validation subsets. ST models were trained for each modality, and one MT model was trained to predict doses for all modalities simultaneously. Model performance was evaluated across the test dataset in terms of Mean Absolute Percent Error (MAPE). We conducted statistical analysis of model performance using the two‐tailed Wilcoxon signed‐rank test. Results: Training times for five ST models ranged from 255 to 430 min per modality, totaling 1925 min, while the MT model required 2384 min. MT model prediction required an average of 1.82 s per patient, compared to ST model predictions at 0.93 s per modality. The MT model yielded MAPE of 1.1033 ± 0.3627% as opposed to the collective MAPE of 1.2386 ± 0.3872% from ST models, and the differences were statistically significant (p = 0.0003, 95% confidence interval = [–0.0865, –0.0712]). Conclusion: Our study highlights the potential benefits of a MT learning framework in predicting RT dose distributions across various modalities without notable compromises. This MT architecture approach offers several advantages, such as flexibility, scalability, and streamlined model management, making it an appealing solution for clinical deployment. With such a MT model, patients can make more informed treatment decisions, physicians gain more quantitative insight for pre‐treatment decision‐making, and clinics can better optimize resource allocation. With our proposed goal array and MT framework, we aim to expand this work to a site‐agnostic dose prediction model, enhancing its generalizability and applicability. [ABSTRACT FROM AUTHOR]

The slow water renewal endows the Baltic Sea a strong retention of pollutants/nutrients. Constraining water age is a practical way to depict the transport timescales for water masses and accompanying soluble substances. Although the water ages in the Baltic Sea have been resolved by 3D ocean models 20 year ago, the simulated results have not been verified. In this work, we exploited two anthropogenic radionuclides (129I and 236U) to constrain the ages of inflowing North Sea saline waters into the Baltic Sea by using the Tracer Age and Transit Time Distribution approaches. Our results indicate that the Baltic Sea has a highly stratified structure with a more diffusive bottom circulation and a more advective surface circulation. The circulation timescale for the saline water in the Baltic Sea was estimated to be >30 years from the bottom of Arkona Basin to the surface of central Baltic Sea. This work demonstrates the power of anthropogenic radiotracers in investigating circulation timescale and mixing processes in the Baltic Sea and provides the first observation‐based proof for the multi‐decadal retention of (radioactive) pollutants within the Baltic Sea. Plain Language Summary: This study focuses on using two man‐made radioisotopes, I‐129 and U‐236, to determine the ages of North Sea saline waters entering the Baltic Sea. Our results show that the Baltic Sea's circulation timescale is more than 30 years. This provides the first evidence, based on observations, of pollutants being retained within the Baltic Sea for several decades. The study highlights the usefulness of anthropogenic radioactive tracers in understanding hydrodynamic processes in the coastal areas of Northwestern Europe. Key Points: 129I and 236U released from nuclear reprocessing plants were used to determine the ages of saline water in the Baltic SeaThe transport timescales and mixing regimes are investigated by the tracer age and transit time distribution approachesThe saline water has a circulation timescale of >30 years within the interior Baltic Sea [ABSTRACT FROM AUTHOR]

Coastal areas face escalating storm surge disasters due to rising sea levels and urban growth, posing greater risks to lives and property. Comprehensive storm surge risk assessment and sensitivity analysis in coastal areas are crucial for effective disaster prevention and mitigation. This research focuses on Huizhou, China, conducting a comparative study of storm surge risk assessment and sensitivity analysis based on an integrated approach, which includes the storm surge inundation numerical model (FVCOM-SWAN), Geographic Information System (GIS) and Remote Sensing (RS) techniques, and Multiple Criteria Decision-Making (MCDM) methods. Ten flood-related risk indicators are selected from the hazard, exposure and vulnerability, the weights of which are evaluated through a comprehensive comparison among Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight (EW), AHP-EW, and FAHP-EW methods. High-precision risk level maps are generated subsequently utilizing GIS and RS techniques. Sensitivities of the indicators are analyzed using One-At-A-Time (OAT) and Fourier Amplitude Sensitivity Test (FAST) methods. The proposed storm surge risk assessment framework, the MCDM comparative study and the sensitivity analysis can offer insights for better understanding and management of storm surge risks, and contribute to the standardization and application of storm surge risk assessment. [ABSTRACT FROM AUTHOR]

Background and Purpose: Blood–brain barrier (BBB) breakdown is one of the crucial pathological changes of cerebral ischaemia–reperfusion (I/R) injury. Trilobatin (TLB), a naturally occurring food additive, exerts neuroprotective effects against cerebral I/R injury as demonstrated in our previous study. This study was designed to investigate the effect of TLB on BBB disruption after cerebral I/R injury. Experimental Approach: Rats with focal cerebral ischaemia caused by transient middle cerebral artery occlusion were studied along with brain microvascular endothelial cells and human astrocytes to mimic BBB injury caused by oxygen and glucose deprivation/reoxygenation (OGD/R). Key Results: The results showed that TLB effectively maintained BBB integrity and inhibited neuronal loss following cerebral I/R challenge. Furthermore, TLB increased tight junction proteins including ZO‐1, Occludin and Claudin 5, and decreased the levels of apolipoprotein E (APOE) 4, cyclophilin A (CypA) and phosphorylated nuclear factor kappa B (NF‐κB), thereby reducing proinflammatory cytokines. TLB also decreased the Bax/Bcl‐2 ratio and cleaved‐caspase 3 levels along with a reduced number of apoptotic neurons. Molecular docking and transcriptomics predicted MMP9 as a prominent gene evoked by TLB treatment. The protective effects of TLB on cerebral I/R‐induced BBB breakdown was largely abolished by overexpression of MMP9, and the beneficial effects of TLB on OGD/R‐induced loss of BBB integrity in human brain microvascular endothelial cells and astrocyte co‐cultures was markedly reinforced by knockdown of MMP9. Conclusions and Implications: Our findings reveal a novel property of TLB: preventing BBB disruption following cerebral I/R via targeting MMP9 and inhibiting APOE4/CypA/NF‐κB axis. [ABSTRACT FROM AUTHOR]

This paper concerns numerical solution of the diffusion equation with strong anisotropy on meshes not aligned with the anisotropic vector field. In order to resolve the numerical pollution for simulations on a non-anisotropy-aligned mesh and reduce the associated high computational cost we propose an effective preconditioner, extending our previous work [D. Green et al., Comput. Phys. Commun., 9 (2022), 108333]. Similar to the anisotropy-aligned mesh case, we apply the auxiliary space preconditioning framework to design a preconditioner where a continuous finite element space is used as the auxiliary space for the discontinuous finite element space. The key component is an effective line smoother that can mitigate the high-frequency errors perpendicular to the magnetic field. We design a graph-based approach to find such a line smoother that is approximately perpendicular to the vector fields when the mesh does not align with the anisotropy. Numerical experiments for several benchmark problems are presented, demonstrating the effectiveness and robustness of the proposed preconditioner when applied to Krylov iterative methods. [ABSTRACT FROM AUTHOR]

Small-field-of-view reconstruction CT images (sFOV-CT) increase the pixel density across airway structures and reduce partial volume effects. Multi-instance learning (MIL) is proposed as a weakly supervised machine learning method, which can automatically assess the image quality. The aim of this study was to evaluate the disparities between conventional CT (c-CT) and sFOV-CT images using a lung nodule system based on MIL and assessments from radiologists. 112 patients who underwent chest CT were retrospectively enrolled in this study between July 2021 to March 2022. After undergoing c-CT examinations, sFOV-CT images with small-field-of-view were reconstructed. Two radiologists analyzed all c-CT and sFOV-CT images, including features such as location, nodule type, size, CT values, and shape signs. Then, an MIL-based lung nodule system objectively analyzed the c-CT (c-MIL) and sFOV-CT (sFOV-MIL) to explore their differences. The signal-to-noise ratio of lungs (SNR-lung) and contrast-to-noise ratio of nodules (CNR-nodule) were calculated to evaluate the quality of CT images from another perspective. The subjective evaluation by radiologists showed that feature of minimal CT value (p = 0.019) had statistical significance between c-CT and sFOV-CT. However, most features (all with p < 0.05), except for nodule type, location, volume, mean CT value, and vacuole sign (p = 0.056–1.000), had statistical differences between c-MIL and sFOV-MIL by MIL system. The SNR-lung between c-CT and sFOV-CT had no statistical significance, while the CNR-nodule showed statistical difference (p = 0.007), and the CNR of sFOV-CT was higher than that of c-CT. In detecting the difference between c-CT and sFOV-CT, features extracted by the MIL system had more statistical differences than those evaluated by radiologists. The image quality of those two CT images was different, and the CNR-nodule of sFOV-CT was higher than that of c-CT. [ABSTRACT FROM AUTHOR]

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Storm surge is a common nature disaster in China southern coastal area, which usually causes heavy human life and economic losses. With the economic development and population concentration of coastal cities, the storm surges may result in more impacts and damage in the future. Therefore, it is of vital importance to conduct risk assessment to identify high-risk areas and evaluate economic losses. However, quantitative study of storm surge risk assessment in undeveloped areas of China is difficult, since there is a lack of building characters and damage assessment data. Aiming at the problem of data missing in undeveloped areas of China, this paper proposes a methodology for conducting storm surge risk assessment quantitatively based on deep learning and geographic information system (GIS) techniques. Five defined storm surge inundation scenarios with different typhoon return periods are simulated by coupled FVCOM-SWAN model, the reliability of which is validated using official measurements. Building footprints of the study area are extracted through TransUNet deep learning model and Remote Sensing Image (RSI), while building heights are obtained through Unmanned Aerial Vehicle (UAV) measurement. Subsequently, economic losses are quantitatively calculated by combing the adjusted depth-damage functions and overlay analysis of the buildings exposed to storm surge inundation. Zonation maps of the study area are illustrated to display the risk levels according to the economic losses. The quantitative risk assessment and zonation maps can help the government to make storm surge disaster prevention measures and optimize land use planning, and thus to reduce the potential economic losses of the coastal area. [ABSTRACT FROM AUTHOR]

Background: Online adaptive radiotherapy (ART) involves the development of adaptable treatment plans that consider patient anatomical data obtained right prior to treatment administration, facilitated by cone‐beam computed tomography guided adaptive radiotherapy (CTgART) and magnetic resonance image‐guided adaptive radiotherapy (MRgART). To ensure accuracy of these adaptive plans, it is crucial to conduct calculation‐based checks and independent verification of volumetric dose distribution, as measurement‐based checks are not practical within online workflows. However, the absence of comprehensive, efficient, and highly integrated commercial software for secondary dose verification can impede the time‐sensitive nature of online ART procedures. Purpose: The main aim of this study is to introduce an efficient online quality assurance (QA) platform for online ART, and subsequently evaluate it on Ethos and Unity treatment delivery systems in our clinic. Methods: To enhance efficiency and ensure compliance with safety standards in online ART, ART2Dose, a secondary dose verification software, has been developed and integrated into our online QA workflow. This implementation spans all online ART treatments at our institution. The ART2Dose infrastructure comprises four key components: an SQLite database, a dose calculation server, a report generator, and a web portal. Through this infrastructure, file transfer, dose calculation, report generation, and report approval/archival are seamlessly managed, minimizing the need for user input when exporting RT DICOM files and approving the generated QA report. ART2Dose was compared with Mobius3D in pre‐clinical evaluations on secondary dose verification for 40 adaptive plans. Additionally, a retrospective investigation was conducted utilizing 1302 CTgART fractions from ten treatment sites and 1278 MRgART fractions from seven treatment sites to evaluate the practical accuracy and efficiency of ART2Dose in routine clinical use. Results: With dedicated infrastructure and an integrated workflow, ART2Dose achieved gamma passing rates that were comparable to or higher than those of Mobius3D. Additionally, it significantly reduced the time required to complete pre‐treatment checks by 3–4 min for each plan. In the retrospective analysis of clinical CTgART and MRgART fractions, ART2Dose demonstrated average gamma passing rates of 99.61 ± 0.83% and 97.75 ± 2.54%, respectively, using the 3%/2 mm criteria for region greater than 10% of prescription dose. The average calculation times for CTgART and MRgART were approximately 1 and 2 min, respectively. Conclusion: Overall, the streamlined implementation of ART2Dose notably enhances the online ART workflow, offering reliable and efficient online QA while reducing time pressure in the clinic and minimizing labor‐intensive work. [ABSTRACT FROM AUTHOR]

Background: Throughout a patient's course of radiation therapy, maintaining accuracy of their initial treatment plan over time is challenging due to anatomical changes‐for example, stemming from patient weight loss or tumor shrinkage. Online adaptation of their RT plan to these changes is crucial, but hindered by manual and time‐consuming processes. While deep learning (DL) based solutions have shown promise in streamlining adaptive radiation therapy (ART) workflows, they often require large and extensive datasets to train population‐based models. Purpose: This study extends our prior research by introducing a minimalist approach to patient‐specific adaptive dose prediction. In contrast to our prior method, which involved fine‐tuning a pre‐trained population model, this new method trains a model from scratch using only a patient's initial treatment data. This patient‐specific dose predictor aims to enhance clinical accessibility, thereby empowering physicians and treatment planners to make more informed, quantitative decisions in ART. We hypothesize that patient‐specific DL models will provide more accurate adaptive dose predictions for their respective patients compared to a population‐based DL model. Methods: We selected 33 patients to train an adaptive population‐based (AP) model. Ten additional patients were selected, and their respective initial RT data served as single samples for training patient‐specific (PS) models. These 10 patients contained an additional 26 ART plans that were withheld as the test dataset to evaluate AP versus PS model dose prediction performance. We assessed model performance using Mean Absolute Percent Error (MAPE) by comparing predicted doses to the originally delivered ground truth doses. We used the Wilcoxon signed‐rank test to determine statistically significant differences in terms of MAPE between the AP and PS model results across the test dataset. Furthermore, we calculated differences between predicted and ground truth mean doses for segmented structures and determined statistical significance in the differences for each of them. Results: The average MAPE across AP and PS model dose predictions was 5.759% and 4.069%, respectively. The Wilcoxon signed‐rank test yielded two‐tailed p‐value = 2.9802×10−8$2.9802\ \times \ {10}^{ - 8}$, indicating that the MAPE differences between the AP and PS model dose predictions are statistically significant, and 95% confidence interval = [−2.1610, −1.0130], indicating 95% confidence that the MAPE difference between the AP and PS models for a population lies in this range. Out of 24 total segmented structures, the comparison of mean dose differences for 12 structures indicated statistical significance with two‐tailed p‐values < 0.05. Conclusion: Our study demonstrates the potential of patient‐specific deep learning models in application to ART. Notably, our method streamlines the training process by minimizing the size of the required training dataset, as only a single patient's initial treatment data is required. External institutions considering the implementation of such a technology could package such a model so that it only requires the upload of a reference treatment plan for model training and deployment. Our single patient learning strategy demonstrates promise in ART due to its minimal dataset requirement and its utility in personalization of cancer treatment. [ABSTRACT FROM AUTHOR]

Background: MRI‐only radiotherapy planning (MROP) is beneficial to patients by avoiding MRI/CT registration errors, simplifying the radiation treatment simulation workflow and reducing exposure to ionizing radiation. MRI is the primary imaging modality for soft tissue delineation. Treatment planning CTs (i.e., CT simulation scan) are redundant if a synthetic CT (sCT) can be generated from the MRI to provide the patient positioning and electron density information. Unsupervised deep learning (DL) models like CycleGAN are widely used in MR‐to‐sCT conversion, when paired patient CT and MR image datasets are not available for model training. However, compared to supervised DL models, they cannot guarantee anatomic consistency, especially around bone. Purpose: The purpose of this work was to improve the sCT accuracy generated from MRI around bone for MROP. Methods: To generate more reliable bony structures on sCT images, we proposed to add bony structure constraints in the unsupervised CycleGAN model's loss function and leverage Dixon constructed fat and in‐phase (IP) MR images. Dixon images provide better bone contrast than T2‐weighted images as inputs to a modified multi‐channel CycleGAN. A private dataset with a total of 31 prostate cancer patients were used for training (20) and testing (11). Results: We compared model performance with and without bony structure constraints using single‐ and multi‐channel inputs. Among all the models, multi‐channel CycleGAN with bony structure constraints had the lowest mean absolute error, both inside the bone and whole body (50.7 and 145.2 HU). This approach also resulted in the highest Dice similarity coefficient (0.88) of all bony structures compared with the planning CT. Conclusion: Modified multi‐channel CycleGAN with bony structure constraints, taking Dixon‐constructed fat and IP images as inputs, can generate clinically suitable sCT images in both bone and soft tissue. The generated sCT images have the potential to be used for accurate dose calculation and patient positioning in MROP radiation therapy. [ABSTRACT FROM AUTHOR]

If the physicists are not familiar with the optimizer of the system, how can physicists be able to oversee or direct the oART process and troubleshoot in real time? Radiotherapy (RT) is at crossroads, as the technological improvements are pivotal in improving its treatment efficacy. While physicists have diverse responsibilities ranging from quality assurance of treatment equipment to safe delivery of patient plans, dosimetrists are dedicated to treatment planning and therefore have time allocated solely to the intricate task of developing optimal treatment plans. [Extracted from the article]

In recent years, electrochemiluminescence resonance energy transfer (ECL-RET) with low background signal and high specificity has attracted much attention among researchers. Herein, we established a novel ECL-RET biosensor for PML/RARα fusion gene detection. In this ECL-RET system, carbon dots (CDs) with low toxicity and prominent electrochemical activity were used as donor and Au@Ag2S core–shell nanoparticles (Au@Ag2S NPs) were employed as ECL acceptor. The Au@Ag2S NPs possessed a wide ultraviolet-visible (UV-vis) absorption spectrum between 500 nm and 700 nm, which completely overlapped with the ECL spectrum of CDs. Furthermore, the CDs-decorated poly-amidoamine/reduced graphene oxide (CDs/PAMAM/rGO) nanocomposites were prepared to improve the ECL signals and served as a substrate to stably load capture probe deoxyribonucleic acid (DNA). Based on the ECL-RET biosensing strategy, the Au@Ag2S NPs-labeled assistant probes and target DNA could pair with capture probes to form the sandwich-type DNA structure and the distance between donor and accepter was closed, leading to quenching of the ECL signal of CDs. The ECL-RET biosensor represented eminent analytical performance for PML/RARα fusion gene detection with a wide linear relationship from 5 fM to 500 pM and a low detection limit of 0.72 fM, which provided a novel technical means and theoretical basis for detection and diagnosis of acute promyelocytic leukemia. [ABSTRACT FROM AUTHOR]

Background: The framework of adaptive radiation therapy (ART) was crafted to address the underlying sources of intra‐patient variation that were observed throughout numerous patients' radiation sessions. ART seeks to minimize the consequential dosimetric uncertainty resulting from this daily variation, commonly through treatment planning re‐optimization. Re‐optimization typically consists of manual evaluation and modification of previously utilized optimization criteria. Ideally, frequent treatment plan adaptation through re‐optimization on each day's computed tomography (CT) scan may improve dosimetric accuracy and minimize dose delivered to organs at risk (OARs) as the planning target volume (PTV) changes throughout the course of treatment. Purpose: Re‐optimization in its current form is time‐consuming and inefficient. In response to this ART bottleneck, we propose a deep learning based adaptive dose prediction model that utilizes a head and neck (H&N) patient's initial planning data to fine‐tune a previously trained population model towards a patient‐specific model. Our fine‐tuned, patient‐specific (FT‐PS) model, which is trained using the intentional deep overfit learning (IDOL) method, may enable clinicians and treatment planners to rapidly evaluate relevant dosimetric changes daily and re‐optimize accordingly. Methods: An adaptive population (AP) model was trained using adaptive data from 33 patients. Separately, 10 patients were selected for training FT‐PS models. The previously trained AP model was utilized as the base model weights prior to re‐initializing model training for each FT‐PS model. Ten FT‐PS models were separately trained by fine‐tuning the previous model weights based on each respective patient's initial treatment plan. From these 10 patients, 26 ART treatment plans were withheld from training as the test dataset for retrospective evaluation of dose prediction performance between the AP and FT‐PS models. Each AP and FT‐PS dose prediction was compared against the ground truth dose distribution as originally generated during the patient's course of treatment. Mean absolute percent error (MAPE) evaluated the dose differences between a model's prediction and the ground truth. Results: MAPE was calculated within the 10% isodose volume region of interest for each of the AP and FT‐PS models dose predictions and averaged across all test adaptive sessions, yielding 5.759% and 3.747% respectively. MAPE differences were compared between AP and FT‐PS models across each test session in a test of statistical significance. The differences were statistically significant in a paired t‐test with two‐tailed p‐value equal to 3.851×10−9$3.851 \times {10}^{ - 9}$ and 95% confidence interval (CI) equal to [−2.483, −1.542]. Furthermore, MAPE was calculated using each individually segmented structure as an ROI. Nineteen of 24 structures demonstrated statistically significant differences between the AP and FT‐PS models. Conclusion: We utilized the IDOL method to fine‐tune a population‐based dose prediction model into an adaptive, patient‐specific model. The averaged MAPE across the test dataset was 5.759% for the population‐based model versus 3.747% for the fine‐tuned, patient‐specific model, and the difference in MAPE between models was found to be statistically significant. Our work demonstrates the feasibility of patient‐specific models in adaptive radiotherapy, and offers unique clinical benefit by utilizing initial planning data that contains the physician's treatment intent. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the significance of miRNA expression levels in peripheral blood lymphocytes of patients clinically diagnosed with pulmonary tuberculosis. Pulmonary tuberculosis-related datasets in the Gene Expression Omnibus (GEO) database were analyzed, and DE-miRNAs were screened for Gene Ontology (GO) analysis and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment to construct a DE-miRNA-DE-mRNA network. The peripheral blood lymphocytes of 10 patients with pulmonary tuberculosis, 10 patients with rifampicin-resistant tuberculosis and 10 healthy volunteers were selected for validation of RNA expression levels. qRT-PCR was done to verify the expression of DE-miRNA, and western blotting was done to check the expression levels of genes of associated pathways. Differential expression of miR-660 was found in pulmonary tuberculosis through data analysis and literature mining. The differential expression was also confirmed by qRT-PCR in samples from patients and healthy controls. The expression of miR-660 was significantly upregulated (p < 0.01) in patients with pulmonary tuberculosis and rifampicin-resistant pulmonary tuberculosis compared with the healthy controls. According to western blotting results, the expression levels of P-NF-κB and AKT in patients with pulmonary tuberculosis and NF-κB, P-NF-κB, AKT and p-AKT in patients with rifampicin-resistant tuberculosis were significantly upregulated (p < 0.01). The high expression levels of miR-660 may activate the AKT/NF-κB signalling pathway and has the potential to serve as a potential biomarker for the diagnosis of pulmonary tuberculosis. [ABSTRACT FROM AUTHOR]

Background and purpose: A reliable neuroimaging biomarker to predict language improvement after neuromodulation in post‐stroke aphasia is lacking. It is hypothesized that aphasic patients with stroke injuries in the left primary language circuits but with sufficient right arcuate fasciculus (AF) integrity might respond to low‐frequency repetitive transcranial magnetic stimulation (LF‐rTMS), leading to language improvement. This study aimed to assess the microstructural indices of the right AF before LF‐rTMS treatment and further correlate with language improvement after the treatment. Methods: Thirty‐three patients with at least 3 months after stroke in the left hemisphere and nonfluent aphasia were recruited in this randomized double‐blind study. All patients received real 1‐Hz LF‐rTMS (n = 16) or sham stimulation (n = 17) at the right pars triangularis for 10 consecutive weekdays. Fractional anisotropy, axial diffusivity, radial diffusivity and apparent diffusion coefficient of the right AF were extracted using diffusion tensor imaging before the rTMS treatment and correlated with the measured functional improvement by the Concise Chinese Aphasia Test. Results: The Concise Chinese Aphasia Test change scores revealed a stronger language improvement in auditory/reading comprehension and expression in the rTMS group than in the sham group. Regression analysis showed that the pre‐treatment fractional anisotropy, axial diffusivity and apparent diffusion coefficient of the right AF significantly correlated with the expression abilities (R2 > 0.700, p < 0.044) and comprehension abilities (R2 > 0.702, p < 0.039) in the rTMS group. Conclusions: It was concluded that the right AF could be a predictor in language recovery induced by LF‐rTMS after the injuries of primary language circuits. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the genetic causes and teeth characteristics of dentin dysplasia Shields type II(DD-II) in three Chinese families. Materials and methods: Data from three Chinese families affected with DD-II were collected. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) were conducted to screen for variations, and Sanger sequencing was used to verify mutation sites. The physical and chemical characteristics of the affected teeth including tooth structure, hardness, mineral content, and ultrastructure were investigated. Results: A novel frameshift deletion mutation c.1871_1874del(p.Ser624fs) in DSPP was found in families A and B, while no pathogenic mutation was found in family C. The affected teeth's pulp cavities were obliterated, and the root canals were smaller than normal teeth and irregularly distributed comprising a network. The patients' teeth also had reduced dentin hardness and highly irregular dentinal tubules. The Mg content of the teeth was significantly lower than that of the controls, but the Na content was obviously higher than that of the controls. Conclusions: A novel frameshift deletion mutation, c.1871_1874del (p.Ser624fs), in the DPP region of the DSPP gene causes DD-II. The DD-II teeth demonstrated compromised mechanical properties and changed ultrastructure, suggesting an impaired function of DPP. Our findings expand the mutational spectrum of the DSPP gene and strengthen the understanding of clinical phenotypes related to the frameshift deletion in the DPP region of the DSPP gene. Clinical relevance: A DSPP mutation can alter the characteristics of the affected teeth, including tooth structure, hardness, mineral content, and ultrastructure. [ABSTRACT FROM AUTHOR]

Purpose: Varian Ethos utilizes novel intelligent‐optimization‐engine (IOE) designed to automate the planning. However, this introduced a black box approach to plan optimization and challenge for planners to improve plan quality. This study aims to evaluate machine‐learning‐guided initial reference plan generation approaches for head & neck (H&N) adaptive radiotherapy (ART). Methods: Twenty previously treated patients treated on C‐arm/Ring‐mounted were retroactively re‐planned in the Ethos planning system using a fixed 18‐beam intensity‐modulated radiotherapy (IMRT) template. Clinical goals for IOE input were generated using (1) in‐house deep‐learning 3D‐dose predictor (AI‐Guided) (2) commercial knowledge‐based planning (KBP) model with universal RTOG‐based population criteria (KBP‐RTOG) and (3) an RTOG‐based constraint template only (RTOG) for in‐depth analysis of IOE sensitivity. Similar training data was utilized for both models. Plans were optimized until their respective criteria were achieved or DVH‐estimation band was satisfied. Plans were normalized such that the highest PTV dose level received 95% coverage. Target coverage, high‐impact organs‐at‐risk (OAR) and plan deliverability was assessed in comparison to clinical (benchmark) plans. Statistical significance was evaluated using a paired two‐tailed student t‐test. Results: AI‐guided plans were superior to both KBP‐RTOG and RTOG‐only plans with respect to clinical benchmark cases. Overall, OAR doses were comparable or improved with AI‐guided plans versus benchmark, while they increased with KBP‐RTOG and RTOG plans. However, all plans generally satisfied the RTOG criteria. Heterogeneity Index (HI) was on average <1.07 for all plans. Average modulation factor was 12.2 ± 1.9 (p = n.s), 13.1 ± 1.4 (p = <0.001), 11.5 ± 1.3 (p = n.s.) and 12.2 ± 1.9 for KBP‐RTOG, AI‐Guided, RTOG and benchmark plans, respectively. Conclusion: AI‐guided plans were the highest quality. Both KBP‐enabled and RTOG‐only plans are feasible approaches as clinics adopt ART workflows. Similar to constrained optimization, the IOE is sensitive to clinical input goals and we recommend comparable input to an institution's planning directive dosimetric criteria. [ABSTRACT FROM AUTHOR]

Objective: To investigate whether meranzin hydrate (MH) can alleviate depression-like behavior and hypomotility similar to Chaihu Shugan Powder (CSP), and further explore the potential common mechanisms. Methods: Totally 120 Spraque-Dawley rats were randomly divided into 5–8 groups including sham, vehicle, fluoxetine (20 mg/kg), mosapride (10 mg/kg), CSP (30 g/kg), MH (9.18 mg/kg), [D-Lys3]-GHRP-6 (Dlys, 0.5 mg/kg), and MH+Dlys groups by a random number table, 8 rats in each group. And 32 mice were randomly divided into wild-type, MH (18 mg/kg), growth hormone secretagogue receptor-knockout (GHSR-KO), and GHSR+MH groups, 8 mice in each group. The forced swimming test (FST), open field test (OFT), tail suspension test (TST), gastric emptying (GE) test, and intestinal transit (IT) test were used to assess antidepressant and prokinetic (AP) effects after drug single administration for 30 min with absorbable identification in rats and mice, respectively. The protein expression levels of brain-derived neurotrophic factor (BDNF) and phosphorylated mammalian target of rapamycin (p-mTOR) in the hippocampus of rats were evaluated by Western blot. The differences in functional brain changes were determined via 7.0 T functional magnetic resonance imaging-blood oxygen level-dependent (fMRI-BOLD). Results: MH treatment improved depression-like behavior (FST, OFT) and hypomotility (GE, IT) in the acute forced swimming (FS) rats (all P<0.05), and the effects are similar to the parent formula CSP. The ghrelin antagonist [D-Lys3]-GHRP-6 inhibited the effect of MH on FST and GE (P<0.05). Similarly, MH treatment also alleviated depression-like behavior (FST, TST) in the wild-type mice, however, no effects were found in the GHSR KO mice. Additionally, administration of MH significantly stimulated BDNF and p-mTOR protein expressions in the hippocampus (both P<0.01), which were also prevented by [D-Lys3]-GHRP-6 (P<0.01). Besides, 3 main BOLD foci following acute FS rats implicated activity in hippocampus—thalamus—basal ganglia (HTB) circuits. The [D-Lys3]-GHRP-6 synchronously inhibited BOLD HTB foci. As expected, prokinetic mosapride only had effects on the thalamus and basal ganglia, but not on the hippocampus. Within the HTB, the hippocampus is implicated in depression and FD. Conclusions: MH accounts for part of AP effects of parent formula CSP in acute FS rats, mainly via ghrelin-related shared regulation coupled to BOLD signals in brain areas. This novel functionally connection of HTB following acute stress, treatment, and regulation highlights anti-depression unified theory. [ABSTRACT FROM AUTHOR]

DFT calculations have been applied to study the mechanisms of [3+2] and [3+2+1] reactions of ene/yne‐vinylcyclopropanes (shorted as ene/yne‐VCPs). The [3+2] reactions of ene/yne‐VCPs start from C−C cleavage of cyclopropane (CP cleavage) to form six‐membered rhodacycle, followed by alkene/alkyne insertion and reductive elimination. The [3+2+1] reactions have two competing pathways, one is the [3+2+1] pathway (CP cleavage, ene/yne insertion, CO insertion and reductive elimination) and the other is the [3+1+2] pathway (CP cleavage, CO insertion, ene/yne insertion and reductive elimination). The length of tether in substrates affects the ene/yne insertion steps in these cycloadditions, making some reactions fail or changing the reaction pathways. The reasons for these tether length effects are discussed. [ABSTRACT FROM AUTHOR]

Introduction: T cell activation can lead to osteoporosis and while there are several case reports of fractures occurring after immune checkpoint inhibitor (ICI) use, to date, there are no population level studies looking at fracture risk related to ICI use. Methods: Using Alberta Cancer Registry data, we identified all individuals treated with ICI for cancer between September 29, 2010, and March 31, 2019. Linked records from Alberta's healthcare administrative databases were assessed for the presence of fracture diagnostic codes in the year prior to and up to two years after ICI initiation. Fracture rate was stratified based on the time-period before and after ICI initiation. Fracture rates after ICI were compared to baseline. Results: The study cohort consisted of 1600 ICI users (mean age 65.7 years, 60% male). Most patients were treated with an anti-PD-1 agent (73.9%). ICIs were initiated on average 707.8 days after cancer diagnosis. 76 (4.8%) individuals had a remote history of a major fracture, and 141 (8.8%) had been treated with an osteoporosis medication prior to ICI treatment. The fracture rate in the year prior to ICI initiation was 11.3 per 1000 patient-years. The fracture rate in the year after ICI initiation was significantly higher at 27.3 per 1000 patient-years. The fracture rate dropped to 17.6 per 1000 patient-years in the second year after ICI initiation. The incidence rate ratio of sustaining a major fracture in the year after compared to the year prior to ICI initiation was 2.43 (95% CI 1.34–4.27). Conclusions: Fracture risk may be increased in cancer patients early after initiation of ICI, and this may represent a novel immune-related adverse event. [ABSTRACT FROM AUTHOR]

Purpose: Ethos CBCT‐based adaptive radiotherapy (ART) system can generate an online adaptive plan by re‐optimizing the initial reference plan based on the patient anatomy at the treatment. The optimization process is fully automated without any room for human intervention. Due to the change in anatomy, the ART plan can be significantly different from the initial plan in terms of plan parameters such as the aperture shapes and number of monitor units (MUs). In this study, we investigated the feasibility of using calculation‐based patient specific QA for ART plans in conjunction with measurement‐based and calculation‐based QA for initial plans to establish an action level for the online ART patient‐specific QA. Methods: A cohort of 98 cases treated on CBCT‐based ART system were collected for this study. We performed measurement‐based QA using ArcCheck and calculation‐based QA using Mobius for both the initial plan and the ART plan for analysis. For online the ART plan, Mobius calculation was conducted prior to the delivery, while ArcCheck measurement was delivered on the same day after the treatment. We first investigated the modulation factors (MFs) and MU numbers of the initial plans and ART plans, respectively. The γ passing rates of initial and ART plan QA were analyzed. Then action limits were derived for QA calculation and measurement for both initial and online ART plans, respectively, from 30 randomly selected patient cases, and were evaluated using the other 68 patient cases. Results: The difference in MF between initial plan and ART‐plan was 12.9% ± 12.7% which demonstrates their significant difference in plan parameters. Based on the patient QA results, pre‐treatment calculation and measurement results are generally well aligned with ArcCheck measurement results for online ART plans, illustrating their feasibility as an indicator of failure in online ART QA measurements. Furthermore, using 30 randomly selected patient cases, the γ analysis action limit derived for initial plans and ART plans are 89.6% and 90.4% in ArcCheck QA (2%/2 mm) and are 92.4% and 93.6% in Mobius QA(3%/2 mm), respectively. According to the calculated action limits, the ArcCheck measurements for all the initial and ART plans passed QA successfully while the Mobius calculation action limits flagged seven and four failure cases respectively for initial plans and ART plans, respectively. Conclusion: An ART plan can be substantially different from the initial plan, and therefore a separate session of ART plan QA is needed to ensure treatment safety and quality. The pre‐treatment QA calculation via Mobius can serve as a reliable indicator of failure in online ART plan QA. However, given that Ethos ART system is still relatively new, ArcCheck measurement of initial plan is still in practice. It may be skipped as we gain more experience and have better understanding of the system. [ABSTRACT FROM AUTHOR]

Background: The study aimed to identify core biomarkers related to diagnosis and immune microenvironment regulation and explore the immune molecular mechanism of diabetic nephropathy (DN) through bioinformatics analysis. Methods: GSE30529, GSE99325, and GSE104954 were merged with removing batch effects, and different expression genes (DEGs) were screened at a criterion |log2FC| >0.5 and adjusted P <0.05. KEGG, GO, and GSEA analyses were performed. Hub genes were screened by conducting PPI networks and calculating node genes using five algorithms with CytoHubba, followed by LASSO and ROC analysis to accurately identify diagnostic biomarkers. In addition, two different GEO datasets, GSE175759 and GSE47184, and an experiment cohort with 30 controls and 40 DN patients detected by IHC, were used to validate the biomarkers. Moreover, ssGSEA was performed to analyze the immune microenvironment in DN. Wilcoxon test and LASSO regression were used to determine the core immune signatures. The correlation between biomarkers and crucial immune signatures was calculated by Spearman analysis. Finally, cMap was used to explore potential drugs treating renal tubule injury in DN patients. Results: A total of 509 DEGs, including 338 upregulated and 171 downregulated genes, were screened out. "chemokine signaling pathway" and "cell adhesion molecules" were enriched in both GSEA and KEGG analysis. CCR2, CX3CR1, and SELP, especially for the combination model of the three genes, were identified as core biomarkers with high diagnostic capabilities with striking AUC, sensitivity, and specificity in both merged and validated datasets and IHC validation. Immune infiltration analysis showed a notable infiltration advantage for APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. In addition, the correlation analysis showed that CCR2, CX3CR1, and SELP were strongly and positively correlated with checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. Finally, dilazep was screened out as an underlying compound for DN analyzed by CMap. Conclusions: CCR2, CX3CR1, and SELP are underlying diagnostic biomarkers for DN, especially in their combination. APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation may participate in the occurrence and development of DN. At last, dilazep may be a promising drug for treating DN. [ABSTRACT FROM AUTHOR]

The valve‐side bushing of the UHV converter transformer is the key equipment in the DC transmission project, and its running state directly affects the security and stability of the power system. This paper analyses the attributes of the physical entities, uses the digital twins to establish the state feature set and proposes a zero‐sample state evaluation algorithm for the valve‐side bushing. First, the geometric, materials and electrical characteristics are analyzed, and the detailed components of carrier current are obtained by empirical mode decomposition. Then, COMSOL is used to establish digital twins, verify the validity of twins with axial heat distribution of bushing, and establish a state feature set with the extreme temperature inside and outside bushing. Finally, the fuzzy clustering algorithm is used to classify the state feature set, and the similarity is used as the index to realize the zero‐sample state evaluation of the valve‐side bushing. Through the demonstration and analysis of examples, the evaluation model solves the problems of difficulty in extracting the internal features, fewer fault samples, and training difficulty, which is conducive to improving the operation and maintenance management level of power transmission equipment. [ABSTRACT FROM AUTHOR]

Seeking land use development strategies is an effective policy tool to support economic development, especially in developing countries. Previous studies evidence the indispensable role of urban construction land use (UCLU) in regional economic development. However, neglecting the two-stage characteristic and mismatch of UCLU could misinterpret the strategy. This study, considering a two-stage characteristic, aims to explore how land use development strategy affects economic development. First, we create a measure for UCLU mismatch. Second, using both linear and nonlinear models, we explore the possible relationship between the land use strategy and economic development. Subsequently, robustness and the potential path-dependent reinforcement loop (PDRL) are discussed further. Finally, the fundamental channels are investigated in the mechanism analysis section. The results confirm that temporary positive effects stimulate economic development, whereas permanent potential negative effects undermine robust economic development. In addition, the PDRL shows that irrational adoption of the strategy would mean succumbing to low- and medium-industries. We also find that land and capital demonstrate exogenous properties that function as visible hands, with economic regulation exploring UCLU mismatches and misallocation of resources. However, the overuse of these two policies could lead to an unhealthy cycle of mutually reinforcing adverse effects. Based on these findings, we propose policy recommendations to support the rational use of this strategy. [ABSTRACT FROM AUTHOR]

Objectives: This paper presents a randomized controlled trial on assimilative integration, which is aimed at integrating elements from other orientations within one approach to enrich its conceptual and practical repertoire. Elements from Emotion‐Focused Therapy (EFT) were integrated into a form of cognitive behavior therapy: Psychological Therapy (PT). In one treatment condition, EFT was added to PT (+EFT) with the intent to enhance therapists' working with emotions. In the other condition, concepts and interventions based on the socialpsychological self‐regulation approach were added to PT (+SR). Our assumption was that the +EFT would lead to greater and deeper change, particularly in the follow‐up assessments. Method: Patients (n = 104) with anxiety, depression, or adjustment disorders were randomized to the two conditions and treated by 38 therapists who self‐selected between the conditions. Primary outcome was symptom severity at 12‐month follow‐up; secondary outcomes included several measures such as interpersonal problems and quality of life. Variables were assessed at baseline, after 8 and 16 sessions, at posttreatment, and at 6‐ and 12‐month follow‐up. Results: Contrary to our hypothesis, no significant between‐group effects were found. Conclusion: The findings first suggest the difficulty of topping an already very effective approach to psychotherapy. Alternative interpretations were that the EFT training, while corresponding to regular practice in AI, was not sufficient to make a difference in outcome, or that while profiting from the enhancement of abilities for working with emotions, this was outbalanced by negative effects of difficulties related to the implementation of the new elements. [ABSTRACT FROM AUTHOR]

Purpose: Describe an early‐adopting institution's experience with online adaptive radiation for stereotactic partial breast irradiation. Methods and materials: Retrospective review of 22 women treated between May 2021 and March 2022 with adaptive stereotactic partial breast irradiation. A total of 106 of 110 fractions were evaluated for dosimetric changes in target coverage and organ‐at‐risk (OAR) dose. Patient set up with stereotactic wooden frame and adapted per fraction. Treatment and planning times were collected prospectively by radiation therapists. Results: Scheduled PTV30 Gy was <95% in 72.1% and <90% in 38.5% of fractions, and both PTV and CTV coverage were improved significantly after adaption, and 83.7% of fractions were delivered as adapted per physician choice. There was no difference in OAR coverage. Average adaptive treatment planning took 15 min and average time‐on‐couch was 34.4 min. Conclusions: Adaptive stereotactic breast irradiation resulted in improved target coverage with equivalent dosing to OARs in an efficient and tolerated treatment time. Improved target coverage allowed for decreased PTV margins compared to prior trial protocols that may improve acute and late toxicities. [ABSTRACT FROM AUTHOR]

Extensive upfront validation and testing are needed prior to the clinical implementation of AI-based auto-segmentation tools Keywords: AI model prevalidation; Artificial Intelligence; Automatic segmentation; Model performance and QA EN AI model prevalidation Artificial Intelligence Automatic segmentation Model performance and QA 1 6 6 01/27/23 20230101 NES 230101 INTRODUCTION Artificial intelligence (AI) has emerged as a promising approach for automatic contouring in radiation therapy, compared to previous attempts, that is, image value thresholding, Atlas-based, and so forth. Amongst those early adopters of AI tools, the consensus is lacking regarding the required prior validation tests of AI auto-segmentation models before releasing them for clinical use. REBUTTAL For the proposition: Justin Roper, PhD Dr. Mu-Han Lin is an exceptional colleague who has real-world experience with AI at an institution broadly recognized as a leader in AI research. [Extracted from the article]

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Simple Summary: Black pigs are the most competitive hogs in Taiwan as they have a higher price than commercial non-black hogs due to their desirable meat quality for locals, and they have accounted for 15% of Taiwan's hog production since 2011. However, the litter sizes before the weaning of local black sows are much smaller than those of maternal breeds such as Landrace and Yorkshire, which are commonly used in Taiwan. Herd productivity is highly influenced by dam litter size and piglet weight before weaning. The KHAPS Black Pig was registered as a new breed in Taiwan in 2010 and was designed as a maternal line for black hog production. The direct–maternal genetic effects on litter size and piglet weight were tested for selection efficiency. The direct–maternal genetic correlation had a great impact on the prediction of maternal breeding value. Therefore, it is crucial to estimate the direct–maternal genetic parameters of litter size and piglet weight to assist future selection decisions for the genetic progress of this new breed. The objective of this study was to estimate the genetic parameters of litter size and piglet weight from farrowing to weaning in KHAPS Black sows. The genetic parameters investigated were the direct (h2d), maternal (h2m), realized (h2r), and total (h2T) heritability, as well as correlations (rd, rm, and rdm) within and between traits. The analyses were performed using single- and three-trait animal models with and without maternal genetic effects. In the three-trait model with maternal genetic effect, all estimates of h2d and h2m were significantly different from zero except the h2d of mean birth weight. Positive values of rd and rm between traits were observed as expected in the range of 0.322–1.000. Negative values of rdm were found within and between traits and were less associated with mean piglet weight traits than litter size traits. Estimates of h2T were consistently larger than those of h2r in both the single- and three-trait model analyses. In addition, the three-trait model can take into account the association between the traits, so the estimates are more accurate with smaller SEs. In conclusion, maternal genetic effects were not negligible in this study, and thus, a multiple-trait animal model with maternal genetic effects and full pedigree is recommended to assist future pig breeding decisions in this new breed. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore platinum resistance-related biomarkers andmechanisms in lung adenocarcinoma. Through the analysis of gene expression data of lung adenocarcinoma patients and normal patients from The Cancer Genome Atlas, Gene Expression Omnibus database, and A database of genes related to platinum resistance, platinum resistance genes in lung adenocarcinoma and platinum resistance-related differentially expressed geneswere obtained. After screening by a statistical significance threshold, a total of 252 geneswere defined as platinum resistance geneswith significant differential expression, of which 161were up-regulated and 91 were down-regulated. The enrichment results of upregulated gene Gene Ontology (GO) showed that TOP3 entries related to biological processes (BP) were double-strand break repair, DNA recombination, DNA replication, the down-regulated gene GO enriches the TOP3 items about biological processes (BP) as a response to lipopolysaccharide, muscle cell proliferation, response to molecule of bacterial origin. Gene Set Enrichment Analysis showed that the top three were e2f targets, g2m checkpoint, and rgf beta signaling. A prognostic model based on non-negative matrix factorization classification showed the characteristics of high- and low-risk groups. The prognostic model established by least absolute shrinkage and selection operator regression and risk factor analysis showed that genes such as HOXB7, NT5E, and KRT18 were positively correlated with risk score. By analyzing the differences in m6A regulatory factors between high- and low-risk groups, it was found that FTO, GPM6A, METTL3, and YTHDC2 were higher in the low-risk group, while HNRNPA2B1, HNRNPC, TGF2BP1, IGF2BP2, IGF2BP3, and RBM15B were higher in the high-risk group. Immune infiltration and drug sensitivity analysis also showed the gene characteristics of the platinum-resistant population in lung adenocarcinoma. ceRNA analysis showed that has-miR-374a-5p and RP6-24A23.7 were lower in the tumor expression group, and that the survival of the low expression group was worse than that of the high expression group. In conclusion, the results of this study show that platinum resistance-related differentially expressed genes in lung adenocarcinoma are mainly concentrated in biological processes such as DNA recombination and response to lipopolysaccharide. The validation set proved that the high-risk group of our prognostic model had poor survival. M6A regulatory factor analysis, immune infiltration, and drug sensitivity analysis all showed differences between high and low-risk groups. ceRNA analysis showed that has-miR-374a-5p and RP6-24A23.7 could be protective factors. Further exploration of the potential impact of these genes on the risk and prognosis of drug-resistant patients with lung adenocarcinoma would provide theoretical support for future research. [ABSTRACT FROM AUTHOR]

Background and aims: Methamphetamine (MA) is a psychostimulant associated with a high relapse rate among patients with MA use disorder (MUD). Long-term use of MA is associated with mental disorders, executive dysfunction, aggressive behaviors, and impulsivity among patients with MUD. However, identifying which factors may be more closely associated with relapse has not been investigated. Thus, we aimed to investigate the psychological factors and the history of MA use thatmay influence MA relapse. Methods: This cross-sectional study included 168 male MUD patients (MUD group) and 65 healthy male residents (control group). Each patient was evaluated with self-report measures of executive dysfunction, psychopathological symptoms, impulsiveness, aggressiveness, and history of MA use. Data were analyzed with t-tests, analyses of variance, and correlation and regression analyses. Results: The MUD group reported greater executive dysfunction, psychopathological symptoms, impulsivity, and aggression than the control group. Lower age of first MA use was associated both with having relapsed one or more times and with having relapsed two or more times; greater executive dysfunction was associated only with having relapsed two or more times. Conclusion: Patients withMUD reported worse executive function andmental health. Current results also suggest that lower age of firstMA usemay influence relapse rate in general, while executive dysfunction may influence repeated relapse in particular. The present results add to the literature concerning factors that may increase the risk of relapse in individuals with MUD. [ABSTRACT FROM AUTHOR]

To analyze the overheating problem of the valve‐side bushings of high voltage direct current (HVDC) converter transformer, which is mainly caused by current‐carrying connections defects at copper–aluminium transition region in the bushing, this paper set up a 3‐D electromagnetic‐thermal analysis model for the valve‐side bushings, and a method was proposed to calculate the thermal distribution of the valve‐side bushings under the influence of current‐carrying connections defects. Based on this model, the details of an example were shown in this paper, and the numerical results agreed with the measured results within 1%. Then, the temperature field distribution characteristics of valve‐side bushing under different working current I, defect degree k, and ambient temperature Tamb were determined. According to the characteristics, the temperature difference ΔT and working current I of the bushing were applied to evaluate contact resistance at the copper–aluminium transition region of the bushing, which can be provided by a novel method to evaluate for current‐carrying connections defects in the valve‐side bushings. [ABSTRACT FROM AUTHOR]

Purpose: Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART process is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such as cone beam computed tomography (CBCT). Direct application of deep learning (DL)‐based segmentation to CBCT images suffered from issues such as low image quality and limited available contour labels for training. To overcome these obstacles to online CBCT segmentation, we propose a registration‐guided DL (RgDL) segmentation framework that integrates image registration algorithms and DL segmentation models. Methods: The RgDL framework is composed of two components: image registration and RgDL segmentation. The image registration algorithm transforms/deforms planning contours that were subsequently used as guidance by the DL model to obtain accurate final segmentations. We had two implementations of the proposed framework—Rig‐RgDL (Rig for rigid body) and Def‐RgDL (Def for deformable)—with rigid body (RB) registration or deformable image registration (DIR) as the registration algorithm, respectively, and U‐Net as the DL model architecture. The two implementations of RgDL framework were trained and evaluated on seven OARs in an institutional clinical head‐and‐neck dataset. Results: Compared to the baseline approaches using the registration or the DL alone, RgDLs achieved more accurate segmentation, as measured by higher mean Dice similarity coefficients (DSCs) and other distance‐based metrics. Rig‐RgDL achieved a DSC of 84.5% on seven OARs on average, higher than RB or DL alone by 4.5% and 4.7%. The average DSC of Def‐RgDL was 86.5%, higher than DIR or DL alone by 2.4% and 6.7%. The inference time required by the DL model component to generate final segmentations of seven OARs was less than 1 s in RgDL. By examining the contours from RgDLs and DL case by case, we found that RgDL was less susceptible to image artifacts. We also studied how the performances of RgDL and DL vary with the size of the training dataset. The DSC of DL dropped by 12.1% as the number of training data decreased from 22 to 5, whereas RgDL only dropped by 3.4%. Conclusion: By incorporating the patient‐specific registration guidance to a population‐based DL segmentation model, RgDL framework overcame the obstacles associated with online CBCT segmentation, including low image quality and insufficient training data, and achieved better segmentation accuracy than baseline methods. The resulting segmentation accuracy and efficiency show promise for applying this RgDL framework for online ART. [ABSTRACT FROM AUTHOR]

Effect of ICS II on SIRT3-KO Mice To further verify if loss of functional SIRT3 offsets ICS II preconditioning-induced cardioprotection in MI, WT and SIRT3-KO mice were treated with or without ICS II for 7 days prior to MI. ICS II Mitigated MI-Induced Myocardial Injury in Mice The left anterior descending coronary artery ligation-induced MI mice model was used to determine the effect of ICS II on MI-induced myocardial injury. Icariside II, a Naturally Occurring SIRT3 Agonist, Protects against Myocardial Infarction through the AMPK/PGC-1 /Apoptosis Signaling Pathway ICS II Inhibited Myocardial Apoptosis in Mice after MI We next investigated the role of apoptosis in the inhibitory effect of ICS II on MI. [Extracted from the article]

Automated fare collection (AFC) systems can provide tap-in and tap-out records of passengers, allowing us to conduct a comprehensive analysis of spatiotemporal patterns for urban mobility. These temporal and spatial patterns, especially those observed over long periods, provide a better understanding of urban transportation planning and community historical development. In this paper, we explored spatiotemporal evolution of travel patterns using the smart card data of subway traveling from 2011 to 2017 in Shenzhen. To this end, a Gaussian mixture model with expectation–maximization (EM) algorithm clusters the travel patterns according to the frequency characteristics of passengers' trips. In particular, we proposed the Pareto principle to negotiate diversified evaluation criteria on model parameters. Seven typical travel patterns are obtained using the proposed algorithm. Our findings highlighted that the proportion of each pattern remains relatively stable from 2011 to 2017, but the regular commuting passengers play an increasingly important position in the passenger flow. Additionally, focusing on the busiest commuting passengers, we depicted the spatial variations over years and identified the characters in different periods. Their cross-year usage of smart cards was finally examined to understand the migration of travel patterns over years. With reference to these methods and insights, transportation planners and policymakers can intuitively understand the historical variations of passengers' travel patterns, which lays the foundation for improving the service of the subway system. [ABSTRACT FROM AUTHOR]

Overall, Google Scholar seems to be the most effective system in many languages, but using multiple search systems, especially Google Scholar and local search systems, is important for identifying important non-English-language articles. Assessment of relevance, quality, and visibility of non-English-language articles Because we used only two keywords, search results may include articles not relevant to biodiversity conservation. Keywords: biodiversity information; evidence synthesis; global biodiversity databases; languages; language barrier; publication bias; ; ; ; ; ; ; barrera del lenguaje; bases de datos de la biodiversidad mundial; idiomas; información sobre la biodiversidad; sesgo de publicación; síntesis de evidencias EN biodiversity information evidence synthesis global biodiversity databases languages language barrier publication bias ; ; ; ; ; barrera del lenguaje bases de datos de la biodiversidad mundial idiomas información sobre la biodiversidad sesgo de publicación síntesis de evidencias 1 11 11 08/01/22 20220801 NES 220801 INTRODUCTION Nearly, 77% of Earth's land and 87% of its ocean have been modified owing to human activities (Ceballos et al., 2015; Chowdhury et al., 2021; Venter et al., 2016). Both types of language barriers (i.e., language barriers to publications and to the use of science in decision-making) continue to be pervasive issues in countries where English is not widely spoken, which may explain the increasing trend of non-English-language articles on biodiversity conservation. [Extracted from the article]

Objectives: We aimed at investigating whether serum exosomal miR‐16‐5p could be utilized as an immunotherapy biomarker in lung adenocarcinoma (LUAD) patients administered by programmed cell death ligand‐1 (PD‐L1) inhibitors, and to evaluate its functions in LUAD progression. Methods: Sixty LUAD sufferers and 20 healthy controls (HCs) were covered in this work. We applied both IHC and WB to examine PD‐L1 level in clinical tissue samples and utilized WB to quantify PD‐L1 expression in LUAD cells and LUAD xenograft tissues, respectively. Transmission electron microscopy (TEM), WB, and nanoparticle tracking analysis (NTA) were executed to confirm the exosomes isolated from serum specimens and cell culture media. To quantify of exosomal miR‐16‐5p level from serum and culture medium of cultured cell, qRT‐PCR experiment was utilized. The connection between tissue PD‐L1 level and serum exosomal miR‐16‐5p expression in PD‐L1‐positive sufferers administered by PD‐L1 inhibitors was verified using Spearman correlation coefficient analysis. In addition, the overall survival (OS) and progression‐free survival (PFS) rates among PD‐L1 inhibitor managed sufferers were acquired through a follow‐up visit. Finally, we used a group of assays, including 5‐bromo‐2′‐dexoyuridine (BrdU) and colony formation test, wound healing experiment, flow cytometry, and nude mice xenograft experiment, to explore the functions of circulating exosomal miR‐16‐5p on LUAD cell proliferation, apoptosis, and migration, as well as tumor development, respectively. Results: PD‐L1 expression was positively related to T stage (tumor size stage), and PD‐L1 inhibitor treatment reduced the PD‐L1 expression and mitigated T stage in PD‐L1‐positive LUAD sufferers. For PD‐L1‐positive LUAD sufferers, elevated PD‐L1 expression or reduced serum exosomal miR‐16‐5p level were linked to longer PFS and OS upon PD‐L1 inhibitor treatment. The number of exosomes in patient's serum was more than that in the serum of healthy individuals, and PD‐L1 inhibitor treatment decreased the number of serum‐derived exosomes in PD‐L1‐positive LUAD sufferers. Exosome‐derived miR‐16‐5p was downregulated in patient's serum and cell culture medium, and this was negatively linked to tumor stage and PD‐L1 expression. Meanwhile, PD‐L1 inhibitor treatment could increase the serum exosomal miR‐16‐5p expression, and the expression change of serum exosomal miR‐16‐5p was diametrically related to PD‐L1 after the treatment. Moreover, the overexpression of PD‐L1 accelerated tumor growth and decreased the exosomal miR‐16‐5p content in cell culture media, while exosomal miR‐16‐5p overexpression in cell culture media inhibited tumor development by decreasing the PD‐L1 expression. Exosomal miR‐16‐5p overexpression in cell culture media also depressed LUAD cell proliferation and migration, and stimulated cell apoptosis, especially in the cells which cultured in the mediums with PD‐L1 inhibitor in vitro. Conclusions: Serum exosomal miR‐16‐5p may be a latent tumor inhibitor and a new biomarker for PD‐L1 inhibitor‐dependent immunotherapy in LUAD by regulating the PD‐L1 expression. [ABSTRACT FROM AUTHOR]

A novel complex (C6H16N)3[Tb2(Hsal)3(NO3)6] (1) was synthesised and characterized using X-ray crystallography. The PXRD patterns indicate that complex 1 is stable after heating at 180 °C for half an hour. More interestingly, high luminescence quantum yields of 90%, 78%, and 53% and long lifetimes of 1.71, 1.72, and 1.71 ms were obtained at room temperature, 80 °C, and 180 °C, respectively. Thus, complex 1 is one of the rare examples of luminescent lanthanide complexes with both high luminescence efficiency and thermostability. [ABSTRACT FROM AUTHOR]