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

Abstract The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network based on a deep convolutional neural network for categorizing wounds into four categories: diabetic, pressure, surgical, and venous ulcers. Our multi-modal network uses wound images and their corresponding body locations for more precise classification. A unique aspect of our methodology is incorporating a body map system that facilitates accurate wound location tagging, improving upon traditional wound image classification techniques. A distinctive feature of our approach is the integration of models such as VGG16, ResNet152, and EfficientNet within a novel architecture. This architecture includes elements like spatial and channel-wise Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated Multi-Layer Perceptron, providing a robust foundation for classification. Our multi-modal network was trained and evaluated on two distinct datasets comprising relevant images and corresponding location information. Notably, our proposed network outperformed traditional methods, reaching an accuracy range of 74.79–100% for Region of Interest (ROI) without location classifications, 73.98–100% for ROI with location classifications, and 78.10–100% for whole image classifications. This marks a significant enhancement over previously reported performance metrics in the literature. Our results indicate the potential of our multi-modal network as an effective decision-support tool for wound image classification, paving the way for its application in various clinical contexts.

Abstract Recent postmortem transcriptomic studies of schizophrenia (SCZ) have shown hundreds of differentially expressed genes. However, the extent to which these gene expression changes reflect antipsychotic drug (APD) exposure remains uncertain. We compared differential gene expression in the prefrontal cortex of SCZ patients who tested positive for APDs at the time of death with SCZ patients who did not. APD exposure was associated with numerous changes in the brain transcriptome, especially among SCZ patients on atypical APDs. Brain transcriptome data from macaques chronically treated with APDs showed that APDs affect the expression of many functionally relevant genes, some of which show expression changes in the same directions as those observed in SCZ. Co-expression modules enriched for synaptic function showed convergent patterns between SCZ and some of the APD effects, while those associated with inflammation and glucose metabolism exhibited predominantly divergent patterns between SCZ and APD effects. In contrast, major cell-type shifts inferred in SCZ were primarily unaffected by APD use. These results show that APDs may confound SCZ-associated gene expression changes in postmortem brain tissue. Disentangling these effects will help identify causal genes and improve our neurobiological understanding of SCZ.

Abstract Wound classification is an essential step of wound diagnosis. An efficient classifier can assist wound specialists in classifying wound types with less financial and time costs and help them decide on an optimal treatment procedure. This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize them into multiple classes, including diabetic, pressure, surgical, and venous ulcers. A body map was also developed to prepare the location data, which can help wound specialists tag wound locations more efficiently. Three datasets containing images and their corresponding location information were designed with the help of wound specialists. The multi-modal network was developed by concatenating the image-based and location-based classifier outputs with other modifications. The maximum accuracy on mixed-class classifications (containing background and normal skin) varies from 82.48 to 100% in different experiments. The maximum accuracy on wound-class classifications (containing only diabetic, pressure, surgical, and venous) varies from 72.95 to 97.12% in various experiments. The proposed multi-modal network also showed a significant improvement in results from the previous works of literature.

Abstract Background Limited studies have evaluated the use of ultrasound for detection of intracerebral hemorrhage (ICH) using diagnostic ultrasound Transcranial Doppler machines in adults. The feasibility of ICH detection using Point of care Ultrasound (POCUS) machines has not been explored. We evaluated the feasibility of using cranial POCUS B mode imaging performed using intensive care unit (ICU) POCUS device for ICH detection with a secondary goal of mapping optimal imaging technique and brain topography likely to affect sensitivity and specificity of ICH detection with POCUS. Materials and methods After obtaining IRB approval, a blinded investigator performed cranial ultrasound (Fujifilm, Sonosite® Xporte, transcranial and abdominal presets) through temporal windows on 11 patients with intracerebral pathology within 72 h of last CT/MRI (computed tomography scan/magnetic resonance imaging) brain after being admitted to a neurocritical care unit in Aug 2020 and Nov 2020–Mar 2021. Images were then compared to patient’s CT/MRI to inform topography. Inferential statistics were reported. Results Mean age was 57 (28–77 years) and 6/11 were female. Six patients were diagnosed with ICH, 3 with ischemic stroke, 1 subarachnoid hemorrhage, and 1 brain tumor. The sensitivity and specificity of point of care diagnosis of ICH compared to CT/MRI brain was 100% and 50%, respectively. Mean time between ultrasound scan and CT/MRI was 13.3 h (21 min–39 h). Falx cerebri, choroid calcification and midbrain-related artifacts were the most reproducible hyperechoic signals. Abdominal preset on high gain yielded less artifact than Transcranial Doppler preset for cranial B mode imaging. False positive ICH diagnosis was attributed to intracerebral tumor and midbrain-related artifact. Conclusions Our exploratory analysis yielded preliminary data on use of point of care cranial ultrasound for ICH diagnosis to inform imaging techniques, cranial topography on B mode and sample size estimation for future studies to evaluate sensitivity and specificity of cranial POCUS in adult patients. This pilot study is limited by small sample size and over representation of ICH in the study. Cranial POCUS is feasible using POCUS machines and may have potential as a screening tool if validated in adequately powered studies.

Abstract Background Bone morphogenetic proteins (BMP) are evolutionarily conserved morphogens that are reactivated in lung carcinomas. In lung cancer cells, BMP signaling suppresses AMP activated kinase (AMPK) by inhibiting LKB1. AMPK is activated by mitochondrial stress that inhibits ATP production, which is enhanced 100-fold when phosphorylated by LKB1. Activated AMPK can promote survival of cancer cells but its “hyperactivation” induces cell death. The studies here reveal novel cell death mechanisms induced by BMP inhibitors, together with agents targeting the mitochondria, which involves the “hyperactivation” of AMPK. Methods This study examines the synergistic effects of two BMP inhibitors together with mitochondrial targeting agents phenformin and Ym155, on cell death of lung cancer cells expressing LKB1 (H1299), LKB1 null (A549), and A549 cells transfected with LKB1 (A549-LKB1). Cell death mechanisms evaluated were the activation of caspases and the nuclear localization of apoptosis inducing factor (AIF). A769662 was used to allosterically activate AMPK. Knockdown of BMPR2 and LKB1 using siRNA was used to examine their effects on nuclear localization of AMPK. Validation studies were performed on five passage zero primary NSCLC. Results Both BMP inhibitors synergistically suppressed growth when combined with Ym155 or phenformin in cells expressing LKB1. The combination of BMP inhibitors with mitochondrial targeting agents enhanced the activation of AMPK in lung cancer cells expressing LKB1. Allosteric activation of AMPK with A769662 induced cell death in both H1299 and A549 cells. Cell death induced by the combination of BMP inhibitors and mitochondrial-targeting agents did not activate caspases. The combination of drugs induced nuclear localization of AIF in cells expressing LKB1, which was attenuated by knockdown of LKB1. Knockdown of BMPR2 together with Ym155 increased nuclear localization of AIF. Combination therapy also enhanced cell death and AIF nuclear localization in primary NSCLC. Conclusions These studies demonstrate that inhibition of BMP signaling together with mitochondrial targeting agents induce AIF caspase-independent cell death, which involves the “hyperactivation” of AMPK. AIF caspase-independent cell death is an evolutionarily conserved cell death pathway that is infrequently studied in cancer. These studies provide novel insight into mechanisms inducing AIF caspase-independent cell death in cancer cells using BMP inhibitors. Video Abstract

Purpose of Review: Functional stress testing is frequently used to assess for coronary artery disease (CAD) in symptomatic, stable patients with low to intermediate pretest probability. However, patients with highly vulnerable plaque may have preserved luminal patency and, consequently, a falsely negative stress test. Cardiac computed tomography angiography (CCTA) has emerged at the forefront of primary prevention screening and has excellent agency in ruling out obstructive CAD with high negative predictive value while simultaneously characterizing nonobstructive plaque for high-risk features, which invariably alters risk-stratification and pre-procedural decision making. Recent Findings: We review the literature detailing the utility of CCTA in its ability to risk-stratify patients with CAD based on calcium scoring as well as high-risk phenotypic features and to qualify the functional significance of stenotic lesions. Summary: Calcium scores ≥ 100 should prompt consideration of statin and aspirin therapy. Spotty calcifications < 3 mm, increased non-calcified plaque > 4 mm3 per mm of the vessel wall, low attenuation < 30 HU soft plaque and necrotic core with a rim of higher attenuation < 130 HU, and a positive remodeling index ratio > 1.1 all confer additive risk for acute plaque rupture when present. Elevations in the perivascular fat attenuation index > −70.1 HU are a strong predictor of all-cause mortality and can further the risk stratification of patients in the setting of a non-to-minimal plaque burden. Lastly, a CT-derived fractional flow reserve (FFRCT) < 0.75 or values from 0.76 to 0.80 in conjunction with additional risk factors is suggestive of flow-limiting disease that would benefit from invasive testing. The wealth of information available through CCTA can allow clinicians to risk-stratify patients at elevated risk for an acute ischemic event and engage in advanced revascularization planning.

The article reviews and benchmarks machine learning methods for automatic image-based plant species recognition and proposes a novel retrieval-based method for recognition by nearest neighbor classification in a deep embedding space. The image retrieval method relies on a model trained via the Recall@k surrogate loss. State-of-the-art approaches to image classification, based on Convolutional Neural Networks (CNN) and Vision Transformers (ViT), are benchmarked and compared with the proposed image retrieval-based method. The impact of performance-enhancing techniques, e.g., class prior adaptation, image augmentations, learning rate scheduling, and loss functions, is studied. The evaluation is carried out on the PlantCLEF 2017, the ExpertLifeCLEF 2018, and the iNaturalist 2018 Datasets—the largest publicly available datasets for plant recognition. The evaluation of CNN and ViT classifiers shows a gradual improvement in classification accuracy. The current state-of-the-art Vision Transformer model, ViT-Large/16, achieves 91.15% and 83.54% accuracy on the PlantCLEF 2017 and ExpertLifeCLEF 2018 test sets, respectively; the best CNN model (ResNeSt-269e) error rate dropped by 22.91% and 28.34%. Apart from that, additional tricks increased the performance for the ViT-Base/32 by 3.72% on ExpertLifeCLEF 2018 and by 4.67% on PlantCLEF 2017. The retrieval approach achieved superior performance in all measured scenarios with accuracy margins of 0.28%, 4.13%, and 10.25% on ExpertLifeCLEF 2018, PlantCLEF 2017, and iNat2018–Plantae, respectively.

Diagnosis, management, and treatment of Dieulafoy’s lesion have been complicated since its first mention in 1884. With increased awareness over the decades, Dieulafoy’s lesions are being managed differently based on the physician’s expertise. Herein, we report a case of a 58-year-old female who was found to have a Dieulafoy’s lesion on endoscopy, with her treatment complicated by gastric necrosis and perforation after multiple embolizations and limited surgical resection.

Abstract Cortical thinning occurs throughout the entire life and extends to late-life neurodegeneration, yet the neurobiological substrates are poorly understood. Here, we used a virtual-histology technique and gene expression data from the Allen Human Brain Atlas to compare the regional profiles of longitudinal cortical thinning through life (4004 magnetic resonance images [MRIs]) with those of gene expression for several neuronal and non-neuronal cell types. The results were replicated in three independent datasets. We found that inter-regional profiles of cortical thinning related to expression profiles for marker genes of CA1 pyramidal cells, astrocytes and, microglia during development and in aging. During the two stages of life, the relationships went in opposite directions: greater gene expression related to less thinning in development and vice versa in aging. The association between cortical thinning and cell-specific gene expression was also present in mild cognitive impairment and Alzheimer’s Disease. These findings suggest a role of astrocytes and microglia in promoting and supporting neuronal growth and dendritic structures through life that affects cortical thickness during development, aging, and neurodegeneration. Overall, the findings contribute to our understanding of the neurobiology underlying variations in MRI-derived estimates of cortical thinning through life and late-life disease.

Summary: The increased prevalence of obesity and metabolic diseases has heightened interest in adipose tissue biology and its potential as a therapeutic target. To better understand cellular heterogeneity and complexity of white adipose tissue (WAT), we employed cytometry by time-of-flight (CyTOF) to characterize immune and stromal cells in visceral and subcutaneous WAT depots under normal and high-fat diet feeding, by quantifying the expression levels of 32 surface marker proteins. We observed comparable proportions of immune cells in two WAT depots under steady state, but depot-distinct subtypes of adipose precursor cells (APC), suggesting differences in their adipogenic and fibrogenic potential. Furthermore, in addition to pro-inflammatory immune cell shifts, significant pro-fibrotic changes were observed in APCs under high-fat diet, suggesting that APCs are early responders to dietary challenges. We propose CyTOF as a complementary and alternative tool to current high-throughput single-cell transcriptomic analyses to better understand the function and plasticity of adipose tissue.

General psychopathology and cognition are likely to have a bidirectional influence on each other. Yet, the relationship between brain structure, psychopathology, and cognition remains unclear. This brief report investigates the association between structural properties of the cerebral cortex [surface area, cortical thickness, intracortical myelination indexed by the T1w/T2w ratio, and neurite density assessed by restriction spectrum imaging (RSI)] with general psychopathology and cognition in a sample of children from the Adolescent Brain Cognitive Development (ABCD) study. Higher levels of psychopathology and lower levels of cognitive ability were associated with a smaller cortical surface area. Inter-regionally—across the cerebral cortex—the strength of association between an area and psychopathology is strongly correlated with the strength of association between an area and cognition. Taken together, structural deviations particularly observed in the cortical surface area influence both psychopathology and cognition.