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Aerosols play a critical role in the Arctic's radiative balance, influencing solar radiation and cloud formation. Limited observations in the central Arctic leave gaps in understanding aerosol dynamics year-round, affecting model predictions of climate-relevant aerosol properties. Here, we present the first annual high-time-resolution observations of submicron aerosol chemical composition in the central Arctic during the Arctic Ocean 2018 (AO2018) and the 2019–2020 Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expeditions. Seasonal variations in the aerosol mass concentrations and chemical composition in the central Arctic were found to be driven by typical Arctic seasonal regimes and resemble those of pan-Arctic land-based stations. Organic aerosols dominated the pristine summer, while anthropogenic sulfate prevailed in autumn and spring under haze conditions. Ammonium, which impacts aerosol acidity, was consistently less abundant, relative to sulfate, in the central Arctic compared to lower latitudes of the Arctic. Cyclonic (storm) activity was found to have a significant influence on aerosol variability by enhancing emissions from local sources and the transport of remote aerosol. Local wind-generated particles contributed up to 80 % (20 %) of the cloud condensation nuclei population in autumn (spring). While the analysis presented herein provides the current central Arctic aerosol baseline, which will serve to improve climate model predictions in the region, it also underscores the importance of integrating short-timescale processes, such as seasonal wind-driven aerosol sources from blowing snow and open leads/ocean in model simulations. This is particularly important, given the decline in mid-latitude anthropogenic emissions and the increase in local ones.

Person re-identification (Re-ID) emerges as a captivating realm within computer vision, dedicated to the task of recognizing the same individual across diverse camera angles or locations. The realm of video-based person re-identification (video re-ID) has recently captivated increasing interest, owing to its wide array of practical applications spanning surveillance, smart city solutions, and public safety measures. Nevertheless, video re-ID proves to be a formidable challenge, an ever-evolving domain fraught with a multitude of uncertainties like viewpoint variations, occlusions, pose changes, and unpredictable video sequences. Over the past few years, the realm of deep learning applied to video re-ID has consistently delivered remarkable outcomes on public datasets, showcasing a range of innovative strategies devised to tackle the array of issues encountered in video re-ID. In stark contrast to image-based re-ID, video re-ID stands out as significantly more intricate and demanding. In a bid to inspire forthcoming research endeavors and confronts emerging challenges, this paper presents a comprehensive overview of the latest advancements in deep learning methodologies tailored for video re-ID. It delves into three crucial facets; encompassing succinct explanations of video re-ID techniques along with their constraints, pivotal breakthroughs coupled with the technical hurdles faced, and the architectural framework underpinning these developments. The paper further furnishes a comparative analysis of performance across diverse datasets, offers insightful guidance on enhancing video re-ID strategies, and outlines compelling avenues for future research exploration.

In the Namib Desert, fog is the only regular water input and, thus, is a crucial water source for its fauna and flora. Each year, between June and October, absorbing biomass burning aerosols (BBAs) overlie the stratocumulus clouds in the adjacent Southeast Atlantic. In some synoptic settings, this layer of BBAs reaches Namibia and its desert, where it interacts with coastal fog and low clouds (FLCs). In this study, a novel 15-year data set of geostationary satellite observations of FLC dissipation time in the Namib Desert is used, along with reanalysis data, to better understand the potential semi-direct effects of BBAs on FLC dissipation in the Namib Desert, i.e., through adjustments of atmospheric stability and thermodynamics via the interaction of aerosols with radiation. This is done by investigating both the time of day when FLCs dissolve and the synoptic-scale meteorology depending on BBA loading. It is found that FLC dissipation time is significantly later on high-BBA-loading days. BBAs are transported to the Namib along moist free-tropospheric air by a large-scale anticyclonic recirculation pattern. At the surface, the associated longwave heating strengthens a continental heat low, which modifies the circulation and boundary layer moisture along the coastline, complicating the attribution of BBA effects. During high-BBA days, the vertical profiles of the temporal development of air temperatures highlight contrasting daytime and nighttime processes modifying the local inversion. These processes are thought to be driven by greenhouse warming as a result of the moisture in the BBA plumes and BBA absorption (only during the daytime). A statistical learning framework is used to quantify meteorological and BBA influences on FLC dissipation time. The statistical model is able to reproduce the observed differences in FLC dissipation time between high- and low-BBA days and attributes these differences mainly to differences in circulation, boundary layer moisture and near-surface air temperature along the coastline. However, the model is prone to underfitting and is not able to reproduce the majority of the FLC dissipation variability. While the model does not suggest that BBA patterns are important for FLC dissipation, the findings show how the moist BBA plumes modify local thermodynamics, to which FLC dissipation is shown to be sensitive. The findings highlight the challenges of disentangling meteorological and aerosol effects on cloud development using observations and invite detailed modeling analyses of the underlying processes, for example, with large-eddy simulations.

A left-lateralized cortical reading circuit underlies successful reading and fails to engage in individuals with reading problems. Studies identifying this circuit included youth from economically advantaged backgrounds and focused on cortical, not subcortical, structures. However, among youth with low scores on reading tests who are living in the context of economic disadvantage, this brain network is actively engaged during reading, despite persistent reading problems. This finding suggests that other brain circuits may underlie reading in these youth. A hippocampal circuit is one likely candidate, as it has recently been shown to support domain-general processes like working memory (WM) that are also associated with reading. Given age-related increases in hippocampal volume, WM, and reading, and known associations between WM and reading, we hypothesized that hippocampal volume would be associated with reading via WM processes. Using a cross-sectional developmental design, we explored this in middle childhood (average age at MRI scan ∼10; N = 50) and adolescence (average age at MRI scan ∼17; N = 175). Results suggest that the hippocampus is a critical contributor to word reading in adolescents living in economically disadvantaged contexts, and that this operates through working memory processes. Such findings point to new targets for reading intervention in adolescents.

Aerosol–cloud interactions (ACI) have a pronounced influence on the Earth's radiation budget but continue to pose one of the most substantial uncertainties in the climate system. Marine boundary-layer clouds (MBLCs) are particularly important since they cover a large portion of the Earth's surface. One of the biggest challenges in quantifying ACI from observations lies in isolating adjustments of cloud fraction (CLF) to aerosol perturbations from the covariability and influence of the local meteorological conditions. In this study, this isolation is attempted using 9 years (2011–2019) of near-global daily satellite cloud products in combination with reanalysis data of meteorological parameters. With cloud-droplet number concentration (Nd) as a proxy for aerosol, MBLC CLF is predicted by region-specific gradient boosting machine learning (ML) models. By means of SHapley Additive exPlanation (SHAP) regression values, CLF sensitivity to Nd and meteorological factors as well as meteorological influences on the Nd–CLF sensitivity are quantified. The regional ML models are able to capture, on average, 45 % of the CLF variability. Based on our statistical approach, global patterns of CLF sensitivity suggest that CLF is positively associated with Nd, particularly in the stratocumulus-to-cumulus transition regions and the Southern Hemispheric midlatitudes. However, Nd retrieval bias may contribute to non-causality in these positive sensitivities, and hence they should be considered upper-bound estimates. CLF sensitivity to estimated inversion strength (EIS) is ubiquitously positive and strongest in tropical and subtropical regions topped by stratocumulus and within the midlatitudes. Globally, increased sea-surface temperature (SST) reduces CLF, particularly in stratocumulus regions. The spatial patterns of CLF sensitivity to horizontal wind components in the free troposphere may point to the impact of synoptic-scale weather systems and vertical wind shear on MBLCs. The Nd–CLF relationship is found to depend more on the selected thermodynamical variables than dynamical variables and in particular on EIS and SST. In the midlatitudes, a stronger inversion is found to amplify the Nd–CLF relationship, while this is not observed in the stratocumulus regions. In the stratocumulus-to-cumulus transition regions, the Nd–CLF sensitivity is found to be amplified by higher SSTs, potentially pointing to Nd more frequently delaying this transition in these conditions. The expected climatic changes in EIS and SST may thus influence future forcings from the CLF adjustment. The novel data-driven framework, whose limitations are also discussed, produces a quantification of the response of MBLC CLF to aerosols, taking into account the covariations with meteorology.

In this study, we present and analyze the first continuous time series of relevant aerosol precursor vapors from the central Arctic (north of 80° N) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. These precursor vapors include sulfuric acid (SA), methanesulfonic acid (MSA), and iodic acid (IA). We use FLEXPART simulations, inverse modeling, sulfur dioxide (SO2) mixing ratios, and chlorophyll a (chl a) observations to interpret the seasonal variability in the vapor concentrations and identify dominant sources. Our results show that both natural and anthropogenic sources are relevant for the concentrations of SA in the Arctic, but anthropogenic sources associated with Arctic haze are the most prevalent. MSA concentrations are an order of magnitude higher during polar day than during polar night due to seasonal changes in biological activity. Peak MSA concentrations were observed in May, which corresponds with the timing of the annual peak in chl a concentrations north of 75° N. IA concentrations exhibit two distinct peaks during the year, namely a dominant peak in spring and a secondary peak in autumn, suggesting that seasonal IA concentrations depend on both solar radiation and sea ice conditions. In general, the seasonal cycles of SA, MSA, and IA in the central Arctic Ocean are related to sea ice conditions, and we expect that changes in the Arctic environment will affect the concentrations of these vapors in the future. The magnitude of these changes and the subsequent influence on aerosol processes remains uncertain, highlighting the need for continued observations of these precursor vapors in the Arctic.

Compared to the general population, science trainees experience challenges and heightened stressors that often lead to adverse mental health outcomes. With COVID-19, the stressors of social distancing, isolation, truncated lab time, and uncertainty about the future have all likely exacerbated these issues. Now, more than ever, practical and effective interventions are vitally needed to address the core causes of stress among science trainees and increase their resilience. This paper introduces a new resilience program targeted to biomedical trainees and scientists - Becoming a Resilient Scientist Series (BRS), a 5-part workshop complemented by facilitated group discussions all aimed at bolstering resilience, particularly in the context of academic and research environments. To assess the program’s efficacy, participants completed resilience measures and related assessments before and after completing the series. The results suggest that BRS is associated with improvements in trainee resilience (primary outcome) and with reductions in perceived stress, anxiety, and work-related presenteeism, as well as enhancements in adaptability, self-awareness, and self-efficacy (secondary outcomes). Furthermore, program participants reported a high level of satisfaction, a strong willingness to recommend the program to others, and perceived positive changes in their resilience skills. To the best of our knowledge, this is the first resilience program designed explicitly for biomedical trainees and scientists, tailored to their unique professional culture and work environment.

Reading difficulties and exposure to air pollution are both disproportionately high among youth living in economically disadvantaged contexts. Critically, variance in reading skills in youth living in higher socioeconomic status (SES) contexts largely derives from genetic factors, whereas environmental factors explain more of the variance in reading skills among youth living in lower SES contexts. Although reading research has focused closely on the psychosocial environment, little focus has been paid to the effects of the chemical environment. In this study, we measured prenatal exposure to a common air pollutant, polycyclic aromatic hydrocarbons (PAH), via the presence (versus absence) of PAH-DNA adducts in maternal blood during the third trimester of pregnancy. We examined the impact of prenatal PAH exposure on adolescent hippocampal subfield volume and on word reading in a sample of youth followed prospectively since birth (N = 165). Compared to those without prenatal exposure, those with detectable PAH-DNA adducts (N = 63) exhibited significantly smaller hippocampal volumes (CA2/3 subfield, t = -2.413, p

Abstract Realizations of fully implantable cochlear implants (FICIs) for providing adequate solution to esthetic concerns and frequent battery replacement have lacked of addressing system level criteria as a complete device. Here, we present a full-custom FICI that considers design of both an implantable sensor for wide range sound sensing and a signal conditioning circuit for electrical stimulation of the auditory nerve. The microelectromechanical system (MEMS)-based acoustic sensor utilizes multiple cantilever beam structures to sense and filter the mechanical vibrations on the ossicular chain. The area optimized bilayer design of the piezoelectric sensor met with the volume limitation in the middle ear while achieving high signal-to-noise-ratio. The sensor outputs are processed by a current mode low-power signal conditioning circuit that stimulates the auditory neurons through intracochlear electrodes. The FICI is validated with an in vivo model where the electrical auditory brainstem response (eABR) of the animal was observed while applying sound excitation. The eABR results demonstrate that the system is able to evoke responses in the auditory nerves of a guinea pig for sound range of 45–100 dB SPL within the selected frequency bands.

Abstract Interjurisdictional migrations lead to seasonally changing patterns of exploitation risk, emphasizing the importance of spatially explicit approaches to fishery management. Understanding how risk changes along a migration route supports time-area based fishery management, but quantifying risk can be complicated when multiple fishing methods are geographically segregated and when bycatch species are considered. Further, habitat selection in dynamic environments can influence migration behavior, interacting with other management objectives such as water quality and habitat restoration. As a case study, we examined a novel acoustic telemetry data set for Lake Whitefish in Lake Erie, where they migrate through multiple spatial management units that are variably affected by seasonal hypoxia and host a variety of fisheries. Combining telemetry results with fishery catch and water quality monitoring, we demonstrate three exploitation risk scenarios: (i) high risk due to high residency and high catch, (ii) high risk due to high residency in time-areas with moderate catch, and (iii) low risk due to residency in time-areas with low catch. Interestingly, occupation of low risk refugia was increased by the development of hypoxia in adjacent areas. Consequently, fishery management goals to sustainably manage other target species may be directly and indirectly linked to water quality management goals through Lake Whitefish.

Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. “Cloud controlling factor” (CCF) analysis derives relationships between large-scale meteorological drivers and cloud radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear (ΔU300). We identify an optimal configuration for predicting high-cloud radiative anomalies that includes SUT and ΔU300 and show that spatial domain size is more important than the selection of CCFs for predictive skill. We also find an important discrepancy between the optimal domain sizes required for predicting locally and globally aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks and deduce that the inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback.

Abstract Large language models (LLMs) such as Open AI’s GPT-4 (which power ChatGPT) and Google’s Gemini, built on artificial intelligence, hold immense potential to support, augment, or even eventually automate psychotherapy. Enthusiasm about such applications is mounting in the field as well as industry. These developments promise to address insufficient mental healthcare system capacity and scale individual access to personalized treatments. However, clinical psychology is an uncommonly high stakes application domain for AI systems, as responsible and evidence-based therapy requires nuanced expertise. This paper provides a roadmap for the ambitious yet responsible application of clinical LLMs in psychotherapy. First, a technical overview of clinical LLMs is presented. Second, the stages of integration of LLMs into psychotherapy are discussed while highlighting parallels to the development of autonomous vehicle technology. Third, potential applications of LLMs in clinical care, training, and research are discussed, highlighting areas of risk given the complex nature of psychotherapy. Fourth, recommendations for the responsible development and evaluation of clinical LLMs are provided, which include centering clinical science, involving robust interdisciplinary collaboration, and attending to issues like assessment, risk detection, transparency, and bias. Lastly, a vision is outlined for how LLMs might enable a new generation of studies of evidence-based interventions at scale, and how these studies may challenge assumptions about psychotherapy.

To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.

Subconjunctival hemorrhages following forceps delivery are common. However, their presence with hyphema is rare. This case report describes a full-term neonate who was initially examined for trauma and subconjunctival hemorrhage in both eyes with diffuse hyphema in right eye following forceps delivery. The anterior segment showed nondilating pupil with fine blood vessels on the iris in one eye along with tunica vasculosa lentis. B-scan showed a hyperechoic line in the vitreous suggestive of unilateral persistent fetal vasculature.

Abstract In the most comprehensive population surveys, mental health is only broadly captured through questionnaires asking about “mentally unhealthy days” or feelings of “sadness.” Further, population mental health estimates are predominantly consolidated to yearly estimates at the state level, which is considerably coarser than the best estimates of physical health. Through the large-scale analysis of social media, robust estimation of population mental health is feasible at finer resolutions. In this study, we created a pipeline that used ~1 billion Tweets from 2 million geo-located users to estimate mental health levels and changes for depression and anxiety, the two leading mental health conditions. Language-based mental health assessments (LBMHAs) had substantially higher levels of reliability across space and time than available survey measures. This work presents reliable assessments of depression and anxiety down to the county-weeks level. Where surveys were available, we found moderate to strong associations between the LBMHAs and survey scores for multiple levels of granularity, from the national level down to weekly county measurements (fixed effects β = 0.34 to 1.82; p

An increasing number of climate model simulations is becoming available for the transition from the Last Glacial Maximum to the Holocene. Assessing the simulations' reliability requires benchmarking against environmental proxy records. To date, no established method exists to compare these two data sources in space and time over a period with changing background conditions. Here, we develop a new algorithm to rank simulations according to their deviation from reconstructed magnitudes and temporal patterns of orbital and millennial-scale temperature variations. The use of proxy forward modeling allows for accounting for non-climatic processes that affect the temperature reconstructions. It further avoids the need to reconstruct gridded fields or regional mean temperature time series from sparse and uncertain proxy data. First, we test the reliability and robustness of our algorithm in idealized experiments with prescribed deglacial temperature histories. We quantify the influence of limited temporal resolution, chronological uncertainties, and non-climatic processes by constructing noisy pseudo-proxies. While model–data comparison results become less reliable with increasing uncertainties, we find that the algorithm discriminates well between simulations under realistic non-climatic noise levels. To obtain reliable and robust rankings, we advise spatial averaging of the results for individual proxy records. Second, we demonstrate our method by quantifying the deviations between an ensemble of transient deglacial simulations and a global compilation of sea surface temperature reconstructions. The ranking of the simulations differs substantially between the considered regions and timescales, which suggests that optimizing for agreement with the temporal patterns of a small set of proxies might be insufficient for capturing the spatial structure of the deglacial temperature variability. We attribute the diversity in the rankings to more regionally confined temperature variations in reconstructions than in simulations, which could be the result of uncertainties in boundary conditions, shortcomings in models, or regionally varying characteristics of reconstructions such as recording seasons and depths. Future work towards disentangling these potential reasons can leverage the flexible design of our algorithm and its demonstrated ability to identify varying levels of model–data agreement. Additionally, the algorithm can be applied to variables like oxygen isotopes and climate transitions such as the penultimate deglaciation and the last glacial inception.

Abstract The present paper develops a new framework to predict the mean flow through an array of cylinders in which the flow around the array (array‐scale) and the flow around individual cylinders (element‐scale) are modeled separately using actuator disc theory and empirical drag models respectively, and then coupled through the net drag force. Applying this framework only requires knowledge of the array geometry and incident flow. The framework is validated using high‐fidelity direct numerical simulations for arrays of between 7 and 109 cylinders having different arrangements (staggered, concentric, random) and bounding shapes (circular, square) in both two‐ and three‐dimensional flows. In general, the framework outperforms existing models which require calibration and are only valid for part of the practical parameter space. The demonstrated scale separation suggests different combinations of element‐scale and array‐scale models/theories may be used for other arrangements of bluff bodies.

Abstract Two Earth system models are analyzed to gain insight into the processes that govern projected changes in the South Asian monsoon. Warmer present‐day base state tropical SSTs contribute to coupled processes that produce greater future tropical Pacific warming in CESM2 with less of an increase in season‐mean monsoon precipitation compared to E3SMv2. This is attributed to changes in the large‐scale east‐west atmospheric Walker circulation, with relatively larger increases in precipitation and upper‐level divergence over the tropical Pacific and increases in upper‐level convergence over South Asia in CESM2. The stronger El Niño‐like response in CESM2, which increases Pacific precipitation and upper‐level divergence farther to the east, and larger future ENSO amplitude in E3SMv2, produce a greater relative increase in future monsoon‐ENSO connections in E3SMv2 compared to CESM2. This analysis indicates that the key processes that affect future monsoon‐ENSO connections are ENSO amplitude and size of the future tropical Pacific El Niño‐like response.

Red-bellied pacu (Piaractus brachypomus) is a popular exotic fish cultured globally for ornamental and human consumption purposes. In Indonesia, this fish is mainly cultured in Java, including to supply seed to other regions for the ornamental fish trade or aquaculture. Although this exotic fish in widely cultured, information on parasitic infections is negligible. Red-bellied pacus were purchased from a fish farmer and examined for monogenean parasite infection. Monogenean parasites were identified based on morphological and molecular characteristics. Histopathological examinations were performed on gills from heavily infected fish. DNA was extracted from an individual monoge-nean; 18S rRNA PCR product was sequenced and a phylogenetic tree constructed. Morphological and molecular traits identified the monogenean as Mymarothecium viatorum, the first report of the neotropical M. viatorum in this region. The parasite was most likely introduced to Indonesia through the importation of ornamental fish. This parasite had a prevalence of 79% and a mean intensity of 7.1. There was no significant correlation between the parasite abundance and the condition factor of the fish. M. viatorum was more abundant on smaller fish than on larger fish. Gill histopathology showed oedema, severe haemorrhage, and infiltration of inflammatory cells. The occurrence of these parasites could pose a serious threat to the development of red-bellied pacu culture.

Predicting future conservation needs can help inform conservation management but is subject to uncertainty. We measured deforestation rates during 2015–2017 for 114 protected areas in Madagascar, linked deforestation to the status of protection according to IUCN categories I–VI, used recent deforestation rates to extrapolate forest cover over 2017–2050 and linked the size of forest blocks to the projected persistence of lemur subpopulations. In the six IUCN categories for protected areas in Madagascar the median size of forest blocks is 9–37 km2 and median annual deforestation rates range from 0.02% in the single IUCN category III site to 0.19% in category II and 1.95% in category VI sites. In 2017, 40% of all forest blocks within protected areas were < 10 km2, and this is projected to increase to 45% in 2050. Apart from these small forest fragments, the modal site of forest blocks was 160–320 km2 in 2017, and this is projected to decrease to 80–160 km2 in 2050. The range of > 50% of all lemur species exclusively contains forest blocks of < 10 km2. The modal size of forest blocks > 10 km2 is predicted to remain at 120 km2 until 2050. Although uncertainty remains, these analyses provide hope that forest blocks within the protected areas of Madagascar will remain large enough to maintain lemur subpopulations for most species until 2050. This should allow sufficient time for the implementation of effective conservation measures.

This study investigates the cooling features of sweeping jets with phase changes, providing insights into how parameters affect heat transfer. The study aims to improve heat transfer by investigating the cooling effects of a sweeping jet impinging on a concave wall. The Eulerian-Lagrangian particle tracking method was used to examine the impact of Reynolds number, droplet diameter, mist capacity, and impingement distance on heat transfer properties during the sweeping jet impingement cooling. Increasing the Reynolds number from 20,000 to 35,200 results in a 7.1% and 3.3% decrease in average temperature at the axial centerline of the impingement wall, attributed to the cooling effect from droplet phase change. Decreasing droplet diameter from 20 µm to 10 µm reduces temperature amplitude by 11K. At 5% and 7.5% mist ratios, the cooling performance is similar to that of dry air. However, a mist injection of 10% significantly amplifies the cooling effect by 18.8%, providing a more efficient cooling experience. This investigation provides essential perspectives on impingement cooling, offering insights into the impact of various parameters on heat transfer enhancement.

Temporal Lobe Epilepsy (TLE) is a prevalent neurological disorder affecting millions worldwide, including a significant proportion in India. Precise diagnosis and effective treatment planning are critical for TLE patients, necessitating advanced neuroimaging techniques. Magnetoencephalography (MEG) offers a non-invasive method for evaluating brain function, providing detailed insights into TLE. In this study, we aim to evaluate the potential of functional connectivity metrics derived from MEG data at the source level for distinguishing TLE patients from healthy controls (HCs). We analyse the data across various brain frequency bands, including alpha, beta, delta, gamma, theta, broadband, and high-frequency oscillations (HFO), using amplitude envelope correlation and graph theory metrics. We employ machine learning algorithms to classify TLE and HC groups based on these metrics. Chi2 feature importance analysis reveals significant importance of connectivity metrics such as local efficiency, mean clustering coefficient, mean shortest path length, small worldness score, weighted degree centrality, binary degree centrality, global efficiency across frequency bands, particularly in theta, alpha, beta, broadband and HFO bands. Various machine learning models demonstrate high classification performance, with accuracies reaching up to 100% in particular frequency bands in agreement with the Chi2 feature importance analysis. Overall, the Subspace Discriminant Ensemble model, especially in the Theta and Alpha frequency bands, show exceptional potential for classifying TLE and HCs. Overall, this study underscores the potential of MEG and functional connectivity analysis using specific frequency bands and machine learning models for classifying TLE and HC with high accuracy, which may contribute to improved diagnosis and management of epilepsy.

This paper presents the first investigation of the solubility of iron in mineral dust aerosols collected at the Henties Bay Aerosol Observatory (HBAO), in Namibia, from April to December 2017. During the study period, 10 intense dust events occurred. Elemental iron reached peak concentrations as high as 1.5 µg m−3, significantly higher than background levels. These events are attributed to wind erosion of natural soils from the surrounding gravel plains of the Namib desert. The composition of the sampled dust is found to be overall similar to that of aerosols from northern Africa but is characterized by persistent and high concentrations of fluorine which are attributed to local fugitive dust. The fractional solubility of Fe (%SFe) for both the identified dust episodes and background conditions ranged between 1.3 % and 20 % and averaged at 7.9 % (±4.1 %) and 6.8 (±3.3 %), respectively. Even under background conditions, the %SFe was correlated with that of Al and Si. The solubility was lower between June and August and increased from September onwards during the austral spring. The relation to measured concentrations of particulate MSA (methane sulfonic acid), solar irradiance, and wind speed suggests a possible two-way interaction whereby marine biogenic emissions from the coastal Benguela upwelling to the atmosphere would increase the solubility of iron-bearing dust according to the photo-reduction processes. This first investigation points to the western coast of southern Africa as a complex environment with multiple processes and active exchanges between the atmosphere and the Atlantic Ocean, requiring further research.

Detecting insider threats is the foremost challenge in many institutions because of the abnormal behavior of legitimate access and network crawling in the Internet of Things (IoT) environment. The insider activities of the institution’s data are submerged in many regular activities, leading to a data imbalance problem. Existing insider threat detection techniques often fail to address the data imbalance problem in the insider threat data of IoT-enabled institutions, thereby causing deterioration in detection performance. Thus, this paper presents a novel Enhanced Bidirectional Generative Adversarial Network (EBiGAN) for adversarial sample generation and a Deep Neural Network (DNN) with the Probability of Improvement (PI) acquisition function of Bayesian optimization to detect insiders in an IoT enabled institutions. The proposed model involves three modules: (1) Improved PCA for extracting user functionality samples and outlier estimators of k-means for grouping scenario-based user functional data. (2) Bidirectional GAN with an additional discriminator to ensure the quality of the generated samples (3) The PI acquisition function of Bayesian Optimization for tuning the hyperparameter to improve the performance of the DNN model for insider threat detection to secure IoT-enabled institutions. The performance of the Enhanced BiGAN and DNN-PI was evaluated using a benchmark institutional dataset. The experimental results show that the proposed model identifies the suspicious behavior of insiders with a high detection rate and minimal false alarm rate in an IoT infrastructure.

The significance of fiducial marker detection in neuroimaging cannot be overstated, as these markers serve as vital reference points for accurate spatial alignment during image registration. Our proposed model addresses challenges in consistent marker placement and variability during MRI scanning, ensuring reliable localization for subsequent analysis. This paper introduces an innovative approach to fiducial marker detection in T1-weighted MRI volumes, specifically targeting the Left Preauricular Point (LPA), Right Pre-auricular Point (RPA), and Nasion. The implementation employs a 3D Convolutional Neural Network (CNN) to achieve precise localization of these crucial anatomical landmarks. Operating on a high-performance system our algorithm demonstrated exceptional accuracy and sensitivity using MATLAB R2023a as the primary tool for development and evaluation. Rigorous experiments on a diverse dataset showcased the algorithm’s robust performance. For RPA detection, the model achieved 96.55% accuracy, emphasizing sensitivity (96.78% recall) and precision (96.35%). LPA detection demonstrated an impressive accuracy of 96.88%, with heightened sensitivity (96.95%) and precision (96.83%). The nasion detection process exhibited precise localization, with a Mean Square Error (MSE) of 0.3439 for 36 volume data. These results highlight the algorithm’s potential to enhance accuracy and efficiency in fiducial point detection for improved neuroimaging studies.

Trust is predictive of civic cooperation and economic growth. Recently, the U.S. public has demonstrated increased partisan division and a surveyed decline in trust in institutions. There is a need to quantify individual and community levels of trust unobtrusively and at scale. Using observations of language across more than 16,000 Facebook users, along with their self-reported generalized trust score, we develop and evaluate a language-based assessment of generalized trust. We then apply the assessment to more than 1.6 billion geotagged tweets collected between 2009 and 2015 and derive estimates of trust across 2,041 U.S. counties. We find generalized trust was associated with more affiliative words (love, we, and friends) and less angry words (hate and stupid) but only had a weak association with social words primarily driven by strong negative associations with general othering terms (“they” and “people”). At the county level, associations with the Centers for Disease Control and Prevention (CDC) and Gallup surveys suggest that people in high-trust counties were physically healthier and more satisfied with their community and their lives. Our study demonstrates that generalized trust levels can be estimated from language as a low-cost, unobtrusive method to monitor variations in trust in large populations.

Introduction The Acute Organic Change of Character (AOCC) is an organic mental disorder subtype in which perception, thought, mood and personality impairment predominate. It consists in a change in the individual’s general behaviour or attitude, which is shown to be closely associated with or caused by an underlying organic process, and which is rapidly resolved when the organic noxious agent is eliminated (Pintor et al. Journal of Psychiatry and Psychiatric Disorders 4 (2020): 354-358). Objectives To describe the importance of taking AOCC diagnosis into consideration and the role of liaison psychiatrists in AOCC management by presenting two AOCC cases admitted to the Hospital Clinic of Barcelona. Methods We retrospectively reviewed two AOCC cases in patients followed by our hospital’s liaison psychiatry unit during the summer of 2023. We also searched for previous case reports of AOCC using a PubMed query. Results Case 1: A 50-year-old male who suffered a polytrauma with diffuse axonal injury (DAI). His relatives and the referring medical team observed a change in his behaviour consisting in irritability, suspicion, hostility and impatience. No cognitive impairment nor fluctuation in the described symptoms were observed. At the time of discharge character changes were still present due to DAI slow and unpredictable clinical course. Symptomatic treatment with risperidone 6mg/day and quetiapine 100mg/day was administered achieving a satisfactory clinical response. Case 2: A 47-year-old woman with type 2 diabetes who suffered an infectious cellulitis that spread causing sepsis. The patient began to appear disruptive with verbose and tangential speech during her admission. No cognitive impairment nor fluctuation in the described symptoms were observed. Symptomatic treatment with risperidone 10mg/day and olanzapine 5mg/day was administered achieving a satisfactory clinical response. At the time of discharge character changes described before were almost resolved. Conclusions The clinical presentation of both cases suggested organic mental disorders in which a change in general behaviour predominates. Liaison psychiatrists play a key role in AOCC management by recognizing the clinical pattern, helping if needed with psychopharmacological treatment and ensuring a good understanding of the disorder both by the referring medical team and the patient’s relatives. To our knowledge, it would be of great importance to achieve a better understanding of this clinical condition which to date we consider to be underdiagnosed. Disclosure of Interest None Declared

Atypical choroid plexus papilloma is a rare World Health Organization grade 2 intraventricular tumor arising from the epithelium of the plexus choroid with intermediate clinical-pathological features between the benign choroid plexus papilloma and the malignant choroid plexus carcinoma. The main criteria for differentiation are histopathologic, with difficulties in distinguishing it from choroid plexus papilloma based on imaging features. We report the case of a 4-year-old female presenting with headaches and altered mental status. Brain magnetic resonance imaging revealed a right lateral ventricular mass with some atypical characteristics, which were confirmed on pathological examination as an atypical choroid plexus papilloma.

Abstract Based on the available literature and the studies presented by the previous speakers, Dr. Andreu will provide a summary of predictive and protective factors associated with mood relapse or recurrence in bipolar disorder, with a special focus on the distinction between modifiable and non-modifiable factors and on the identification of specific phenotypes at higher risk of relapse. The speaker will also mention the role of psychotherapeutic and pharmacological treatments, and will summarize the available evidence regarding lithium response. Disclosure of Interest None Declared

Introduction Psychiatric comorbidity is common in cancer patients, emphasizing the need for comprehensive care. While depressive symptoms in pancreatic cancer (PC) have been studied, there is limited attention given to manic symptoms. This case report aims to contribute to the knowledge of PC psychiatric comorbidities by describing a case of a 61-year-old patient with stage IV PC, with no personal or family psychiatric history, who presented a sudden onset manic episode. Objectives Our goal is to contribute to the growing knowledge of psychiatric comorbidities of PC focusing on manic symptoms by describing the case of a patient with stage IV PC without previous psychiatric history who presented a sudden onset of a manic episode. Methods We describe the mentioned clinical case. We also searched for previous case reports of maniac episodes in pancreatic cancer using a PubMed query. Results The patient, a 61-year-old male with stage IV PC, presented at the Emergency Room with abrupt behavioural changes suggestive of a manic episode of two weeks of evolution. The patient had been undergoing chemotherapy and short 3-day cycles of corticosteroids for the past 9 months but had been off this treatment for 20 days when the episode began. Acute organic causes were ruled out. The patient was admitted to the psychiatric unit, where organic screening was expanded and treatment with antipsychotics and a mood stabilizer was initiated with subsequent remission of symptoms after two weeks. This article describes the case of a man with a PC diagnosis who had no prior psychiatric history and was admitted to the inpatient psychiatry unit due to a manic episode involving high-risk behavioral disturbances and megalomaniac psychotic symptoms. Several factors may have contributed to the onset of these symptoms, including corticosteroid use after chemotherapy and certain chemotherapy agents. However, due to temporal factors, these factors do not fully explain the episode. The exact biological mechanisms behind the manic symptoms remain unknown, but hypotheses include gene-environment interactions in bipolar disorder and immunodysregulation related to the production of inflammatory cytokines. We found in the literature four cases that have reported new-onset mania as an initial symptom of PC, but the causal relationship is unclear. Conclusions Notably, this case differs from others due to the rapid remission of symptoms and the use of lithium therapy. While the underlying mechanisms are still unclear, this case contributes to understanding this rare complication of PC and may help in developing consensus on clinical management. Future research will further explore the pathophysiology of psychiatric symptoms in PC and appropriate therapeutic approaches. This case shows a manic episode as a rare psychiatric complication in PC. In the literature reviewed, four other similar cases have been observed. Disclosure of Interest None Declared

Introduction Chemsex refers to the use of drugs, typically stimulants and/or psychoactive substances, in a sexual context, often in the context of casual or group sex encounters. Currently, the practice of chemsex focuses on men who have sex with men (MSM). On the other hand, Pre-exposure prophylaxis (PrEP) is a biomedical method that has proven effective in preventing HIV transmission, particularly among individuals at a heightened risk, including those who engage in chemsex. MSM account for two thirds of new HIV cases in the US. It is estimated that in 70% of cases seroconversion occurs through “condomless anal sex” (CAS). According to the CDC, one in six MSM will be infected with HIV during their lifetime. The consumption of methamphetamine (MA) has been identified as the main driver of the practice of CAS, alteration of rectal immunological function and faster seroconversion. One in three new HIV infections have been associated with MA consumption. (Grov C et al. JAIDS 2020; 85 272-279). Objectives The primary goal of this study is to describe the prevalence of chemsex engagement among PrEP users, delineate user characteristics and requirements, gain deeper insights into this phenomenon within the Barcelona region, and formulate customized strategies accordingly. Methods This study conducts a literature review to explore the current correlation between engaging in chemsex and the utilization of PrEP. We identified research articles published between January 2020 and December 2022, that discussed the utilization of chemsex drugs prior to or during sexual activities. The findings were synthesised using a narrative approach and conceptualised using a behavioural analysis framework. Results According to a recent cross-sectional study performed at Hospital Clínic de Barcelona, SUD among patients who are being followed-up in the outpatient clinic of PrEP was higher (89%) compared with other European regions such as England (38.5%) or Amsterdam (41%). Moreover, according to data collected in the EMIS 2017 survey, Barcelona is the city with the highest prevalence of chemsex in Spain. (De La Mora L et al. AIDS Beh. 2022; 26: 4055-4062). Conclusions The frequency of chemsex practice among individuals using PrEP in Barcelona surpasses what has been observed in other groups. Nearly 25% of the participants express worries and a requirement for assistance regarding the management of drug use, matters associated with their sexuality, and sexually transmitted infections (STIs). MSM who suffers from substance use disorder may experience difficulty achieving effective daily oral PrEP adherence prevention levels that may serve as early indicators of increased risk of disengagement from PrEP care and discontinuation the PrEP. These results highlight the importance of adopting aninterdisciplinary approach that incorporates education about substances and the implementation of risk mitigation strategies within the context of riskier sexual behaviors. Disclosure of Interest None Declared

Introduction With the rapid changes of attitude, investigation and legislation around cannabis and its subproducts in the Western world, there is a need to profoundly examine the consequences of its use in the general population and, specifically, in people affected by mental disorders. There is a clear relationship between cannabis use and psychosis, but there is also growing evidence of its relationship with manic episodes (Sideli et al, 2019). A systematic review published by the CANMAT Task Force in 2022 examined again the relationship between cannabis use and bipolar disorder (BD), establishing association with worsened course and functioning of BD in frequent users (Tourjman et al., 2023). On the other hand, some recent papers have highlighted the role of the endocannabinoid system (ECS) in BD, suggesting even possible beneficial effects, mainly through the CB2 receptor (Arjmand et al, 2019). Objectives To describe the impact of cannabis in the psychiatric readmission in BD and to approach the differences in course in cannabis users with regards to non-users. Methods We conducted a prospective cohort study including the patients admitted to our acute psychiatric unit with the diagnosis of manic or mixed episode during the period between 2015 and 2019 (including patients with one of both final diagnosis: BD or schizoaffective disorder). We established a follow-up of 3 years from the date of admission in which hospital readmissions are examined. Results The study, which included 309 patients, concluded that cannabis users were admitted and had the first episode at a younger age (p=0.005), a higher percentage of them did not have a previous diagnosis (p=0.026) nor a previous history of mental health issues (p=0.019) and it was more likely to be their first admission (p=0.011) and to suffer psychotic symptoms (p=0.002). As of treatment, the results were statistically significant regarding the fact that a lower proportion of patients had received previous psychiatric treatment (p=0.004) and previous electroconvulsive therapy (p=0.003). There was a higher chance of them being non-adherent with medication (p

Introduction In recent years, interest has grown in understanding the particularities of addiction in women. One of these singularities, with an important impact on public health, is pregnancy. Substance use during pregnancy has increased in recent decades. Given that addiction is mainly a chronic disease of the brain circuits of reward, motivation and memory, an event such as pregnancy does not exempt people who suffer from substance use disorder (SUD) from the difficulties of achieving abstinence. Moreover, as addiction often involves cycles of relapse and remission, pregnant women can also suffer a relapse even if they previously had achieved abstinence. On the other hand, given that addiction is a disease with a significant social component, we can find patients in precarious economic and social situations who suffer unwanted and therefore unplanned pregnancies. Objectives To describe the case of a pregnant woman with SUD for multiple drugs who is admitted to the inpatient ward of the Hospital Clínic of Barcelona for detoxification. Also to reflect, taking in to account gender perspective, on the particularities of substance use in women on childbearing age. Methods We present the case of a 25-year-old woman, six weeks pregnant and homeless, who was admitted to the inpatient ward for presumed psychotic symptoms. Even if the patient had a history of intravenous heroin, cocaine, and methamphetamine use, during the admission she only admitted current alcohol consumption (3 UBE/day). During admission, the patient decided to undergo a legal voluntary abortion. Regarding this case, we did a literature review on the consequences of different substances use to pregnancy (to both the fetus and the mother’s health). Likewise, we reflected on interventions that could be carried out in community mental health facilities to detect cases like the one exposed and provide help. Results Poor obstetric outcomes are six times higher in patients who use substances. Newborns can have withdrawal syndrome, spontaneous abortion, pre-term birth, fetal malformations and fetal growth restriction. More than 50% of women of reproductive age use drugs, mainly alcohol. Routine screening and education of women of reproductive age is the best way to reduce substance use during pregnancy. Conclusions We consider it essential for a better management of these patients to remember that the evidence suggests that the decisions that addicts make are mainly driven by a pathology, not by a moral failure. However, pregnancy can be a moment of greater motivation to stop using drugs, given that it is a high risk situation for the mother and the fetus. Disclosure of Interest None Declared

Introduction Bipolar disorder (BD) is a chronic and recurrent disease characterized by acute mood episodes alternated with periods of euthymia. The available literature postulates that a biphasic dysregulation of mitochondrial bioenergetics might be observed in BD. Objectives We aimed to explore differences in in vivo mitochondrial respiration (1) intra-individually: longitudinally within patients during an acute mood episode of BD and after clinical remission, and (2) inter-individually: between patients with BD on depressive or manic episodes and healthy controls (HC). Methods Patients admitted to our acute psychiatric ward with a manic episode or bipolar depression were recruited. Different mitochondrial oxygen consumption rates (OCRs) were assessed during the acute episode (T0) and after clinical remission (T1) in one million of peripheral blood mononuclear cells (PBMC): Routine, Leak, ETC and Rox. They were measured as picomoles of oxygen per million cells (pmol O2/million). This experiment was also conducted in HC. High-resolution respirometry was performed at 37°C by polarographic oxygen sensors in a two-chamber Oxygraph-2k system. Manic and depressive symptoms were assessed using standardized psychometric scales. Oxygen consumption capacity was compared (1) intra-individually, during acute episodes and after clinical remission, and (2) inter-individually, during acute manic and depressive episodes, and in HC. Statistical analyses were performed with SPSS, GraphPad and R Statistics. Results 20 patients with BD (15 manic, 5 depressed) and 10 HC were included. A significant increase in the maximal oxygen consumption capacity (ETC) was observed in clinical remission (27.4 ± 17.4) compared to the acute episodes (21.1 ± 11.7, p = 0.001), which remained significant after subtracting Rox from the other rates (p = 0.001). At T1, patients admitted with a manic episode tended to show higher mean ETC (31.2 ± 18.7) compared with T0 (24.1 ± 12.0, p = 0.074); the tendency persisted after Rox subtraction (p = 0.076). Patients admitted with a depressive episode also showed higher ETC means in T1 (16.3 ± 3.8) compared to T0 (12.1 ± 3.4), but there were not significant differences (p = 0.231). When HC, manic and depressive patients at T0 were compared between them, significant differences were observed in ETC (H =8.5; p =0.014) and Rox (H =13.8; p = 0.001). After Rox deduction, differences in ETC remained (H =11.7; p = 0.003). Individuals with bipolar depression showed lower ETC rates (12.1 ± 3.4) than those with a manic episode (24.1 ± 12.0; t = -3.5, p = 0.003), which was also found after Rox deduction (p = 0.001). Conclusions In both manic and depressive episodes in BD, mitochondrial respiration might be reduced and increase after clinical remission. Further studies with larger samples will allow to confirm these results and also to identify potential mitochondrial state-dependent biomarkers. Disclosure of Interest A. Giménez-Palomo Grant / Research support from: AGP is supported by a Rio Hortega 2021 grant (CM21/00094) from the Spanish Ministry of Health financed by ISCIII and cofinanced by Fondo Social Europeo Plus (FSE+)., M. Guitart-Mampel: None Declared, A. Meseguer: None Declared, M. Valentí: None Declared, L. Bracco: None Declared, H. Andreu: None Declared, E. Vieta: None Declared, G. Garrabou: None Declared, I. Pacchiarotti: None Declared

Introduction In Spain and other European countries, patients coming to the emergency room (ER) are usually classified as “organic” or “psychiatric” on arrival, but this may be complicated when psychiatric history is present as the focus can be misplaced (Leeman. IJPM 1975;6(4):544-40; Alam et al. Psychiatr. Clin. North Am. 2017;40(3):425–33). Objectives To describe three cases seen in the psychiatric emergency room (PER) in which triage errors occurred and to review whether it is widespread for psychiatric patients with organic pathology or in need of medical care to be wrongly triaged. Methods We retrospectively reviewed three cases seen in the PER of Hospital Clínic in July 2023 in which triage errors happened. Triage error was considered when patients triaged directly to the PER presented symptoms that either needed medical treatment or required medical clearance before being considered purely psychiatric. Results Case 1: A 27-year-old woman with history of depressive syndrome was triaged for a speech disturbance that had occurred fifteen minutes after intercourse. After being evaluated, she was referred to neurology where she was diagnosed with an acute ischaemic stroke in left middle cerebral artery territory, requiring thrombectomy and posterior admission to neurology. Case 2: A 50-year-old man with history of alcohol use disorder was brought to the PER after saying that “he was seeing people doing magic” at home. When evaluated, significant distal tremor, tachycardia and hypertension were observed, being compatible with withdrawal symptoms, so he was transferred to the ER. There he was monitored and treated, finally requiring admission to internal medicine due to persistent symptoms. Case 3: A 26-year-old man with history of substance use disorder was triaged for loss of consciousness and “spasms”. After evaluation, he was transferred to the ER, where organic screening was carried out, being oriented as a probable tonic-clonic seizure and discharged with outpatient follow-up. Conclusions The cases presented are instances in which somatic diseases in patients pre-labelled with psychiatric histories were wrongly assumed to be recurrences of their psychiatric disorders. In all cases, they needed to be re-examined by the corresponding medical specialty and required diagnostic tests, in two cases hospital admission was needed. Emergency physicians and emergency psychiatrists often disagree on how to medically clear patients (Alam et al. Psychiatr. Clin. North Am. 2017;40(3):425–33; Janiak et al. JEM. 2012;43(5):866–70), some authors have even proposed protocols for doing this in a more systematic way (Shah et al. JEM. 2012;43(5):871–5). To avoid a delay in diagnosis and treatment and the consequences that may result from it, establishing guidelines for proper triage of patients with psychiatric history should be considered. Disclosure of Interest None Declared

Wind loads of high-rise buildings are a key parameter in architectural design. The magnitude and distribution characteristics of wind loads are of great importance for the safety and economy of structural design. The wind loads of high-rise buildings are quite different from those of monomer buildings. The wind-induced interference effect could significantly increase the local wind pressure of buildings, causing potential safety hazards for the main structure and enclosure structure. For the three common high-rise buildings, we adopted the wind tunnel test method to measure the surface pressure of each building. The corresponding Re number was 8.2×106. This paper studied the shape coefficients, fluctuating wind pressure coefficients and base bending moment coefficient of each building with different wind direction angles and different spacing ratios, and the maximum value of each parameter and the corresponding working condition were statistically analyzed. The results showed that, under any wind direction angle, the fluctuating wind pressure coefficients on all sides of the building were affected by the spacing ratio, and the fluctuation range was large. When the wind angle was 180º, the fluctuating wind pressure coefficients on the sides of Building 1 were most affected by the slope ratio. At this wind angle, the maximum value was 0.43 at a slope ratio of 5.0, which was 65% different from the minimum. Partition shape coefficients of some sides and top surfaces changed significantly with the spacing ratio. When the spacing ratio was 5.0, the base bending moment coefficients in the downwind and crosswind directions reached their maximum values, and the wind direction angles where the maximum values of the base bending moment coefficients in the downwind direction were 40º and 50º, respectively, and the wind direction angle where the maximum value of the base bending moment coefficients in the crosswind direction was 10º. Due to the influence of the wind angle and the building spacing ratio, the wind loads on the facades of the pyramidal group of buildings varied greatly, and the wind-induced interference effect was evident. The wind load between the building facades in the three buildings was different, and the wind disturbance effect was evident. Therefore, the most unfavorable stress state and interference state of the structure should be comprehensively considered in the wind resistance design of the three buildings. The building spacing ratio should preferably be set to 3.0, and wind angles of 10º, 40º, and 50º should be avoided whenever possible.

In the title compound, C38H28O6, the dihedral angles between the naphthalene ring system and its pendant benzyloxy rings A and B are 88.05 (7) and 80.84 (7)°, respectively. The dihedral angles between the A and B rings and their attached phenyl rings are 49.15 (8) and 80.78 (8)°, respectively. In the extended structure, the molecules are linked by weak C—H...O and C—H...π hydrogen bonds, and π–π stacking interactions, which variously generate C(11) chains and R22(12) loops as part of a three-dimensional network. The Hirshfeld surface [fingerprint contributions = H...H (42.3%), C...H/H...C (40.3%) and O...H/H...O (15.7%)] and intermolecular interaction energies are reported, with dispersion (Edis = −428.6 kJ mol−1) being the major contributor.

The radiative effects of clouds make a large contribution to the Earth's energy balance, and changes in clouds constitute the dominant source of uncertainty in the global warming response to carbon dioxide forcing. To characterize and constrain this uncertainty, cloud-controlling factor (CCF) analyses have been suggested that estimate sensitivities of clouds to large-scale environmental changes, typically in cloud-regime-specific multiple linear regression frameworks. Here, local sensitivities of cloud radiative effects to a large number of controlling factors are estimated in a regime-independent framework from 20 years (2001–2020) of near-global (60∘ N–60∘ S) satellite observations and reanalysis data using statistical learning. A regularized linear regression (ridge regression) is shown to skillfully predict anomalies of shortwave (R2=0.63) and longwave cloud radiative effects (CREs) (R2=0.72) in independent test data on the basis of 28 CCFs, including aerosol proxies. The sensitivity of CREs to selected CCFs is quantified and analyzed. CRE sensitivities to sea surface temperature and estimated inversion strength are particularly pronounced in low-cloud regions and generally in agreement with previous studies. The analysis of CRE sensitivities to three-dimensional wind field anomalies reflects the fact that CREs in tropical ascent regions are mainly driven by variability of large-scale vertical velocity in the upper troposphere. In the subtropics, CRE is sensitive to free-tropospheric zonal and meridional wind anomalies, which are likely to encapsulate information on synoptic variability that influences subtropical cloud systems by modifying wind shear and thus turbulence and dry-air entrainment in stratocumulus clouds, as well as variability related to midlatitude cyclones. Different proxies for aerosols are analyzed as CCFs, with satellite-derived aerosol proxies showing a larger CRE sensitivity than a proxy from an aerosol reanalysis, likely pointing to satellite aerosol retrieval biases close to clouds, leading to overestimated aerosol sensitivities. Sensitivities of shortwave CREs to all aerosol proxies indicate a pronounced cooling effect from aerosols in stratocumulus regions that is counteracted to a varying degree by a longwave warming effect. The analysis may guide the selection of CCFs in future sensitivity analyses aimed at constraining cloud feedback and climate forcings from aerosol–cloud interactions using data from both observations and global climate models.

Abstract The recent decade has seen an explosion in the e-commerce industry with the support of modern technologies (e.g., artificial intelligence) to maximize conversion rates. Many recommendation systems, therefore, have been developed to predict the customer behaviors to take appropriate actions. Customization of promotions or items for distinct groups of online customers considerably contributes to enhancing the sales. The purchasing records are essential information that can be used to investigate the customer behaviors. These kinds of information, however, are mainly expressed in tubular forms. Several computational models were developed using conventional machine learning algorithms to deal with that data type. However, these approaches struggle with large-volume and high-dimensional data, feature engineering, and high computational cost. In our study, we propose a deep learning model based on the Feature Tokenizer Transformer architecture to predict the customer purchasing intention. This novel architecture is a simplified adaptive version of the Transformer tabular data. The results demonstrated that our model showed better performance compared to conventional machine learning models. Furthermore, the model’s stability was also confirmed by the results of multiple repeated experiments.

Abstract Opioid poisoning mortality is a substantial public health crisis in the United States, with opioids involved in approximately 75% of the nearly 1 million drug related deaths since 1999. Research suggests that the epidemic is driven by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and isolation. Hindering this research is a lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolutions. To address this issue, we use a multi-modal data set consisting of natural language from Twitter, psychometric self-reports of depression and well-being, and traditional area-based measures of socio-demographics and health-related risk factors. Unlike previous work using social media data, we do not rely on opioid or substance related keywords to track community poisonings. Instead, we leverage a large, open vocabulary of thousands of words in order to fully characterize communities suffering from opioid poisoning, using a sample of 1.5 billion tweets from 6 million U.S. county mapped Twitter users. Results show that Twitter language predicted opioid poisoning mortality better than factors relating to socio-demographics, access to healthcare, physical pain, and psychological well-being. Additionally, risk factors revealed by the Twitter language analysis included negative emotions, discussions of long work hours, and boredom, whereas protective factors included resilience, travel/leisure, and positive emotions, dovetailing with results from the psychometric self-report data. The results show that natural language from public social media can be used as a surveillance tool for both predicting community opioid poisonings and understanding the dynamic social and psychological nature of the epidemic.

Researchers are increasingly acknowledging that psychopathological conditions usually grouped together under the generic label “depression” are highly diverse. However, no differential therapeutic approach currently exists that is sensitive to the varieties of depression afflicting young people. In fact, the discussion is missing something much more fundamental: a specification of the types of adolescent depression. Recent research that has aimed to classify different kinds of depression has mainly studied adult populations and predominantly used technically complicated measurements of biological markers. The neglect of the potential particularities of dysphoric disorders affecting youths is unfortunate, and the exclusive focus on biological parameters unnecessarily restrictive. Moreover, this one-sidedness obfuscates more directly available sources of clinically relevant data that could orient conceptualization efforts in child and adolescent psychiatry. Particularly, clues for discriminating different types of adolescent depression may be obtained by analyzing personally articulated accounts of how affected young people experience changes in their relation to the world and to themselves. Thus, here we present and discuss the findings of a study that explored the possibility of specifying types of adolescent depression in a phenomenological way. The study investigated the association between these types and the vicissitudes of personality development. In accounts given by youths diagnosed with depression during semi-structured interviews, we identified themes and examined their phenomenological centrality. Specifically, our qualitative analyses aimed to determine the relative importance of certain themes with respect to the overall intelligibility of the described changes to the relational space. Based on the findings of these analyses, we differentiate three specifiers of adolescent depression and suggest an association between particular types of experiences and the trajectory of affected adolescents’ personality development. To our knowledge, this is the first phenomenologically grounded specification of types of adolescent depression with potential therapeutic significance. Thus, based on this contribution, we propose that modes of scientific exploration that are close to phenomenological philosophy—which have been ignored in the context of developmental psychopathology—could offer a foundation to theories developed in the field of child and adolescent psychiatry.

The hard X-ray emission from magnetars and other isolated neutron stars remains under-explored. An instrument with higher sensitivity to hard X-rays is critical to understanding the physics of neutron star magnetospheres and also the relationship between magnetars and Fast Radio Bursts (FRBs). High sensitivity to hard X-rays is required to determine the number of magnetars with hard X-ray tails, and to track transient non-thermal emission from these sources for years post-outburst. This sensitivity would also enable previously impossible studies of the faint non-thermal emission from middle-aged rotation-powered pulsars (RPPs), and detailed phase-resolved spectroscopic studies of younger, bright RPPs. The High Energy X-ray Probe (HEX-P) is a probe-class mission concept that will combine high spatial resolution X-ray imaging (

Introduction: Accurate risk assessment of bone marrow failure syndrome (BMFS) is crucial for early diagnosis and intervention. Methods: We used complete blood count (CBC) data to develop a predictive model for BMFS. Retrospective CBC data were collected from Seoul National University Hospital and Seoul St. Mary's Hospital of the Catholic Medical Center in South Korea. We developed binary classifiers for aplastic anaemia (AA) and myelodysplastic syndrome (MDS) and generated a BMFS classifier to determine the maximum probability. Classifiers were developed using multiple feature sets consisting of 13, 17, 25, or 28 CBC features to ensure applicability to various CBC testing settings. Performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results: XGBoost achieved the best AUROCs, 0·953–0·961 for the AA classifier and 0·910–0·935 for the MDS classifier, across multiple CBC feature sets. The BMFS classifier, combining the AA and MDS classifiers, demonstrated an AUROC of 0·915–0·936. When using cut-off probabilities to achieve a 95% sensitivity, the specificities ranged from 68% to 79%. External validation on an independent dataset yielded an AUROC of 0·932–0·942, a sensitivity of 93–96%, and a specificity of 65–82% at the aforementioned cut-offs. Conclusions: Our predictive model provides a practical guide for diagnosing BMFS based on basic demographics and CBC data available during the first clinical encounter. It provides a reliable risk assessment tool for primary physicians, facilitating a more effective triage, timely referrals, and improved patient care.

Abstract Targeting of location-specific aid for the U.S. opioid epidemic is difficult due to our inability to accurately predict changes in opioid mortality across heterogeneous communities. AI-based language analyses, having recently shown promise in cross-sectional (between-community) well-being assessments, may offer a way to more accurately longitudinally predict community-level overdose mortality. Here, we develop and evaluate, TrOP (Transformer for Opiod Prediction), a model for community-specific trend projection that uses community-specific social media language along with past opioid-related mortality data to predict future changes in opioid-related deaths. TOP builds on recent advances in sequence modeling, namely transformer networks, to use changes in yearly language on Twitter and past mortality to project the following year’s mortality rates by county. Trained over five years and evaluated over the next two years TrOP demonstrated state-of-the-art accuracy in predicting future county-specific opioid trends. A model built using linear auto-regression and traditional socioeconomic data gave 7% error (MAPE) or within 2.93 deaths per 100,000 people on average; our proposed architecture was able to forecast yearly death rates with less than half that error: 3% MAPE and within 1.15 per 100,000 people.

The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance and hence the climate response in the region, yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long data set of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k-means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019–2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds to the global radiation, surface air temperature, and timing of sea ice melting/freezing, which drive changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic haze signals in the PNSD and BC observations, which contributed to higher accumulation-mode PNC and BC mass concentrations in the central Arctic than at land-based observatories. We also show that the wintertime Arctic Oscillation (AO) phenomenon, which was reported to achieve a record-breaking positive phase during January–March 2020, explains the unusual timing and magnitude of Arctic haze across the Arctic region compared to longer-term observations. In summer, the aerosol PNCs of the nucleation and Aitken modes are enhanced; however, concentrations were notably lower in the central Arctic over the ice pack than at land-based sites further south. The analysis presented herein provides a current snapshot of Arctic aerosol processes in an environment that is characterized by rapid changes, which will be crucial for improving climate model predictions, understanding linkages between different environmental processes, and investigating the impacts of climate change in future Arctic aerosol studies.

High-resolution models have become widely available for the study of the ocean's small-scale processes. Although these models simulate more turbulent ocean dynamics and reduce uncertainties of parameterizations, they are not practical for long-term simulations, especially for climate studies. Besides scientific research, there are also growing needs from key applications for multi-resolution, flexible modeling capabilities. In this study we introduce the Ocean Modeling with Adaptive REsolution (OMARE), which is based on refactoring Nucleus for European Modelling of the Ocean (NEMO) with the parallel computing framework of JASMIN (J parallel Adaptive Structured Mesh applications INfrastructure). OMARE supports adaptive mesh refinement (AMR) for the simulation of the multi-scale ocean processes with improved computability. We construct an idealized, double-gyre test case, which simulates a western-boundary current system with seasonally changing atmospheric forcings. This paper (Part 1) focuses on the ocean physics simulated by OMARE at two refinement scenarios: (1) 0.5–0.1∘ static refinement and the transition from laminar to turbulent, eddy-rich ocean, and (2) the short-term 0.1–0.02∘ AMR experiments, which focus on submesoscale processes. Specifically, for the first scenario, we show that the ocean dynamics on the refined, 0.1∘ region is sensitive to the choice of refinement region within the low-resolution, 0.5∘ basin. Furthermore, for the refinement to 0.02∘, we adopt refinement criteria for AMR based on surface velocity and vorticity. Results show that temporally changing features at the ocean's mesoscale, as well as submesoscale process and its seasonality, are captured well through AMR. Related topics and future plans of OMARE, including the upscaling of small-scale processes with AMR, are further discussed for further oceanography studies and applications.