bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interplay of combat deployment harassment, testosterone concentrations and post-deployment suicide risk in male veterans
Authors: Sher Leo , Bierer Linda M. , Flory Janine , Makotkine Iouri , Yehuda Rachel 
Published: March 2024

AbstractObjective:Many combat veterans exhibit suicidal ideation and behaviour, but the relationships among experiences occurring during combat deployment and suicidality are still not fully understood. In this study, we tested the hypothesis that harassment during a combat deployment is associated with post-deployment suicidality and testosterone function.Methods:Male combat veterans who made post-deployment suicide attempts and demographically matched veterans without a history of suicide attempts were enrolled in the study. Demographic and clinical parameters of study participants were assessed and recorded. Study participants were interviewed by a trained clinician using the Mini-International Neuropsychiatric Interview (MINI), the Deployment Risk and Resilience Inventory (DRRI) – Relationships within unit scale, the Scale for Suicidal Ideation (SSI), and the Brown–Goodwin Aggression Scale. Free testosterone levels were assessed in morning blood samples.Results:DRRI harassment scores were higher and free testosterone levels were lower among suicide attempters in comparison with non-attempters. In the whole sample, DRRI harassment scores positively correlated with SSI scores and negatively correlated with free testosterone levels. Free testosterone levels negatively correlated with SSI scores. Aggression scale scores positively correlated with DRRI harassment scores among non-attempters but not among attempters.Conclusion:Our observations that harassment scores are associated with suicidality and testosterone levels, and suicidality is associated with testosterone levels may indicate that there is a link between deployment harassment, testosterone function and suicidality.
Inverse dynamics analysis of a 6-RR-RP-RR parallel manipulator with offset universal joints
Authors: Huang Huze , Han Hasiaoqier , Li Dawei , Xu Zhenbang , Wu Qingwen 
Published: March 2024

AbstractThis paper presents an algorithm for solving the inverse dynamics of a parallel manipulator (PM) with offset universal joints (RR–joints) via the Newton–Euler method. The PM with RR–joints increase the joint stiffness and enlarge the workspace but introduces additional joint parameters and constraint torques, rendering the dynamics more complex. Unlike existing studies on PMs with RR–joints, which emphasize the kinematics and joint performance, this paper studies the dynamical model. First, an iterative algorithm is established through a rigid body velocity transformation, which calculates the input parameters of the link velocity and acceleration. A linear system of equations in matrix form is then established for the entire PM through the Newton–Euler method. By using the generalized minimal residual method (GMRES) to solve the equation system, all the forces and torques on the joints can be obtained, from which the required actuation force can be derived. This method is validated through numerical simulations using the automatic dynamic analysis of multibody systems software. The proposed method is suitable for establishing the dynamic model of complex PMs with redundant or hybrid structures.
The Reflection Principle and the Ex-Ante Pareto Principle in Anna Mahtani’s Objects of Credence
Authors: Bovens Luc 
Published: March 2024

AbstractFirst, Mahtani argues that both in the game The Mug and in the Sleeping Beauty we should not defer to a trusted person under a particular designation if they do not self-identify under this designation. This invites a more complex Reflection Principle. I respond that there are more parsimonious ways to avoid the challenges posed to the Reflection Principle. Second, Mahtani argues that preferences create a hyperintensional context, which poses a challenge to the Ex-Ante Pareto Principle that can be averted by supervaluation. I respond that such an appeal to supervaluation would block randomization as a fair allocation device.
Epistemic Violence and Colonial Legacies in the Representation of Refugee Women: Contesting Narratives of Vulnerability and Victimhood
Authors: Benslama-Dabdoub Malak 
Published: March 2024

AbstractThe traditional drafting and subsequent implementation of international refugee law have been criticised for relying on a male-centric understanding of persecution. Whilst this framework has recently shifted to include a more gender-sensitive interpretation, I argue that this introduction of gender within refugee status determination has traditionally relied on narratives infused with gendered and racialised stereotypes. In particular, it relies on a ‘white saviour’ colonial narrative that perceives refugee women as vulnerable victims in need of saving. Drawing on a decolonial and critical epistemological analysis that includes both a race and gender dimension, I unpack the epistemic violence and hidden colonial legacies in the representation of refugee women in case-law. Ultimately, this article concludes with a call for reframing the legal narrative around refugee women by approaching them as political actors rather than oppressed and vulnerable subjects.
Global‐Scale Evaluation of Coastal Ocean Alkalinity Enhancement in a Fully Coupled Earth System Model
Authors: Palmiéri Julien , Yool Andrew 
Published: March 2024

AbstractThe Paris Agreement plans for “net‐zero” carbon dioxide (CO2) emissions during the second half of the 21st century. However, reducing emissions from some sectors is challenging, and “net‐zero” permits carbon dioxide removal (CDR) activities. One CDR scheme is ocean alkalinity enhancement (OAE), which proposes dissolving basic minerals into seawater to increase its buffering capacity for CO2. While modeling studies have often investigated OAE at basin or global scale, some proposals focus on readily accessible coastal shelves, with TA added through the dissolution of seafloor olivine sands. Critically, by settling and dissolving sands on shallow seafloors, this retains the added TA in near‐surface waters in direct contact with atmospheric CO2. To investigate this, we add dissolved TA at a rate of ∼29 Teq y−1 to the global shelves (<100m) of an Earth system model (UKESM1) running a high emissions scenario. As UKESM1 is fully coupled, wider effects of OAE‐mediated increase in ocean CO2 uptake –e.g. atmospheric xCO2, air temperature and marine pH– are fully quantified. Applying OAE from 2020 to 2100 decreases atmospheric xCO2 ∼10 ppm, and increases air‐to‐sea CO2 uptake ∼8%. In‐line with other studies, CO2 uptake per unit of TA added occurs at a rate of ∼0.8 mol C (mol TA)−1. Significantly for monitoring, advection of added TA results in ∼50% of CO2 uptake occurring remotely from OAE operations, and the model also exhibits noticeable land carbon reservoir changes. While practical uncertainties and model representation caveats remain, this analysis estimates the effectiveness of this specific OAE scheme to assist with net‐zero planning.
A Unifying Model for Turbulent Hyporheic Mass Flux Under a Wide Range of Near‐Bed Hydrodynamic Conditions
Authors: Chen Chieh‐Ying , Fytanidis Dimitrios K. , Garcia Marcelo H. 
Published: March 2024

AbstractExisting models for estimating hyporheic solute mass flux often require numerous parameters related to flow, bed, and channel characteristics, which are frequently unavailable. We performed a meta‐analysis on existing data set, enhanced with high Reynolds number cases from a validated Computational Fluid Dynamics model, to identify key parameters influencing effective diffusivity at the sediment water interface. We applied multiple linear regression to generate empirical models for predicting eddy diffusivity. To simplify this, we developed two single‐parameter models using either a roughness or permeability‐based Reynolds number. These models were validated against existing models and literature data. The model using roughness Reynolds number is easy to use and can provide an estimate of the mass transfer coefficient for solutes like dissolved oxygen, particularly in scenarios where detailed bed characteristics such as permeability might not be readily available.
The Generation of 150 km Echoes Through Nonlinear Wave Mode Coupling
Authors: Longley William J. 
Published: March 2024

AbstractA fundamental problem in plasma turbulence is understanding how energy cascades across multiple scales. In this paper, a new weak turbulence theory is developed to explain how energy can be transferred from Langmuir and Upper‐Hybrid waves to ion‐acoustic waves. A kinetic approach is used where the Boltzmann equation is Fourier‐Laplace transformed, and the nonlinear term is retained. A unique feature of this approach is the ability to calculate power spectra at low frequencies, for any wavelength or magnetic aspect angle. The results of this theory explain how the predominant type of 150‐km radar echoes are generated in the ionosphere. First, peaks in the suprathermal electron velocity distribution drive a bump‐on‐tail like instability that excites the Upper‐Hybrid mode. This excited wave then couples nonlinearly to the ion‐acoustic mode, generating the ∼10 dB enhancement observed by radars. This theory also explains why higher frequency radars like ALTAIR do not observe these echoes.
Proteomics for optimizing therapy in acute myeloid leukemia: venetoclax plus hypomethylating agents versus conventional chemotherapy
Authors: de Camargo Magalhães Eduardo Sabino , Hubner Stefan Edward , Brown Brandon Douglas , Qiu Yihua , Kornblau Steven Mitchell 
Published: March 2024

AbstractThe use of Hypomethylating agents combined with Venetoclax (VH) for the treatment of Acute Myeloid Leukemia (AML) has greatly improved outcomes in recent years. However not all patients benefit from the VH regimen and a way to rationally select between VH and Conventional Chemotherapy (CC) for individual AML patients is needed. Here, we developed a proteomic-based triaging strategy using Reverse-phase Protein Arrays (RPPA) to optimize therapy selection. We evaluated the expression of 411 proteins in 810 newly diagnosed adult AML patients, identifying 109 prognostic proteins, that divided into five patient expression profiles, which are useful for optimizing therapy selection. Furthermore, using machine learning algorithms, we determined a set of 14 proteins, among those 109, that were able to accurately recommend therapy, making it feasible for clinical application. Next, we identified a group of patients who did not benefit from either VH or CC and proposed target-based approaches to improve outcomes. Finally, we calculated that the clinical use of our proteomic strategy would have led to a change in therapy for 30% of patients, resulting in a 43% improvement in OS, resulting in around 2600 more cures from AML per year in the United States.
Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number
Authors: Corponi Filippo , Li Bryan M. , Anmella Gerard , Mas Ariadna , Pacchiarotti Isabella , Valentí Marc , Grande Iria , Benabarre Antoni , Garriga Marina , Vieta Eduard , Lawrie Stephen M. , Whalley Heather C. , Hidalgo-Mazzei Diego , Vergari Antonio 
Published: March 2024

AbstractMood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician’s office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen’s κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.
Outcomes of patients with multiple myeloma refractory to standard dose vs low dose lenalidomide
Authors: Goel Utkarsh , Charalampous Charalampos , Kapoor Prashant , Binder Moritz , Buadi Francis K. , Dingli David , Dispenzieri Angela , Fonder Amie , Gertz Morie A. , Gonsalves Wilson I. , Hayman Suzanne R. , Hobbs Miriam A. , Hwa Yi L. , Kourelis Taxiarchis , Lacy Martha Q. , Leung Nelson , Lin Yi , Warsame Rahma M. , Kyle Robert A. , Rajkumar S. Vincent , Kumar Shaji K. 
Published: March 2024

AbstractRefractoriness to lenalidomide is an important factor determining the choice of therapy at first relapse in multiple myeloma (MM). It remains debatable if resistance to lenalidomide varies among MM refractory to standard doses vs low dose maintenance doses. In this study, we assessed the outcomes with subsequent therapies in patients with MM refractory to standard dose vs low dose lenalidomide. We retrospectively reviewed all patients with MM at our institution who received first line therapy with lenalidomide containing regimens, and assessed progression free survival (PFS) and overall survival for these patients for second line therapy, and with lenalidomide retreatment. For second line therapy, we found no difference in the PFS between standard dose refractory and low dose refractory groups (median PFS 14 months vs 14 months, p = 0.95), while the PFS for both these groups was inferior to the not refractory group (median PFS 30 months, p < 0.001 for both pairs). Similar trends were seen among these groups on lenalidomide retreatment, and on multivariable analysis. These data suggest that refractoriness to lenalidomide is not dose dependent, and definition of lenalidomide refractoriness should not depend on the dose of lenalidomide to which the disease was considered refractory.
DIP-MS: ultra-deep interaction proteomics for the deconvolution of protein complexes
Authors: Frommelt Fabian , Fossati Andrea , Uliana Federico , Wendt Fabian , Xue Peng , Heusel Moritz , Wollscheid Bernd , Aebersold Ruedi , Ciuffa Rodolfo , Gstaiger Matthias 
Published: March 2024

AbstractMost proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing most cellular processes. Affinity purification of the protein of interest combined with liquid chromatography coupled to tandem mass spectrometry (AP–MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can, however, not be resolved from a single AP–MS experiment but requires computational inference from multiple time- and resource-intensive reciprocal AP–MS experiments. Here we introduce deep interactome profiling by mass spectrometry (DIP-MS), which combines AP with blue-native-PAGE separation, data-independent acquisition with mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. We applied DIP-MS to probe the organization of the human prefoldin family of complexes, resolving distinct prefoldin holo- and subcomplex variants, complex–complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution.
A systematic review and meta-analysis of socio-cognitive impairments in multiple sclerosis
Authors: Roheger Mandy , Grothe Lydia , Hasselberg Laura , Grothe Matthias , Meinzer Marcus 
Published: March 2024

AbstractSocio-cognitive impairment is frequent in multiple sclerosis (MS). However, little is known about the relationship between other potentially relevant clinical symptoms (i.e., cognition, depression, fatigue) and the degree of socio-cognitive impairment, and neural mechanisms underlying socio-cognitive deficits in MS. Therefore, we meta-analytically quantified socio-cognitive impairment in MS. A systematic literature search in MEDLINE Ovid, Web of Science Core Collection, CENTRAL, and PsycInfo was conducted until December 2022. Studies investigating affective or cognitive theory of mind (a/cToM), visual perspective taking (VPT) and social decision making (SDM) in MS patients relative to healthy controls were included. Risk-of-bias (RoB) was assessed using the CLARITY group “Tool for Assessing RoB in Cohort Studies”. Mediation analysis investigated the contribution of clinical symptoms to socio-cognitive impairment. In total, n = 8534 studies were screened, 58 were included in the systematic review, 27 in the meta-analyses. Most studies were rated with a moderate RoB. Meta-analyses confirmed impairment of both aToM and cToM in MS patients, with larger effect sizes for aToM. Mediation analysis demonstrated that higher levels of fatigue selectively predicted the degree of cToM impairment. There was insufficient data available to quantify impairment in other socio-cognitive domains. Fourteen structural and functional imaging studies were identified and characterized by substantial heterogeneity. Summarized, this study confirmed substantial socio-cognitive impairment in MS and highlights the potential exacerbating role of comorbid clinical symptoms. We identify several evidence gaps that need to be addressed in future large-scale studies using comprehensive and coordinated assessments of socio-cognitive parameters, potential mediators, and neural correlates.Trial registration: The pre-registered review protocol can be assessed at www.crd.york.ac.uk/PROSPERO/ (ID: CRD42020206225).
Psilocybin enhances insightfulness in meditation: a perspective on the global topology of brain imaging during meditation
Authors: Singer Berit , Meling Daniel , Hirsch-Hoffmann Matthias , Michels Lars , Kometer Michael , Smigielski Lukasz , Dornbierer Dario , Seifritz Erich , Vollenweider Franz X. , Scheidegger Milan 
Published: March 2024

AbstractIn this study, for the first time, we explored a dataset of functional magnetic resonance images collected during focused attention and open monitoring meditation before and after a five-day psilocybin-assisted meditation retreat using a recently established approach, based on the Mapper algorithm from topological data analysis. After generating subject-specific maps for two groups (psilocybin vs. placebo, 18 subjects/group) of experienced meditators, organizational principles were uncovered using graph topological tools, including the optimal transport (OT) distance, a geometrically rich measure of similarity between brain activity patterns. This revealed characteristics of the topology (i.e. shape) in space (i.e. abstract space of voxels) and time dimension of whole-brain activity patterns during different styles of meditation and psilocybin-induced alterations. Most interestingly, we found that (psilocybin-induced) positive derealization, which fosters insightfulness specifically when accompanied by enhanced open-monitoring meditation, was linked to the OT distance between open-monitoring and resting state. Our findings suggest that enhanced meta-awareness through meditation practice in experienced meditators combined with potential psilocybin-induced positive alterations in perception mediate insightfulness. Together, these findings provide a novel perspective on meditation and psychedelics that may reveal potential novel brain markers for positive synergistic effects between mindfulness practices and psilocybin.
Risk and prosocial behavioural cues elicit human-like response patterns from AI chatbots
Authors: Zhao Yukun , Huang Zhen , Seligman Martin , Peng Kaiping 
Published: March 2024

AbstractEmotions, long deemed a distinctly human characteristic, guide a repertoire of behaviors, e.g., promoting risk-aversion under negative emotional states or generosity under positive ones. The question of whether Artificial Intelligence (AI) can possess emotions remains elusive, chiefly due to the absence of an operationalized consensus on what constitutes 'emotion' within AI. Adopting a pragmatic approach, this study investigated the response patterns of AI chatbots—specifically, large language models (LLMs)—to various emotional primes. We engaged AI chatbots as one would human participants, presenting scenarios designed to elicit positive, negative, or neutral emotional states. Multiple accounts of OpenAI's ChatGPT Plus were then tasked with responding to inquiries concerning investment decisions and prosocial behaviors. Our analysis revealed that ChatGPT-4 bots, when primed with positive, negative, or neutral emotions, exhibited distinct response patterns in both risk-taking and prosocial decisions, a phenomenon less evident in the ChatGPT-3.5 iterations. This observation suggests an enhanced capacity for modulating responses based on emotional cues in more advanced LLMs. While these findings do not suggest the presence of emotions in AI, they underline the feasibility of swaying AI responses by leveraging emotional indicators.
Estimating the size of populations at risk for malaria: a case study in cattle herders and agricultural workers in Northern Namibia
Authors: Rerolle Francois , Jacobson Jerry O. , Smith Gueye Cara , Bennett Adam , Carrillo Sidney , Ntuku Henry , Smith Jennifer L. 
Published: March 2024

AbstractCattle herders and agricultural workers have been identified has key high-risk populations for malaria in northern Namibia. Population size estimates for these groups are lacking but are important for planning, monitoring and evaluating the effectiveness of targeted strategies towards malaria elimination in the region. In this analysis, we extend population size estimation methods routinely used in HIV research, specifically social mapping and multiple source capture-recapture, to the context of malaria to estimate how many cattle herders and agricultural workers lived in two regions of northern Namibia over the course of the 2019–2020 malaria season. Both methods estimated two to three times more agricultural workers than cattle herders but size estimates based on the multiple source capture-recapture method were two to three times greater than the mapping-based, highlighting important methodological considerations to apply such methods to these highly mobile populations. In particular, we compared open versus closed populations assumptions for the capture-recapture method and assessed the impact of sensitivity analyses on the procedure to link records across multiple data sources on population size estimates. Our results are important for national control programs to target their resources and consider integrating routine population size estimation of high risk populations in their surveillance activities.