bluuebunny/crossref_metadata_2025_split huggingface dataset:
The bodily self from psychosis to psychedelics
Authors: Harduf Amir , Panishev Gabriella , Harel Eiran V. , Stern Yonatan , Salomon Roy 
Published: December 2023

AbstractThe sense of self is a foundational element of neurotypical human consciousness. We normally experience the world as embodied agents, with the unified sensation of our selfhood being nested in our body. Critically, the sense of self can be altered in psychiatric conditions such as psychosis and altered states of consciousness induced by psychedelic compounds. The similarity of phenomenological effects across psychosis and psychedelic experiences has given rise to the “psychotomimetic” theory suggesting that psychedelics simulate psychosis-like states. Moreover, psychedelic-induced changes in the sense of self have been related to reported improvements in mental health. Here we investigated the bodily self in psychedelic, psychiatric, and control populations. Using the Moving Rubber Hand Illusion, we tested (N = 75) patients with psychosis, participants with a history of substantial psychedelic experiences, and control participants to see how psychedelic and psychiatric experience impacts the bodily self. Results revealed that psychosis patients had reduced Body Ownership and Sense of Agency during volitional action. The psychedelic group reported subjective long-lasting changes to the sense of self, but no differences between control and psychedelic participants were found. Our results suggest that while psychedelics induce both acute and enduring subjective changes in the sense of self, these are not manifested at the level of the bodily self. Furthermore, our data show that bodily self-processing, related to volitional action, is disrupted in psychosis patients. We discuss these findings in relation to anomalous self-processing across psychedelic and psychotic experiences.
A Gestalt account of human behavior is supported by evidence from switching between single and dual actions
Authors: Huestegge Lynn , Pieczykolan Aleks , Koch Iring 
Published: December 2023

AbstractThe question of how behavior is represented in the mind lies at the core of psychology as the science of mind and behavior. While a long-standing research tradition has established two opposing fundamental views of perceptual representation, Structuralism and Gestalt psychology, we test both accounts with respect to action representation: Are multiple actions (characterizing human behavior in general) represented as the sum of their component actions (Structuralist view) or holistically (Gestalt view)? Using a single-/dual-response switch paradigm, we analyzed switches between dual ([A + B]) and single ([A], [B]) responses across different effector systems and revealed comparable performance in partial repetitions and full switches of behavioral requirements (e.g., in [A + B] → [A] vs. [B] → [A], or [A] → [A + B] vs. [B] → [A + B]), but only when the presence of dimensional overlap between responses allows for Gestalt formation. This evidence for a Gestalt view of behavior in our paradigm challenges some fundamental assumptions in current (tacitly Structuralist) action control theories (in particular the idea that all actions are represented compositionally with reference to their components), provides a novel explanatory angle for understanding complex, highly synchronized human behavior (e.g., dance), and delimitates the degree to which complex behavior can be analyzed in terms of its basic components.
Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning
Authors: Tritt Andrew , Yue John K. , Ferguson Adam R. , Torres Espin Abel , Nelson Lindsay D. , Yuh Esther L. , Markowitz Amy J. , Manley Geoffrey T. , Bouchard Kristofer E. ,   the TRACK-TBI Investigators, Keene C. Dirk , Madden Christopher , McCrea Michael , Merchant Randall , Mukherjee Pratik , Ngwenya Laura B. , Robertson Claudia , Schnyer David , Taylor Sabrina R. , Zafonte Ross 
Published: December 2023

AbstractTraumatic brain injury (TBI) affects how the brain functions in the short and long term. Resulting patient outcomes across physical, cognitive, and psychological domains are complex and often difficult to predict. Major challenges to developing personalized treatment for TBI include distilling large quantities of complex data and increasing the precision with which patient outcome prediction (prognoses) can be rendered. We developed and applied interpretable machine learning methods to TBI patient data. We show that complex data describing TBI patients' intake characteristics and outcome phenotypes can be distilled to smaller sets of clinically interpretable latent factors. We demonstrate that 19 clusters of TBI outcomes can be predicted from intake data, a ~ 6× improvement in precision over clinical standards. Finally, we show that 36% of the outcome variance across patients can be predicted. These results demonstrate the importance of interpretable machine learning applied to deeply characterized patients for data-driven distillation and precision prognosis.
Assessing population structure and migration patterns of wild boar (Sus scrofa) in Japan
Authors: Sawai Kotaro , Arakawa Aisaku , Taniguchi Masaaki , Xiao Bo , Sawai Miwa , Osaki Makoto , Yamaguchi Emi , Hayama Yoko , Murato Yoshinori , Shimizu Yumiko , Kondo Sonoko , Yamamoto Takehisa 
Published: December 2023

AbstractGeographical wildlife patterns reflect historical range expansion and connectivity and provide insights into wildlife population management. In our large-scale phylogeographic population analysis of wild boars (Sus scrofa leucomystax) in Japan, we identified 15 clusters using 29 microsatellite markers, each structured within a range of approximately 200 km. This suggests that evolution was essentially driven by isolation by distance, and that the range of gene flow was limited. One cluster contained subpopulations located approximately 900 km apart, indicating the occurrence of past anthropogenic introductions. Moreover, we estimated effective migration to visualize the geographic genetic population diversity. This analysis identified six potential barriers, one of which involved large plains and mountainous areas in the Kanto region of eastern Japan. This barrier likely persisted in the two eastern clusters for an extended period, restricting migration to the neighboring areas. Overall, our study sheds light on the demographic history of wild boar in Japan, provides evidence of past anthropogenic introductions from distant areas, and highlights the importance of geographic barriers in shaping genetic diversity and population dynamics. This knowledge will be beneficial for forming informed wildlife management strategies toward the conservation of genetic integrity and ecological balance of wild boar populations in Japan.
A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection
Authors: Hossain Md. Alamgir , Islam Md. Saiful 
Published: December 2023

AbstractIn the age of sophisticated cyber threats, botnet detection remains a crucial yet complex security challenge. Existing detection systems are continually outmaneuvered by the relentless advancement of botnet strategies, necessitating a more dynamic and proactive approach. Our research introduces a ground-breaking solution to the persistent botnet problem through a strategic amalgamation of Hybrid Feature Selection methods—Categorical Analysis, Mutual Information, and Principal Component Analysis—and a robust ensemble of machine learning techniques. We uniquely combine these feature selection tools to refine the input space, enhancing the detection capabilities of the ensemble learners. Extra Trees, as the ensemble technique of choice, exhibits exemplary performance, culminating in a near-perfect 99.99% accuracy rate in botnet classification across varied datasets. Our model not only surpasses previous benchmarks but also demonstrates exceptional adaptability to new botnet phenomena, ensuring persistent accuracy in a landscape of evolving threats. Detailed comparative analyses manifest our model's superiority, consistently achieving over 99% True Positive Rates and an unprecedented False Positive Rate close to 0.00%, thereby setting a new precedent for reliability in botnet detection. This research signifies a transformative step in cybersecurity, offering unprecedented precision and resilience against botnet infiltrations, and providing an indispensable blueprint for the development of next-generation security frameworks.
Structure-based discovery of novel P-glycoprotein inhibitors targeting the nucleotide binding domains
Authors: Moesgaard Laust , Pedersen Maria L. , Uhd Nielsen Carsten , Kongsted Jacob 
Published: December 2023

AbstractP-glycoprotein (P-gp), a membrane transport protein overexpressed in certain drug-resistant cancer cells, has been the target of numerous drug discovery projects aimed at overcoming drug resistance in cancer. Most characterized P-gp inhibitors bind at the large hydrophobic drug binding domain (DBD), but none have yet attained regulatory approval. In this study, we explored the potential of designing inhibitors that target the nucleotide binding domains (NBDs), by computationally screening a large library of 2.6 billion synthesizable molecules, using a combination of machine learning-guided molecular docking and molecular dynamics (MD). 14 of the computationally best-scoring molecules were subsequently tested for their ability to inhibit P-gp mediated calcein-AM efflux. In total, five diverse compounds exhibited inhibitory effects in the calcein-AM assay without displaying toxicity. The activity of these compounds was confirmed by their ability to decrease the verapamil-stimulated ATPase activity of P-gp in a subsequent assay. The discovery of these five novel P-gp inhibitors demonstrates the potential of in-silico screening in drug discovery and provides a new stepping point towards future potent P-gp inhibitors.
A retrospective cohort study on the clinical outcomes of patients admitted to intensive care units with dysnatremia
Authors: Ng Pauline Yeung , Cheung Regina Yui Ting , Ip April , Chan Wai Ming , Sin Wai Ching , Yap Desmond Yat-Hin 
Published: December 2023

AbstractWith evolving patient characteristics and patterns of ICU utilization, the impact of dysnatremias on patient outcomes and healthcare costs in the present era have not been well studied. Patients ≥ 18 years admitted to the ICUs in public hospitals in Hong Kong between January 2010 and June 2022 and had at least one serum sodium measurement obtained within 24 h prior to or following ICU admission were stratified into normonatremic (135-145 mmol/L), hyponatremic (< 135 mmol/L) and hypernatremic (> 145 mmol/L) groups. A total of 162,026 patients were included—9098 (5.6%), 40,533 (25.0%) and 112,395 (69.4%) patients were hypernatremic, hyponatremic and normonatremic at the time of ICU admission, respectively. The odds of patients with hypernatremia and hyponatremia dying in the ICU were 27% and 14% higher (aOR 1.27, 95% CI 1.19–1.36 and aOR 1.14, 95% CI 1.08–1.19, respectively; P < 0.001 for both), and 52% and 21% higher for dying in the hospital (aOR 1.52, 95% CI 1.43–1.62 and aOR 1.21, 95% CI 1.17–1.26, respectively; P < 0.001 for both] compared with those with normonatremia. Patients with dysnatremia also had longer ICU length of stay (LOS), hospital LOS, and higher healthcare costs than the normonatremic group. Dysnatremias at ICU admission were associated with increased ICU and in-hospital mortality and overall healthcare burden.
CNN-based flow field prediction for bus aerodynamics analysis
Authors: Garcia-Fernandez Roberto , Portal-Porras Koldo , Irigaray Oscar , Ansa Zugatz , Fernandez-Gamiz Unai 
Published: December 2023

AbstractThe aim of this article is to evaluate the ability of a convolutional neural network (CNN) to predict velocity and pressure aerodynamic fields in heavy vehicles. For training and testing the developed CNN, various CFD simulations of three different vehicle geometries have been conducted, considering the RANS-based k-ω SST turbulent model. Two geometries correspond to the SC7 and SC5 coach models of the bus manufacturer SUNSUNDEGUI and the third one corresponds to Ahmed body. By generating different variants of these three geometries, a large number of representations of the velocity and pressure fields are obtained that will be used to train, verify, and evaluate the convolutional neural network. To improve the accuracy of the CNN, the field representations obtained are discretized as a function of the expected velocity gradient, so that in the areas where there is a greater variation in velocity, the corresponding neuron is smaller. The results show good agreement between numerical results and CNN predictions, being the CNN able to accurately represent the velocity and pressure fields with very low errors. Additionally, a substantial improvement in the computational time needed for each simulation is appreciated, reducing it by four orders of magnitude.
Digitally-enhanced dog behavioral testing
Authors: Farhat Nareed , Lazebnik Teddy , Monteny Joke , Moons Christel Palmyre Henri , Wydooghe Eline , van der Linden Dirk , Zamansky Anna 
Published: December 2023

AbstractBehavioral traits in dogs are assessed for a wide range of purposes such as determining selection for breeding, chance of being adopted or prediction of working aptitude. Most methods for assessing behavioral traits are questionnaire or observation-based, requiring significant amounts of time, effort and expertise. In addition, these methods might be also susceptible to subjectivity and bias, negatively impacting their reliability. In this study, we proposed an automated computational approach that may provide a more objective, robust and resource-efficient alternative to current solutions. Using part of a ‘Stranger Test’ protocol, we tested n = 53 dogs for their response to the presence and neutral actions of a stranger. Dog coping styles were scored by three dog behavior experts. Moreover, data were collected from their owners/trainers using the Canine Behavioral Assessment and Research Questionnaire (C-BARQ). An unsupervised clustering of the dogs’ trajectories revealed two main clusters showing a significant difference in the stranger-directed fear C-BARQ category, as well as a good separation between (sufficiently) relaxed dogs and dogs with excessive behaviors towards strangers based on expert scoring. Based on the clustering, we obtained a machine learning classifier for expert scoring of coping styles towards strangers, which reached an accuracy of 78%. We also obtained a regression model predicting C-BARQ scores with varying performance, the best being Owner-Directed Aggression (with a mean average error of 0.108) and Excitability (with a mean square error of 0.032). This case study demonstrates a novel paradigm of ‘machine-based’ dog behavioral assessment, highlighting the value and great promise of AI in this context.
Comparative effectiveness of first-line antihypertensive drug classes on the maintenance of estimated glomerular filtration rate (eGFR) in real world primary care
Authors: Gao Qiao , Tan Ngiap Chuan , Lee Mong Li , Hsu Wynne , Choo Jason 
Published: December 2023

AbstractRenin-angiotensin system inhibitors (RASi), particularly angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs), are commonly used in the treatment of hypertension and are recommended for kidney protection. Uncertainty remains about the effectiveness of RASi being used as first-line antihypertensive therapy on eGFR maintenance compared to its alternatives, especially for those with no or early-stage chronic kidney disease (CKD). We conducted a retrospective cohort study of 19,499 individuals (mean age 64.1, 43.5% males) from primary care in Singapore with 4.5 median follow-up years. The study cohort included newly diagnosed individuals with hypertension (whose eGFR was mainly in CKD stages G1-G2) and initiated on ACEIs, ARBs, beta-blockers (BBs), calcium channel blockers (CCBs) or diuretics (Ds) as first-line antihypertensive monotherapy. We compared the estimated glomerular filtration rate (eGFR) curve before/after the drug initiation over time of patients under different drug classes and analyzed the time to declining to a more advanced stage CKD. Inverse probability of treatment weighting (IPTW) was used to adjust for baseline confounding factors. Two key findings were observed. First, after initiating antihypertensive drugs, the eGFR almost maintained the same as the baseline in the first follow-up year, compared with dropping 3 mL/min/1.73 m2 per year before drug initiation. Second, ARBs were observed to be slightly inferior to ACEIs (HR = 1.14, 95% CI = (1.04, 1.23)) and other antihypertensive agents (HR = 1.10, 95% CI = (1.01, 1.20)) in delaying eGFR decline to a more advanced CKD stage in the study population. Our results showed that initiating antihypertensive agents can significantly maintain eGFR for those newly diagnosed patients with hypertension. However, RASi may not be superior to other antihypertensive agents in maintaining eGFR levels for non-CKD or early stages CKD patients.
Predictors of unfavourable outcome in adults with suspected central nervous system infections: a prospective cohort study
Authors: ter Horst Liora , van Zeggeren Ingeborg E. , Olie Sabine E. ,   I-PACE Study Group, Brenner J. , Citroen J. , van Geel B.M. , Heckenberg S.G.B. , Jellema K. , Kester M.I. , Killestein J. , Mook B.B. , Resok Y.C. , Titulaer M.J. , van Veen K.E.B. , Verschuur C.V.M. , van de Beek Diederik , Brouwer Matthijs C. 
Published: December 2023

AbstractSuspected central nervous system (CNS) infections may pose a diagnostic challenge, and often concern severely ill patients. We aim to identify predictors of unfavourable outcome to prioritize diagnostics and treatment improvements. Unfavourable outcome was assessed on the Glasgow Outcome Scale at hospital discharge, defined by a score of 1 to 4. Of the 1152 episodes with suspected CNS infection, from two Dutch prospective cohorts, the median age was 54 (IQR 37–67), and 563 episodes (49%) occurred in women. The final diagnoses were categorized as CNS infection (N = 358 episodes, 31%), CNS inflammatory disease (N = 113, 10%), non-infectious non-inflammatory neurological disorder (N = 388, 34%), non-neurological infection (N = 252, 22%), and other systemic disorder (N = 41, 4%). Unfavourable outcome occurred in 412 of 1152 (36%), and 99 died (9%). Predictors for unfavourable outcomes included advanced age, absence of headache, tachycardia, altered mental state, focal cerebral deficits, cranial nerve palsies, low thrombocytes, high CSF protein, and the final diagnosis of CNS inflammatory disease (odds ratio 4.5 [95% confidence interval 1.5–12.6]). Episodes suspected of having a CNS infection face high risk of experiencing unfavourable outcome, stressing the urgent need for rapid and accurate diagnostics. Amongst the suspected CNS infection group, those diagnosed with CNS inflammatory disease have the highest risk.
2 × 4.5 kW bidirectional output near-single-mode quasi-continuous wave monolithic fiber laser
Authors: Ding Xinyi , Zeng Lingfa , Wang Li , Wu Hanshuo , Wang Peng , Zhang Hanwei , Wang Xiaolin , Ning Yu , Xi Fengjie , Xu Xiaojun 
Published: December 2023

AbstractQuasi-continuous wave (QCW) laser has a very broad application in the industrial field, especially in additive manufacturing, surface treatment, laser cutting, laser cleaning, and laser drilling. Compared with the unidirectional fiber laser, the bidirectional output can be achieved two ports high power output with only one resonator, which can greatly reduce the industrial cost. However, there are few researches on QCW fiber lasers with bidirectional output. Here, we optimized and demonstrated a bidirectional output QCW laser with output power of 2 × 4.5 kW based on a double-clad ytterbium-doped fiber with a core/cladding diameter of 25/400 μm. The peak power at both ends reached 4515 W and 4694 W, respectively. The Raman suppression ratio at both ends of A and B is about 12 dB, and the beam quality factor M2 is about 1.37 and 1.42, respectively. The corresponding optical-to-optical efficiency is 79%. To the best of our knowledge, this is the highest peak power of QCW laser with near-single-mode beam quality in a bidirectional structure laser.
Heat generation/absorption effect on natural convective heat transfer in a wavy triangular cavity filled with nanofluid
Authors: Islam Tarikul , Alam Md. Nur , Niazai Shafiullah , Khan Ilyas , Fayz-Al-Asad Md. , Alqahtani Sultan 
Published: December 2023

AbstractThis study is numerically executed to investigate the influence of heat generation or absorption on free convective flow and temperature transport within a wavy triangular enclosure filled by the nanofluid taking the Brownian effect of nanoparticles. The water (H2O) is employed as base fluid and copper (Cu) as nanoparticles for making effective Cu–H2O nanofluids. The perpendicular sinusoidally wavy wall is cooled at low temperature while the horizontal bottom sidewall is heated non-uniformly (sinusoidal). The inclined wall of the enclosure is insulated. The governing dimensionless non-linear PDEs are executed numerically with the help of the Galerkin weighted residual type finite element technique. The numerically simulated results are displayed through average Nusselt number, isothermal contours, and streamlines for the various model parameters such as Hartmann number, Rayleigh number, heat generation or absorption parameter, nanoparticles volume fraction, and undulation parameter. The outcomes illustrate that the temperature transport rate augments significantly for the enhancement of Rayleigh number as well as nanoparticles volume fraction whereas reduces for the increment of Hartman number. The heat transfer is significantly influenced by the size, shape, and Brownian motion of the nanoparticles. The rate of heat transport increases by 20.43% considering the Brownian effect for 1% nanoparticle volume. The thermal performance increases by 8.66% for the blade shape instead of the spherical shape of nanoparticles. In addition, heat transfer is impacted by the small size of nanoparticles. The thermal transport rate increases by 35.87% when the size of the nanoparticles reduces from 100 to 10 nm. Moreover, the rate of heat transmission increases efficiently as the undulation parameter rises. It is also seen that a crucial factor in the flow of nanofluids and heat transmission is the heat generation/absorption parameter that influences temperature distribution, heat transfer rates, and overall thermal performance.