bluuebunny/crossref_metadata_2025_split huggingface dataset:
A megastudy on the predictability of personal information from facial images: Disentangling demographic and non-demographic signals
Authors: Tkachenko Yegor , Jedidi Kamel 
Published: November 2023

AbstractWhile prior research has shown that facial images signal personal information, publications in this field tend to assess the predictability of a single variable or a small set of variables at a time, which is problematic. Reported prediction quality is hard to compare and generalize across studies due to different study conditions. Another issue is selection bias: researchers may choose to study variables intuitively expected to be predictable and underreport unpredictable variables (the ‘file drawer’ problem). Policy makers thus have an incomplete picture for a risk-benefit analysis of facial analysis technology. To address these limitations, we perform a megastudy—a survey-based study that reports the predictability of numerous personal attributes (349 binary variables) from 2646 distinct facial images of 969 individuals. Using deep learning, we find 82/349 personal attributes (23%) are predictable better than random from facial image pixels. Adding facial images substantially boosts prediction quality versus demographics-only benchmark model. Our unexpected finding of strong predictability of iPhone versus Galaxy preference variable shows how testing many hypotheses simultaneously can facilitate knowledge discovery. Our proposed L1-regularized image decomposition method and other techniques point to smartphone camera artifacts, BMI, skin properties, and facial hair as top candidate non-demographic signals in facial images.
Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines
Authors: Viswanathan Karthik , Goel Manan , Laghuvarapu Siddhartha , Varma Girish , Priyakumar U. Deva 
Published: November 2023

AbstractThe discovery of potential therapeutic agents for life-threatening diseases has become a significant problem. There is a requirement for fast and accurate methods to identify drug-like molecules that can be used as potential candidates for novel targets. Existing techniques like high-throughput screening and virtual screening are time-consuming and inefficient. Traditional molecule generation pipelines are more efficient than virtual screening but use time-consuming docking software. Such docking functions can be emulated using Machine Learning models with comparable accuracy and faster execution times. However, we find that when pre-trained machine learning models are employed in generative pipelines as oracles, they suffer from model degradation in areas where data is scarce. In this study, we propose an active learning-based model that can be added as a supplement to enhanced molecule generation architectures. The proposed method uses uncertainty sampling on the molecules created by the generator model and dynamically learns as the generator samples molecules from different regions of the chemical space. The proposed framework can generate molecules with high binding affinity with $$\sim$$
∼
a 70% improvement in runtime compared to the baseline model by labeling only $$\sim$$
∼
30% of molecules compared to the baseline oracle.
Harnessing deep learning to forecast local microclimate using global climate data
Authors: Zanchi Marco , Zapperi Stefano , La Porta Caterina A. M. 
Published: November 2023

AbstractMicroclimate is a complex non-linear phenomenon influenced by both global and local processes. Its understanding holds a pivotal role in the management of natural resources and the optimization of agricultural procedures. This phenomenon can be effectively monitored in local areas by employing models that integrate physical laws and data-driven algorithms relying on climate data and terrain conformation. Climate data can be acquired from nearby meteorological stations when available, but in their absence, global climate datasets describing 10 km-scale areas are often utilized. The present research introduces an innovative microclimate model that combines physical laws and deep learning to reproduce temperature and relative humidity variations at the meter-scale within a study area located in the Lombardian foothills. The model is exploited to perform a comparative study investigating whether employing the global climate dataset ERA5 as input reduces model’s accuracy in reproducing the microclimate variations compared to using data collected by the Lombardy Regional Environment Protection Agency (ARPA) from a nearby meteorological station. The comparative analysis shows that using local meteorological data as inputs provides more accurate results for microclimate modeling. However, in situations where local data is not available, the use of global climate data remains a viable and reliable approach.
The online language of work-personal conflict
Authors: Liou Gloria , Mittal Juhi , Sehgal Neil K. R. , Tay Louis , Ungar Lyle , Guntuku Sharath Chandra 
Published: November 2023

AbstractWith the blurring of boundaries in this digital age, there is increasing concern around work-personal conflict. Assessing and tracking work-personal conflict is critical as it not only affects individual workers but is also a vital measure among broader well-being and economic indices. This inductive study examines the extent to which work-personal conflict corresponds to individuals’ language use on social media. We apply an open-vocabulary analysis to the posts of 2810 Facebook users who also completed a survey for an established work-personal conflict scale. It was found that the language-based model can predict personal-to-work conflict (r = 0.23) and work-to-personal conflict (r = 0.15) and provide important insights into such conflicts. Specifically, we found that high personal-to-work conflict was associated with netspeak and swearing, while low personal-to-work conflict was associated with language about work and positivity. We found that high work-to-personal conflict was associated with negative emotion and negative tone, while low work-to-personal conflict was associated with positive emotion and language about birthdays.
Quantum optical tomography based on time-resolved and mode-selective single-photon detection by femtosecond up-conversion
Authors: Namekata Naoto , Kobayashi Nobuaki , Nomura Kenya , Sako Tokuei , Takata Norio , Inoue Shuichiro 
Published: November 2023

AbstractWe developed an optical time-of-flight measurement system using a time-resolved and mode-selective up-conversion single-photon detector for acquiring tomographic images of a mouse brain. The probe and pump pulses were spectrally carved from a 100-femtosecond mode-locked fiber laser at 1556 nm using 4f systems, so that their center wavelengths were situated at either side of the phase matching band separated by 30 nm. We demonstrated a sensitivity of 111 dB which is comparable to that of shot-noise-limited optical coherence tomography and an axial resolution of 57 μm (a refractive index of 1.37) with 380 femtosecond probe and pump pulses whose average powers were 1.5 mW and 30 μW, respectively. The proposed technique will open a new way of non-contact and non-invasive three-dimensional structural imaging of biological specimens with ultraweak optical irradiation.
Synthesis of MWCNTs by chemical vapor deposition of methane using FeMo/MgO catalyst: role of hydrogen and kinetic study
Authors: Chotmunkhongsin Chawalkul , Ratchahat Sakhon , Chaiwat Weerawut , Charinpanitkul Tawatchai , Soottitantawat Apinan 
Published: November 2023

AbstractThis study aims to investigate the role of hydrogen on CNTs synthesis and kinetics of CNTs formation. The CNTs were synthesized by catalytic chemical vapor deposition of methane over FeMo/MgO catalyst. The experimental results revealed that hydrogen plays an important role in the structural changes of catalyst during the pre-reduction process. The catalyst structure fully transformed into metallic FeMo phases, resulting in an increased yield of 5 folds higher than those of the non-reduced catalyst. However, the slightly larger diameter and lower crystallinity ratio of CNTs was obtained. The hydrogen co-feeding during the synthesis can slightly increase the CNTs yield. After achieving the optimum amount of hydrogen addition, further increase in hydrogen would inhibit the methane decomposition, resulting in lower product yield. The hydrogenation of carbon to methane was proceeded in hydrogen co-feed process. However, the hydrogenation was non-selective to allotropes of carbon. Therefore, the addition of hydrogen would not benefit neither maintaining the catalyst stability nor improving the crystallinity of the CNT products. The kinetic model of CNTs formation, derived from the two types of active site of dissociative adsorption of methane, corresponded well to the experimental results. The rate of CNTs formation greatly increases with the partial pressure of methane but decreases when saturation is exceeded. The activation energy was found to be 13.22 kJ mol−1, showing the rate controlling step to be in the process of mass transfer.
Advancing diabetes prediction with a progressive self-transfer learning framework for discrete time series data
Authors: Lim Heeryung , Kim Gihyeon , Choi Jang-Hwan 
Published: November 2023

AbstractAlthough diabetes mellitus is a complex and pervasive disease, most studies to date have focused on individual features, rather than considering the complexities of multivariate, multi-instance, and time-series data. In this study, we developed a novel diabetes prediction model that incorporates these complex data types. We applied advanced techniques of data imputation (bidirectional recurrent imputation for time series; BRITS) and feature selection (the least absolute shrinkage and selection operator; LASSO). Additionally, we utilized self-supervised algorithms and transfer learning to address the common issues with medical datasets, such as irregular data collection and sparsity. We also proposed a novel approach for discrete time-series data preprocessing, utilizing both shifting and rolling time windows and modifying time resolution. Our study evaluated the performance of a progressive self-transfer network for predicting diabetes, which demonstrated a significant improvement in metrics compared to non-progressive and single self-transfer prediction tasks, particularly in AUC, recall, and F1 score. These findings suggest that the proposed approach can mitigate accumulated errors and reflect temporal information, making it an effective tool for accurate diagnosis and disease management. In summary, our study highlights the importance of considering the complexities of multivariate, multi-instance, and time-series data in diabetes prediction.
Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning
Authors: Xia Yuhao , Zhao Kailong , Liu Dong , Zhou Xiaogen , Zhang Guijun 
Published: December 2023

AbstractAccurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.
The expansion of land plants during the Late Devonian contributed to the marine mass extinction
Authors: Smart Matthew S. , Filippelli Gabriel , Gilhooly William P. , Ozaki Kazumi , Reinhard Christopher T. , Marshall John E. A. , Whiteside Jessica H. 
Published: November 2023

AbstractThe evolution and expansion of land plants brought about one of the most dramatic shifts in the history of the Earth system — the birth of modern soils — and likely stimulated massive changes in marine biogeochemistry and climate. Multiple marine extinctions characterized by widespread anoxia, including the Late Devonian mass extinction around 372 million years ago, may have been linked to terrestrial release of the nutrient phosphorus driven by newly-rooted landscapes. Here we use recently published Devonian lake records as variable inputs in an Earth system model of the coupled carbon-nitrogen-phosphorus-oxygen-sulfur biogeochemical cycles to evaluate whether recorded changes to phosphorus fluxes could sustain Devonian marine anoxia sufficient to drive mass extinction. Results show that globally scaled increases in riverine phosphorus export during the Late Devonian mass extinction could have generated widespread marine anoxia, as modeled perturbations in carbon isotope, temperature, oxygen, and carbon dioxide data are generally consistent with the geologic record. Similar results for large scale volcanism suggest the Late Devonian mass extinction was likely multifaceted with both land plants and volcanism as contributing factors.
Psyche und Fibromyalgie
Authors: Klose Petra , Werner Marc 
Published: November 2023

ZusammenfassungFibromyalgie ist eine komplexe Erkrankung, bei der sowohl körperliche als auch psychische Faktoren eine Rolle spielen. Die psychischen Faktoren verursachen allerdings bei Fibromyalgie nicht die Erkrankung an sich, sondern können vielmehr den Verlauf und die Schwere der Symptome beeinflussen. Eine umfassende Behandlung berücksichtigt daher sowohl medizinische als auch psychologische Aspekte, um eine ganzheitliche Betreuung und Unterstützung für Menschen mit Fibromyalgie zu gewährleisten. Solch eine Behandlung ist interdisziplinär und umfasst eine Kombination aus integrativmedizinischer Therapie, psychologischer Unterstützung und Lebensstiländerungen, um die bestmöglichen Ergebnisse für die Betroffenen zu erzielen.
Imaging Nasal Obstruction: An Objective Evaluation for a Subjective Complaint
Authors: Abreu Vasco R.L.S. , Xavier João A. 
Published: November 2023

AbstractNasal obstruction (NO) is a common clinical symptom characterized by a subjective sensation of insufficient airflow through the nasal cavity and may result from various factors, including changes in nasal anatomy, inflammatory conditions, tumoral lesions, and other etiologies. While a thorough medical history and physical examination can often identify its cause, imaging is usually necessary to fully understand the problem. Computed tomography (CT) is the primary imaging modality used to evaluate the nasal cavity and paranasal sinuses, allowing for the identification of potential causes and structural abnormalities. However, when soft tissue characterization is required, magnetic resonance imaging (MRI) is also useful. Understanding the anatomical and pathological basis of NO is crucial for accurate diagnosis and appropriate management. Imaging techniques provide valuable information for identifying the underlying causes of NO and guiding treatment decisions. This article reviews the normal anatomy of the nasal cavity and adjacent paranasal sinuses as well as the several conditions that may affect breathing comfort (tumors, inflammatory diseases, bony and cartilaginous anatomical variants, and “nonobstructive” mucosal thickenings), showing their normal presentation on CT and MRI.
Boron Lewis Acid Catalyzed Hydrophosphorylation of N-Heteroaryl-Substituted Alkenes
Authors: Lee Sarah Yunmi , Kwak Soojin , Choi Jeongin 
Published: November 2023

AbstractThe hydrophosphorylation of N-heteroaryl-substituted alkenes catalyzed by a boron Lewis acid catalyst is reported. This reaction occurs with a range of alkenes bearing N-heterocycles, including pyridines, a quinoline, a pyrrole, and a benzothiazole, resulting in the production of β-N-heteroaryl alkylphosphonates in good yields under additive-free, operationally simple conditions. The mechanistic insights suggest that this hydrophosphorylation involves the deprotonation of the P–H bond of dialkyl phosphites, enabled by the cooperative effects of a boron acid and the basic N-heterocyclic moiety of the alkenes. The resulting phosphonate anion serves as an effective phosphorus nucleophile for the conjugate addition to the concurrently formed N-protonated alkenes. These β-N-heteroaryl alkylphosphonates can be further converted into other valuable organophosphorus compounds through the introduction of an alcohol group or the reduction of the phosphine oxide moiety.
Pyrazolopyridine Ligands in Transition-Metal-Catalyzed C–C and C–Heteroatom Bond-Forming Reactions
Authors: Joo Jung Min , Kang Eunsu 
Published: November 2023

AbstractPyrazole-substituted pyridines have emerged as versatile bidentate ligands in transition-metal catalysis, providing opportunities to fine-tune reactivity and selectivity beyond what conventional bipyridine ligands can achieve. This review focuses on two representative pyrazolopyridine ligands: 2-(1H-pyrazol-1-yl)pyridine (1-PzPy) and 2-(1H-pyrazol-3-yl)pyridine (3-PzPy). The 1-PzPy series, characterized by a pyrazole ring serving as a weakly coordinating Lewis basic ligand, offer flexibility in ligand binding. Alternatively, the 3-PzPy series provide both L2- and LX-type binding modes, functioning as hydrogen bond donors and σ-donors, respectively. The structural diversity of pyrazolopyridine ligands enables the development of various synthetic strategies, facilitating cross-coupling, cycloaddition, photocatalytic, and asymmetric reactions. This review highlights the roles of these ligands in advancing transition-metal-catalyzed C–C and C–heteroatom bond-forming reactions.1	Introduction2	Synthesis of Pyrazolopyridine Ligands3	Applications of 1-PzPy Ligands4	Applications of 3-PzPy Ligands5	Conclusion
Abdominal Hypertension after Abdominal Plication in Postbariatric Patients: The Consequence in the Postoperative Recovery
Authors: Morales-Olivera Martin , Hanson-Viana Erik , Rodríguez-Segura Armando , Rendón-Medina Marco A. 
Published: December 2023

Abstract
Background Abdominoplasty with abdominal plication increases intra-abdominal pressure (IAP) and has been previously associated with limited diaphragmatic excursion and respiratory dysfunctions. Many factors found in abdominoplasties and among postbariatric patients predispose them to a higher occurrence. This study aims to evaluate the impact of abdominal plication among postbariatric patients, assess whether the plication increases their IAP, and analyze how these IAP correlate to their postoperative outcome.
Methods This prospective study was performed on all patients who underwent circumferential Fleur-De-Lis abdominoplasty. For this intended study, the IAP was measured by an intravesical minimally invasive approach in three stages: after the initiation of general anesthesia, after a 10-cm abdominal wall plication and skin closure, and 24 hours after the procedure.
Results We included 46 patients, of which 41 were female and 5 were male. Before the bariatric procedure, these patients had an average maximum weight of 121.4 kg and an average maximum body mass index of 45.78 kg/m2; 7 were grade I obese patients, 10 were grade II, and 29 were grade III. Only three patients were operated on with a gastric sleeve and 43 with gastric bypass. We presented six patients with transitory intra-abdominal hypertension in the first 24 hours, all of them from the grade I obesity group, the highest presented was 14.3 mm Hg. We presented 15% (7/46) of complication rates, which were only four seroma and five dehiscence; two patients presented both seroma and wound dehiscence.
Conclusion Performing a 10-cm abdominal wall plication or greater represents a higher risk for intra-abdominal hypertension, slower general recovery, and possibly higher complication rate in patients who presented a lower degree of obesity (grade I) at the moment of the bariatric surgery.
Adolescent Female Victims of Sexual Violence: Analysis of Loss of Follow-up after Emergency Care and Outpatient Follow-up
Authors: Torres Alejandra Suyapa Becerra , Alabarse Otávio Prado , Alves Ândria Cléia , Teixeira Ana Luiza , Azevedo Renata Cruz Soares de , Fernandes Arlete 
Published: November 2023

Abstract
Objective To assess the loss to follow-up after emergency care and during 6-months of outpatient follow-up, and the associated variables, among adolescent sexual violence survivors.
Methods This is a retrospective study with review of the medical records of 521 females, aged 10 to 18 years, who received emergency care in a referral service in São Paulo, Brazil. The variables were sociodemographic; personal history; characteristics of abuse, disclosure, and reactions triggered after abuse (physical and mental disorders as well as social changes), psychotropic prescription needs, and moment of abandonment: after emergency care and before completing 6 months of outpatient follow-up. To compare groups of patients lost to follow-up at each time point, we used the Chi-square and Fisher exact tests followed by multiple logistic regression with stepwise criterion for selection of associated variables. We calculated the odds ratio with confidence interval (OR, CI 95%). The level of significance adopted was 5%.
Results A total of 249/521 (47.7%) adolescents discontinued follow-up, 184 (35.3%) after emergency care and 65 (12.4%) before completing outpatient follow-up. The variables of living with a partner (OR = 5.94 [CI 95%; 2.49–14.20]); not having a religion (OR = 2.38 [CI 95%;1.29–4.38)]), having a Catholic religion [OR =  2.11 (CI 95%; 1.17–3.78)]; and not disclosing the abuse [OR = 2.07 (CI 95%; 1.25–3.44)] were associated with loss to follow-up after emergency care. Not needing mental disorder care (OR = 2.72 [CI 95%; 1.36–5.46]) or social support (OR = 2.33 [CI 95%; 1.09–4.99]) were directly associated with loss to outpatient follow-up.
Conclusion Measures to improve adherence to follow-up should be aimed at adolescents who live with a partner and those who do not tell anyone about the violence.