bluuebunny/crossref_metadata_2025_split huggingface dataset:
The history of sexual selection research provides insights as to why females are still understudied
Authors: Ah-King Malin 
Published: November 2022

AbstractWhile it is widely acknowledged that Darwin’s descriptions of females were gender-biased, gender bias in current sexual selection research is less recognized. An examination of the history of sexual selection research shows prevalent male precedence—that research starts with male-centered investigations or explanations and thereafter includes female-centered equivalents. In comparison, the incidence of female precedence is low. Furthermore, a comparison between the volume of publications focusing on sexual selection in males versus in females shows that the former far outnumber the latter. This bias is not only a historical pattern; sexual selection theory and research are still male-centered—due to conspicuous traits, practical obstacles, and continued gender bias. Even the way sexual selection is commonly defined contributes to this bias. This history provides an illustrative example by which we can learn to recognize biases and identify gaps in knowledge. I conclude with a call for the scientific community to interrogate its own biases and suggest strategies for alleviating biases in this field and beyond.
Cryo-EM structure of the agonist-bound Hsp90-XAP2-AHR cytosolic complex
Authors: Gruszczyk Jakub , Grandvuillemin Loïc , Lai-Kee-Him Josephine , Paloni Matteo , Savva Christos G. , Germain Pierre , Grimaldi Marina , Boulahtouf Abdelhay , Kwong Hok-Sau , Bous Julien , Ancelin Aurélie , Bechara Cherine , Barducci Alessandro , Balaguer Patrick , Bourguet William 
Published: November 2022

AbstractThe aryl hydrocarbon receptor (AHR) is a ligand-dependent transcription factor that mediates a broad spectrum of (patho)physiological processes in response to numerous substances including pollutants, natural products and metabolites. However, the scarcity of structural data precludes understanding of how AHR is activated by such diverse compounds. Our 2.85 Å structure of the human indirubin-bound AHR complex with the chaperone Hsp90 and the co-chaperone XAP2, reported herein, reveals a closed conformation Hsp90 dimer with AHR threaded through its lumen and XAP2 serving as a brace. Importantly, we disclose the long-awaited structure of the AHR PAS-B domain revealing a unique organisation of the ligand-binding pocket and the structural determinants of ligand-binding specificity and promiscuity of the receptor. By providing structural details of the molecular initiating event leading to AHR activation, our study rationalises almost forty years of biochemical data and provides a framework for future mechanistic studies and structure-guided drug design.
First-in-human phase Ia study of the PI3Kα inhibitor CYH33 in patients with solid tumors
Authors: Wei Xiao-Li , Liu Fu-Rong , Liu Ji-Hong , Zhao Hong-Yun , Zhang Yang , Wang Zhi-Qiang , Qiu Miao-Zhen , Xu Fei , Yu Qiu-Qiong , Du Yi-Wu , Shi Yan-Xia , Wang De-Sheng , Wang Feng-Hua , Xu Rui-Hua 
Published: November 2022

AbstractPIK3CA mutations are highly prevalent in solid tumors. Targeting phosphatidylinositol 3-kinase α is therefore an attractive strategy for treating cancers harboring PIK3CA mutations. Here, we report the results from a phase Ia, open label, dose-escalation and -expansion study (NCT03544905) of CYH33, a highly selective PI3Kα inhibitor, in advanced solid tumors. The primary outcomes were the safety, tolerability, maximum tolerated dose (MTD) and recommended phase 2 dose (RP2D) of CYH33. The secondary outcomes included evaluation of pharmacokinetics, preliminary efficacy and changes in pharmacodynamic biomarkers in response to CYH33 treatment. The exploratory outcome was the relationship between the efficacy of CYH33 treatment and tumor biomarker status, including PIK3CA mutations. A total of 51 patients (19 in the dose escalation stage and 32 in the dose expansion stage) including 36 (70.6%) patients (4 in the dose escalation stage and 32 in the dose expansion stage) with PIK3CA mutations received CYH33 1–60 mg. The MTD of CYH33 was 40 mg once daily, which was also selected as the RP2D. The most common grade 3/4 treatment-related adverse events were hyperglycemia, rash, platelet count decreased, peripheral edema, and fatigue. Forty-two out of 51 patients were evaluable for response, the confirmed objective response rate was 11.9% (5/42). Among 36 patients harboring PIK3CA mutations, 28 patients were evaluable for response, the confirmed objective response rate was 14.3% (4/28). In conclusion, CYH33 exhibits a manageable safety profile and preliminary anti-tumor efficacy in solid tumors harboring PIK3CA mutations.
Breaking through water-splitting bottlenecks over carbon nitride with fluorination
Authors: Wu Ji , Liu Zhonghuan , Lin Xinyu , Jiang Enhui , Zhang Shuai , Huo Pengwei , Yan Yan , Zhou Peng , Yan Yongsheng 
Published: November 2022

AbstractGraphitic carbon nitride has long been considered incapable of splitting water molecules into hydrogen and oxygen without adding small molecule organics despite the fact that the visible-light response and proper band structure fulfills the proper energy requirements to evolve oxygen. Herein, through in-situ observations of a collective C = O bonding, we identify the long-hidden bottleneck of photocatalytic overall water splitting on a single-phased g-C3N4 catalyst via fluorination. As carbon sites are occupied with surface fluorine atoms, intermediate C=O bonding is vastly minimized on the surface and an order-of-magnitude improved H2 evolution rate compared to the pristine g-C3N4 catalyst and continuous O2 evolution is achieved. Density functional theory calculations suggest an optimized oxygen evolution reaction pathway on neighboring N atoms by C–F interaction, which effectively avoids the excessively strong C-O interaction or weak N-O interaction on the pristine g-C3N4.
A global dynamic runoff application and dataset based on the assimilation of GPM, SMAP, and GCN250 curve number datasets
Authors: Sujud Lara H. , Jaafar Hadi H. 
Published: November 2022

AbstractRunoff modelling is a crucial element in hydrologic sciences. However, a global runoff database is not currently available at a resolution higher than 0.1°. We use the recently developed Global Curve Number dataset (GCN250) to develop a dynamic runoff application (2015 – present) and that can be accessed via a Google Earth Engine application. We also provide a global mean monthly runoff dataset for April 2015-2021 in GeoTIFF format at a 250-meter resolution. We utilize soil moisture and GPM rainfall to dynamically retrieve the appropriate curve number and generate the corresponding runoff anywhere on Earth. Mean annual global runoff ratio results for 2021 were comparable to the runoff ratio from the Global Land Data Assimilation System (0.079 vs. 0.077, respectively). Mean annual global runoff from GCN and GLDAS were within 11% each other for 2020–2021 (0.18 vs. 0.16 mm/day, respectively). The GCN250 runoff application and the dataset are useful for many water applications such hydrologic design, land management, water resources management, and flood risk assessment.
Evaluating different web applications to assess the toxicity of plasticizers
Authors: Arulanandam Charli Deepak , Hwang Jiang-Shiou , Rathinam Arthur James , Dahms Hans-Uwe 
Published: November 2022

AbstractPlasticizers increase the flexibility of plastics. As environmental leachates they lead to increased water and soil pollution, as well as to serious harm to human health. This study was set out to explore various web applications to predict the toxicological properties of plasticizers. Web-based tools (e.g., BOILED-Egg, LAZAR, PROTOX-II, CarcinoPred-EL) and VEGA were accessed via an 5th–10th generation computer in order to obtain toxicological predictions. Based on the LAZAR mutagenicity assessment was only bisphenol F predicted as mutagenic. The BBP and DBP in RF; DEHP in RF and XGBoost; DNOP in RF and XGBoost models were predicted as carcinogenic in the CarcinoPred-EL web application. From the bee predictive model (KNN/IRFMN) BPF, di-n-propyl phthalate, diallyl phthalate, dibutyl phthalate, and diisohexyl phthalate were predicted as strong bee toxicants. Acute toxicity for fish using the model Sarpy/IRFMN predicted 19 plasticizers as strong toxicants with LC50 values of less than 1 mg/L. This study also considered plasticizer effects on gastrointestinal absorption and other toxicological endpoints.
A Bayesian generative neural network framework for epidemic inference problems
Authors: Biazzo Indaco , Braunstein Alfredo , Dall’Asta Luca , Mazza Fabio 
Published: November 2022

AbstractThe reconstruction of missing information in epidemic spreading on contact networks can be essential in the prevention and containment strategies. The identification and warning of infectious but asymptomatic individuals (i.e., contact tracing), the well-known patient-zero problem, or the inference of the infectivity values in structured populations are examples of significant epidemic inference problems. As the number of possible epidemic cascades grows exponentially with the number of individuals involved and only an almost negligible subset of them is compatible with the observations (e.g., medical tests), epidemic inference in contact networks poses incredible computational challenges. We present a new generative neural networks framework that learns to generate the most probable infection cascades compatible with observations. The proposed method achieves better (in some cases, significantly better) or comparable results with existing methods in all problems considered both in synthetic and real contact networks. Given its generality, clear Bayesian and variational nature, the presented framework paves the way to solve fundamental inference epidemic problems with high precision in small and medium-sized real case scenarios such as the spread of infections in workplaces and hospitals.
Robustness and efficiency of international pesticide trade networks subject to link removal strategies
Authors: Xie Wen-Jie , Li Jian-An , Wei Na , Wang Li , Zhou Wei-Xing 
Published: November 2022

AbstractThe international pesticide trade network (iPTN) is a key factor affecting global food production and food security. The trade relationship is a key component in iPTNs. In a complex international trade environment, we model the impacts of uncertain factors such as trade wars, economic blockades and local wars, as removing vital relationships in the trade network. There are many complex network studies on node centrality, but few on link centrality or link importance. We propose a new method for computing network link centrality. The main innovation of the method is in converting the original network into a dual graph, the nodes in the dual graph corresponding to the links of the original network. Through the dual graph, the node centrality indicators can measure the centrality of the links in the original network. We verify the effectiveness of the network link centrality indicator based on the dual graph in the iPTN, analyze the relationship between the existing network link centrality indicators and the indicator proposed in this paper, and compare their differences. It is found that the trade relationships with larger indicators (hub, outcloseness, outdegree) based on the dual graph have a greater impact on network efficiency than those based on the original pesticide trade networks.
Differential effects of periodontal microbiome on the rheumatoid factor induction during rheumatoid arthritis pathogenesis
Authors: Kim Ji-Won , Jung Hyerin , Baek In-Pyo , Nam Yoojun , Kang Jaewoo , Chung Min Kyung , Park Jun-Beom , Lee Jennifer , Kwok Seung-Ki , Kim Wan-Uk , Park Sung-Hwan , Ju Ji Hyeon 
Published: November 2022

AbstractAssociation between exposure to periodontal bacteria and development of autoantibodies related to rheumatoid arthritis (RA) has been widely accepted; however, direct causal relationship between periodontal bacteria and rheumatoid factor (RF) is currently not fully understood. We investigated whether periodontal bacteria could affect RF status. Patients with preclinical, new-onset, or chronic RA underwent periodontal examination, and investigation of subgingival microbiome via 16S rRNA sequencing. Degree of arthritis and RF induction was examined in collagen-induced arthritis (CIA) mice that were orally inoculated with different periodontal bacteria species. Subsequently, single-cell RNA sequencing analysis of the mouse spleen cells was performed. Patients with preclinical RA showed an increased abundance of the Porphyromonadacae family in the subgingival microbiome compared to those with new-onset or chronic RA, despite comparable periodontitis severity among them. Notably, a distinct subgingival microbial community was found between patients with high-positive RF and those with negative or low-positive RF (p=0.022). Oral infections with the periodontal pathogensP. gingivalisandTreponema denticolain CIA mice similarly enhanced arthritis score, but resulted in different levels of RF induction. Genes related to B cell receptor signaling, B cell proliferation, activation, and differentiation, and CD4+T cell costimulation and cytokine production were involved in the differential induction of RF in mice exposed to different bacteria. In summary, periodontal microbiome might shape RF status by affecting the humoral immune response during RA pathogenesis.
λ-Carrageenan promotes plant growth in banana via enhancement of cellular metabolism, nutrient uptake, and cellular homeostasis
Authors: Thye Kah-Lok , Wan Abdullah Wan Muhamad Asrul Nizam , Balia Yusof Zetty Norhana , Wee Chien-Yeong , Ong-Abdullah Janna , Loh Jiun-Yan , Cheng Wan-Hee , Lamasudin Dhilia Udie , Lai Kok-Song 
Published: November 2022

AbstractBanana (Musa acuminata) is an important fruit crop and source of income for various countries, including Malaysia. To date, current agrochemical practice has become a disputable issue due to its detrimental effect on the environment. λ-carrageenan, a natural polysaccharide extracted from edible red seaweed, has been claimed to be a potential plant growth stimulator. Hence, the present study investigates the effects of λ-carrageenan on plant growth using Musa acuminata cv. Berangan (AAA). Vegetative growth such as plant height, root length, pseudostem diameter, and fresh weight was improved significantly in λ-carrageenan-treated banana plants at an optimum concentration of 750 ppm. Enhancement of root structure was also observed in optimum λ-carrageenan treatment, facilitating nutrients uptake in banana plants. Further biochemical assays and gene expression analysis revealed that the increment in growth performance was consistent with the increase of chlorophyll content, protein content, and phenolic content, suggesting that λ-carrageenan increases photosynthesis rate, protein biosynthesis, and secondary metabolites biosynthesis which eventually stimulate growth. Besides, λ-carrageenan at optimum concentration also increased catalase and peroxidase activities, which led to a significant reduction in hydrogen peroxide and malondialdehyde, maintaining cellular homeostasis in banana plants. Altogether, λ-carrageenan at optimum concentration improves the growth of banana plants via inducing metabolic processes, enhancing nutrient uptake, and regulation of cell homeostasis. Further investigations are needed to evaluate the effectiveness of λ-carrageenan on banana plants under field conditions.
DFT analogue of prospecting the spin-polarised properties of layered perovskites Ba2ErNbO6 and Ba2TmNbO6 influenced by electronic structure
Authors: Khandy Saveer Ahmad , Gupta Dinesh C. 
Published: November 2022

AbstractSince the unexpected accelerated discovery of half-metallic perovskites is continuously on the rise both from basic sciences and application-oriented sides. Herein, for the first time in this carried research work, we significantly delivered a detailed analysis on one of experimentally synthesized perovskite structure Ba2ErNbO6 and in related to Ba2TmNbO6 within the realm of unified density functional theory. Initially, the structural stability of two molecular perovskite structures were critically established interms of their total ground state and cohesive energies by the expendition of Brich Murnaghan equation of state. Also, the tolerance factor (τ) oversees the cubic structural stability without possessing any geometrical strains. More likely, the density functional perturbation theory (DFPT) has been calibrated to perceive the dynamical context of these layered structures. Also, from the understandings of second order elastic and mechanical parameters adresses their suitable ductile characteristics. The quantum mechanical refinement of their intrinsic electronic structures were systematically tuned by the exploitation of Generalised gradient approximation (GGA), on-site Hubbard scheme (GGA + U) selected to the strongly correlated electrons of particular angular momentum and modified Becke-Johnson (mBJ) potential. Moreover, the two-dimensional representation of asymmetric density of states (DOS) pinned around the Fermi-level (EF) and the interpretation linked to their corresponding spin-polarised band structures signatures the well-known half-metallic nature. Subsequently, the transport properties especially the value of figure of merit (ZT) equals to unity (1) along the selected chemical potential range at different temperatures. The summed-up properties and the overall tendency triggers the possibility of these materials to register their extending applications in spintronics, thermoelectrics, nanoengineering, and radioisotope generator perspectives.
The study of aggression and affiliation motifs in bottlenose dolphins’ social networks
Authors: Pérez-Manrique Ana , Fernández-Gracia Juan , Gomila Antoni , Ramasco José J. 
Published: November 2022

AbstractNetworks in biology have provided a powerful tool to describe and study very complex biological processes and systems such as animal societies. Social network analysis allows us to assess different processes occurring in animal groups. In the current study, we use this approach to investigate how conflict dynamics and post-conflict interactions shape the social networks of groups of captive bottlenose dolphins. We first examined temporal changes and aggression-affiliation motifs in the observed dolphins’ network structure. Using the results of the previous analysis, we built two models that simulate the dynamics of aggression and affiliation in a small dolphin group. The first model is based only on the observed statistics of interactions, whereas the second includes post conflict memory effects as well. We found that the resulting social networks and their most common motifs matched the association patterns observed in wild and captive dolphins. Furthermore, the model with memory was able to capture the observed dynamics of this group of dolphins. Thus, our models suggest the presence and influence of post-conflict behaviors on the structure of captive dolphins’ social networks. Therefore, the network approach reveals as an effective method to define animal social networks and study animal sociality. Finally, this approach can have important applications in the management of animal populations in captive settings.
Cyclododecane shaping, sublimation rate and residue analysis for the extraction of painting micro-samples from resin cross-sections
Authors: Jaques Victory Armida Janine , Zikmundová Eva , Holas Jiří , Zikmund Tomáš , Kaiser Jozef , Holcová Katarína 
Published: November 2022

AbstractCross-section preparation of painting micro-samples is part of their routine analysis. This type of preparation can be used for several analytical techniques, such as scanning electron microscopy, Fourier-transform infrared spectroscopy, Raman spectroscopy, and optical microscopy. These techniques offer high-resolution imaging and/or elemental information, providing access to technical and material data important for the interpretation, preservation, and restoration of painted artworks. However, it also means that the material from the sample embedded in the resin becomes unreachable for further analysis, except for the polished surface of the cross-section. Degradation of the embedding medium can also occur over time, which can lead to misinterpretation, loss of information, or even complete destruction of the embedded sample. In the field of cultural heritage, cyclododecane (CDD) is commonly used for the consolidation and protection of objects, and is used in the preparation of cross-sections to prevent contamination of the sample by the embedding medium. This study enhanced the existing preparation process by shaping the CDD layer to enable extraction of the micro-sample from the resin if needed, without compromising the integrity of the sample. Moreover, the purity, the sublimation rate in a normal environment and a vacuum, and the impact of CDD on three different types of samples (historical painting on a canvas, wall painting fragment, model sample) were examined.
Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases
Authors: Toyohara Yusuke , Sone Kenbun , Noda Katsuhiko , Yoshida Kaname , Kurokawa Ryo , Tanishima Tomoya , Kato Shimpei , Inui Shohei , Nakai Yudai , Ishida Masanori , Gonoi Wataru , Tanimoto Saki , Takahashi Yu , Inoue Futaba , Kukita Asako , Kawata Yoshiko , Taguchi Ayumi , Furusawa Akiko , Miyamoto Yuichiro , Tsukazaki Takehiro , Tanikawa Michihiro , Iriyama Takayuki , Mori-Uchino Mayuyo , Tsuruga Tetsushi , Oda Katsutoshi , Yasugi Toshiharu , Takechi Kimihiro , Abe Osamu , Osuga Yutaka 
Published: November 2022

AbstractUterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-based diagnosis in patients with uterine sarcomas. Fifteen sequences of MRI for patients (uterine sarcoma group: n = 63; uterine leiomyoma: n = 200) were used to train the models. Six radiologists (three specialists, three practitioners) interpreted the same images for validation. The most important individual sequences for diagnosis were axial T2-weighted imaging (T2WI), sagittal T2WI, and diffusion-weighted imaging. These sequences also represented the most accurate combination (accuracy: 91.3%), achieving diagnostic ability comparable to that of specialists (accuracy: 88.3%) and superior to that of practitioners (accuracy: 80.1%). Moreover, radiologists’ diagnostic accuracy improved when provided with DNN results (specialists: 89.6%; practitioners: 92.3%). Our DNN models are valuable to improve diagnostic accuracy, especially in filling the gap of clinical skills between interpreters. This method can be a universal model for the use of deep learning in the diagnostic imaging of rare tumors.