bluuebunny/crossref_metadata_2025_split huggingface dataset:
Global Ocean Particulate Organic Phosphorus, Carbon, Oxygen for Respiration, and Nitrogen (GO-POPCORN)
Authors: Tanioka Tatsuro , Larkin Alyse A. , Moreno Allison R. , Brock Melissa L. , Fagan Adam J. , Garcia Catherine A. , Garcia Nathan S. , Gerace Skylar D. , Lee Jenna A. , Lomas Michael W. , Martiny Adam C. 
Published: November 2022

AbstractConcentrations and elemental stoichiometry of suspended particulate organic carbon, nitrogen, phosphorus, and oxygen demand for respiration (C:N:P:−O2) play a vital role in characterizing and quantifying marine elemental cycles. Here, we present Version 2 of the Global Ocean Particulate Organic Phosphorus, Carbon, Oxygen for Respiration, and Nitrogen (GO-POPCORN) dataset. Version 1 is a previously published dataset of particulate organic matter from 70 different studies between 1971 and 2010, while Version 2 is comprised of data collected from recent cruises between 2011 and 2020. The combined GO-POPCORN dataset contains 2673 paired surface POC/N/P measurements from 70°S to 73°N across all major ocean basins at high spatial resolution. Version 2 also includes 965 measurements of oxygen demand for organic carbon respiration. This new dataset can help validate and calibrate the next generation of global ocean biogeochemical models with flexible elemental stoichiometry. We expect that incorporating variable C:N:P:-O2 into models will help improve our estimates of key ocean biogeochemical fluxes such as carbon export, nitrogen fixation, and organic matter remineralization.
A parallel integrated learning technique of improved particle swarm optimization and BP neural network and its application
Authors: Li Jingming , Dong Xu , Ruan Sumei , Shi Lei 
Published: November 2022

AbstractSwarm intelligence algorithm has attracted a lot of interest since its development, which has been proven to be effective in many application areas. In this study, an enhanced integrated learning technique of improved particle swarm optimization and BPNN (Back Propagation Neural Network) is proposed. First, the theory of good point sets is used to create a particle swarm with a uniform initial spatial distribution. So a good point set adaptive particle swarm optimization (GPSAPSO) algorithm was created by using a multi-population co-evolution approach and introducing a function that dynamically changes the inertia weights with the number of iterations. Sixteen benchmark functions were used to confirm the efficacy of the algorithm. Secondly, a parallel integrated approach combining the GPSAPSO algorithm and the BPNN was developed and utilized to build a water quality prediction model. Finally, four sets of cross-sectional data of the Huai River in Bengbu, Anhui Province, China, were used as simulation data for experiments. The experimental results show that the GPSAPSO-BPNN algorithm has obvious advantages compared with TTPSO-BPNN, NSABC-BPNN, IGSO-BPNN and CRBA-BPNN algorithms, which improves the accuracy of water quality prediction results and provides a scientific basis for water quality monitoring and management.
Microstructures at the distal tip of ant chemosensory sensilla
Authors: Gellert Hannah R. , Halley Daphné C. , Sieb Zackary J. , Smith Jody C. , Pask Gregory M. 
Published: November 2022

AbstractAnts and other eusocial insects emit and receive chemical signals to communicate important information within the colony. In ants, nestmate recognition, task allocation, and reproductive distribution of labor are largely mediated through the detection of cuticular hydrocarbons (CHCs) that cover the exoskeleton. With their large size and limited volatility, these CHCs are believed to be primarily detected through direct contact with the antennae during behavioral interactions. Here we first use scanning electron microscopy to investigate the unique morphological features of CHC-sensitive basiconic sensilla of two ant species, the black carpenter ant Camponotus pennsylvanicus and the Indian jumping ant Harpegnathos saltator. These basiconic sensilla possess an abundance of small pores typical of most insect olfactory sensilla, but also have a large concave depression at the terminal end. Basiconic sensilla are enriched at the distal segments of the antennae in both species, which aligns with their proposed role in contact chemosensation of CHCs. A survey of these sensilla across additional ant species shows varied microstructures at their tips, but each possess surface textures that would also increase sensory surface area. These unique ant chemosensory sensilla represent yet another example of how specialized structures have evolved to serve the functional requirements of eusocial communication.
Benchmarking AutoML for regression tasks on small tabular data in materials design
Authors: Conrad Felix , Mälzer Mauritz , Schwarzenberger Michael , Wiemer Hajo , Ihlenfeldt Steffen 
Published: November 2022

AbstractMachine Learning has become more important for materials engineering in the last decade. Globally, automated machine learning (AutoML) is growing in popularity with the increasing demand for data analysis solutions. Yet, it is not frequently used for small tabular data. Comparisons and benchmarks already exist to assess the qualities of AutoML tools in general, but none of them elaborates on the surrounding conditions of materials engineers working with experimental data: small datasets with less than 1000 samples. This benchmark addresses these conditions and draws special attention to the overall competitiveness with manual data analysis. Four representative AutoML frameworks are used to evaluate twelve domain-specific datasets to provide orientation on the promises of AutoML in the field of materials engineering. Performance, robustness and usability are discussed in particular. The results lead to two main conclusions: First, AutoML is highly competitive with manual model optimization, even with little training time. Second, the data sampling for train and test data is of crucial importance for reliable results.
Optogenetic restoration of high sensitivity vision with bReaChES, a red-shifted channelrhodopsin
Authors: Too Lay Khoon , Shen Weiyong , Protti Dario A. , Sawatari Atomu , A. Black Dylan , Leamey Catherine A. , Y. Huang Jin , Lee So-Ra , E. Mathai Ashish , Lisowski Leszek , Y. Lin John , C. Gillies Mark , Simunovic Matthew P. 
Published: November 2022

AbstractThe common final pathway to blindness in many forms of retinal degeneration is the death of the light-sensitive primary retinal neurons. However, the normally light-insensitive second- and third-order neurons persist optogenetic gene therapy aims to restore sight by rendering such neurons light-sensitive. Here, we investigate whether bReaChES, a newly described high sensitivity Type I opsin with peak sensitivity to long-wavelength visible light, can restore vision in a murine model of severe early-onset retinal degeneration. Intravitreal injection of an adeno-associated viral vector carrying the sequence for bReaChES downstream of the calcium calmodulin kinase IIα promoter resulted in sustained retinal expression of bReaChES. Retinal ganglion cells (RGCs) expressing bReaChES generated action potentials at light levels consistent with bright indoor lighting (from 13.6 log photons cm−2 s−1). They could also detect flicker at up to 50 Hz, which approaches the upper temporal limit of human photopic vision. Topological response maps of bReaChES-expressing RGCs suggest that optogenetically activated RGCs may demonstrate similar topographical responses to RGCs stimulated by photoreceptor activation. Furthermore, treated dystrophic mice displayed restored cortical neuronal activity in response to light and rescued behavioral responses to a looming stimulus that simulated an aerial predator. Finally, human surgical retinal explants exposed to the bReaChES treatment vector demonstrated transduction. Together, these findings suggest that intravitreal gene therapy to deliver bReaChES to the retina may restore vision in human retinal degeneration in vivo at ecologically relevant light levels with spectral and temporal response characteristics approaching those of normal human photopic vision.
An Online experiment during the 2020 US–Iran crisis shows that exposure to common enemies can increase political polarization
Authors: Jahani Eaman , Gallagher Natalie , Merhout Friedolin , Cavalli Nicolo , Guilbeault Douglas , Leng Yan , Bail Christopher A. 
Published: November 2022

AbstractA longstanding theory indicates that the threat of a common enemy can mitigate conflict between members of rival groups. We tested this hypothesis in a pre-registered experiment where 1670 Republicans and Democrats in the United States were asked to complete an online social learning task with a bot that was labeled as a member of the opposing party. Prior to this task, we exposed respondents to primes about (a) a common enemy (involving Iran and Russia); (b) a patriotic event; or (c) a neutral, apolitical prime. Though we observed no significant differences in the behavior of Democrats as a result of priming, we found that Republicans—and particularly those with very strong conservative views—were significantly less likely to learn from Democrats when primed about a common enemy. Because our study was in the field during the 2020 Iran Crisis, we were able to further evaluate this finding via a natural experiment—Republicans who participated in our study after the crisis were even less influenced by the beliefs of Democrats than those Republicans who participated before this event. These findings indicate common enemies may not reduce inter-group conflict in highly polarized societies, and contribute to a growing number of studies that find evidence of asymmetric political polarization in the United States. We conclude by discussing the implications of these findings for research in social psychology, political conflict, and the rapidly expanding field of computational social science.
Ensemble machine learning identifies genetic loci associated with future worsening of disability in people with multiple sclerosis
Authors: Fuh-Ngwa Valery , Zhou Yuan , Melton Phillip E. , van der Mei Ingrid , Charlesworth Jac C. , Lin Xin , Zarghami Amin , Broadley Simon A. , Ponsonby Anne-Louise , Simpson-Yap Steve , Lechner-Scott Jeannette , Taylor Bruce V. 
Published: November 2022

AbstractLimited studies have been conducted to identify and validate multiple sclerosis (MS) genetic loci associated with disability progression. We aimed to identify MS genetic loci associated with worsening of disability over time, and to develop and validate ensemble genetic learning model(s) to identify people with MS (PwMS) at risk of future worsening. We examined associations of 208 previously established MS genetic loci with the risk of worsening of disability; we learned ensemble genetic decision rules and validated the predictions in an external dataset. We found 7 genetic loci (rs7731626: HR 0.92, P = 2.4 × 10–5; rs12211604: HR 1.16, P = 3.2 × 10–7; rs55858457: HR 0.93, P = 3.7 × 10–7; rs10271373: HR 0.90, P = 1.1 × 10–7; rs11256593: HR 1.13, P = 5.1 × 10–57; rs12588969: HR = 1.10, P = 2.1 × 10–10; rs1465697: HR 1.09, P = 1.7 × 10–128) associated with risk worsening of disability; most of which were located near or tagged to 13 genomic regions enriched in peptide hormones and steroids biosynthesis pathways by positional and eQTL mapping. The derived ensembles produced a set of genetic decision rules that can be translated to provide additional prognostic values to existing clinical predictions, with the additional benefit of incorporating relevant genetic information into clinical decision making for PwMS. The present study extends our knowledge of MS progression genetics and provides the basis of future studies regarding the functional significance of the identified loci.
The role of hand size in body representation: a developmental investigation
Authors: Cowie Dorothy , Gottwald Janna M. , Bird Laura-Ashleigh , Bremner Andrew J. 
Published: November 2022

AbstractKnowledge of one’s own body size is a crucial facet of body representation, both for acting on the environment and perhaps also for constraining body ownership. However, representations of body size may be somewhat plastic, particularly to allow for physical growth in childhood. Here we report a developmental investigation into the role of hand size in body representation (the sense of body ownership, perception of hand position, and perception of own-hand size). Using the rubber hand illusion paradigm, this study used different fake hand sizes (60%, 80%, 100%, 120% or 140% of typical size) in three age groups (6- to 7-year-olds, 12- to 13-year-olds, and adults; N = 229). We found no evidence that hand size constrains ownership or position: participants embodied hands which were both larger and smaller than their own, and indeed judged their own hands to have changed size following the illusion. Children and adolescents embodied the fake hands more than adults, with a greater tendency to feel their own hand had changed size. Adolescents were particularly sensitive to multisensory information. In sum, we found substantial plasticity in the representation of own-body size, with partial support for the hypothesis that children have looser representations than adults.
Evaluation of the competitiveness of the container multimodal port hub
Authors: Wan Min , Kuang Haibo , Yu Yue , Zhang Ruirui 
Published: November 2022

AbstractThe research on the competitiveness of container multimodal transport port hub, comprehensively considering the software and hardware strength of port hub, is of great significance to analyze the specific source competitiveness of port hub, the optimal allocation of multimodal transport capacity resources and port operation management planning. Based on this, according to the principle of fuzzy set and membership function in fuzzy theory, this paper adopts the modeling method of multi-attribute decision-making to deeply analyze the key influencing factors of port hub competitiveness, extract the core indicators from the four aspects of business capacity, transport capacity resources, infrastructure and service quality, and then construct the fuzzy multi-attribute decision-making evaluation model of port hub competitiveness, and take the container transportation in Northeast China as an example. The research shows that the key influencing factors of multimodal transport port hub competitiveness have changed from traditional infrastructure factors to flexible influencing factors such as transportation service quality, transportation capacity and resource integration ability, transportation business efficiency, and port enterprises are in the critical period of industry transformation and upgrading. Strengthening inter industry integration, building industrial ecological alliance and building a multi-mode transportation integrated service chain with port as the core play an obvious role in promoting the formation of the core competitiveness of port hub. Among the ports along the coast of Liaoning, Yingkou Port has the strongest competitiveness against the supply of corn containers in Northeast China, and the competitiveness of other ports from strong to weak is Jinzhou port, Beiliang port, Panjin Port, Huludao port, Dandong port, Dalian port and Suizhong port. The research results enrich the theoretical system of multimodal transport port hub competitiveness research, and provide reference suggestions for port enterprises, transportation enterprises, government departments and other relevant subjects in strategy formulation, policy implementation, layout planning and so on.
Harnessing interpretable machine learning for holistic inverse design of origami
Authors: Zhu Yi , Filipov Evgueni T. 
Published: November 2022

AbstractThis work harnesses interpretable machine learning methods to address the challenging inverse design problem of origami-inspired systems. We established a work flow based on decision tree-random forest method to fit origami databases, containing both design features and functional performance, and to generate human-understandable decision rules for the inverse design of functional origami. First, the tree method is unique because it can handle complex interactions between categorical features and continuous features, allowing it to compare different origami patterns for a design. Second, this interpretable method can tackle multi-objective problems for designing functional origami with multiple and multi-physical performance targets. Finally, the method can extend existing shape-fitting algorithms for origami to consider non-geometrical performance. The proposed framework enables holistic inverse design of origami, considering both shape and function, to build novel reconfigurable structures for various applications such as metamaterials, deployable structures, soft robots, biomedical devices, and many more.
Improved identification of pollution source attribution by using PAH ratios combined with multivariate statistics
Authors: Mali Matilda , Ragone Rosa , Dell’Anna Maria Michela , Romanazzi Giuseppe , Damiani Leonardo , Mastrorilli Piero 
Published: November 2022

AbstractPolycyclic aromatic hydrocarbons (PAHs) are contaminants introduced by different pathways in the marine ecosystem, affecting both aquatic and sediment bodies. Identification of their sources is of vital importance for protecting the marine ecosystem. The attribution of the pollution sources is usually made by using diagnostic molecular ratios of PAHs isomers. The reliability of this approach diminishes when PAHs contents are measured far from their original source, for example in water bodies or in bottom sediments. Conventionally the source attribution is based on time consuming univariate methods. In the present work coupling of molecular ratios with advanced supervised statistical techniques was used to increase the accuracy of the PAH source attribution in bottom sediments. Data on PAHs distribution within 5 port areas, with known pattern port activity, were collected. Evaluation of multiple PAHs ratios at once by means of supervised OPLS-DA technique was performed. A robust descriptive and predictive model was set up and successfully validated. The proposed methodology helps identify PAH transport pathways, highlighting interactions between pollution patterns, port activities and coastal land-use supporting decision makers in defining monitoring and mitigation procedures.
Female sexual function in different phenotypes of polycystic ovarian syndrome: a comparative cross-sectional study
Authors: Yarjanli Mahsa , Jahanian Sadatmahalleh Shahideh , Mirzaei Negin , Azarbajani Khadijeh 
Published: November 2022

AbstractPolycystic ovary syndrome (PCOS) coexisting mood disorders along with a combination of aesthetic manifestations may have a detrimental effect on women's sexual function. Hence, different phenotypes of PCOS have different clinical and biochemical signs and symptoms. The aim of this study was to compare women's sexual function (SF) in different phenotypes of PCOS. This cross-sectional study was conducted on 364 women who met the Rotterdam diagnostic criteria to compare different PCOS phenotypes (A = 95, B = 79, C = 95, and D = 95) and 100 non PCOS women in control group. All participants were invited to fill out the female sexual function index (FSFI). Significant differences were observed between the different phenotypes and control group in terms of the total score, sexual desire, arousal, lubrication, and satisfaction (P < 0.001); however, no significant differences were found between different phenotypes in terms of pain (P > 0.05) and orgasm (P > 0.05) but difference was significant between different phenotypes and control group. In addition, phenotype B had the lowest mean score of total FSFI (P < 0.05). The results indicated that women's SF is significantly different in different PCOS phenotypes. It is concluded that in order to solve the SF problems of women with PCOS, different treatment and care measures should be considered according to the relevant phenotype.
Landscape of potentially targetable receptor tyrosine kinase fusions in diverse cancers by DNA-based profiling
Authors: Wang Tiantian , Wei Li , Lu Qiang , Shao Yanmei , You Shuqing , Yin Jiani C. , Wang Sha , Shao Yang , Chen Zhanhong , Wang Zhe 
Published: November 2022

AbstractRecurrent fusions of receptor tyrosine kinases (RTKs) are often driving events in tumorigenesis that carry important diagnostic value and are potentially targetable by the increasing number of tyrosine kinase inhibitors (TKIs). Here, we characterized the spectrum of 1324 RTK fusions with intact kinase domains in solid tumors by DNA-based high-throughput sequencing. Overall, the prevalence of RTK fusions were 4.7%, with variable frequencies and diverse genomic structures and fusion partners across cancer types. Cancer types, such as thyroid cancers, urological cancers and neuroendocrine tumors are selective in the RTK fusions they carry, while others exhibit highly complex spectra of fusion events. Notably, most RTKs were promiscuous in terms of the partner genes they recombine with. A large proportion of RTK fusions had one of the breakpoints localized to intergenic regions. Comprehensive genomic profiling revealed differences in co-mutational patterns pre- and post-TKI treatments across various RTK fusions. At baseline, multiple cases were detected with co-occurring RTK fusions or concomitant oncogenic mutations in driver genes, such as KRAS and EGFR. Following TKI resistance, we observed differences in potential on- and off-target resistance mutations among fusion variants. For example, the EML4-ALK v3 variant displayed more complex on-target resistance mechanisms, which might explain the reduced survival outcome compared with the v1 variant. Finally, we identified two lung cancer patients with MET+ and NTRK1+ tumors, respectively, who responded well to crizotinib treatment. Taken together, our findings demonstrate the diagnostic and prognostic values of screening for RTK fusions using DNA-based sequencing in solid tumors.
High-throughput determination of dry mass of single bacterial cells by ultrathin membrane resonators
Authors: Sanz-Jiménez Adrián , Malvar Oscar , Ruz Jose J. , García-López Sergio , Kosaka Priscila M. , Gil-Santos Eduardo , Cano Álvaro , Papanastasiou Dimitris , Kounadis Diamantis , Mingorance Jesús , Paulo Álvaro San , Calleja Montserrat , Tamayo Javier 
Published: November 2022

AbstractHow bacteria are able to maintain their size remains an open question. Techniques that can measure the biomass (dry mass) of single cells with high precision and high-throughput are demanded to elucidate this question. Here, we present a technological approach that combines the transport, guiding and focusing of individual bacteria from solution to the surface of an ultrathin silicon nitride membrane resonator in vacuum. The resonance frequencies of the membrane undergo abrupt variations at the instants where single cells land on the membrane surface. The resonator design displays a quasi-symmetric rectangular shape with an extraordinary capture area of 0.14 mm2, while maintaining a high mass resolution of 0.7 fg (1 fg = 10−15 g) to precisely resolve the dry mass of single cells. The small rectangularity of the membrane provides unprecedented frequency density of vibration modes that enables to retrieve the mass of individual cells with high accuracy by specially developed inverse problem theory. We apply this approach for profiling the dry mass distribution in Staphylococcus epidermidis and Escherichia coli cells. The technique allows the determination of the dry mass of single bacterial cells with an accuracy of about 1% at an unparalleled throughput of 20 cells/min. Finally, we revisit Koch & Schaechter model developed during 60 s to assess the intrinsic sources of stochasticity that originate cell size heterogeneity in steady-state populations. The results reveal the importance of mass resolution to correctly describe these mechanisms.