bluuebunny/crossref_metadata_2025 huggingface dataset:
Loess deposits in the low latitudes of East Asia reveal the ~20-kyr precipitation cycle
Authors: Li Xusheng , Zhou Yuwen , Han Zhiyong , Yuan Xiaokang , Yi Shuangwen , Zeng Yuqiang , Qin Lisha , Lu Ming , Lu Huayu
Published: February 2024

The cycle of precipitation change is key to understanding the driving mechanism of the East Asian summer monsoon (EASM). However, the dominant cycles of EASM precipitation revealed by different proxy indicators are inconsistent, leading to the “Chinese 100 kyr problem”. In this study, we examine a high-resolution, approximately 350,000-year record from a low-latitude loess profile in China. Our analyses show that variations in the ratio of dithionite−citrate−bicarbonate extractable iron to total iron are dominated by the ~20-kyr cycle, reflecting changes in precipitation. In contrast, magnetic susceptibility varies with the ~100-kyr cycle and may be mainly controlled by temperature-induced redox processes or precipitation-induced signal smoothing. Our results suggest that changes in the EASM, as indicated by precipitation in this region, are mainly forced by precession-dominated insolation variations, and that precipitation and temperature may have varied with different cycles over the past ~350,000 years.
RETRACTED ARTICLE: Systems engineering of Escherichia coli for high-level glutarate production from glucose
Authors: Zhang Zhilan , Chu Ruyin , Wei Wanqing , Song Wei , Ye Chao , Chen Xiulai , Wu Jing , Liu Liming , Gao Cong
Published: February 2024

Glutarate is a key monomer in polyester and polyamide production. The low efficiency of the current biosynthetic pathways hampers its production by microbial cell factories. Herein, through metabolic simulation, a lysine-overproducing E. coli strain Lys5 is engineered, achieving titer, yield, and productivity of 195.9 g/L, 0.67 g/g glucose, and 5.4 g/L·h, respectively. Subsequently, the pathway involving aromatic aldehyde synthase, monoamine oxidase, and aldehyde dehydrogenase (AMA pathway) is introduced into E. coli Lys5 to produce glutarate from glucose. To enhance the pathway’s efficiency, rational mutagenesis on the aldehyde dehydrogenase is performed, resulting in the development of variant Mu5 with a 50-fold increase in catalytic efficiency. Finally, a glutarate tolerance gene cbpA is identified and genomically overexpressed to enhance glutarate productivity. With enzyme expression optimization, the glutarate titer, yield, and productivity of E. coli AMA06 reach 88.4 g/L, 0.42 g/g glucose, and 1.8 g/L·h, respectively. These findings hold implications for improving glutarate biosynthesis efficiency in microbial cell factories.
Oral and middle ear delivery of otitis media standard of care antibiotics, but not biofilm-targeted antibodies, alter chinchilla nasopharyngeal and fecal microbiomes
Authors: Duff Audrey F. , Jurcisek Joseph A. , Kurbatfinski Nikola , Chiang Tendy , Goodman Steven D. , Bakaletz Lauren O. , Bailey Michael T.
Published: February 2024

Otitis media (OM) is one of the most globally pervasive pediatric conditions. Translocation of nasopharynx-resident opportunistic pathogens like nontypeableHaemophilus influenzae(NTHi) assimilates into polymicrobial middle ear biofilms, which promote OM pathogenesis and substantially diminish antibiotic efficacy. Oral or tympanostomy tube (TT)-delivered antibiotics remain the standard of care (SOC) despite consequences including secondary infection, dysbiosis, and antimicrobial resistance. Monoclonal antibodies (mAb) against two biofilm-associated structural proteins, NTHi-specific type IV pilus PilA (anti-rsPilA) and protective tip-region epitopes of NTHi integration host factor (anti-tip-chimer), were previously shown to disrupt biofilms and restore antibiotic sensitivity in vitro. However, the additional criterion for clinical relevance includes the absence of consequential microbiome alterations. Here, nine chinchilla cohorts (n = 3/cohort) without disease were established to evaluate whether TT delivery of mAbs disrupted nasopharyngeal or fecal microbiomes relative to SOC-OM antibiotics. Cohort treatments included a 7d regimen of oral amoxicillin-clavulanate (AC) or 2d regimen of TT-delivered mAb, AC, Trimethoprim-sulfamethoxazole (TS), ofloxacin, or saline. Fecal and nasopharyngeal lavage (NPL) samples were collected before and several days post treatment (DPT) for 16S sequencing. While antibiotic-treated cohorts displayed beta-diversity shifts (PERMANOVA,P < 0.05) and reductions in alpha diversity (q < 0.20) relative to baseline, mAb antibodies failed to affect diversity, indicating maintenance of a eubiotic state. Taxonomic and longitudinal analyses showed blooms in opportunistic pathogens (ANCOM) and greater magnitudes of compositional change (P < 0.05) following broad-spectrum antibiotic but not mAb treatments. Collectively, results showed broad-spectrum antibiotics induced significant fecal and nasopharyngeal microbiome disruption regardless of delivery route. Excitingly, biofilm-targeting antibodies had little effect on fecal and nasopharyngeal microbiomes.
A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments
Authors: Biswas Arpan , Liu Yongtao , Creange Nicole , Liu Yu-Chen , Jesse Stephen , Yang Jan-Chi , Kalinin Sergei V. , Ziatdinov Maxim A. , Vasudevan Rama K.
Published: February 2024

Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined a priori with the ability to shift the trajectory of the optimization based on human-identified findings during the experiment is lacking. Thus, to highlight the best of both human operators and AI-driven experiments, here we present the development of a human–AI collaborated experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly with human real-time feedback. Here, the human guidance overpowers AI at early iteration when prior knowledge (uncertainty) is minimal (higher), while the AI overpowers the human during later iterations to accelerate the process with the human-assessed goal. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and in real-time on an atomic force microscope, with human assessment to find symmetric hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human–AI approaches for curiosity driven exploration of systems across experimental domains.
Systematic review of the use of ultrasound for venous assessment and venous thrombosis screening in spaceflight
Authors: Elias Antoine , Weber Tobias , Green David A. , Harris Katie M. , Laws Jonathan M. , Greaves Danielle K. , Kim David S. , Mazzolai-Duchosal Lucia , Roberts Lara , Petersen Lonnie G. , Limper Ulrich , Bergauer Andrej , Elias Michael , Winnard Andrew , Goswami Nandu
Published: February 2024

The validity of venous ultrasound (V-US) for the diagnosis of deep vein thrombosis (DVT) during spaceflight is unknown and difficult to establish in diagnostic accuracy and diagnostic management studies in this context. We performed a systematic review of the use of V-US in the upper-body venous system in spaceflight to identify microgravity-related changes and the effect of venous interventions to reverse them, and to assess appropriateness of spaceflight V-US with terrestrial standards. An appropriateness tool was developed following expert panel discussions and review of terrestrial diagnostic studies, including criteria relevant to crew experience, in-flight equipment, assessment sites, ultrasound modalities, and DVT diagnosis. Microgravity-related findings reported as an increase in internal jugular vein (IJV) cross-sectional area and pressure were associated with reduced, stagnant, and retrograde flow. Changes were on average responsive to venous interventions using lower body negative pressure, Bracelets, Valsalva and Mueller manoeuvres, and contralateral IJV compression. In comparison with terrestrial standards, spaceflight V-US did not meet all appropriateness criteria. In DVT studies (n = 3), a single thrombosis was reported and only ultrasound modality criterion met the standards. In the other studies (n = 15), all the criteria were appropriate except crew experience criterion, which was appropriate in only four studies. Future practice and research should account for microgravity-related changes, evaluate individual effect of venous interventions, and adopt Earth-based V-US standards.
Ultrafast readout, crosstalk suppression iontronic array enabled by frequency-coding architecture
Authors: Li Zhibin , Yang Jing , Zhang Yuxuan , Geng Peiyan , Feng Jiansong , Chen Bin , Zhang Xu , Yuan Guojiang , Chen Xiaolong , Wang Taihong
Published: February 2024

The development of iontronic skin (I-skin) capable of ultrafast sensing in a wide pressure range, comparable to human skin, is of paramount importance for intelligent robotics. However, this remains a major challenge due to the lack of iontronic array architectures that can achieve ultrafast readout and crosstalk-free under large capacitance response generated within a wide pressure range. Here, we report a frequency-coding architecture of artificial ion mechanoreceptor skin (AIM-skin) that can provide a universal mode of iontronic array sensing and bypass the dependence of complex integrated back-end interface electronics. Notably, the successful implementation of orthogonal frequency coding in the AIM-skin with high sensitivity and ultrawide pressure range achieve ultrafast parallel readout for the spatiotemporal mechanical stimuli. Furthermore, the parallel zero-potential mechanism (PZPM) of the architecture effectively mitigates electrical crosstalk between sensing units. We have demonstrated that combhination of proposed device and deep learning has a broad application prospect in intelligent human-machine interaction and real-time dynamic robotic manipulation.
Impact of the aqueous corrosion induced alteration layer on mechanical properties of pharmaceutical glasses
Authors: Zheng Qiuju , Ma Xinlin , Ma Youze , Yu Jiaxin , Yue Yuanzheng , He Hongtu
Published: February 2024

It is known that network modifying ions (such as alkali or alkaline earth ions) make glasses susceptible to aqueous corrosion, resulting in the alteration of their surface layers. However, the effect of the altered layers on the mechanical properties of glasses has not been well understood. In this work we study this effect using the pharmaceutical boroaluminosilicate (BAS) glasses as objects by performing nano- and macroscale mechanical tests. The results show that extending the corrosion time increases the thickness of the alteration layer of the BAS glass. The water-related species in the alteration layer lowers the nanohardness, the reduced modulus, the nanowear resistance and Vickers hardness. The corrosion-induced “silica-like” structure in alteration layer benefits the densification of the subsurface caused by nanoindentation and nanowear, and thereby enhances the fracture toughness of the BAS glass. The correlation between the water content in the alteration layer and the mechanical properties has been revealed. This work is instrumental in the design of the next generation of pharmaceutical glasses with higher toughness.
High temperature spin selectivity in a quantum dot qubit using reservoir spin accumulation
Authors: Jansen R. , Yuasa S.
Published: February 2024

Employing spins in quantum dots for fault-tolerant quantum computing in large-scale qubit arrays with on-chip control electronics requires high-fidelity qubit operation at elevated temperature. This poses a challenge for single spin initialization and readout. Existing schemes rely on Zeeman splitting or Pauli spin blockade with typical energy scales of 0.1 or 1 meV for electron-based qubits, so that sufficient fidelity is obtained only at temperatures around or below 0.1 or 1 K, respectively. Here we describe a method to achieve high temperature spin selectivity in a quantum dot using a reservoir with a spin accumulation, which deterministically sets the spin of a single electron on the dot. Since spin accumulation as large as 10 meV is achievable in silicon, spin selection with electrically adjustable error rates below 10−4 is possible even in a liquid He bath at 4 K. Via the reservoir spin accumulation, induced and controlled by a nearby ferromagnet, classical information (magnetization direction) is mapped onto a spin qubit. These features provide the prospect of spin qubit operation at elevated temperatures and connect the worlds of quantum computing and spintronics.
The gut virome is associated with stress-induced changes in behaviour and immune responses in mice
Authors: Ritz Nathaniel L. , Draper Lorraine A. , Bastiaanssen Thomaz F. S. , Turkington Christopher J. R. , Peterson Veronica L. , van de Wouw Marcel , Vlckova Klara , Fülling Christine , Guzzetta Katherine E. , Burokas Aurelijus , Harris Hugh , Dalmasso Marion , Crispie Fiona , Cotter Paul D. , Shkoporov Andrey N. , Moloney Gerard M. , Dinan Timothy G. , Hill Colin , Cryan John F.
Published: February 2024

The microbiota–gut–brain axis has been shown to play an important role in the stress response, but previous work has focused primarily on the role of the bacteriome. The gut virome constitutes a major portion of the microbiome, with bacteriophages having the potential to remodel bacteriome structure and activity. Here we use a mouse model of chronic social stress, and employ 16S rRNA and whole metagenomic sequencing on faecal pellets to determine how the virome is modulated by and contributes to the effects of stress. We found that chronic stress led to behavioural, immune and bacteriome alterations in mice that were associated with changes in the bacteriophage classCaudoviricetesand unassigned viral taxa. To determine whether these changes were causally related to stress-associated behavioural or physiological outcomes, we conducted a faecal virome transplant from mice before stress and autochthonously transferred it to mice undergoing chronic social stress. The transfer of the faecal virome protected against stress-associated behaviour sequelae and restored stress-induced changes in select circulating immune cell populations, cytokine release, bacteriome alterations and gene expression in the amygdala. These data provide evidence that the virome plays a role in the modulation of the microbiota–gut–brain axis during stress, indicating that these viral populations should be considered when designing future microbiome-directed therapies.
A framework for automated scalable designation of viral pathogen lineages from genomic data
Authors: McBroome Jakob , de Bernardi Schneider Adriano , Roemer Cornelius , Wolfinger Michael T. , Hinrichs Angie S. , O’Toole Aine Niamh , Ruis Christopher , Turakhia Yatish , Rambaut Andrew , Corbett-Detig Russell
Published: February 2024

Pathogen lineage nomenclature systems are a key component of effective communication and collaboration for researchers and public health workers. Since February 2021, the Pango dynamic lineage nomenclature for SARS-CoV-2 has been sustained by crowdsourced lineage proposals as new isolates were sequenced. This approach is vulnerable to time-critical delays as well as regional and personal bias. Here we developed a simple heuristic approach for dividing phylogenetic trees into lineages, including the prioritization of key mutations or genes. Our implementation is efficient on extremely large phylogenetic trees consisting of millions of sequences and produces similar results to existing manually curated lineage designations when applied to SARS-CoV-2 and other viruses including chikungunya virus, Venezuelan equine encephalitis virus complex and Zika virus. This method offers a simple, automated and consistent approach to pathogen nomenclature that can assist researchers in developing and maintaining phylogeny-based classifications in the face of ever-increasing genomic datasets.
A probabilistic computation framework to estimate the dawn phenomenon in type 2 diabetes using continuous glucose monitoring
Authors: Barua Souptik , Glantz Namino , Larez Arianna , Bevier Wendy , Sabharwal Ashutosh , Kerr David
Published: February 2024

In type 2 diabetes (T2D), the dawn phenomenon is an overnight glucose rise recognized to contribute to overall glycemia and is a potential target for therapeutic intervention. Existing CGM-based approaches do not account for sensor error, which can mask the true extent of the dawn phenomenon. To address this challenge, we developed a probabilistic framework that incorporates sensor error to assign a probability to the occurrence of dawn phenomenon. In contrast, the current approaches label glucose fluctuations as dawn phenomena as a binary yes/no. We compared the proposed probabilistic model with a standard binary model on CGM data from 173 participants (71% female, 87% Hispanic/Latino, 54 ± 12 years, with either a diagnosis of T2D for six months or with an elevated risk of T2D) stratified by HbA1c levels into normal but at risk for T2D, with pre-T2D, or with non-insulin-treated T2D. The probabilistic model revealed a higher dawn phenomenon frequency in T2D [49% (95% CI 37–63%)] compared to pre-T2D [36% (95% CI 31–48%), p = 0.01] and at-risk participants [34% (95% CI 27–39%), p < 0.0001]. While these trends were also found using the binary approach, the probabilistic model identified significantly greater dawn phenomenon frequency than the traditional binary model across all three HbA1c sub-groups (p < 0.0001), indicating its potential to detect the dawn phenomenon earlier across diabetes risk categories.
Seasonal variation of net ecosystem carbon exchange and gross primary production over a Loess Plateau semi-arid grassland of northwest China
Authors: Zhang Xueteng , Bi Jianrong , Zhu Di , Meng Zhaozhao
Published: February 2024

Grassland ecosystems store approximately one-third of the global terrestrial carbon stocks, which play a crucial role in regulating the carbon cycle on regional and global scales, but the current scientific understanding of the variation in net carbon dioxide exchange (NEE) on grassland ecosystems is still limited. Based on the eddy covariance technique, this study investigated the seasonal variation of ecosystem respiration (Reco) and gross primary production (GPP) from 2018 to 2020 in a semi-arid grassland on the Loess Plateau in northwest China. The results indicated that the annual cumulative average NEE value was − 0.778 kg C/m2, the growing season cumulative value accounted for approximately 83.81%, which suggested that the semiarid grassland showed a notable soil carbon sink. The correlation analysis revealed that soil temperature (Ts) (RReco = 0.71, RGPP = 0.61) and soil water content (SWC) (RReco = 0.47, RGPP = 0.44) were the two main driving factors in modulating the variation of daily average GPP and Reco (P < 0.01). Therefore, the monthly average of GPP and Reco increased with the increase in Ts (RGPP = 0.716, P < 0.01; RReco = 0.586, P < 0.05), resulting in an increase in the carbon sequestration capacity of the grass ecosystem. This study also showed that soil moisture has a promoting effect on the response of Reco and GPP to Ts, and the correlation among GPP, Reco, and Ts was much stronger under wet conditions. For instance, the coefficient of determination of Reco and GPP with Ts under wet conditions in 2018 increased by 0.248 and 0.286, respectively, compared to those under droughty conditions. Additionally, the temperature sensitivity of Reco (Q10) increased by 46.13% compared to dry conditions. In addition, carbon exchange models should consider the synergistic effect of Ts and SWC as one of the main driving factors for theoretical interpretation or modeling. Under the potential scenario of future global warming and the frequent extreme weather events, our findings have important implications for predicting future CO2 exchange and establishing an optimal ecological model of carbon flux exchange.
Predicting soil cone index and assessing suitability for wind and solar farm development in using machine learning techniques
Authors: Hassan Marwa , Beshr Eman
Published: February 2024

This study proposes a novel approach that combines machine learning models to predict soil compaction using the soil cone index values. The methodology incorporates support vector regression (SVR) to gather input data on key soil parameters, and the output data from SVR are used as inputs for additional machine learning techniques such as Gradient Boosting, Decision Tree, Artificial Neural Networks, and Adaptive Neuro-Fuzzy Inference System. Evaluation of Artificial Intelligent techniques shows that the XGBoost model outperforms others, exhibiting high accuracy and reliability with low mean square error and high correlation coefficient. The effectiveness of the XGBoost model has implications for soil management, agricultural productivity, and land suitability evaluations, particularly for renewable energy projects. By integrating advanced AI techniques, stakeholders can make informed decisions about land use planning, sustainable farming practices, and the feasibility of renewable energy installations. Overall, this research contributes to soil science by demonstrating the potential of AI techniques, specifically the XGBoost model, in accurately predicting soil compaction and supporting optimal soil management practices.
Intelligent robotics harvesting system process for fruits grasping prediction
Authors: Alaaudeen K. M. , Selvarajan Shitharth , Manoharan Hariprasath , Jhaveri Rutvij H.
Published: February 2024

This paper proposes and executes an in-depth learning-based image processing approach for self-picking apples. The system includes a lightweight one-step detection network for fruit recognition. As well as computer vision to analyze the point class and anticipate a correct approach position for each fruit before grabbing. Using the raw inputs from a high-resolution camera, fruit recognition and instance segmentation are done on RGB photos. The computer vision classification and grasping systems are integrated and outcomes from tree-grown foods are provided as input information and output methodology poses for every apple and orange to robotic arm execution. Before RGB picture data is acquired from laboratory and plantation environments, the developed vision method will be evaluated. Robot harvest experiment is conducted in indoor as well as outdoor to evaluate the proposed harvesting system's performance. The research findings suggest that the proposed vision technique can control robotic harvesting effectively and precisely where the success rate of identification is increased above 95% in case of post prediction process with reattempts of less than 12%.
Evaluating AI in medicine: a comparative analysis of expert and ChatGPT responses to colorectal cancer questions
Authors: Peng Wen , feng Yifei , Yao Cui , Zhang Sheng , Zhuo Han , Qiu Tianzhu , Zhang Yi , Tang Junwei , Gu Yanhong , Sun Yueming
Published: February 2024

Colorectal cancer (CRC) is a global health challenge, and patient education plays a crucial role in its early detection and treatment. Despite progress in AI technology, as exemplified by transformer-like models such as ChatGPT, there remains a lack of in-depth understanding of their efficacy for medical purposes. We aimed to assess the proficiency of ChatGPT in the field of popular science, specifically in answering questions related to CRC diagnosis and treatment, using the book “Colorectal Cancer: Your Questions Answered” as a reference. In general, 131 valid questions from the book were manually input into ChatGPT. Responses were evaluated by clinical physicians in the relevant fields based on comprehensiveness and accuracy of information, and scores were standardized for comparison. Not surprisingly, ChatGPT showed high reproducibility in its responses, with high uniformity in comprehensiveness, accuracy, and final scores. However, the mean scores of ChatGPT’s responses were significantly lower than the benchmarks, indicating it has not reached an expert level of competence in CRC. While it could provide accurate information, it lacked in comprehensiveness. Notably, ChatGPT performed well in domains of radiation therapy, interventional therapy, stoma care, venous care, and pain control, almost rivaling the benchmarks, but fell short in basic information, surgery, and internal medicine domains. While ChatGPT demonstrated promise in specific domains, its general efficiency in providing CRC information falls short of expert standards, indicating the need for further advancements and improvements in AI technology for patient education in healthcare.
Development of stable S-scheme 2D–2D g-C3N4/CdS nanoheterojunction arrays for enhanced visible light photomineralisation of nitrophenol priority water pollutants
Authors: Saad Muhammad , Bahadur Ali , Iqbal Shahid , Mahmood Sajid , Tayyab Muhammad , Alshalwi Matar , Shah Mazloom
Published: February 2024

The investigation focused on creating and studying a new 2D–2D S-scheme CdS/g-C3N4 heterojunction photocatalyst. Various techniques examined its structure, composition, and optical properties. This included XRD, XPS, EDS, SEM, TEM, HRTEM, DRS, and PL. The heterojunction showed a reduced charge recombination rate and more excellent stability, helping to lessen photocorrosion. This was due to photogenerated holes moving more quickly out of the CdS valence band. The interface between g-C3N4 and CdS favored a synergistic charge transfer. A suitable flat band potential measurement supported enhanced reactive oxygen species (ROS) generation in degrading 4-nitrophenol and 2-nitrophenol. This resulted in remarkable degradation efficiency of up to 99% and mineralization of up to 79%. The findings highlighted the practical design of the new 2D–2D S-scheme CdS/g-C3N4 heterojunction photocatalyst and its potential application in various energy and environmental settings, such as pollutant removal, hydrogen production, and CO2 conversion.
Reactive transport modeling of organic carbon degradation in marine methane hydrate systems
Authors: Wei Li , Malinverno Alberto , Colwell Frederick , Goldberg David S.
Published: February 2024

Natural methane hydrate has often been observed in sand layers that contain no particulate organic carbon (POC), but are surrounded by organic-rich, fine-grained marine muds. In this paper, we develop a reactive transport model (RTM) of a microbially-mediated set of POC degradation reactions, including hydrolysis of POC driven by extracellular enzymes, fermentation of the resulting high-molecular weight dissolved organic carbon (HMW-DOC), and methanogenesis that consumes low-molecular weight dissolved organic carbon (LMW-DOC). These processes are mediated by two groups of microbes, fermenters and methanogens that are heterogeneously distributed in different lithologies, with the largest numbers of microbes in the large pores of coarse-grained layers. We find that the RTM can reproduce methane hydrate occurrences observed in two different geological environments, at Walker Ridge Site 313-H (Gulf of Mexico) and IODP Site U1325 (Cascadia Margin). We also find that microbes can degrade POC even if they are physically separated, as extracellular enzymes and DOC can diffuse away from where they are produced by microbes. Microbial activity is highest at relatively early times after burial at shallow depths and near lithological boundaries, where concentration gradients transport solutes to intervals that contain the most microbes.
The role of the six factors model of athletic mental energy in mediating athletes’ well-being in competitive sports
Authors: Singh Amisha , Kaur Arora Mandeep , Boruah Bahniman
Published: February 2024

In the realm of high-performance sports, athletes often prioritize success at the expense of their well-being. Consequently, sports psychology researchers are now focusing on creating psychological profiles for athletes that can forecast their performance while safeguarding their overall well-being. A recent development in this field is the concept of athletic mental energy (AME), which has been associated with both sporting success and positive emotions. Therefore, the aim of this study was to explore if AME in athletes can mediate this directly observed relationship between performance and psychological well-being. For stronger predictive validity these relationships were examined across two studies with each involving distinct sets of participants engaged in various sports disciplines, including football, cricket, basketball, archery, and more. The self-report measures of sports performance, athletic mental energy (AME), and psychological well-being (PWB) were administered post-competition on the local, regional, state, national, international, and professional level athletes of age 18 and above. Our study found that both, the affective and cognitive components of AME mediated the athletes’ performance and psychological well–being relationship. Interestingly, the study found no significant gender differences in AME and PWB scores. While family structures didn’t yield significant variations in AME scores, there were some descriptive distinctions in PWB scores across different family structures. Our research offers preliminary evidence suggesting that AME can play a pivotal role in preserving athletes’ psychological well-being following competitive events.
Improving the efficiency of DNA extraction from iron incrustations and oilfield-produced water
Authors: Foysal Md Javed , Salgar-Chaparro Silvia J.
Published: February 2024

The quantity and quality of DNA isolated from environmental samples are crucial for getting robust high-throughput sequencing data commonly used for microbial community analysis. The differences in the nature and physicochemical properties of environmental samples impact DNA yields, and therefore, an optimisation of the protocols is always recommended. For instance, samples collected from corroded areas contain high concentrations of metals, salts, and hydrocarbons that can interfere with several steps of the DNA extraction protocols, thereby reducing yield and quality. In this study, we compared the efficiency of commercially available DNA extraction kits and laboratory-adopted methods for microbial community analysis of iron incrustations and oilfield-produced water samples. Modifications to the kits manufacturers’ protocols were included to maximise the yield and quality. For iron incrustations, the modified protocol for FastDNA Spin Kit for Soil yielded higher DNA and resulted in higher diversity, including the recovery of low-abundant and rare taxa in the samples, compared to DNeasy PowerSoil Pro Kit. The DNA extracted with modified phenol–chloroform methods yielded higher DNA but failed to pass quality control PCR for 16S sequencing with and without purification. The protocols mentioned here can be used to maximise DNA recovery from iron incrustations and oilfield-produced water samples.
Design of a binary programmable transmitarray based on phase change material for beam steering applications in D-band
Authors: Gharbieh Samara , Milbrandt Jorick , Reig Bruno , Mercier Denis , Allain Marjolaine , Clemente Antonio
Published: February 2024

This paper introduces the design of a reconfigurable transmitarray operating within the D-band frequency range (110–170 GHz). The transmitarray unit cell is composed of three metal layers and two quartz dielectric substrates. It achieves a 1-bit phase shift resolution through the alternating states of two innovative switches integrated into the active transmitting patch of the unit cell. To address the challenge of miniaturization in the D-band, compact switches compatible with the proposed unit cell dimensions are introduced. These switches are constructed using phase change materials (PCM) that change between amorphous and crystalline states when exposed to heat. The paper includes a full-wave simulation of the unit cell, demonstrating an insertion loss below 1.5 dB across a wide frequency band of 27%. Additionally, a 10 $$\times $$
×
10 elements transmitarray is synthesized using a numerical tool and its theoretical results are compared to full-wave electromagnetic simulations for validation purposes. The results indicate that by incorporating the proposed switches into the unit cell, the transmitarray achieves promising reconfiguration capabilities within the D-band. Moreover, the paper presents the architecture of a command line designed to bias the PCM switches. Notably, this command line represents a novel approach, as it enables individual biasing of each PCM switch using direct current (DC). The influence of these command lines on the transmitarray’s performance is thoroughly investigated. Although there is a compromise in the 1-dB gain bandwidth, the overall behavior of the transmitarray remains encouraging.
A large therian mammal from the Late Cretaceous of South America
Authors: Chimento Nicolás R. , Agnolín Federico L. , García-Marsà Jordi , Manabe Makoto , Tsuihiji Takanobu , Novas Fernando E.
Published: February 2024

Theria represent an extant clade that comprises placental and marsupial mammals. Here we report on the discovery of a new Late Cretaceous mammal from southern Patagonia, Patagomaia chainko gen. et sp. nov., represented by hindlimb and pelvic elements with unambiguous therian features. We estimate Patagomaia chainko attained a body mass of 14 kg, which is considerably greater than the 5 kg maximum body mass of coeval Laurasian therians. This new discovery demonstrates that Gondwanan therian mammals acquired large body size by the Late Cretaceous, preceding their Laurasian relatives, which remained small-bodied until the beginning of the Cenozoic. Patagomaia supports the view that the Southern Hemisphere was a cradle for the evolution of modern mammalian clades, alongside non-therian extinct groups such as meridiolestidans, gondwanatherians and monotremes.
Unsupervised machine learning combined with 4D scanning transmission electron microscopy for bimodal nanostructural analysis
Authors: Kimoto Koji , Kikkawa Jun , Harano Koji , Cretu Ovidiu , Shibazaki Yuki , Uesugi Fumihiko
Published: February 2024

Unsupervised machine learning techniques have been combined with scanning transmission electron microscopy (STEM) to enable comprehensive crystal structure analysis with nanometer spatial resolution. In this study, we investigated large-scale data obtained by four-dimensional (4D) STEM using dimensionality reduction techniques such as non-negative matrix factorization (NMF) and hierarchical clustering with various optimization methods. We developed software scripts incorporating knowledge of electron diffraction and STEM imaging for data preprocessing, NMF, and hierarchical clustering. Hierarchical clustering was performed using cross-correlation instead of conventional Euclidean distances, resulting in rotation-corrected diffractions and shift-corrected maps of major components. An experimental analysis was conducted on a high-pressure-annealed metallic glass, Zr-Cu-Al, revealing an amorphous matrix and crystalline precipitates with an average diameter of approximately 7 nm, which were challenging to detect using conventional STEM techniques. Combining 4D-STEM and optimized unsupervised machine learning enables comprehensive bimodal (i.e., spatial and reciprocal) analyses of material nanostructures.
Early transcriptomic responses of rice leaves to herbivory by Spodoptera frugiperda
Authors: Leclerc Laëtitia , Nguyen Trang Hieu , Duval Pénélope , Mariotti Victoria , Petitot Anne-Sophie , Orjuela Julie , Ogier Jean-Claude , Gaudriault Sophie , Champion Antony , Nègre Nicolas
Published: February 2024

During herbivory, chewing insects deposit complex oral secretions (OS) onto the plant wound. Understanding how plants respond to the different cues of herbivory remains an active area of research. In this study, we used an herbivory-mimick experiment to investigate the early transcriptional response of rice plants leaves to wounding, OS, and OS microbiota from Spodoptera frugiperda larvae. Wounding induced a massive early response associated to hormones such as jasmonates. This response switched drastically upon OS treatment indicating the activation of OS specific pathways. When comparing native and dysbiotic OS treatments, we observed few gene regulation. This suggests that in addition to wounding the early response in rice is mainly driven by the insect compounds of the OS rather than microbial. However, microbiota affected genes encoding key phytohormone synthesis enzymes, suggesting an additional modulation of plant response by OS microbiota.
Prediction of the axial compression capacity of stub CFST columns using machine learning techniques
Authors: Megahed Khaled , Mahmoud Nabil Said , Abd-Rabou Saad Elden Mostafa
Published: February 2024

Concrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing uncertainty for engineering designers. Moreover, conventional regression analysis fails to accurately predict the intricate relationship between column properties and compressive strength. To address these issues, this study proposes the use of two machine learning (ML) models—Gaussian process regression (GPR) and symbolic regression (SR). These models accept a variety of input variables, encompassing geometric and material properties of stub CFST columns, to estimate their strength. An experimental database of 1316 specimens was compiled from various research papers, including circular, rectangular, and double-skin stub CFST columns. In addition, a dimensionless output variable, referred to as the strength index, is introduced to enhance model performance. To validate the efficiency of the introduced models, predictions from these models are compared with those from two established standard codes and various ML algorithms, including support vector regression optimized with particle swarm optimization (PSVR), artificial neural networks, XGBoost (XGB), CatBoost (CATB), Random Forest, and LightGBM models. Through performance metrics, the CATB, GPR, PSVR and XGB models emerge as the most accurate and reliable models from the evaluation results. In addition, simple and practical design equations for the different types of CFST columns have been proposed based on the SR model. The developed ML models and proposed equations can predict the compressive strength of stub CFST columns with reliable and accurate results, making them valuable tools for structural engineering. Furthermore, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The results of the feature analysis reveal that section slenderness ratio and concrete strength parameters negatively impact the compressive strength index.
Clinico-radiological features of intracranial atherosclerosis-related large vessel occlusion prior to endovascular treatment
Authors: Rodrigo-Gisbert Marc , García-Tornel Alvaro , Requena Manuel , Vielba-Gómez Isabel , Bashir Saima , Rubiera Marta , De Dios Lascuevas Marta , Olivé-Gadea Marta , Piñana Carlos , Rizzo Federica , Muchada Marian , Rodriguez-Villatoro Noelia , Rodríguez-Luna David , Juega Jesus , Pagola Jorge , Hernández David , Molina Carlos A. , Terceño Mikel , Tomasello Alejandro , Ribo Marc
Published: February 2024

The identification of large vessel occlusion with underlying intracranial atherosclerotic disease (ICAS-LVO) before endovascular treatment (EVT) continues to be a challenge. We aimed to analyze baseline clinical-radiological features associated with ICAS-LVO that could lead to a prompt identification. We performed a retrospective cross-sectional study of consecutive patients with stroke treated with EVT from January 2020 to April 2022. We included anterior LVO involving intracranial internal carotid artery and middle cerebral artery. We analyzed baseline clinical and radiological variables associated with ICAS-LVO and evaluated the diagnostic value of a multivariate logistic regression model to identify ICAS-LVO before EVT. ICAS-LVO was defined as presence of angiographic residual stenosis or a trend to re-occlusion during EVT procedure. A total of 338 patients were included in the study. Of them, 28 patients (8.3%) presented with ICAS-LVO. After adjusting for confounders, absence of atrial fibrillation (OR 9.33, 95% CI 1.11–78.42; p = 0.040), lower hypoperfusion intensity ratio (HIR [Tmax > 10 s/Tmax > 6 s ratio], (OR 0.69, 95% CI 0.50–0.95; p = 0.025), symptomatic intracranial artery calcification (IAC, OR .15, 95% CI 1.64–26.42, p = 0.006), a more proximal occlusion (ICA, MCA-M1: OR 4.00, 95% CI 1.23–13.03; p = 0.021), and smoking (OR 2.91, 95% CI 1.08–7.90; p = 0.035) were associated with ICAS-LVO. The clinico-radiological model showed an overall well capability to identify ICAS-LVO (AUC = 0.88, 95% CI 0.83–0.94; p < 0.001). In conclusion, a combination of clinical and radiological features available before EVT can help to identify an ICAS-LVO. This approach could be useful to perform a rapid assessment of underlying etiology and suggest specific pathophysiology-based measures. Prospective studies are needed to validate these findings in other populations.
Automated resolution of the spiral torsion spring inverse design problem
Authors: Silva Alejandro , López-Navarrete Gonzalo , García-Martos Carlos , Muñoz-Guijosa Juan Manuel
Published: February 2024

Many mechanical applications take advantage of spiral torsion springs due to their robustness, compactness, and simplicity. Brand-new manufacturing methods allow to create spiral springs with unconventional geometries and materials that suit a wider range of uses demanding either linearity or nonlinearity. Designing a spiral torsion spring with a nonlinear desired torque curve may be a great challenge, due to their many degrees-of-freedom (length, width, thickness, arbor, and barrel diameters, etc.) and the complexity of the geometrical and mechanical requirements to ensure their manufacturability, system compatibility, operation safety and reliability; and the solution is never unique. This manuscript proposes and validates an innovative methodology for the resolution of this inverse design problem based on the application of a nonlinear restrained global optimization algorithm. This algorithm is adjusted to converge, out of the infinity of designs that match the desired torque curve and hold all the functional and manufacturing constraints, to a design solution that minimizes strip mass. The methodology is built on a formulation for the calculation of the torque curve of a generalized spiral spring, with or without coiling and with any along-the-length cross-section, already published by the authors.
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases
Authors: Yuan Xiao , Su Jieqiong , Wang Jing , Dai Bing , Sun Yanfang , Zhang Keke , Li Yinghua , Chuan Jun , Tang Chunyan , Yu Yan , Gong Qiang
Published: February 2024

Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3–91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools.
The effect of data resampling methods in radiomics
Authors: Demircioğlu Aydin
Published: February 2024

Radiomic datasets can be class-imbalanced, for instance, when the prevalence of diseases varies notably, meaning that the number of positive samples is much smaller than that of negative samples. In these cases, the majority class may dominate the model's training and thus negatively affect the model's predictive performance, leading to bias. Therefore, resampling methods are often utilized to class-balance the data. However, several resampling methods exist, and neither their relative predictive performance nor their impact on feature selection has been systematically analyzed. In this study, we aimed to measure the impact of nine resampling methods on radiomic models utilizing a set of fifteen publicly available datasets regarding their predictive performance. Furthermore, we evaluated the agreement and similarity of the set of selected features. Our results show that applying resampling methods did not improve the predictive performance on average. On specific datasets, slight improvements in predictive performance (+ 0.015 in AUC) could be seen. A considerable disagreement on the set of selected features was seen (only 28.7% of features agreed), which strongly impedes feature interpretability. However, selected features are similar when considering their correlation (82.9% of features correlated on average).
Epigenetic age acceleration is associated with speed of pubertal growth but not age of pubertal onset
Authors: Kim Catherine , Harrall Kylie K. , Glueck Deborah H. , Hockett Christine , Dabelea Dana
Published: February 2024

Using data from a longitudinal cohort of children, we examined whether epigenetic age acceleration (EAA) was associated with pubertal growth and whether these associations were mediated by adiposity. We examined associations between EAA at approximately 10 years of age with pubertal growth metrics, including age at peak height velocity (PHV), PHV, and sex steroid levels and whether these associations were mediated by measures of adiposity including body mass index (BMI) and MRI-assessed visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Children (n = 135) with accelerated EAA had higher PHV (β 0.018, p = 0.0008) although the effect size was small. The association between EAA and age at PHV was not significant (β  − 0.0022, p = 0.067). Although EAA was associated with higher BMI (β 0.16, p = 0.0041), VAT (β 0.50, p = 0.037), and SAT (β 3.47, p = 0.0076), BMI and VAT did not mediate associations between EAA and PHV, while SAT explained 8.4% of the association. Boys with higher EAA had lower total testosterone (β  − 12.03, p = 0.0014), but associations between EAA and other sex steroids were not significant, and EAA was not associated with sex steroid levels in girls. We conclude that EAA did not have strong associations with either age at onset of puberty or pubertal growth speed, although associations with growth speed were statistically significant. Studies with larger sample sizes are needed to confirm this pattern of associations.