bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel pathway for the sonochemical synthesis of silver nanoparticles with near-spherical shape and high stability in aqueous media
Authors: Calderón-Jiménez Bryan , Montoro Bustos Antonio R. , Pereira Reyes Reinaldo , Paniagua Sergio A. , Vega-Baudrit José R. 
Published: January 2022

AbstractThe present study shows the development of a novel sonochemical synthesis pathway of sub-15 nm silver nanoparticles (AgNPs) with quasi-spherical shape and high stability in aqueous suspension. Different analytical techniques such as on-line UV–Vis spectroscopy, Atomic Force Microscopy (AFM), and Transmission Electron Microscopy (TEM) were complementarily used to characterize the evolution of the properties of AgNPs synthesized with this new route. Furthermore, different centrifugation conditions were studied to establish a practical, simple and straightforward purification method. Particle size was determined by TEM employing two different deposition methods, showing that purified AgNPs have a size of 8.1 nm ± 2.4 nm with a narrow dispersion of the size distribution (95% coverage interval from 3.4 to 13 nm). Critical information of the shape and crystalline structure of these sub-15 nm AgNPs, provided by shape descriptors (circularity and roundness) using TEM and high resolution (HR)-TEM measurements, confirmed the generation of AgNPs with quasi-spherical shapes with certain twin-fault particles promoted by the high energy of the ultrasonic treatment. Elemental analysis by TEM-EDS confirmed the high purity of the sub-15 nm AgNPs, consisting solely of Ag. At the optical level, these AgNPs showed a bandgap energy of (2.795 ± 0.002) eV. Finally, the evaluation of the effects of ultraviolet radiation (UVC: 254 nm and UVA: 365 nm) and storage temperature on the spectral stability revealed high stability of the optical properties and subsequently dimensional properties of sub-15 nm AgNPs in the short-term (600 min) and long-term (24 weeks).
The impact of security countermeasures on human behavior during active shooter incidents
Authors: Zhu Runhe , Lucas Gale M. , Becerik-Gerber Burcin , Southers Erroll G. , Landicho Earl 
Published: January 2022

AbstractActive shooter incidents represent an increasing threat to American society, especially in commercial and educational buildings. In recent years, a wide variety of security countermeasures have been recommended by public and governmental agencies. Many of these countermeasures are aimed to increase building security, yet their impact on human behavior when an active shooter incident occurs remains underexplored. To fill this research gap, we conducted virtual experiments to evaluate the impact of countermeasures on human behavior during active shooter incidents. A total of 162 office workers and middle/high school teachers were recruited to respond to an active shooter incident in virtual office and school buildings with or without the implementation of multiple countermeasures. The experiment results showed countermeasures significantly influenced participants’ response time and decisions (e.g., run, hide, fight). Participants’ responses and perceptions of the active shooter incident were also contingent on their daily roles, as well as building and social contexts. Teachers had more concerns for occupants’ safety than office workers. Moreover, teachers had more positive perceptions of occupants in the school, whereas office workers had more positive perceptions of occupants in the office.
The influence of stimuli valence, extraversion, and emotion regulation on visual search within real-world scenes
Authors: Bendall Robert C. A. , Eachus Peter , Thompson Catherine 
Published: January 2022

AbstractAffective traits, including extraversion and emotion regulation, are important considerations in clinical psychology due to their associations with the occurrence of affective disorders. Previously, emotional real-world scenes have been shown to influence visual search. However, it is currently unknown whether extraversion and emotion regulation can influence visual search towards neutral targets embedded within real-world scenes, or whether these traits can impact the effect of emotional stimuli on visual search. An opportunity sample of healthy individuals had trait levels of extraversion and emotion regulation recorded before completing a visual search task. Participants more accurately identified search targets in neutral images compared to positive images, whilst response times were slower in negative images. Importantly, individuals with higher trait levels of expressive suppression displayed faster identification of search targets regardless of the emotional valence of the stimuli. Extraversion and cognitive reappraisal did not influence visual search. These findings add to our understanding regarding the influence of extraversion, cognitive reappraisal, and expressive suppression on our ability to allocate attention during visual search when viewing real-world scenes.
The effects of the interaction between BMI and dyslipidemia on hypertension in adults
Authors: Tang Na , Ma Jian , Tao Rongqin , Chen Zhijun , Yang Yide , He Quanyuan , Lv Yuan , Lan Zelong , Zhou Junhua 
Published: January 2022

AbstractBody mass index (BMI) and dyslipidemia are indicators of human health and are often associated with high blood pressure. In this study,we explored the relationship between BMI or dyslipidemia and the risk of hypertension and further verified the possible interacting influences of BMI with dyslipidemia on the risk of hypertension. The aim is to explore the possible risk factors of hypertension and to provide scientific basis for the prevention and treatment of hypertension. Eligible subjects were selected from a cross-sectional survey in Changsha City, and we collected relevant data and clinical indicators for each participant. Body mass index (BMI) was calculated as weight (kg)/height2 (m2), and divided into four categories according to the Chinese standard. Dyslipidemia is defined according to Chinese guideline. Unconditional logistic regression models were used for dichotomous variables to determine the risk or protective factors of dependent variables. Multivariate Logistic model was used to study the influence of BMI and dyslipidemia on hypertension. The following indicators were used to assess the interaction effects: (1) Relative excess risk due to interaction (RERI); (2) Attributable proportion due to interaction(AP); (3) Synergy index (SI). SPSS software was used for statistical analysis. A total of 2740 eligible participants were enrolled in the cross-sectional study, of which 765 subjects (27.9%) were diagnosed with hypertension. Multivariate Logistic model showed that overweight (OR: 1.70, 95%CI: 1.39–2.09) or obese (OR: 2.60, 95%CI: 1.84–3.66) subjects had a significantly higher risk of hypertension than normal weight people, and underweight was a protective factor for hypertension(OR: 0.52, 95%CI: 0.29–0.93). People with dyslipidemia have a higher risk of hypertension than those with normal lipids (OR: 3.05, 95%CI: 2.36–3.90). In addition,there was a significant potentiating interaction effect between overweight or obesity and dyslipidemia(overweight: RERI (1.91, 95%CI: 0.17–3.66), AP (0.40, 95%CI:0.14–0.66), SI (2.03, 95%CI:1.11–3.74) and obesity: RERI (2.20, 95%CI:1.01–3.40), AP (0.38, 95%CI:0.18–0.58), SI (1.84, 95%CI:1.18–2.89), while no interaction was found between underweight and dyslipidemia. Low body weight is an independent protective factor for hypertension, but overweight, obesity and dyslipidemia are risk factors for hypertension, and dyslipidemia significantly shared interactions with overweight and obesity that influenced the risk of hypertension.
Membrane rigidity regulates E. coli proliferation rates
Authors: Salinas-Almaguer Samuel , Mell Michael , Almendro-Vedia Victor G. , Calero Macarena , Robledo-Sánchez Kevin Carlo Martín , Ruiz-Suarez Carlos , Alarcón Tomás , Barrio Rafael A. , Hernández-Machado Aurora , Monroy Francisco 
Published: January 2022

AbstractCombining single cell experiments, population dynamics and theoretical methods of membrane mechanics, we put forward that the rate of cell proliferation in E. coli colonies can be regulated by modifiers of the mechanical properties of the bacterial membrane. Bacterial proliferation was modelled as mediated by cell division through a membrane constriction divisome based on FtsZ, a mechanically competent protein at elastic interaction against membrane rigidity. Using membrane fluctuation spectroscopy in the single cells, we revealed either membrane stiffening when considering hydrophobic long chain fatty substances, or membrane softening if short-chained hydrophilic molecules are used. Membrane stiffeners caused hindered growth under normal division in the microbial cultures, as expected for membrane rigidification. Membrane softeners, however, altered regular cell division causing persistent microbes that abnormally grow as long filamentous cells proliferating apparently faster. We invoke the concept of effective growth rate under the assumption of a heterogeneous population structure composed by distinguishable individuals with different FtsZ-content leading the possible forms of cell proliferation, from regular division in two normal daughters to continuous growing filamentation and budding. The results settle altogether into a master plot that captures a universal scaling between membrane rigidity and the divisional instability mediated by FtsZ at the onset of membrane constriction.
Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine
Authors: Hu Jianhua , Zhou Tan , Ma Shaowei , Yang Dongjie , Guo Mengmeng , Huang Pengli 
Published: January 2022

AbstractThe rock mass is one of the key parameters in engineering design. Accurate rock mass classification is also essential to ensure operational safety. Over the past decades, various models have been proposed to evaluate and predict rock mass. Among these models, artificial intelligence (AI) based models are becoming more popular due to their outstanding prediction results and generalization ability for multiinfluential factors. In order to develop an easy-to-use rock mass classification model, support vector machine (SVM) techniques are adopted as the basic prediction tools, and three types of optimization algorithms, i.e., particle swarm optimization (PSO), genetic algorithm (GA) and grey wolf optimization (GWO), are implemented to improve the prediction classification and optimize the hyper-parameters. A database was assembled, consisting of 80 sets of real engineering data, involving four influencing factors. The three combined models are compared in accuracy, precision, recall, F1 value and computational time. The results reveal that among three models, the GWO-SVC-based model shows the best classification performance by training. The accuracy of training and testing sets of GWO-SVC are 90.6250% (58/64) and 93.7500% (15/16), respectively. For Grades I, II, III, IV and V, the precision value is 1, 0.93, 0.90, 0.92, 0.83, the recall value is 1, 1, 0.93, 0.73, 0.83, and the F1 value is 1, 0.96, 0.92, 0.81, 0.83, respectively. Sensitivity analysis is performed to understand the influence of input parameters on rock mass classification. It shows that the sensitive factor in rock mass quality is the RQD. Finally, the GWO-SVC is employed to assess the quality of rocks from the southeastern ore body of the Chambishi copper mine. Overall, the current study demonstrates the potential of using artificial intelligence methods in rock mass assessment, rendering far better results than the previous reports.
CO2 outgassing during collisional orogeny is facilitated by the generation of immiscible fluids
Authors: Groppo Chiara , Rolfo Franco , Frezzotti Maria Luce 
Published: January 2022

AbstractOrogenic degassing is emerging as a potentially relevant source of carbon dioxide (CO2) from the continental crust. However, the processes of carbon mobilization are still poorly explored. Here, we use thermodynamic modeling to investigate the decarbonation of sediments metamorphosed under high geothermal gradients. Our modeling shows that immiscible CO2-rich vapors and hydrosaline brines are generated at these conditions, with different properties and mobility through the crust. The CO2-rich fluid fraction could rapidly rise toward the surface without interacting with the host rocks by carbo-fracturing the host rocks or through deep faults. The denser hydrosaline brines likely permeate the source rocks. When applied to the active Himalayan orogen, these observations reconcile measured CO2fluxes at the surface and positive conductivity anomalies associated with micro-seismicity at depth. Our modeling shows that the continental crust represents a relevant reservoir of CO2that can be efficiently degassed during hot collisions.
Direct evidence for crust-mantle differentiation in the late Hadean
Authors: Maltese Alessandro , Caro Guillaume , Pandey Om Prakash , Upadhyay Dewashish , Mezger Klaus 
Published: January 2022

AbstractConstraints on the evolution of the silicate Earth between 4.5 and 3.8 billion years ago are limited by the scarcity of pristine geological material from that period. The geodynamic evolution of the early Earth, prior to the preserved rock record, is thus mainly inferred from numerical modelling. To evaluate the geological significance of these simulations, geochronological constraints pertaining to the evolution of the Hadean crust are required. Here we show using Neodymium isotope variations generated by decay of now-extinct 146-Samarium that Paleoarchean rocks from the Singhbhum Craton, India derived from a Hadean depleted mantle reservoir that differentiated $${4.19}_{-0.12}^{+0.06}$$

4.19

−
0.12

+
0.06

billion years ago. The event postdates Neodymium model ages of mantle depletion inferred from other Archean rocks by 200 million years. This geochronological record is mirrored in the Hafnium isotope composition of the oldest zircon grains, suggesting that Hadean mantle differentiation proceeded via distinct pulses of large-scale magmatic activity and crustal rejuvenation.
Preferential solvation of meso-methyl BODIPYs with pyridine via pseudo-hydrogen-bonds
Authors: Morisue Mitsuhiko , Kawanishi Miho , Kusukawa Takahiro 
Published: January 2022

This study explored unexpected pseudo-hydrogen bond interactions between meso-methyl BODIPYs and pyridine and acridine. Both experimental and theoretical investigations indicated the existence of pseudo-hydrogen bonds, although the static views based on theoretical aspects were inconsistent with dynamic properties observed by experimental investigations.
CeO2 decorated bimetallic phosphide nanowire arrays for enhanced oxygen evolution reaction electrocatalysis via interface engineering
Authors: Cong Yikang , Chen Xingnan , Mei Yan , Ye Jun , Li Ting-Ting 
Published: January 2022

A novel heterostructure comprising of Co0.4Ni1.6P nanowire arrays decorated with CeO2 nanoparticles grown on porous Ni foam is easily fabricated and shows remarkable OER performance.
Insights into the neuroprotective effects ofSalvia officinalisL. andSalvia microphyllaKunth in the memory impairment rat model
Authors: Ayoub Iriny M. , George Mina Y. , Menze Esther T. , Mahmoud Maryhan , Botros Mariam , Essam Mariam , Ashmawy Israa , Shendi Pussey , Hany Andrew , Galal Mohammed , Ayman Mohamed , Labib Rola M. 
Published: January 2022

The present study demonstrates the beneficial effects of Salvia microphylla and S. officinalis from Egypt and Jordan against scopolamine-induced AD-like disorder.
S-20, a steroidal saponin from the berries of black nightshade, exerts anti-multidrug resistance activity in K562/ADR cells through autophagic cell death and ERK activation
Authors: Wang Yi , Xu Jingwen , Wang Yihai , Xiang Limin , He Xiangjiu 
Published: January 2022

The steroidal saponin S-20 isolated from Solanum nigrum L. is a potent inhibitor of drug-resistant proteins. The EKR activation triggered by autophagic cell death in S-20-treated K562/ADR cells contributed to overcome the multidrug resistance.
Cognitive-Communication Complaints and Referrals for Speech-Language Pathology Services Following Concussion
Authors: O'Brien Katy H. , Wallace Tracey , Kemp Amy M. , Pei Yalian 
Published: January 2022

Purpose:Speech-language pathologists are increasingly being recognized as key members of concussion management teams. This study investigates whether self-report of communication problems postconcussion may be useful in identifying clients who could benefit from speech-language pathology services.Method:Participants included 41 adolescents and adults from an outpatient specialty concussion clinic. All completed the La Trobe Communication Questionnaire (LCQ) at admission, and 23 repeated this measure at discharge. Participants were prospectively enrolled, with chart reviews providing demographic, injury, and medical factors. The analysis considered (a) communication complaints and resolution over time, including comparison to two previously published LCQ studies of typical adults and adults with and without traumatic brain injury (TBI); (b) the relationship between communication complaints, participant factors, and common concussion assessments; and (c) factors related to speech-language pathology service referral for rehabilitation.Results:At first visit, 12 of 41 participants (29%) reported communication problems, although 19 (46%) reported difficulty with greater than half of LCQ items. At a group level, compared to published reference data of both people with chronic mixed severity TBI and controls, participants in this study reported more problems at first visit with communication overall, as well as greater difficulty with the LCQ Initiation/Conversation Flow subscale. Partner Sensitivity subscale scores at first visit were also greater than published control data. LCQ subscale scores of Initiation/Conversation Flow and Partner Sensitivity decreased from first visit to last visit, demonstrating resolution over time. Only concussion symptom scales and not demographic, injury, or cognitive screenings were related to LCQ scores. The same two LCQ subscales, Initiation/Conversation Flow and Partner Sensitivity, predicted referral for speech-language pathology services, along with symptom scales and being injured due to motor vehicle crash.Discussion:A subset of people recovering from concussion report experiencing communication problems. Reporting of particular communication problems was related to referral for speech-language pathology rehabilitation services and may be useful in directing care after concussion.
Outcomes of Gender Affirming Voice Training: A Comparison of Hybrid and Individual Training Modules
Authors: Merrick Gwen , Figol Andrea , Anderson Jennifer , Lin R. Jun 
Published: January 2022

Purpose:
Gender affirming voice training supports individuals as they explore and develop a healthy voice that feels congruent with their gender and sense of self. Although gender affirming voice training is increasingly available, there is no research comparing individual versus hybrid training outcomes in the existing literature.

Method:
A retrospective chart review was performed on trans women and/or nonbinary individuals who attended either individual or hybrid (group and individual training) gender affirming voice and communication training at St. Michael's Hospital in Toronto between 2012 and 2019. Participant demographics, self-reported goal achievement, attendance, pre/post–Trans Woman Voice Questionnaire (TWVQ) scores, and pre/postspeaking fundamental frequency (SFF) were compared between individual and hybrid training participants. Multiple logistic regression analysis was performed to determine factors that could predict self-reported goal achievement.

Results:

Sixty-two trans women and/or nonbinary individuals who attended either individual (
n
= 43) or hybrid (
n
= 19) gender affirming voice and communication training were included in the study. Hybrid participants had a significantly higher goal achievement rate and therapy adherence rate compared with individual participants (74% vs. 21%; 84% vs. 28%, respectively). Both training models resulted in significant elevation of SFF and reduction of TWVQ scores. Inflection range did not change significantly for either training formats. Multiple logistic regression showed that hybrid training was a positive predictor for goal achievement (odds ratio = 2.2, 95% confidence interval [0.3, 4.1]).

Conclusions:
Although clients in individual and hybrid gender affirming voice and communication training achieved significant pitch elevation and lower TWVQ scores, hybrid participants demonstrated significantly higher goal achievement and therapy adherence. This study suggests that hybrid training may provide a valuable support system that improves therapy success in this client population.
MAMask: Multi‐feature aggregation instance segmentation with pyramid attention mechanism
Authors: Wang Gaihua , Lin Jinheng , Zhai Qianyu , Cheng Lei , Dai Yingying , Zhang Tianlun 
Published: January 2022

AbstractInstance segmentation is a fundamental yet challenging vision task. Recently, many instance segmentation methods have attempted to use attention mechanisms to improve model efficiency. However, these methods still ignore the problem of information loss in lateral connection of Feature Pyramid Networks (the supplement operation of low‐resolution, semantically strong features in FPN). The paper presents an effective detection‐based approach named MAMask, which is closely tied to the one‐stage method, Fully Convolutional One‐Stage Object Detection (FCOS). In particular, it adopts the multi‐feature aggregation decoder with pyramid integrate attention (PIA) to instance segmentation. The pyramid integrate attention block can prevent the loss of important channel information by learning richer multi‐scale representation. Meanwhile, it also brings significant improvements in performance for existing FPN‐based frameworks at slight additional computational costs. The proposed MAMask achieves 37.3% in box AP on the COCO dataset. The method outperforms a few recent methods without longer training time. Compared with the current typical algorithms, the proposed method has achieved excellent performance.
Medical image segmentation using deep learning: A survey
Authors: Wang Risheng , Lei Tao , Cui Ruixia , Zhang Bingtao , Meng Hongying , Nandi Asoke K. 
Published: January 2022

AbstractDeep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation using deep learning techniques is presented. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi‐level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyse literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.