bluuebunny/crossref_metadata_2025 huggingface dataset:
Longitudinal assessment of plasma biomarkers for early detection of cognitive changes in subjective cognitive decline
Authors: Hsieh Cheng-Hao , Ko Chien-An , Liang Chih-Sung , Yeh Po-Kuan , Tsai Chia-Kuang , Tsai Chia-Lin , Lin Guan-Yu , Lin Yu-Kai , Tsai Ming-Chen , Yang Fu-Chi
Published: May 2024

Background

Individuals experiencing subjective cognitive decline (SCD) are at an increased risk of developing mild cognitive impairment and dementia. Early identification of SCD and neurodegenerative diseases using biomarkers may help clinical decision-making and improve prognosis. However, few cross-sectional and longitudinal studies have explored plasma biomarkers in individuals with SCD using immunomagnetic reduction.

Objective

To identify plasma biomarkers for SCD.

Methods

Fifty-two participants [38 with SCD, 14 healthy controls (HCs)] underwent baseline assessments, including measurements of plasma Aβ42, Aβ40, t-tau, p-tau, and α-synuclein using immunomagnetic reduction (IMR) assays, cognitive tests and the Mini-Mental State Examination (MMSE). Following initial cross-sectional analysis, 39 individuals (29 with SCD, 10 HCs) entered a longitudinal phase for reassessment of these biomarkers and the MMSE. Biomarker outcomes across different individual categories were primarily assessed using the area under the receiver operating characteristic (ROC) curve. The SCD subgroup with an MMSE decline over one point was compared to those without such a decline.

Results

Higher baseline plasma Aβ1-42 levels significantly discriminated participants with SCD from HCs, with an acceptable area under the ROC curve (AUC) of 67.5% [95% confidence interval (CI), 52.7–80.0%]. However, follow-up and changes in MMSE and IMR data did not significantly differ between the SCD and HC groups (p > 0.05). Furthermore, lower baseline plasma Aβ1-42 levels were able to discriminate SCD subgroups with and without cognitive decline with a satisfied performance (AUC, 75.0%; 95% CI, 55.6–89.1%). At last, the changes in t-tau and Aβ42 × t-tau could differentiate between the two SCD subgroups (p < 0.05).

Conclusion

Baseline plasma Aβ42 may help identify people with SCD and predict SCD progression. The role of plasma Aβ42 levels as well as their upward trends from baseline in cases of SCD that progress to mild cognitive impairment and Alzheimer’s disease require further investigation.
Ultra-high pressure treatment improve the content of characteristic aromatic components of melon juice from the view of physical changes
Authors: Liu Xiao , Liang Feng , Wang Bing Su , Ren Fei Yue , Wang Wei , Zhang Chao
Published: May 2024

Introduction

The effectiveness of ultra-high pressure (UHP) technology in retaining the flavor of fresh fruit and vegetable juices has been acknowledged in recent years. Along with previously hypothesized conclusions, the improvement in melon juice flavor may be linked to the reduction of its surface tension through UHP.

Methods

In this paper, the particle size, free-water percentage, and related thermodynamic parameters of melon juice were evaluated in a physical point for a deeper insight.

Results

The results showed that the UHP treatment of P2-2 (200 MPa for 20 min) raised the free water percentage by 7,000 times than the other treatments and both the melting enthalpy, binding constant and Gibbs free energy of P2-2 were minimized. This significantly increased the volatility of characteristic aromatic compounds in melon juice, resulting in a 1.2-5 times increase in the content of aromatic compounds in the gas phase of the P2-2 group compared to fresh melon juice.
Racial and survival disparities in inflammatory breast cancer (IBC) and non-IBC: a population-based study focused on Native Hawaiians and other Pacific Islanders
Authors: Yoshikawa Gene T. , Miyazaki Kyle SY. , Acoba Jared D. , Fujii Takeo
Published: May 2024

Background

It is well known that race is an independent predictor of breast cancer mortality and advanced stage at diagnosis. Inflammatory breast cancer (IBC) is the most aggressive type of breast cancer and has distinct clinical and biological features. Previous studies have shown that Blacks have a higher incidence of IBC than Whites. However, the proportion of IBC and the role of race on prognosis in Native Hawaiian and other Pacific Islander (NH/PI) populations with breast cancer are poorly understood. In this study, we aimed to examine the proportion of IBC to non-IBC in NH/PIs and to identify the clinicopathological, biological, and socioeconomic factors associated with the overall survival of NH/PIs compared to other races.

Methods

Utilizing a comprehensive cancer registry from the largest hospital in Hawaii, newly diagnosed primary invasive breast cancer patients diagnosed between 2000 and 2018 were identified. Univariate and multivariate Cox proportional hazards models were used to test the association between race and clinical outcomes. Variables with P-values <0.05 in the univariate analysis and race (variable of interest) were included in a multivariate analysis.

Results

The cohort included 3691 patients, 60 of whom had IBC. NH/PI race had the highest proportion of IBC compared to other races (3.44%) but was not found to be an independent poor prognostic factor in IBC (HR 1.17 [95%CI 0.26-5.22]). Conversely, NH/PI race was associated with worse survival outcomes in patients with non-IBC (HR 1.65 [95%CI, 1.14-2.39]) along with other factors such as lack of insurance, underinsured status, triple-negative breast cancer (TNBC) subtype, age, and advanced clinical stage.

Conclusions

The findings of this study highlight that NH/PIs had higher rates of IBC and inferior survival in non-IBC compared to other races but not in IBC. It is essential to disaggregate NH/PI race from Asians in future population-based research studies. Further research is needed to understand the factors contributing to higher rates of IBC and poor survival outcomes in NH/PIs with non-IBC as well as targeted interventions to improve breast cancer outcomes in this population to ultimately help improve survival rates and reduce health inequities in NH/PIs with breast cancer.
A single-dose, randomized, open-label, four-period, crossover equivalence trial comparing the clinical similarity of the proposed biosimilar rupatadine fumarate to reference Wystamm® in healthy Chinese subjects
Authors: Lin Sisi , Lou Yutao , Hao Rui , Shao Yiming , Yu Jin , Fang Lu , Bao Meihua , Yi Wu , Zhang Yiwen
Published: May 2024

Purpose

The aim of this study was to evaluate the bioequivalence of two formulations of rupatadine (10-mg tablets) under fasting and fed conditions in healthy Chinese subjects.

Methods

A total of 72 subjects were randomly assigned to the fasting cohort (n = 36) and fed cohort (n = 36). Each cohort includes four single-dose observation periods and 7-day washout intervals. Blood samples were collected at several timepoints for up to 72 h post-dose. The plasma concentration of rupatadine and the major active metabolites (desloratadine and 3-hydroxydesloratadine) were analyzed by a validated HPLC–MS/MS method. The non-compartmental analysis method was employed to determine the pharmacokinetic parameters. Based on the within-subject standard deviation of the reference formulation, a reference-scaled average bioequivalence or average bioequivalence method was used to evaluate the bioequivalence of the two formulations.

Results

For the fasting status, the reference-scaled average bioequivalence method was used to evaluate the bioequivalence of the maximum observed rupatadine concentration (Cmax; subject standard deviation > 0.294), while the average bioequivalence method was used to evaluate the bioequivalence of the area under the rupatadine concentration–time curve from time 0 to the last detectable concentration (AUC0-t) and from time 0 to infinity (AUC0-∞). The geometric mean ratio (GMR) of the test/reference for Cmax was 95.91%, and the upper bound of the 95% confidence interval was 95.91%. For AUC0-t and AUC0-∞ comparisons, the GMR and 90% confidence interval (CI) were 98.76% (93.88%–103.90%) and 98.71% (93.93%–103.75%), respectively. For the fed status, the subject standard deviation values of Cmax, AUC0-t, and AUC0-∞ were all <0.294; therefore, the average bioequivalence method was used. The GMR and 90% CI for Cmax, AUC0-t, and AUC0-∞ were 101.19% (91.64%–111.74%), 98.80% (94.47%–103.33%), and 98.63% (94.42%–103.03%), respectively. The two-sided 90% CI of the GMR for primary pharmacokinetic endpoints of desloratadine and 3-hydroxydesloratadine was also within 80%–125% for each cohort. These results met the bioequivalence criteria for highly variable drugs. All adverse events (AEs) were mild and transient.

Conclusion

The test drug rupatadine fumarate showed a similar safety profile to the reference drug Wystamm® (J. Uriach y Compañía, S.A., Spain), and its pharmacokinetic bioequivalence was confirmed in healthy Chinese subjects based on fasting and postprandial status.

Clinical trial registration:

http://www.chinadrugtrials.org.cn/index.html, identifier CTR20213217
Analysis of regional carbon productivity differences and influencing factors—based on new green decomposition model
Authors: Fu Min , Mei Ying , Tian Lixin , Zhang Chao
Published: May 2024

This paper introduces a new green decomposition model of carbon productivity that aims to further analyze the regional differences in carbon productivity and its interaction with regional industrial performance. We combine desired outputs and undesired outputs orientation, and multiple factor inputs to derive a new green decomposition theorem, establish a new green decomposition model of carbon productivity, and obtain nine effects of regional carbon productivity differences. Empirical analysis is conducted using input-output data from 29 provinces and 15 industries in China, comparing the differences in carbon productivity from both the provincial and industry perspectives and exploring the mechanism of action. This paper provides theoretical basis and empirical evidence for regional carbon productivity enhancement and economic and industrial optimization from the perspective of multi-factor inputs, as well as policy insights for regional low-carbon transition development.
Potential non-invasive detection of lesions in broiler femur heads: application of the DXA imaging system
Authors: Ramser Alison , Greene Elizabeth S. , Wideman Robert , Dridi Sami
Published: May 2024

Leg health is a significant economic and welfare concern for the poultry industry. Current methods of detection rely on visual assessment of the legs and gait scores and bone scoring during necropsy for full characterization. Additionally, the current scoring of femurs only examines the external surface of the femoral head. Through the use of the dual-energy X-ray absorptiometry (DXA) imaging system, we show the presence of a necrotic region in the femurs that would otherwise be considered healthy based on the current evaluation procedures. Importantly, these lesions were present in almost 60% (22 of 37) of femurs that scored normal for femoral head necrosis (FHN). Additionally, these femurs showed greater bone mineral content (BMC) relative to weight compared to their counterparts with no lucent lesions (6.95% ± 0.20% vs. 6.26% ± 0.25; p = 0.038). Identification of these lesions presents both a challenge and an opportunity. These subclinical lesions are likely to be missed in routine scoring procedures for FHN and can inadvertently impact the characterization of the disease and genetic selection programs. Furthermore, this imaging system can be used for in vivo, ex vivo, and embryonic (egg) studies and, therefore, constitutes a potential non-invasive method for early detection of bone lesions in chickens and other avian species.
Using machine learning algorithms and techniques for defining the impact of affective temperament types, content search and activities on the internet on the development of problematic internet use in adolescents’ population
Authors: Jović Jelena , Ćorac Aleksandar , Stanimirović Aleksandar , Nikolić Mina , Stojanović Marko , Bukumirić Zoran , Ignjatović Ristić Dragana
Published: May 2024

Background

By using algorithms and Machine Learning – ML techniques, the aim of this research was to determine the impact of the following factors on the development of Problematic Internet Use (PIU): sociodemographic factors, the intensity of using the Internet, different contents accessed on the Internet by adolescents, adolescents’ online activities, life habits and different affective temperament types.

Methods

Sample included 2,113 adolescents. The following instruments were used: questionnaire about: socio-demographic characteristics, intensity of the Internet use, content categories and online activities on the Internet; Facebook (FB) usage and life habits; The Internet Use Disorder Scale (IUDS). Based on their scores on the scale, subjects were divided into two groups – with or without PIU; Temperament Evaluation of Memphis, Pisa, Paris, and San Diego scale for adolescents (A-TEMPS-A).

Results

Various ML classification models on our data set were trained. Binary classification models were created (class-label attribute was PIU value). Models hyperparameters were optimized using grid search method and models were validated using k-fold cross-validation technique. Random forest was the model with the best overall results and the time spent on FB and the cyclothymic temperament were variables of highest importance for these model. We also applied the ML techniques Lasso and ElasticNet. The three most important variables for the development of PIU with both techniques were: cyclothymic temperament, the longer use of the Internet and the desire to use the Internet more than at present time. Group of variables having a protective effect (regarding the prevention of the development of PIU) was found with both techniques. The three most important were: achievement, search for contents related to art and culture and hyperthymic temperament. Next, 34 important variables that explain 0.76% of variance were detected using the genetic algorithms. Finally, the binary classification model (with or without PIU) with the best characteristics was trained using artificial neural network.

Conclusion

Variables related to the temporal determinants of Internet usage, cyclothymic temperament, the desire for increased Internet usage, anxious and irritable temperament, on line gaming, pornography, and some variables related to FB usage consistently appear as important variables for the development of PIU.
Mental health inequality between urban and rural youth under COVID-19 from survey data from China
Authors: Xiao Yang
Published: May 2024

Background

While health inequality has been the focus of past scholarly discussions, COVID-19’s outbreak and spread have provided a new arena for discussing health inequality, particularly in the context of urban–rural disparities in China. This paper explores the impact of COVID-19 on urban–rural health inequality, and the roles played by socioeconomic status and social capital.

Methods

A cross-sectional observational collected data on demographics, mental health, socioeconomic status, and social capital. An online survey was administered from August 27 to August 30, 2020, and, 1936 valid samples were received. Mental health was measured using the Brief Symptom Inventory (BSI-18). This study applied the ordinary least squares regression (OLS) model, and data analysis was performed using STATA.

Results

There were 1936 participants, with an equal distribution of genders. Multiple regression analysis showed that the mental health levels of rural youth were superior to those of urban youth (p = 0.049), especially when the epidemic was not severe (p = 0.013). Socioeconomic status had a significant positive promotion effect on mental health (p = 0.008), but the interaction effect between socioeconomic status and the urban–rural divide indicated that the promotion effect of socioeconomic status on the mental health of urban youth was greater than that of rural youth (p = 0.04). Social capital had a significant positive promotion effect on mental health (p = 0.000), and the interaction effect indicated that this promoting effect did not differ between urban and rural areas (p > 0.05).
Bibliometric analysis of most cited Peyronie's disease and its management publications
Authors: Şahin Mehmet Fatih , Doğan Çağrı , Akgül Murat , Yazıcı Cenk Murat , Şeramet Serkan , Dayısoylu Hulusi Sıtkı
Published: May 2024

Introduction

Peyronie's disease (PD) is a common urologic illness, motivating numerous scientific investigations and publications. Scientific publications have more authors each year. A bibliometric review of the PD literature might help urologists and sexual medicine professionals comprehend publication tendencies in this subject. The current study was aimed at presenting a bibliometric analysis of PD, which is one of the important and trending subjects of andrology.

Methods

On January 5, 2023, Web of Science scanned documents with the terms Peyronie's disease” “Peyronie's disease treatment”, “Peyronie's disease management”, “Peyronie's disease surgery” and “Peyronie's disease injection” from 1975 through 2023. Titles, years, authors, citations, citation indices, journal names, authors' countries of origin, article categories, and funding sources were recorded.

Results

“Clinical Efficacy, Safety and Tolerability of Collagenase Clostridium Histolyticum for the Treatment of Peyronie Disease in 2 Large Double-Blind, Randomized, Placebo Controlled Phase 3 Studies” has the most citations and citation index. Most of the T100 articles were published in 2020, primarily in the Journal of Urology. These articles mainly focused on treatment, especially surgeries. All of these articles were in English, and the vast majority of them were by authors from the US who were most frequently collaborated with by other authors.

Conclusion

This research analyzed the top 100 PD studies. This research focused on pathophysiology, innovative surgical procedures, and new approaches of PD. It also recommended bigger databases and more financing for research.
Exploration and Maintenance of Homeomorphic Orbit Revs in the Elliptic Restricted Three-Body Problem
Authors: Alvarado Kevin I. , Singh Sandeep K.
Published: May 2024

A novel station-keeping strategy leveraging periodic revolutions of homeomorphic orbits in the Elliptic Restricted Three-Body Problem within the pulsating frame is presented. A systemic approach founded on arc-length continuation is presented for the discovery, computation, and classification of periodic revolutions that morph from their traditional circular restricted three-body counterparts to build an a priori dataset. The dataset is comprehensive in covering all possible geometric architectures of the restricted problem. Shape similarity is quantified using Hausdorff distance and works as a filter for the station-keeping algorithm in relation to appropriate target conditions. Finally, an efficient scheme to quantify impulsive orbit maintenance maneuvers that minimize the total fuel cost is presented. The proposed approach is salient in its generic applicability across any elliptic three-body system and any periodic orbit family. Finally, average annual station-keeping costs using the described methodology are quantified for selected “orbits of interest” in the cis-lunar and the Sun–Earth systems. The robustness and efficacy of the approach instill confidence in its applicability for realistic mission design scenarios.
Transcriptome Analysis Reveals the Induction of Apoptosis-Related Genes by a Monoclonal Antibody against a New Epitope of CD99 on T-Acute Lymphoblastic Leukemia
Authors: Takheaw Nuchjira , Kotemul Kamonporn , Chaiwut Ratthakorn , Pata Supansa , Laopajon Witida , Rangnoi Kuntalee , Yamabhai Montarop , Kasinrerk Watchara
Published: May 2024

CD99 was demonstrated to be a potential target for antibody therapy on T-acute lymphoblastic leukemia (T-ALL). The ligation of CD99 by certain monoclonal antibodies (mAbs) induced T-ALL apoptosis. However, the molecular basis contributing to the apoptosis of T-ALL upon anti-CD99 mAb engagement remains elusive. In this study, using our generated anti-CD99 mAb clone MT99/3 (mAb MT99/3), mAb MT99/3 engagement strongly induced apoptosis of T-ALL cell lines, but not in non-malignant peripheral blood cells. By transcriptome analysis, upon mAb MT99/3 ligation, 13 apoptosis-related genes, including FOS, TNF, FASLG, BCL2A1, JUNB, SOCS1, IL27RA, PTPN6, PDGFA, NR4A1, SGK1, LPAR5 and LTB, were significantly upregulated. The epitope of CD99 recognized by mAb MT99/3 was then identified as the VDGENDDPRPP at residues 60–70 of CD99, which has never been reported. To the best of our knowledge, this is the first transcriptome data conducted in T-ALL with anti-CD99 mAb engagement. These findings provide new insights into CD99 implicated in the apoptosis of T-ALL. The identification of a new epitope and apoptosis-related genes that relate to the induction of apoptosis by mAb MT99/3 may serve as a new therapeutic target for T-ALL. The anti-CD99 mAb clone MT99/3 might be a candidate for further development of a therapeutic antibody for T-ALL therapy.
Myelin Oligodendrocyte Glycoprotein-Antibody Associated Disease: An Updated Review of the Clinical Spectrum, Pathogenetic Mechanisms and Therapeutic Management
Authors: Gklinos Panagiotis , Dobson Ruth
Published: May 2024

Clinical syndromes associated with antibodies against myelin oligodendrocyte glycoprotein (MOG) are now recognized as a distinct neurological disease entity, and are gaining increasing attention. The pathogenic mechanisms underlying MOG-antibody disease (MOGAD) remain incompletely understood. Case series, facilitated by registries, and observational studies over the past few years have shed increasing light on the clinical aspects and therapeutic approaches of MOGAD. MOGAD may manifest with a variety of clinical syndromes, including acute disseminated encephalomyelitis (ADEM), autoimmune encephalitis, optic neuritis (ON) and transverse myelitis (TM). MOGAD can be either monophasic or relapsing. This review aims to provide a comprehensive updated description of the clinical spectrum, paraclinical features, and prognosis of MOG-antibody disease, as well as summarize its therapeutic considerations. Randomized clinical trials, standardized diagnostic criteria and treatment guidelines are the steps forward.
Vibration Isolation and Launch Performance Enhancement of the Spacecraft In-Orbit Launch Design Using the Nonlinear Dynamic Feature
Authors: Zhou Xu , Tong Weihao , Dai Lu , Wei Boyuan
Published: May 2024

This paper proposes a new spacecraft in-orbit launch design using a nonlinear configuration to utilize nonlinear dynamics for the enhancement of vibration isolation and launch performance. The in-orbit launch device has four springs, where the stroke directions of two springs are perpendicular to the launch direction so as to produce nonlinearity with negative stiffness for enhancing the launch velocity. The other two springs are designed to counterbalance the above negative stiffness when the launch outlet is shut down, leading to quasi-zero dynamic stiffness for vibration isolation enhancement. The dynamic equations of the in-orbit launch device for both the on- and off-launch are presented. Then the performance enhancement of both the vibration isolation and launch performance is thoroughly investigated via comparative study and parametric study. The resonance peak is reduced by 4.16 dB, the effective vibration isolation bandwidth is increased by 57%, and the launch speed is increased 1.64 times. This validates the performance improvement of the new launch device design and presents a useful guideline for application.
Indoor AR Navigation Framework Based on Geofencing and Image-Tracking with Accumulated Error Correction
Authors: Lu Min , Arikawa Masatoshi , Oba Kohei , Ishikawa Keiichi , Jin Yuhan , Utsumi Tomihiro , Sato Ryo
Published: May 2024

This study presents a novel framework for improving indoor augmented reality (AR) navigation with modern smartphone technology, which is achieved by addressing two major challenges: managing large absolute coordinate spaces and reducing error accumulation in camera-based spatial tracking. Our contribution is significant in two ways. First, we integrate geofencing with indoor navigation by considering spatial tracking errors, timing for audio guidance, and dynamic 3D arrow visualization for effective local-to-global spatial coordinate transformation. This method achieves precise local positioning and seamlessly integrates with larger spatial contexts, overcoming the limitations of current AR systems. Second, we introduce a periodic image-based calibration approach to minimize the inherent error accumulation in camera-based tracking, enhancing accuracy over longer distances. Unlike prior studies focusing on individual technologies, our work explores the software architecture of indoor AR navigation by providing a comprehensive framework for its design and practical use. The practicality of our approach is validated through the implementation of a smartphone application at the Mineral Industry Museum of Akita University, highlighting the limitations of component technologies and demonstrating our framework’s effectiveness.
AdvancingTire Safety: Explainable Artificial Intelligence-Powered Foreign Object Defect Detection with Xception Networks and Grad-CAM Interpretation
Authors: Saleh Radhwan A. A. , Al-Areqi Farid , Konyar Mehmet Zeki , Kaplan Kaplan , Öngir Semih , Ertunc H. Metin
Published: May 2024

Automatic detection of tire defects has become an important issue for tire production companies since these defects cause road accidents and loss of human lives. Defects in the inner structure of the tire cannot be detected with the naked eye; thus, a radiographic image of the tire is gathered using X-ray cameras. This image is then examined by a quality control operator, and a decision is made on whether it is a defective tire or not. Among all defect types, the foreign object type is the most common and may occur anywhere in the tire. This study proposes an explainable deep learning model based on Xception and Grad-CAM approaches. This model was fine-tuned and trained on a novel real tire dataset consisting of 2303 defective tires and 49,198 non-defective. The defective tire class was augmented using a custom augmentation technique to solve the imbalance problem of the dataset. Experimental results show that the proposed model detects foreign objects with an accuracy of 99.19%, recall of 98.75%, precision of 99.34%, and f-score of 99.05%. This study provided a clear advantage over similar literature studies.
Research on a Neutron Detector with a Boron-Lined Multilayer Converter
Authors: Deng Chao , Hu Qin , Li Pengcheng , Wang Qibiao , Xie Bo , Yang Jianbo , Tuo Xianguo
Published: May 2024

3He is a splendid neutron detection material due to its high neutron reaction cross section, gaseous state, and nonelectronegative and nonpoisonous nature. With the worldwide problem of the “3He supply crisis” arising, boron-lined gaseous neutron detectors are being widely used in neutron detection to replace 3He neutron detectors. In this work, to reduce the scattering neutron background coming from the substrate of a boron-lined neutron detector in the application of neutron scattering, a new design of the boron-lined gaseous neutron detector composed of a boron-lined multichip converter and a multiwire proportional chamber was proposed. The electron drift efficiency matrix simulated by Garfield++ (Version 2023.4) and the values and positions of electron energy deposition simulated by Geant4 were obtained. The α, 7Li, and total charged particle energy deposition spectra were acquired via coupling calculations of the electron drift efficiency matrix and the values and positions of electron energy deposition, and the width of the slit was selected as 3 mm. The boron-lined multilayer converter neutron detector (BMCND) was tested using a 241Am–239Pu mixture α source, and the total count rate of α charged particles was measured as 599.5 s−1, which is 89% of the theoretical α particle emission rate of 672.9 s−1. The drift voltage experiments showed that 1200 V is enough to acquire a relatively ideal count, and a 2500 V drift voltage was confirmed, considering the higher count and instrument safety. We also performed the neutron detection experiments using a photo-neutron source, and a characteristic spectrum shape of “two stairs” was measured. When borated polyethylene was used to shield the BMCND, the detected total count decreased while keeping the characteristic spectrum shape, demonstrating that the BMCND was equipped with the ability to detect neurons, indicating that BMCNDs have the potential to be an outstanding 3He alternative neutron detector.
The Functional Significance of High Cysteine Content in Eye Lens γ-Crystallins
Authors: Serebryany Eugene , Martin Rachel W. , Takahashi Gemma R.
Published: May 2024

Cataract disease is strongly associated with progressively accumulating oxidative damage to the extremely long-lived crystallin proteins of the lens. Cysteine oxidation affects crystallin folding, interactions, and light-scattering aggregation especially strongly due to the formation of disulfide bridges. Minimizing crystallin aggregation is crucial for lifelong lens transparency, so one might expect the ubiquitous lens crystallin superfamilies (α and βγ) to contain little cysteine. Yet, the Cys content of γ-crystallins is well above the average for human proteins. We review literature relevant to this longstanding puzzle and take advantage of expanding genomic databases and improved machine learning tools for protein structure prediction to investigate it further. We observe remarkably low Cys conservation in the βγ-crystallin superfamily; however, in γ-crystallin, the spatial positioning of Cys residues is clearly fine-tuned by evolution. We propose that the requirements of long-term lens transparency and high lens optical power impose competing evolutionary pressures on lens βγ-crystallins, leading to distinct adaptations: high Cys content in γ-crystallins but low in βB-crystallins. Aquatic species need more powerful lenses than terrestrial ones, which explains the high methionine content of many fish γ- (and even β-) crystallins. Finally, we discuss synergies between sulfur-containing and aromatic residues in crystallins and suggest future experimental directions.
The Impact of Frailty Components and Preoperative Mechanical Cardiac Support Changes with Time after Heart Transplantation
Authors: Szentgróti Rita , Khochanskiy Dmitry , Szécsi Balázs , Németh Flóra , Szabó András , Koritsánszky Kinga , Vereb Alexandra , Cserép Zsuzsanna , Sax Balázs , Merkely Béla , Székely Andrea
Published: May 2024

Background: Frailty has been proven to be associated with mortality after orthotopic heart transplantation (OHT). The aim of our study was to determine the impact of frailty on mortality in the current era using pretransplant mechanical cardiac support (MCS). Methods: We retrospectively calculated the frailty scores of 471 patients undergoing OHT in a single institution between January 2012 and August 2022. The outcome was all-cause mortality. Results: The median survival time was 1987 days (IQR: 1487 days) for all patients. In total, 266 (56.5%) patients were categorized as nonfrail, 179 (38.0%) as prefrail, and 26 (5.5%) as frail. The survival rates were 0.73, 0.54, and 0.28 for nonfrail, prefrail, and frail patients, respectively. The frailty score was associated with mortality [HR: 1.34 (95% CI: 1.22–1.47, p < 0.001)]. Among the components of the frailty score, age above 50 years, creatinine ≥ 3.0 mg/dL or prior dialysis, and hospitalization before OHT were independently associated with mortality. Continuous-flow left ventricular assist devices (CF-LVAD) were associated with an increased risk for all-cause mortality [AHR: 1.80 (95% CI: 1.01–3.24, p = 0.047)]. Conclusions: The components of the frailty score were not equally associated with mortality. Frailty and pretransplant MCS should be included in the risk estimation.
Whole-Body Photobiomodulation Therapy Propels the Fibromyalgia Patient into the Recomposition Phase: A Reflexive Thematic Analysis
Authors: Fitzmaurice Bethany C. , Grenfell Rebecca L. , Heneghan Nicola R. , Rayen Asius T. A. , Soundy Andrew A.
Published: May 2024

Background: Recent evidence has identified great promise for the novel whole-body photobiomodulation therapy (PBMT) for individuals with fibromyalgia (FM). However, currently no evidence has documented the experiences of participants. The objective of this study was to qualitatively assess treatment experience and response in a group of participants with FM undergoing a course of whole-body PBMT. Methods: An interpretive hermeneutic phenomenological study situated within the worldview of pragmatism was undertaken. A convenience sample of individuals with FM were included if they had undertaken a novel 6-week trial of PBMT. Individuals undertook semi-structured interviews exploring treatment experience and multidimensional treatment responses during Week 3 and Week 6. Results: Sixteen trial participants (47.3 ± 10.9 years) took part in this study. The analysis produced three overarching themes that were previously identified from a baseline study (namely, ‘Body Structure & Function’, ‘Activities & Participation’, and ‘Environment’) with an additional five sub-themes that highlighted the intervention experience. Subsequently, four important processes were observed and identified: increased motivation; feeling proud; improved confidence; feeling like ‘old self’. This ultimately culminated in the identification of a positive spiral, which we have termed ‘recomposition’. Conclusions: We believe our study is the first in the field of chronic pain management to utilise qualitative methodology to directly assess the acceptability and efficacy of a specific medical intervention in a clinical trial, and the first study to qualitatively assess whole-body PBMT experience. The findings are compelling and warrant further work to support the introduction of this device into the National Health Service (NHS).
Insights into the Electrocatalytic Activity of Fe,N-Glucose/Carbon Nanotube Hybrids for the Oxygen Reduction Reaction
Authors: Morais Rafael G. , Rey-Raap Natalia , Figueiredo José L. , Pereira Manuel F. R.
Published: May 2024

Glucose-derived carbon hybrids were synthesized by hydrothermal treatment in the presence of oxidized carbon nanotubes. Additionally, iron and nitrogen functionalities were incorporated into the carbon structure using different methodologies. The introduction of iron and nitrogen in a single step under a H2 atmosphere favored the formation of quaternary nitrogen and oxidized nitrogen, whereas the incorporation of nitrogen under an N2 atmosphere after doping the hybrids with iron mainly produced pyridinic nitrogen. The samples were characterized by scanning electron microscopy, X-ray spectroscopy, adsorption isotherms, inductively coupled plasma optical emission spectrometry, and Raman spectroscopy. The presence of iron and nitrogen in the carbons increases the onset potential toward oxygen reduction in KOH 0.1 mol L−1 by 130 mV (0.83 V), in comparison to carbonized glucose, whereas the reaction mechanism shifts closer to a direct pathway and the formation of HO2− decreases to 25% (3.5 electrons). The reaction rate also increased in comparison to the carbonized glucose, as observed by the decrease in the Tafel slope value from 117 to 61 mV dec−1. Furthermore, the incorporation of iron and nitrogen in a single step enhanced the short-term performance of the prepared electrocatalysts, which may also be due to the higher relative amount of quaternary nitrogen.
The Machine Learning Attribution of Quasi-Decadal Precipitation and Temperature Extremes in Southeastern Australia during the 1971–2022 Period
Authors: Speer Milton , Hartigan Joshua , Leslie Lance
Published: May 2024

Much of eastern and southeastern Australia (SEAUS) suffered from historic flooding, heat waves, and drought during the quasi-decadal 2010–2022 period, similar to that experienced globally. During the double La Niña of the 2010–2012 period, SEAUS experienced record rainfall totals. Then, severe drought, heat waves, and associated bushfires from 2013 to 2019 affected most of SEAUS, briefly punctuated by record rainfall over parts of inland SEAUS in the late winter/spring of 2016, which was linked to a strong negative Indian Ocean Dipole. Finally, from 2020 to 2022 a rare triple La Niña generated widespread extreme rainfall and flooding in SEAUS, resulting in massive property and environmental damage. To identify the key drivers of the 2010–2022 period’s precipitation and temperature extremes due to accelerated global warming (GW), since the early 1990s, machine learning attribution has been applied to data at eight sites that are representative of SEAUS. Machine learning attribution detection was applied to the 52-year period of 1971–2022 and to the successive 26-year sub-periods of 1971–1996 and 1997–2022. The attributes for the 1997–2022 period, which includes the quasi-decadal period of 2010–2022, revealed key contributors to the extremes of the 2010–2022 period. Finally, some drivers of extreme precipitation and temperature events are linked to significant changes in both global and local tropospheric circulation.
Major Perioperative Cardiac Risk Assessment: A Review for Cardio-Oncologists and Perioperative Physicians
Authors: Johnson Emily P. , Monsour Robert , Hafez Osama , Kotha Rohini , Ackerman Robert S.
Published: May 2024

The Revised Cardiac Risk Index (RCRI) and the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) preoperative risk assessment tools are the most widely used methods for quantifying the risk of major negative perioperative cardiac outcomes that a patient may face during and after noncardiac surgery. However, these tools were created to include as wide a range of surgical factors as possible; thus, some predictive accuracy is sacrificed when it comes to certain surgical subpopulations. In this review, we explore the various surgical oncology patient populations for whom these assessment tools can be reliably applied and for whom they demonstrate poor reliability.
The Effect of Resin Interleafing on the Wedge Peel Strength of CF/PEEK Manufactured by Laser-Assisted In Situ Consolidation
Authors: Wang Ruozhou , Xu Entao , Wen Liwei
Published: May 2024

In this work, a novel approach involving coating fine PEEK powder on prepreg is introduced to improve wedge peel strength and reduce interlaminar voids. CF/PEEK laminates with resin interleaving are in situ consolidated by laser-assisted fiber placement. The morphology of the powdered surface is obtained using an optical profilometer, and the surface roughness and volume of added resin are calculated accordingly. Interface and surface temperature are measured during the layup process. Thermal history indicates that very short bonding time is the dominating factor for voids and limited interlayer strength. Laminate porosity and microscopic features are characterized with an optical microscope. The porosity of resin-interleaved laminates decreases to 3.7%, while the resin content only increases by 4.5% in the meantime. This is because interlayer resin particles rapidly melt under laser heating and quickly fill the voids between layers. The wedge peel strength of resin-interleaved laminates can increase by 30.1% without a repass treatment. This could be attributed to the increase in resin intimate contact and reduction in interlayer voids.
Enhancing Brain Segmentation in MRI through Integration of Hidden Markov Random Field Model and Whale Optimization Algorithm
Authors: Daoudi Abdelaziz , Mahmoudi Saïd
Published: May 2024

The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures in MR images. In this study, we present an automatic approach for robust and accurate brain tissue boundary outlining in MR images. This algorithm is proposed for the tissue classification of MR brain images into White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). The proposed segmentation process combines two algorithms, the Hidden Markov Random Field (HMRF) model and the Whale Optimization Algorithm (WOA), to enhance the treatment accuracy. In addition, we use the Whale Optimization Algorithm (WOA) to optimize the performance of the segmentation method. The experimental results from a dataset of brain MR images show the superiority of our proposed method, referred to HMRF-WOA, as compared to other reported approaches. The HMRF-WOA is evaluated on multiple MRI contrasts, including both simulated and real MR brain images. The well-known Dice coefficient (DC) and Jaccard coefficient (JC) were used as similarity metrics. The results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice coefficient and Jaccard coefficient above 0.9.
Continuous Wave Measurements Collected in Intermediate Depth throughout the North Sea Storm Season during the RealDune/REFLEX Experiments
Authors: Rutten Jantien , Tissier Marion , van Wiechen Paul , Zhang Xinyi , de Vries Sierd , Reniers Ad , Mol Jan-Willem
Published: May 2024

High-resolution wave measurements at intermediate water depth are required to improve coastal impact modeling. Specifically, such data sets are desired to calibrate and validate models, and broaden the insight on the boundary conditions that force models. Here, we present a wave data set collected in the North Sea at three stations in intermediate water depth (6–14 m) during the 2021/2022 storm season as part of the RealDune/REFLEX experiments. Continuous measurements of synchronized surface elevation, velocity and pressure were recorded at 2–4 Hz by Acoustic Doppler Profilers and an Acoustic Doppler Velocimeter for a 5-month duration. Time series were quality-controlled, directional-frequency energy spectra were calculated and common bulk parameters were derived. Measured wave conditions vary from calm to energetic with 0.1–5.0 m sea-swell wave height, 5–16 s mean wave period and W-NNW direction. Nine storms, i.e., wave height beyond 2.5 m for at least six hours, were recorded including the triple storms Dudley, Eunice and Franklin. This unique data set can be used to investigate wave transformation, wave nonlinearity and wave directionality for higher and lower frequencies (e.g., sea-swell and infragravity waves) to compare with theoretical and empirical descriptions. Furthermore, the data can serve to force, calibrate and validate models during storm conditions.
Robotic Manipulator in Dynamic Environment with SAC Combing Attention Mechanism and LSTM
Authors: Kuang Xinghong , Zhou Sucheng
Published: May 2024

The motion planning task of the manipulator in a dynamic environment is relatively complex. This paper uses the improved Soft Actor Critic Algorithm (SAC) with the maximum entropy advantage as the benchmark algorithm to implement the motion planning of the manipulator. In order to solve the problem of insufficient robustness in dynamic environments and difficulty in adapting to environmental changes, it is proposed to combine Euclidean distance and distance difference to improve the accuracy of approaching the target. In addition, in order to solve the problem of non-stability and uncertainty of the input state in the dynamic environment, which leads to the inability to fully express the state information, we propose an attention network fused with Long Short-Term Memory (LSTM) to improve the SAC algorithm. We conducted simulation experiments and present the experimental results. The results prove that the use of fused neural network functions improved the success rate of approaching the target and improved the SAC algorithm at the same time, which improved the convergence speed, success rate, and avoidance capabilities of the algorithm.
Machine Learning-Based Hand Pose Generation Using a Haptic Controller
Authors: Choi Jongin , Lee Jaehong , Oh Daniel , Lee Eung-Joo
Published: May 2024

In this study, we present a novel approach to derive hand poses from data input via a haptic controller, leveraging machine learning techniques. The input values received from the haptic controller correspond to the movement of five fingers, each assigned a value between 0.0 and 1.0 based on the applied pressure. The wide array of possible finger movements requires a substantial amount of motion capture data, making manual data integration difficult. This challenge is primary due to the need to process and incorporate large volumes of diverse movement information. To tackle this challenge, our proposed method automates the process by utilizing machine learning algorithms to convert haptic controller inputs into hand poses. This involves training a machine learning model using supervised learning, where hand poses are matched with their corresponding input values, and subsequently utilizing this trained model to generate hand poses in response to user input. In our experiments, we assessed the accuracy of the generated hand poses by analyzing the angles and positions of finger joints. As the quantity of training data increased, the margin of error decreased, resulting in generated poses that closely emulated real-world hand movements.
Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning
Authors: Zhu Yanqiu , Fan Shuxiang , Zuo Min , Zhang Baohua , Zhu Qingzhen , Kong Jianlei
Published: May 2024

The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed.