bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sex—the most underappreciated variable in research: insights from helminth-infected hosts
Authors: Wesołowska Agnieszka 
Published: November 2022

AbstractThe sex of a host affects the intensity, prevalence, and severity of helminth infection. In many cases, one sex has been found to be more susceptible than the other, with the prevalence and intensity of helminth infections being generally higher among male than female hosts; however, many exceptions exist. This observed sex bias in parasitism results primarily from ecological, behavioural, and physiological differences between males and females. Complex interactions between these influences modulate the risk of infection. Indeed, an interplay among sex hormones, sex chromosomes, the microbiome and the immune system significantly contributes to the generation of sex bias among helminth-infected hosts. However, sex hormones not only can modulate the course of infection but also can be exploited by the parasites, and helminths appear to have developed molecules and pathways for this purpose. Furthermore, host sex may influence the efficacy of anti-helminth vaccines; however, although little data exist regarding this sex-dependent efficacy, host sex is known to influence the response to vaccines. Despite its importance, host sex is frequently overlooked in parasitological studies. This review focuses on the key contributors to sex bias in the case of helminth infection. The precise nature of the mechanisms/factors determining these sex-specific differences generally remains largely unknown, and this represents an obstacle in the development of control methods. There is an urgent need to identify any protective elements that could be targeted in future therapies to provide optimal disease management with regard to host sex. Hence, more research is needed into the impact of host sex on immunity and protection.
Nonstandard competing anisotropic $(p,q)$-Laplacians with convolution
Authors: Razani A. 
Published: November 2022

AbstractA competing anisotropic $(p,q)$
(
p
,
q
)
-Laplacian $$ -\overset{N}{\underset{i=1}{\sum}}\frac{\partial }{\partial x_{i}} \biggl( \biggl\vert \frac{\partial u}{\partial x_{i}} \biggr\vert ^{p_{i}-2}- \mu \biggl\vert \frac{\partial u}{\partial x_{i}} \biggr\vert ^{q_{i}-2} \biggr) \frac{\partial u}{\partial x_{i}} =f \bigl(x, \phi \star u,\nabla (\phi \star u)\bigr) $$
−

∑

i
=
1

N

∂

∂

x
i

(
|

∂
u

∂

x
i

|

p
i

−
2

−
μ
|

∂
u

∂

x
i

|

q
i

−
2

)

∂
u

∂

x
i

=
f

(
x
,
ϕ
⋆
u
,
∇
(
ϕ
⋆
u
)
)

as a nonstandard Dirichlet problem with convolutions on a bounded smooth domain in $\mathbb{R}^{N}$

R
N

, $N\geq 3$
N
≥
3
is considered. Assume $f:\Omega \times \mathbb{R}\times \mathbb{R}^{N}\to \mathbb{R}$
f
:
Ω
×
R
×

R
N

→
R
is a Carathéodory function and $\phi \in L^{1}(\mathbb{R}^{N})$
ϕ
∈

L
1

(

R
N

)
. If $\mu >0$
μ
>
0
, the existence of a generalized solution is proved. By the Galerkin basis for the space, a sequence that converges strongly to the solution is constructed. If $\mu \leq 0$
μ
≤
0
, it is proved that any generalized solution is a weak solution.
Correction of severe lower extremity deformity with digital hexapod external fixator based on CT data
Authors: Lu Yufeng , Li Jinfeng , Qiao Feng , Xu Zhaochen , Zhang Baogang , Jia Bin , He Jinlong , Qi Liang , Wang Min , Fei Chen , Cao Xiaoming 
Published: November 2022

Abstract
Purpose
Our goal was to examine the therapeutic effect of a self-designed digital six-axis external fixator technique for the correction of severe lower extremity deformities.

Patients and methods
Between January 2017 and December 2020, our institution employed self-developed digital hexapod external fixator technology (QSF), based on CT data, to gradually correct 28 severe tibial deformities, and 15 femurs underwent osteotomy and internal fixation. The mean patient age was 32.6 ± 14.3 years, and the mean follow-up duration was 27.4 ± 16.1 months. We also recoded and analyzed the values of preoperative and final follow-up MAD, mFTA, MPTA, LLD, mLDFA, LEFS, KSS, and functional score.

Results
The QSF adjustment duration was 21.4 ± 10.8 days, and the healing duration of the tibial osteotomy site was 17.6 ± 7.0 weeks. The preoperative MAD, mFTA, and MPTA were 54.1 ± 26.2 mm, 167.7 ± 15.7°, and 75.2 ± 12.0°, respectively. At the last follow-up, the MAD was 8.2 ± 9.9 mm, mFTA was 177.6 ± 3.4°, and MPTA was 87.6 ± 2.4°. Based on these data, we achieved significant improvement post operation. The preoperative LLD and mLDFA values were 13.8 ± 18 mm and 83.7 ± 10.8°, respectively, and the values were 7.6 ± 7.6 mm and 87.8 ± 2.6°, respectively, at the last follow-up. This indicated no significant difference in these values before and after the operation. Finally, the LEFS, KSS, and functional scores improved from preoperative 51.6 ± 11.2, 68.5 ± 11.7, and 67.8 ± 11.2 to postoperative 72.3 ± 6.1, 92.9 ± 3.4, and 94.2 ± 6.3, respectively.

Conclusions
Based on our analyses, the QSF technique accurately corrected severe multiplanar tibial deformities in adults. When combined with femoral osteotomy, satisfactory lower extremity alignment was obtained while correcting for femoral deformity. This technology has the advantages of simple operation, reliable fixation, less trauma, and less complications.
Relationships between personality traits and disordered eating among Chinese female exercisers: the role of symptoms of exercise dependence and obsessive-compulsiveness
Authors: Yang Peiying , Wang Ting , Herold Fabian , Müller Notger G. , Taylor Alyx , Szabo Attila , Granziol Umberto , Cook Brian , Landolfi Emilio , Solmi Marco , Zou Liye 
Published: November 2022

Abstract
Background
Although numerous studies have examined associations between personality traits and eating disorders in females, few studies have been conducted on female exercisers. Given the high risk of disordered eating in female exercisers, this study investigated the associations between the Big Five personality traits and disordered eating in female exercisers, and further explored the potential mediators, namely exercise dependence symptoms, and obsessive–compulsive symptoms underlying this association.

Methods
A total of 295 female exercisers aged between 18 to 67 years (M =  22.11, SD =  6.65) participated in this study.

Results
Negative and statistically significant correlations between conscientiousness (r = − 0.17, p < 0.01), emotional stability (r = − 0.27, p < 0.001) and agreeableness (r = − 0.18, p < 0.01) and disordered eating were observed in our sample of female exercisers. The multiple mediation analyses revealed that exercise dependence symptoms and obsessive–compulsive symptoms mediate the relationship between conscientiousness (β = 0.016, CI = [0.003, 0.031]), emotional stability (β = -0.012, CI = [− 0.028, − 0.002]), and disordered eating in female exercisers, whereas obsessive–compulsive symptoms (β = − 0.041, CI = [− 0.088, − 0.001]) but not exercise dependence symptoms are a mediator of the relationship between agreeableness and disordered eating.

Conclusions
Our findings can be used to improve the screening procedures for eating disorders in female exercisers as they contribute to a better understanding of the psychological mechanisms that underlie the associations between the Big Five personality traits and disordered eating.
Multiple time series database on microservice architecture for IoT-based sleep monitoring system
Authors: Simanjuntak Eko , Surantha Nico 
Published: November 2022

AbstractMonitoring health status requires collecting a large amount of data from the human body. The sensor can be used to collect data from the human body. The sensor transmits data for almost every second across the internet. The challenge of the health monitoring system is the massive amount of incoming data. Therefore, a system capable of sending, storing, analyzing, and visualizing vast amounts of data is required for health monitoring. A previous study proposed microservice and event-driven architecture. It also proposed a single database for all services and a relational database management system (RDBMS) for storing time series data, which might reduce the data transmission performance and reliability. This research intends to improve the monitoring system from the previous study to accommodate a greater throughput, faster database read and write operations, and a more reliable system design. The improvement consists of multiple changes in system architecture and technology. A multi-database is proposed in the system architecture to improve system reliability. Time series database and Message Queue Telemetry Protocol (MQTT) server are proposed as an upgrade on technology. As a result, the proposed system throughput is 2.43 times faster than the old system. On database performance, the new system's database write speed is 20.95 times faster and the database read speed is 1.64 times faster than the old system. The proposed system also achieves better scalability, resilience, and independence.
Arduino-based wireless spectrometer: a practical application
Authors: Shin Jaeseong , Choi Han-Kyu 
Published: November 2022

AbstractWe provide a technical report on designing an Arduino-based wireless spectrometer using an old spectrometer. The designed spectrometer is environmentally friendly and cost-effective and can be used in physical and analytical laboratories. The Arduino-based wireless spectrometer enables students to understand the fundamental theory of spectroscopy and wireless communication among instruments. The rotating motions of the mirror and grating turret inside the spectrometer are controlled by a pre-written source code based on the Arduino program. This procedure can give an insight into spectroscopic analysis and telecommunication skills using computer-based software. Using our customized spectrometer, we measure Raman spectra from a silicon wafer, 4-nitrobenzenethiol, and rhodamine 6G, which serve as representative Raman probe molecules. From our results, we conclude that our approach provides intuitions for wide applications of Arduino-based chemical instruments.
Graphical Abstract
Imaging features associated with H3 K27-altered and H3 G34-mutant gliomas: a narrative systematic review
Authors: Lasocki Arian , Abdalla Gehad , Chow Geoffrey , Thust Stefanie C. 
Published: November 2022

Abstract
Background
Advances in molecular diagnostics accomplished the discovery of two malignant glioma entities harboring alterations in the H3 histone: diffuse midline glioma, H3 K27-altered and diffuse hemispheric glioma, H3 G34-mutant. Radiogenomics research, which aims to correlate tumor imaging features with genotypes, has not comprehensively examined histone-altered gliomas (HAG). The aim of this research was to synthesize the current published data on imaging features associated with HAG.

Methods
A systematic search was performed in March 2022 using PubMed and the Cochrane Library, identifying studies on the imaging features associated with H3 K27-altered and/or H3 G34-mutant gliomas.

Results
Forty-seven studies fulfilled the inclusion criteria, the majority on H3 K27-altered gliomas. Just under half (21/47) were case reports or short series, the remainder being diagnostic accuracy studies. Despite heterogeneous methodology, some themes emerged. In particular, enhancement of H3 K27M-altered gliomas is variable and can be less than expected given their highly malignant behavior. Low apparent diffusion coefficient values have been suggested as a biomarker of H3 K27-alteration, but high values do not exclude this genotype. Promising correlations between high relative cerebral blood volume values and H3 K27-alteration require further validation. Limited data on H3 G34-mutant gliomas suggest some morphologic overlap with 1p/19q-codeleted oligodendrogliomas.

Conclusions
The existing data are limited, especially for H3 G34-mutant gliomas and artificial intelligence techniques. Current evidence indicates that imaging-based predictions of HAG are insufficient to replace histological assessment. In particular, H3 K27-altered gliomas should be considered when occurring in typical midline locations irrespective of enhancement characteristics.
The role of hydrogen bonds in hydrous minerals stable at lower mantle pressure conditions
Authors: Tsuchiya Jun , Thompson Elizabeth C. 
Published: November 2022

AbstractOver the past few decades, hydrous minerals were thought to be absent in the lower mantle, due to their instability at high-pressure conditions. Recently, however, hydrous phases including phase H (MgSiO$$_4$$

4

H$$_2$$

2

), pyrite-type FeOOH, and $$\delta$$
δ
-AlOOH have been discovered to be thermodynamically stable at lower mantle pressures. Investigations using ab initio calculations methods play a key role in identifying these novel phases and determining their geophysical properties (i.e., compressibility, elasticity, and sound velocities). These calculations suggest that the hydrous minerals which are stable at lower mantle pressure conditions (i.e., phase H, FeOOH, and AlOOH), have symmetric hydrogen bonds at these pressures. This indicates that hydrogen bond strength is closely connected to the stability and physical properties of hydrous minerals at extreme pressures. In this review, we summarize the theoretical and experimental studies of hydrous minerals stable at the high-pressure conditions of the Earth’s lower mantle in light of the role of hydrogen bonding.
HYPR4D kernel method on TOF PET data with validations including image-derived input function
Authors: Cheng Ju-Chieh Kevin , Bevington Connor W. J. , Sossi Vesna 
Published: November 2022

Abstract
Background
Positron emission tomography (PET) images are typically noisy especially in dynamic imaging where the PET data are divided into a number of short temporal frames often with a low number of counts. As a result, image features such as contrast and time–activity curves are highly variable. Noise reduction in PET is thus essential. Typical noise reduction methods tend to not preserve image features/patterns (e.g. contrast and size dependent) accurately. In this work, we report the first application of our HYPR4D kernel method on time-of-flight (TOF) PET data (i.e. PSF-HYPR4D-K-TOFOSEM). The proposed HYPR4D kernel method makes use of the mean 4D high frequency features and inconsistent noise patterns over OSEM subsets as well as the low noise property of the early reconstruction updates to achieve prior-free de-noising. The method was implemented and tested on the GE SIGNA PET/MR and was compared to the TOF reconstructions with PSF resolution modeling available on the system, namely PSF-TOFOSEM with and without standard post filter and PSF-TOFBSREM (TOF Q.Clear) with various beta values (regularization strengths).

Results
Results from experimental contrast phantom and human subject data with various PET tracers showed that the proposed method provides more robust and accurate image features compared to other regularization methods. The preservation of contrast for the PSF-HYPR4D-K-TOFOSEM was observed to be better and less dependent on the contrast and size of the target structures as compared to TOF Q.Clear and PSF-TOFOSEM with filter. At the same contrast level, PSF-HYPR4D-K-TOFOSEM achieved better 4D noise suppression than other methods (e.g. >2 times lower noise than TOF Q.Clear at the highest contrast). We also present a novel voxel search method to obtain an image-derived input function (IDIF) and demonstrate that the obtained IDIF is the most quantitative w.r.t. the measured blood samples when the acquired data are reconstructed with PSF-HYPR4D-K-TOFOSEM.

Conclusions
The overall results support superior performance of the PSF-HYPR4D-K-TOFOSEM for TOF PET data and demonstrate that the proposed method is likely suitable for all imaging tasks including the generation of IDIF without requiring any prior information as well as further improving the effective sensitivity of the imaging system.
Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms
Authors: Deforth Manja , Gebhard Caroline E. , Bengs Susan , Buehler Philipp K. , Schuepbach Reto A. , Zinkernagel Annelies S. , Brugger Silvio D. , Acevedo Claudio T. , Patriki Dimitri , Wiggli Benedikt , Twerenbold Raphael , Kuster Gabriela M. , Pargger Hans , Schefold Joerg C. , Spinetti Thibaud , Wendel-Garcia Pedro D. , Hofmaenner Daniel A. , Gysi Bianca , Siegemund Martin , Heinze Georg , Regitz-Zagrosek Vera , Gebhard Catherine , Held Ulrike 
Published: November 2022

Abstract
Background
The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19.

Methods
The prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance.

Results
In total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was −0.06 (95% CI: −0.22 to 0.09).

Conclusion
The proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended.
Early prediction of Alzheimer's disease using convolutional neural network: a review
Authors: Patil Vijeeta , Madgi Manohar , Kiran Ajmeera 
Published: November 2022

AbstractIn this paper, a comprehensive review on Alzheimer's disease (AD) is carried out, and an exploration of the two machine learning (ML) methods that help to identify the disease in its initial stages. Alzheimer's disease is a neurocognitive disorder occurring in people in their early onset. This disease causes the person to suffer from memory loss, unusual behavior, and language problems. Early detection is essential for developing more advanced treatments for AD. Machine learning (ML), a subfield of Artificial Intelligence (AI), uses various probabilistic and optimization techniques to help computers learn from huge and complicated data sets. To diagnose AD in its early stages, researchers generally use machine learning. The survey provides a broad overview of current research in this field and analyses the classification methods used by researchers working with ADNI data sets. It discusses essential research topics such as the data sets used, the evaluation measures employed, and the machine learning methods used. Our presentation suggests a model that helps better understand current work and highlights the challenges and opportunities for innovative and useful research. The study shows which machine learning method holds best for the ADNI data set. Therefore, the focus is given to two methods: the 18-layer convolutional network and the 3D convolutional network. Hence, CNNs with multi-layered fetch more accurate results as compared to 3D CNN. The work also contributes to the use of the ADNI data set, where the classification of training and testing samples is divided with such a number that brings the highest accuracy achieved with 18-layer CNN. The work concentrates on the early prediction of Alzheimer's disease with machine learning methods. Thus, the accuracy achieved is 98% for 18-layer CNN.
Cancer research activity in the Arab world: a 15-year bibliometric analysis
Authors: Machaalani Marc , El Masri Jad , El Ayoubi Lemir Majed , Matar Bassam 
Published: November 2022

Abstract
Background
The Arab region comprises 22 countries located in the Middle East and North Africa, sharing cultural and linguistic ties. Arab countries have continued to lag in terms of biomedical research compared to other nations for several past decades. Cancer is a major public health concern, being the second leading cause of death globally. Given that high research activity on cancer reflects positively on screening programs, awareness, and clinical practice, this article aimed to examine the activity and trend of cancer research in the Arab world between 2005 and 2019.

Methods
Between 2005 and 2019, the number of cancer-related articles published by each Arab country, and regarding 27 different types, was assessed using the PubMed database. Numbers were normalized with respect to each country’s average population and average Gross Domestic Product (GDP).

Results
Arab countries contributed to 1.52% of total cancer publications. The number of cancer publications has steadily grown since 2005, with the last 7 years alone witnessing 75.69% of the total Arab cancer-related publications. In terms of publications per million persons, Qatar ranked first (393.74 per million persons), while in terms of publications per national GDP, Egypt ranked first (464.27 per billion US dollars). Breast, liver, and colorectal cancers had the highest numbers of all Arab cancer-related publications, while testicular, vulvar, and gallbladder cancers had the least.

Conclusions
This paper pools information and insight for scientists, clinicians, funders, and decision-makers on the actualities and developments of cancer research in the Arab world. Addressing the barriers facing cancer research remains a cornerstone in the plan to improve the Arab world’s output and contribution to the field of oncology.
Frequency spectrum of engineering structures with time varying masses
Authors: Tu Phung , Vimonsatit Vanissorn , Hansapinyo Chayanon 
Published: November 2022

AbstractTime varying mass engineering structure is the type of structure where the total mass of the structure changes rapidly, and the loads generated are dependent on the properties of the structure, discharge rate and the properties of the materials inside the structure. A discharging silo is a typical time varying mass civil engineering structure where the total mass of the entire structure changes during the discharge of the stored materials. This paper presents a novel combinatory approach using Hilbert Transform and Variational Mode Decomposition methods to analyse nonlinear and non-stationary signals collected from the experimental silo filled with iron ore and one filled with sand. In particular, the silo filled with sand was producing loud foghorn like sound during discharge. The results revealed the existence of a frequency spectrum that can be developed further to assess the structural integrity of a time varying mass civil engineering structure such as a silo without the impacting silo operation and throughput. The obtained frequency spectrum shows that there is a noticeable difference between the spectra for the silo filled with iron ore and the silo filled with sand.