bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring Different Encodings and Distance Metrics for a Quantum Instance-based Classifier
Authors: Berti Alessandro , Bernasconi Anna , Corso Gianna M. Del , Guidotti Riccardo 
Published: December 2023

Abstract

Over the past few years, we observed a rethinking of classical artificial intelligence algorithms from a quantum computing perspective. This trend is driven by the peculiar properties of quantum mechanics, which offer the potential to enhance artificial intelligence capabilities, enabling it to surpass the constraints of classical computing.
However, redesigning classical algorithms into their quantum equivalents is not straightforward and poses numerous challenges. In this study, we analyze in-depth two orthogonal designs of the Quantum K-Nearest Neighbor classifier. In particular, we show two solutions based on amplitude encoding and basis encoding of data, respectively. These two types of encoding impact the overall structure of the respective algorithms, which employ different distance metrics and show different performances. By breaking down each quantum algorithm, we clarify implementation aspects ranging from data preparation to classification.
Eventually, we discuss the difficulties associated with data preparation, the theoretical advantage of quantum algorithms, and their impact on performance with respect to the classical counterpart.
Photobiomodulation Therapy Moderates Cancer Cachexia-associated Muscle wasting through Activating PI3K/AKT/FoxO3a Pathway succinct title: PBMT Alleviates Muscle Wasting via PI3K/AKT/FoxO3a Pathway
Authors: Li Yonghua , Chen Yibing , Lai Dongming , Huang Ting , Chang Haocai , Liao Yuan , Xia Qing , Li Hongsheng , Xu Liu , Li Haolong , Xing Da , Liu Quentin , Zou Zhengzhi 
Published: December 2023

Abstract
Cancer cachexia-associated muscle wasting as a multifactorial wasting syndrome, is an important factor affecting the long-term survival rate of tumor patients. Photobiomodulation therapy (PBMT) has emerged as a promising tool to cure and prevent many diseases. However, the effect of PBMT on skeletal muscle atrophy during cancer progression has not been fully demonstrated yet. Here, we found PBMT alleviated the atrophy of myotube diameter induced by cancer cells in vitro, and prevented cancer-associated muscle atrophy in mice bearing tumor. Mechanistically, the alleviation of muscle wasting by PBMT was found to be involved in inhibiting E3 ubiquitin ligases MAFbx and MuRF-1. In addition, transcriptomic analysis using RNA-seq and GSEA revealed that PI3K/AKT pathway might be involved in PBMT-prevented muscle cachexia. Next, we showed the protective effect of PBMT against muscle cachexia was totally blocked by AKT inhibitor in vitro and in vivo. Moreover, PBMT-activated AKT promoted FoxO3a phosphorylation and thus inhibiting the nucleus entry of FoxO3a. Lastly, in cisplatin-treated muscle cachexia model, PBMT had also been shown to ameliorate muscle atrophy through enhancing PI3K/AKT pathway to suppress MAFbx and MuRF-1 expression. These novel findings revealed that PBMT could be a promising therapeutic approach in treating muscle cachexia induced by cancer.
Unsteady gust interaction with tidal rotors: Importance of 3D wake effects and implications for turbine load modelling
Authors: Smyth Amanda S. M. , de Arcos Federico S. Zilic , Young Anna M. 
Published: December 2023

Abstract

This study investigates the blade loads on a model tidal turbine subject to unsteady gust forcing in the form of uniform small-amplitude oscillations in the axial inflow velocity. The validity of industry-standard 2D strip-theory models for calculating unsteady hydrodynamic loading on 3D rotor geometries is evaluated, by comparing predictions by the Theodorsen function to numerical Reynolds-Averaged Navier Stokes (RANS) simulations and three-dimensional inviscid vortex lattice modelling (VLM). The results show that the 2D function captures neither the trends nor the magnitudes of the unsteady turbine loads. The inviscid VLM corresponds well to the unsteady RANS simulations, suggesting that 3D wake effects are highly consequential for the unsteady loads. The primary non-dimensional parameters determining the unsteady load magnitudes are identified, and it is observed that industrial turbines are likely to experience operation at the "critical forcing period" corresponding to peak unsteady load amplitudes in realistic wave conditions. These peak loads exceed those predicted by assuming quasi-steady conditions, which is otherwise often considered to give conservative load estimates. We demonstrate the potential hazard in applying quasi-steady tip-loss corrections and induction factors in unsteady flow conditions, as this can substantially under-predict the loads, and show that the shortcomings of strip-theory models stem from their lack of 3D unsteady wake effects. These outcomes have implications for the evaluation of peak and lifetime loads on tidal devices, and for any rotor application which relies on 2D strip-theory for load predictions.
Quantum Diffusion Models
Authors: Cacioppo Andrea , Colantonio Lorenzo , Bordoni Simone , Giagu Stefano 
Published: December 2023

Abstract

Denoising diffusion probabilistic models have become increasingly important in machine learning, but their quantum counterpart has not been studied yet. In this work, we propose a quantum version of a generative diffusion model. In this algorithm, artificial neural networks are replaced with parameterized quantum circuits, in order to manipulate quantum states directly. We present both a full quantum version and a latent classical-quantum version of the algorithm. In the latent model, the parameterized quantum circuits are trained in a low dimensional representation of data, obtained by employing a pre-trained classical autoencoder. For both models, we show a method for conditioning the output distribution, utilizing ancillary qubits. The models' performance have been evaluated using quantitative metrics complemented by qualitative assessments. For the latent model, we show the implementation of a simplified version on real quantum hardware. The execution on a NISQ device allows to evaluate the performance of the algorithm in the presence of noise.
Sustained ecological impacts of invasive crayfish following claw injury
Authors: Soto Ismael , Hen Gwendaline Le , Buřič Miloš , Cuthbert Ross N. , Haubrock Phillip J. , Sentis Arnaud , Veselý Lukáš , Kouba Antonín 
Published: December 2023

Abstract

The rapid acceleration of human-mediated translocation of species has led to the introduction of thousands of species outside their native range with severe consequences for global biodiversity. Freshwater crayfish are a particularly successful group of invasive species as exemplified by the increasingly widespread marbled crayfish. A significant portion of crayfish populations has regenerating or missing claws. At the same time, claws play important roles in e.g., feeding and intraspecific interactions, altering crayfish ability to invade new areas and impact native communities. Here, we combined laboratory experiments and modeling to investigate whether the number of claws on marbled crayfish modulates its ecological impact (focusing on predation rate) using functional response analyses, as well as its intraspecific interactions via population dynamic modeling. We found that the number of claws did not affect the marbled crayfish's functional response type (hyperbolic type II), attack rates, or handling times when preying on chironomid larvae. However, claw numbers had a significant effect on the partial consumption rate of prey, which tended to increase with the number of claws and prey density. The presence of multiple intraspecific predators influenced marbled crayfish non-trophic behavior, with antagonistic effects prevalent between crayfish pairs regardless of claw status. Moreover, the impact of multiple predators was prey density-dependent, with highest levels of antagonism shown at the lowest prey density across crayfish groups. Our findings show that the potential impacts of invasive crayfish was not affected by the number of claws, highlighting the growing context-independent risk that these species pose to freshwater ecosystems.
Change in control intermittency with practice during humanpostural balancing on an unstable unrestrained platform
Authors: Rao Aishwarya , Palanthandalam-Madapusi Harish 
Published: December 2023

Abstract
Purpose: The central nervous system (CNS) employs an intermittent control strategy in humanquiet stance and other balancing activities as stated in the literature. We investigate the less exploredaspect of how the intermittent control strategy changes as humans practice balancing on an unstableunrestrained platform.
Method: We perform human subject experiments with a rocker board, obtain the Center of Pres-sure (COP), ankle and rocker board angle, and EMG data for the Medial Gastrocnemius (MG) and Tibialis Anterior (TA) muscles for the right leg (active leg). We analyze the trends in various postur-ography, kinematic, and muscle-firing parameters while taking reference from simulation analysis todraw insights about the nature of changes in the intermittent control strategy with practice.
Result: Multi-modal or skewed histograms of COP, power law in power spectrum, and 2-slope diffu-sion stabilogram are observed in all data. Total kernel density of the peaks in the COP histogramsincrease with practice trials and increases with decrease in intermittent threshold during simulation.The low-frequency slope sL of the ankle angle power spectrum decreases with practice and decreaseswith decrease in intermittency threshold in simulations. Average Energy AE and Simple SquareIntegral SSI decrease with practice. Wavelet spectrum of MG and TA muscle EMG show intermit-tent bursts in the signals.
Conclusion: The intermittent control strategy is retained throughout the balanc-ing practice on the rocker board. The trends in parameters with practice are bestexplained by a decrease in intermittency threshold in the control strategy with practice.
Comparison of two methods in multi-criteria decision-making: application in transmission rod material selection
Authors: Thinh Hoang Xuan , Mai Nguyen Trong 
Published: December 2023

Transmission rod is an indispensable part in diesel and gasoline engines. Its job is to convert rotation into translational motion or vice versa. The transmission rod material selection plays a very important role, affecting its working function and durability. This study was conducted to compare two Multi Criteria Decision Making (MCDM) methods in transmission rod material selection. They are PIV (Proximity Indexed Value) method, and FUCA (Faire Un Choi Adéquat) method. Seven types of steel commonly used in transmission rods were reviewed for ranking, inclusive of: 20 steel, 40 steel, 45 steel, 18Cr2Ni4WA steel, 30 CrMoA steel, 45Mn2 steel and 40CrNi steel. Nine parameters were used as criteria to evaluate each steel including minimum yield strength, ultimate tensile strength, minimum elongation ratio, contraction ratio, modulus of elasticity, mean coefficient of thermal expansion, thermal conductivity, specific thermal capacity, and density. The weights of the criteria were calculated using three methods inclusive of MEAN weight method, Entropy weight method and MEREC weight method (Method based on the Removal Effects of Criteria). Each MCDM method was combined with the three weight methods mentioned above to rank the alternatives. The obtained results show that when using both PIV and FUCA methods to rank the alternatives, the best and worst alternatives are found regardless of the weight of the criteria. The best alternative determined using the PIV method is also the best alternative determined using the FUCA method. It means that the two PIV and FUCA methods have been shown to be equally effective. Among the seven transmission rod materials reviewed, 20 steel was identified as the best, and 40CrNi steel was identified as the worst
An Online Shared Decision-making Intervention for Dementia Prevention: A Parallel-group Randomized Pilot Study
Authors: Ownby Raymond L. , Davenport Rosemary 
Published: December 2023

Objectives:
Evaluate the acceptability and efficacy of an online dementia prevention intervention
based on a cognitive behavioral shared decision-making model.

Materials and Method:
This was an unblinded pilot study in which participants were randomly
assigned to one of two treatment groups. This study was carried out remotely via telephone, video
conferencing, and online data collection. Eighteen English-speaking persons 40 years of age and
older interested in developing more brain-healthy lifestyles. Both groups received 12 weekly sessions
on lifestyle factors related to cognitive decline. The treatment-as-usual (TAU) group received
the information and was encouraged to make lifestyle changes. The cognitive behavioral shared decision-
making model (CBSDM) group received structured weekly sessions with support for evidence-
informed personal goal choices and behavior change strategies. Primary outcome measures
were the Alzheimer's Disease Risk Inventory and the Memory Self-Efficacy and Dementia
Knowledge Assessment Scales. Participants reported brain health activities during the first, sixth,
and 12th weeks of the study.

Results:
No significant between-group changes were seen in the three primary outcome measures.
The intervention was viewed positively by participants, who all said they would participate in it
again. Participants in the CBSDM group showed increases in knowledge of dementia risk factors
and exercise. Other outcomes were consistent with moderate to large effect sizes for both groups.

Conclusion:
An online intervention providing psychoeducation and behavior change support was
viewed positively by older adults. Results provide preliminary support for the CBSDM intervention’s
efficacy in promoting brain health in older adults.

Clinical Trial Registration Number:
NCT04822129.
In Search of Novel SGLT2 Inhibitors by High-throughput Virtual Screening
Authors: Debnath Abhijit , Sharma Shalini , Mazumder Rupa , Mazumder Avijit , Singh Rajesh , Kumar Ankit , Dua Arpita , Singhal Priya , Kumar Arvind , Singh Gurvinder 
Published: December 2023

Background:
Type 2 diabetes mellitus constitutes approximately 90% of all reported forms
of diabetes mellitus. Insulin resistance characterizes this manifestation of diabetes. The prevalence of
this condition is commonly observed in patients aged 45 and above; however, there is an emerging
pattern of younger cohorts receiving diagnoses primarily attributed to lifestyle-related variables, including obesity, sedentary behavior, and poor dietary choices. The enzyme SGLT2 exerts a negative regulatory effect on insulin signaling pathways, resulting in the development of insulin resistance and subsequent elevation of blood glucose levels. The maintenance of glucose homeostasis relies on the proper
functioning of insulin signaling pathways, while disruptions in insulin signaling can contribute to the
development of type 2 diabetes.

Objective:
Our study aimed to investigate the role of SGLT2. This enzyme interferes with insulin signaling pathways and identifies potential SGLT2 inhibitors as a treatment for managing type 2 diabetes.

Methods:
We screened the Maybridge HitDiscover database to identify potent hits followed by druglikeness, Synthetic Accessibility, PAINS alert, toxicity estimation, ADME assessment, and Consensus
Molecular docking

Results:
The screening process led to the identification of three molecules that demonstrated significant
binding affinity, favorable drug-like properties, effective ADME, and minimal toxicity.

Conclusion:
The identified molecules could manage T2DM effectively by inhibiting SGLT2, providing
a promising avenue for future therapeutic strategies
Discovering Microbe-disease Associations with Weighted Graph
Convolution Networks and Taxonomy Common Tree
Authors: Xing Jieqi , Shi Yu , Su Xiaoquan , Wu Shunyao 
Published: December 2023

Background:
Microbe-disease associations are integral to understanding complex diseases
and their screening procedures.

Objective:
While numerous computational methods have been developed to detect these associations,
their performance remains limited due to inadequate utilization of weighted inherent similarities
and microbial taxonomy hierarchy. To address this limitation, we have introduced
WTHMDA (weighted taxonomic heterogeneous network-based microbe-disease association), a
novel deep learning framework.

Methods:
WTHMDA combines a weighted graph convolution network and the microbial taxonomy
common tree to predict microbe-disease associations effectively. The framework extracts multiple
microbe similarities from the taxonomy common tree, facilitating the construction of a microbe-
disease heterogeneous interaction network. Utilizing a weighted DeepWalk algorithm, node
embeddings in the network incorporate weight information from the similarities. Subsequently, a
deep neural network (DNN) model accurately predicts microbe-disease associations based on this
interaction network.

Results:
Extensive experiments on multiple datasets and case studies demonstrate WTHMDA's
superiority over existing approaches, particularly in predicting unknown associations.

Conclusion:
Our proposed method offers a new strategy for discovering microbe-disease linkages,
showcasing remarkable performance and enhancing the feasibility of identifying disease risk.
Stacking-Kcr: A Stacking Model for Predicting the Crotonylation Sites of
Lysine by Fusing Serial and Automatic Encoder
Authors: Liang Ying , Li Suhui , You Xiya , Guo You , Tang Jianjun 
Published: December 2023

Background:
Protein lysine crotonylation (Kcr), a newly discovered important posttranslational
modification (PTM), is typically localized at the transcription start site and regulates
gene expression, which is associated with a variety of pathological conditions such as developmental
defects and malignant transformation.

Objective:
Identifying Kcr sites is advantageous for the discovery of its biological mechanism and
the development of new drugs for related diseases. However, traditional experimental methods for
identifying Kcr sites are expensive and inefficient, necessitating the development of new computational
techniques.

Methods:
In this work, to accurately identify Kcr sites, we propose a model for ensemble learning
called Stacking-Kcr. Firstly, extract features from sequence information, physicochemical properties,
and sequence fragment similarity. Then, the two characteristics of sequence information and
physicochemical properties are fused using automatic encoder and serial, respectively. Finally, the
fused two features and sequence fragment similarity features are then respectively input into the
four base classifiers, a meta classifier is constructed using the first level prediction results, and the
final forecasting results are obtained.

Results:
The five-fold cross-validation of this model has achieved an accuracy of 0.828 and an
AUC of 0.910. This shows that the Stacking-Kcr method has obvious advantages over traditional
machine learning methods. On independent test sets, Stacking-Kcr achieved an accuracy of
84.89% and an AUC of 92.21%, which was higher than 1.7% and 0.8% of other state-of-the-art
tools. Additionally, we trained Stacking-Kcr on the phosphorylation site, and the result is superior
to the current model.

Conclusion:
These outcomes are additional evidence that Stacking-Kcr has strong application potential
and generalization performance.
Mechanism of Houpu Wenzhong Decoction in the Treatment of Chronic
Gastritis and Depression Based on Network Pharmacology and Molecular
Docking
Authors: Zhang Liang , Yu Wenying 
Published: December 2023

Objective:
The study aimed to discuss the possible mechanisms of Houpu Wenzhong
Decoction in the treatment of chronic gastritis and depression based on network pharmacology
and molecular docking.

Methods:
The chemical components and corresponding targets of seven crude drugs in Houpu
Wenzhong Decoction were retrieved from the database TCMSP. The targets for treating chronic
gastritis and depression were obtained from the database GeneCards. STRING 11.5 was used to
establish the protein-protein interaction network of common targets among “chemical components-
chronic gastritis-depression”, and then Cytoscape was employed for visual analysis. The
targets with the top ten degrees were selected as key targets, which were then imported to BioGPS
to obtain the distribution in organs and tissues. The common targets were analyzed via Go and
KEGG pathway enrichment analyses using Metascape. The top ten degrees of active components
were verified by molecular docking of key targets.

Results:
Under the conditions of OB ≥ 30% and DL ≥ 0.18, a total of 144 chemical components
and related 251 targets of Houpu Wenzhong Decoction were retrieved. 1192 and 12902 targets
were retrieved for chronic gastritis and depression from the GeneCards database, respectively. 104
common targets were obtained by intersection among the two diseases and drug targets. The key
common targets have been found to be mainly distributed in organs and tissues, such as the adrenal
cortex, amygdala, appendix, adrenal gland, colorectal, liver, and prostates, as observed through
BioGPS analysis. 5141 biological processes, 354 cell components, and 615 molecular functions
were obtained by GO functional enrichment analysis, and 302 signal pathways were obtained by
KEGG enrichment analysis. Among these, IL-17, TNF, PI3K-Akt, and toll-like receptor signaling
pathway have been found to be involved. Molecular docking results showed the key active components,
naringenin and hesperidin, to have good binding activities with targets STAT3 and Jun.

Conclusion:
104 common targets between chronic gastritis and depression have been obtained as
the basis for Houpu Wenzhong Decoction to treat the two diseases. The ten key active ingredients
have been found to act on 15 key signal pathways through 104 common key targets to treat the
two diseases.
Designing Telemedicine for Older Adults With Multimorbidity: Content Analysis Study
Authors: Buawangpong Nida , Pinyopornpanish Kanokporn , Pliannuom Suphawita , Nantsupawat Nopakoon , Wiwatkunupakarn Nutchar , Angkurawaranon Chaisiri , Jiraporncharoen Wichuda 
Published: December 2023

Background
Telemedicine is a potential option for caring for older adults with multimorbidity. There is a need to explore the perceptions about telemedicine among older adults with multimorbidity to tailor it to the needs of older adults with multiple chronic conditions.

Objective
This study aims to explore the perceptions about telemedicine among older patients with multimorbidity.

Methods
A qualitative study was conducted using semistructured interviews. The interview questions examined older adults’ perspectives about telemedicine, including their expectations regarding telemedicine services and the factors that affect its use. Thematic analysis was performed using NVivo (version 12; Lumivero). The study was reported using the Standards for Reporting Qualitative Research guidelines.

Results
In total, 29 patients with multimorbidity—21 (72%) female patients and 8 (28%) male patients with a mean age of 69 (SD 10.39) years—were included. Overall, 4 themes and 7 subthemes emerged: theme 1—perceived benefit of telemedicine among older adults with multimorbidities, theme 2—appropriate use of telemedicine for multimorbid care, theme 3—telemedicine system catering to the needs of older patients, and theme 4—respect patients’ decision to decline to use telemedicine.

Conclusions
Telemedicine for older adults with multimorbidity should focus on those with stable conditions. This can help increase access to care for those requiring continuous condition monitoring. A structured telemedicine program and patient-centered services can help increase patient acceptance of telemedicine. However, health care providers must accept the limitations of older patients that may prevent them from receiving telemedicine services.