bluuebunny/crossref_metadata_2025_split huggingface dataset:
A multi-region single nucleus transcriptomic atlas of Parkinson’s disease
Authors: N. M. Prashant , Fullard John F. , Clarence Tereza , Mathur Deepika , Casey Clara , Hennigan Evelyn , Alvia Marcela , Krause-Massaguer Joana , Barreda Ayled , Davis David A. , Vontell Regina T. , Garamszegi Susanna P. , Vance Jeffery M. , Sang Lorelle , Chatigny Michael , Vismer David , Landin Barry , Burstein David , Lee Donghoon , Voloudakis Georgios , Berretta Sabina , Haroutunian Vahram , Scott William K. , Bendl Jaroslav , Roussos Panos 
Published: November 2024

AbstractParkinson’s Disease (PD) is a debilitating neurodegenerative disorder, characterized by motor and cognitive impairments, that affects >1% of the population over the age of 60. The pathogenesis of PD is complex and remains largely unknown. Due to the cellular heterogeneity of the human brain and changes in cell type composition with disease progression, this complexity cannot be fully captured with bulk tissue studies. To address this, we generated single-nucleus RNA sequencing and whole-genome sequencing data from 100 postmortem cases and controls, carefully selected to represent the entire spectrum of PD neuropathological severity and diverse clinical symptoms. The single nucleus data were generated from five brain regions, capturing the subcortical and cortical spread of PD pathology. Rigorous preprocessing and quality control were applied to ensure data reliability. Committed to collaborative research and open science, this dataset is available on the AMP PD Knowledge Platform, offering researchers a valuable tool to explore the molecular bases of PD and accelerate advances in understanding and treating the disease.
Improved facial emotion recognition model based on a novel deep convolutional structure
Authors: Elsheikh Reham A. , Mohamed M. A. , Abou-Taleb Ahmed Mohamed , Ata Mohamed Maher 
Published: November 2024

AbstractFacial Emotion Recognition (FER) is a very challenging task due to the varying nature of facial expressions, occlusions, illumination, pose variations, cultural and gender differences, and many other aspects that cause a drastic degradation in quality of facial images. In this paper, an anti-aliased deep convolution network (AA-DCN) model has been developed and proposed to explore how anti-aliasing can increase and improve recognition fidelity of facial emotions. The AA-DCN model detects eight distinct emotions from image data. Furthermore, their features have been extracted using the proposed model and numerous classical deep learning algorithms. The proposed AA-DCN model has been applied to three different datasets to evaluate its performance: The Cohn-Kanade Extending (CK+) database has been utilized, achieving an ultimate accuracy of 99.26% in (5 min, 25 s), the Japanese female facial expressions (JAFFE) obtained 98% accuracy in (8 min, 13 s), and on one of the most challenging FER datasets; the Real-world Affective Face (RAF) dataset; reached 82%, in low training time (12 min, 2s). The experimental results demonstrate that the anti-aliased DCN model is significantly increasing emotion recognition while improving the aliasing artifacts caused by the down-sampling layers.
Microresonator photonic wire bond integration for Kerr-microcomb generation
Authors: Takabayashi Alain Yuji , Pavlov Nikolay , Rosborough Victoria , Hoffman Galen , Kanger Lou , Koushyar Farzad Mokhtari , Huffman Taran , Nelson Mike , Turner Charles , Johansson Leif , Musolf Juergen , Garrett Henry , Liu Thomas , Morrison Gordon , Chembo Yanne , Mattis Brian , Nguyen Thien-An , Van Camp Mackenzie , Turner Steven Eugene , Karpov Maxim , Jost John , Burkley Zakary 
Published: November 2024

AbstractExtremely high-Q microresonators provide an attractive platform for a plethora of photonic applications including optical frequency combs, high-precision metrology, telecommunication, microwave generation, narrow linewidth lasers, and stable frequency references. Moreover, the desire for compactness and a low power threshold for nonlinear phenomena have spurred investigation into integrated and scalable solutions. Historically, crystalline microresonators with Q $$\sim$$ 109 were one of the first material platforms providing unprecedented optical performance in a small form factor. A key challenge, though, with these devices is in finding alternatives to fragile, bulky, and free-space couplers, such as tapered fibers, prisms, and cleaved fibers. Here, we present for the first time, the evanescent coupling of a photonic wire bond (PWB) to a MgF2-based microresonator to generate solitons and a pure, low-noise microwave signal based on Kerr-microcombs. These results open a path towards scalable integration of crystalline microresonators with integrated photonics. Moreover, because PWBs possess advantages over traditional coupling elements in terms of ease of fabrication, size, and flexibility, they constitute a more advanced optical interface for linear and nonlinear photonics.
EEG features and synek scale indicate severity of neurotoxicity in adult patients treated with CD19 CAR T-cell therapy
Authors: Mao David , Reiner Anne S. , Chen Xi , Park Jae , Pennisi Martina , Perales Miguel-Angel , Avila Edward K. , Santomasso Bianca D. 
Published: November 2024

AbstractPatients who develop chimeric antigen receptor (CAR) T-cell-related immune effector cell-associated neurotoxicity syndrome (ICANS) frequently undergo evaluation with electroencephalography (EEG). We hypothesize that EEG features and Synek scale score, a measure of degree of EEG abnormality, are associated with ICANS severity. Here, we performed a retrospective review of 125 adult patients at Memorial Sloan Kettering Cancer Center (MSKCC) who received CAR-T cell therapy from 2010 to 2019, including 53 patients with B-acute lymphoblastic leukemia treated with 1928z CAR T cells (NCT01044069) and 72 patients with large B-cell lymphoma (LBCL) treated with the commercial CAR T products axicabtagene ciloleucel or tisagenlecleucel. We collected video EEG monitoring (27 with B-ALL and 20 with LBCL) and recorded daily EEG features, Synek scores, and ICANS grade for 47 eligible patients. Synek scale and ICANS grade were positively correlated (correlation coefficient 0.47, 95% CI: 0.31–0.60). This was further corroborated in the univariable model associating high Synek scale (3 or 4) with high ICANS grade (OR = 15.2; 95%CI:7.8–29.7, p < 0.0001). EEG features such as discontinuity, absence of posterior dominant rhythm, and presence of generalized sharp waves were statistically significantly associated with higher ICANS grade in univariable models. In the multivariable model, discontinuity (OR = 4.2 (95%CI:1.3–13.8, p = 0.02) and absence of posterior dominant rhythm (OR = 10.5 (95%CI:4.6–23.9, p < 0.0001) were statistically associated with higher ICANS grade. Overall, EEG discontinuity and absence of posterior dominant rhythm were independently associated with higher severity of neurotoxicity. Further, our data suggest that Synek Scale, may be a severity marker for neurotoxicity.
Comparison of two negative pressure ureteral access sheaths combined with day-case flexible ureteroscopy for renal stones randomized trial
Authors: Ma Qinghong , Chen Guoqiang , Li Guanghai , Cui Deheng 
Published: November 2024

Abstract To compare the safety and effectiveness of the combination of intelligent intrarenal pressure control platforms and flexible end ureteral access sheath combined with flexible ureteroscopic lithotripsy (FURL) for the treatment of renal stones less than 2 cm with day case mode. From November 2023 to July 2024, a prospective, randomized, double-blind, parallel-controlled study was conducted to recruit 60 patients with upper urinary tract calculi measuring ≤ 2 cm in longest diameter. A total of 60 eligible patients were consecutively enrolled. Based on a pre-generated random number table and allocation scheme using SPSS 27.0, patients were assigned to either the intelligent pressure control group (IFURL, n = 30) or the head bending group (BFURL, n = 30). Blinding measures were implemented for patients, data collectors, statisticians, and analysts, with the allocation results disclosed to the surgeons prior to the start of the procedure in the operating room. The operation duration for IFURL and BFURL was 52.50 (48.00, 60.00) vs. 46.00 (36.00, 56.25), respectively (p = 0.047). Stone free rate (SFR) on the first postoperative day was 73.33% vs. 93.33% for IFURL and BFURL, respectively (p = 0.038). While, SFR was no significant difference between the two groups after two months(90.00% vs. 96.67%, p = 0.301). One patient in IFURL was readmitted after discharge due to fever 2 days post-discharge, and improved after 2 days of anti-infection treatment. One patient in BFURL was readmitted due to lumbar and abdominal pain from urine extravasation, which improved after 3 days of anti-infection treatment. SFR of one-month post-surgery for renal calculi ≤ 2 cm treated with intelligent pressure control and flexible UAS combined with FURL in day surgery mode was similar, with low infection-related complications and rehospitalization rates, showing no statistical difference. However, the overall hospitalization costs for the BFURL was lower than IFURL.
Identifying future challenges for climate change adaptation through insights from participatory scenario-downscaling in Mumbai
Authors: Petzold Jan , Garschagen Matthias , Deshpande Shankar , Dhiman Ravinder , Doshi Deepal , Katzschner Antje , Santos Alexandre Pereira , Parthasarathy D. 
Published: November 2024

AbstractPopulations in many coastal urban areas are increasingly exposed to climate-related hazards. At the same time, the number of people residing in coastal cities is growing, and, especially in the Global South, these cities are characterised by rapid urbanisation and social inequality. However, the progress of adaptation is lagging, and there is a limited understanding of how future socioeconomic urban developments will affect cities’ social vulnerability and challenges to adaptation. We use the case study of Mumbai to apply a participatory scenario approach, in which we downscale the global Shared Socioeconomic Pathway (SSP) narratives to the local level. Our results stress the relevance of addressing social inequality in urban change processes across different sectors, including labour, housing, transport, and health and streamlining urban planning across different governance scales. Our study lays the ground for integrated modelling of future vulnerability and exposure scenarios and the development of local adaptation pathways.
Using network analysis to understand the association between menopause and depressive symptoms
Authors: Martin-Key Nayra A. , Funnell Erin L. , Benacek Jiri , Spadaro Benedetta , Bahn Sabine 
Published: November 2024

AbstractIt has been proposed that depressive symptoms occurring throughout the menopause may be linked to specific menopause symptoms. The objectives of this study were (i) to use network analysis to explore the symptom structure of menopause and depressive symptoms, and (ii) to compare the symptom structure of menopause and depressive symptoms between the perimenopause and the post-menopause. The Menopause-specific Quality of Life (MENQOL) and the eight-item Patient Health Questionnaire (PHQ-8) were used to measure menopause and depressive symptoms, with online survey data from 858 respondents included for analysis. The most central symptoms were decreased stamina, decreased physical strength, feeling depressed, and change in sexual desire. The network demonstrated good stability (CS-strength = 0.75 and CS-influence = 0.75), with no significant differences in structure between the perimenopause and post-menopause groups. Addressing decreased stamina and psychosocial symptoms could prove instrumental in managing menopause symptoms. Early intervention is crucial for overcoming barriers and providing essential support.
Blockchain controlled trustworthy federated learning platform for smart homes
Authors: Biswas Sujit , Sharif Kashif , Latif Zohaib , Alenazi Mohammed J. F. , Pradhan Ashok Kumar , Bairagi Anupam Kumar 
Published: November 2024

AbstractSmart device manufacturers rely on insights from smart home (SH) data to update their devices, and similarly, service providers use it for predictive maintenance. In terms of data security and privacy, combining distributed federated learning (FL) with blockchain technology is being considered to prevent single point failure and model poising attacks. However, adding blockchain to a FL environment can worsen blockchain's scaling issues and create regular service interruptions at SH. This article presents a scalable Blockchain‐based Privacy‐preserving Federated Learning (BPFL) architecture for an SH ecosystem that integrates blockchain and FL. BPFL can automate SHs' services and distribute machine learning (ML) operations to update IoT manufacturer models and scale service provider services. The architecture uses a local peer as a gateway to connect SHs to the blockchain network and safeguard user data, transactions, and ML operations. Blockchain facilitates ecosystem access management and learning. The Stanford Cars and an IoT dataset have been used as test bed experiments, taking into account the nature of data (i.e. images and numeric). The experiments show that ledger optimisation can boost scalability by 40–60% in BCN by reducing transaction overhead by 60%. Simultaneously, it increases learning capacity by 10% compared to baseline FL techniques.
Digital twin framework and platform for zero‐emission heavy haul locomotive design and development
Authors: Spiryagin Maksym , Bernal Esteban , Ahmad Sanjar , Wu Qing , Cole Colin , Mcsweeney Tim 
Published: November 2024

AbstractIn recent years, significant development activity has been seen in battery electric and hydrogen locomotives. To fully understand the potential benefits of these zero‐emission technologies and their application in locomotive design, a design study utilising a digital twin framework can be employed. While current research on the use of digital twins for battery and hydrogen‐powered rail vehicles is limited, recent studies conducted at the Centre for Railway Engineering at CQUniversity indicate potential challenges with implementing standard 6‐axle locomotives for zero‐emission designs considering heavy haul operational needs and scenarios. These challenges are related to limitations in energy storage capacity and optimisation of train operation scenarios. By considering an 8‐axle locomotive design concept and employing a digital twin framework in the design process, a more comprehensive assessment of conceptual development, design and requirements can be achieved. This will ensure the locomotive design meets standards, guidelines, and codes of practice, ultimately contributing to achieving net‐zero emission goals in locomotive traction.
Machine tool operating vibration prediction based on multi‐sensor fusion and LSTM neural network
Authors: Shi Zhonglou , Duan Jinjie , Li Faquan 
Published: November 2024

AbstractThis study proposes a machine tool vibration prediction method based on multi‐sensor fusion and a long short‐term memory (LSTM) network. Machine tool vibration significantly impacts machining quality, surface roughness, dimensional accuracy, and tool wear. By combining deep learning with industrial applications, this method achieves high‐precision vibration prediction through multi‐sensor data fusion. Data is input into the LSTM model to predict the next moment's vibration. Experimental results demonstrate strong prediction capability for periodic vibrations and machining‐specific vibration errors, effectively enhancing machining accuracy.
Human essential gene identification based on feature fusion and feature screening
Authors: Zhang Zhao‐Yue , Fan Yue‐Er , Huang Cheng‐Bing , Du Meng‐Ze 
Published: November 2024

AbstractEssential genes are necessary to sustain the life of a species under adequate nutritional conditions. These genes have attracted significant attention for their potential as drug targets, especially in developing broad‐spectrum antibacterial drugs. However, studying essential genes remains challenging due to their variability in specific environmental conditions. In this study, the authors aim to develop a powerful prediction model for identifying essential genes in humans. The authors first obtained the essential gene data from human cancer cell lines and characterised gene sequences using 7 feature encoding methods such as Kmer, the Composition of K‐spaced Nucleic Acid Pairs, and Z‐curve. Subsequently, feature fusion and feature optimisation strategies were employed to select the impactful features. Finally, machine learning algorithms were applied to construct the prediction models and evaluate their performance. The single‐feature‐based model achieved the highest area under the Receiver Operating Characteristic curve (AUC) of 0.830. After fusing and filtering these features, the classical machine learning models achieved the highest AUC at 0.823 while the deep learning model reached 0.860. Results obtained by the authors show that compared to using individual features, feature fusion and feature optimisation strategies significantly improved model performance. Moreover, the study provided an advantageous method for essential gene identification compared to other methods.
Circular Chemistry: An Enabler of Circular Economy To Achieve the Zero-Waste Goal
Authors: Singh Santosh Bahadur 
Published: November 2024

AbstractThe main aims of zero-waste goals are the promotion of sustainable production and consumption through the societal move toward circular approaches. The chemical industry includes a variety of processes to produce various useful consumables, but many of these processes have serious negative environmental, health, and safety impacts at every level of their design, production, processing, and uses. Circularity is at the core of eco-design and the production technology in which waste is repurposed and their environmental impacts are reduced via the 3Rs concepts: reduce, reuse, and recycle. The integration of circular approaches with chemistry makes it a circular chemistry (CC). This article provides a brief literature review on CC and why it is important to tackle the various sustainability-related issues. Here we conduct a structured opinion as well as evidence-based review to explore the role of CC to make it more sustainable. Fundamental aspects of CC and its role
in the circular economy have been discussed, and it is concluded that the design of clean chemical processes, recovery, and reuse of wastes, and reintroducing recovered materials back to the industrial production chain is possible and scalable. This article aligns with 7 UN’s Sustainable Development Goals, that is, 3, 6, 9, 12, 13, 14, and 15.
Chirurgische Versorgung typischer osteoporotischer
Frakturen
Authors: Pankratz Carlos , Gebhard Florian , Schütze Konrad 
Published: November 2024

ZusammenfassungDie steigende Inzidenz Osteoporose-assoziierter Frakturen stellt eine zunehmende
Herausforderung für die operative Versorgung dar. Diese Arbeit bietet einen
Überblick über die aktuellen Strategien zur Versorgung der häufigsten
osteoporotischen Frakturen. Ziel ist es Wissen über aktuelle Techniken zu
vermitteln und für spezifische Herausforderungen zu sensibilisieren. Hierzu
wurde eine Übersicht über operative Verfahren für Frakturen des proximalen
Femurs, des Beckens, der Wirbelsäule, des distalen Radius und des proximalen
Humerus erstellt. Die operative Versorgung Osteoporose-assoziierter Frakturen
erfordert aufgrund der verminderten Knochenqualität und -quantität sowie der
häufigen Multimorbidität der Patienten einige Besonderheiten. Zunehmend
angewendet werden verschiedene Augmentationstechniken, die die Stabilität von
Osteosynthesen steigern und eine frühzeitige Mobilisation ermöglichen. Bei
Frakturen der Wirbelsäule und des Beckenringes hat die schmerzbedingte
Immobilität oftmals fatale Folgen für die Betroffenen. Die operative Versorgung
beinhaltet hier einen teilweise präventiven Aspekt. Minimalinvasive Verfahren,
wie die perkutane Schraubenosteosynthese bei Beckenringfrakturen, haben sich als
risikoarme Methode etabliert. Die Kyphoplastie bei osteoporotischen
Wirbelkörperfrakturen kann durch eine Hybrid-Versorgung mit zusätzlicher
dorsaler Instrumentierung ergänzt werden. Vor allem bei Frakturen der oberen
Extremität hat die konservative Therapie weiterhin ihren Stellenwert.
Hüftgelenksnahe Femurfrakturen werden unmittelbar operativ versorgt. Weitere
Forschung ist notwendig, um evidenzbasierte Standardverfahren für die Anwendung
von Augmentationstechniken bei verschiedenen Frakturtypen zu etablieren.
Radiographic Assessment of Total Knee Arthroplasty Alignment With and Without Accelerometer-Based Navigation at a Resident Training Institution
Authors: Brazier Brett G. , Allen Christian B. , Hilyard Daryl G. , Shah Darshan S. , Vizurraga David E. , Hope Donald N. 
Published: November 2024

AbstractMalalignment and lack of surgeon experience are cited as risk factors for prosthetic failure and the need for subsequent revision surgery in total knee arthroplasty (TKA). Various conventional (CON) and computer-assisted surgical (CAS) methods have been developed to try and prevent malalignment and limit outliers. One of these methods is through an accelerometer-based CAS (aCAS), which intraoperatively helps determine the angulation and amount of resection necessary to restore alignment in TKA. The purpose of this study was to determine the number of TKA alignment outliers in TKAs performed both with CON and aCAS methods at a single training institution in cases that trainees were involved in. In this retrospective single-center study, radiographic analysis of 150 primary TKAs performed at our training institution was performed. The medial distal femoral angle (DFA), medial proximal tibial angle (PTA), and posterior slope angle (PSA) were evaluated on immediate postoperative radiographs of 75 aCAS and 75 CON knees. Outlier measurements were defined as DFA outside of 5 ± 3 degrees valgus, PTA > ± 3 degrees from neutral axis, and PSA outside 0 to 7 degrees for cruciate retaining and 0 to 5 degrees for posterior stabilized implants. Data was analyzed using chi-squared, analysis of variance, and Student's t-tests. There was no significant difference found between these two groups in the total number of outliers (8% aCAS vs. 9.8% CON, p = 0.508). Additionally, there was also no significant difference found for any of the radiographic measurements independently; DFA (8% vs. 8%, p = 1.00), PTA (4% vs. 9%, p = 0.184), and PSA (12% vs. 12%, p = 1.00), when comparing aCAS and CON TKAs. Primary TKAs performed at a single training institution demonstrated no significant difference between aCAS and CON methods in the total number of outliers or in the number of outliers for each postoperative radiographic measurement. This study would suggest that there is no advantage in limiting outliers when utilizing aCAS for TKAs at training institutions in cases that trainees are involved in.
Level of Evidence Level III Retrospective Cohort Study