bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ex vivo propagation of synaptically-evoked cortical depolarizations in a mouse model of Alzheimer’s disease at 20 Hz, 40 Hz, or 83 Hz
Authors: Patel Aayushi A. , Zhu Mei Hong , Yan Riqiang , Antic Srdjan D. 
Published: October 2024

AbstractSensory stimulations at 40 Hz gamma (but not any other frequency), have shown promise in reversing Alzheimer’s disease (AD)-related pathologies. What distinguishes 40 Hz? We hypothesized that stimuli at 40 Hz might summate more efficiently (temporal summation) or propagate more efficiently between cortical layers (vertically), or along cortical laminas (horizontally), compared to inputs at 20 or 83 Hz. To investigate these hypotheses, we used brain slices from AD mouse model animals (5xFAD). Extracellular (synaptic) stimuli were delivered in cortical layer 4 (L4). Leveraging a fluorescent voltage indicator (VSFP) expressed in cortical pyramidal neurons, we simultaneously monitored evoked cortical depolarizations at multiple sites, at 1 kHz sampling frequency. Experimental groups (AD-Female, CTRL-Female, AD-Male, and CTRL-Male) were tested at three stimulation frequencies (20, 40, and 83 Hz). Despite our initial hypothesis, two parameters—temporal summation of voltage waveforms and the strength of propagation through the cortical neuropil—did not reveal any distinct advantage of 40 Hz stimulation. Significant physiological differences between AD and Control mice were found at all stimulation frequencies tested, while the 40 Hz stimulation frequency was not remarkable.
Profiling neuroinflammatory markers and response to nusinersen in paediatric spinal muscular atrophy
Authors: Zhang Qiang , Hong Ying , Brusa Chiara , Scoto Mariacristina , Cornell Nikki , Patel Parth , Baranello Giovanni , Muntoni Francesco , Zhou Haiyan 
Published: October 2024

AbstractNeuroinflammation is an emerging clinical feature in spinal muscular atrophy (SMA). Characterizing neuroinflammatory cytokines in cerebrospinal fluid (CSF) in SMA and their response to nusinersen is important for identifying new biomarkers and understanding the pathophysiology of SMA. We measured twenty-seven neuroinflammatory markers in CSF from twenty SMA children at different time points, and correlated the findings with motor function improvement. At baseline, MCP-1, IL-7 and IL-8 were significantly increased in SMA1 patients compared to SMA2, and were significantly correlated with disease severity. After six months of nusinersen treatment, CSF levels of eotaxin and MIP-1β were markedly reduced, while IL-2, IL-4 and VEGF-A were increased. The decreases in eotaxin and MIP-1β were associated with changes in motor scores in SMA1. We also detected a transient increase in MCP-1, MDC, MIP-1α, IL-12/IL-23p40 and IL-8 after the first or second injection of nusinersen, followed by a steady return to baseline levels within six months. Our study provides a detailed profile of neuroinflammatory markers in SMA CSF. Our data confirms the potential of MCP-1, eotaxin and MIP-1β as new neuroinflammatory biomarkers in SMA1 and indicates the presence of a subtle inflammatory response to nusinersen during the early phase of treatment.
A double-layered fully automated insomnia identification model employing synthetic data generation using MCSA and CTGAN with single-channel EEG signals
Authors: Philip Mulamoottil Steffi , Vigneswaran T. 
Published: October 2024

AbstractInsomnia was diagnosed by analyzing sleep stages obtained during polysomnography (PSG) recording. The state-of-the-art insomnia detection models that used physiological signals in PSG were successful in classification. However, the sleep stages of unbalanced data in small-time intervals were fed for classification in previous studies. This can be avoided by analyzing the insomnia detection structure in different frequency bands with artificially generated data from the existing one at the preprocessing and post-processing stages. Hence, the paper proposes a double-layered augmentation model using Modified Conventional Signal Augmentation (MCSA) and a Conditional Tabular Generative Adversarial Network (CTGAN) to generate synthetic signals from raw EEG and synthetic data from extracted features, respectively, in creating training data. The presented work is independent of sleep stage scoring and provides double-layered data protection with the utility of augmentation methods. It is ideally suited for real-time detection using a single-channel EEG provides better mobility and comfort while recording. The work analyzes each augmentation layer’s performance individually, and better accuracy was observed when merging both. It also evaluates the augmentation performance in various frequency bands, which are decomposed using discrete wavelet transform, and observed that the alpha band contributes more to detection. The classification is performed using Decision Tree (DT), Ensembled Bagged Decision Tree (EBDT), Gradient Boosting (GB), Random Forest (RF), and Stacking classifier (SC), attaining the highest classification accuracy of 94% using RF with a greater Area Under Curve (AUC) value of 0.97 compared to the existing works and is best suited for small datasets.
An intrinsic low-frequency atmospheric mode of the Indonesian-Australian summer monsoon
Authors: Liang Yu , Xie Shang-Ping , Zhang Honghai 
Published: October 2024

AbstractDeep convection in the Indo-Pacific warm pool is vital in driving global atmospheric overturning circulations. Year-to-year variations in the strength and location of warm pool precipitation can lead to significant local and downstream hydroclimatic impacts, including floods and droughts. While the El Niño-Southern Oscillation (ENSO) is recognized as a key factor in modulating interannual precipitation variations in this region, atmospheric internal variability is often as important. Here, through targeted atmospheric model experiments, we identify an intrinsic low-frequency atmospheric mode in the warm pool region during the austral summer, and show that its impact on seasonal rainfall is comparable to ENSO. This mode resembles the horizontal structure of the Madden-Julian Oscillation (MJO), and may play a role in initiating ENSO as stochastic forcing. We show that this mode is not merely an episodic manifestation of MJO events but primarily arises from barotropic energy conversion aided by positive feedback between convection and circulation.
FluencyBank Timestamped: An Updated Data Set for Disfluency Detection and Automatic Intended Speech Recognition
Authors: Romana Amrit , Niu Minxue , Perez Matthew , Provost Emily Mower 
Published: October 2024

Purpose:
This work introduces updated transcripts, disfluency annotations, and word timings for FluencyBank, which we refer to as FluencyBank Timestamped. This data set will enable the thorough analysis of how speech processing models (such as speech recognition and disfluency detection models) perform when evaluated with typical speech versus speech from people who stutter (PWS).

Method:
We update the FluencyBank data set, which includes audio recordings from adults who stutter, to explore the robustness of speech processing models. Our update (semi-automated with manual review) includes new transcripts with timestamps and disfluency labels corresponding to each token in the transcript. Our disfluency labels capture typical disfluencies (filled pauses, repetitions, revisions, and partial words), and we explore how speech model performance compares for Switchboard (typical speech) and FluencyBank Timestamped. We present benchmarks for three speech tasks: intended speech recognition, text-based disfluency detection, and audio-based disfluency detection. For the first task, we evaluate how well Whisper performs for intended speech recognition (i.e., transcribing speech without disfluencies). For the next tasks, we evaluate how well a Bidirectional Embedding Representations from Transformers (BERT) text-based model and a Whisper audio-based model perform for disfluency detection. We select these models, BERT and Whisper, as they have shown high accuracies on a broad range of tasks in their language and audio domains, respectively.

Results:
For the transcription task, we calculate an intended speech word error rate (isWER) between the model's output and the speaker's intended speech (i.e., speech without disfluencies). We find isWER is comparable between Switchboard and FluencyBank Timestamped, but that Whisper transcribes filled pauses and partial words at higher rates in the latter data set. Within FluencyBank Timestamped, isWER increases with stuttering severity. For the disfluency detection tasks, we find the models detect filled pauses, revisions, and partial words relatively well in FluencyBank Timestamped, but performance drops substantially for repetitions because the models are unable to generalize to the different types of repetitions (e.g., multiple repetitions and sound repetitions) from PWS. We hope that FluencyBank Timestamped will allow researchers to explore closing performance gaps between typical speech and speech from PWS.

Conclusions:
Our analysis shows that there are gaps in speech recognition and disfluency detection performance between typical speech and speech from PWS. We hope that FluencyBank Timestamped will contribute to more advancements in training robust speech processing models.
An EfficientNet integrated ResNet deep network and explainable AI for breast lesion classification from ultrasound images
Authors: Jabeen Kiran , Khan Muhammad Attique , Hamza Ameer , Albarakati Hussain Mobarak , Alsenan Shrooq , Tariq Usman , Ofori Isaac 
Published: October 2024

AbstractBreast cancer is one of the major causes of deaths in women. However, the early diagnosis is important for screening and control the mortality rate. Thus for the diagnosis of breast cancer at the early stage, a computer‐aided diagnosis system is highly required. Ultrasound is an important examination technique for breast cancer diagnosis due to its low cost. Recently, many learning‐based techniques have been introduced to classify breast cancer using breast ultrasound imaging dataset (BUSI) datasets; however, the manual handling is not an easy process and time consuming. The authors propose an EfficientNet‐integrated ResNet deep network and XAI‐based framework for accurately classifying breast cancer (malignant and benign). In the initial step, data augmentation is performed to increase the number of training samples. For this purpose, three‐pixel flip mathematical equations are introduced: horizontal, vertical, and 90°. Later, two pre‐trained deep learning models were employed, skipped some layers, and fine‐tuned. Both fine‐tuned models are later trained using a deep transfer learning process and extracted features from the deeper layer. Explainable artificial intelligence‐based analysed the performance of trained models. After that, a new feature selection technique is proposed based on the cuckoo search algorithm called cuckoo search controlled standard error mean. This technique selects the best features and fuses using a new parallel zero‐padding maximum correlated coefficient features. In the end, the selection algorithm is applied again to the fused feature vector and classified using machine learning algorithms. The experimental process of the proposed framework is conducted on a publicly available BUSI and obtained 98.4% and 98% accuracy in two different experiments. Comparing the proposed framework is also conducted with recent techniques and shows improved accuracy. In addition, the proposed framework was executed less than the original deep learning models.
Blockchain‐IoT: A revolutionary model for secure data storage and fine‐grained access control in internet of things
Authors: Ullah Zia , Husnain Ghassan , Mohmand Muhammad Ismail , Qadir Mansoor , Alzahrani Khalid J. , Ghadi Yazeed Yasin , Alkahtani Hend Khalid 
Published: October 2024

AbstractWith the rapid expansion of the Internet of Things (IoT), cloud storage has emerged as one of the cornerstones of data management, facilitating ubiquitous access and seamless sharing of information. However, with the involvement of a third party, traditional cloud‐based storage systems are plagued by security and availability concerns, stemming from centralized control and management architectures. A novel blockchain‐IoT model that leverages blockchain technology and decentralized storage mechanisms to address these challenges is presented. The model combines the Ethereum blockchain, interplanetary file system, and attribute‐based encryption to ensure secure and resilient storage and sharing of IoT data. Through an in‐depth exploration of the system architecture and underlying mechanisms, it is demonstrated how the framework decouples storage functionality from resource‐constrained IoT devices, mitigating security risks associated with on‐device storage. In addition, data owners and users can easily exchange data with one another through the use of Ethereum smart contracts, fostering a collaborative environment and providing incentives for data sharing. Moreover, an incentive mechanism powered by the FileCoin cryptocurrency is introduced, which motivates and ensures data sharing transparency and integrity between stakeholders. Furthermore, in the proposed blockchain‐IoT model, the proof‐of‐authority system consensus algorithm has been replaced by a delegated proof‐of‐capacity system, which reduces transaction costs and energy consumption. Using the Rinkby Ethereum official testing network, the proposed model has been demonstrated to be feasible and economical, emphasizing its potential to redefine IoT data management.
The impact of spatial representation in energy transition modelling on systemwide energy return on investment
Authors: Sahin Hasret , Solomon Abebe Asfaw , Aghahosseini Arman , Breyer Christian 
Published: October 2024

AbstractAdopting aggregation techniques in power sector modelling led to disregarding the key characteristics of regions in terms of resource use, which may not completely capture the bottlenecks in the energy transition. This study provides a holistic approach to estimate its impact on the transition of the European power system from the perspective of energy return on investment (EROI) by using six energy transition scenarios based on three different spatial representations. The findings indicate that EROI trends are highly dependent on the spatial representation, technology selection and energy mix. Further additional capacities of complementary technologies along with an upsurge in renewable capacities drive EROI values down. Disregarding the physical distances in the energy modelling results in large EROI enhancement due to the artificial smoothing effect of the aggregation method. EROI values of the aggregated scenarios remain between 18 and 24 by 2050. In the case of 20 independent sub‐regions, the lowest EROI is obtained at about 14 by 2050, due to the limitation on optimal resource utilisation. Interconnection of the sub‐regions, which represents the best proximation to the real situation, increases the EROI to 17 by 2050.
Chromospheric Mg I emission lines of pre-main-sequence stars
Authors: Yamashita Mai , Itoh Yoichi , Takagi Yuhei 
Published: October 2024

Context. To reveal details of the internal structure, the relationship between chromospheric activity and the Rossby number has been extensively examined for main-sequence stars. For active pre-main-sequence (PMS) stars, it is suggested that the level of activity be assessed using optically thin emission lines, such as Mg I.
Aims. We aim to detect Mg I chromospheric emission lines from PMS stars and to determine whether the chromosphere is activated by the dynamo process or by mass accretion from protoplanetary disks.
Methods. We analyzed high-resolution optical spectra of 64 PMS stars obtained with the Very Large Telescope (VLT)/X-shooter and UVES and examined the infrared Ca II (8542 Å) and Mg I (8807 Å) emission lines. To detect the weak chromospheric emission lines, we determined the atmospheric parameters (Teff and log 𝑔) and the degree of veiling of the PMS stars by comparing the observed spectra with photospheric model spectra.
Results. After subtracting the photospheric model spectrum from the PMS spectrum, we detected Ca II and Mg I as emission lines. The strengths of the Mg I emission lines in PMS stars with no veiling are comparable to those in zero-age main-sequence (ZAMS) stars if both types of stars have similar Rossby numbers. The Mg I emission lines in these PMS stars are thought to be formed by a dynamo process similar to that in ZAMS stars. In contrast, the Mg I emission lines in PMS stars with veiling are stronger than those in ZAMS stars. These objects are believed to have protoplanetary disks, where mass accretion generates shocks near the photosphere, heating the chromosphere.
Conclusions. The chromosphere of PMS stars is activated not only by the dynamo process but also by mass accretion.
Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist
Authors: Yao Mylene W.M. , Jenkins Julian , Nguyen Elizabeth T. , Swanson Trevor , Menabrito Marco 
Published: October 2024

AbstractAlthough in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF by patients who could benefit from such treatment. In order for patients to choose to consider IVF treatment when appropriate, it is critical for them to be provided with an accurate, understandable IVF prognosis. Machine learning (ML) can meet the challenge of personalized prognostication based on data available prior to treatment. The development, validation, and deployment of ML prognostic models and related patient counseling report delivery require specialized human and platform expertise. This review article takes a pragmatic approach to review relevant reports of IVF prognostic models and draws from extensive experience meeting patients' and providers' needs with the development of data and model pipelines to implement validated ML models at scale, at the point-of-care. Requirements of using ML-based IVF prognostics at point-of-care will be considered alongside clinical ML implementation factors critical for success. Finally, we discuss health, social, and economic objectives that may be achieved by leveraging combined human expertise and ML prognostics to expand fertility care access and advance health and social good.
Premature Coconut Nutfall and
Amblypelta cocophaga
China (Hemiptera, Coreidae) in Solomon Islands
Authors: Macfarlane R. 
Published: October 2024

Abstract

The causal organism of premature nutfall of coconuts is the coreiid bug,
Amblypelta cocophaga
China. There are several species of
Amblypelta
which are spread throughout Melanesia, Northern Australia, and Indonesia. Only
Amblypelta cocophaga cocophaga
and
Amblypelta cocophaga malaitensis
are associated with severe premature nutfall of coconuts.
Amblypelta cocophaga
has also been reported by EPPO and CABI to be present in Fiji and Singapore, but both records are erroneous. Premature nutfall of coconuts has always been associated with four dominant ant species,
Oecophylla smaragdina
and
Anoploleplis gracilipes
are antagonistic to
A. cocophaga
whereas
Pheidole megacephala
and
Philidris myrmecodiae
are not. Spraying the base of the palm to kill ants, and inter-planting with soursop trees to encourage
O. smaragdina
has shown promise in controlling nutfall. The little fire ant,
Wasmannia auropunctata,
was accidentally introduced into Solomon Islands in the 1960s and was accidentally spread around the country. It has proven beneficial in stopping premature nutfall. Currently premature nutfall is hard to find. Biosecurity is an essential component of effective pest management. Eradication of new pests is often not possible, economic, or necessary. Prevention of movement of organisms into new areas is still the most effective biosecurity tool.

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Single transition layer in mass-conserving reaction-diffusion systems with bistable nonlinearity
Authors: Kuwamura Masataka , Teramoto Takashi , Ikeda Hideo 
Published: October 2024

Abstract
Mass-conserving reaction-diffusion systems with bistable nonlinearity are useful models for studying cell polarity formation, which is a key process in cell division and differentiation. We rigorously show the existence and stability of stationary solutions with a single internal transition layer in such reaction-diffusion systems under general assumptions by the singular perturbation theory. Moreover, we present a meaningful model for understanding the existence of an unstable transition layer solution; our numerical simulations show that the unstable solution is a separatrix of the dynamics of the model.
The applicability study of the large wind farm wake effect model
Authors: Han Yi , Li Qian , Liu Wei , Peng Huaiwu , Zhang Lihui 
Published: October 2024

Abstract
As the scale of the wind farm becomes bigger and the wind turbines are increasingly clustered, a large wind farm wake effect occurs. In this work, the initial distance where the large wind farm wake effect model starts to take effect is investigated analytically. The large wind farm wake effect is evaluated by constructing a large-scale wind farm with a regular arrangement of wind turbines similar to Horns Rev. The essential variables that influence the wind deficit trend of the large wind farm wake effect model are explored numerically. The wake deficit becomes more obvious as the surface roughness gets lower, the apparent turbine spacing gets smaller, or the thrust coefficient decreases. The wake deficits between the large wind farm wake effect model and the wake superposition of the modified Park model are compared. The transition distance, where the dominator of the wind deficit switches from the modified Park model to the large wind farm wake effect model, moves upstream along the incoming wind for the rougher surface or the lower thrust coefficient. In addition, for the similar resultant turbine spacing, the slightly staggered wind farm layout does not show an obvious influence on the wake deficit obtained by the large wind farm wake effect model.
Reactive power and voltage control based on reactive power compensation coefficient and sensitivity factor
Authors: Xie Zhihua , Wei Wenqi , Jia Peng 
Published: October 2024

Abstract
Aiming at the problems that the existing reactive power allocation methods do not take into account the specific operating state of each doubly-fed induction generator (DFIG) and the established optimization model has many variables and is complicated, this paper proposes to construct an optimization model based on reactive power compensation coefficient and sensitivity factor, to reduce the number of variables in the mathematical model and simplify the expression of the relationship between each key parameter, so the speed of the reactive power and voltage control are improved. In the process of constructing specific optimization models, the objective of minimizing the active loss takes into account the operating copper consumption of each DFIG. The objective of minimizing the voltage fluctuation of each DFIG is also taken into account, in addition to the operating state constraints of each DFIG, so that the final reactive power allocation is more practical. In solving the multi-objective optimization problem, the improved NSGA-II algorithm is utilized, which can achieve the simultaneous optimization of multiple objectives, and when combined with the simplified model established in this paper, the correctness and speed of obtaining the optimal solution set of Pareto can be further improved. Finally, the effectiveness of the proposed strategy in reducing active losses and voltage fluctuations in wind farms is verified by simulation examples.