bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance aware algorithm design for elastic resource workflow management of cluster consolidation to handle enterprise big data
Authors: Neelima Sarabu , Krishna Murthy Pannala , Kalyani BJD
Published: July 2024

Integration and deployment of big data and business analytics application with cloud computing are more attractive as a service and are trending practice. This hybrid workflow is rapidly increasing and will trigger a revolution for enterprise data handling, information retrieval and computing. This paper presents hybrid workflow management framework for big data and multi cloud computing systems in a two-step approach. Linear optimization-based resource assessment algorithm is planned in the first step. Cluster oriented elastic resource allocation and workflow management techniques are concentrated in the second step. This paper also focus on performance evaluation parameters includes execution time, through put with multi task work flow optimization model. The proposed framework is efficiently managed the implementation of hybrid workflows by finetuning the evaluation attributes and provides improvement in terms of response time an average of 6%.
Classification of Tri Pramana learning activities in virtual reality environment using convolutional neural network
Authors: Sindu I Gede Partha , Sudarma Made , Hartati Rukmi Sari , Gunantara Nyoman
Published: July 2024

Tri Pramana as the local genius of Balinese society, is now adopted in the education system. This adaptation results in a Learning Cycle Model which essentially consists of three classes namely Sabda Pramana (theoretical study), Pratyaksa Pramana (direct observation), and Anumana Pramana (practicum). In learning activities, it is difficult for educators to fully observe individuals to find out the most suitable learning model. Through Virtual Environment Technology, educators can observe students more freely through the recording of students' activities. However, in its implementation, manual analysis requires large resources. Deep Learning approach based on Convolutional Neural Network (CNN) is able to automate this analysis process through the classification ability of the image of the recorded learner activity. To produce a robust CNN model, this research compares four of the most commonly used architectures, namely ResNet-50, MobileNetV2, InceptionV3, and Xception. Each architecture is tuned using a combination of learning rate and batch size. Through a 512 x 512 resolution dataset with 70% training subset (4,541 images), 20% validation (1,296 images), and 10% test (652 images), the best ResNet model is obtained with a learning rate configuration of 1e-3 and batch size 64 with an accuracy of 99.39%, precision of 99.37%, and recall of 99.42%.
Optimizing the long short-term memory algorithm to improve the accuracy of infectious diseases prediction
Authors: Sediyono Eko , Wahyuni Sri Ngudi , Sembiring Irwan
Published: July 2024

This study discusses the implementation of the proposed optimizedlong short-term memory (LSTM) to predict the number of infectious disease cases that spread in Central Java, Indonesia. The proposed model is developed by optimizing the output layer, which affects the output value of the cell state. This study used cases of four infectious diseases in Indonesia's Central Java Province, namely COVID-19, dengue, diarrhea, and hepatitis A. This model was compared to basic LSTM and MinMax schaler LSTM improvement to see the difference in the accuracy of each disease. The results showed a significant difference in the average prediction results with real cases between the three models. The main objectives of this study were: modifying the LSTM algorithm to predict the number of infectious disease cases to get a smaller residual value, comparing the results of the optimization accuracy of the LSTM algorithm with the LSTM algorithm in previous studies, and evaluating the use of spatial variables in applying infectious disease prediction models using the LSTM algorithm. The results found that the performance difference between the proposed optimization algorithm and the model in the previous study was obtained. The proposed LSTM optimization algorithm had an accuracy improvement of about 2% over the previous model.
Optimizing pulmonary carcinoma detection through image segmentation using evolutionary algorithms
Authors: Elavarasu Moulieswaran , Govindaraju Kalpana
Published: July 2024

This paper’s goal is to suggest an image segmentation technique for use with medical images, specifically computer tomography scan images, to aid doctors in understanding the images. To address a variety of picture segmentation issues, it is necessary to investigate and apply novel evolutionary algorithms. The study focuses on pulmonary carcinoma, which is the cancer that affects males the most frequently across the globe. For proper treatment and life-saving measures, early identification of lung cancer is essential. To identify lung cancer, doctors frequently employ the computed tomography imaging technique. In order to extract tumours from lung scans, the study analyses the effectiveness of three optimization algorithms: k-means clustering, particle swarm optimization, and modified guaranteed convergence particle swarm optimization. The study also examines the pre-processing performance of four filters, namely the mean, bilateral, gaussian, and laplacian filters, shows that the bilateral filter is best suited for CT scans of the body. To test the proposed technique on 30 examples of lung scans. The proposed algorithm is tested on 30 sample lung images. The results show that the modified guaranteed convergence particle swarm optimization algorithm has the highest accuracy of 96.01%.
Enhancing machine failure prediction with a hybrid model approach
Authors: Khattach Ouiam , Moussaoui Omar , Hassine Mohammed
Published: July 2024

The industrial sector is undergoing a substantial transformation by embracing predictive maintenance approaches, aiming to minimize downtime and reduce operational expenses. This transformative shift involves the incorporation of machine learning techniques to refine the accuracy of predicting machinery failures. In this article, we delve into an in-depth exploration of machine failure prediction, employing a hybrid model amalgamating long short-term memory (LSTM) and support vector machine (SVM). Our comprehensive study meticulously assesses the hybrid model’s performance, comparing it with standalone LSTM and SVM models across three distinct datasets. The results showcase that the hybrid model outperformed, providing the modest dependable, and highest F1-score values in our evaluation.
A three-step combination strategy for addressing outliers and class imbalance in software defect prediction
Authors: Rizky Pribadi Muhammad , Dwi Purnomo Hindriyanto , Hendry Hendry
Published: July 2024

Software defect prediction often involves datasets with imbalanced distributions where one or more classes are underrepresented, referred to as the minority class, while other classes are overrepresented, known as the majority class. This imbalance can hinder accurate predictions of the minority class, leading to misclassification. While the synthetic minority oversampling technique (SMOTE) is a widely used approach to address imbalanced learning data, it can inadvertently generate synthetic minority samples that resemble the majority class and are considered outliers. This study aims to enhance SMOTE by integrating it with an efficient algorithm designed to identify outliers among synthetic minority samples. The resulting method, called reduced outliers (RO)-SMOTE, is evaluated using an imbalanced dataset, and its performance is compared to that of SMOTE. RO-SMOTE first performs oversampling on the training data using SMOTE to balance the dataset. Next, it applies the mining outlier algorithm to detect and eliminate outliers. Finally, RO-SMOTE applies SMOTE again to rebalance the dataset before introducing it to the underlying classifier. The experimental results demonstrate that RO-SMOTE achieves higher accuracy, precision, recall, F1-score, and area under curve (AUC) values compared to SMOTE.
Emergency patient forecasting with models based on support vector machines
Authors: Hernandez Carlos , Lagos Dafne , Leal Paola , Castillo Jaime
Published: July 2024

Understanding the dynamic nature of the influx of patients is crucial for efficiently managing supplies, medical personnel, and infrastructure in an emergency room (ER). While overestimation can lead to resource wastage, underestimation can result in shortages and compromised service quality. This study addresses emergency patient forecast by means of implementing support vector machine (SVM) algorithms. Along four phases (analysis, design, development, and validation), more than 50,000 ER records were preprocessed and analyzed. Traditional error metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were utilized alongside monthly consolidated forecasts. To benchmark performance, actual values and forecasts derived from linear regression (LR) models were used. Experiments revealed that LR models had lower errors compared to SVM models. However, monthly consolidated forecasts showed that SVM-based models underestimated less than LR-based models. In conclusion, SVM-based models could help planners to accurately estimate the requirements for supplies and medical personnel during the period under study.
Barcode-less Fruits Classification Using Deep Learning
Authors: Abdel-raouf Amal , Sheta Alaa , Baareh AbdelKarim , Rausch Peter
Published: July 2024

Barcode-less fruit recognition technology has revolutionized the checkout process by eliminating manual barcode scanning. This technology automatically identifies and adds fruit items to the purchase list, significantly reducing waiting times at the cash register. Faster checkouts enhance customer convenience and optimize operational efficiency for retailers. Adding barcode to fruits require using adhesives on the fruit surface that may cause health hazards. Leveraging deep learning techniques for barcode-less fruit recognition brings valuable advantages to industries, including advanced automation, enhanced accuracy, and increased efficiency. These benefits translate into improved productivity, cost reduction, and superior quality control. This study introduces a Convolutional Neural Network (CNN) designed explicitly for automatic fruit recognition, even in challenging real-world scenarios. The proposed method assists fruit sellers in accurately identifying and distinguishing between different types of fruit that may exhibit similarities. A dataset that includes 44,406 images of different fruit types is used to train and test our technique. Employing a CNN, the developed model achieves an impressive classification accuracy of 97.4% during the training phase and 88.6% during the testing phase respectively, showcasing its effectiveness in precise fruit recognition.
Design of novel convolution neural network model for lung cancer detection by using sensitivity maps
Authors: Saxena Sugandha , Narasimha Prasad Sarappadi
Published: July 2024

Despite the existence of numerous models for detecting lung cancer, there is still room for achieving higher levels of accuracy. In this paper, a maximum sensitivity neural network (MSNN) has been proposed. As the name suggests, the model aims to achieve high sensitivity and offers a viable remedy to minimize the number of false positive in oder to improve the overall accuracy for lung cancer detection. The MSNN model is a promising model since it can efficiently interpret grayscale lung computed tomography (CT) scan images as inputs and can be trained using just a few images also. This model has surpassed previous deep learning models by obtaining a remarkable sensitivity of 94.6% and an accuracy of 96.9%. A sensitivity map is created, offering important insights into the critical regions for finding malignant nodules. This innovative method has shown outstanding performance in identifying lung cancer with a low false positive rate which can increase the accuracy of medical diagnoses.
Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales
Authors: Arini Parhusip Hanna , Trihandaru Suryasatriya , Indrajaya Denny , Labadin Jane
Published: July 2024

You only look once v8 (YOLOv8)-seg and its variants are implemented to accelerate the collection of goods for a store for selling activity in Indonesia. The method used here is object detection and segmentation of these objects, a combination of detection and segmentation called instance segmentation. The novelty lies in the customization and optimization of YOLOv8-seg for detecting and segmenting 30 specific Indonesian products. The use of augmented data (125 images augmented into 1,250 images) enhances the model's ability to generalize and perform well in various scenarios. The small number of data points and the small number of epochs have proven reliable algorithms to implement on store products instead of using QR codes in a digital manner. Five models are examined, i.e., YOLOv8-seg, YOLOv8s-seg, YOLOv8m-seg, YOLOv8l-seg, and YOLOv8x-seg, with a data distribution of 64% for the training dataset, 16% for the validation dataset, and 20% for the testing dataset. The best model, YOLOv8l-seg, was obtained with the highest mean average precision (mAP) box value of 99.372% and a mAPmask value of 99.372% from testing the testing dataset. However, the YOLOv8mseg model can be the best alternative model with a mAPbox value of 99.330% since the number of parameters and the computational speed are the best compared to other models.
An optimised deep learning approach for alzheimer’s disease classification
Authors: Pawan Phanieswar Perla , Sarvari Harshitha Konda , Marka Venkatrajam , Srinivasa Rao Battula , Aparna Mudiyala
Published: July 2024

Alzheimer’s disease (AD) is a progressive and incurable brain disorder. It starts out subtly and gets worse with time. 60 to 70 percent of dementia cases are brought on by this illness. An Alzheimer’s patient is diagnosed every two seconds, according to research. The complexity of the brain makes it often very challenging to identify in elderly people. In the area of medical imaging, deep learning is growing. Several deep learning techniques that attempted to identify and categorise the magnetic resonance imaging (MRI) brain images into four stages of AD will be compared in this work. 6400 MRI brain images were extracted from a dataset and divided into training, validation, and testing datasets. In our research on twelve deep learning architectures, inceptionV3 has given the best results with 99.56% and 97.75% accuracy on train and validation, respectively, and on test data, the model has achieved an accuracy of 95.81%. We trained the models using optimised ImageNet weights, which resulted in higher accuracy across all twelve models.
Javanese part-of-speech tagging using cross-lingual transfer learning
Authors: Enrique Gabriel , Alfina Ika , Yulianti Evi
Published: July 2024

Large datasets that are publicly available for POS tagging do not always exist for some languages. One of those languages is Javanese, a local language in Indonesia, which is considered as a low-resource language. This research aims to examine the effectiveness of cross-lingual transfer learning for Javanese POS tagging by fine-tuning the state-of-the-art Transformer-based models (such as IndoBERT, mBERT, and XLM-RoBERTa) using different kinds of source languages that have a higher resource (such as Indonesian, English, Uyghur, Latin, and Hungarian languages), and then fine-tuning it again using the Javanese language as the target language. We found that the models using cross-lingual transfer learning can increase the accuracy of the models without using cross-lingual transfer learning by 14.3%–15.3% over LSTM-based models, and by 0.21%–3.95% over Transformer-based models. Our results show that the most accurate Javanese POS tagger model is XLM-RoBERTa that is fine-tuned in two stages (the first one using Indonesian language as the source language, and the second one using Javanese language as the target language), capable of achieving an accuracy of 87.65%
Sectoral electricity micro-spatial load forecasting based on partitional clustering technique
Authors: Senen Adri , Jamani Jamian Jasrul , Satya Dini Hasna , Supriyanto Eko , Anggaini Dwi
Published: July 2024

Load demand forecasting is crucial in energy supply planning due to economic progress and territorial expansion, where land utilization transforms dynamically. An accurate sectoral load prediction can preclude the loss of beneficial opportunities arising from excessive load demand or excessive investment at a low-growth juncture. However, the particular area in this sectoral approach is still relatively large, rendering it incapable of precisely projecting load at minor points (micro-spatial). This study has proposed a micro-spatial load prediction strategy that categorizes identified areas into smaller grids or districts. This procedure includes clustering similar sites together for improved accuracy. K-Means is one of the partitional clustering approaches, a clustering algorithm utilizing object-based centroid-based partitioning approaches. The algorithm determines a cluster's centroid or centre as the average point for the cluster. This technique is advantageous as it can process extensive data efficiently and is appropriate for circular data. This technique can divide the data into multiple partitions, ensuring that each object belongs to precisely one cluster. Subsequently, mathematical modelling is used to predict the load of each cluster, which can then be utilized to more accurately evaluate the positions and sizes of prospective substations, transmission, and distribution facilities.
Enhancing microgrid production through particle swarm optimization and genetic algorithm
Authors: Mohamed Benydir , Imodane Belkasem , M’hand Oubella , Mohamed Ajaamoum , Brahim Bouachrine , Abdellah El idrissi , Najib Abekiri , Kaoutar Dahmane
Published: July 2024

The growing demand for sustainable and efficient energy solutions has led to research on optimizing renewable energy sources within microgrid systems. This study presents a comparative analysis of two prominent optimization techniques, particle swarm optimization (PSO) and genetic algorithm (GA), to enhance solar photovoltaic PV and wind production in microgrids. The aim is to achieve a balanced and efficient energy generation that closely matches the load demand, thereby minimizing energy wastage and ensuring a reliable energy supply. The two algorithms are employed using data representing PV and wind production, as well as load consumption, over a 24-hour period. The results are evaluated based on their ability to reduce the gap between energy production and load demand. Our findings reveal compelling insights into the performance of GA and PSO in the context of microgrid optimization. To validate the results obtained from the simulation, the PSO algorithm is implemented on an actual cart Digital Signal Processor DSP platform, using a processor-in-the-loop (PIL). This successful real-world application highlights the practical viability of utilizing PSO to improve solar PV and wind energy generation within microgrids.
Mycobacterium leprae Survival Inside Acanthamoeba sp. Isolated from Water Source in Leprosy Endemic Area, Indonesia
Authors: Wahyuni Ratna ,   Department of Health, Faculty of Vocational Studies, Universitas Airlangga, Surabaya, Astari Linda , Iswahyudi Iswahyudi , Paling Sepling , Adriaty Dinar , Kurniawati Siti , Aulia Syifa , Rahmatari Bandaru , Prakoeswa Cita Rosita , Agusni Indropo , Izumi Shinzo ,   Department of Dermatovenereology, Faculty of Medicine, Universitas Airlangga, Surabaya 4 Department of Primary Teacher Education, STKIP Kristen Wamena, Papua,   Institute of Tropical Disease, Universitas Airlangga, Surabaya,   Department of Primary Teacher Education, STKIP Kristen Wamena, Papua,   Institute of Tropical Disease, Universitas Airlangga, Surabaya,   Laboratory of Veterinary Microbiology and Immunology, Faculty of Veterinary Medicine, Universitas  Brawijaya, Malang,   Department of Health, Faculty of Vocational Studies, Universitas Airlangga, Surabaya,   Faculty of Dentistry, Universitas Muhammadiyah Surabaya, Surabaya,   Department of Dermatovenereology, Faculty of Medicine, Universitas Airlangga, Surabaya 4 Department of Primary Teacher Education, STKIP Kristen Wamena, Papua,   Department of Dermatovenereology, Faculty of Medicine, Universitas Airlangga, Surabaya 4 Department of Primary Teacher Education, STKIP Kristen Wamena, Papua,   Institute of Tropical Disease, Universitas Airlangga, Surabaya
Published: July 2024

Mycobacterium leprae is an acid-fast bacterium that causes leprosy diseases, which remains a problem worldwide. Even though leprosy prevalence in the world has decreased significantly, many endemic pocket areas continue reporting new cases and harbor M. leprae in the environment, including water and soil. The presence of obligate intracellular bacteria-M. leprae in the environment raises a question on how it survives. Free-living amoeba has been proposed as its reservoir host in the environment. The study was conducted to give evidence that M. leprae can survive inside free-living amoeba isolated from water sources of leprosy endemic areas. M. leprae from leprosy patients was cultured together with Acanthamoeba sp. isolated from the water source of the leprosy endemic area. Viability and duplication of M. leprae inside amoeba then observed at days 14 and 28 using reverse transcriptase PCR and qPCR. The results showed that M. leprae survived inside the amoeba until day 28, but no bacterial replication was observed. The study reveals in vitro evidence of viable M. leprae inside free-living amoeba of leprosy endemic area environment
Validation of Hand Grading Technique of Corpora Lutea in Embryo Recipient Cows
Authors: Perera Gamage Dammika Rohan Kumara ,   Animal Embryo Biotechnology Laboratory, Department of Farm Animal Production and Health, Faculty of  Veterinary   Medicine and Animal Science, University of Peradeniya, Sri Lanka, Pushpakumara Pupulewatte Gedara Anil , Alexander Ponweera Arachchige   Basil Douglas ,   School of Veterinary Science, Massey University, New Zealand,   Animal Embryo Biotechnology Laboratory, Department of Farm Animal Production and Health, Faculty of  Veterinary   Medicine and Animal Science, University of Peradeniya, Sri Lanka
Published: July 2024

The success of embryo transfer in cattle may depend on several factors. The blood progesterone level of the embryo recipient would be one of the main factors secondary to the size and quality of the corpus luteum. Per-rectal palpation and by-hand grading of corpora lutea have been practiced routinely, even under commercial embryo transfer programs in cattle. This experiment aimed to evaluate the accuracy of the per-rectal hand grading technique of mid-phase corpora lutea in embryo recipient cows. A group of healthy cows (Friesian× Jersey, n=25), which had been selected as recipient animals with the special consideration of reproductive history in an embryo transfer program, was used in this study. Estrous of those animals were synchronized using a standard progesterone intravaginal devise-PGF2α protocol. Conven-tional evaluation and grading of corpora lutea were conducted at the per-rectal hand grading as 'A', 'B', 'C' and 'Cystic' (If diameters; < 1cm=C, 1-1.9cm=B, 2-2.5cm= A, >2.5cm= Cystic). In this study, the accuracy of those hand-grading techniques was evaluated using ultra-sound scanning technology on the same day. Identification accuracy of grades C, B, A, and Cysts in hand grading technique was 45%, 56%, 67%, and 50%, respectively. The average accuracy in the hand grading technique was 52%, and identification accuracy as suitable (A or B) corpora lutea for embryo transfer was 76%. Results raised the 24% probable loss of embryo transferrable recipients due to the hand-grading technique. Further, 8% of unsuitable cows can be selected as embryo recipients due to the practice of hand-grading techniques at embryo transfer programs. Although higher accuracy is a characteristic of the hand grading technique, the technology causes considerable loss due to inaccurate selection probability of recipients at embryo transfer programs.
The Role of Mycorrhizae on the Growth of Banana Planlets of Cultivar Raja (Musa Paradisiaca C.V. Raja) from Bali at Post Acclimatization
Authors: Dwiyani Rindang ,   Faculty of Agriculture, Udayana University, Denpasar 80361, Indonesia, Wirya I Gusti Ngurah Alit Susanta , Gunadi I Gusti Alit , Darmawati Ida Ayu Putri , Yuswanti Hestin , Susrusa Ketut Budi , Astiningsih Anak Agung Made ,   Faculty of Agriculture, Udayana University, Denpasar 80361, Indonesia,   Faculty of Agriculture, Udayana University, Denpasar 80361, Indonesia,   Faculty of Agriculture, Udayana University, Denpasar 80361, Indonesia,   Faculty of Agriculture, Udayana University, Denpasar 80361, Indonesia,   Faculty of Agriculture, Udayana University, Denpasar 80361, Indonesia,   Faculty of Agriculture, Udayana University, Denpasar 80361, Indonesia
Published: July 2024

The effect of inoculation of a combination of the arbuscular mycorrhizal fungus of Glomus and Acaulospora on micropropagated banana plantlets cultivar Raja from Bali during the acclimatization phase was investigated. The experiment was conducted at the Experimental Station of the Faculty of Agriculture, Udayana University, Denpasar, Bali, Indonesia, over the period of March to June 2022. The objective of this research was to evaluate the impact of various doses of mycorrhizae on the growth of tissue culture banana plantlets that had undergone acclimatization for a period of six weeks. A total of five different doses of mycorrhiza were applied to the acclimatized plantlets, namely M0=without mycorrhiza and M5=5g mycorrhizal. kg1, M10=10 g mycorrhiza.kg-1, M15=15 g mycorrhiza.kg-1, M20=20 g mycorrhizal. kg 1. A significant effect was discovered at M15; however, compared to M20, M20 gave better results for root length and fresh weight of the above-ground part, indicating the best dose was 20 g mycorrhiza kg-1. This study concluded that mycorrhiza has a very significant role in supporting the growth of micro-propagated banana plantlets at post-acclimatization. Variables observed, such as the average number of leaves, stem diameter, above-ground plant fresh and dry weight, and root fresh and dry weight, increased with the application of AMF (Glomus and Acaulospora).
Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching
Authors: Wulan Naren , An Lijun , Zhang Chen , Kong Ru , Chen Pansheng , Bzdok Danilo , Eickhoff Simon B. , Holmes Avram J. , Yeo B.T. Thomas
Published: July 2024

Individualized phenotypic prediction based on structural magnetic resonance imaging (MRI) is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a “meta-matching” framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants (“meta-matching finetune” and “meta-matching stacking”) from our previous study to work with T1-weighted MRI data by changing the base neural network architecture to a 3D convolution neural network. We compare the two meta-matching variants with elastic net and classical transfer learning using the UK Biobank (N = 36,461), the Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017), and the HCP-Aging dataset (N = 656). We find that meta-matching outperforms elastic net and classical transfer learning by a large margin, both when translating models within the same dataset and when translating models across datasets with different MRI scanners, acquisition protocols, and demographics. For example, when translating a UK Biobank model to 100 HCP-YA participants, meta-matching finetune yielded a 136% improvement in variance explained over transfer learning, with an average absolute gain of 2.6% (minimum = –0.9%, maximum = 17.6%) across 35 phenotypes. Overall, our results highlight the versatility of the meta-matching framework.
The trouble with free-water elimination using single-shell diffusion MRI data: A case study in ageing
Authors: Correia Marta M. , Henriques Rafael Neto , Golub Marc , Winzeck Stefan , Nunes Rita G.
Published: July 2024

Free-water elimination (FWE) modelling for diffusion tensor imaging (DTI) can be used to estimate the free-water (FW) volume fraction, as well as FW-compensated DTI parameters. Single-shell (SS) diffusion magnetic resonance imaging (MRI) acquisitions are more common in clinical cohorts due to time constraints, but the FWE-DTI model is a two-compartment model, hence only well posed for multi-shell (MS) data. A regularised gradient descent (RGD) method is often applied to SS datasets and has been used to study healthy ageing, Alzheimer’s and Parkinson’s disease, amongst others, largely ignoring the methodological limitations of this approach. In this study, we compared the performance of RGD fitting with SS data, to a non-linear least squares (NLS) fitting applied to MS data, using simulations and data from 620 participants aged 18 to 88 years. Consistent with previous studies, our simulations show that RGD fitting using SS data flattens the relationship between mean diffusivity (MD) estimates and their ground truth values, and introduces an artificial positive correlation between fractional anisotropy (FA) estimates and the underlying tissue ground truth MD. Neither of these biases were observed when NLS fitting was applied to MS data. In human data, a smaller number of significant voxels with positive correlations between MD and age were observed when the RGD SS algorithm was used, which is consistent with the flattening of MD profiles observed in simulations. FW-compensated FA maps produced strikingly different results depending on the method employed: the maps obtained with RGD SS identified some brain areas with a strong positive association with age, while no such positive correlations were found with MS NLS. While similar positive correlations between age and FW-compensated FA maps obtained with SS RGD have been reported, these results are only replicated when the RGD SS was used, suggesting that this apparent FA increase was likely an artefact introduced by inappropriate modelling using SS data. Our study, therefore, suggests that previous findings reported in the literature using the RGD approach should be interpreted with extreme care.
“Iliacus muscle abscess as an unexpected cause of posterior hip pain in a healthy young adult female”: a case report
Authors: Huang Caleb Weihao , Yeo Mathew Yi Wen
Published: July 2024

Background

Iliacus muscle abscess is an uncommon but potentially life-threatening condition that can present with nonspecific symptoms, posing diagnostic challenges. This case report highlights the importance of considering iliopsoas abscess in patients presenting with fever and hip pain, especially in the absence of obvious risk factors or penetrating trauma. The novelty of this case lies in its atypical presentation mimicking a respiratory viral infection and musculoskeletal injury, impeding accurate diagnosis and appropriate management.

Case Presentation

A previously healthy 21-year-old female who had a mechanical fall 3 weeks prior presented with fever, right hip pain, and respiratory symptoms, initially suggestive of a respiratory infection and musculoskeletal injury. However, initial investigations revealing a markedly high C-reactive protein (CRP) concentration prompted further computed tomography (CT) imaging of her abdomen and pelvis, which uncovered an iliopsoas abscess presumably stemming from antecedent trauma. Subsequent CT guided aspiration along with culture-sensitive antibiotics led to successful treatment and resolution of her symptoms.

Conclusions

This case emphasizes the importance of considering iliopsoas abscess as a possible differential, even in young patients without typical risk factors. Markedly elevated inflammatory markers such as CRP concentrations can serve as a vital indicator, directing attention towards the possibility of septicemia or the presence of an occult abscess, facilitating prompt imaging and accurate diagnosis.
Paediatric Uveitis - the uniqueness in clinical presentation and the efficacy of biologics treatment
Authors: Yiu Mei Kwan , Ho Mary , Ho Assunta C.H. , Chan Winnie K.Y. , Yung Wing , Yip Wilson W.K. , Young Alvin L.
Published: July 2024

Aims

To evaluate unique clinical characteristics of paediatric uveitis in our locality and treatment outcomes especially the efficacy of biologics.

Methods

This was a retrospective cohort.

Results

37 paediatric uveitis cases involving 67 eyes were included. Male-to-female ratio was 1:1.3. Mean age of uveitis onset was 11 ± 3.7 (4–18). 81.1% cases suffered from bilateral uveitis. 75.7% cases were chronic uveitis. Nearly half of the cases (40.5%) presented with anterior uveitis. The predominant diagnosis of uveitis in our cohort was idiopathic. Unlike studies from other populations, the associated systemic conditions in this mostly Chinese cohort were Behçet’s disease (8.1%), tubulointerstitial nephritis and uveitis (8.1%) and HLA-B27 associated uveitis (8.1%). Steroid response was a common phenomenon, observed in 40.5% of cases. The most common complication was posterior synechiae (45.9%), followed by cataract (37.8%), glaucoma (27.0%), band keratopathy (18.9%) and macular oedema (13.5%). 3/37 patients encountered either first attack of uveitis or flare after receiving COVID-19 vaccine. 54.1% of patients required systemic steroid for disease control. The majority required steroid sparing immunotherapy, including Methotrexate (43.2%), Mycophenolate Mofetil (24.3%), Cyclosporine A (8.1%), Azathioprine (5.4%) and Tacrolimus (2.7%). Resistant cases required biologics including tumour necrosis factor alpha inhibitors (Adalimumab 32.4%, Infliximab 2.7%) and interleukin-6 inhibitors (Tocilizumab 2.7%).

Conclusions

Clinical presentation of the local paediatric uveitis differs from previously described features in Caucasian and other populations. According to our experience as a tertiary eye centre, Behçet’s disease, tubulointerstitial nephritis and uveitis and HLA-B27 associated uveitis were more often encountered than Juvenile Idiopathic Arthritis associated uveitis. Our report evaluated the efficacy of immunomodulatory therapy and biologics in controlling uveitis and reducing ocular complications.
Microglia mediate the increase in slow-wave sleep associated with high ambient temperature
Authors: Hatori Sena , Matsui Futaba , Zhou Zhiwen , Norimoto Hiroaki
Published: July 2024

An increase in ambient temperature leads to an increase in sleep. However, the mechanisms behind this phenomenon remain unknown. This study aimed to investigate the role of microglia in the increase of sleep caused by high ambient temperature. We confirmed that at 35 °C, slow-wave sleep was significantly increased relative to those observed at 25 °C. Notably, this effect was abolished upon treatment with PLX3397, a CSF1R inhibitor that can deplete microglia, while sleep amount at 25 °C was unaffected. These observations suggest that microglia play a pivotal role in modulating the homeostatic regulation of sleep in response to the fluctuations in ambient temperature.
Test-based measurement of skill mismatch: a validation of five different measurement approaches using the NEPS
Authors: Bischof Stephan
Published: July 2024

Skill mismatch is a key indicator of labour market research that has received significant attention. To date, various approaches of test-based measurement of skill mismatch have been used in research, generating differing results. However, it remains unclear which method is the most valid for measuring skill mismatch. This study provides a comparative validation of five commonly used approaches to test-based measurement of skill mismatches in reading and mathematics to detect the most valid method. Drawing on the 2016 wave of the German National Educational Panel Study (NEPS) Adult Cohort, I find significantly varying distributions for the different measurement approaches, and highly valid skill mismatch measures for the statistical and the mixed approach. Overall, the mixed approach emerges as the most valid method. The findings highlight the critical importance of measurement approaches in skill mismatch research.
Harlequin syndrome in a patient with probable hemicrania continua and exertional headache – is there a link? a case report
Authors: Miedl Markus , Baumgartner Philipp , Disse Leah Raffaela , Weber Konrad Peter , Pohl Heiko , Wegener Susanne
Published: July 2024

Background

The harlequin syndrome is a rare disorder of the autonomic nervous system characterized by unilateral diminished flushing and sweating of the face following exposure to heat or physical activity. It results from sympathetic dysfunction and most commonly occurs idiopathically. A secondary development due to an underlying pathology (e.g., carotid artery dissection, tumors) must be excluded at first appearance. There is evidence that the cranial autonomic system is involved in the pathophysiology of trigeminal autonomic headaches like hemicrania continua. Therefore, an overlap in the pathophysiology of harlequin syndrome and trigeminal autonomic headache disorders seems plausible. However, the association of a harlequin syndrome with hemicrania continua was never reported.

Case presentation

This work describes the case of a 42‐year‐old female patient presenting to our headache unit. The patient reported persisting unilateral headache of the right side of dragging or squeezing character accompanied by trigeminal autonomic symptoms, including lacrimation, nasal congestion, conjunctival injection and Horner’s syndrome, and was responsive to treatment with 75mg/d indomethacin. Five months after the initial consultation, the patient noted that the upper right quadrant of her face was pale after jogging. A harlequin syndrome was diagnosed. Further, she developed a short-lasting, bilateral headache of pulsatile character during strenuous exercise consistent with exertional headache. Comprehensive diagnostic evaluations, encompassing cranial and cervical MRI scans, laboratory tests, and biopsies, culminated in the diagnosis of Sjögren’s syndrome. This finding suggests that the trigemino-autonomic dysfunction may either be idiopathic or a direct manifestation of Sjögren’s syndrome.

Conclusions

This report documents the case of a rare combination of a headache resembling probable hemicrania continua and the harlequin syndrome (and even exertional headache). It illustrates the underlying anatomy of the autonomic nervous system in a clinical context and emphasizes the hypothesis of a pathophysiological link between abnormal sympathetic activity and trigeminal autonomic headaches.
Acute pancreatitis and refractory hypercalcemia in the third trimester caused by parathyroid carcinoma
Authors: Nie Qingwen , Ouyang Shunlin , He Fang
Published: July 2024

Background

Hypercalcemia can be a rare contributor to acute pancreatitis (AP) in pregnancy. This is primarily due to primary hyperparathyroidism (PHPT), resulting from parathyroid carcinoma. We exhibited a case report to analyze the diagnosis and treatment during the onset of hypercalcemia-induced AP.

Case presentation

A 32-year-old primigravida presented with acute pancreatitis near full-term gestation. Following a cesarean delivery, there was a reduction in serum amylase and peripancreatic exudate, but her serum calcium concentrations persistently elevated over 4.0 mmol/L. Interventions to lower the hypercalcemia were only temporarily effective, until a high serum parathyroid hormone (PTH) concentration of 1404 pg/mL was detected. Ultrasound revealed a 31 mm × 24 mm hypoechoic oval nodule in the left lower lobe of the thyroid gland. She underwent a parathyroidectomy, resulting in a dramatic decrease in serum PTH level, from preoperative levels of 2051 pg/mL to 299 pg/mL just 20 minutes after removal. Similarly, her serum calcium declined from 3.82 mmol/L to 1.73 mmol/L within 24 hours postoperatively. The final histopathology suggested parathyroid carcinoma.

Conclusion

When refractory hypercalcemia is present, serum PTH levels should be measured to determine PHPT. Parathyroidectomy is the optimal strategy for alleviating hypercalcemia and clarifying the underlying pathology.
Causal association between skin cancer and immune cells: mendelian randomization (MR) study
Authors: Yin Wei , Li Ruilei , Zhang Zhaoqi , Wang Ying , Tang Xinghua , Zhu Lin , Yao Hong , Li Ke
Published: July 2024

Background

Numerous meta-analyses and clinical studies have shown that subtypes of immune cells are associated with the development of skin cancer, but it is not clear whether this association is causal or biased. Mendelian randomization (MR) analysis reduces the effect of confounding factors and improves the accuracy of the results when compared to traditional studies. Thus, in order to examine the causal relationship between various immune cell and skin cancer, this study employs two-sample MR.

Methods

This study assesses the causal association between 731 immune cell characteristics and skin cancer using a two-sample Mendel randomization (MR) methodology. Multiple MR methods were used to bias and to derive reliable estimates of causality between instrumental variables and outcomes. Comprehensive sensitivity analyses were used to validate the stability, heterogeneity and horizontal multiplicity of the results.

Results

We discovered that potential causal relationships between different types of immune cells and skin cancer disease. Specifically, one type of immune cell as potentially causal to malignant melanoma of skin (MM), eight different types of immune cells as potentially causal to basal cell carcinoma (BCC), four different types of immune cells as potentially causal to actinic keratosis (AK), and no different types of immune cells were found to have a potential causal association with squamous cell carcinoma(SCC), with stability in all of the results.

Conclusion

This study demonstrates the close connection between immune cells and skin cancer disease by genetic means, which enriches the current knowledge about the role of immune cells in skin cancer and also contributes to the design of therapeutic strategies from an immunological perspective.
Effect of PR status on the prognosis of advanced ER-high HER2-negative breast cancer patients receiving CDK4/6 inhibitor combined with endocrine as first-line therapy
Authors: Jia Lin , Peng Junning , Sun Nan , Chen Hongying , Liu Zhenyu , Zhao Wenhui , Zhang Qingyuan , Li Liru
Published: July 2024

Background

This study was designed to evaluate the effect of progesterone receptor (PR) status on the prognosis of advanced estrogen receptor (ER)-high human epidermal growth factor receptor 2 (HER2)-negative breast cancer patients receiving CDK4/6 inhibitor combined with endocrine as first-line therapy.

Methods

Advanced ER-high HER2-negative breast cancer patients who were admitted to Harbin Medical University Cancer Hospital and received cyclin-dependent kinase (CDK)4/6 inhibitor combined with endocrine as first-line therapy were included for analysis. Patients were divided into PR-high group (11-100%), PR-low group (1-10%), and PR-negative group (< 1%) according to the expression of PR. Chi-square test was used to analyze the correlation of variables between groups. COX regression analysis were used to analyze the risk factors of survival. Kaplan-Meier survival curve was used to analyze the differences of progression-free survival (PFS) and overall survival (OS) between groups.

Results

Among the 152 patients, 72 were PR-high, 32 were PR-low, and 48 were PR-negative. Compared with PR-negative group, the proportions of disease-free survival (DFS) ≥ 5 years and Ki-67 index ≤ 30% in PR-low group and PR-high group were significant higher. PR-negative patients were more likely to occur first-line progression of disease within 24 months (POD24) than PR-high(P = 0.026). Univariate and multivariate analysis showed that PR-negative and first-line POD24 occurrence were risk factors for survival. Survival curve analysis showed that compared with PR-high group, the PFS and OS were significantly lower in PR-negative group (P = 0.001, P = 0.036, respectively). Patients with first-line POD24 had shorter OS in the overall population as well as in subgroups stratified by PR status.

Conclusions

PR-negative and first-line POD24 occurrence were risk factors of advanced ER-high HER2-negative breast cancer patients receiving CDK4/6 inhibitor combined with endocrine as first-line therapy. PR-negative patients had shortest PFS and OS. Regardless of PR status, first-line POD24 occurrence predicted shorter OS.
LncRNA SH3PXD2A-AS1 facilitates cisplatin resistance in non-small cell lung cancer by regulating FOXM1 succinylation
Authors: Chen Yunfeng , Wu Siyan , Han Yu , Shi Hai , Yuan Jieqing , Cui Wenjie
Published: July 2024

Background

Long noncoding RNAs (lncRNAs) play vital regulatory functions in non-small cell lung cancer (NSCLC). Cisplatin (DDP) resistance has significantly decreased the effectiveness of DDP-based chemotherapy in NSCLC patients. This study aimed to investigate the effects of SH3PXD2A antisense RNA 1 (SH3PXD2A-AS1) on DDP resistance in NSCLC.

Methods

Proliferation and apoptosis of DDP-resistant NSCLC cells were detected using cell counting kit-8 and flow cytometry assays. The interaction between SH3PXD2A-AS1 and sirtuin 7 (SIRT7) was assessed using co-immunoprecipitation (Co-IP), RNA pull-down, RNA immunoprecipitation (RIP), RNA fluorescence in situ hybridization, and immunofluorescence assays, while succinylation (SUCC) of Forkhead Box M1 (FOXM1) was analyzed by IP and Western blot assays. The role of SH3PXD2A-AS1 in vivo was explored using a xenografted tumor model.

Results

Expression of SH3PXD2A-AS1 was found elevated in DDP-resistant NSCLC cells, while it’s knocking down translated into suppression of cell viability and promotion of apoptosis. Moreover, silencing of SH3PXD2A-AS1 resulted in decreased FOXM1 protein level and enhanced FOXM1-SUCC protein level. The SIRT7 was found to interact with FOXM1, translating into inhibition of FOXM1 SUCC at the K259 site in human embryonic kidney (HEK)-293T cells. Overexpressing of SIRT7 reversed the increase of FOXM1-SUCC protein level and apoptosis, and the decrease of cell viability induced by silencing of SH3PXD2A-AS1. In tumor-bearing mice, SH3PXD2A-AS1 inhibition suppressed tumor growth and the protein levels of Ki67, SIRT7, and FOXM1.

Conclusion

SH3PXD2A-AS1 promoted DDP resistance in NSCLC cells by regulating FOXM1 SUCC via SIRT7, offering a promising therapeutic approach for NSCLC.