bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ultrasound-based deep learning radiomics nomogram for comprehensive prediction of tumor, axillary lymph node status and prognosis of breast cancer patients after neoadjuvant chemotherapy
Authors: Liu YueXia , Liu QingHua , Hu QuanHui , Shi JiaYao , Liu GuiLian , Liu Han , Shu ShengChun 
Published: December 2023

Abstract
Objectives Our study aims to explore the feasibility of the deep learning radiomics nomogram (DLRN) for predicting the status of tumors and axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) in breast cancer patients, and employ a Cox regression model for survival analysis to validate the effectiveness of the fusion algorithm.
Methods A total of 243 patients who underwent NAC were retrospectively included between October 2014 and July 2022. The DLRN integrated clinical characteristics as well as radiomics and deep transfer learning features extracted from ultrasound images. Evaluating the diagnostic performance of DLRN by constructing ROC curve , and assessing the clinical usefulness of models by using decision curve analysis (DCA). A survival model was developed to validate the effectiveness of the fusion algorithm.
Results In the training cohort, the DLRN yielded an area under the receiver operating characteristic curve (AUC) values of 0.984 and 0.985 in the tumor and LNM, while 0.892 and 0.870, respectively, in the test cohort. The consistency index (C-index) of the nomogram was 0.761 and 0.731, respectively, in the training and test cohort.The Kaplan-Meier survival curves showed that patients in the high-risk group had significantly poorer overall survival than patients in the low-risk group (P<0.05).
Conclusion The US-based DLRN model could hold promise as a clinical guidance for predicting the status of tumor and LNM after NAC in breast cancer patients. This fusion model also can predict the prognosis of patients , which could help clinicians to make better clinical decisions.
HSF1 is a direct sensor of proteotoxic stress.
Authors: Muller Petr , Simoncik Oliver , Tichy Vlastimil , Durech Michal , Hernychova Lenka , Trcka Filip , Uhrik Lukas , Bardelcik Miroslav , Coates Philip , Vojtesek Borivoj 
Published: December 2023

Abstract
Stress responses play a pivotal role in normal physiology, a feature exploited by cancer cells to facilitate growth, counteract genomic instability, and resist therapeutic stress. Heat shock factor-1 (HSF1), a major stress-response transcription factor, exhibits a significant upregulation in activity in cancer. While HSF1's role in transcription is relatively well-known, the mechanisms governing its activation, particularly in tumors, remain unclear. To determine whether HSF1 is the primary sensor of proteotoxic stress, we studied the range of conditions that modulate HSF1 activation both in vitro and in cells. We show that purified HSF1 adopts a stable monomeric conformation in vitro. Conditions that lead to protein denaturation, such as heat stress, molecular crowding, Hsp90 inhibition, and proteasome inhibition, directly trigger the activation of HSF1 by inducing conformational changes and trimer assembly. In contrast, proteosynthesis inhibition prevents HSF1 activation by reducing denatured proteins in the cell. Our findings highlight HSF1 as a direct sensor of proteotoxic stress, independent of post-translational modifications and molecular chaperones. Abrupt environmental changes causing protein denaturation induce a conformational change in monomeric HSF1, explaining the universal cellular response to proteotoxic stress. This activation mechanism underscores the cells' ability to initiate a protective response, enhancing chaperone activities to restore homeostasis.
The Clinical Efficacy of single-hole Punch Excision Combined With Intralesional Steroid Injection for Nodular Keloid Treatment
Authors: Liu Bingbing , Lin Haoying , Zhang Minghai 
Published: December 2023

Abstract

There are many methods to treat keloid, including various excision operations,laser, injection and radiotherapy.However, few studies have explored the effectiveness of single-hole punch excision in keloid treatment.This study aimed to investigate the efficacy and safety of lateral punch excision combined with intralesional steroid injection for keloid treatment through self-control experiment.In this self-controlled trial, all patients meet the diagnosis of nodular keloid, and try to choose left-right symmetrical control, one in the control group (50 in total) and the other in the observation group (50 in total).The keloids in the treatment group were initially treated with punch excision combined with intralesional steroid injection, followed by injection treatment alone. Keloids in the control group received intralesional steroid injection alone.The Vancouver Scar Scale (VSS)of the keloid before and after the punch excision wasevaluated; the keloid scores at different time points and the number of injection treatments required in both groups were compared, and adverse reactions were observed.The effective rate of the observation group was 86.0%, which was significantly higher than that of the control group (66.0%), and the recurrence rate of 22% was lower than that of the control group (χ2 = 4.141,63417), all of which were statistically significant (all P < 0.05). At the end of treatment, the VSS and total injection times in the observation group were significantly lower than those in the control group (t = 5.900,3.361), with statistical significance (P < 0.01).The combination of single-hole punch excision and intralesional steroid injection is an effective method to treat multiple nodular keloids, shortening the treatment course of tralesional steroid injection without obvious adverse reactions.
High-percentage new energy distribution network line loss frequency division prediction based on wavelet transform and BIGRU-LSTM
Authors: Wu Xiangming , Song Nan , Liang Jifeng , Lv Ye , Wang Zitian , Yang LIjun 
Published: December 2023

Abstract
With the large-scale integration of new energy sources, the control complexity of the distribution network has substantially increased, thereby complicating the mechanisms and factors affecting power loss. Traditional power loss calculation methods have become inadequate. Addressing this challenge, we propose a novel power loss frequency-decomposition prediction model that integrates wavelet transform with BIGRU-LSTM, significantly enhancing the prediction accuracy and adaptability for power loss in distribution networks with a high proportion of new energy. Key temporal and non-temporal feature parameters were selected from electrical and environmental characteristics using grey relational analysis and the NARMA method to construct a multidimensional feature dataset. A predictive architecture combining wavelet transform with BIGRU-LSTM was developed, where historical power loss data were decomposed into frequency components to form a multidimensional input for the network prediction. The BIGRU-LSTM network facilitated frequency-decomposition prediction of the components using deterministic and probabilistic prediction channels. The results were then reconstructed using inverse wavelet transform to produce power loss prediction intervals. Simulation examples validated the effectiveness and suitability of the proposed prediction model.
Performance Evaluation Of The Deep Learning System For Weed Recognization
Authors: Mosslah Abd Abrahim , Mahdi Reyadh Hazim , Al-Barzinji Shokhan M. 
Published: December 2023

Abstract
Numerous approaches based on machine learning have emerged in recent years to enhance crop protection efficiency. One example is the utilisation of deep neural networks to differentiate between various weed types in actual event scenarios. Nevertheless, these methods often need substantial input from experts who work iteratively to design robust deep learning systems. To simplify such processes and conserve resources, researchers have explored a fresh method known as automated deep learning. The our technology's recognition of weeds through the use of machine learning was evaluated using plant seedlings and weed collection from plants as a dataset to address the issue of weed recognition. The study compared various configurations, including plant segmentation, using a collection of classifiers in place of Softmax, and training on datasets that contain noise. The findings indicated ensuring performance, with F1-scores of 93.1% and 90.2% based on the dataset utilised. These results align with AutoML-linked studies while falling short of manually fine-tuned deep-learning-based systems created by human specialists. To conclude, exploring the potential of combining manual expert work and automated deep learning could be a promising direction for enhancing efficiency in plant defence.
Transforming healthcare: A pilot study to improve primary healthcare professionals’ self- management support behaviour through blended learning
Authors: Timmermans Lotte , Decat Peter , Foulon Veerle , Hecke Ann Van , Vermandere Mieke , Schoenmakers Birgitte 
Published: December 2023

Abstract

Background: Self-management of a chronic condition is a complex but increasingly important issue. However, a supportive attitude and behaviour among healthcare professionals is hampered by a lack of awareness, knowledge and motivation. In addition, the role of professionals in supporting self-management seems unclear.
Methods: A blended learning program for primary healthcare professionals was developed to strengthen self-management support in primary care. The program was piloted in community health centres and multidisciplinary medical practices in Flanders. Using the Kirkpatrick model, the impact on healthcare professionals’ reaction, learning and behaviour regarding self-management support was evaluated.
Results: A total of 60 healthcare professionals registered for the educational program. Post-learning questionnaires and verbal feedback showed a positive response, with professionals highly appreciating the innovative blended learning approach. In terms of learning, participants showed a good understanding of self-management support, although nuances were observed in the application of acquired knowledge to practice scenarios. Finally, preliminary insights into behavioural change were explored, revealing a positive impact of the intervention on participants’ supportive self-management behaviours in healthcare practice.
Conclusions: Our study provides preliminary evidence of the impact of a blended learning program in raising awareness and providing knowledge to professionals about self-management support. The program needs to be refined for general implementation in primary care.
Identifying contemporary challenges pertaining to Distributed Software Development using Fuzzy Delphi Method
Authors: Gul Maimoona , Alam Khubaib Amjad 
Published: December 2023

Abstract
Distributed Software Development (DSD) is a process in which team members are located in different geographical locations and collaborate using communication and collaboration tools. DSD has gained prominence in recent years, driven by the surge in globalization, the rapid expansion of the IT outsourcing market, and the widespread accessibility of high-speed internet. However, DSD also presents numerous challenges, including team dynamics, employee well-being, HR management, communication & collaboration, requirements management, tooling & infrastructure, and development process & risk management issues. This paper aims at identifying critical challenges related to DSD. Initially, a systematic literature review was conducted to construct an initial list of 77 challenges; subsequently, a Fuzzy Delphi Method (FDM) survey was adopted to identify the critical challenges in this context. Results of the FDM indicate that, out of the 41 screened-out challenges, differences in work ethics & practices, language barriers, meeting scheduling difficulties, different time zones, burnout, physical fatigue/mental stress, and lack of opportunities for personal development are the most critical challenges of DSD. The findings of this study have implications for both practitioners and researchers related to DSD.
Factor Influencing Recruitment of Training and Development in Automobile Industry
Authors: Anika Afrin , Islam Mahia , Ahmed Farhad 
Published: December 2023

Abstract
This quantitative research delves into the recruiting, training, and development practices within the expanding automobile industry in Bangladesh. The primary goal is to analyze how these human resources practices influence employee retention and organizational performance. Over the past decade, the automotive sector has become the third-largest contributor to the nation's manufacturing GDP, propelled by increasing consumer demand, rising disposable income, and supportive government policies. Effective recruitment, training, and development strategies are vital for cultivating a skilled and motivated workforce within automobile companies. Recruitment strategies must align with the unique requirements of the industry, attracting candidates with technical proficiency and adaptability. Both traditional and modern recruitment methods, such as social media and online platforms, have proven successful in attracting highly qualified personnel. Training and development programs are crucial for enhancing employee competencies, job performance, and overall job satisfaction. Various methods, including on-the-job training, workshops, seminars, and e-learning initiatives, are employed to disseminate industry-specific knowledge and skills. Tailoring training material to align with organizational objectives and employee needs is essential for optimizing the effectiveness of these initiatives. The study's findings reveal a significant correlation between efficient human resources practices and higher levels of employee retention. Well-designed HR strategies positively impact employee engagement, job satisfaction, and commitment, ultimately leading to decreased turnover rates. Employee retention is acknowledged to have a positive impact on productivity and the overall competitiveness of organizations operating within the automobile industry. Several recommendations emerge from the study to improve human resources practices and enhance organizational performance. These include diversifying recruitment channels, enhancing employer branding efforts, and incorporating e-learning into training initiatives. The importance of fostering a culture of continuous learning, acknowledging employee accomplishments, and providing avenues for professional development is also emphasized as crucial. It is crucial to recognize the limitations of this research, including a restricted sample size and the specific context of the automotive sector in Bangladesh during the study period. Further investigation is suggested to explore enduring effects and involve a more extensive population sample to enhance the generalizability of the findings. In conclusion, implementing the recommended strategies enables automobile companies to attract and retain highly skilled individuals, create a positive work environment, and instill a sense of loyalty among employees, fostering sustainable growth and success in Bangladesh's competitive automotive industry.
Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments
Authors: Shihab Ibne Farabi , Alvee Benjir Islam , Bhagat Sudesh Ramesh , Sharma Anuj 
Published: December 2023

Abstract
The study demonstrates the effectiveness of a robust ensemble model specifically designed for accurate sidewalk detection - a crucial aspect of road safety and urban planning. The model's performance was evaluated on Cityscapes, Ade20k, and the Boston Dataset. The results indicate that the ensemble model surpasses the individual models, achieving mIOU scores of 93.1\%, 90.3\%, and 90.6\% on these datasets under ideal conditions. Furthermore, the ensemble model displayed consistent performance levels, even under challenging conditions, with a gradual decrease in efficiency observed only in various noise types such as Salt-and-Pepper and Speckle. In contrast, despite its slight advantage in ideal scenarios, the state-of-the-art ONE-PEACE Large Language Model (LLM) showed a notable drop in performance in noisy environments. These findings highlight the robustness and dependability of the ensemble model, making it a promising tool for enhancing road safety and managing curb space in complex urban environments.
Effects of ion source etching on the impedance performances of quartz component
Authors: Yang Chih-Fu F. , Chang Chih-Tsung , Tsai Yuan-Nan , Lin Chih-Lung , Hsu Chun-Yao 
Published: December 2023

Abstract

Quartz element (i.e., active quartz crystal oscillator or resonator) is coated on the quartz crystal with an Ag/Ni double-layer film. An ion source etching system is used to etch the Ag film surface of the quartz crystal through masks of different sizes. Three groups of masks were adopted for quartz crystal etching, the sizes of which are A (1.30×1.10 mm2), B (1.70×1.30 mm2), and C (2.42×1.62 mm2) with the remaining area not being etched. During the etching process, a frequency counter was connected to monitor the load resonance frequency (FL, MHz), load resonant frequency difference (FLD, ppm), resistance at series resonant frequency (RR, Ω, related to impedance performance). After ion source etching, the quartz element (mask A) corresponds to FLD (-385.5 ~ -256.8 ppm), and load resonant impedance RR ranges from 8.7 to 9.1 Ω, representing a 6.32% average impedance reduction. Using masks C, the quartz element corresponds to FLD (-282.9 ~ -241.1 ppm), and load resonant impedance RR ranges from 10.0 to 21.1 Ω. The average impedance divergence is 13.04 Ω. It is speculated that the size of the etching area of the quartz crystal using mask B or mask C was too large, and some remaining zones have been etched improperly, which led to an increase in impedance and also caused crystal frequency instability or oscillation failure. Precise etching and mask selection when producing quartz components is essential to maintain their functionality and stability.
High expression of COX-2 and MDR1 implicate resistance development to imatinib therapy in chronic myeloid leukemia patients
Authors: Dharmapuri Gangappa , Aribandi Anil , Kalle Arunasree M 
Published: December 2023

Abstract
Unresponsiveness of the chronic myeloid leukemia (CML) patients to frontline therapy of imatinib results in either dose escalation or change of drug leading to increased toxicity and financial burden. We have previously demonstrated cyclooxygenase-2 (COX-2)-dependent expression of efflux pumps via Wnt-signalling pathway as the underlying mechanism of imatinib resistance in vitro in K562 cells. The present study aimed at evaluating the gene expression of COX-2 and 3 of the efflux pumps, MDR1, MRP1and ABCG2, in lymphocytes of CML patients (n = 50) treated with different doses of imatinib as a means to diagnose resistance development. The results clearly indicated that the dosage of imatinib treatment correlated with expression levels of of efflux pumps and the COX-2-GSK3β- β-catenin-LEF signaling molecules. Although further studies are warrented, the results indicate the predictive therapeutic biomarker value of COX-2 and MDR1 during follow-up visits for early diagosis of resistance development.
Birth-pulse models to assess the effects of Wolbachia-carrying mosquito releases on the control of dengue fever
Authors: Zhang Xianghong , Li Juan , Liu Xianning 
Published: December 2023

Abstract

Dengue fever as a mosquito-borne disease caused by dengue virus is responsible for a substantial disease burden to the world.Wolbachia as an innovative technique has been approved to inhibit the replication of dengue viruses in mosquitoes or reduce the number of wild mosquitoes. We firstly build a birth-pulse model with sex structures to depict the nonlinear dynamics of mosquito population and then to investigate how Wolbachia can supress or replace wild mosquitoes. The existence and stability of periodic solutions of the system are proved by analyzing its stroboscopic map. Theoretical results show that under complete maternal transmission, Wolbachia will successfully establish and completely replace wild mosquitoes. Under incomplete maternal transmission, there may be two pairs of bistable periodic solutions, including the coexistence of replacement failure and partial replacement periodic solutions, or the coexistence of replacement failure and mosquito eradication periodic solutions. For these bistable situations, if the initial proportion of Wolbachia-carrying mosquitoes is greater than the critical threshold, then the strategy of partial replacement or mosquito eradication can be succeeded. Since the initial proportion of Wolbachia mosquitoes is not always greater enough in the field, we introduce mosquito releases into the birth-pulse model, and obtain the condition for the stability of complete replacement periodic solution. Finally, we give rich numerical simulations to show the multiple attractors of the systems, the comparison of three common release strategies and the effects of Wolbachia-induced parameters on control strategies. At last section, we summarize some key suggestions on the control of mosquitoes and dengue fever in practice. This work will be helpful for public health authorities in designing proper release strategies to control the spread of dengue fever.
Selective Differential Pulse Voltammetry Determination of Uric Acid in Clinical Urine Samples Using Copper Oxide Nanoparticles Modified Pencil Graphite Electrodes
Authors: Hirbo Hirbo Geremew , Kuse Kenenisa Abdisa , Hola Roba Nebi 
Published: December 2023

Abstract
The level of uric acid (UA) in clinical samples serves as a useful test in the diagnosis of related health disorders such as gout, hypertension & cardiovascular diseases, sclerosis, Alzheimer’s disease & optic neuritis. Interference by coexisting components of physiological fluids is a key problem to solve while attempting to demonstrate electrochemical determination of UA. CuO NPs was synthesized by aqueous chemical growth method. The deposition of the synthesized NPs on the pretreated PGE surface was carried out by drop casting of a suspension of the NPs. The current response of uric acid detection was improved by a factor of 2.63 in differential pulse voltammetric measurement compared to unmodified electrode. Under the optimized method and solution parameters, linear dependence of peak current on uric acid concentration in a wide range of 1.21x10 − 6 M to 1.142x10− 4 M, Limit of detection 6.25x10− 7 M, and limit of quantification 2.084 x10− 6 M, and excellent recovery results (90.0-99.3%) respectively. The developed sensor displayed well separated and discerned peaks for UA in presence of the potential interferent (ascorbic acid and tartaric acid). The electrode was successfully applied for the detection of UA in clinical urine samples in a phosphate buffer solution (pH = 7). The proposed sensor showed a very high reproducibility and repeatability with the relative standard deviation of 0.94%. In conclusion, a simple and low cost sensor based on PGEs is developed for sensitive and selective detection of uric acid in clinical urine samples.