bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of machine learning in the prognosis prediction of malignant large bowel obstruction: a two-cohort study
Authors: Chen Shuai , Zhang Jun-Rong , Li Zhen-Lu , Huang Cang-Dian , Tu Peng-Sheng , Chen Wen-Xuan , Shang-Guan Xin-Chang , Wu Chang-Liang , Chen Xian-Qiang 
Published: May 2024

Abstract

Background
The Colon and Rectal NCCN Clinical Practice Guidelines currently identify obstructions as risk factors rather than as specific types. A personalized and intelligent prognostic evaluation system for malignant large bowel obstruction (MLBO) is urgently needed.
Methods
We conducted a retrospective study on 170 MLBO patients who underwent radical excision at two centers. The training and validation sets were randomly derived from the combined data of each center at a 7:3 ratio. We employed machine learning methods, including the logistic regression classifier (LR), linear discriminant analysis classifier (LDA), extreme gradient boosting classifier (XGB), AdaBoost classifier (AB), and light gradient boosting machine classifier (LGBM). These classifiers were based on clinical features (clinical model), radiological features (radiomics model), and their combination (merged model). The best model was identified through the area under the operating characteristic curve (AUC).
Results
Using clinicopathologic parameters, clinicopathologic models XGB achieved an impressive AUC of 0.97 for DFS, and LDA maintained strong performance with an AUC of 0.92 for OS, rather than radio-omics and dual-omics models. Using the Qingdao Center(QD) dataset as a single validation set, the model performance was not ideal due to demographic differences, with AUC values of 0.42 and 0.50 for DFS and OS, respectively. Finally, when cross-training and validating clinicopathological features from two centers were conducted, LDA exhibited exceptional performance for both DFS and OS, with AUCs of 0.96 and 0.95, respectively. Regardless of DFS or OS, the worse prognosis group had higher levels of the following metrics compared to the better prognosis group. [For DFS: pT(p < 0.001), pN(p < 0.006), pM(p < 0.001), monocyte count(0.64 vs. 0.52, p = 0.038), and carbohydrate antigen 199(CA199) (27.59 vs. 15.14, p = 0. 006); For OS: pT(p = 0.002), pN(p = 0.002) and pM(p < 0.001), as well as LVI (p = 0.037), monocyte count(0.68 vs. 0.51, p = 0.005) and CA199 (31.78 vs. 15.88, p = 0.006)].
Conclusions
High-efficacy models for the prognosis prediction of MLBO via clinicopathological features across two centers was constructed. We recommend heightened vigilance for MLBO patients with a high TNM stage, lymphovascular invasion occurrence, elevated CA199 levels, and high monocyte count.
Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics
Authors: Ockenden Helen , Bingham Robert , Goldberg Daniel , Curtis Andrew 
Published: May 2024

Abstract

The landscape shrouded by Antarctica’s ice sheet provides important insights into its history and influences the ice response to climate forcing, yet much of this critical basal boundary remains poorly understood. With ice-penetrating-radar technology yet to be developed to sound the bed of the Antarctic Ice Sheet from space, our understanding of the subglacial landscape has depended upon interpolation between irregularly distributed geophysical surveys. Despite increasing geophysical coverage and sophisticated developments in spatial interpolation, this has led to significant spatial biases in maps of Antarctica’s subglacial landscape. Recently-acquired, high-resolution maps of the ice surface now offer a transformative basis for recovering subglacial topography. We present a new map of Antarctica’s subglacial topography produced by an inversion technique that leverages the principle that stress changes associated with ice flow over bedrock obstacles produce ice-surface topography. Our results significantly enrich our understanding of mesoscale (10s of km width) landforms beneath the Antarctic Ice Sheet, revealing many new features and accentuating knowledge of subglacial mountain ranges and basins. The new map unmasks the true continent-wide spatial distribution of Antarctica’s subglacial roughness and geomorphology, outlines a pathway towards more accurate interpolation of poorly surveyed areas, and guides future targeted geophysical surveying.
Fabrication of robust and sustainable superhydrophobic cotton fabrics via surface micro-dissolve methode for oil/water separation
Authors: Ji Wanli , Zhong Shaofeng 
Published: May 2024

Abstract

The preparation of durable super-hydrophobic cotton fabrics (CFs) poses a significant challenge for oil-water separation, primarily due to nanoparticle loss and the utilization of toxic auxiliaries. This study proposes a sustainable method for creating superhydrophobic CFs. Initially, CFs are treated with a mixture of NaOH and urea at low temperatures to enhance surface roughness while preserving mechanical integrity. Subsequently, hexadecyl trimethoxysilane (HDTMS) and butane tetracarboxylic acid (BTCA) are applied to reduce fiber surface energy. This combined approach results in CFs with outstanding superhydrophobic properties, boasting a water contact angle of up to 155°, surpassing nanoparticle-based surfaces. Furthermore, these fabrics exhibit remarkable mechanical and chemical stability, along with enduring washing durability. Notably, they demonstrate effective self-cleaning abilities in the presence of liquid contaminants and excellent oil/water separation performance with a high separation efficiency. The developed CFs hold promise for diverse applications in both household and industrial settings.
Technological and Electrophysical Characteristics of Receiving Detectors Based on Si (Ge), Si (Li) and Si (Au) Structures With Scintilator Semiconductor
Authors: A Radjapov S. , I. Maripov 
Published: May 2024

Abstract

The scientific significance of the research results is that new technological processes for the preparation of semiconductor detectors based on the Si(Li) p-i-n structure with nuclear radiation were identifed and their electrophysical characteristics were determined. These results are explained by the fact that they are of great importance in the practical application of various semiconductor devices. The practical significance of the research results is the development and implementation of Si(Li) p-i-n structural detectors based on semiconductor monocrystalline silicon.
Optimal-order Trotter-Suzuki decomposition for quantum simulation on noisy quantum computers
Authors: Avtandilyan A. A. , Pogosov W. V. 
Published: May 2024

Abstract

The potential of employing higher orders of the Trotter-Suzuki decomposition of the evolution operator for more effective simulations of quantum systems on a noisy quantum computer is explored. By examining the transverse-field Ising model and the XY model, it is demonstrated that when the gate error is decreased by approximately an order of magnitude relative to typical modern values, higher-order Trotterization becomes advantageous. This form of Trotterization yields a global minimum of the overall simulation error, comprising both the mathematical error of Trotterization and the physical error arising from gate execution.
Mulching techniques impact on soil chemical and biological characteristics affecting physiology of lemon trees
Authors: Olmos-Ruiz Rafael , Hurtado-Navarro María , Pascual Jose Antonio , Carvajal Micaela 
Published: May 2024

Abstract

Aims
The lemon cultivation methods and techniques are crucial to ensure maximum productivity in the face of climate change. Mulching with plastic is commonly used in citrus production for saving water, but some side effects need to be investigated. In our study, we investigated different plastic and biological mulching on lemon trees determining growth and physiological parameters in relation to soil chemical and biological composition.
Methods
The experiment was divided into four different lines with ten trees per treatment, the effect of mulching with white and black plastic film, dry pruning mulching respect to a non-mulched treatment of lemon tree orchard during a crop season. The impact of these treatments on vegetative growth, stomatal gas exchange and mineral nutrition on plant and soil bacterial communities were evaluated.
Results
Our results showed that the type of mulching significantly influenced in the parameters studied. All mulching treatments increased temperature and soil moisture levels; plastic mulching treatments had significantly higher values in terms of intrinsic water use efficiency; while mulching with dry pruning showed higher microbial activity and higher soil nutrient concentration, leading to increased water use efficiency and productivity.
Conclusion
The results showed that different methods of mulching affected the physiology of lemon trees interacting in a complex way to determine their growth. Specifically, mulching using dry pruning improved the exchange of gases in the plant and plant nutrition which was related to the biological soil health.
The importance of on-request reports for the correct assessment of low macroseismic intensities: the experience of "Hai Sentito Il Terremoto"
Authors: Sbarra Paola , Tosi Patrizia , De Rubeis Valerio , Sorrentino Diego 
Published: May 2024

Abstract

The lower degrees of the macroseismic scales are estimated mainly using the diagnostic related to the percentage of people who felt the earthquake (“felt” percentage). However, estimating this percentage by examining only a sub-sample of people may be biased, as those who complete the questionnaires are predominantly those who have felt the earthquake. One way to solve this problem is to send a specific request to them when an earthquake occurs. The 'Hai Sentito il Terremoto' (HSIT) site began implementing this strategy in December 2008 by allowing users to register on the website. Thanks to contributions from registered users, the HSIT database has been populated with "not felt" reports, which since 2009 have outnumbered the “felt” ones. The diagnostics of registered and unregistered users were analysed separately in a sample of municipalities, showing no difference between the two types of users with the exception of “not felt” reports number. Despite the availability of the "not felt" reports, the experimental “not felt” number is still underestimated when compared to those indicated in the Mercalli-Cancani-Sieberg scale. It was therefore necessary to multiply the number of "not felt" reports by a correction factor, estimated to be 6, to properly use the “felt” percentage as a diagnostic effect. The macroseismic intensity and event magnitude do not affect this correction factor. By using both on-request reports and a correction factor it is possible to overcome the problem of underreporting "not felt" reports, more accurately estimate low intensities, and better define the “felt”-“not felt” boundary.
Endoscopic, ultrasound guided, radiofrequency ablation of aldosterone producing adenomas: A prospective, proof-of-concept trial
Authors: Brown Morris , Argentesi Giulia , Wu Xilin , Ney Alexander , Goodchild Emily , Laycock Kate , Lee Yun-Ni , Senanayake Russell , MacFarlane James , Ng Elisabeth , Kearney Jessica , O'Toole Samuel , Palma August , Munteanu Iulia , Salsbury Jackie , Benu Elena , Ebano Patrizia , Carroll Nick , Gillett Daniel , Tadross John , Marker Alison , Wilson Patrick , Godfrey Edmund , Goodchild George , Bestwick Jonathan , Gurnell Mark , Cheow Heok , Pereira Stephen , Drake William 
Published: May 2024

Abstract

Unilateral aldosterone-producing adrenal adenomas (APAs) are the potentially curable cause of 5% of all cases of hypertension. At present surgical removal of a whole gland is the only approved option for achieving cure, with uncertain long-term outcomes. Endoscopic ultrasound-guided trans-gastric radiofrequency ablation (EUS-RFA) offers a less invasive alternative to total adrenalectomy for the treatment of left-sided APAs. We aimed to determine whether EUS-RFA of APAs is safe and evaluate its likelihood of success in curing primary aldosteronism.
We conducted a multi-centre feasibility study of EUS-RFA as a nonsurgical, adrenal-sparing treatment for left-sided APAs. The primary endpoint was safety, judged by the occurrence of pre-specified major hazards (intestinal haemorrhage, infarction or viscus perforation); and the secondary endpoints were biochemical and clinical efficacy (Primary Aldosterone Surgical Outcome (PASO) criteria).
28 participants (21 male, 7 female, mean age 57.7 +/- 10.3 years; 16 White, 11 Black, 1 Asian), underwent 35 ablations on one (n=21) or two (n=7) occasions. None of the pre-specified major hazards occurred. There were 21 biochemical and 12 clinical successes. These were complete in 16 and 4 participants, respectively, associated with >75% eradication of the APA, judged by molecular imaging.
EUS-guided trans-gastric RFA is a safe alternative to total adrenalectomy for the treatment of left-sided APAs and can lead to complete PASO success when most of the APA is ablated.
Harnessing Medicinal Chemical Intuition from Collective Intelligence
Authors: Gkeka Paraskevi , Llompart Pierre , Minoletti claire , Amaning Kwame , Bianciotto Marc , Filoche-Romme Bruno , Foricher Yann , Mas Pablo , Papin David , Rameau Jean-Philippe , Schio Laurent , Marcou Gilles , Varnek Alexandre , Moussaid Mehdi 
Published: May 2024

Abstract

Over the last decade, the combination of collective intelligence with computational methods has transformed complex problem-solving. Here, we investigate if and how collective intelligence can be applied to drug discovery, focusing on the lead optimization stage of the discovery process. For this study, 92 Sanofi researchers with diverse scientific expertise participated anonymously in a lead optimization exercise. Their feedback was used to build a collective intelligence agent that was compared to an artificial intelligence model developed in parallel. This work has led to three major conclusions. First, a significant improvement of collective versus individual decisions in optimizing ADMET endpoints is observed. Second, for all endpoints apart from hERG inhibition, the collective intelligence performance exceeds the artificial intelligence model. Third, we observe a complementarity between collective intelligence and AI for complex tasks, demonstrating the potential of hybrid predictions. Overall, this research highlights the potential of collective intelligence in drug discovery. The entire dataset, including questionnaire responses, and developed models are available for access on GitHub.
Experimental Detection of Embedded Rebar Corrosion in Concrete with Ground Penetrating Radar
Authors: Jalal Khadiza Binte , Yazdani Nur , Beneberu Eyosias , Rahman Mohd Mezanur 
Published: May 2024

Abstract

Reinforced concrete structures have been extensively investigated using Ground Penetrating Radar (GPR) for qualitatively analyzing corrosion-induced degradation. However, no reliable quantitative model exists for the corrosion evaluation of the embedded steel rebars. Such corrosion may significantly impact the strength, serviceability and long-term durability of concrete structures. This study involved an experimental work to determine the relationship between the rebar corrosion quantity and the maximum amplitude as well as the two-way travel time (TWTT) of the GPR electromagnetic wave. A direct current was impressed into embedded steel rebars in concrete beams immersed in a 5% saltwater solution to induce accelerated corrosion. GRP data were collected before saline submersion and at 10-day intervals. A multivariate regression equation with high reliability was developed to estimate corrosion-indued rebar mass loss with independent variables, including concrete cover, rebar diameter, age of the reinforced concrete, concrete strength and GPR scanning parameters.
Motor-free telerobotic endomicroscopy for steerable and programmable imaging in complex curved and localized areas
Authors: Ren Hongliang , Yuan Sishen , Xu Chao , Cui Beilei , Zhang Tinghua , Liang Baijia , Yuan Wu 
Published: May 2024

Abstract

Aberrations in intraluminal epithelia are precursors to a plethora of critical conditions, including various cancers, where early detection is crucial for patient prognosis. Optical Coherence Tomography (OCT) endoscopy has emerged as a promising imaging technique that provides high resolution in micrometers and imaging depth in millimeters. However, conventional proximal-scanning OCT endoscopes face challenges in imaging tortuous lumens. Motor-driven distal-scanning OCT endoscopes afford potential solutions but pose thermal and electrical risks to tissues. Crucially, these systems lack the requisite compact steering mechanisms for effective navigation within complex luminal organs, commonly involving tooltip deformations, including bending, torsion, and compression. Here, we present a motor-free telerobotic OCT endoscope of a 1.28 ~ 2.28-mm long magnetic rotator integrating a rotatable diametrically magnetized cylinder permanent magnet (RDPM) with a light reflector. Utilizing an external magnetic field effectively mitigates heat effects induced by current and voltage, yielding a maximum voltage of less than 0.02 mV and a temperature rise of no more than 0.5 °C even during continuous operation lasting one hour. Furthermore, a learning-based approach is developed to correct the common OCT imaging distortions resulting from nonuniform rotation within curved luminal organs. The endoscope demonstrates remarkable maneuverability, enabling steerable angles up to 110° under a magnetic field strength of ~ 500 mT and a gradient of ~ 0.06 mT/mm. In-vivo studies on mouse colons validate the endoscope's ability to deliver reliable angular control for localized 3D imaging, unaffected by bending and without the need for feedback. This advancement in intraluminal microimaging introduces a new guidewire-independent technique in endomicroscopy, enhancing safety and potentially improving patient care.
Lab to Emergency: Establishment and validation of automated method for rapid biodosimetry
Authors: Vijayalakshmi J. , Chaurasia Rajesh Kumar , Nair Pooja , Shirsath K.B. , Priya T Lekha , Paul Solomon F.D. , Srini K. Satish , Bhat N.N. , Khan Arshad , Sapra B.K. 
Published: May 2024

Abstract

In inadvertent radiation exposure scenarios, accurate measurement of absorbed dose by biodosimetric techniques is crucial, especially when physical dosimetry is unavailable or in dispute. Chromosomal aberrations like dicentric (DC) and micronuclei (MN) are widely used for biodosimetry, with the former being the gold standard for individual dose assessment. However, both methods are labour-intensive requiring skilled analysis. This study aims to assess and compare the frequency and accuracy of DC and MN scoring through manual and automated methods. It investigates the establishment and validation of dose-response curves, in the dose range of 0-5 Gy, for 60Co-γ-ray-induced DC and MN using peripheral blood samples from three human volunteers. Statistical analysis confirmed dose-dependent increases in aberration frequencies, with Poisson distribution validation for all dose points. The manual and automated scoring methods yielded notable differences in linear and quadratic coefficients. Correlation analyses demonstrated substantial agreement between manual and automated scoring methods, particularly for MN (R2= 0.98). Towards validation, 16 dose-blinded samples were analysed for both endpoints using manual and automated scoring methods. Results yielded a close match between estimated and delivered doses, particularly evident with automated scoring (of both DC and MN) displaying superior accuracy for most of the doses. Notably, the variation was minimal, with -1.96% for DC and 2.85% for MN at 0.5 and 4 Gy, respectively. Automated scoring was approximately 15-20 times faster than manual scoring, suggestive of a faster practical option, especially in emergency scenarios such as criticality accidents and radiological emergencies, thus facilitating speedy triage and medical decisions.
An enhanced work research method for improving efficiency of cuttings of tree plantations on flat and sloping terrains using a cutting-cycle productivity model
Authors: Palander Teijo , Ovaskainen Heikki , Lauren Annamari , Pasi Antti 
Published: May 2024

Abstract

The aim was to model and identify the most productive cutting methods by comparing forward felling technique (C) with sideways techniques outside (A and D) or inside cutting edge (B and E). First, drone video material of each tree was analyzed by comparing time distributions of work phases. Then, the relation between this input data and harvester’s production data was analyzed by regression models. A Quadratic model predicted the productivity precisely (R2 = 0.95). This model explained the effective-hour productivity in cutting cycle with dummy variables of harvesting conditions. The productivity was explained by tree size and cutting cycle time, while effects of operator and harvester were eliminated successfully according to statistical analysis. In the pine (Pinus taeda) plantations on flat terrain, cutting method B was 4.8 m3/E0h more productive than method A, and 6.7 m3/E0h more productive than method C. In eucalyptus (Eucalyptus saligna) plantations cutting method E was identified 1.8 m3/E0h more productive as compared to cutting method D on sloping terrain. Of the time-cycle variables, time consumption of the “moving of tree” changed statistically most significantly between the cutting methods, of which the methods that used sideways felling technique inside cutting edge were most efficient. This modeling structure can be recommended for precise work studies in similar harvesting conditions.
Forecasting Bitcoin Prices: A Comparative Study of Machine Learning and Deep Learning Algorithms
Authors: Alizadegan Hamed , Radmehr Arian , Ilani Mohsen Asghari 
Published: May 2024

Abstract

The cryptocurrency market, particularly Bitcoin, has witnessed significant volatility, making accurate price prediction a challenging yet crucial task. This research explores the application of four powerful machine learning algorithms), Light Gradient Boosting Machine (LightGBM , Long Short Term Memory (LSTM), Bidirectional Long Short Term Memory (BiLSTM) and Extreme Gradient Boosting (XGBoost), for forecasting Bitcoin prices. The study focuses on evaluating the predictive performance using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as the evaluation metrics. The LSTM and Bi-LSTM, a type of recurrent neural network (RNN), are known for that ability to capture long-term dependencies in time series data. On the other hand, LightGBM and XGBoost, a gradient boosting framework, excels in handling large datasets efficiently and delivering accurate predictions. By employing these algorithms, this research aims to enhance the accuracy of Bitcoin price predictions compared to traditional methods. The experimental setup involves training and validating the models on historical Bitcoin price data. The MAE and RMSE metrics are utilized to assess the models' predictive accuracy, providing a comprehensive evaluation of their performance. The comparative analysis of machine learning models sheds light on their strengths and weaknesses in the context of cryptocurrency price prediction. The results showcase the importance of employing advanced machine learning techniques in forecasting financial time series, highlighting the potential for improved decision-making in cryptocurrency trading and investment strategies.