bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lake level rise modeling with Artificial Neural Networks (ANN) and Hydrodynamic model
Authors: Bulti Abebe Tadesse Tadesse , yutura Gonse Amelo 
Published: December 2022

Abstract
The water level in a lake increases as a result of changes in the hydrologic processes in the lake catchment that can cause a flood in the lake's surrounding areas. The lake system needs a comprehensive investigation to alleviate an increase in the water level and consequently flooding. Some different methods and approaches can be applied to predict the lake level increase. A water balance study, hydrodynamic model application, and ANN simulation applications are some of the methods. Each method has merit and complexity in terms of data requirement, the reflection of the physical processes, and ease of modeling application. A simple water balance model application is relatively good in considering all the physical processes. However, model outputs are not good during the model performance evaluation. The hydrodynamic models are simulation models which base on the physical processes of the lake system, with the same modification. Somehow it considers all the real processes with simplification. A good understanding of the application provides a favorable condition to provide all the input/factors contributing/ indirectly to a system modeling. The simulation through a hydrodynamic model is within a good range on performance indicators. Artificial Neural networks (ANN) are recent applications that focus on data science rather than physical processes. The ANN provides an opportunity to include all the contributing factors in the modeling processes. The output from ANN modeling processes is in a very good range. The choice of the method of prediction needs to understand the actual event in a lake system and the model's background for good prediction.
Finite element simulation of residual stress in milling of aluminum alloy with different passes
Authors: Xu Yongshi , Yue Caixu , Chen Zhitao , Li Ming , Wang Le , Liu Xianli 
Published: December 2022

Abstract
Machining-induced residual stress is an important measure of surface integrity. Better residual stress distribution will improve the quality and performance of the workpiece. In the process of cutting, it often encounters the situation of multi-pass cutting. The formation of residual stress in the process of single- or multi-pass cutting has been widely studied, but few researchers consider the same total radial cut depth, divided into single-pass cutting and multi-pass cutting, and the last cutting in the process of multi-pass cutting is less studied. The difference between single-pass cutting and multi-pass cutting is that the former machining of cutting is used as the initial state of the latter machining. The stress/strain and temperature generated on the surface of the former machining can affect the subsequent cutting force, temperature, plastic deformation, and the final residual stress. This paper studies the distribution of cutting force and residual stress generated by single- and multi-pass cutting of Al-7075-T6, and focuses on the analysis of the multi-pass cutting strategy. The three-dimensional finite element method is used to simulate and the experimental results are in good agreement with the simulation results. The results show that compared with single-pass cutting, multi-pass cutting can reduce the surface tensile stress and the magnitude and depth of underground residual compressive stress, and the effect of decreasing method is more obvious when multi-pass cutting. The last machining is affected by the previous cutting. When the next radial cutting depth is greater than the plastic deformation depth produced by the previous cutting, the next radial cutting depth is the same, and the trend of residual stress is approximately the same. The results can guide the optimization of residual stress in the multi-pass cutting strategy.
Influence of weight-bearing on the 3D movement of lumbar facet joints in the sitting position
Authors: Han Ye , Wang Xiaodong , Wang Jianzhong , Sun Shaosong , Xia Xi , Wang Jing , Miao Jun 
Published: December 2022

Abstract
Objective
To analyze the motion characteristics of lumbar facet joints and to observe the effect of weight-bearing on lumbar facet joints in the sitting position.
Methods
Ten normal subjects (5 males and 5 females) were recruited and scanned by CT, and their lumbar 3D models were reconstructed by software. The images of flexion and extension of lumbar facet joints in the sitting position were collected without weight-bearing and weight-bearing 10 kg, and the 2D model was constructed by software. The 2D-3D model was matched to restore the flexion and extension motion changes of the subjects' lumbar spine in the sitting position, and the relevant data of facet joinets were collected.
Results
In the L3/4 segment, after weight loading, the displacement of the left facet joint in the X axis became larger, while that in the Y axis and Z axis decreased. The displacement of the right facet joint in the X axis and Y axis increased, and the Z axis displacement decreased. The rotation angle of the bilateral facet joints also decreased. In the L4/5 segment, after loading, the displacements of the X, Y, and Z axis displacements of both sides increase, while the rotation angles of α and β increase, while the rotation angle of γ decreases. In the L5/S1 segment, the displacements of the X, Y, and Z axes on the left side decrease. The displacement of the X and Y axes on the right side decreases, while the displacement on the Z axis increases. The rotation angles of α and γ increase, and the rotation angle of the β axis decreases.
Conclusion
When sitting, the flexion and extension distance and rotational displacement of lumbar facet joints are not affected by weight-bearing. In addition, there is asymmetry in the movement of the left and right facet joints, and weight bearing has no effect on the asymmetry of the motion.
Performance Assessment of High-Resolution Remote Sensing Rainfall Products and their utility in simulating Extreme Hydrological Events in a Peri-urban Catchment
Authors: Yaswanth P , Bindhu V M , Kannan Bagavath Singh Arul Malar , Balaji Chakaravarthy , Narasimhan Balaji 
Published: December 2022

Abstract
Flood risk management studies require reliable estimates of extreme precipitation at high spatial-temporal distribution to force hydrologic models. Recently, Remote Sensing Rainfall Products (RRPs) have gained significant importance in the field of hydrometeorology, but their applicability in urban hydrologic predictions remains uncertain. The current study evaluates the accuracy of RRPs in comparison with observed rainfall and the significance of space-time representation of rain in simulating single and bimodal flood hydrographs. The current study is conducted for the Adyar river basin, a rapidly developing urban area in Chennai experiencing frequent floods. Sub-daily rainfall retrievals from three different satellites and Doppler Weather Radar (DWR) are the Remote sensing Rainfall products (RRPs) selected in the present study. Continuous and categorical statistical indices are selected to evaluate the performance of satellite rainfall estimates. Then the hydrologic utility of RRPs is conducted using the HEC-HMS model for five extreme precipitation events. The RRPs simulated the rising and recession portion of flood hydrographs accurately with a bias in peak discharge. Then, two approaches are selected to further improve the flood hydrograph simulations in the current study; 1) Hydrologic model simulations after disaggregating the daily station data to sub-daily scale using time characteristics of RRPs, 2) Hydrologic simulations after bias adjusting the RRPs with station data. We found substantial improvements in model results in the two approaches. The disaggregation approach using satellite rainfall estimates has overcome the insufficiency of sub-daily rainfall observations. The bias adjusted radar rainfall data is found as best performing for the flood hydrograph simulations.
Dysfunction in parkin aggravates inflammatory bone erosion by reinforcing osteoclast activity
Authors: Kim Eun-Young , Kim Ji-Eun , Kim Young-Eun , Choi Bongkun , Sohn Dong Hyun , Park Si-On , Chung Yeon-Ho , Kim Yongsub , Robinson William H. , Kim Yong-Gil , Chang Eun-Ju 
Published: December 2022

Abstract
Background: Parkin dysfunction associated with the progression of parkinsonism contributes to a progressive systemic skeletal disease characterized by low bone mineral density. However, the role of parkin in bone remodeling has not yet been elucidated in detail.
Result: We observed that decreased parkin in monocytes is linked to osteoclastic bone-resorbing activity. siRNA-mediated knockdown of parkin significantly enhanced the bone-resorbing activity of osteoclasts (OCs) on dentin without any changes in osteoblast differentiation. Moreover, Parkin-deficient mice exhibited an osteoporotic phenotype with a lower bone volume accompanied by increased OC-mediated bone-resorbing capacity displaying increased acetylation of α-tubulin compared to wild-type (WT) mice. Notably, compared to WT mice, the Parkin-deficient mice displayed increased susceptibility to inflammatory arthritis, reflected by a higher arthritis score and a marked bone loss after arthritis induction using K/BxN serum transfer, but not ovariectomy-induced bone loss. Intriguingly, parkin colocalized with microtubules and parkin-depleted-osteoclast precursor cells (Parkin-/- OCPs) displayed augmented ERK-dependent acetylation of α-tubulin due to failure of interaction with histone deacetylase 6 (HDAC6), which was promoted by IL-1β signaling. The ectopic expression of parkin in Parkin-/- OCPs limited the increase in dentin resorption induced by IL-1β, accompanied by the reduced acetylation of α-tubulin and diminished cathepsin K activity.
Conclusion: These results indicate that a deficiency in the function of parkin caused by a decrease in parkin expression in OCPs under the inflammatory condition may enhance inflammatory bone erosion by altering microtubule dynamics to maintain OC activity.
SAMNA: Accurate Alignment of Multiple Biological Networks Based on Simulated Annealing
Authors: Chen Jing , Wang Zixiang , Huang Jia 
Published: December 2022

Abstract

Background:Protein is an important part of biological tissue and contains a lot of biological information. Protein-protein interaction network alignment is a method for analyzing proteins that helps discover conserved functions between organisms and predict unknown functions. In particular, multi-network alignment aims to find the mapping relationship among multiple network nodes, so as to transfer the knowledge of species. However, with the increasing complexity of PPI networks, how to perform network alignment more accurately and efficiently is a new challenge.Results:This paper proposes a new global network alignment algorithm called SAMNA (Simulated Annealing Multiple Network Alignment), using both network topology and sequence homology information. To generate the alignment, SAMNA first generates cross-network candidate clusters by a clustering algorithm on a k-partite similarity graph constructed with sequence similarity information, and then selects candidate cluster nodes as alignment results and optimizes them using an improved simulated annealing algorithm.Conclusion:The SAMNA algorithm was experimented on synthetic and real-world network datasets, and the results showed that SAMNA outperformed the state-of-the-art algorithm in biological performance.
Studies on Organic Mediated Synthesis of Y 2 O 3 :Eu 3+ Nanophosphors and its Temperature Dependent Photoluminescence Properties
Authors: Abhila S. R. , Lalan Veena , Sr T. S. , Krishna G. Dhanu , Gopchandran K. G. 
Published: December 2022

Abstract
In this work, thermally stable Y2O3:Eu3+ nanophosphors by organic mediated combustion technique using dilute citric acid / citric acid mixed with diethylene glycol as fuel was prepared. The structural analysis establishes that these oxides crystallize into body-centered cubic structure with a space group la3. The nanophosphor prepared by citric acid with diethylene glycol as fuel exhibits better surface morphology and enhanced photoemission. The addition of polymeric precursors greatly enhanced the photoluminescence intensity of Y2O3: Eu phosphor by reducing the surface defects via the morphological control and improvement of crystallite growth. Investigations on temperature dependent photoluminescence studies reveal that the materials are thermally stable and the respective activation energy was 0.130 and 0.143 eV. The nanophosphor, fueled with diethylene glycol, exhibit remarkable CIE chromaticity coordinates (0.655, 0.345),in agreement with the National Television System Committee standards, and good CCT values with high color purity (96.48%), making them potential red phosphor in the fabrication of white light-emitting diodes.
Influence of Uncertain Parameters on Machining Distortion of Thin-walled Parts
Authors: Li Xiaoyue , Qi Hao , Tao Qiang , Li Liang 
Published: December 2022

Abstract
Thin-walled parts refer to lightweight structural parts comprised of thin plates and stiffeners. During the machining process of thin-walled parts, machining distortion often occurs due to uncertain factors such as varying stiffness, cutting force, cutting temperature, residual stress and other factors. This paper studied the minimization of the failure probability of machining distortion by controlling the uncertainties of inputs. For this, a fuzzy inference model for the machining system was proposed to determine the effects of uncertain factors on the machining distortion errors, which was composed of rule frame and result frame. In the rule frame, machining parameters, outline size, and wall thickness were used as inputs. In the result frame, linear stiffness, cutter path, as well as cutting force were taken as the input parameters. The values of machining distortion were the output, taken into a threshold function. Comprehensive matching was defined to measure the importance of uncertain inputs to outputs. Machining distortion will exceed the specification (failure) with the increase in comprehensive matching. Therefore, the comprehensive matching index evaluates the effects of the uncertainties on the machining distortion and quantify the effects of given uncertain parameters. Two engineering examples were employed to illustrate the accuracy and efficiency of the proposed approach. It revealed that the comprehensive matching of cutting force to the failure probability of machining distortion was the maximum, 0.040, which was 12 to 13 times greater than that of linear stiffness or cutter path.
Development of a nomogram for predicting 90-day mortality in patients with sepsis- associated liver injury: a clinical study of the MIMIC-IV database
Authors: Cui Liangwen , Bao Junjie , Yu Chao , Zhang Cheng , Huang Rui , Liu Lian , Shao Min 
Published: December 2022

Abstract
Background
The high mortality rate in sepsis patients is related to sepsis-associated liver injury (SALI). We sought to develop an accurate forecasting nomogram to estimate individual 90-day mortality in SALI patients.
Methods
Data from 34329 patients were extracted from the public Medical Information Mart for Intensive Care (MIMIC-IV) database. SALI was defined by total bilirubin (TBIL) > 2 mg/dL and the occurrence of an international normalized ratio (INR) > 1.5 in the presence of sepsis. Logistic regression analysis was performed to establish a prediction model called the nomogram based on the training set (n = 727), which was subsequently subjected to internal validation.
Results
Multivariate logistic regression analysis showed that SALI was an independent risk factor for mortality in patients with sepsis. The Kaplan‒Meier curves for 90-day survival were different between the SALI and non-SALI groups after propensity score matching (PSM) (log rank: P < 0.001 versus P = 0.038), regardless of PSM balance. The nomogram demonstrated better discrimination than the Sequential Organ Failure Assessment (SOFA) score, Logistic Organ Dysfunction System (LODS) score, Simplified Acute Physiology II (SAPS II) score, and Albumin-Bilirubin (ALBI) score in the training and validation sets, with areas under the receiver operating characteristic curve (AUROC) of 0.778 (95% CI 0.730–0.799, P < 0.001) and 0.804 (95% CI 0.713–0.820, P < 0.001), respectively. The calibration plot showed that the nomogram was sufficiently successful to predict the probability of 90-day mortality in both groups.The DCA of the nomogram demonstrated a higher net benefit regarding clinical usefulness than SOFA, LODS, SAPSII, and ALBI scores in the two groups.
Conclusions
The nomogram performs exceptionally well in predicting the 90-day mortality rate in SALI patients, which can be used to assess the prognosis of patients with SALI and may assist in guiding clinical practice to enhance patient outcomes.
Targeting estrogen-mediated CYP4F2/CYP4F11-20-HETE metabolic disorder decelerates tumorigenesis in ER+ breast cancer
Authors: Yang Juan , Li Yin , Han Xiao , Li Tianjiao , Li Ding , Liu Qiao , Yan Lizhong , Li Fei , Pei Xiaolin , Feng Ya , Lin Zhoujun , Fu Zhenkun , Wang Changjun , Sun Qiang , Li Chenggang 
Published: December 2022

Abstract
Background
The female hormone estrogen (E2) is essential for mammary gland development and unequivocally drives 80% of breast malignancies. The elucidation of molecular events in the E2-estrogen receptor (ER) α signaling pathway in ER + BC progression is of major scientific and therapeutic importance.
Methods
Lipid metabolomics profiling was used to examine the membrane phospholipid stimulated by E2. Clinical BC samples were used to assess the association of CYP4F2, CYP4F11 expression with clinicopathological characteristics and patient outcomes. ER + and ER- BC cells were used to examine the positive correlation between E2-ERα and CYP4F2, CYP4F11. Some Inhibitors of main enzymes in AA metabolism were used combined with E2 to assess roles of CYP4F2/CYP4F11 in the progression of ER + BC. CYP4F2, CYP4F11 overexpression and knockdown BC cell lines were employed to examine the effects of CYP4F2, CYP4F11 on cellular proliferation, apoptosis and tumor growth. Western blotting, qPCR, Immunohistochemical staining and flow cytometry were also conducted to determine the underlying mechanisms related to CYP4F2, CYP4F11 function.
Results
The activation of the CYP450 signaling pathway in arachidonic acid metabolism contributed to ER + BC tumorigenesis. In ER + BC, CYP4F2 and CYP4F11 overexpression induced by E2 could promote cancer cell proliferation and resistance to apoptosis by producing the metabolite 20-HETE and activating the antiapoptotic protein Bcl-2. CYP4F2 and CYP4F11 elevation correlates with poorer overall survival and disease-free survival in ER + BC patients.
Conclusion
CYP4F2, CYP4F11 and their metabolite 20-HETE could serve as effective prognostic markers and attractive therapeutic targets for novel anticancer drug development about ER + BC.
The role of tumor model in magnetic targeting of magnetosomes and ultramagnetic liposomes
Authors: Curcio Alberto , Perez Jose Efrain , Prévéral Sandra , Fromain Alexandre , Genevois Coralie , Michel Aude , de Walle Aurore Van , Lalatonne Yoann , Faivre Damien , Ménager Christine , Wilhelm Claire 
Published: December 2022

Abstract
The combined passive and active targeting of tumoral tissue remains an active and relevant cancer research field. Here, we exploit the properties of two highly magnetic nanomaterials, magnetosomes and ultramagnetic liposomes, in order to magnetically target prostate adenocarcinoma tumors, implanted orthotopically or subcutaneously to take into account the role of tumor vascularization in the targeting efficiency. Analysis of organ biodistribution in vivo analysis revealed that, for all conditions, both nanomaterials accumulate mostly in the liver and spleen, with an overall low tumor retention. However, both nanomaterials were more readily identified in orthotopic tumors, reflecting their higher tumor vascularization. Additionally, a 2- and 3- fold increase in nanomaterial accumulation was achieved with magnetic targeting. In summary, ultramagnetic nanomaterials show promise mostly in the targeting of highly-vascularized orthotopic tumors.
4-PBA inhibits endoplasmic reticulum stress to improve autophagic flux in the treatment of protamine/lipopolysaccharide-induced interstitial cystitis in rats
Authors: Jia Li , Jingzhen Zhu , Xinliang Yang , Bishao Sun , Xin Luo , Ji Zheng , Zhenqiang Fang 
Published: December 2022

Abstract
Interstitial cystitis (IC) has severe clinical symptoms with unclear mechanism. The continuous inflammatory response of the bladder is the basis of its pathogenesis. Endoplasmic reticulum stress (ERS) is involved in the regulation and development of various inflammatory diseases. And autophagy plays an important role in IC. In this study, we mainly focus on the therapeutic effect of endoplasmic reticulum stress and autophagy on protamine/lipopolysaccharide-induced interstitial cystitis. Female Sprague–Dawley rats were randomized into three experimental groupsas follows: sham controls(N), IC alone, and IC+4-PBA.Rats in group IC received 10 mg/ml PS in the urinary bladder, followed by 2 mg/ml LPS instillation after 30 minutes, IC +4-PBA group SD rats received 4-PBA solution administered intragastrically once a day for 5days.ERS biomarker (GRP78), autophagy-related proteins (LC3I/II, and Beclin1), autophagic flux biomarker (P62), inflammatory biomarkers (IL-6, TNF-a, NF-kB) , apoptotic biomarkers (Caspase 3, Bax), anti-apoptotic biomarkers (Bcl-2) and oxidative stress biomarkers (HO-1 , NQO-1)revealed significantly different results among the groups. The histological score and mast cell count demonstrated most severe in the IC group than those in the IC+USC group. TUNEL assay examined the level of apoptosis in IC group was higher than in the IC+USC group. The bladder micturition function was significantly improved with4-PBA treatment.4-PBA inhibits ERS to recover autophagic flux, and then to suppress the bladder oxidative stress, the inflammatory reaction and apoptosis, finally improve the bladder urinary function in PS/LPS induced IC.
Projection of Groundwater Level Fluctuations Using Different Machine Learning Algorithms under Climate Change in the Mashhad Aquifer, Iran
Authors: Panahi Ghasem , Eskafi Mahya Hassanzadeh , Faridhosseini Alireza , Khodashenas Saeed Reza , Rohani Abbas 
Published: December 2022

Abstract

Due to population growth in recent years and climate change in arid and semi-arid regions, the lack of rainfall and the reduction of surface water flows required in various sectors, monitoring and projection of the climate change impact on the Groundwater Level (GWL) in the future is vital in the management and control of these resources. The purpose of this study is the projection of climate change impact on the GWL fluctuations in the Mashhad aquifer during the future period (2022-2064). In the first step, the climatic variables using ACCESS-CM2 under the Shared Socio-economic Pathways (SSPs) 5-8.5 scenario from the CMIP6 model were extracted. We used the CMhyd model to downscale the climatic data from the GCMs model. In the second step, different machine learning algorithms, including Multilayer Perceptron Neural Network(MLP), Adaptive Neuro-fuzzy Inference System Neutral Network(ANFIS), Radial Basis Function Neural Network(RBF), and Support Vector Machine(SVM) were used to predict the GWL fluctuations under climate change in the future period. Our results point out that temperatures and evaporation will increase in the autumn season, and precipitation will decrease by 26% in the future in the Mashhad aquifer. The results showed that the RBF model was an excellent performance in predicting GWL compared to other models. Based on the result of the RBF model, the GWL will decrease by 6.60 meters under the SSP5-8.5 scenario in the future. The findings of this research have a practical role in making helpful groundwater resources management decisions.