bluuebunny/crossref_metadata_2025_split huggingface dataset:
Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring
Authors: Yu Feng , Liu Jianchang , Liu Dongming , Wang Honghai , Shang Liangliang 
Published: November 2022

AbstractRecently, stacked autoencoder (SAE)‐based deep learning has been widely employed for industrial process fault detection, which can extract representative low‐dimensional features. However, SAE fails to take the distribution structure of raw data into consideration in the unsupervised self‐reconstruction learning, and this may lead to limited monitoring performance. Focusing on this issue, this paper proposes a new fault detection method based on entropy estimation density peak clustering and stacked autoencoder (EEDPC‐SAE) for industrial process monitoring. First, an improved automatic clustering algorithm, namely, entropy estimation density peak clustering (EEDPC), is presented to analyze the distribution structure of process data. Then, the data distribution information is integrated into the learning procedure of SAE to capture the data intrinsic structure. EEDPC‐SAE can describe the distribution characteristics of process data, and more informative high‐level features can be learned for fault detection. Finally, two statistics, that is, Hotelling'sT‐squared () and squared prediction error (SPE), are established based on the encoder features and residual features generated by EEDPC‐SAE. The effectiveness of the proposed method is evaluated by the Tennessee Eastman (TE) process and fed‐batch fermentation penicillin (FBFP) process.
Effectiveness of triennial screening with clinical breast examination: 14‐years follow‐up outcomes of randomized clinical trial in Trivandrum, India
Authors: Ramadas Kunnambath , Basu Partha , Mathew Beela S. , Muwonge Richard , Venugopal Muraleedharan , Prakasan Aparna M. , Malu Rafi , Lucas Eric , Augustine Paul , Mony Rari P. , Thara Somanathan , Sankaranarayanan Rengaswamy 
Published: November 2022

AbstractBackgroundThis study presents the preliminary results of a randomized controlled trial (RCT) initiated in January 2006 in India to evaluate the effectiveness of clinical breast examination (CBE) in reducing breast cancer mortality as compared to a no‐screening control group reported significant downstaging in the intervention group. The present manuscript reports long‐term follow‐up outcomes.MethodsWomen 30–69 years old from 133 intervention clusters and 141 control clusters were invited to participate. Women in the intervention arm underwent three rounds of CBE every 3 years. CBE‐positive women were reexamined by a physician, and triple‐assessment was performed on those confirmed to have abnormalities. All participants were followed through home visits and linkage with population‐based cancer registry.ResultsOf the 55,843 eligible women in the intervention arm, 95.7% had CBE at least once and 11.5% were CBE‐positive. Breast cancers were diagnosed in 335 participants in the intervention group and 273 in the control group (N = 59,447). Age‐standardized incidence rate of early cancer was 30.4 of 100,000 in the intervention and 21.9 of 100,000 in the control group, with a rate ratio (RR) of 1.4 (95% confidence interval [CI], 1.1–1.8). The age‐standardized breast cancer mortality rates were 11.3 and 11.1 per 100,000 in intervention and control arms, respectively (RR, 1.1; 95% CI, 0.8–1.5) after 15 years. Five‐year breast cancer survival rates were 77.0% in the intervention and 71.2% in the control groups (overall p value = .043).ConclusionsTriennial CBE screening failed to demonstrate any mortality benefit despite achieving a shift toward earlier stage at detection and improved survival in the intervention arm. CBE is a valuable tool for diagnosis of breast cancer in symptomatic women especially in areas where mammography and/or breast cancer screening programs are not widely available.
Modeling the impact of novel systemic treatments on lung cancer screening benefits
Authors: Gogebakan Kemal Caglar , Lange Jane , Slatore Christopher G. , Etzioni Ruth 
Published: November 2022

AbstractBackgroundSince low‐dose computed tomography (LDCT) screening was shown to be effective in the National Lung Screening Trial (NLST), novel targeted therapies and immunotherapies for advanced lung cancer have become available. This study investigated the impact of these treatment advances on the expected benefits of LDCT screening.MethodsA microsimulation model of LDCT screening for high‐risk individuals under standard systemic treatments (chemotherapy and radiation therapy) and novel treatments (immunotherapy and targeted therapy) was used. The model assumed a reduction in advanced‐stage disease consistent with the NLST, and given the stage at diagnosis, it projected survival. The disease‐specific relative mortality reduction (MR) due to LDCT screening was projected in the trial setting and in a population eligible for LDCT screening under the current US Preventive Services Task Force (USPSTF) recommendations.ResultsThe availability of novel treatments reduced the MR in the LDCT arm of the NLST from 15% to 13.5% and the number of lung cancer deaths prevented from 310 to 224 per 100,000 persons screened. Over 10 years, population LDCT screening based on USPSTF recommendations prevented 374 lung cancer deaths per 100,000 under standard treatments (13.3% MR) and 236 per 100,000 under fully adopted novel treatments (10.6% MR). The number needed to screen to avert one death over 10 years was 270 under standard treatments and 440 under novel treatments.ConclusionsThe transition from standard systemic treatments to novel treatments is expected to reduce the relative and absolute mortality benefits of LDCT screening. Benefit–harm tradeoffs of LDCT screening are likely to change as novel treatments become widespread.
Pharmacokinetic and Bioequivalent Study of Potassium Chloride Sustained‐Release Tablet Under Different Dietary Conditions in Healthy Chinese Subjects
Authors: Li Qin , Yang Zhuan , Liu Shi‐Jing , Liu Lin , Chen Lu , Zhang Qian , Zhou Yan , Du Peng , Zeng Chen , Li Na , Zeng Yan , Xiong Yun , Liu Di Jia , Chen Jiyu , He Yan 
Published: November 2022

AbstractPotassium (K+) is an endogenous substance that is an essential dietary component. However, the interaction between dietary arrangements and specific effects of dietary K+ intake in bioequivalence studies remains unclear. To investigate the influence of dietary arrangement on the bioequivalence of potassium chloride (KCl) sustained‐release tablets in healthy Chinese volunteers, the pharmacokinetics of KCl were compared in two open‐label, single‐center, randomized, two‐period crossover studies with different dietary conditions. All volunteers received an oral dose of 6 g of KCl sustained‐release tablets under fasting conditions, with different dietary arrangements. Urine samples were collected on baseline days and 48 hours after tablet consumption. Inductively coupled plasma–optical emission spectrometry was used to measure the concentration of K+ in the urine samples. Pharmacokinetic parameters were analyzed using Phoenix WinNonlin software in a noncompartmental model. In either clinical trial, no significant differences were observed in the maximal rate of urinary excretion and cumulative urinary excretion from 0 to 24 hours of K+ between the reference and test drugs. The bioequivalence studies of both KCl sustained‐release tablet formulations were successfully conducted under different dietary conditions.
Improving the efficiency of graph algorithm executions on high‐performance computing
Authors: Moori Marcelo K. , de A. Rocha Hiago Mayk G. , Schwarzrock Janaina , Lorenzon Arthur F. , Beck Antonio Carlos S. 
Published: November 2022

SummaryThe growing need for extracting information from large graphs has been pushing the development of parallel graph algorithms. However, the highly irregular structure of the real‐world graphs limits the performance and energy improvements of graph applications. In this paper, we show that, in most cases, using all the available cores of the multiprocessor is not the best option in terms of the aforementioned non‐functional requirements. Based on that, we proposeGraphKat, a framework that enables the simultaneous processing of several algorithms/graphs instead of executing them serially (i.e., one after another), increasing efficiency in terms of performance and energy.GraphKatworks in two steps: (i) it characterizes the graph applications with a specific number of threads based on their efficiency levels; and (ii) it defines the execution order of all graph applications in the target system. Experimental results on three multicore processors (Intel and AMD) show thatGraphKatimproves the overall system's efficiency related to performance (up to ) and energy‐saving (up to 245.21), and reduces the graph applications' execution time (up to ) and energy consumption (up to 6.64) compared to the default execution of parallel applications on HPC systems.
LDP‐Fed+: A robust and privacy‐preserving federated learning based classification framework enabled by local differential privacy
Authors: Wang Yufeng , Zhang Xu , Ma Jianhua , Jin Qun 
Published: November 2022

AbstractAs a distributed learning framework, Federated Learning (FL) allows different local learners/participants to collaboratively train a joint model without exposing their own local data, and offers a feasible solution to legally resolve data islands. However, among them, the data privacy and model security are two challenges. The former means that, if original data are used for trained FL models, various methods can be used to deduce the original data samples, thereby causing the leakage of data. The latter implies that unreliable/malicious participants may affect or destroy the joint FL model, through uploading wrong local model parameters. Therefore, this paper proposes a novel distributed FL training framework, namely LDP‐Fed+, which takes into account differential privacy protection and model security defense. Specifically, firstly, a local perturbation module is added at the local learner side, which perturbs the original data of local learners through feature extraction, binary encoding and decoding, and random response. Then, through using the perturbed data, local neural network model is trained to obtain the network parameters that meet local differential protection, to effectively deal with model inversion attacks. Secondly, a security defense module is added on the server side, which uses the auxiliary model and differential index mechanism to select an appropriate number of local disturbance parameters for aggregation to enhance model security defense and deal with membership inference attacks. The experimental results show that, compared with other federated learning models based on differential privacy, LDP‐Fed+ has stronger robustness for model security and higher accuracy for model training while ensuring strict privacy protection.
A review on optimization techniques for medical image analysis
Authors: Kaur Palwinder , Singh Rajesh Kumar 
Published: November 2022

SummaryData mining of medical imaging approaches makes it difficult to determine their value in the disease's insight, analysis, and diagnosis. Image classification presents a significant difficulty in image analysis and plays a vital part in computer‐aided diagnosis. This task concerned the use of optimization techniques for the utilization of image processing, pattern recognition, and classification techniques, as well as the validation of image classification results in medical expert reports. The primary intention of this study is to analyze the performance of optimization techniques explored in the area of medical image analysis. For this motive, the optimization techniques employed in existing literature from 2012 to 2021 are reviewed in this study. The contribution of optimization‐based medical image classification and segmentation utilized image modalities, data sets, and tradeoffs for each technique are also discussed in this review study. Finally, this review study provides the gap analysis of optimization techniques used in medical image analysis with the possible future research direction.
Development of neural‐network‐based stereo bionic compound eyes with fiber bundles
Authors: Chen Juan , Chen Ying , Zhao Han , Ma Tao 
Published: November 2022

SummaryThe compound eye system has many unique advantages, enabling organisms to quickly and accurately obtain the three‐dimensional spatial information of the target. Therefore, its bionic applications have great potential in object localization, dynamic tracking, and 3D reconstruction. However, the current bionic compound eye systems still have great difficulties in target detection. Most of the bionic compound eye systems only verify the possibility of three‐dimensional (3D) detection, and there is no suitable calibration and detection scheme. In this article, a fiber‐optic stereo bionic compound eye is designed, and a simple and rapid calibration method suitable for this system is selected by comparing two effective optical information. Further, the system quantitatively analyzes the detection performance of the target to achieve precise positioning and dynamic trajectory tracking. In the static detection experiment, the average detection errors of the 3D position at the horizontal angle, the elevation angle and the depth are 0.33°, 0.32° and 1.84 mm, respectively. In the dynamic trajectory tracking experiment, the circle radius errors in the X‐Z plane and Y‐Z plane are 0.04 and 2.20 mm, respectively. The proposed system provides a new perspective to understand the nature of the compound eye.
Ionization of Cp2ZrMe2 and Lewis Bases by Methylaluminoxane: Computational Insights
Authors: Collins Scott , Linnolahti Mikko 
Published: November 2022

AbstractThe interactions of the Lewis bases CO, octamethyltrisiloxane (OMTS) and 2,2’‐bipyridine (bipy) with a sheet model for the principal activator (MeAlO)16(Me3Al)6 (16,6) in hydrolytic methylaluminoxane (MAO) were investigated by DFT. These studies reveal that OMTS and bipy form adducts with Me3Al prior to methide abstraction by 16,6 to form the ion‐pairs [Me2Al(κ2‐L)][16,6] (5: L=OMTS, 6: L=bipy, [16,6]−=[(MeAlO)16(Me3Al)6 Me]−) while CO simply binds to a reactive edge site without ionization. The binding and activation of Cp2ZrMe2 with 16,6 to form both neutral adducts 1 Cp2ZrMe2⋅16,6 and contact ion‐pairs 4 and 7, both with formula [Cp2ZrMe][μ‐Me(MeAlO)16(Me3Al)6], featuring terminal and chelated MAO‐anions, respectively was studied by DFT. The displacement of the anion with either excess Cp2ZrMe2 or Me3Al was also studied, forming outer‐sphere ion‐pairs [(Cp2ZrMe)2μ‐Me][16,6] (2) and [Cp2Zr(μ‐Me)2AlMe2][16,6] (3). The theoretical NMR spectra of these species were compared to experimental spectra of MAO and Cp2ZrMe2 and found to be in good agreement with the reported data and assignments. These studies confirm that 16,6 is a very suitable model for the activators present in MAO but highlight the difficulty in accurately calculating thermodynamic quantities for molecules in this size regime.
Synthesis of Phosphonates with Identical and Different Alkyl Groups by the Microwave‐Assisted Alkylation of Phosphonic Ester‐Acid Derivatives
Authors: Keglevich György , Harsági Nikoletta , Drahos László , Zsuzsa Kiss Nóra 
Published: November 2022

AbstractMonoalkyl phosphonic derivatives obtained by the microwave (MW)‐ and ionic liquid‐promoted direct esterification of alkylphosphonic acids were converted to the corresponding dialkyl alkylphosphonates on reaction with alkyl halides in the presence of triethylamine, under solvent‐free MW‐assisted conditions. Derivatives with different alkoxy groups were also synthesized. A minor “disproportionation” side reaction was identified during the preparation of dialkyl alkylphosphonates with different alkoxy groups. All together 12 alkylphosphonates were prepared by the efficient method developed.
Interheteromolecular Hyperconjugation Boosts (De)hydrogenation for Reversible H2Storage
Authors: Xue Wenjie , Liu Hongxia , Zhao Binbin , Tang Conghui , Xia Bao Yu , You Bo 
Published: November 2022

AbstractInterheteromolecular hyperconjugation is ubiquitous in organic systems, affecting bond length, dipole moments, conformations and so on, while its effect on (de)hydrogenation reactivity in a heterogeneous thermo‐catalytic system has rarely been explored. Herein, the N‐heterocycles containing a benzene ring and aliphatic chain [N‐ethylcarbazole (NEC) andN‐propylcarbazole (NPC)] were utilized to study the correlation between interheteromolecular hyperconjugation and catalytic (de)hydrogenation. Density functional theory calculations, variable‐temperature1H nuclear magnetic resonance spectroscopy, and catalytic experiments showed that the presented hyperconjugation between NEC and NPC weakened the electron cloud density of aromatic rings and thus facilitated the reactivity with hydrogen featuring unpaired electrons. Therefore, an extremely low temperature of 80 °C was enough for the hydrogenation. Moreover, this interheteromolecular hyperconjugation was general in other N‐heterocycles (e. g.,N‐methyindole and NPC) and was also effective to (de)deuterate as revealed by isotope experiments. This work expands the application of interheteromolecular hyperconjugation to heterogeneous thermocatalysis for reversible H2storage.
Mature B‐ and plasma‐cell flow cytometric analysis: A review of the impact of targeted therapy
Authors: Gao Qi , Chen Xueyan , Cherian Sindhu , Roshal Mikhail 
Published: November 2022

AbstractFlow cytometry has been indispensable in diagnosing B cell lymphoma and plasma cell neoplasms. The advances in novel multicolor flow cytometry have also made this technology a robust tool for monitoring minimal/measurable residual disease in chronic lymphocytic leukemia and multiple myeloma. However, challenges using conventional gating strategies to isolate neoplastic B or plasma cells are emerging due to the rapidly increasing number of antibody therapeutics targeting single or multiple classic B/plasma cell‐lineage markers, such as CD19, CD20, and CD22 in B cells and CD38 in plasma cells. This review is the first of a two‐part series that summarizes the most current targeted therapies used in B and plasma cell neoplasms and proposes detailed alternative approaches to overcome post‐targeted therapy analysis challenges by flow cytometry. The second review in this series (Chen et al.) focuses on challenges encountered in the use of targeted therapy in precursor B cell neoplasms.
A cytotoxic survey on 2‐amino‐1H‐imidazol based synthetic marine sponge alkaloid analogues
Authors: Gémes Nikolett , Makra Zsófia , Neuperger Patrícia , Szabó Enikő , Balog József Á. , Flink Lili Borbála , Kari Beáta , Hackler László , Puskás László. G. , Kanizsai Iván , Szebeni Gábor J. 
Published: November 2022

AbstractHere, we describe the synthesis and biologic activity evaluation of 20 novel synthetic marine sponge alkaloid analogues with 2‐amino‐1H‐imidazol (2‐AI) core. Cytotoxicity was tested on murine 4T1 breast cancer, A549 human lung cancer, and HL‐60 human myeloid leukemia cells by the resazurin assay. A total of 18 of 20 compounds showed cytotoxic effect on the cancer cell lines with different potential. Viability of healthy human fibroblasts and peripheral blood mononuclear cells upon treatment was less hampered compared to cancer cell lines supporting tumor cell specific cytotoxicity of our compounds. The most cytotoxic compounds resulted the following IC50 values 28: 2.91 µM on HL‐60 cells, and 29: 3.1 µM on 4T1 cells. The A549 cells were less sensitive to the treatments with IC50 15 µM for both 28 and 29. Flow cytometry demonstrated the apoptotic effect of the most active seven compounds inducing phosphatidylserine exposure and sub‐G1 fragmentation of nuclear DNA. Cell cycle arrest was also observed. Four compounds caused depolarization of the mitochondrial membrane potential as an early event of apoptosis. Two lead compounds inhibited tumor growth in vivo in the 4T1 triple negative breast cancer and A549 human lung adenocarcinoma xenograft models. Novel marine sponge alkaloid analogues are demonstrated as potential anticancer agents for further development.
Analytical profile of the lysergamide 1cP‐AL‐LAD and detection of impurities
Authors: Kavanagh Pierce V. , Westphal Folker , Pulver Benedikt , Schwelm Hannes M. , Stratford Alexander , Auwärter Volker , Chapman Stephen J. , Halberstadt Adam L. , Brandt Simon D. 
Published: November 2022

AbstractThe development of novel lysergamides continues to occur, based on both the needs of psychedelic medicine and commercial interest in new recreational substances. The present study continues the authors' research on novel lysergamides and describes the analytical profile of 1‐cyclopropanoyl‐AL‐LAD (IUPAC name: 1‐(cyclopropanecarbonyl)‐N,N‐diethyl‐6‐(prop‐2‐en‐1‐yl)‐9,10‐didehydroergoline‐8β‐carboxamide; 1cP‐AL‐LAD), using various chromatographic, mass spectrometric, and spectroscopic methods. Analysis of a powdered sample of 1cP‐AL‐LAD, obtained from an online vendor, by high performance liquid chromatography‐electrospray ionization‐quadrupole time‐of‐flight mass spectrometry in full scan/AutoMS/MS mode revealed the detection of 17 impurities based on high‐resolution tandem mass spectral data; tentative determination of their identity was based on mass spectral grounds alone, though detection of AL‐LAD and 1P‐AL‐LAD was confirmed using available reference standards. Other tentative compound identifications included 1‐acetyl‐AL‐LAD and several other substances potentially reflecting oxidation of the N6‐allyl group as well as other positions on the ergoline ring system. These data may assist those interested in the chemistry of lysergamides. Finally, 1cP‐AL‐LAD was also detected in samples of “blotters” sold online for recreational use.
Community‐level phylogenetic diversity does not differ between rare and common lineages across tallgrass prairies in the northern Great Plains
Authors: Herzog Sarah A. , Latvis Maribeth 
Published: November 2022

AbstractNiche differentiation has served as one explanation for species coexistence, and phylogenetic relatedness provides a means to approximate how ecologically similar species are to each other. To explore the contribution of rare species to community phylogenetic diversity, we sampled 21 plant communities across the Prairie Coteau ecoregion, an area of high conservation concern. We used breakpoint analysis through the iterative addition of less abundant species to the phylogenetic tree for each community to assess the contribution of rare species to community phylogenetic diversity. We also quantify the phylogenetic signal of abundance using Blomberg's K statistic and calculated the phylogenetic similarity between rare and common species using a phylogenetic beta‐diversity metric (Dnn). To estimate the phylogenetic structuring of these prairie communities, we calculated two common metrics that capture evolutionary relatedness at different scales (MPD and MNTD). Additionally, we examine the correlation between Faith's PD, MPD, and MNTD and species richness. We found rare species do not generally contribute higher levels of phylogenetic diversity than common species. Eight communities had significant breakpoints, with only four communities having an increasing trend for the rarest species. The phylogenetic signal for abundance was low but significant in only four communities, and communities had lower phylogenetic diversity than expected from the regional species pool. Finally, the strength of the correlation between species richness and phylogenetic diversity was mixed. Our results indicate niche differentiation does not explain the persistence of rare species in tallgrass prairies, as they were more closely related than expected from random, suggesting high functional redundancy between rare and common species. This is promising for the long‐term resilience of this ecosystem, but only insofar as enough species remain in the system. With ongoing biodiversity loss, it is essential that we understand the role rare species play in their communities.