bluuebunny/crossref_metadata_2025_split huggingface dataset:
Brain Tumor Classification using Machine Learning
Authors: Pavitha N. , Bakde Atharva , Avhad Shantanu , Korate Isha , Mahajan Shaunak , Padole Rudraksha 
Published: January 2022

This paper presents a technical analysis of tumor data with Machine Learning and Classification Approach. Feature parameters which are dependent for classification of tumor are used for analyzing and classifying the class of tumor. In the classification of tumor, KNN-Classifier is implemented with cross validating accuracy score and tuning hyper parameters. Experimental simulation for best average score for K makes it to the cross validation. Approaching the prediction with the best accuracy score, hyper parameters of KNN Classifier states the best score. Using Principal Component Analysis on the data, miss-classification of tumor class in data is visualized.
Aims: To declare and analyse tumor data from the source of MRI, CT scan, etc. for medication of tumor. To utilize smart predictions for the upcoming tumor patients using Machine Learning.
Study Design: Tumor classification using K Nearest Neighbor algorithm and analysis of the miss-classification.
Methodology: We included 11 different studies and research papers which were relevant with tumor classification. Research papers include classification of tumors with different supervised learning approaches. Our proposed analysis and classification give visualization of two classes of tumor.
Results: The Project results in classification of tumor data using Machine Learning and analyzing the miss-classification of tumor. In implementation of KNN Algorithm, the accuracy score after cross validation and tuning K values is 0.97. The confusion matrix shows 4 false positives and 1 false negative value in testing.
Conclusion: Less miss-classification of tumor results best accuracy score and more efficient working on testing data. Visualizing the classification with 3-dimensional scatter plots made the analysis accurate.
Light‐Triggered Efficient Sequential Drug Delivery of Biomimetic Nanosystem for Multimodal Chemo‐, Antiangiogenic, and Anti‐MDSC Therapy in Melanoma
Authors: Lai Xing , Liu Xue‐Liang , Pan Hong , Zhu Mao‐Hua , Long Mei , Yuan Yihang , Zhang Zhong , Dong Xiao , Lu Qin , Sun Peng , Lovell Jonathan F. , Chen Hong‐Zhuan , Fang Chao 
Published: January 2022

AbstractIn view of the multiple pathological hallmarks of tumors, nanosystems for the sequential delivery of various drugs whose targets are separately located inside and outside tumor cells are desired for improved cancer therapy. However, current sequential delivery is mainly achieved through enzyme‐ or acid‐dependent degradation of the nanocarrier, which would be influenced by the heterogeneous tumor microenvironment, and unloading efficiency of the drug acting on the target outside tumor cells is usually unsatisfactory. Here, a light‐triggered sequential delivery strategy based on a liposomal formulation of doxorubicin (DOX)‐loaded small‐sized polymeric nanoparticles (DOX‐NP) and free sunitinib in the aqueous cavity, is developed. The liposomal membrane is doped with photosensitizer porphyrin–phospholipid (PoP) and hybridized with red blood cell membrane to confer biomimetic features. Near‐infrared light‐induced membrane permeabilization triggers the “ultrafast” and “thorough” release of sunitinib (100% release in 5 min) for antiangiogenic therapy and also myeloid‐derived suppressor cell (MDSC) inhibition to reverse the immunosuppressive tumor environment. Subsequently, the small‐sized DOX‐NP liberated from the liposomes is more easily uptaken by tumor cells for improved immunogenic chemotherapy. RNA sequencing and immune‐related assay indicates therapeutic immune enhancement. This light‐triggered sequential delivery strategy demonstrates the potency in cancer multimodal therapy against multiple targets in different spatial positions in tumor microenvironment.
Fabrication of Stretchable Superamphiphobic Surfaces with Deformation‐Induced Rearrangeable Structures
Authors: Zhou Xiaoteng , Liu Jie , Liu Wendong , Steffen Werner , Butt Hans‐Jürgen 
Published: January 2022

AbstractStretchable superamphiphobic surfaces with a high deformation resistance are in demand to achieve liquid‐repellent performance in flexible electronics, artificial skin, and textile dressings. However, it is challenging to make mechanically robust superamphiphobic coatings, which maintain their superliquid repellency in a highly stretched state. Here, a stretchable superamphiphobic surface is reported, on which the microstructures can rearrange during stretching to maintain a stable superamphiphobicity even under a high tensile strain. The surface is prepared by spray‐coating silicone nanofilaments onto a prestretched substrate (e.g., cis‐1,4‐polyisoprene) with poly(dimethylsiloxane) (PDMS) layer as a binder. After subsequent fluorination, this surface keeps its superamphiphobicity to both water and n‐hexadecane up to the tensile strain of at least 225%. The binding PDMS layer and rearrangeable structures maximize the deformation resistance of the surface during the stretching process. The superamphiphobicity and morphology of the surface are maintained even after 1000 stretch–release cycles. Taking advantage of the mentioned benefits, a liquid manipulation system is designed, which has the potential for fabricating reusable and low‐cost platforms for biochemical detection and lab‐on‐a‐chip systems.
Lymph‐Directed Self‐Immolative Nitric Oxide Prodrug for Inhibition of Intractable Metastatic Cancer
Authors: Kim Taejeong , Suh Jeeyeon , Kim Jihoon , Kim Won Jong 
Published: January 2022

AbstractThere has been a significant clinical demand for lymph‐directed anti‐metastatic therapy as tumor‐draining lymph nodes play pivotal roles in cancer metastasis which accounts for more than 90% of tumor‐related deaths. Despite the high potential of nitric oxide (NO) in anti‐cancer therapy owing to its biocompatibility and tumor cell‐specific cytotoxicity, the poor stability and lack of target specificity of present NO donors and delivery systems have limited its clinical applications. Herein, a redox‐triggered self‐immolative NO prodrug that can be readily conjugated to various materials containing free thiol groups such as albumin, is reported. The prodrug and its conjugates demonstrate smart release of NO donor via intramolecular cyclization under reductive conditions, followed by spontaneously generating NO in physiological conditions. The albumin‐prodrug conjugate inhibits tumor metastasis by inducing cytotoxicity preferentially on tumor cells after efficiently draining into lymph nodes. This novel prodrug can contribute to the development of on‐demand NO delivery systems for anti‐metastatic therapy and other treatments.
Light‐Triggered In Situ Biosynthesis of Artificial Melanin for Skin Protection
Authors: Lee Uk‐Jae , Ko Junghyeon , Kim Su‐Hwan , Lee Pyung‐Gang , An Young‐Hyeon , Yun Hyungdon , Flood Dillon T. , Dawson Philip E. , Hwang Nathaniel S. , Kim Byung‐Gee 
Published: January 2022

AbstractTyrosinase‐mediated melanin synthesis is an essential biological process that can protect skin from UV radiation and radical species. This work reports on in situ biosynthesis of artificial melanin in native skin using photoactivatable tyrosinase (PaTy). The I41Y mutant of Streptomyces avermitilis tyrosinase (SaTy) shows enzymatic activity comparable to that of wild‐type SaTy. This Y41 is replaced with photocleavable o‐nitrobenzyl tyrosine (ONBY) using the introduction of amber codon and ONBY‐tRNA synthetase/tRNA pairs. The ONBY efficiently blocks the active site and tyrosinase activity is rapidly recovered by the photo‐cleavage of ONBY. The activated PaTy successfully oxidizes L‐tyrosine and tyramine‐conjugated hyaluronic acid (HA_T) to synthesize melanin particles and hydrogel, respectively. To produce artificial melanin in living tissues, PaTy is encapsulated into lipid nanoparticles as an artificial melanosome. Using liposomes containing PaTy (PaTy_Lip), PaTy is transdermally delivered into ex vivo porcine skin and in vivo mouse skin tissues, thus achieving the in situ biosynthesis of artificial melanin for skin tissue protection under UV irradiation. The results of this study demonstrate that this biomimetic system can recapitulate the biosynthetic analogs of naturally occurring melanin. It should therefore be considered to be a promising strategy for producing protective biological molecules within living systems for tissue protection.
A Weavable and Scalable Cotton‐Yarn‐Based Battery Activated by Human Sweat for Textile Electronics
Authors: Xiao Gang , Ju Jun , Lu Hao , Shi Xuemei , Wang Xin , Wang Wei , Xia Qingyou , Zhou Guangdong , Sun Wei , Li Chang Ming , Qiao Yan , Lu Zhisong 
Published: January 2022

AbstractSweat‐activated batteries (SABs) are lightweight, biocompatible energy generators that produce sufficient power for skin‐interface electronic devices. However, the fabrication of 1D SABs that are compatible with conventional textile techniques for self‐powered wearable electronics remains challenging. In this study, a cotton‐yarn‐based SAB (CYSAB) with a segmental structure is developed, in which carbon‐black‐modified, pristine yarn and Zn foil‐wrapped segments are prepared to serve as the cathode, salt bridge, and anode, respectively. Upon electrolyte absorption, the CYSAB can be rapidly activated. Its performance is closely related to the ion concentration, infiltrated electrolyte volume, and evaporation rate. The CYSAB can tolerate repeated bending and washing without any significant influence on its power output. Moreover, the CYSABs can be woven into fabrics and connected in series and parallel configurations to produce an energy supplying headband, which can be activated by the sweat secreted from a volunteer during a cycling exercise to power light‐emitting diode headlights. The developed CYSAB can also be integrated with yarn‐based strain sensors to achieve a smart textile for the self‐powered sensing of human motion and breathing. This weavable, washable, and scalable CYSAB is expected to contribute to the manufacturing of self‐powered smart textiles for future applications in wearable healthcare monitoring.
Time‐Resolved Encryption via a Kinetics‐Tunable Supramolecular Photochromic System
Authors: Li Dong , Feng Zefen , Han Yujie , Chen Chen , Zhang Qi‐Wei , Tian Yang 
Published: January 2022

AbstractWith the advancement of forgery and decryption methods, conventional static encryption technology is becoming more and more powerless, which strongly demands the development of multistate anticounterfeiting materials as well as advanced multidimensional encryption strategies and technologies. Here a new strategy to realize time‐resolved encryption based on a self‐assembled supramolecular ternary complex is presented, which exhibits tunable dynamic photochromic features caused by the reversible photodimerization/cleavage reactions of the guest chromophores inside the cavity of cucurbit[8]uril (CB[8]). This supramolecular system shows excellent photochromic properties, including extremely rapid response time, high conversion rate, and product‐stereoselectivity, etc. More importantly, the kinetics of the photoreaction can be modulated by simply varying the host–guest ratios in aqueous or quasi‐solid phase, providing the material with finely tunable time‐dependent features, which cannot only be employed in data processing with more extended information, but also construct confidential materials by time‐resolved multidimensional encryption and dynamic anticounterfeiting. The strategic design of kinetics‐tunable supramolecular photochromic materials may provide valuable guidance for the development of more advanced materials for information security.
Subthreshold Swing of 59 mV decade−1 in Nanoscale Flexible Ultralow‐Voltage Organic Transistors
Authors: Geiger Michael , Lingstädt Robin , Wollandt Tobias , Deuschle Julia , Zschieschang Ute , Letzkus Florian , Burghartz Joachim N. , van Aken Peter A. , Weitz R. Thomas , Klauk Hagen 
Published: January 2022

AbstractOrganic thin‐film transistors (TFTs) that provide subthreshold swings near the theoretical limit together with large on/off current ratios at very low operating voltages require high‐capacitance gate dielectrics with a vanishingly small defect density. A promising approach to the fabrication of such dielectrics at temperatures sufficiently low to allow TFT fabrication on polymeric substrates are hybrid dielectrics consisting of a thin metal oxide layer in combination with a molecular self‐assembled monolayer (SAM). Here, the electrical and surface properties of titanium oxide produced by the plasma‐assisted oxidation of the surface of vacuum‐deposited titanium gate electrodes and its use as the first component of a hybrid TiOx/SAM gate dielectric in flexible organic TFTs are investigated. These transistors have a gate‐dielectric capacitance of about 1 µF cm−2, a subthreshold swing of 59 mV decade−1 (within measurement error of the physical limit at room temperature) for a wide range of channel lengths as small as 0.7 µm, and an on/off current ratio of 107 for a gate‐source‐voltage range of 1 V.
Comparing the agronomic effectiveness of fertilizers on the basis of the rate of application: An interpretation
Authors: Chien Sen H. 
Published: January 2022

AbstractSeveral recently developed and marketed fertilizers containing S and/or Zn that have been evaluated for their agronomic effectiveness are often compared with the traditional standard fertilizers on the basis of a single high fertilizer rate of the tested nutrient in terms of crop yield. This approach can provide an inaccurate or misleading conclusion that they are as good as or even better than the standard fertilizer sources. A better and more accurate method is to compare the different fertilizer sources on the basis of multiple rates. In addition, measurements of the nutrient taken up by a crop can provide useful information to help achieve the objectives of a study.
Deep learning classification of inverted papilloma malignant transformation using 3D convolutional neural networks and magnetic resonance imaging
Authors: Liu George S. , Yang Angela , Kim Dayoung , Hojel Andrew , Voevodsky Diana , Wang Julia , Tong Charles C.L. , Ungerer Heather , Palmer James N. , Kohanski Michael A. , Nayak Jayakar V. , Hwang Peter H. , Adappa Nithin D. , Patel Zara M. 
Published: January 2022

AbstractBackgroundDistinguishing benign inverted papilloma (IP) tumors from those that have undergone malignant transformation to squamous cell carcinoma (IP‐SCC) is important but challenging to do preoperatively. Magnetic resonance imaging (MRI) can help differentiate these 2 entities, but no established method exists that can automatically synthesize all potentially relevant MRI image features to distinguish IP and IP‐SCC. We explored a deep learning approach, using 3‐dimensional convolutional neural networks (CNNs), to address this challenge.MethodsRetrospective chart reviews were performed at 2 institutions to create a data set of preoperative MRIs with corresponding surgical pathology reports. The MRI data set included all available MRI sequences in the axial plane, which were used to train, validate, and test 3 CNN models. Saliency maps were generated to visualize areas of MRIs with greatest influence on predictions.ResultsA total of 90 patients with IP (n = 64) or IP‐SCC (n = 26) tumors were identified, with a total of 446 images of distinct MRI sequences for IP (n = 329) or IP‐SCC (n = 117). The best CNN model, All‐Net, demonstrated a sensitivity of 66.7%, specificity of 81.5%, overall accuracy of 77.9%, and receiver‐operating characteristic area under the curve of 0.80 (95% confidence interval, 0.682‐0.898) for test classification performance. The other 2 models, Small‐All‐Net and Elastic‐All‐Net, showed similar performance levels.ConclusionA deep learning approach with 3‐dimensional CNNs can distinguish IP and IP‐SCC with moderate test classification performance. Although CNNs demonstrate promise to enhance the prediction of IP‐SCC using MRIs, more data are needed before they can reach the predictive value already established by human MRI evaluation.
Polyelectrolyte complex nanofiltration membranes by surface deposition of polyethylenimine on polyanion supports
Authors: Li Chenwei , Hu Dujuan , Liu Ling , Zhu Li , Xu Man , Wang Cunwen , Li Yanbo 
Published: January 2022

AbstractPolyethylenimine (PEI) is a water‐soluble polymer with higher density of cations and strong adsorbability. In the present work, loose nanofiltration membranes based on PEI were prepared by surface deposition of PEI on hydrolyzed polyacrylonitrile (PANH) supports through a surface crosslink method without using extra crosslinkers. The thinner selective layer (around 50 nm) of polyelectrolyte complex was formed through the crosslinks (ionic and co‐valent) between amine groups in PEI and carboxyl groups in PANH. The PEI/PAN membranes were characterized by SEM, ATR‐IR, and XPS. Nanofiltration of PEGs and salt solutions were performed to evaluate the membrane performance. Loose nanofiltration membranes, with molecular weight cut‐off from 500 to 950 Da, permeances form 7 to 16 L m−2 h−1 bar−1, were obtained by the simple surface crosslink method. The membrane performance can be facile tailored by controlling the deposition parameters, including deposition time and post‐treatments.
Robust Anion‐Shielding Metal‐Organic Frameworks Based Composite Interlayers To Achieve Uniform Li Deposition for Stable Li‐Metal Anode
Authors: Li Chuanfu , Lu Ruichao , Amin Kamran , Zhang Binbin , Liu Hao , Zheng Wei , Guo Jinze , Du Peiyao , Mao Lijuan , Lu Xiaoquan , Wei Zhixiang 
Published: January 2022

AbstractLithium (Li) metal is one of the most promising anode candidates for next‐generation rechargeable batteries with a high energy density because of its high theoretical specific capacity and low reduction potential. However, the growth of lithium dendrites causes intolerable safety risks and poor electrochemical performances. Limiting the migration of anions near the Li‐metal anode is regarded as an effective strategy to achieve uniform Li deposition and thus suppress the dendritic growth. Here, a metal‐organic frameworks based polyvinylidene fluoride‐hexafluoropropylene copolymer composite interlayer is designed and prepared through an in situ heat‐assisted method. Being guided and assisted by the anion‐shielded composite interlayer, a homogenous Li‐ion flux as well as a high Li‐ion transference number are achieved, which promotes the uniform and stable lithium deposition. At the same time, the robust composite interlayer is expected to suppress the growth of Li dendrites by its intrinsically high modulus (≈2.49 GPa). As a result, symmetrical batteries exhibit excellent cyclic stability for over 1600 h with functional composite interlayer. Furthermore, full cells with a commercial lithium iron phosphate cathode demonstrate remarkably enhanced cycling performance with a satisfying capacity retention of 96 % after 500 cycles. This work provides an effective strategy for the design and fabrication of functional composite interlayers to realize stable lithium metal anodes.
Simultaneous Analysis of Ascorbic Acid, Uric Acid, and Dopamine at Bare Polystyrene Thermoplastic Electrodes
Authors: McCord Cynthia P. , Summers Bali , Henry Charles S. 
Published: January 2022

AbstractSimultaneous analysis of ascorbic acid (AA), uric acid (UA), and dopamine (DA) is desirable as they are important biomarkers that coexist in biological fluids. Electrochemical detection of these molecules can be challenging as their oxidation peaks are poorly resolved at most unmodified electrodes. Modifications are typically necessary for analysis which increase cost and complicate fabrication. Herein, inexpensive and moldable polystyrene thermoplastic electrodes (PS TPEs), that can be used for simultaneous analysis of AA, DA, and UA, without surface modification, were presented. Electrode composition was optimized for conductivity, capacitance, and electrochemistry. Detection limits were 4, 0.9, and 4 μm for AA, DA and UA respectively. PS TPEs were also challenged in urine; excellent recoveries (90–110 %) were seen for AA and UA. DA showed lower recoveries (82 % for 10 μm and 47 % for 5 μm), but in the future, higher recoveries can be achieved through design considerations and further material optimization.
Performance enhancement‐based active learning sample selection method
Authors: He Zhonghai , Song Shijie , Shen Kun , Zhang Xiaofang 
Published: January 2022

AbstractRepresentative samples are important for multivariate calibration. The highly efficient selection of representative samples to be labelled can save money and time. Existing methods, such as Kennard‐Stone and net analyte signal selection, are usually based on the distance between candidate samples and labelled calibration sets in feature space. However, these distances are influenced by the feature space, which is spanned by an information vector extracted from labelled samples. To overcome the negative effects of the distance‐based selection method, a model performance enhancement‐based sample selection method is proposed to select calibration samples efficiently. Based on loss function optimization, the samples that can improve model performance the most, as estimated by bootstrap, are sequentially selected and added to the calibration set. Due to the high representation of each sample, a few samples can build a model that has no significant loss of prediction ability when compared with a model built with the large number set of calibration samples. The performance enhancement‐based active learning (PEAL) sample selection method is both effective and efficient.
Gender and risk‐taking behaviors influence the clinical presentation of oral squamous cell carcinoma
Authors: Wolfer Susanne , Kunzler Annika , Foos Tatjana , Ernst Cornelia , Leha Andreas , Schultze‐Mosgau Stefan 
Published: January 2022

AbstractObjectiveThe common risk factors for oral squamous cell carcinoma (OSCC) are smoking and alcohol abuse. A small percentage of patients, mostly women, are demonstrating oral cancer without the common risk behavior. This study investigates how gender and different patterns of lifestyle factors influence the clinical presentation of OSCC.Patients and MethodsFrom this retrospective study, demographical and tumor‐specific data and lifestyle factors were analyzed. Statistical analyses were performed using the χ2 test or Fisher's exact test for categorical analysis and the t test, ANOVA test, or Kruskal–Wallis test for continuous variables. The influence of the respective lifestyle factors together with their interactions with the gender on tumor characteristics has been tested using logistic and ordinal cumulative link regression models.ResultsAmong a total of 308 patients, men represented the majority of smokers (87.2%) and the female cohort were largely non‐smokers and non‐drinkers (64.9%). For age, tumor site and N‐stage it looks like that differences of men and women are driven by the different risk behavior. But if the lifestyle factors are taken into account, we observe contrary effects between men and women for T‐, N‐, and UICC‐stage. For different cancer locations we saw opposite effects with gender and risk profile. These effects are not dose‐dependent explainable for gender.ConclusionSome but not all differences in the development of OSCC for men and women are explainable by the respective difference in lifestyle behavior. Some further investigations are necessary to find explanations for the obvious differences between men and women in developing OSCC.