bluuebunny/crossref_metadata_2025 huggingface dataset:
A study of nitrogen dioxide (NO2) periodicity over the United Arab Emirates using wavelet analysis
Authors: Al Yammahi Aishah , Aung Zeyar
Published: October 2022

NO2and nitric oxide (NO) are the most reactive gases in the atmosphere. The interaction of NOxmolecules with oxygen, water and other chemicals leads to the formation of acid rain. The presence of NO2in the air affects human health and forms a photochemical smog. In this study, we utilize wavelet analysis, namely, the Morlet wavelet, which is a type of continuous wavelet transform, to conduct a spectral analysis of the periodicity of nitrogen dioxide (NO2). The study is conducted using data from 14 weather stations located in diverse geographic areas of the United Arab Emirates (UAE) over a period of two years (2019 and 2020). We explain and relate the significance of human activities to the concentration level of NO2, particularly considering the effect of the COVID-19 lockdown to the periodicity of NO2. The results show that NO2concentrations in desert areas such as Liwa and Al Quaa were unaffected by the lockdown period (April–July 2020) resulting from the COVID-19 pandemic. The other stations in the urban areas of Abu Dhabi city, Al Dhafra and Al Ain, showed a reduction in NO2during the lockdown. NO2is more highly concentrated during winter seasons than during other seasons. The periodicity of NO2lasted from a few days up to 16 days in most regions. However, some stations located in the Al Dhafra region, such as Al Ruwais and the Gayathi School stations, exhibited a longer period of more than 32 days with a 0.05 significance test. In the Abu Dhabi region, NO2lasted between 64 and 128 days at the Al Mafraq station. The correlation between the NO2concentration across several ground stations was studied using wavelet coherence.
Design considerations for photovoltaic energy harvesting in wearable devices
Authors: Kim Katherine A. , Bagci F. Selin , Dorsey Kristen L.
Published: October 2022

Wearable technology is emerging as a solution for various bio-mechanic and health-metric applications. Solar photovoltaic energy is a viable supplemental power source that can reduce battery size requirements in wearables. This study outlines the considerations for a wearable sleeve device and its associated power converter system using commercially-available flexible photovoltaic panels located on the forearm. Investigation of the effects of curvature shows that while curvature of the panel around a forearm does reduce output power, the angle relative to the light source has a more pronounced effect on both output power and voltage characteristics. Among various panel arrangements on the forearm, that with five individual panels of smaller width provided the highest output power after the boost converter power stage. Testing under various static positions, the PV sleeve provided up to 94 mW outdoors, which can effectively reduce the battery size while maintaining user safety.
Real-time COVID-19 detection via graphite oxide-based field-effect transistor biosensors decorated with Pt/Pd nanoparticles
Authors: Wasfi Asma , Awwad Falah , Qamhieh Naser , Al Murshidi Badria , Palakkott Abdul Rasheed , Gelovani Juri George
Published: October 2022

Coronavirus 2019 (COVID-19) spreads an extremely infectious disease where there is no specific treatment. COVID-19 virus had a rapid and unexpected spread rate which resulted in critical difficulties for public health and unprecedented daily life disruption. Thus, accurate, rapid, and early diagnosis of COVID-19 virus is critical to maintain public health safety. A graphite oxide-based field-effect transistor (GO-FET) was fabricated and functionalized with COVID-19 antibody for the purpose of real-time detection of COVID-19 spike protein antigen. Thermal evaporation process was used to deposit the gold electrodes on the surface of the sensor substrate. Graphite oxide channel was placed between the gold electrodes. Bimetallic nanoparticles of platinum and palladium were generated via an ultra-high vacuum (UHV) compatible system by sputtering and inert-gas condensation technique. The biosensor graphite oxide channel was immobilized with specific antibodies against the COVID-19 spike protein to achieve selectivity and specificity. This technique uses the attractive semiconductor characteristics of the graphite oxide-based materials resulting in highly specific and sensitive detection of COVID-19 spike protein. The GO-FET biosensor was decorated with bimetallic nanoparticles of platinum and palladium to investigate the improvement in the sensor sensitivity. The in-house developed biosensor limit of detection (LOD) is 1 fg/mL of COVID-19 spike antigen in phosphate-buffered saline (PBS). Moreover, magnetic labelled SARS-CoV-2 spike antibody were studied to investigate any enhancement in the sensor performance. The results indicate the successful fabrication of a promising field effect transistor biosensor for COVID-19 diagnosis.
Serum biomarkers, including nitric oxide metabolites (NOx), for prognosis of cardiovascular death and acute myocardial infarction in an ESSE-RF case–control cohort with 6.5-year follow up
Authors: Gumanova Nadezhda G. , Bogdanova Natalya L. , Metelskaya Victoria A. , Tarasov Vladimir I. , Kots Alexander Ya. , Kutsenko Vladimir A. , Kontsevaya Anna V. , Drapkina Oksana M.
Published: October 2022

The present case–control study aimed to assess associations of routine and experimental biomarkers with risk for cardiovascular death and acute myocardial infarction (AMI) in a cohort recruited from the multicenter study “Cardiovascular Epidemiology in Russian Federation” (ESSE-RF) to identify experimental biomarkers potentially suitable for expanded evaluation. A total of 222 subjects included cardiovascular death (N = 48) and AMI cases (N = 63) during 6.5-year follow up and matched healthy controls. Seven routine and eight experimental biomarkers were assayed to analyze associations with outcomes using logistic and Cox proportional hazard regressions. Elevated levels of cardiac troponin I (cTnI), C-reactive protein (CRP), and nitric oxide metabolites (NOx) were independently associated (P < 0.001) with higher risk of cardiovascular death (estimated hazard ratio (eHR) = 1.83–3.74). Elevated levels of NOx and cTnI were independently (P < 0.001) associated with higher risk of nonfatal AMI (eHRs = 1.78–2.67). Elevated levels of angiopoietin-like protein 3 (ANGPTL3) were independently associated (P < 0.001) with lower risk of cardiovascular death (eHRs 0.09–0.16) and higher risk of nonfatal AMI (eHR = 2.07; P = 0.01). These results indicated that subsequent expanded validation should focus on predictive impact of cTnI, NOx, CRP, and ANGPTL3 to develop nationwide recommendations for individual stratification of patients with cardiovascular risks.
The clinical and pathological features of low-grade epilepsy-associated glioneuronal tumors
Authors: Xie Mingguo , Wang Xiongfei , Qiao Jiao , Zhou Jian , Guan Yuguang , Li Tianfu , Qi Xueling , Luan Guoming
Published: October 2022

The aim of the study was to evaluate the clinicopathological features, as well as the surgical prognosis, of epilepsy-associated glioneuronal tumors (GNT) with CD34 expression and BRAF mutation. Clinical data of patients who underwent epilepsy surgery for GNT were retrospectively studied. Univariate and multivariate analyses were performed to evaluate the correlations of clinical and pathological factors with molecular markers of CD34 expression and BRAFV600E mutation in GNT. A total of 247 patients with GNT had immunohistochemical detection of CD34 expression (CD34 positive vs. negative: 198/49), and among them, 102 patients had immunohistochemical detection of BRAFV600E mutation (BRAF positive vs. negative: 59/43). Univariate analysis found that tumor types (P < 0.001), patient population (P = 0.015), seizure aura (P = 0.007), drug-resistant epilepsy (P = 0.036), concordance of ictal electroencephalogram (EEG) findings (P = 0.032), surgical resection extent (P = 0.045), tumor location (P = 0.007) and duration of epilepsy (P = 0.027) were related to CD34 expression, and that concordance of ictal EEG findings (P = 0.031) and age at surgery (P = 0.015) were related to BRAFV600E mutation. In addition, history of generalized tonic–clonic seizure (HR 0.12; P = 0.035), drug-resistant epilepsy (HR 0.13; P = 0.030) and concordance of interictal EEG findings (HR 8.01; P = 0.039) were associated with tumor progression-free survival (PFS). However, CD34 expression or BRAFV600E mutation in GNT was not associated with surgical outcomes of seizure control and tumor PFS. The CD34 expression or BRAFV600E mutation in GNT may partly influence the distribution of clinicopathological features of patients with epilepsy, but they may be not able to predict the surgical prognosis of seizure outcome and tumor recurrence.
A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study
Authors: Cisterna-García Alejandro , Guillén-Teruel Antonio , Caracena Marcos , Pérez Enrique , Jiménez Fernando , Francisco-Verdú Francisco J. , Reina Gabriel , González-Billalabeitia Enrique , Palma José , Sánchez-Ferrer Álvaro , Botía Juan A.
Published: October 2022

The development of tools that provide early triage of COVID-19 patients with minimal use of diagnostic tests, based on easily accessible data, can be of vital importance in reducing COVID-19 mortality rates during high-incidence scenarios. This work proposes a machine learning model to predict mortality and risk of hospitalization using both 2 simple demographic features and 19 comorbidities obtained from 86,867 electronic medical records of COVID-19 patients, and a new method (LR-IPIP) designed to deal with data imbalance problems. The model was able to predict with high accuracy (90–93%, ROC-AUC = 0.94) the patient's final status (deceased or discharged), while its accuracy was medium (71–73%, ROC-AUC = 0.75) with respect to the risk of hospitalization. The most relevant characteristics for these models were age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure. Finally, to facilitate its use by clinicians, a user-friendly website has been developed (https://alejandrocisterna.shinyapps.io/PROVIA).
Anthropogenic pollution gradient along a mountain river affects bacterial community composition and genera with potential pathogenic species
Authors: Lenart-Boroń Anna , Boroń Piotr , Kulik Klaudia , Prajsnar Justyna , Żelazny Mirosław , Chmiel Maria J.
Published: October 2022

Mountain regions in Poland are among the most frequently visited tourist destinations, causing a significant anthropogenic pressure put on the local rivers. In this study, based on numbers of 9 microorganisms, content of 17 antibiotics and 17 physicochemical parameters, we determined a pollution gradient in six sites along Białka, a typical mountain river in southern Poland. The E.coli/Staphylococcus ratio varied evidently between polluted and non-polluted sites, indicating that the possible utility of this parameter in assessing the anthropogenic impact on river ecosystems is worth further investigation. Then, using next generation sequencing, we assessed the changes in bacterial community structure and diversity as a response to the pollution gradient. Proteobacteria and Bacteroidetes were the most abundant phyla in the majority of samples. Actinobacteria were the most abundant in the most pristine (groundwater) sample, while Firmicutes and Verrucomicrobia were more prevalent in polluted sites. Bacterial diversity at various levels increased with water pollution. Eleven bacterial genera potentially containing pathogenic species were detected in the examined samples, among which Acinetobacter, Rhodococcus, and Mycobacterium were the most frequent. At the species level, Acinetobacter johnsonii was most prevalent potential pathogen, detected in all surface water samples, including the pristine ones. Two bacterial taxa—genus Flectobacillus and order Clostridiales showed very distinct variation in the relative abundance between the polluted and non-polluted sites, indicating their possible potential as biomarkers of anthropogenic impact on mountain river waters.
Skin lesion classification of dermoscopic images using machine learning and convolutional neural network
Authors: Shetty Bhuvaneshwari , Fernandes Roshan , Rodrigues Anisha P. , Chengoden Rajeswari , Bhattacharya Sweta , Lakshmanna Kuruva
Published: October 2022

Detecting dangerous illnesses connected to the skin organ, particularly malignancy, requires the identification of pigmented skin lesions. Image detection techniques and computer classification capabilities can boost skin cancer detection accuracy. The dataset used for this research work is based on the HAM10000 dataset which consists of 10015 images. The proposed work has chosen a subset of the dataset and performed augmentation. A model with data augmentation tends to learn more distinguishing characteristics and features rather than a model without data augmentation. Involving data augmentation can improve the accuracy of the model. But that model cannot give significant results with the testing data until it is robust. The k-fold cross-validation technique makes the model robust which has been implemented in the proposed work. We have analyzed the classification accuracy of the Machine Learning algorithms and Convolutional Neural Network models. We have concluded that Convolutional Neural Network provides better accuracy compared to other machine learning algorithms implemented in the proposed work. In the proposed system, as the highest, we obtained an accuracy of 95.18% with the CNN model. The proposed work helps early identification of seven classes of skin disease and can be validated and treated appropriately by medical practitioners.
Relative importance of composition structures and biologically meaningful logics in bipartite Boolean models of gene regulation
Authors: Yadav Yasharth , Subbaroyan Ajay , Martin Olivier C. , Samal Areejit
Published: October 2022

Boolean networks have been widely used to model gene networks. However, such models are coarse-grained to an extent that they abstract away molecular specificities of gene regulation. Alternatively,bipartiteBoolean network models of gene regulation explicitly distinguish genes from transcription factors (TFs). In such bipartite models, multiple TFs may simultaneously contribute to gene regulation by forming heteromeric complexes, thus giving rise tocomposition structures. Since bipartite Boolean models are relatively recent, an empirical investigation of their biological plausibility is lacking. Here, we estimate the prevalence of composition structures arising through heteromeric complexes. Moreover, we present an additional mechanism where composition structures may arise as a result of multiple TFs binding tocis-regulatory regions and provide empirical support for this mechanism. Next, we compare the restriction in BFs imposed by composition structures and by biologically meaningful properties. We find that though composition structures can severely restrict the number of Boolean functions (BFs) driving a gene, the two types of minimally complex BFs, namely nested canalyzing functions (NCFs) and read-once functions (RoFs), are comparatively more restrictive. Finally, we find that composition structures are highly enriched in real networks, but this enrichment most likely comes from NCFs and RoFs.
PDE12 in type 1 diabetes
Authors: Tekin Hasim , Josefsen Knud , Krogvold Lars , Dahl-Jørgensen Knut , Gerling Ivan , Pociot Flemming , Buschard Karsten
Published: October 2022

Type 1 diabetes (T1D) incidence is increased after COVID-19 infection in children under 18 years of age. Interferon-α-activated oligoadenylate synthetase and downstream RNAseL activation degrade pathogen RNA, but can also damage host RNA when RNAseL activity is poorly regulated. One such regulator is PDE12 which degrades 2′-5′ oligoadenylate units, thereby decreasing RNAseL activity. We analyzed PDE12 expression in islets from non-diabetic donors, individuals with newly (median disease duration 35 days) and recently (5 years) diagnosed T1D, and individuals with type 2 diabetes (T2D). We also analyzed PDE12 single-nucleotide polymorphisms (SNPs) relative to T1D incidence. PDE12 expression was decreased in individuals with recently diagnosed T1D, in three of five individuals with newly diagnosed T1D, but not in individuals with T2D. Two rare PDE12 SNPs were found to have odds ratios of 1.80 and 1.74 for T1D development. We discuss whether decreased PDE12 expression after COVID-19 infection might be part of the up to 2.5-fold increase in T1D incidence.
Design and characterization of an urea-bridged PMO supporting Cu(II) nanoparticles as highly efficient heterogeneous catalyst for synthesis of tetrazole derivatives
Authors: Valiey Ehsan , Dekamin Mohammad G.
Published: October 2022

In this work, a new periodic mesoporous organosilica with urea-bridges produced by the reaction of (3-aminopropyl)triethoxysilane and toluene-2,4-diisocyanate (APS-TDU-PMO) is introduced. The obtained APS-TDU-PMO was found to be an appropriate support for loading of Cu(II) nanoparticles to afford supramolecular Cu@APS-TDU-PMO nanocomposite. Uniformity and mesoporosity of both synthesized nanomaterials including APS-TDU-PMO and Cu@APS-TDU-PMO were proved by different spectroscopic, microscopic or analytical techniques including FTIR, EDX, XRD, FESEM, TEM, BET, TGA and DTA. Furthermore, the prepared Cu@APS-TDU-PMO nanomaterial was also used, as a heterogeneous and recyclable catalyst, for the synthesis of tetrazole derivatives through cascade condensation, concerted cycloaddition and tautomerization reactions. Indeed, the main advantages of this Cu@APS-TDU-PMO is its simple preparation and high catalytic activity as well as proper surface area which enable it to work under solvent-free conditions. Also, the introduced Cu@APS-TDU-PMO heterogeneous catalyst showed good stability and reusability for six consecutive runs to address more green chemistry principles.
DeepLPI: a novel deep learning-based model for protein–ligand interaction prediction for drug repurposing
Authors: Wei Bomin , Zhang Yue , Gong Xiang
Published: October 2022

The substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by identifying interactions between drug molecules and target proteins based on computational methods have gained growing attention. Here, we propose the DeepLPI, a novel deep learning-based model that mainly consists of ResNet-based 1-dimensional convolutional neural network (1D CNN) and bi-directional long short term memory network (biLSTM), to establish an end-to-end framework for protein–ligand interaction prediction. We first encode the raw drug molecular sequences and target protein sequences into dense vector representations, which go through two ResNet-based 1D CNN modules to derive features, respectively. The extracted feature vectors are concatenated and further fed into the biLSTM network, followed by the MLP module to finally predict protein–ligand interaction. We downloaded the well-known BindingDB and Davis dataset for training and testing our DeepLPI model. We also applied DeepLPI on a COVID-19 dataset for externally evaluating the prediction ability of DeepLPI. To benchmark our model, we compared our DeepLPI with the baseline methods of DeepCDA and DeepDTA, and observed that our DeepLPI outperformed these methods, suggesting the high accuracy of the DeepLPI towards protein–ligand interaction prediction. The high prediction performance of DeepLPI on the different datasets displayed its high capability of protein–ligand interaction in generalization, demonstrating that the DeepLPI has the potential to pinpoint new drug-target interactions and to find better destinations for proven drugs.
Experimental and TDDFT materials simulation of thermal characteristics and entropy optimized of Williamson Cu-methanol and Al2O3-methanol nanofluid flowing through solar collector
Authors: Jamshed Wasim , Eid Mohamed R. , Al-Hossainy Ahmed F. , Raizah Zehba , Tag El Din El Sayed M. , Sajid Tanveer
Published: October 2022

Current investigation emphasizes the evaluation of entropy in a porous medium of Williamson nanofluid (WNF) flow past an exponentially extending horizontal plate featuring Parabolic Trough Solar Collector (PTSC). Two kinds of nanofluids such as copper-methanol (Cu-MeOH) and alumina-methanol (Al2O3-MeOH) were tested, discussed and plotted graphically. The fabricated nanoparticles are studied using different techniques, including TDDFT/DMOl3 method as simulated and SEM measurements as an experimental method. The centroid lengths of the dimer are 3.02 Å, 3.27 Å, and 2.49 Å for (Cu-MeOH), (Al2O3-MeOH), and (Cu-MeOH-αAl-MOH), respectively. Adequate similarity transformations were applied to convert the partial differential equation (PDEs) into nonlinear ordinary differential equations (ODEs) with the corresponding boundary constraints. An enhancement in Brinkmann and Reynolds numbers increases the overall system entropy. WNF parameter enhances the heat rate in PTSC. The thermal efficiency gets elevated for Cu-MeOH than that of Al2O3-MeOH among 0.8% at least and 6.6% in maximum for varying parametric values.
Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission
Authors: Gao Chengcheng , Zhang Rui , Chen Xicheng , Yao Tianhua , Song Qiuyue , Ye Wei , Li PengPeng , Wang Zhenyan , Yi Dong , Wu Yazhou
Published: October 2022

With the recent prevalence of COVID-19, cryptic transmission is worthy of attention and research. Early perception of the occurrence and development risk of cryptic transmission is an important part of controlling the spread of COVID-19. Previous relevant studies have limited data sources, and no effective analysis has been carried out on the occurrence and development of cryptic transmission. Hence, we collect Internet multisource big data (including retrieval, migration, and media data) and propose comprehensive and relative application strategies to eliminate the impact of national and media data. We use statistical classification and regression to construct an early warning model for occurrence and development. Under the guidance of the improved coronavirus herd immunity optimizer (ICHIO), we construct a “sampling-feature-hyperparameter-weight” synchronous optimization strategy. In occurrence warning, we propose an undersampling synchronous evolutionary ensemble (USEE); in development warning, we propose a bootstrap-sampling synchronous evolutionary ensemble (BSEE). Regarding the internal training data (Heilongjiang Province), the ROC-AUC of USEE3 incorporating multisource data is 0.9553, the PR-AUC is 0.8327, and the R2 of BSEE2 fused by the “nonlinear + linear” method is 0.8698. Regarding the external validation data (Shaanxi Province), the ROC-AUC and PR-AUC values of USEE3 were 0.9680 and 0.9548, respectively, and the R2 of BSEE2 was 0.8255. Our method has good accuracy and generalization and can be flexibly used in the prediction of cryptic transmission in various regions. We propose strategy research that integrates multiple early warning tasks based on multisource Internet big data and combines multiple ensemble models. It is an extension of the research in the field of traditional infectious disease monitoring and has important practical significance and innovative theoretical value.
Computer vision in surgery: from potential to clinical value
Authors: Mascagni Pietro , Alapatt Deepak , Sestini Luca , Altieri Maria S. , Madani Amin , Watanabe Yusuke , Alseidi Adnan , Redan Jay A. , Alfieri Sergio , Costamagna Guido , Boškoski Ivo , Padoy Nicolas , Hashimoto Daniel A.
Published: October 2022

Hundreds of millions of operations are performed worldwide each year, and the rising uptake in minimally invasive surgery has enabled fiber optic cameras and robots to become both important tools to conduct surgery and sensors from which to capture information about surgery. Computer vision (CV), the application of algorithms to analyze and interpret visual data, has become a critical technology through which to study the intraoperative phase of care with the goals of augmenting surgeons’ decision-making processes, supporting safer surgery, and expanding access to surgical care. While much work has been performed on potential use cases, there are currently no CV tools widely used for diagnostic or therapeutic applications in surgery. Using laparoscopic cholecystectomy as an example, we reviewed current CV techniques that have been applied to minimally invasive surgery and their clinical applications. Finally, we discuss the challenges and obstacles that remain to be overcome for broader implementation and adoption of CV in surgery.
Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline
Authors: Mikolajewicz Nicholas , Gacesa Rafael , Aguilera-Uribe Magali , Brown Kevin R. , Moffat Jason , Han Hong
Published: October 2022

Single-cell RNA-sequencing (scRNA-seq) offers functional insight into complex biology, allowing for the interrogation of cellular populations and gene expression programs at single-cell resolution. Here, we introduce scPipeline, a single-cell data analysis toolbox that builds on existing methods and offers modular workflows for multi-level cellular annotation and user-friendly analysis reports. Advances to scRNA-seq annotation include: (i) co-dependency index (CDI)-based differential expression, (ii) cluster resolution optimization using a marker-specificity criterion, (iii) marker-based cell-type annotation with Miko scoring, and (iv) gene program discovery using scale-free shared nearest neighbor network (SSN) analysis. Both unsupervised and supervised procedures were validated using a diverse collection of scRNA-seq datasets and illustrative examples of cellular transcriptomic annotation of developmental and immunological scRNA-seq atlases are provided herein. Overall, scPipeline offers a flexible computational framework for in-depth scRNA-seq analysis.
Dysregulated transforming growth factor-beta mediates early bone marrow dysfunction in diabetes
Authors: Kum Jina J. Y. , Howlett Christopher J. , Khan Zia A.
Published: October 2022

Diabetes affects select organs such as the eyes, kidney, heart, and brain. Our recent studies show that diabetes also enhances adipogenesis in the bone marrow and reduces the number of marrow-resident vascular regenerative stem cells. In the current study, we have performed a detailed spatio-temporal examination to identify the early changes that are induced by diabetes in the bone marrow. Here we show that short-term diabetes causes structural and molecular changes in the marrow, including enhanced adipogenesis in tibiae of mice, prior to stem cell depletion. This enhanced adipogenesis was associated with suppressed transforming growth factor-beta (TGFB) signaling. Using human bone marrow-derived mesenchymal progenitor cells, we show that TGFB pathway suppresses adipogenic differentiation through TGFB-activated kinase 1 (TAK1). These findings may inform the development of novel therapeutic targets for patients with diabetes to restore regenerative stem cell function.
Sterane and hopane biomarkers capture microbial transformations of complex hydrocarbons in young hydrothermal Guaymas Basin sediments
Authors: Mara Paraskevi , Nelson Robert K. , Reddy Christopher M. , Teske Andreas , Edgcomb Virginia P.
Published: October 2022

In Guaymas Basin, organic-rich hydrothermal sediments produce complex hydrocarbon mixtures including saturated, aromatic and alkylated aromatic compounds. We examined sediments from push cores from Guyamas sites with distinct temperature and geochemistry profiles to gain a better understanding on abiotic and biological hydrocarbon alteration. Here we provide evidence for biodegradation of hopanoids, producing saturated hydrocarbons like drimane and homodrimane as intermediate products. These sesquiterpene by-products are present throughout cooler sediments, but their relative abundance is drastically reduced within hotter hydrothermal sediments, likely due to hydrothermal mobilization. Within the sterane pool we detect a trend toward aromatization of steroidal compounds within hotter sediments. The changes in hopane and sterane biomarker composition at different sites reflect temperature-related differences in geochemical and microbial hydrocarbon alterations. In contrast to traditionally observed microbial biodegradation patterns that may extend over hundreds of meters in subsurface oil reservoirs, Guaymas Basin shows highly compressed changes in surficial sediments.
Reaction Mechanism in the Study of Amplifying Asymmetric Autocatalysis
Authors: Brown John M.
Published: October 2022

This article first explores the challenges involved in defining a mechanism for a catalytic asymmetric reaction and then the additional factors that have to be considered for autocatalytic reactions. Any major discovery is based on scientific infrastructure, and the developments that provided that infrastructure for the Soai reaction are discussed in detail. The current status of mechanistic understanding is assessed though the various approaches and methodologies that have been utilized. On this basis a view of the present understanding of amplifying asymmetric autocatalysis is given. The final sections are concerned with mechanistic aspects of application to autocatalytic reactions at phase boundaries and with the extreme sensitivities of this unique chemistry towards perturbation.
Stochastic Modeling of Asymmetric Autocatalysis in the Soai Reaction
Authors: Lente Gábor
Published: October 2022

For a theoretical chemist, one of the most exciting features of the Soai reaction is the experimentally confirmed occurrence of absolute asymmetric synthesis and the stochastic distribution of enantiomers observed in it. This chapter shows how highly successful models of this rare phenomenon can be built based on the principles of the continuous time discrete state (CDS) approach to stochastic chemical kinetics. First, an updated, particle-based definition of racemic mixtures is discussed, then a minimal model is presented for absolute asymmetric synthesis that is already able to interpret the core features using a minimum number of chemical processes. An 18-step mechanism proposed for the Soai reaction is also discussed in detail and is shown to lead to practically the same enantiomer distribution as the minimal model with second-order autocatalysis does. Finally, it is shown that about 20 initial chiral molecules as an accidental impurity in the initial reaction mixture may already cause the experimentally measured, slight but significant asymmetry in one of the two most analyzed product distributions known in the Soai reaction.
Challenges and recent advancements in the transformation of CO2into carboxylic acids: straightforward assembly with homogeneous 3d metals
Authors: Cauwenbergh Robin , Goyal Vishakha , Maiti Rakesh , Natte Kishore , Das Shoubhik
Published: October 2022

Transformation of carbon dioxide (CO2) into valuable organic carboxylic acids is essential for maintaining sustainability. In this review, such CO2thermo-, photo- and electrochemical transformations under 3d-transition metal catalysis are described from 2017 until 2022.
Synthesis of hierarchical porous zirconium dioxide and its application in the detection of sulfonamides in animal-derived food
Authors: Luan Xiaofang , Nie Wen , Tian Xinxin , Xu Jinglei , Fang Wenqiang , Liu Shuang , Lan Xinyu , Jia Wenxuan , Liu Yongming , Liu Zhenbo
Published: October 2022

In order to effectively remove grease for the detection of sulfonamides, a non-toxic and low-cost hierarchical porous zirconia material was synthesized using the dual template method.
Mass photometry reveals SARS-CoV-2 spike stabilisation to impede ACE2 binding through altered conformational dynamics
Authors: Burnap Sean A. , Struwe Weston B.
Published: October 2022

Here we show using mass photometry how proline substitutions, commonly used for SARS-CoV-2 spike stabilisation in vaccine design, directly affects ACE2 receptor interactions via dynamics of open and closed states.
Quantum yield enhancement of firefly bioluminescence with biomolecular condensates
Authors: Nishihara Ryo , Kihara Yoshiki , Niwa Kazuki , Mimura Masahiro , Tomita Shunsuke , Kurita Ryoji
Published: October 2022

Encapsulation of the firefly bioluminescence system into biomolecular condensates enhanced the bioluminescence quantum yield, an index of brightness.
Absolute configuration determination of SMTP-7 via microcrystal electron diffraction (MicroED)
Authors: Wang Bo , Lin Yiqing
Published: October 2022

The absolute configuration of a clinically important drug candidate, SMTP-7, with only micron-sized powders available, is directly obtained via microcrystal electron diffraction (MicroED) analysis.
Elucidating the oxygen reduction reaction mechanism on the surfaces of 2D monolayer CsPbBr3 perovskite
Authors: Upadhyay Shrish Nath , Sardar Verma Bunty , Singh Ashok , Kumar Vikash , Pakhira Srimanta
Published: October 2022

The oxygen reduction reaction (ORR) is an indispensable reaction in electrochemical energy converting systems such as fuel cells.
The combination of skeleton-engineering and periphery-engineering: a design strategy for organic doublet emitters
Authors: Zhou Hai-Ping , Wu Shui-Xing , Duan Ying-Chen , Gao Feng-Wei , Pan Qing-Qing , Kan Yu-He , Su Zhong-Min
Published: October 2022

A series of radicals based on tris(2,4,6-trichlorophenyl)methyl (TTM) were theoretically designed and evaluated by combining skeleton-engineering and periphery-engineering strategies.
A new double-reference correction scheme for accurate and efficient computation of NMR chemical shieldings
Authors: Crittenden Deborah L.
Published: October 2022

Our novel correction procedure yields high-accuracy DFT predictions of absolute NMR shieldings and enables outliers due to relativistic effects or manifestly inadequate modelling of electron correlation to be easily and unambiguously identified.
Overlapping hydration shells in salt solutions causing non-monotonic Soret coefficients with varying concentration
Authors: Mohanakumar Shilpa , Kriegs Hartmut , Briels W. J. , Wiegand Simone
Published: October 2022

We develop an intuitive picture that overlapping hydration shells in salt solutions cause non-monotonic Soret coefficients with varying concentration.
Cyclic(alkyl)(amino)carbene ruthenium complexes for Z-stereoselective (asymmetric) olefin metathesis
Authors: Morvan Jennifer , Vermersch François , Lorkowski Jan , Talcik Jakub , Vives Thomas , Roisnel Thierry , Crévisy Christophe , Vanthuyne Nicolas , Bertrand Guy , Jazzar Rodolphe , Mauduit Marc
Published: October 2022

The first Z-stereoselective catechodithiolate ruthenium complexes containing achiral and chiral cyclic(alkyl)(amino)carbene ligands are reported.
VUV-UV-vis photoluminescence, X-ray radioluminescence and energy transfer dynamics of Ce3+ and Eu2+ in Sr2MgSi2O7
Authors: Wen Donghao , Li Quanfeng , Ou Yiyi , Yang Yunlin , Qi Zeming , Dorenbos Pieter , Liang Hongbin
Published: October 2022

The VUV-UV-vis photoluminescence, X-ray radioluminescence, energy transfer dynamics of Ce3+ and Eu2+ in Sr2MgSi2O7 and the dependencies of Ce3+ 4f–5d transitions on the local structure of Sr2MgSi2O7, Ba2MgSi2O7 and BaMg2Si2O7 are discussed.
Synthesis of 2D MoO3−x/N-doped-carbon nanocomposites via in situ carbonization of layered (NH4)Mo3O9–(NH4)2Mo4O13-organic hybrid nanomaterials for exceptionally efficient adsorption and separation of organic dyes
Authors: Gaikwad Supriya Hanmant , Porel Mukherjee Shatabdi
Published: October 2022

2D MoO3−x/N-doped-carbon nanocomposites were synthesized via the in situ carbonization of layered (NH4)Mo3O9–(NH4)2Mo4O13-organic hybrid nanomaterials for the effective adsorption and separation of organic dye pollutants from environmental water.
Hybrid double-spiral microfluidic chip for RBC-lysis-free enrichment of rare cells from whole blood
Authors: Shirai Kentaro , Guan Guofeng , Meihui Tan , Xiaoling Peng , Oka Yuma , Takahashi Yusuke , Bhagat Ali Asgar S. , Yanagida Masatoshi , Iwanaga Shigeki , Matsubara Nobuaki , Mukohara Toru , Yoshida Tomokazu
Published: October 2022

Hybrid double-spiral chip was developed to enrich circulating tumor cells (CTCs) without red blood cell lysis for liquid biopsy. Combined with the imaging flow cytometry, CTCs detection from cancer patient blood was demonstrated.
Promotion of Suzuki–Miura reaction under bimetallic green catalyst
Authors: Binandeh Mansour , Nasseri Mohammad Ali , Allahresani Ali
Published: October 2022

The aim of the project was to design and synthesize a magnetic nanocatalyst with a new organic ligand to perform the Suzuki reaction.
Democratizing the rapid screening of protein expression for materials development
Authors: Morris Melody A. , Bataglioli Rogério A. , Mai Danielle J. , Yang Yun Jung , Paloni Justin M. , Mills Carolyn E. , Schmitz Zachary D. , Ding Erika A. , Huske Allison C. , Olsen Bradley D.
Published: October 2022

Low-cost, high-throughput methods for the determination of high-yield protein expression conditions are developed and verified, to enable the rapid development of new protein materials, such as biosensors and biomaterials.
Optimization of metamaterials and metamaterial-microcavity based on deep neural networks
Authors: Lan Guoqiang , Wang Yu , Ou Jun-Yu
Published: October 2022

We use a deep-learning method to optimize the design of split-ring metamaterials and metamaterial-microcavities. We use this method to quickly realize the design of the metamaterial-microcavity with the absorptance peak at 1310 nm.
Locally varying formation of nanoclusters across a low-intensity ultra-short laser spot
Authors: Osterloh Niklas , Pan Tianluo , Morgenstern Karina
Published: October 2022

Substantial variations in nanoclusters formed in an unexpected low-fluence range of an ultra-short laser are visualized on the microscale in a novel scanning approach across the laser spot.
Tuning excited state energy levels by achieving coordination-induced thermally activated delayed fluorescence
Authors: Liang Dong , Jia Ji-Hui , Cai Xian-Bao , Zhao Yu-Qing , Wang Zhan-Qi , Lu Can-Zhong
Published: October 2022

In this study, a low steric hindrance D–A type spiro-ligand which possesses long afterglow was used to construct Ag(i) complexes. The excited states energy level is regulated for resulting in tiny ΔEST and efficient TADF through metal coordination.
Load-bearing study and interfacial interactions of hydroxyapatite composite coatings for bone tissue engineering
Authors: Awasthi Shikha , Pandey Sarvesh Kumar , Gaur Jeet Kumar , Srivastava Chandan
Published: October 2022

A HAP–hBN composite with a synergetic effect of lubrication and strong bonding between HAP and h-BN coating shows perfect hydrophilicity, wear resistance and biocompatibility, and can be used as an ideal implant for orthopaedic applications.
Kinetic investigation of para-nitrophenol reduction with photodeposited platinum nanoparticles onto tunicate cellulose
Authors: Thiel T. A. , Zhang X. , Radhakrishnan B. , van de Krol R. , Abdi F. F. , Schroeter M. , Schomäcker R. , Schwarze M.
Published: October 2022

Modified cellulose (ModCe) was used in a photodeposition process as a support material for platinum nanoparticles. The supported catalysts were investigated for the transfer hydrogenation of para-nitrophenol (PNP) to para-aminophenol (PAP).
Pyridine ionic liquid functionalized bimetallic MOF solid-phase extraction coupled with high performance liquid chromatography for separation/analysis sunset yellow
Authors: Wu Jun , Wan Shuyu , Xu Ouwen , Song Hanyang , Yang Jing , Zhu Xiashi
Published: October 2022

An effective method based on the pyridine ionic liquid functionalized bimetallic MOF solid-phase extractant (Cu/Co-MOF@[PrPy][Br]) coupled with high performance liquid chromatography (HPLC) for the separation/analysis sunset yellow was established.
C(sp3)–C(sp3) coupling of non-activated alkyl-iodides with electron-deficient alkenes via visible-light/silane-mediated alkyl-radical formation
Authors: Mistry Sanesh , Kumar Roopender , Lister Andrew , Gaunt Matthew J.
Published: October 2022

Here, we present a remarkably mild and general initiation protocol for alkyl-radical generation from non-activated alkyl-iodides.
Enhanced performance and microbial communities of anaerobic digestion with biochar in pilot-scale continuous stirred-tank reactors: effects of substrate concentration and hydraulic retention time
Authors: Shen Ruixia , Yao Zonglu , Yu Jiadong , Luo Juan , Zhao Lixin
Published: October 2022

AD was enhanced with low-cost corn straw biochar in 16 L CSTRs, and the highest biogas production rate was 1.41 L L−1 d−1 at a substrate concentration of 12%. Methanosarcina showed a better performance in relieving ammonia nitrogen inhibition.
One step electrochemical fabrication of high performance Ni@Fe-doped Ni(oxy)hydroxide anode for practical alkaline water electrolysis
Authors: Jiang Tao , Jiang Xinge , Hnát Jaromír , Michalcova Alena , Biswas Indro , Reissner Regine , Kyriakou Vasileios , Razmjooei Fatemeh , Liao Hanlin , Bouzek Karel , Ansar Syed-asif
Published: October 2022

Charge-transfer process (CTP) and mass-transfer process (MTP) are essential in water electrolysis. The engineered core@shell nanoarrays-based anode can significantly improve not only CTP but also MTP in AWE cell.
Mussel-inspired polymeric coatings with the antifouling efficacy controlled by topologies
Authors: Xiong Chenxi , Xiong Wenjuan , Mu Youbing , Pei Danfeng , Wan Xiaobo
Published: October 2022

Block copolymers with different topologies (linear, loop, 3-armed and 4-armed polymers) containing poly(N-vinylpyrrrolidone) (PVP) antifouling blocks and terminal poly(dopamine-acrylamide) (PDAA) anchoring blocks were synthesized.
Exploring the optoelectronic properties of SnSe: a new insight
Authors: Kumar Manoj , Rani Sanju , Vashishtha Pargam , Gupta Govind , Wang Xu , Singh V. N.
Published: October 2022

Open-air annealing of SnSe led to a stable and enhanced photoconductive device. The structure was grown into a complete solar cell.
Accelerated design for magnetic high entropy alloys using data-driven multi-objective optimization
Authors: Li Xin , Shan Guangcun , Zhang Jiliang , Shek Chan-Hung
Published: October 2022

High entropy alloys (HEAs) with excellent combination performance of magnetic and mechanical properties were rationally designed via intelligent algorithms combined with metallurgical knowledge.
Immunoregulatory signal networks and tumor immune evasion mechanisms: insights into therapeutic targets and agents in clinical development
Authors: Wei Qian , Taskén Kjetil
Published: October 2022

Through activation of immune cells, the immune system is responsible for identifying and destroying infected or otherwise damaged cells including tumorigenic cells that can be recognized as foreign, thus maintaining homeostasis. However, tumor cells have evolved several mechanisms to avoid immune cell detection and killing, resulting in tumor growth and progression. In the tumor microenvironment, tumor infiltrating immune cells are inactivated by soluble factors or tumor promoting conditions and lose their effects on tumor cells. Analysis of signaling and crosstalk between immune cells and tumor cells have helped us to understand in more detail the mechanisms of tumor immune evasion and this forms basis for drug development strategies in the area of cancer immunotherapy. In this review, we will summarize the dominant signaling networks involved in immune escape and describe the status of development of therapeutic strategies to target tumor immune evasion mechanisms with focus on how the tumor microenvironment interacts with T cells.
Identification and validation of a genomic mutation signature as a predictor for immunotherapy in NSCLC
Authors: Wang Zemin , Ge You , Li Han , Fei Gaoqiang , Wang Shuai , Wei Pingmin
Published: October 2022

Currently, the benefits of immune checkpoint inhibitor (ICI) therapy prediction via emerging biomarkers have been identified, and the association between genomic mutation signatures (GMS) and immunotherapy benefits has been widely recognized as well. However, the evidence about non-small cell lung cancer (NSCLC) remains limited. We analyzed 310 immunotherapy patients with NSCLC from the Memorial Sloan Kettering Cancer Center (MSKCC) cohort. Lasso Cox regression was used to construct a GMS, and the prognostic value of GMS could be able to verify in the Rizvi cohort (N=240) and Hellmann cohort (N=75). We further conducted immunotherapy-related characteristics analysis in The Cancer Genome Atlas (TCGA) cohort (N=1052). A total of seven genes (ZFHX3, NTRK3, EPHA7, MGA, STK11, EPHA5, TP53) were identified for GMS model construction. Compared with GMS-high patients, patients with GMS-low had longer overall survival (OS; P<0.001) in the MSKCC cohort and progression-free survival (PFS; P<0.001) in the validation cohort. Multivariate Cox analysis revealed that GMS was an independent predictive factor for NSCLC patients in both the MSKCC and validation cohort. Meanwhile, we found that GMS-low patients reflected enhanced antitumor immunity in TCGA cohort. The results indicated that GMS had not only potential predictive value for the benefit of immunotherapy but also may serve as a potential biomarker to guide clinical ICI treatment decisions for NSCLC.
Integrating Verbal Short-Term Memory Into the Assessment and Treatment of Aphasia
Authors: Sayers Matthew J. , Greenspan Wendy , Martin Nadine
Published: October 2022

Purpose:

Verbal short-term memory (STM) impairment is ubiquitous in people with aphasia, and its severity generally parallels the severity of word processing impairment. In language-based models of STM, verbal STM arises from the activation and maintenance of linguistic representations. Measures of verbal STM can provide insight into the nature of language processing deficits in aphasia and should be a component of comprehensive assessment and treatment plans. Although verbal STM impairment in aphasia is widely studied in the research community, inclusion of verbal STM assessment and treatment in clinical practice remains limited.

Method:

This tutorial presents a description of language-based models of verbal STM and factors that contribute to performance in verbal STM tasks. We outline a model of verbal STM assessment and intervention that considers two levels of linguistic processing deficits (i.e., semantic vs. phonological), temporal variability in performance and access to representations (activation transmission vs. maintenance), and the effects of memory load. This tutorial presents a practical framework for the integration of measures of verbal STM in both assessment and treatment. Case studies are included to facilitate learning.

Conclusions:

This tutorial provides clinicians with the theoretical background and practical framework for integration of verbal STM in both treatment and assessment. While the assessment and treatment of verbal STM deficits in aphasia is in the beginning stages of implementation in the clinical setting, it offers a promising avenue for aphasia rehabilitation.