bluuebunny/crossref_metadata_2025_split huggingface dataset:
Powder metallurgy as a perfect technique for preparation of Cu–TiO2 composite by identifying their microstructure and optical properties
Authors: Eessaa Ashraf K. , Elkady Omayma A. , El-Shamy A. M. 
Published: April 2023

AbstractPowder metallurgy (PM) is a technique that involves the manufacturing of metal powders and their consolidation into finished products or components. This process involves the mixing of metal powders with other materials such as ceramics or polymers, followed by the application of heat and pressure to produce a solid, dense material. The use of PM has several advantages over traditional manufacturing techniques, including the ability to create complex shapes and the production of materials with improved properties. Cu–TiO2 composite materials are of great interest due to their unique properties, such as high electrical conductivity, improved mechanical strength, and enhanced catalytic activity. The synthesis of Cu–TiO2 composites using the PM technique has been gaining popularity in recent years due to its simplicity, cost-effectiveness, and ability to produce materials with excellent homogeneity. The novelty of using the PM technique for the preparation of Cu–TiO2 composite lies in the fact that it enables the production of materials with controlled microstructures and optical properties. The microstructure of the composite can be fine-tuned by controlling the particle size and distribution of the starting powders, as well as the processing parameters such as temperature, pressure, and sintering time. The optical properties of the composite can also be tailored by adjusting the size and distribution of the TiO2 particles, which can be used to control the absorption and scattering of light. This makes Cu–TiO2 composites particularly useful for applications such as photocatalysis and solar energy conversion. In summary, the use of Powder Metallurgy for the preparation of Cu–TiO2 composite is a novel and effective technique for producing materials with controlled microstructures and optical properties. The unique properties of Cu–TiO2 composites make them attractive for a wide range of applications in various fields, including energy, catalysis, and electronics.
Identification and validation of fatty acid metabolism-related lncRNA signatures as a novel prognostic model for clear cell renal cell carcinoma
Authors: Shen Cheng , Chen Zhan , Jiang Jie , Zhang Yong , Chen Xinfeng , Xu Wei , Peng Rui , Zuo Wenjing , Jiang Qian , Fan Yihui , Fang Xingxing , Zheng Bing 
Published: April 2023

AbstractClear cell renal cell carcinoma (ccRCC) is a main subtype of renal cancer, and advanced ccRCC frequently has poor prognosis. Many studies have found that lipid metabolism influences tumor development and treatment. This study was to examine the prognostic and functional significance of genes associated with lipid metabolism in individuals with ccRCC. Using the database TCGA, differentially expressed genes (DEGs) associated with fatty acid metabolism (FAM) were identified. Prognostic risk score models for genes related to FAM were created using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Our findings demonstrate that the prognosis of patients with ccRCC correlate highly with the profiles of FAM-related lncRNAs (AC009166.1, LINC00605, LINC01615, HOXA-AS2, AC103706.1, AC009686.2, AL590094.1, AC093278.2). The prognostic signature can serve as an independent predictive predictor for patients with ccRCC. The predictive signature's diagnostic effectiveness was superior to individual clinicopathological factors. Between the low- and high-risk groups, immunity research revealed a startling difference in terms of cells, function, and checkpoint scores. Chemotherapeutic medications such lapatinib, AZD8055, and WIKI4 had better outcomes for patients in the high-risk group. Overall, the predictive signature can help with clinical selection of immunotherapeutic regimens and chemotherapeutic drugs, improving prognosis prediction for ccRCC patients.
Dietary phytochemical index is favorably associated with oxidative stress status and cardiovascular risk factors in adults with obesity
Authors: Hamedi-Shahraki Soudabeh , Jowshan Mohammad-Reza , Zolghadrpour Mohammad-Amin , Amirkhizi Farshad , Asghari Somayyeh 
Published: April 2023

AbstractPhytochemicals are bioactive compounds found in plant-based foods. Consumption of phytochemical-rich foods has been associated with cardiovascular and metabolic diseases prevention in various populations. To quantify the phytochemical content of the diet, dietary phytochemical index (DPI) was established which is defined as the proportion of daily energy intake derived from foods rich in phytochemicals. The purpose of this study was to evaluate the association between the DPI and oxidative stress markers and cardiovascular risk factors in obese adults. In this cross-sectional study, a total of 140 adults aged 20–60 years and body mass index (BMI) of ≥ 30 kg/m2 were included. A validated food frequency questionnaire (FFQ) was used to collect information on dietary intakes. The DPI was calculated based on the following formula: DPI = [daily energy obtained from foods rich in phytochemicals (kcal)/total daily energy intake (kcal) × 100]. There was an inverse association between DPI and serum concentrations of Malondialdehyde (MDA) (P = 0.004), triglyceride (TG) (P-trend = 0.003), high-sensitive C-reactive protein (hs-CRP) (P = 0.017), and erythrocyte superoxide dismutase (SOD) activity (P = 0.024). Total antioxidant capacity (TAC) was positively associated with DPI score (P = 0.045). No significant relationship was found between the DPI score and fasting blood sugar (FBS), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total oxidant status (TOS), glutathione peroxidase (GPx), catalase (CAT), and anthropometric parameters as well as systolic and diastolic blood pressure. The current study found that there was a significant inverse association between DPI and oxidative stress, inflammation, and hypertriglyceridemia as cardiovascular disease (CVD) risk factors in obese population. However, further research is needed to confirm these findings.
Short-term inflow forecasting in a dam-regulated river in Southwest Norway using causal variational mode decomposition
Authors: Yousefi Mojtaba , Wang Jinghao , Fandrem Høivik Øivind , Rajasekharan Jayaprakash , Hubert Wierling August , Farahmand Hossein , Arghandeh Reza 
Published: April 2023

AbstractClimate change affects patterns and uncertainties associated with river water regimes, which significantly impact hydropower generation and reservoir storage operation. Hence, reliable and accurate short-term inflow forecasting is vital to face climate effects better and improve hydropower scheduling performance. This paper proposes a Causal Variational Mode Decomposition (CVD) preprocessing framework for the inflow forecasting problem. CVD is a preprocessing feature selection framework that is built upon multiresolution analysis and causal inference. CVD can reduce computation time while increasing forecasting accuracy by down-selecting the most relevant features to the target value (inflow in a specific location). Moreover, the proposed CVD framework is a complementary step to any machine learning-based forecasting method as it is tested with four different forecasting algorithms in this paper. CVD is validated using actual data from a river system downstream of a hydropower reservoir in the southwest of Norway. The experimental results show that CVD-LSTM reduces forecasting error metric by almost 70% compared with a baseline (scenario 1) and reduces by 25% compared to an LSTM for the same composition of input data (scenario 4).
Content-based filter queries on DNA data storage systems
Authors: El-Shaikh Alex , Seeger Bernhard 
Published: April 2023

AbstractRecent developments in DNA data storage systems have revealed the great potential to store large amounts of data at a very high density with extremely long persistence and low cost. However, despite recent contributions to robust data encoding, current DNA storage systems offer limited support for random access on DNA storage devices due to restrictive biochemical constraints. Moreover, state-of-the-art approaches do not support content-based filter queries on DNA storage. This paper introduces the first encoding for DNA that enables content-based searches on structured data like relational database tables. We provide the details of the methods for coding and decoding millions of directly accessible data objects on DNA. We evaluate the derived codes on real data sets and verify their robustness.
Reward maximization assessed using a sequential patch depletion task in a large sample of heterogeneous stock rats
Authors: Gancarz Amy M. , Mitchell Suzanne H. , George Anthony M. , Martin Connor D. , Turk Marisa C. , Bool Heather M. , Aktar Fahmida , Kwarteng Francis , Palmer Abraham A. , Meyer Paul J. , Richards Jerry B. , Dietz David M. , Ishiwari Keita 
Published: April 2023

AbstractChoice behavior requires animals to evaluate both short- and long-term advantages and disadvantages of all potential alternatives. Impulsive choice is traditionally measured in laboratory tasks by utilizing delay discounting (DD), a paradigm that offers a choice between a smaller immediate reward, or a larger more delayed reward. This study tested a large sample of Heterogeneous Stock (HS) male (n = 896) and female (n = 898) rats, part of a larger genetic study, to investigate whether measures of reward maximization overlapped with traditional models of delay discounting via the patch depletion model using a Sequential Patch Depletion procedure. In this task, rats were offered a concurrent choice between two water “patches” and could elect to “stay” in the current patch or “leave” for an alternative patch. Staying in the current patch resulted in decreasing subsequent reward magnitudes, whereas the choice to leave a patch was followed by a delay and a resetting to the maximum reward magnitude. Based on the delay in a given session, different visit durations were necessary to obtain the maximum number of rewards. Visit duration may be analogous to an indifference point in traditional DD tasks. Males and females did not significantly differ on traditional measures of DD (e.g. delay gradient; AUC). When examining measures of patch utilization, females made fewer patch changes at all delays and spent more time in the patch before leaving for the alternative patch compared to males. Consistent with this, there was some evidence that females deviated from reward maximization more than males. However, when controlling for body weight, females had a higher normalized rate of reinforcement than males. Measures of reward maximization were only weakly associated with traditional DD measures and may represent distinctive underlying processes. Taken together, females performance differed from males with regard to reward maximization that were not observed utilizing traditional measures of DD, suggesting that the patch depletion model was more sensitive to modest sex differences when compared to traditional DD measures in a large sample of HS rats.
Inhibition of DYRK1B suppresses inflammation in allergic contact dermatitis model and Th1/Th17 immune response
Authors: Wongchang Thamrong , Pluangnooch Panwadee , Hongeng Suradej , Wongkajornsilp Adisak , Thumkeo Dean , Soontrapa Kitipong 
Published: April 2023

AbstractAllergic contact dermatitis (ACD) is a type IV hypersensitivity mainly mediated by Th1/Th17 immune response. Topical corticosteroid is currently the first-line treatment for allergic contact dermatitis (ACD) and systemic administration of immunosuppressive drugs are used in patients with severe disseminated cases. However, increased risk of adverse effects has limited their use. Thus, the development of a novel immunosuppressant for ACD with low toxicity is a challenging issue. In this study, we began our study by using a murine contact hypersensitivity (CHS) model of ACD to examine the immunosuppressive effects of DYRK1B inhibition. We found that mice treated with a selective DYRK1B inhibitor show reduced ear inflammation. In addition, a significant reduction of Th1 and Th17 cells in the regional lymph node upon DYRK1B inhibition was observed by FACS analysis. Studies in vitro further revealed that DYRK1B inhibitor does not only suppressed Th1 and Th17 differentiation, but also promotes regulatory T cells (Treg) differentiation. Mechanistically, FOXO1 signaling was enhanced due to the suppression of FOXO1Ser329 phosphorylation in the presence of DYRK1B inhibitor. Therefore, these findings suggest that DYRK1B regulates CD4 T cell differentiation through FOXO1 phosphorylation and DYRK1B inhibitor has a potential as a novel agent for treatment of ACD.
Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer
Authors: Ning Jingyuan , Sun Keran , Fan Xiaoqing , Jia Keqi , Meng Lingtong , Wang Xiuli , Li Hui , Ma Ruixiao , Liu Subin , Li Feng , Wang Xiaofeng 
Published: April 2023

AbstractGastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then validated in the test 1 and the test 2 cohort. Based on a literature search, we selected 93 previously published prognostic signatures for gastric cancer and compared them with our prognostic signatures. At the single-cell level, the algorithms "Seurat," "SCEVAN", "scissor", and "Cellchat" were used to demonstrate the cell communication disturbance of high-risk cells. WGCNA and univariate Cox regression analysis identified 52 prognosis-related genes, which were subjected to 98 machine-learning integration processes. A prognostic signature consisting of 24 genes was identified using the StepCox[backward] and Enet[alpha = 0.7] machine learning algorithms. This signature demonstrated the best prognostic performance in the overall, test1 and test2 cohort, and outperformed 93 previously published prognostic signatures. Interaction perturbations in cellular communication of high-risk T cells were identified at the single-cell level, which may promote disease progression in patients with gastric cancer. We developed an immune-related prognostic signature with reliable validity and high accuracy for clinical use for predicting the prognosis of patients with gastric cancer.
Short-term forecasts of streamflow in the UK based on a novel hybrid artificial intelligence algorithm
Authors: Di Nunno Fabio , de Marinis Giovanni , Granata Francesco 
Published: April 2023

AbstractIn recent years, the growing impact of climate change on surface water bodies has made the analysis and forecasting of streamflow rates essential for proper planning and management of water resources. This study proposes a novel ensemble (or hybrid) model, based on the combination of a Deep Learning algorithm, the Nonlinear AutoRegressive network with eXogenous inputs, and two Machine Learning algorithms, Multilayer Perceptron and Random Forest, for the short-term streamflow forecasting, considering precipitation as the only exogenous input and a forecast horizon up to 7 days. A large regional study was performed, considering 18 watercourses throughout the United Kingdom, characterized by different catchment areas and flow regimes. In particular, the predictions obtained with the ensemble Machine Learning-Deep Learning model were compared with the ones achieved with simpler models based on an ensemble of both Machine Learning algorithms and on the only Deep Learning algorithm. The hybrid Machine Learning-Deep Learning model outperformed the simpler models, with values of R2 above 0.9 for several watercourses, with the greatest discrepancies for small basins, where high and non-uniform rainfall throughout the year makes the streamflow rate forecasting a challenging task. Furthermore, the hybrid Machine Learning-Deep Learning model has been shown to be less affected by reductions in performance as the forecasting horizon increases compared to the simpler models, leading to reliable predictions even for 7-day forecasts.
Mzion enables deep and precise identification of peptides in data-dependent acquisition proteomics
Authors: Zhang Qiang 
Published: April 2023

AbstractSensitive and reliable identification of proteins and peptides pertains the basis of proteomics. We introduce Mzion, a new database search tool for data-dependent acquisition (DDA) proteomics. Our tool utilizes an intensity tally strategy and achieves generally a higher performance in terms of depth and precision across 20 datasets, ranging from large-scale to single-cell proteomics. Compared to several other search engines, Mzion matches on average 20% more peptide spectra at tryptic enzymatic specificity and 80% more at no enzymatic specificity from six large-scale, global datasets. Mzion also identifies more phosphopeptide spectra that can be explained by fewer proteins, demonstrated by six large-scale, local datasets corresponding to the global data. Our findings highlight the potential of Mzion for improving proteomic analysis and advancing our understanding of protein biology.
Circadian rhythm biomarker from wearable device data is related to concurrent antidepressant treatment response
Authors: Ali Farzana Z. , Parsey Ramin V. , Lin Shan , Schwartz Joseph , DeLorenzo Christine 
Published: April 2023

AbstractMajor depressive disorder (MDD) is associated with circadian rhythm disruption. Yet, no circadian rhythm biomarkers have been clinically validated for assessing antidepressant response. In this study, 40 participants with MDD provided actigraphy data using wearable devices for one week after initiating antidepressant treatment in a randomized, double-blind, placebo-controlled trial. Their depression severity was calculated pretreatment, after one week and eight weeks of treatment. This study assesses the relationship between parametric and nonparametric measures of circadian rhythm and change in depression. Results show significant association between a lower circadian quotient (reflecting less robust rhythmicity) and improvement in depression from baseline following first week of treatment (estimate = 0.11, F = 7.01, P = 0.01). There is insufficient evidence of an association between circadian rhythm measures acquired during the first week of treatment and outcomes after eight weeks of treatment. Despite this lack of association with future treatment outcome, this scalable, cost-effective biomarker may be useful for timely mental health care through remote monitoring of real-time changes in current depression.
Regulation of synaptic connectivity in schizophrenia spectrum by mutual neuron-microglia interaction
Authors: Breitmeyer Ricarda , Vogel Sabrina , Heider Johanna , Hartmann Sophia-Marie , Wüst Richard , Keller Anna-Lena , Binner Anna , Fitzgerald Julia C. , Fallgatter Andreas J. , Volkmer Hansjürgen 
Published: April 2023

AbstractThe examination of post-mortem brain tissue suggests synaptic loss as a central pathological hallmark of schizophrenia spectrum (SCZ), which is potentially related to activated microglia and increased inflammation. Induced pluripotent stem cells serve as a source for neurons and microglia-like cells to address neuron-microglia interactions. Here, we present a co-culture model of neurons and microglia, both of human origin, to show increased susceptibility of neurons to microglia-like cells derived from SCZ patients. Analysis of IBA-1 expression, NFκB signaling, transcription of inflammasome-related genes, and caspase-1 activation shows that enhanced, intrinsic inflammasome activation in patient-derived microglia exacerbates neuronal deficits such as synaptic loss in SCZ. Anti-inflammatory pretreatment of microglia with minocycline specifically rescued aberrant synapse loss in SCZ and reduced microglial activation. These findings open up possibilities for further research in larger cohorts, focused clinical work and longitudinal studies that could facilitate earlier therapeutic intervention.
RNA-DNA interactomes of three prokaryotes uncovered by proximity ligation
Authors: Gavrilov Alexey A. , Evko Grigory S. , Galitsyna Aleksandra A. , Ulianov Sergey V. , Kochetkova Tatiana V. , Merkel Alexander Y. , Tyakht Alexander V. , Razin Sergey V. 
Published: April 2023

AbstractProximity ligation approaches, which are widely used to study the spatial organization of the genome, also make it possible to reveal patterns of RNA-DNA interactions. Here, we use RedC, an RNA-DNA proximity ligation approach, to assess the distribution of major RNA types along the genomes of E. coli, B. subtilis, and thermophilic archaeon T. adornatum. We find that (i) messenger RNAs preferentially interact with their cognate genes and the genes located downstream in the same operon, which is consistent with polycistronic transcription; (ii) ribosomal RNAs preferentially interact with active protein-coding genes in both bacteria and archaea, indicating co-transcriptional translation; and (iii) 6S noncoding RNA, a negative regulator of bacterial transcription, is depleted from active genes in E. coli and B. subtilis. We conclude that the RedC data provide a rich resource for studying both transcription dynamics and the function of noncoding RNAs in microbial organisms.
Tetramethylphosphinane as a new secondary phosphine synthon
Authors: Nobbs James D. , Sugiarto Sigit , See Xin Yi , Cheong Choon Boon , Aitipamula Srinivasulu , Stubbs Ludger P. , van Meurs Martin 
Published: April 2023

AbstractSecondary phosphines are important building blocks in organic chemistry as their reactive P—H bond enables construction of more elaborate molecules. In particular, they can be used to construct tertiary phosphines that have widespread applications as organocatalysts, and as ligands in metal-complex catalysis. We report here a practical synthesis of the bulky secondary phosphine synthon 2,2,6,6-tetramethylphosphinane (TMPhos). Its nitrogen analogue tetramethylpiperidine, known for over a century, is used as a base in organic chemistry. We obtained TMPhos on a multigram scale from an inexpensive air-stable precursor, ammonium hypophosphite. TMPhos is also a close structural relative of di-tert-butylphosphine, a key component of many important catalysts. Herein we also describe the synthesis of key derivatives of TMPhos, with potential applications ranging from CO2 conversion to cross-coupling and beyond. The availability of a new core phosphine building block opens up a diverse array of opportunities in catalysis.