Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibition of IL17A Using an Affibody Molecule Attenuates Inflammation in ApoE-Deficient Mice

The balance between pro- and anti-inflammatory cytokines released by immune and non-immune cells plays a decisive role in the progression of atherosclerosis. Interleukin (IL)-17A has been shown to accelerate atherosclerosis. In this study, we investigated the effect on pro-inflammatory mediators and atherosclerosis development of an Affibody molecule that targets IL17A. Affibody molecule neutralizing IL17A, or sham were administered in vitro to human aortic smooth muscle cells (HAoSMCs) and murine NIH/3T3 fibroblasts and in vivo to atherosclerosis-prone, hyperlipidaemic ApoE−/− mice. Levels of mediators of inflammation and development of atherosclerosis were compared between treatments. Exposure of human smooth muscle cells and murine NIH/3T3 fibroblasts in vitro to αIL-17A Affibody molecule markedly reduced IL6 and CXCL1 release in supernatants compared with sham exposure. Treatment of ApoE−/− mice with αIL-17A Affibody molecule significantly reduced plasma protein levels of CXCL1, CCL2, CCL3, HGF, PDGFB, MAP2K6, QDPR, and splenocyte mRNA levels of Ccxl1, Il6, and Ccl20 compared with sham exposure. There was no significant difference in atherosclerosis burden between the groups. In conclusion, administration of αIL17A Affibody molecule reduced levels of pro-inflammatory mediators and attenuated inflammation in ApoE−/− mice.
Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Background The presence of microvascular invasion (MVI) is considered an independent prognostic factor associated with early recurrence and poor survival in hepatocellular carcinoma (HCC) patients after resection. Artificial intelligence (AI), mainly consisting of non-deep learning algorithms (NDLAs) and deep learning algorithms (DLAs), has been widely used for MVI prediction in medical imaging. Aim To assess the diagnostic accuracy of AI algorithms for non-invasive, preoperative prediction of MVI based on imaging data. Methods Original studies reporting AI algorithms for non-invasive, preoperative prediction of MVI based on quantitative imaging data were identified in the databases PubMed, Embase, and Web of Science. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) scale. The pooled sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated using a random-effects model with 95% CIs. A summary receiver operating characteristic curve and the area under the curve (AUC) were generated to assess the diagnostic accuracy of the deep learning and non-deep learning models. In the non-deep learning group, we further performed meta-regression and subgroup analyses to identify the source of heterogeneity. Results Data from 16 included studies with 4,759 cases were available for meta-analysis. Four studies on deep learning models, 12 studies on non-deep learning models, and two studies compared the efficiency of the two types. For predictive performance of deep learning models, the pooled sensitivity, specificity, PLR, NLR, and AUC values were 0.84 [0.75–0.90], 0.84 [0.77–0.89], 5.14 [3.53–7.48], 0.2 [0.12–0.31], and 0.90 [0.87–0.93]; and for non-deep learning models, they were 0.77 [0.71–0.82], 0.77 [0.73–0.80], 3.30 [2.83–3.84], 0.30 [0.24–0.38], and 0.82 [0.79–0.85], respectively. Subgroup analyses showed a significant difference between the single tumor subgroup and the multiple tumor subgroup in the pooled sensitivity, NLR, and AUC. Conclusion This meta-analysis demonstrates the high diagnostic accuracy of non-deep learning and deep learning methods for MVI status prediction and their promising potential for clinical decision-making. Deep learning models perform better than non-deep learning models in terms of the accuracy of MVI prediction, methodology, and cost-effectiveness. Systematic Review Registration https://www.crd. york.ac. uk/PROSPERO/display_record. php? RecordID=260891, ID:CRD42021260891.
Modeling Discontinuous Cultural Evolution: The Impact of Cross-Domain Transfer

This paper uses autocatalytic networks to model discontinuous cultural transitions involving cross-domain transfer, using as an illustrative example, artworks inspired by the oldest-known uncontested example of figurative art: the carving of the Hohlenstein-Stadel Löwenmensch, or lion-human. Autocatalytic networks provide a general modeling setting in which nodes are not just passive transmitters of activation; they actively galvanize, or “catalyze” the synthesis of novel (“foodset-derived”) nodes from existing ones (the “foodset.”) This makes them uniquely suited to model how new structure grows out of earlier structure, i.e., cumulative, generative network growth. They have been used to model the origin and early evolution of biological life, and the emergence of cognitive structures capable of undergoing cultural evolution. We conducted a study in which six individual creators and one group generated music, prose, poetry, and visual art inspired by the Hohlenstein-Stadel Löwenmensch, and answered questions about the process. The data revealed four through-lines by which they expressed the Löwenmensch in an alternative art form: (1) lion-human hybrid, (2) subtracting from the whole to reveal the form within, (3) deterioration, and (4) waiting to be found with a story to tell. Autocatalytic networks were used to model how these four spontaneously derived through-lines form a cultural lineage from Löwenmensch to artist to audience. We used the resulting data from three creators to model the cross-domain transfer from inspirational source (sculpted figurine) to creative product (music, poetry, prose, visual art). These four spontaneously-generated threads of cultural continuity formed the backbone of this Löwenmensch-inspired cultural lineage, enabling culture to evolve even in the face of discontinuity at the level conventional categories or domains. We know of no other theory of cultural evolution that accommodates cross-domain transfer or other forms of discontinuity. The approach paves the way for a broad scientific framework for the origins of evolutionary processes.
Microbiome Resilience and Health Implications for People in Half-Year Travel

Travel entail change in geography and diet, both of which are known as determinant factors in shaping the human gut microbiome. Additionally, altered gut microbiome modulates immunity, bringing about health implications in humans. To explore the effects of the mid-term travel on the gut microbiome, we generated 16S rRNA gene and metagenomic sequencing data from longitudinal samples collected over six months. We monitored dynamic trajectories of the gut microbiome variation of a Chinese volunteer team (VT) in their whole journey to Trinidad and Tobago (TAT). We found gut microbiome resilience that VT’s gut microbial compositions gradually transformed to the local TAT’s enterotypes during their six-month stay in TAT, and then reverted to their original enterotypes after VT’s return to Beijing in one month. Moreover, we identified driven species in this bi-directional plasticity that could play a role in immunity modulation, as exemplified by Bacteroides dorei that attenuated atherosclerotic lesion formation and effectively suppressed proinflammatory immune response. Another driven species P. copri could play a crucial role in rheumatoid arthritis pathogenesis, a chronic autoimmune disease. Carbohydrate-active enzymes are often implicated in immune and host-pathogen interactions, of which glycoside hydrolases were found decreased but glycosyltransferases and carbohydrate esterases increased during the travel; these functions were then restored after VT’ returning to Beijing. Furthermore, we discovered these microbial changes and restoration were mediated by VT people’s dietary changes. These findings indicate that half-year travel leads to change in enterotype and functional patterns, exerting effects on human health. Microbial intervention by dietary guidance in half-year travel would be conducive to immunity modulation for maintaining health.
Dishevelled-Associated Activator of Morphogenesis 2 (DAAM2) Predicts the Immuno-Hot Phenotype in Pancreatic Adenocarcinoma

Background: DAAM2 participates in the oncogenesis and progression of human cancers. Although the role of DAAM2 in cancers has been preliminarily investigated, its correlations with antitumor immunity are unclear. Methods: A pancancer analysis was conducted to explore the immunological role of DAAM2 based on RNA sequencing (RNA-seq) data downloaded from The Cancer Genome Atlas (TCGA). Next, correlations between DAAM2 and immunological characteristics in the tumor microenvironment (TME) of pancreatic adenocarcinoma (PAAD) were evaluated. In addition, the role of DAAM2 in predicting the clinical characteristics and the response to various therapies in PAAD were also assessed. In addition, the correlations between DAAM2 and the emerging immunobiomarker N6-methyladenosine (m6A) genes were also evaluated. Results: Pancancer analysis revealed that DAAM2 exhibited positive correlations with a majority of immunomodulators, tumor-infiltrating immune cells (TIICs) and inhibitory immune checkpoints in several cancer types, including PAAD. In addition, DAAM2 was associated with an inflamed phenotype in the tumor microenvironment (TME). DAAM2 also predicted significantly higher responses to chemotherapy, anti-EGFR therapy and immunotherapy but lower responses to anti-ERBB2 and antiangiogenic therapy. In addition, DAAM2 was correlated with immune-related microbiota. Conclusion: In PAAD, DAAM2 is associated with an immuno-hot phenotype and can help predict the outcome of various therapeutic options. Overall, DAAM2 is a promising indicator for assessing high immunogenicity in PAAD.
High-Throughput Sequencing-Based Analysis of Rhizosphere and Diazotrophic Bacterial Diversity Among Wild Progenitor and Closely Related Species of Sugarcane (Saccharum spp. Inter-Specific Hybrids)

Considering the significant role of genetic background in plant-microbe interactions and that most crop rhizospheric microbial research was focused on cultivars, understanding the diversity of root-associated microbiomes in wild progenitors and closely related crossable species may help to breed better cultivars. This study is aimed to fill a critical knowledge gap on rhizosphere and diazotroph bacterial diversity in the wild progenitors of sugarcane, the essential sugar and the second largest bioenergy crop globally. Using a high-throughput sequencing (HTS) platform, we studied the rhizosphere and diazotroph bacterial community of Saccharum officinarum L. cv. Badila (BRS), Saccharum barberi (S. barberi) Jesw. cv Pansahi (PRS), Saccharum robustum [S. robustum; (RRS), Saccharum spontaneum (S. spontaneum); SRS], and Saccharum sinense (S. sinense) Roxb. cv Uba (URS) by sequencing their 16S rRNA and nifH genes. HTS results revealed that a total of 6,202 bacteria-specific operational taxonomic units (OTUs) were identified, that were distributed as 107 bacterial groups. Out of that, 31 rhizobacterial families are commonly spread in all five species. With respect to nifH gene, S. barberi and S. spontaneum recorded the highest and lowest number of OTUs, respectively. These results were validated by quantitative PCR analysis of both genes. A total of 1,099 OTUs were identified for diazotrophs with a core microbiome of 9 families distributed among all the sugarcane species. The core microbiomes were spread across 20 genera. The increased microbial diversity in the rhizosphere was mainly due to soil physiochemical properties. Most of the genera of rhizobacteria and diazotrophs showed a positive correlation, and few genera negatively correlated with the soil properties. The results showed that sizeable rhizospheric diversity exists across progenitors and close relatives. Still, incidentally, the rhizosphere microbial abundance of progenitors of modern sugarcane was at the lower end of the spectrum, indicating the prospect of Saccharum species introgression breeding may further improve nutrient use and disease and stress tolerance of commercial sugarcane. The considerable variation for rhizosphere microbiome seen in Saccharum species also provides a knowledge base and an experimental system for studying the evolution of rhizobacteria-host plant association during crop domestication.
Podocyte VEGF-A Knockdown Induces Diffuse Glomerulosclerosis in Diabetic and in eNOS Knockout Mice

Vascular endothelial growth factor-a (VEGF-A) and nitric oxide (NO) are essential for glomerular filtration barrier homeostasis, and are dysregulated in diabetic kidney disease (DKD). While NO availability is consistently low in diabetes, both high and low VEGF-A have been reported in patients with DKD. Here we examined the effect of inducible podocyte VEGF-A knockdown (VEGFKD ) in diabetic mice and in endothelial nitric oxide synthase knockout mice (eNOS−/− ). Diabetes was induced with streptozotocin using the Animal Models of Diabetic Complications Consortium (AMDCC) protocol. Induction of podocyte VEGFKD led to diffuse glomerulosclerosis, foot process effacement, and GBM thickening in both diabetic mice with intact eNOS and in non-diabetic eNOS−/−:VEGFKD mice. VEGFKD diabetic mice developed mild proteinuria and maintained normal glomerular filtration rate (GFR), associated with extremely high NO and thiol urinary excretion. In eNOS−/−:VEGFKD (+dox) mice severe diffuse glomerulosclerosis was associated with microaneurisms, arteriolar hyalinosis, massive proteinuria, and renal failure. Collectively, data indicate that combined podocyte VEGF-A and eNOS deficiency result in diffuse glomerulosclerosis in mice; compensatory NO and thiol generation prevents severe proteinuria and GFR loss in VEGFKD diabetic mice with intact eNOS, whereas VEGFKD induction in eNOS−/−:VEGFKD mice causes massive proteinuria and renal failure mimicking DKD in the absence of diabetes. Mechanistically, we identify VEGFKD -induced abnormal S-nitrosylation of specific proteins, including β3-integrin, laminin, and S-nitrosoglutathione reductase (GSNOR), as targetable molecular mechanisms involved in the development of advanced diffuse glomerulosclerosis and renal failure.
ZNF655 Promotes the Progression of Glioma Through Transcriptional Regulation of AURKA

Objectives Glioma has a high degree of malignancy, strong invasiveness, and poor prognosis, which is always a serious threat to human health. Previous studies have reported that C2H2 zinc finger (ZNF) protein is involved in the progression of various cancers. In this study, the clinical significance, biological behavior, and molecule mechanism of ZNF655 in glioma were explored. Methods The expression of ZNF655 in glioma and its correlation with prognosis were analyzed through public datasets and immunohistochemical (IHC) staining. The shRNA-mediated ZNF655 knockdown was used to explore the effects of ZNF655 alteration on the phenotypes and tumorigenesis of human glioma cell lines. Chromatin immunoprecipitation (ChIP)-qPCR and luciferase reporter assays were performed to determine the potential mechanism of ZNF655 regulating Aurora kinase A (AURKA). Results ZNF655 was abundantly expressed in glioma tissue and cell lines SHG-44 and U251. Knockdown of suppressed the progression of glioma cells, which was characterized by reduced proliferation, enhanced apoptosis, cycle repression in G2, inhibition of migration, and weakened tumorigenesis. Mechanistically, transcription factor ZNF655 activated the expression of AURKA by directly binding to the promoter of AURKA. In addition, downregulation of AURKA partially reversed the promoting effects of overexpression of ZNF655 on glioma cells. Conclusions ZNF655 promoted the progression of glioma by binding to the promoter of AURKA, which may be a promising target for molecular therapy.
Plastic Scintillators with Bismuth Nanoparticles for Low Energy Gamma Spectroscopy Using Subtraction Method

Nowadays, gamma spectroscopy using a multichannel analyzer (MCA) with scintillator is the most widely used method to identify gamma-emitted radioactive nuclides by finding the photoelectric peaks in the gamma energy spectrum. When the gamma-ray enters the scintillator, the scintillating light is generated and the types of nuclides are determined by analyzing the spectrum of scintillating light. In most cases, inorganic scintillators are used to identify gamma-emitted nuclides due to high density and effective atomic number. However, inorganic scintillators are inappropriate to make large sensors for portal monitoring because of their high cost and difficult fabrication process. On the other hand, plastic scintillators generally need a much lower production cost and are easy to make in large sizes. But they have poor light yield, low effective atomic number to occur enough photoelectric effects [1]. In this study, to increase effective atomic number and the density of the plastic scintillator, we use bismuth nanoparticle as it has the highest Z number among nonradioactive isotopes and its derivatives are less toxic than lead or tin equivalents [2,3,4]. A plastic scintillator with bismuth nanoparticles is used to measure gamma spectrum of low energy emitting radionuclides below 150 keV.
Success Factors of Process Digitalization Projects – Two Multiple-Case Studies in the German Manufacturing Industry

: Digitalization entails substantial change for organizations. Novel products emerge, bringing about opportunities in each industrial sector. As things stand, many organizations not only struggle to integrate digital technologies (DTs) into their products, but also fail to capitalize on the ability of DTs to improve business processes. Against this backdrop, our study aims at identifying factors driving the success of process digitalization projects (PDPs). We first conducted a structured literature review to build an ex-ante model of PDP success factors. After that, we conducted a multiple-case study (MCS) of completed PDPs, using its results to revise the ex-ante model from an interpretive stance. Taking a pragmatic approach, we finally performed an MCS of ongoing PDPs to compile an ex-post model. The resulting PDP Success Model covers 29 success factors, whereof ten are new and 19 build on existing research. Our findings extend the knowledge on digital transformation and process digitalization.
Grasshopper Optimization with Stacked Autoencoder based Data Mining Approach for Healthcare Sector

Data mining (DM) is the procedure of pattern discovery and abstraction where massive quantity of data is involved. It finds its applicability in several areas, particularly healthcare. The use of DM in the healthcare industry enables to design of effective early disease diagnosis models based on the available medical data. The recently developed machine learning (ML) models pave the way to design effective DM models for healthcare sector. This article introduces a grasshopper optimization with stacked autoencoder based data mining (GOSAE-DM) technique for healthcare sector. The proposed GOSAE-DM technique primarily aims to handle the class imbalance problem and then performance classification process. The GOSAE-DM technique involves ADASYN technique for handling class imbalance issues. Besides, SAE model is applied for the classification of balanced data, which determines the existence of the disease. In order to boost the classifier results of the SAE model, the GOA is applied and thereby enhances the classifier results. A wide range of experiments was carried out using benchmark medical dataset and the results are inspected under several dimensions. The comparative study reported the betterment of the GOSAE-DM technique over the recent techniques interms of different measures.
Changes of Mental State and Serum Prolactin Levels in Patients with Schizophrenia and Depression after Receiving the Combination Therapy of Amisulpride and Chloroprothixol Tablets

Objective To investigate the changes in mental state and serum prolactin levels in patients with schizophrenia and depression after receiving the combination therapy of amisulpride and chloroprothixol tablets. Methods A total of 148 schizophrenic patients with depression were randomly divided into control group (N = 73) and study group (N = 75). The control group was treated with clopidothiol, and the study group was treated with amisulpride. Symptom scores, sleep quality, adverse reactions, therapeutic effects, prolactin, and progesterone levels, HAMD, PANSS, and PSP scores were compared between the two groups. Results The symptom scores of both groups were significantly reduced, but when compared to the control group, the symptom scores of the research group were significantly reduced more significantly (P < 0.05); serum GDNF levels of both groups were significantly increased, while serum NSE, IL-1, and MBP levels were significantly reduced (P < 0.05). However, the research group altered more substantially (P < 0.05) than the control group; the overall PSQI score of the research group was lower (P < 0.05) than the control group; and the incidence of adverse responses in the control and study groups was 12.3 percent and 4.0 percent. The research group had a lower rate of adverse responses (P < 0.05) than the control group, and the effective treatment of the control and research groups was 82.2 percent and 98.7%, respectively. The research group had a lower rate of adverse reactions (P < 0.05) than the control group, while the control and research groups' successful treatment rates were 82.2 percent and 98.7%, respectively. When compared to the control group, the research group had a greater treatment efficiency (P < 0.05); blood prolactin and progesterone levels were considerably lowered in both groups, but the reductions in the research group were more evident (P < 0.05). Both groups had considerably lower HAMD and PANSS scores, and both had significantly higher PSP scores, although the difference in the research group was more evident (P < 0.05). Conclusion For people with schizophrenia and depression, a combination of amisulpride and chloroprothixol pills has a considerable effect. It can help patients with their clinical symptoms and sleep quality while also lowering their serum prolactin levels, which is favorable to their illness recovery. As a result, the combined treatment of amisulpride and chloroprothixol pills deserves to be promoted and used.
Accurate High-Frequency Modeling of the Input Admittance of PWM Grid-Connected VSCs

This article addresses high-frequency admittance modeling of current-controlled voltage source converters (VSCs). Recent studies have shown that harmonic instability may also occur at frequencies above the Nyquist frequency. To form an accurate multiple-frequency model in this frequency range, sidebands that originate from modulation and sampling must be examined. In this article, an accurate small-signal model is developed taking into account an adequate digital pulsewidth modulator (DPWM) representation, which allows to predict dependence of the frequency response on the steady-state dc (SS-dc) operating point. It is shown that when center-pulse sampling is implemented, pulsewidth modulation sidebands do not create additional loops leaving only sampling sidebands to be considered. Using the same approach as for SS-dc operation, a model that accurately represents admittance measurements during sinusoidal ac (S-ac) operation is developed. Its basis is a novel DPWM model suitable for S-ac regime, which allows to predict dependence of the VSC’s input admittance on the grid voltage magnitude. Experimental and simulated admittance measurements, performed on a single-phase two-level VSC during various SS-dc and S-ac regimes, match with the proposed models up to twice the sampling frequency.