Miracle-dz/UniFAR-FTUs huggingface dataset:
The altered network complexity of resting-state functional brain activity in schizophrenia and bipolar disorder patients

Schizophrenia (SZ) and bipolar disorder (BD) are two of the most frequent mental disorders. These disorders exhibit similar psychotic symptoms, making diagnosis challenging and leading to misdiagnosis. Yet, the network complexity changes driving spontaneous brain activity in SZ and BD patients are still unknown. Functional entropy (FE) is a novel way of measuring the dispersion (or spread) of functional connectivities inside the brain. The FE was utilized in this study to examine the network complexity of the resting-state fMRI data of SZ and BD patients at three levels, including global, modules, and nodes. At three levels, the FE of SZ and BD patients was considerably lower than that of normal control (NC). At the intra-module level, the FE of SZ was substantially higher than that of BD in the cingulo-opercular network. Moreover, a strong negative association between FE and clinical measures was discovered in patient groups. Finally, we classified using the FE features and attained an accuracy of 66.7% (BD vs. SZ vs. NC) and an accuracy of 75.0% (SZ vs. BD). These findings proposed that network connectivity’s complexity analyses using FE can provide important insights for the diagnosis of mental illness.
Valuing interest rate derivatives using Monte Carlo simulation

In dieser Diplomarbeit wird die Anwendung von Monte Carlo Verfahren fur die Bewertung von Zinsderivaten diskutiert. Aufgrund der Vielzahl von unterschiedlichen Zinsmodellen, liegt das Hauptaugenmerk dieser Arbeit auf dem Vasicek, dem Ho- Lee und dem Hull-White Modell. Diese drei Short Rate Modelle werden in der Programmiersprache C++ implementiert. In weiterer Folge werden Verbesserungen der jeweiligen Simulationen diskutiert. Diese Verbesserungen reichen von der Auswahl eines Algorithmus zur Umwandlung von gleich verteilten Pseudo-Zufallszahlen in standardnormal verteilte Pseudo-Zufallszahlen, uber die Wahl einer geeigneten Programmiersprache, hin zu moglichen Optimierungen des Quellcodes bis hin zur Implementierung von Varianz reduzierenden Verfahren. Um die Standardabweichung der simulierten Preise zu verbessern, werden antithetische Zufallsvariablen und Kontrollvariate angewendet. Fur Letztere wurden beobachtete Preise von Anleihen gewahlt. Um das Vasicek Modell, das Ho-Lee Model und das Hull-White Modell zu implementieren, werden alle drei Modelle gemas beobachteten Marktpreisen eines Interest Rate Cap Agreements kalibriert. Die Kalibrierung zeigt, dass das Ho-Lee sowie das Hull-White Modell die beobachteten Marktpreise besser repliziert als das Vasicek Modell. In weiterer Folge werden die Preise eines Interest Rate Cap Agreements simuliert. Die simulierten Preise werden anschliesend mit explizit berechneten Preisen und beobachteten Marktpreisen verglichen. Die Ergebnisse zeigen, dass Verbesserungen der Simulationen aufgrund von Varianz reduzierenden Verfahren stets mit hoherem rechnerischen Aufwand verbunden sind. Schlieslich wird ein Periodic Cap Agreement auf Basis des Hull-White Modells als Fallstudie implementiert. Exemplarisch wird gezeigt, dass pfadabhangige Zinsderivate einfach bewertet werden konnen, sofern die Parameter des zugrunde liegenden Short Rate Prozesses bereits definiert sind. Schlussendlich wird die Relevanz von Monte Carlo Methoden fur die Bewertung von pfadabhangigen Zinsderivaten diskutiert.
Influence diagnostics for elliptical semiparametric mixed models

In this paper we extend semiparametric mixed linear models with normal errors to elliptical errors in order to permit distributions with heavier and lighter tails than the normal ones. Penalized likelihood equations are applied to derive the maximum penalized likelihood estimates (MPLEs) which appear to be robust against outlying observations in the sense of the Mahalanobis distance. A reweighed iterative process based on the back-fitting method is proposed for the parameter estimation and the local influence curvatures are derived under some usual perturbation schemes to study the sensitivity of the MPLEs. Two motivating examples preliminarily analyzed under normal errors are reanalyzed considering some appropriate elliptical errors. The local influence approach is used to compare the sensitivity of the model estimates.
Influencing factors of social entrepreneurship intentions in a higher education context

ABSTRACT The teaching of entrepreneurship has been progressively included in the curricula of several university courses to stimulate the development of empowering attitudes and an entrepreneurial mentality. However, a new form of entrepreneurship has emerged with a focus on sustainability and the creation of new projects that aim to reduce social asymmetries and contribute to a fairer and more balanced society. The role of universities is also to foster the emergence of these projects through the implementation of practices aimed at fostering social entrepreneurship among students. This study aims to understand the determinant dimensions that characterise the students’ social entrepreneurial intention. For this purpose, a sample of 177 students attending a social entrepreneurship course in a higher education institution was employed. The findings indicate that individual, organisation, and context constructs are determinants of students’ entrepreneurial intention. However, not all organisational factors contribute equally. Mentoring and social networks are relevant elements for the entrepreneurial intention of individuals, while curriculum and critical pedagogy are not recognised as determinants.
Neuronal Ndst1 depletion accelerates prion protein clearance and slows neurodegeneration in prion infection

Select prion diseases are characterized by widespread cerebral plaque-like deposits of amyloid fibrils enriched in heparan sulfate (HS), a major extracellular matrix component. HS facilitates fibril formation in vitro, yet how HS impacts fibrillar plaque growth within the brain is unclear. Here we found that prion-bound HS chains are highly sulfated, and that the sulfation is essential for HS accelerating prion conversion in vitro. Using conditional knockout mice to deplete the HS sulfation enzyme, Ndst1 (N-deacetylase, N-sulfotransferase), from neurons or astrocytes, we investigated how reducing HS sulfation impacts survival and prion aggregate distribution during a prion infection. Neuronal Ndst1-depleted mice survived longer and showed fewer and smaller parenchymal plaques, shorter fibrils, and increased vascular amyloid, consistent with enhanced aggregate transit toward perivascular drainage channels. The prolonged survival was strain-dependent, affecting mice infected with extracellular, plaque-forming, but not membrane bound, prion strains. Live PET imaging revealed rapid clearance of prion protein monomers into the CSF in mice expressing unsulfated HS, further suggesting that HS sulfate groups hinder transit of extracellular prion monomers. Our results directly show how a host cofactor slows the spread of prion protein through the extracellular space and identify an enzyme target to facilitate aggregate clearance. Author summary Prions cause a rapidly progressive neurologic disease and death with no curative treatment available. Prion aggregates accumulate exponentially in the brain in affected individuals triggering neuronal loss and neuroinflammation. Yet the additional molecules that facilitate aggregation are largely unknown, and their identification may lead to new therapeutic targets. We have found that prions in the brain preferentially bind to a highly sulfated endogenous polysaccharide, known as heparan sulfate (HS). Here we use genetically modified mice that express poorly sulfated neuron-derived HS, and infect mice with different prions strains. We find that the mice infected with a plaque-forming prion strain show a prolonged survival and fewer plaques compared to the controls. We also found that the prion protein was efficiently transported in the interstitial fluid in mice having poorly sulfated HS, suggesting that the prion protein is more readily cleared from the brain. Our study provides insight into how HS retains prion aggregates in the brain to accelerate disease and indicates the specific HS biosynthetic enzymes to target for enhancing protein clearance.
Orthologous marker groups reveal broad cell identity conservation across plant single-cell transcriptomes

Single-cell RNA sequencing (scRNA-seq) technology has been widely used in characterizing various cell types from in plant growth and development1–6. Applications of this technology in Arabidopsis have benefited from the extensive knowledge of cell-type identity markers7,8. Contrastingly, accurate labeling of cell types in other plant species remains a challenge due to the scarcity of known marker genes9. Various approaches have been explored to address this issue; however, studies have found many closest orthologs of cell-type identity marker genes in Arabidopsis do not exhibit the same cell-type identity across diverse plant species10,11. To address this challenge, we have developed a novel computational strategy called Orthologous Marker Gene Groups (OMGs). We demonstrated that using OMGs as a unit to determine cell type identity enables assignment of cell types by comparing 15 distantly related species. Our analysis revealed 14 dominant clusters with substantial conservation in shared cell-type markers across monocots and dicots.
Case report: Extending the spectrum of clinical and molecular findings in FOXC1 haploinsufficiency syndrome

FOXC1 is a ubiquitously expressed forkhead transcription factor that plays a critical role during early development. Germline pathogenic variants in FOXC1 are associated with anterior segment dysgenesis and Axenfeld-Rieger syndrome (ARS, #602482), an autosomal dominant condition with ophthalmologic anterior segment abnormalities, high risk for glaucoma and extraocular findings including distinctive facial features, as well as dental, skeletal, audiologic, and cardiac anomalies. De Hauwere syndrome is an ultrarare condition previously associated with 6p microdeletions and characterized by anterior segment dysgenesis, joint instability, short stature, hydrocephalus, and skeletal abnormalities. Here, we report clinical findings of two unrelated adult females with FOXC1 haploinsufficiency who have ARS and skeletal abnormalities. Final molecular diagnoses of both patients were achieved using genome sequencing. Patient 1 had a complex rearrangement involving a 4.9 kB deletion including FOXC1 coding region (Hg19; chr6:1,609,721-1,614,709), as well as a 7 MB inversion (Hg19; chr6:1,614,710-8,676,899) and a second deletion of 7.1 kb (Hg19; chr6:8,676,900-8,684,071). Patient 2 had a heterozygous single nucleotide deletion, resulting in a frameshift and a premature stop codon in FOXC1 (NM_001453.3): c.467del, p.(Pro156Argfs*25). Both individuals had moderate short stature, skeletal abnormalities, anterior segment dysgenesis, glaucoma, joint laxity, pes planovalgus, dental anomalies, hydrocephalus, distinctive facial features, and normal intelligence. Skeletal surveys revealed dolichospondyly, epiphyseal hypoplasia of femoral and humeral heads, dolichocephaly with frontal bossin gand gracile long bones. We conclude that haploinsufficiency of FOXC1 causes ARS and a broad spectrum of symptoms with variable expressivity that at its most severe end also includes a phenotype overlapping with De Hauwere syndrome.
The use of novel diffuse optical spectroscopies for improved neuromonitoring during neonatal cardiac surgery requiring antegrade cerebral perfusion

Background Surgical procedures involving the aortic arch present unique challenges to maintaining cerebral perfusion, and optimal neuroprotective strategies to prevent neurological injury during such high-risk procedures are not completely understood. The use of antegrade cerebral perfusion (ACP) has gained favor as a neuroprotective strategy over deep hypothermic circulatory arrest (DHCA) due to the ability to selectively perfuse the brain. Despite this theoretical advantage over DHCA, there has not been conclusive evidence that ACP is superior to DHCA. One potential reason for this is the incomplete understanding of ideal ACP flow rates to prevent both ischemia from underflowing and hyperemia and cerebral edema from overflowing. Critically, there are no continuous, noninvasive measurements of cerebral blood flow (CBF) and cerebral oxygenation (StO2) to guide ACP flow rates and help develop standard clinical practices. The purpose of this study is to demonstrate the feasibility of using noninvasive, diffuse optical spectroscopy measurements of CBF and cerebral oxygenation during the conduct of ACP in human neonates undergoing the Norwood procedure. Methods Four neonates prenatally diagnosed with hypoplastic left heart syndrome (HLHS) or a similar variant underwent the Norwood procedure with continuous intraoperative monitoring of CBF and cerebral oxygen saturation (StO2) using two non-invasive optical techniques, namely diffuse correlation spectroscopy (DCS) and frequency-domain diffuse optical spectroscopy (FD-DOS). Changes in CBF and StO2 due to ACP were calculated by comparing these parameters during a stable 5 min period of ACP to the last 5 min of full-body CPB immediately prior to ACP initiation. Flow rates for ACP were left to the discretion of the surgeon and ranged from 30 to 50 ml/kg/min, and all subjects were cooled to 18°C prior to initiation of ACP. Results During ACP, the continuous optical monitoring demonstrated a median (IQR) percent change in CBF of −43.4% (38.6) and a median (IQR) absolute change in StO2 of −3.6% (12.3) compared to a baseline period during full-body cardiopulmonary bypass (CPB). The four subjects demonstrated varying responses in StO2 due to ACP. ACP flow rates of 30 and 40 ml/kg/min (n = 3) were associated with decreased CBF during ACP compared to full-body CPB. Conversely, one subject with a higher flow6Di rate of 50 ml/kg/min demonstrated increased CBF and StO2 during ACP. Conclusions This feasibility study demonstrates that novel diffuse optical technologies can be utilized for improved neuromonitoring in neonates undergoing cardiac surgery where ACP is utilized. Future studies are needed to correlate these findings with neurological outcomes to inform best practices during ACP in these high-risk neonates.
Out-of-plane behavior of innovative infill walls made of autoclaved aereated concrete (AAC) subjected to shake table tests

Introduction: Seismic damage affecting the non-structural components of buildings (such as infills) can significantly impact the expected economic losses over a structure’s service life. Recently, autoclaved aerated concrete (AAC) infills have gained attention in the construction industry due to their light weight, energy efficiency, and durability. Methods: This study investigates the out-of-plane behavior of an innovative AAC infill wall system by means of shake table tests. The wall system was made of low-density AAC units with innovative joints made from polyurethane resin and steel plates for connection to a structural frame. Shake table tests were carried out using artificial accelerograms based on ICBO-AC156, which can reproduce a wide range of interstory drift demands. The study is articulated in three stages: the mechanical characterization of the AAC infill wall, shake table tests, and seismic behavior analysis of the wall based on current code requirements. Results and Discussions: The results demonstrate the effectiveness of the innovative AAC infill wall system in resisting out-of-plane seismic loads, also considering different levels of seismic action, with base accelerations reaching up to 0.80 g.
Platelets-related signature based diagnostic model in rheumatoid arthritis using WGCNA and machine learning

Background and aim Rheumatoid arthritis (RA) is an autoinflammatory disease that may lead to severe disability. The diagnosis of RA is limited due to the need for biomarkers with both reliability and efficiency. Platelets are deeply involved in the pathogenesis of RA. Our study aims to identify the underlying mechanism and screening for related biomarkers. Methods We obtained two microarray datasets (GSE93272 and GSE17755) from the GEO database. We performed Weighted correlation network analysis (WGCNA) to analyze the expression modules in differentially expressed genes identified from GSE93272. We used KEGG, GO and GSEA enrichment analysis to elucidate the platelets-relating signatures (PRS). We then used the LASSO algorithm to develop a diagnostic model. We then used GSE17755 as a validation cohort to assess the diagnostic performance by operating Receiver Operating Curve (ROC). Results The application of WGCNA resulted in the identification of 11 distinct co-expression modules. Notably, Module 2 exhibited a prominent association with platelets among the differentially expressed genes (DEGs) analyzed. Furthermore, a predictive model consisting of six genes (MAPK3, ACTB, ACTG1, VAV2, PTPN6, and ACTN1) was constructed using LASSO coefficients. The resultant PRS model demonstrated excellent diagnostic accuracy in both cohorts, as evidenced by area under the curve (AUC) values of 0.801 and 0.979. Conclusion We elucidated the PRSs occurred in the pathogenesis of RA and developed a diagnostic model with excellent diagnostic potential.
A review of data abstraction

It is well-known that Artificial Intelligence (AI), and in particular Machine Learning (ML), is not effective without good data preparation, as also pointed out by the recent wave of data-centric AI. Data preparation is the process of gathering, transforming and cleaning raw data prior to processing and analysis. Since nowadays data often reside in distributed and heterogeneous data sources, the first activity of data preparation requires collecting data from suitable data sources and data services, often distributed and heterogeneous. It is thus essential that providers describe their data services in a way to make them compliant with the FAIR guiding principles, i.e., make them automatically Findable, Accessible, Interoperable, and Reusable (FAIR). The notion of data abstraction has been introduced exactly to meet this need. Abstraction is a kind of reverse engineering task that automatically provides a semantic characterization of a data service made available by a provider. The goal of this paper is to review the results obtained so far in data abstraction, by presenting the formal framework for its definition, reporting about the decidability and complexity of the main theoretical problems concerning abstraction, and discuss open issues and interesting directions for future research.
Variational inference for detecting differential translation in ribosome profiling studies

Translational efficiency change is an important mechanism for regulating protein synthesis. Experiments with paired ribosome profiling (Ribo-seq) and mRNA-sequencing (RNA-seq) allow the study of translational efficiency by simultaneously quantifying the abundances of total transcripts and those that are being actively translated. Existing methods for Ribo-seq data analysis either ignore the pairing structure in the experimental design or treat the paired samples as fixed effects instead of random effects. To address these issues, we propose a hierarchical Bayesian generalized linear mixed effects model which incorporates a random effect for the paired samples according to the experimental design. We provide an analytical software tool, “riboVI,” that uses a novel variational Bayesian algorithm to fit our model in an efficient way. Simulation studies demonstrate that “riboVI” outperforms existing methods in terms of both ranking differentially translated genes and controlling false discovery rate. We also analyzed data from a real ribosome profiling experiment, which provided new biological insight into virus-host interactions by revealing changes in hormone signaling and regulation of signal transduction not detected by other Ribo-seq data analysis tools.
Evaluation of neoadjuvant immunotherapy plus chemotherapy in Chinese surgically resectable gastric cancer: a pilot study by meta-analysis

Background Recently, the use of immunochemotherapy in the treatment of advanced gastric cancer (GC) has been increasing and programmed cell death protein 1 (PD-1) inhibitors combined with chemotherapy has become the first-line treatment for advanced GC. However, few studies with small sample sizes have examined this treatment regimen to assess its effectiveness and safety in the neoadjuvant treatment phase of resectable local advanced GC. Materials and methods Herein, we systematically searched PubMed, Cochrane CENTRAL, and Web of Science for clinical trials on neoadjuvant immunochemotherapy (nICT) in advanced GC. The primary outcomes were effectiveness [evaluated by major pathological response (MPR) and pathological complete response (pCR)] and safety [assessed by grade 3–4 treatment-related adverse events (TRAEs) and postoperative complications]. A meta-analysis of non-comparative binary results was performed to aggregate the primary outcomes. Direct comparative analysis was used to compare pooled results of neoadjuvant chemotherapy (nCT) with nICT. The outcomes emerged as risk ratios (RR). Results Five articles with 206 patients were included, and all of them were from the Chinese population. The pooled pCR and MPR rates were 26.5% (95% CI: 21.3%–33.3%) and 49.0% (95% CI: 42.3%–55.9%), while grade 3–4 TRAEs and post-operative complication rates were 20.0% (95% CI: 9.1%–39.8%) and 30.1% (95% CI: 23.1%–37.9%), respectively. Direct comparison showed that with the exception of grade 3–4 TRAEs and postoperative complications, all outcomes including pCR, MPR, and R0 resection rate favoured nICT to nCT. Conclusion nICT is a promising strategy for use as an advisable neoadjuvant treatment for patients with advanced GC in Chinese population. However, more phase III randomized controlled trials (RCTs) will be required to further consolidate the efficacy and safety of this regimen.
Deep convolutional neural networks for multiple histologic types of ovarian tumors classification in ultrasound images

Objective This study aimed to evaluate and validate the performance of deep convolutional neural networks when discriminating different histologic types of ovarian tumor in ultrasound (US) images. Material and methods Our retrospective study took 1142 US images from 328 patients from January 2019 to June 2021. Two tasks were proposed based on US images. Task 1 was to classify benign and high-grade serous carcinoma in original ovarian tumor US images, in which benign ovarian tumor was divided into six classes: mature cystic teratoma, endometriotic cyst, serous cystadenoma, granulosa-theca cell tumor, mucinous cystadenoma and simple cyst. The US images in task 2 were segmented. Deep convolutional neural networks (DCNN) were applied to classify different types of ovarian tumors in detail. We used transfer learning on six pre-trained DCNNs: VGG16, GoogleNet, ResNet34, ResNext50, DensNet121 and DensNet201. Several metrics were adopted to assess the model performance: accuracy, sensitivity, specificity, FI-score and the area under the receiver operating characteristic curve (AUC). Results The DCNN performed better in labeled US images than in original US images. The best predictive performance came from the ResNext50 model. The model had an overall accuracy of 0.952 for in directly classifying the seven histologic types of ovarian tumors. It achieved a sensitivity of 90% and a specificity of 99.2% for high-grade serous carcinoma, and a sensitivity of over 90% and a specificity of over 95% in most benign pathological categories. Conclusion DCNN is a promising technique for classifying different histologic types of ovarian tumors in US images, and provide valuable computer-aided information.