Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cross-Sectional Study: Structural and Related Functional Connectivity Changes in the Brain: Stigmata of Adverse Parenting in Patients with Major Depressive Disorder?

Background: There is a high correlation between the risk of major depressive disorder (MDD) and adverse childhood experiences (ACEs) such as adverse parenting (AP). While there appears to be an association between ACEs and changes in brain structure and function, there have yet to be multimodal neuroimaging studies of associations between parenting style and brain developmental changes in MDD patients. To explore the effect of AP on brain structure and function. Methods: In this cross-sectional study, 125 MDD outpatients were included in the study and divided into the AP group and the optimal parenting (OP) group. Participants completed self-rating scales to assess depressive severity, symptoms, and their parents’ styles. They also completed magnetic resonance imaging within one week of filling out the instruments. The differences between groups of gender, educational level, and medications were analyzed using the chi-squared test and those of age, duration of illness, and scores on scales using the independent samples t-test. Differences in gray matter volume (GMV) and resting-state functional connectivity (RS-FC) were assessed between groups. Results: AP was associated with a significant increase in GMV in the right superior parietal lobule (SPL) and FC between the right SPL and the bilateral medial superior frontal cortex in MDD patients. Limitations: The cross-cultural characteristics of AP will result in the lack of generalizability of the findings. Conclusions: The results support the hypothesis that AP during childhood may imprint the brain and affect depressive symptoms in adulthood. Parents should pay attention to the parenting style and avoid a style that lacks warmth.
Seroprevalence and Risk Factors of Lyme Borreliosis in The Netherlands: A Population-Based Cross-Sectional Study

Lyme borreliosis (LB) is not notifiable in many European countries, and accurate data on the incidence are often lacking. This study aimed to determine the seroprevalence of Borrelia burgdorferi sensu lato (s.l.)-specific antibodies in the general population of The Netherlands, and to determine risk factors associated with seropositivity. Sera and questionnaires were obtained from participants (n = 5592, aged 0–88 years) enrolled in a nationwide serosurveillance study. The sera were tested for B. burgdorferi s.l. -specific IgM and IgG antibodies using ELISA and immunoblot. Seroprevalence was estimated controlling for the survey design. Risk factors for seropositivity were analyzed using a generalized linear mixed-effect model. In 2016/2017, the seroprevalence in The Netherlands was 4.4% (95% CI 3.5–5.2). Estimates were higher in men (5.7% [95% CI 4.4–7.2]) than in women (3.1% [95% CI 2.0–4.0]), and increased with age from 2.6% (95% CI 1.4–4.4) in children to 7.7% (95% CI 5.9–7.9) in 60- to 88-year-olds. The seroprevalence for B. burgdorferi s.l. in the general population in The Netherlands was comparable to rates reported in European countries. The main risk factors for seropositivity were increasing age, being male and the tick bite frequency. The dynamics of LB infection are complex and involve variables from various disciplines. This could be further elucidated using infectious disease modelling.
Development and Evaluation of an Acute Asthma Model Induced by Ovalbumin and Formaldehyde Exposure in Murine

Agrawal et al. : Development of Acute Asthma Model in Murine Asthma is a common prolonged inflammatory disease of the upper respiratory tract, affecting millions of people in all age groups. At present, the treatment of asthma has become a challenge for practitioners because of the regular increment in air pollution worldwide. Subsequently, during the new drug development process, preclinical assessment of active compounds through a quick and robust model is urgently required. Thus, this study aimed to establish an acute asthma model using ovalbumin and formaldehyde in mice. In this experimental study, all animals were sensitized with ovalbumin on 0 th and 5 th d and formaldehyde on the 3 rd , 4 th , 6 th , and 7 th d followed by an ovalbumin challenge from 8 th to 10 th d. During induction of disease, individual groups received beclomethasone and rutin as standard and test drugs, respectively. At the end of the study, the whole lung was harvested, weighed, and assessed for lung weight index. Also, oxidative stress and inflammatory biomarkers were estimated, and lung histopathology was performed. In the results, it was observed that the lung weight index, total leukocyte count and differential leukocyte count, and malondialdehyde values, significantly (p<0.05) decreased, and superoxide dismutase, glutathione and catalase activity were markedly (p<0.05) increased in the rutin treated group as compared to the diseased group. Histopathological studies of lung tissue in the diseased group showed extensive perivascular edema. However, in the rutin-treated group, mucus gland hyperplasia was significantly decreased. Therefore, in this experimental research, an attempt was made to develop a successful acute asthma model within a short period, which reversed on repeated dosing with rutin in murine.
Perhexiline: Old Drug, New Tricks? A Summary of Its Anti-Cancer Effects

Cancer metabolic plasticity, including changes in fatty acid metabolism utilisation, is now widely appreciated as a key driver for cancer cell growth, survival and malignancy. Hence, cancer metabolic pathways have been the focus of much recent drug development. Perhexiline is a prophylactic antianginal drug known to act by inhibiting carnitine palmitoyltransferase 1 (CPT1) and 2 (CPT2), mitochondrial enzymes critical for fatty acid metabolism. In this review, we discuss the growing evidence that perhexiline has potent anti-cancer properties when tested as a monotherapy or in combination with traditional chemotherapeutics. We review the CPT1/2 dependent and independent mechanisms of its anti-cancer activities. Finally, we speculate on the clinical feasibility and utility of repurposing perhexiline as an anti-cancer agent, its limitations including known side effects and its potential added benefit of limiting cardiotoxicity induced by other chemotherapeutics.
Origins and Levels of Monthly and Seasonal Forecast Skill for United States Surface Air Temperatures Determined by Canonical Correlation Analysis

Abstract Statistical techniques have been used to study the ability of SLP, SST and a form of persistence to forecast cold/warm season air temperatures over the United States and to determine the space–time evolution of these fields that give rise to forecast skill. It was found that virtually all forecast skill was due to three climatological features: a decadal scale change in Northern Hemisphere temperature, ENSO-related phenomena, and the occurrence of two distinct short-lived, but large-scale, coherent structures in the atmospheric field of the Northern Hemisphere. The physical mechanisms responsible for the first two signals are currently unknown. One of the large-scale, coherent features seems largely independent of the ENSO phenomena, while the second is at least partially related to ENSO and may be part of a recently discovered global mode of SLP variation. Both features resemble various combinations of known teleconnection patterns. These large-scale coherent structures are essentially stationar.
Weighted pseudo almost periodic solutions of octonion-valued neural networks with mixed time-varying delays and leakage delays

In this paper, we propose a class of octonion-valued neural networks with leakage delays and mixed delays. Considering that the multiplication of octonion algebras does not satisfy the associativity and commutativity, we can obtain the existence and global exponential stability of weighted pseudo almost periodic solutions for octonion-valued neural networks with leakage delays and mixed delays by using the Banach fixed point theorem, the proof by contradiction and the non-decomposition method. Finally, we will give one example to illustrate the feasibility and effectiveness of the main results.
Comparing the Travel Behavior of Older Users Between Station-Based and Dockless Bike Sharing Systems

As the population worldwide continues to age, providing improved mobility services for older people becomes increasingly crucial. In recent years, the maturation of bike sharing has led older people to progressively incorporate it into their everyday lives. In this study, station-based bike sharing (SBBS) and dockless bike sharing (DBS) are investigated in relation to older population mobility. This research relies on the fusion of multiple data sources, including SBBS and DBS trip records, point of interest (POI), and meteorological conditions in Nanjing, China. A zero-inflated negative binomial regression model is used to compare the travel behaviors among various age groups with the exposure variable. SBBS outperforms DBS in relation to age equity, while diverse travel behavior patterns are exhibited between the age groups. Although younger users are the majority of users, older users travel more frequently than younger users in bike sharing services. Also, older users are more likely to use shared bikes near the religious and health POIs, as well as in higher population density areas. This study provides beneficial insights into the transport equity and service improvement of bike sharing systems.
Vector Autoregressions and Macroeconomic Modeling: An Error Taxonomy

In this article, we investigate the theoretical behavior of finite lag models fitted to time series that in truth come from an infinite-order data-generating mechanism. We show that the overall error can be broken down into two basic components, an estimation error that stems from the difference between the parameter estimates and their population ensemble counterparts, and an approximation error that stems from the difference between the and the true . The two sources of error are shown to be present in other performance indicators previously employed in the literature to characterize, so-called, truncation effects. Our theoretical analysis indicates that the magnitude of the estimation error exceeds that of the approximation error, but experimental results based upon a prototypical real business cycle model and a practical example indicate that the approximation error approaches its asymptotic position far more slowly than does the estimation error, their relative orders of magnitude notwithstanding. The experimental results suggest that with sample sizes and lag lengths like those commonly employed in practice models are likely to exhibit serious errors of both types when attempting to replicate the dynamics of the true underlying process and that inferences based on models can be very untrustworthy.
Isotropic PCA and Affine-Invariant Clustering (Extended Abstract)

We present an extension of Principal Component Analysis (PCA) and a new algorithm for clustering points in R n based on it. The key property of the algorithm is that it is affine-invariant. When the input is a sample from a mixture of two arbitrary Gaussians, the algorithm correctly classifies the sample assuming only that the two components are separable by a hyperplane, i.e., there exists a halfspace that contains most of one Gaussian and almost none of the other in probability mass. This is nearly the best possible, improving known results substantially [12, 10, 1]. For k> 2 components, the algorithm requires only that there be some (k − 1)dimensional subspace in which the “overlap” in every direction is small. Our main tools are isotropic transformation, spectral projection and a simple reweighting technique. We call this combination isotropic PCA.
Association Between Preoperative Multidimensional Psychological Distress and Physical Function After Surgery for Sports-Related Injury

Background: Psychological distress after orthopaedic surgery can lead to worse outcomes, including higher levels of disability and pain and lower quality of life. The 10-item Optimal Screening for Prediction for Referral and Outcome–Yellow Flag (OSPRO-YF) survey screens for multiple psychological constructs relevant to recovery from orthopaedic injury and may be useful to preoperatively identify patients who may require further psychological assessment and possible intervention after surgery. Purpose/Hypothesis: To determine the association between the OSPRO-YF and physiological patient-reported outcomes (PROs). It was hypothesized that higher OSPRO-YF scores (indicating worse psychological distress) would be associated with worse PRO scores at time of return to sport. Study Design: Case series; Level of evidence, 4. Methods: This study evaluated 107 patients at a single, academic health center who were assessed at a sports orthopaedics clinic and ultimately treated surgically for injuries to the knee, shoulder, foot, or ankle. Preoperatively, patients completed the OSPRO-YF survey as well as the following PRO measures: Patient-Reported Outcomes Measurement Information System (PROMIS), Single Assessment Numeric Evaluation, numeric rating scale for pain; American Shoulder and Elbow Surgeons standardized shoulder assessment form for patients with shoulder injuries, the International Knee Documentation Committee score (for patients with knee injuries), and the Foot and Ankle Ability Measure (FAAM; for patients with foot or ankle injuries). At the time of anticipated full recovery and/or return to sport, patients again completed the same PRO surveys. Multivariable regression was used to evaluate the association between total OSPRO-YF score at baseline and PRO scores at the time of functional recovery. Results: The baseline OSPRO-YF score predicted postoperative PROMIS Physical Function and FAAM Sports scores only. A 1-unit increase in the OSPRO-YF was associated with a 0.55-point reduction in PROMIS Physical Function (95% CI, –1.05 to –0.04; P = .033) indicating worse outcomes. Among patients who underwent ankle surgery, a 1-unit increase in OSPRO-YF was associated with a 6.45-point reduction in FAAM Sports (95% CI, –12.0 to –0.87; P = .023). Conclusion: The study findings demonstrated that the OSPRO-YF survey predicts certain long-term PRO scores at the time of expected return to sport, independent of baseline scores.
An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI

This paper proposes ensemble strategies for the deep learning object detection models carried out by combining the variants of a model and different models to enhance the anatomical and pathological object detection performance in brain MRI. In this study, with the help of the novel Gazi Brains 2020 dataset, five different anatomical parts and one pathological part that can be observed in brain MRI were identified, such as the region of interest, eye, optic nerves, lateral ventricles, third ventricle, and a whole tumor. Firstly, comprehensive benchmarking of the nine state-of-the-art object detection models was carried out to determine the capabilities of the models in detecting the anatomical and pathological parts. Then, four different ensemble strategies for nine object detectors were applied to boost the detection performance using the bounding box fusion technique. The ensemble of individual model variants increased the anatomical and pathological object detection performance by up to 10% in terms of the mean average precision (mAP). In addition, considering the class-based average precision (AP) value of the anatomical parts, an up to 18% AP improvement was achieved. Similarly, the ensemble strategy of the best different models outperformed the best individual model by 3.3% mAP. Additionally, while an up to 7% better FAUC, which is the area under the TPR vs. FPPI curve, was achieved on the Gazi Brains 2020 dataset, a 2% better FAUC score was obtained on the BraTS 2020 dataset. The proposed ensemble strategies were found to be much more efficient in finding the anatomical and pathological parts with a small number of anatomic objects, such as the optic nerve and third ventricle, and producing higher TPR values, especially at low FPPI values, compared to the best individual methods.
Baseline Predictors of the Long-Term Insufficient Biochemical Response in Patients with Autoimmune Hepatitis: A Single Center Experience

The treatment response criteria in autoimmune hepatitis (AIH) have been recently updated. This study aimed to assess treatment responses in 39 (16 males) patients with AIH confirmed by histology. Prednisone added to azathioprine or mycophenolate was the most frequent first-line treatment. Serum alanine aminotransferase (ALT) levels were periodically checked for a median of 45 months. Eight (20.5%) patients presented 4 weeks non-response (NR). Baseline lower multiples of ALT above the upper normal limit (UNL) (p = 0.005), Ishak liver fibrosis score > 3 (p = 0.029), and less frequent confluent necrosis > 2 (p < 0.001) were independent predictors of NR. 24 (61.5%) patients achieved complete biochemical response (CBR) at six months. Ishak liver fibrosis score ≤ 3 (p < 0.001), lobular eosinophilic infiltrate (p < 0.001), and ≥50% decrease in serum ALT levels at week 4 (p < 0.001) were independent predictors of CBR. In addition, the GLUCRE score, derived from the multiplication of serum creatinine (mg/dL) and glucose (mg/dL) levels, were identified. A baseline GLUCRE value > 100 strongly predicted CBR failure (p = 0.003) at a follow-up greater than 12 months. In conclusion, the absence of cirrhosis and a ≥50% UNL decrease in serum ALT levels were independent predictors for CBR. A baseline GLUCRE score may help identify patients maintaining longer CBR.
Few-shot HRRP recognition with distance-based distribution calibration and dynamic pseudo-label fine-tuning

In recent years, radar high-resolution range profile target recognition(HRRP) based on deep learning has achieved good result in practical recognition tasks. However, due to the limitations of various conditions in the realistic environment, it is difficult to obtain enough samples, which greatly limits recognition performance. From the perspective of transfer learning, this paper proposes a target recognition method of few HRRPs based on distance-based distribution calibration and dynamic pseudo-label fine-tuning(FCPL). This method calibrates the distribution of the features of target domain samples according to the distance in the feature space. Moreover, it creates pseudo-labels with source data in the label space of the target domain to fine-tune the model. Experimental results show that the method this paper proposed achieves higher recognition accuracy than traditional transfer learning and meta-learning on four types of few-shot targets. In addition, the method can be built on most model structures or methods and achieve the smooth transition from few-shot target recognition task to many-shot task.
Revisiting Biophysical Impacts of Greening on Precipitation Over the Loess Plateau of China Using WRF With Water Vapor Tracers

The Loess Plateau of China has witnessed a remarkable greening trend due to vegetation restoration in recent decades. However, the precipitation response to greening remains unclear, and the hydrological effect of greening is controversial. Here, we revisited biophysical effects of greening on precipitation over the plateau during 2002–2015 using the state‐of‐the‐art water vapor tracer embedded in a regional coupled model. We find that greening can promote the growing season precipitation (0.45 mm·day−1), with 15% and 85% of the precipitation increment resulting from increases in the local evapotranspiration and the water vapor inflow from outside the plateau, respectively. As a consequence, the enhanced precipitation can compensate for the terrestrial water loss driven by the increased evapotranspiration, leading to a slight increase in the water yield. This study highlights the dominant role of the nonlocal effect in precipitation responses to greening in this region.
Agreement between Electrical Cardiometry and Pulmonary Artery Thermodilution for Measuring Cardiac Output in Isoflurane-Anesthetized Dogs

Simple Summary Using general anesthesia on animals causes significant changes to the heart and blood vessels that disrupt normal cardiovascular performance. Blood pressure (BP) is used for closely monitoring hemodynamics in anesthetized animals. However, BP is affected by various factors and may not always accurately correlate with the total body blood flow. Pulmonary artery thermodilution (PATD) is the gold standard for cardiac output (CO) measurements, but, due to the high risks associated with its invasiveness, it is not performed in clinical settings. This study evaluates noninvasive electrical cardiometry’s (EC) performance in measuring CO and other hemodynamic variables in healthy anesthetized dogs during acute blood volume manipulation. The EC measurements consistently underpredict the CO values as compared with PATD, but have a better performance when acute blood loss occurs. Even though the EC readings have a slightly higher error than the accepted error, this method is very good at showing trends in the CO measured using PATD. Other EC-derived variables are able to closely track the changes in the CO measured using PATD. In clinics, noninvasive EC may benefit the patient care quality for anesthetized dogs by monitoring the trends in hemodynamics and guiding anesthetists to diagnose and treat cardiovascular complications. Abstract In animals, invasive pulmonary artery thermodilution (PATD) is a gold standard for cardiac output (CO) monitoring, but it is impractical in clinical settings. This study evaluates the agreement between PATD and noninvasive electrical cardiometry (EC) for measuring CO and analyzes the other EC-derived hemodynamic variables in six healthy anesthetized dogs subjected to four different hemodynamic events in a sequential order: (1) euvolemia (baseline); (2) hemorrhage (33% blood volume loss); (3) autologous blood transfusion; and (4) 20 mL/kg colloid bolus. The CO measurements obtained using PATD and EC are compared using Bland–Altman analysis, Lin’s concordance correlation (LCC), and polar plot analysis. Values of p < 0.05 are considered significant. The EC measurements consistently underpredict the CO values as compared with PATD, and the LCC is 0.65. The EC’s performance is better during hemorrhage, thus indicating its capability in detecting absolute hypovolemia in clinical settings. Even though the percentage error exhibited by EC is 49.4%, which is higher than the standard (<30%), EC displays a good trending ability. Additionally, the EC-derived variables display a significant correlation with the CO measured using PATD. Noninvasive EC may have a potential in monitoring trends in hemodynamics in clinical settings.
C and C* among intermediate rings

Given a completely regular Hausdorff space X, an intermediate ring A(X) is a ring of real valued continuous functions between C*(X) and C(X). We discuss two correspondences between ideals in A(X) and z-filters on X, both reviewing old results and introducing new results. One correspondence, ZA, extends the well-known correspondence between ideals in C*(X) and z-filters on X. The other, ζA, extends the natural correspondence between ideals in C(X) and z-filters on X. This paper highlights how these correspondences help clarify what properties of C*(X) and C(X) are shared by all intermediate rings and what properties of C*(X) and C(X) characterize those rings among intermediate rings. Using these correspondences, we introduce new classes of ideals and filters for each intermediate ring that extend the notion of z-ideals and z-filters for C(X), and with ZA, a new class of filters for each intermediate ring A(X) that extends the notion of e-filter for C*(X).