Miracle-dz/UniFAR-FTUs huggingface dataset:
Young women’s transition to motherhood: relations with partners and visions of motherhood

The study uses a processual approach drawing on the modern definition of reproductive health, which recommends individualised measurements of populations. A case study analysis is performed to build a comprehensive empirical model of transitions in young women’s relations with their partners and visions of motherhood, which are critical to understanding young adult females’ reproductive health. Data necessary to reconstruct the setup of individual life paths were collected using exploratory interviews and questionnaires and analysed in the framework of the Strategy of Process Transformation Reconstruction (PTR). The PTR strategy is an innovative tool that combines the qualitative analysis of individual cases and C4.5 Quinlan’s algorithm to make generalisation. The empirical model of internally diverse types of relationships and visions of motherhood formed by young Polish women consists of a sequence of variants showing the evolution of the character, and ways of building and integrating intimate relationships with motherhood. The model can serve as a matrix for creating analytical categories that can be used to describe young women’s relations with their partners and visions of motherhood, construct psychological scales, and plan therapeutic procedures.
Reservoir sedimentation and flushing

This paper is concerned with the engineering assessment of the deposition and re-erosion of sediments in reservoirs. This deposition and re-erosion can be assessed by methods which vary in their complexity. Historically, simple empirical methods have been used which require only a limited amount of data and analysis. More recently, however, computer models have been developed which can simulate the mechanics of sediment movement within the reservoir as flows and levels change. These are used to predict sedimentation patterns and also to look at the possibility of the strategic flushing of sediments through the dam in order to extend the useful life of the scheme. An assessment of catchment sediment yield is a prerequisite to any consideration of sediment problems in reservoirs and this has been shown to be difficult and often imprecise. Sediment yields vary with climate, the geology of the catchment and land-use practices.
Maternal expectations and paternal self-efficacy as factors associated with the occurrence of depressive symptoms in parents after childbirth.

Depressive symptoms with onset after the birth of a child can affect both parents. The aim of the presented study was to determine whether fathers and mothers differ in terms of postpartum depression symptoms and such variables as the sense of parental competence and the prenatal expectations of discrepancy, and whether these variables are related to the occurrence of postpartum depression symptoms in women and men. 271 people, including 165 women and 106 men, who became parents in the last 12 months, were examined. The Edinburgh Postnatal Depression Scale (EPDS), Parental Sense of Competence Scale (PSOC) and Prenatal Expectations Scale (PES) was used. The results indicate differences between mothers and fathers in postpartum depressive symptoms, parental competencies, and prenatal expectations discrepancy as well. While maternal depression symptoms are determined by failure to meet prenatal expectations, depressive symptoms in new fathers are associated with a low sense of paternal competence.
The roles of FLOT1 in human diseases (Review)

FLOT1, a scaffold protein of lipid rafts, is involved in several biological processes, including lipid raft protein-dependent or clathrin-independent endocytosis, and the formation of hippocampal synapses, amongst others. Increasing evidence has shown that FLOT1 can function as both a cancer promoter and cancer suppressor dependent on the type of cancer. FLOT1 can affect the occurrence and development of several types of cancer by affecting epithelial-mesenchymal transition, proliferation of cancer cells, and relevant signaling pathways, and is regulated by long intergenic non-coding RNAs or microRNAs. In the nervous system, overexpression or abnormally low expression of FLOT1 may lead to the occurrence of neurological diseases, such as Alzheimer's disease, Parkinson's disease, major depressive disorder and other diseases. Additionally, it is also associated with dilated cardiomyopathy, pathogenic microbial infection, diabetes-related diseases, and gynecological diseases, amongst other diseases. In the present review, the structure and localization of FLOT1, as well as the physiological processes it is involved in are reviewed, and then the upstream and downstream regulation of FLOT1 in human disease, particularly in different types of cancer and neurological diseases are discussed, with a focus on potentially targeting FLOT1 for the clinical treatment of several diseases.
AN INVESTIGATION INTO HOW UNIVERSITY-LEVEL TEACHERS PERCEIVE CHAT-GPT IMPACT UPON STUDENT LEARNING

This research explores Chat -GPT inclusion into Pakistani higher education, addressing the issue of understanding teacher perspectives. To examine the relevance of this problem, an extensive survey involving 600 university teachers was undertaken using a positivist research paradigm. The findings show that teachers believe Chat-GPT has the ability to improve learning experiences by offering quick access to information and accommodating varied student demands. Concerns are raised, however about overreliance, ethical consequences, and possible impediments to critical thinking. These trends in data were revealed through statistical analysis, which included mean, standard deviation, correlation, and ANOVA. Concerns were raised, however, concerning the overreliance, ethical consequences, and possible barriers to critical thinking. Thus, longitudinal studies, qualitative research, thorough guidelines, and joint efforts are among the recommendations to enable the appropriate incorporation of AI technologies in education. This research adds to our understanding of the challenges of AI integration in education by highlighting need of balanced and well-informed educational methods.
Interpreting Randomized Controlled Trials

Simple Summary We provide an extensive review of the fundamental principles of statistical science that are needed to accurately interpret randomized controlled trials (RCTs). We use these principles to explain how RCTs are motivated by the powerful but strange idea that flipping a coin to choose each patient’s treatment is the most statistically reliable way to compare treatments. Random treatment assignment ensures fair comparisons between treatments because it does away with bias and confounding from variables other than treatment. If the goal is to estimate treatment effects in a patient population, rather than compare two or more treatments, then random sampling, not random treatment assignment, is required. However, random sampling is virtually impossible to carry out in a clinical trial because patients are accrued over time as they arrive in the clinic, subject to a trial’s entry criteria. Consequently, in practice, a trial provides a nonrepresentative convenience sample. Valid treatment comparisons provided by RCT data subsequently require additional causal assumptions of transportability of between-treatment effects from the sample to the patient population of interest. This may be used as a basis for choosing treatments for future patients. The present paper discusses what this means for practicing physicians who encounter RCT data in the literature. Abstract This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial’s sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial’s convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.
Biomedical generative pre-trained based transformer language model for age-related disease target discovery

Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.
The Impact of Institutional Distance on Innovation Performance in Chinese Reverse OFDI: Moderating Effects of Cultural Distance and Internationalization Experience

In contrast to developed economies, emerging market economies exhibit varied directions of investment based on the host country’s economic level. They grapple with the dual impact of institutional disparities while investing in developed countries to acquire strategic assets. This paper investigates the crucial role of institutional distance in reverse outward foreign direct investment (OFDI) from China and its mechanism of influencing corporate innovation. We combine institutional theory with the resource-based view and utilize a zero-inflation negative binomial model to test samples of Chinese listed companies. Our results demonstrate that institutional distance positively impacts parent corporate innovation performance in reverse OFDI. Additionally, we examine the interaction between institutional distance and cultural distance, enriching relevant research context. By studying two types of moderating variables, we attempt to seek effective ways to actively cope with this influence. The dual definition of both an organization’s internationalization and the top management team’s (TMT’s) overseas background expands the theoretical margin of internationalization experience.
Assembling genetic structure of Gardenia remyi, a critically endangered tree endemic to the Hawaiian Islands

Conservation and restoration planning of extremely rare species relies on an understanding of the genetic diversity and population dynamics within a species to overcome potential inbreeding depression. Nānū or Nāʻū (Gardenia remyi H. Mann.) is an endemic tree native to the Hawaiian Islands and is one of more than 200 endangered plant species in Hawaiʻi with less than 50 individuals remaining in the wild. Efforts to understand the genetic diversity and connectivity between wild populations are foundational to conservation management plans, however little is known of the population structure of the species. In this study we utilize double digest restriction‐site associated sequencing (ddRADSeq) on both historical herbarium specimens and samples from living ex situ collections to: (1) Test the hypothesis that we can capture genetic diversity in herbarium material of G. remyi using ddRADSeq, and (2) test the hypothesis that there are genetically distinct populations or subpopulation units among different Hawaiian islands. Usable sequencing data from thirty‐seven samples of herbarium specimens collected between 1952 and 2017 and twenty wild sourced living collection samples were obtained representing all four islands where G. remyi is known to occur. Phylogenetic and population structure analysis revealed a monophyletic ingroup and a clear division between G. remyi samples of the northern island of Kauaʻi and those from the more southeastern younger islands of Molokaʻi, Maui and Hawaiʻi islands. The Kauaʻi samples were further split into a subpopulation from Southern Kauaʻi and the subpopulations from Northern Kauaʻi. Some admixed samples were detected. Our results are consistent with subpopulations of G. remyi, which needs to be considered in future conservation planning and breeding efforts to minimize inbreeding depression.
A Faster Resonance Mode Analysis Approach Based on a Modified Shifted-Inverse Power Iteration Method

Resonance mode analysis (RMA) is a promising approach for harmonic power quality and stability studies because it enables the characterization of resonances. However, RMA requires the determination of the eigenpair decomposition of the nodal admittance matrix over the frequency scan, which is a high time-consuming task. Alternative RMA based on the power iteration (PI) method reduces this computational effort. The PI method is an efficient time-saving tool because it calculates only the dominant eigenvalue of the inverted nodal admittance matrix, but convergence of the method is seriously compromised by the matrix spectrum. To remedy this, the article contributes a faster RMA approach based on a modified shifted-inverse power iteration (MSIPI) method, which reduces the computational time of the above RMA approaches without loss of accuracy in the characterization of resonance frequencies and modal impedances. The MSIPI method applies a linear shift to the nodal admittance matrix, followed by an inverse transformation to the shifted matrix to conduct the iterative method towards the smallest modulus eigenvalue over the frequency scan with a fast convergence. The proposed approach is checked for performance in ten IEEE and three synthetic test power systems. MATLAB/Simulink simulations are compared with the proposed approach.
Enhanced Antibacterial Ability of Electrospun PCL Scaffolds Incorporating ZnO Nanowires

The infection of implanted biomaterial scaffolds presents a major challenge. Existing therapeutic solutions, such as antibiotic treatment and silver nanoparticle-containing scaffolds are becoming increasingly impractical because of the growth of antibiotic resistance and the toxicity of silver nanoparticles. We present here a novel concept to overcome these limitations, an electrospun polycaprolactone (PCL) scaffold functionalised with zinc oxide nanowires (ZnO NWs). This study assessed the antibacterial capabilities and biocompatibility of PCL/ZnO scaffolds. The fabricated scaffolds were characterised by SEM and EDX, which showed that the ZnO NWs were successfully incorporated and distributed in the electrospun PCL scaffolds. The antibacterial properties were investigated by co-culturing PCL/ZnO scaffolds with Staphylococcus aureus. Bacterial colonisation was reduced to 51.3% compared to a PCL-only scaffold. The biocompatibility of the PCL/ZnO scaffolds was assessed by culturing them with HaCaT cells. The PCL scaffolds exhibited no changes in cell metabolic activity with the addition of the ZnO nanowires. The antibacterial and biocompatibility properties make PCL/ZnO a good choice for implanted scaffolds, and this work lays a foundation for ZnO NWs-infused PCL scaffolds in the potential clinical application of tissue engineering.
Mapping and Assessment of Housing Informality Using Object-Based Image Analysis: A Review

Research on the detection of informal settlements has increased in the past three decades owing to the availability of high- to very-high-spatial-resolution satellite imagery. The achievement of development goals, such as the Sustainable Development Goals, requires access to up-to-date information on informal settlements. This review provides an overview of studies that used object-based image analysis (OBIA) techniques to detect informal settlements using remotely sensed data. This paper focuses on three main aspects: image processing steps followed when detecting informal settlements using OBIA; informal settlement indicators and image-based proxies used to detect informal settlements; and a review of studies that extracted and analyzed informal settlement land use objects. The success of OBIA in detecting informal settlements depends on the understanding and selection of informal settlement indicators and image-based proxies used during image classification. To meet the local ontology of informal settlements, the transfer of OBIA mapping techniques requires the fine-tuning of the rulesets. Machine learning OBIA techniques using image proxies derived from multiple sensors increase the opportunities for detecting informal settlements on the city or national level.
Efficacy and safety of immunotherapy combined with single-agent chemotherapy as second- or later-line therapy for metastatic non-small cell lung cancer

Objective This study sought to assess the efficacy and safety of immunotherapy combined with single-agent chemotherapy as a second- or later-line setting for metastatic non-small cell lung cancer (NSCLC) and to provide clinical evidence for this treatment regimen. The predictive value of extracellular vesicle (EV) membrane proteins was explored in patients who underwent this treatment. Methods Clinical data from patients diagnosed with metastatic NSCLC who received immunotherapy plus single-agent chemotherapy as a second- or later-line setting were retrospectively collected between March 2019 and January 2022. A total of 30 patients met the inclusion criteria, and all were pathologically confirmed to have NSCLC. Short-term efficacy, progression-free survival (PFS), EV markers for response prediction, and adverse events were assessed. Results Efficacy data were available for all 30 patients and included a partial response in 5 patients, stable disease in 18 patients, and disease progression in 7 patients. The objective response rate was 16.7%, the disease control rate was 76.7%, and the median PFS was 3.2 months. Univariate analysis showed that PFS was not associated with sex, age, smoking status, treatment lines, prior use of immunotherapy, or prior use of antiangiogenic drugs. The EV membrane proteins MET proto-oncogene, receptor tyrosine kinase (c-MET), epidermal growth factor receptor (EGFR), and vascular endothelial growth factor receptor 2 (VEGFR2) at baseline were associated with poor prognosis and correlated with the efficacy of immunotherapy plus chemotherapy. According to the receiver operating characteristics and Kaplan–Meier curve analyses, patients with high c-MET, EGFR, and VEGFR2 expression at baseline had significantly shorter PFS than those with low expression. In addition, VEGFR2 expression was increased after combined immunotherapy in responders, which was decreased in non-responders. The most common grade 2 or higher adverse events were neutropenia, gastrointestinal reactions, and thyroid dysfunction, all of which were tolerated. Conclusions Immunotherapy plus single-agent chemotherapy as a second- or later-line treatment is safe, effective, and tolerable for metastatic NSCLC. EV markers can be used as predictive markers of efficacy in patients with metastatic NSCLC treated with immunotherapy plus chemotherapy to help monitor treatment efficacy and guide treatment decisions.
Three-Dimensional Printing of Polycaprolactone/Nano-Hydroxyapatite Composite Scaffolds with a Pore Size of 300/500 μm is Histocompatible and Promotes Osteogenesis Using Rabbit Cortical Bone Marrow Stem Cells

Background Many patients have bone defects that exceed the healing size. This study aimed to construct polycaprolactone/nano-hydroxyapatite (PCL/nHA) composite scaffolds with different pore sizes and investigate the osteogenesis and histocompatibility of cortical bone mesenchymal stem cells (BMSCs-C) seeded on it after inoculation. Material/Methods After mixing PCL and nHA proportionally, three-dimensional (3D) printing was used to print scaffolds. Porosity, compressive strength, and elastic modulus of PCL/nHA scaffolds were tested. The proliferation of BMSCs-C cells was examined and osteogenesis, chondrogenesis, and adipogenesis were evaluated. BMSCs-C cells were inoculated into 3D printing scaffolds, and histocompatibility between BMSCs-C cells and scaffolds was observed by the cell count kit (CCK-8) assay and LIVE/DEAD staining. After inoculating BMSCs-C cells into scaffolds, alkaline phosphatase (ALP) activity and calcium content were measured. Results There was no obvious difference in characteristics between the 3 PCL/nHA composite scaffolds. The porosity, compressive strength, and elastic modulus of the 300/500-μm scaffold were between those of the 300-μm and 500-μm scaffolds. With increasing pore size, the mechanical properties of the scaffold decrease. BMSCs-C cells demonstrated faster growth and better osteogenic, adipogenic, and chondrogenic differentiation; therefore, BMSCs-C cells were selected as seed cells. PCL/nHA composite scaffolds with different pore sizes had no obvious toxicity and demonstrated good biocompatibility. All scaffolds showed higher ALP activity and calcium content. Conclusions The 300/500 μm mixed pore size scaffold took into account the mechanical properties of the 300 μm scaffold and the cell culture area of the 500 μm scaffold, therefore, 300/500 μm scaffold is a better model for the construction of tissue engineering scaffolds.