An Exploration of Music Therapists\u27 Perspectives on Preventing and Treating Postpartum Depression

Music therapy has been proposed by many researchers and healthcare professionals (Beck, 2002; Simavli et al., 2014; Tseng et al., 2010) as an effective solution to treat Postpartum Depression (PPD). In this study, the causes of PPD, treatment and prevention of PPD, and the use and results of music therapy with pregnant women and/or mothers with PPD were examined to understand the effectiveness of music therapy and potential treatments for mothers with PPD. This qualitative study employed individual and semi-structured interviews of therapists working with women with PPD. All interviews were recorded and transcribed, then the data were summarized into four main categories of keywords. An analysis of individual and inter-categorical relationships was performed. The results suggested that music therapy is an effective approach of alleviating and preventing PPD, through creating a calm mental state, helping women develop confidence, building interpersonal connections and relationships, and decreasing negative thoughts. Music therapy can also be used as an adjunct treatment to counseling

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models

Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters. However, it is ineffective or even detrimental when applied to reasoning tasks in Smaller Language Models (SLMs) with less than 10 billion parameters. To address this limitation, we introduce Dialogue-guided Chain-of-Thought (DialCoT) which employs a dialogue format to generate intermediate reasoning steps, guiding the model toward the final answer. Additionally, we optimize the model's reasoning path selection using the Proximal Policy Optimization (PPO) algorithm, further enhancing its reasoning capabilities. Our method offers several advantages compared to previous approaches. Firstly, we transform the process of solving complex reasoning questions by breaking them down into a series of simpler sub-questions, significantly reducing the task difficulty and making it more suitable for SLMs. Secondly, we optimize the model's reasoning path selection through the PPO algorithm. We conduct comprehensive experiments on four arithmetic reasoning datasets, demonstrating that our method achieves significant performance improvements compared to state-of-the-art competitors.Comment: Accepted to EMNLP 202

The cellular distribution of Na+/H+ exchanger regulatory factor 1 is determined by the PDZ-I domain and regulates the malignant progression of breast cancer

The oncogenic role of ectopic expression of Na+/H+ exchanger regulatory factor 1 (NHERF1) was recently suggested. Here, we show that NHERF1 was upregulated in high grades compared with low grades. Increased NHERF1 expression was correlated with poor prognosis and poor survival. NHERF1 expression was higher in the nucleus of cancer cells than in contiguous non- mammary epithelial cells. A novel mutation, namely NHERF1 Y24S, was identified in human breast cancer tissues and shown to correspond to a conserved residue in the PDZ-I domain of NHERF1. Truncation and mutation of the PDZ-I domain of NHERF1 increased the nuclear distribution of the NHERF1 protein, and this redistribution was associated with the malignant phenotype of breast cancer cells, including growth, migration, and adhesion. The present results suggest a role for NHERF1 in the progression of breast cancer mediated by the nuclear distribution of the NHERF1 protein, as determined by the truncation or key site mutation of the PDZ-I domain

Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI

Introduction Functional brain networks (FBNs) estimated from functional magnetic resonance imaging (fMRI) data has become a potentially useful way for computer-aided diagnosis of neurological disorders, such as mild cognitive impairment (MCI), a prodromal stage of Alzheimer's Disease (AD). Currently, Pearson's correlation (PC) is the most widely-used method for constructing FBNs. Despite its popularity and simplicity, the conventional PC-based method usually results in dense networks where regions-of-interest (ROIs) are densely connected. This is not accordance with the biological prior that ROIs may be sparsely connected in the brain. To address this issue, previous studies proposed to employ a threshold or l_1-regularizer to construct sparse FBNs. However, these methods usually ignore rich topology structures, such as modularity that has been proven to be an important property for improving the information processing ability of the brain. Methods To this end, in this paper, we propose an accurate module induced PC (AM-PC) model to estimate FBNs with a clear modular structure, by including sparse and low-rank constraints on the Laplacian matrix of the network. Based on the property that zero eigenvalues of graph Laplacian matrix indicate the connected components, the proposed method can reduce the rank of the Laplacian matrix to a pre-defined number and obtain FBNs with an accurate number of modules. Results To validate the effectiveness of the proposed method, we use the estimated FBNs to classify subjects with MCI from healthy controls. Experimental results on 143 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) with resting-state functional MRIs show that the proposed method achieves better classification performance than previous methods

First report of GI.1aP-GI.2 recombinants of rabbit hemorrhagic disease virus in domestic rabbits in China

The rabbit hemorrhagic disease virus 2 (RHDV2 or GI.2) is a highly contagious agent leading to lethal disease in rabbits. It frequently recombines with other Lagovirus genus, generating epidemical variants with high pathogenicity. In this study, twenty-two liver samples tested positive for GI.2 VP60 gene, were collected in rabbit farms from several geographical regions in China. All GI.2 positive specimens were submitted for RT-PCR detection, nucleotide sequencing and phylogenetic analysis. In addition, suspected GI.2 recombinants were evaluated for virus virulence. The results showed that nine presumptive recombinants were identified by testing for RdRp-VP60 recombination. In these recombinants, four were selected to fully characterize the genome of novel GI.2 recombinant variants, which were described as GI.1aP-GI.2. The nucleotide sequence of these novel variants showed unique recombination pattern and phylogenetic features compared to currently prevalent GI.2 variants. Furthermore, this distinctive recombination of new variant SCNJ-2021 moderately enhanced the virulence of GI.2, even for rabbits vaccinated against parental GI.2. In conclusion, the novel GI.1aP-GI.2 recombinants were identified in rabbit industry in China for the first time, which expanded the knowledge on the phylodynamics and genomic diversity of GI.2 genotype. The rapid molecular evolution and varied pathogenicity of these virus recombinants highlight the urgent need for epidemiological surveillance and for future prevention of these neglected GI.2 variants

Designing a novel high-throughput AlphaLISA assay to quantify plasma NHERF1 as a non-small cell lung cancer biomarker

NHERF1 might play a significant role in biological processes including oncogenic transformation and metastasis. Owing to the lack of highly sensitive and quantitative methods of NHERF1 in human plasma, there have been few reports on the plasma levels of NHERF1 and its correlation with cancer. Here, a novel amplified luminescent proximity homogeneous immunoassay (AlphaLISA) has been developed and validated for the quantification of NHERF1 in human plasma. This assay was based on an AlphaScreen detection technique with two different anti-NHERF1 antibodies coupled to donor and acceptor beads, respectively. The developed AlphaLISA assay was further optimized and validated in terms of linearity, limit of detection (LOD), limit of quantification (LOQ), precision, recovery, selectivity and interferences. The linear range of NHERF1 in human plasma was 5.00–100 ng mL−1, with an LOD of 2.00 ng mL−1. This AlphaLISA assay has been successfully applied to the quantification of NHERF1 in the plasma from 75 patients with non-small cell lung cancer (NSCLC). The levels of NHERF1 protein in plasma from patients with NSCLC were significantly higher than those in the healthy group (p = 0.0004). Based on the evaluation of the ROC curves, measuring the content of NHERF1 in human plasma could provide a potential diagnostic tool for NSCLC

GEOCON BRIDGE: Geopolymer Concrete Mixture for Structural Applications

The sustainability of infrastructure projects is becoming increasingly important issue in engineering practice. This means that in the future the construction materials will be selected on the basis of the contribution they can make to reach sustainability requirements. Geopolymers are materials based on by-products from industries. By using geopolymer concrete technology it is possible to reduce our waste and to produce concrete in the environmental-friendly way. An 80% or greater reduction of greenhouse gases compared with Ordinary Portland Cement (OPC) can be achieved through geopolymer technology. However, there are limited practical applications and experience. For a broad and large scale industrial application of geopolymer concrete, challenges still exist in the technological and engineering aspects. The main goal of GeoCon Bridge project was to develop a geopolymer concrete mixture and to upscale it to structural application. The outputs of projects provide input for development of recommendations for structural design of geopolymer based reinforced concrete elements. Through a combination of laboratory experiments on material and structural elements, structural design and finite element simulations, and based on previous experience with OPC concrete, knowledge generated in this project provides an important step towards a “cement free” construction. The project was performed jointly by three team members: Microlab and Group of Concrete Structures from Technical University of Delft and Technical University of Eindhoven

Recent Advances and New Perspectives in Surgery of Renal Cell Carcinoma

Renal cell carcinoma (RCC) is one of the most common types of cancer in the urogenital system. For localized renal cell carcinoma, nephron-sparing surgery (NSS) is becoming the optimal choice because of its advantage in preserving renal function. Traditionally, partial nephrectomy is performed with renal pedicle clamping to decrease blood loss. Furthermore, both renal pedicle clamping and the subsequent warm renal ischemia time affect renal function and increase the risk of postoperative renal failure. More recently, there has also been increasing interest in creating surgical methods to meet the requirements of nephron preservation and shorten the renal warm ischemia time including assisted or unassisted zero-ischemia surgery. As artificial intelligence increasingly integrates with surgery, the three-dimensional visualization technology of renal vasculature is applied in the NSS to guide surgeons. In addition, the renal carcinoma complexity scoring system is also constantly updated to guide clinicians in the selection of appropriate treatments for patients individually. In this article, we provide an overview of recent advances and new perspectives in NSS