Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

Biolinguistic graph fusion model for circRNA-miRNA association prediction.

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

Attractant and repellent induce opposing changes in the four-helix bundle ligand-binding domain of a bacterial chemoreceptor.

Motile bacteria navigate toward favorable conditions and away from unfavorable environments using chemotaxis. Mechanisms of sensing attractants are well understood; however, molecular aspects of how bacteria sense repellents have not been established. Here, we identified malate as a repellent recognized by the MCP2201 chemoreceptor in a bacterium Comamonas testosteroni and showed that it binds to the same site as an attractant citrate. Binding determinants for a repellent and an attractant had only minor differences, and a single amino acid substitution in the binding site inverted the response to malate from a repellent to an attractant. We found that malate and citrate affect the oligomerization state of the ligand-binding domain in opposing way. We also observed opposing effects of repellent and attractant binding on the orientation of an alpha helix connecting the sensory domain to the transmembrane helix. We propose a model to illustrate how positive and negative signals might be generated.

Tetrahedral DNA loaded siCCR2 restrains M1 macrophage polarization to ameliorate pulmonary fibrosis in chemoradiation-induced murine model.

Idiopathic pulmonary fibrosis (IPF) is a chronic lethal disease in the absence of demonstrated efficacy for preventing progression. Although macrophage-mediated alveolitis is determined to participate in myofibrotic transition during disease development, the paradigm of continuous macrophage polarization is still under-explored due to lack of proper animal models. Here, by integrating 2.5 U/kg intratracheal Bleomycin administration and 10 Gy thorax irradiation at day 7, we generated a murine model with continuous alveolitis-mediated fibrosis, which mimics most of the clinical features of our involved IPF patients. In combination with data from scRNA-seq of patients and a murine IPF model, a decisive role of CCL2/CCR2 axis in driving M1 macrophage polarization was revealed, and M1 macrophage was further confirmed to boost alveolitis in leading myofibroblast activation. Multiple sticky-end tetrahedral framework nucleic acids conjunct with quadruple ccr2-siRNA (FNA-siCCR2) was synthesized in targeting M1 macrophages. FNA-siCCR2 successfully blocked macrophage accumulation in pulmonary parenchyma of the IPF murine model, thus preventing myofibroblast activation and leading to the disease remitting. Overall, our studies lay the groundwork to develop a novel IPF murine model, reveal M1 macrophages as potential therapeutic targets, and establish new treatment strategy by using FNA-siCCR2, which are highly relevant to clinical scenarios and translational research in the field of IPF.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

Type-I protein arginine methyltransferase inhibition primes anti-programmed cell death protein 1 immunotherapy in triple-negative breast cancer.

BACKGROUND: Immune-checkpoint blockade (ICB) therapy shows promise for treating aggressive triple-negative breast cancer (TNBC). However, only some patients benefit from ICB, revealing an urgent need for identifying novel strategies for sensitizing patients to ICB. Previously, the authors demonstrated that type-I protein arginine methyltransferases (PRMTs) regulated antiviral innate-immune responses in TNBC by altering RNA splicing. This study aimed to explore the effects of targeting type-I PRMTs on the tumor microenvironment (TME) and the efficacy of ICB therapy against TNBC. METHODS: Single-cell transcriptomic analysis was performed to investigate the effects of type-I PRMT inhibition on the TME, especially T-cell subsets. Single-cell T-cell receptor sequencing was performed to analyze the diversity and dynamics of the T-cell repertoire. A syngeneic murine model of TNBC was used to evaluate the therapeutic efficacy and immune memory effect of combining a type-I PRMT inhibitor (MS023) with an anti-programmed cell death protein 1 (PD-1) antibody. RESULTS: Type-I PRMT inhibition combined with anti-PD-1 therapy reduced tumor growth. Mechanistically, type-I PRMT inhibition reshaped the TME. Increased CD8 T-cell infiltration was verified using flow cytometry. Increased clonotypes and clonal diversity were also observed after MS023 treatment, which contributed to immune memory following combination treatment. CONCLUSIONS: Targeting type-I PRMT can potentially improve immunotherapeutic efficacies in patients with TNBC. By enhancing the tumor immunogenicity and promoting a more favorable immune microenvironment, this combined approach may enable more patients with TNBC to benefit from immunotherapies.

The ligand binding domain of a type IV pilus chemoreceptor PilJ has a different fold from that of another PilJ-type receptor McpN.

Bacterial chemoreceptors sense the extracellular signals and regulate bacterial motilities, biofilm formation, etc. The periplasmic ligand binding domains of chemoreceptors occur as different structural folds and recognize a diversity of chemical molecules. In Pseudomonas aeruginosa (PAO1), two bacterial chemoreceptors, McpN (PA2788) and PilJ (PA0411), are proposed to both contain a PilJ-like ligand-binding domain (LBD) (Pfam motif PF13675) and involved in nitrate chemotaxis and type IV pilus-mediated motility, respectively. The LBDs of McpN and PilJ consist of 135 and 263 residues, respectively, and share very low sequence identity, suggesting they might occur as different structures. Here, we found that PilJ-LBD folded into an HBM module, the same as the sensor domains of McpS-LBD and TorS-LBD, but it differed from that of McpN-LBD. We also observed a trimer in SEC and AUC and proposed a trimeric model based on the crystal structure. Based on the sequence, we classified the Pfam containing McpN-LBD and PilJ-LBD into three classes: sPilJ (single PilJ) represented by McpN-LBD with only one PilJ domain, dPilJ (dual PilJ) that contained dual PilJ domains, and hPilJ (hybrid PilJ) that comprises of a PilJ domain and another non-PilJ domain. Our work indicates a significant structural difference between the ligand binding domains of PilJ and McpN and will help our further study on both kinds of chemoreceptors.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

A novel circRNA-miRNA association prediction model based on structural deep neural network embedding.

A large amount of clinical evidence began to mount, showing that circular ribonucleic acids (RNAs; circRNAs) perform a very important function in complex diseases by participating in transcription and translation regulation of microRNA (miRNA) target genes. However, with strict high-throughput techniques based on traditional biological experiments and the conditions and environment, the association between circRNA and miRNA can be discovered to be labor-intensive, expensive, time-consuming, and inefficient. In this paper, we proposed a novel computational model based on Word2vec, Structural Deep Network Embedding (SDNE), Convolutional Neural Network and Deep Neural Network, which predicts the potential circRNA-miRNA associations, called Word2vec, SDNE, Convolutional Neural Network and Deep Neural Network (WSCD). Specifically, the WSCD model extracts attribute feature and behaviour feature by word embedding and graph embedding algorithm, respectively, and ultimately feed them into a feature fusion model constructed by combining Convolutional Neural Network and Deep Neural Network to deduce potential circRNA-miRNA interactions. The proposed method is proved on dataset and obtained a prediction accuracy and an area under the receiver operating characteristic curve of 81.61% and 0.8898, respectively, which is shown to have much higher accuracy than the state-of-the-art models and classifier models in prediction. In addition, 23 miRNA-related circular RNAs (circRNAs) from the top 30 were confirmed in relevant experiences. In these works, all results represent that WSCD would be a helpful supplementary reliable method for predicting potential miRNA-circRNA associations compared to wet laboratory experiments.

CYP1A2 Polymorphism and Drug Co-administration Affect the Blood Levels and Adverse Effects of Pirfenidone.

BACKGROUND: Mutations in metabolic enzymes and co-administration of drugs may affect the blood concentration of pirfenidone effective in pulmonary fibrosis. To provide a basis for the precise clinical use of pirfenidone, the authors analyzed the correlation between steady-state pirfenidone trough concentration and adverse drug reactions (ADRs) and examined the impact of CYP1A2*1C (rs2069514) and *1F (rs762551) variants and co-administration on pirfenidone blood concentrations and ADRs. METHODS: Forty-four patients were enrolled. The blood concentration of pirfenidone was determined using high-performance liquid chromatography. CYP1A2*1C and *1F genotypes were determined using direct SNP sequencing. Additional information related to drug associations was collected to screen factors affecting drug metabolism. RESULTS: The highest predictive value of ADRs was observed when the steady-state trough concentration of pirfenidone was 3.18 mcg·mL-1 and the area under the receiver operating characteristic curve was 0.701 (P = 0.024). The pirfenidone concentration-to-dose ratio (C/D) in CYP1A2*1F homozygous AA mutants was lower than that in C carriers (CC+AC) (1.28 ± 0.85 vs. 2.03 ± 1.28 mcg·mL-1; P = 0.036). Adverse drug reaction (ADR) incidence in the homozygous AA mutant group (28.0%) was significantly lower than that in the C carriers (CC+AC) (63.2%; P = 0.020), and ADR incidence in the A carriers (AC+AA) was considerably lower than that in the CC group (85.7%; P = 0.039). The C/D value of the combined lansoprazole/rabeprazole group was lower than that of the noncombination group (P < 0.05). CONCLUSIONS: The ADR incidence was positively correlated with pirfenidone blood concentration. The CYP1A2 (rs762551) AA genotype is associated with lower pirfenidone concentrations and fewer ADRs. Lansoprazole/rabeprazole co-administration reduced pirfenidone concentrations. Randomized controlled trials should further explore personalized dosing of pirfenidone and combination therapies.

Hydralazine loaded nanodroplets combined with ultrasound-targeted microbubble destruction to induce pyroptosis for tumor treatment.

Pyroptosis, a novel type of programmed cell death (PCD), which provides a feasible therapeutic option for the treatment of tumors. However, due to the hypermethylation of the promoter, the critical protein Gasdermin E (GSDME) is lacking in the majority of cancer cells, which cannot start the pyroptosis process and leads to dissatisfactory therapeutic effects. Additionally, the quick clearance, systemic side effects, and low concentration at the tumor site of conventional pyroptosis reagents restrict their use in clinical cancer therapy. Here, we described a combination therapy that induces tumor cell pyroptosis via the use of ultrasound-targeted microbubble destruction (UTMD) in combination with DNA demethylation. The combined application of UTMD and hydralazine-loaded nanodroplets (HYD-NDs) can lead to the rapid release of HYD (a demethylation drug), which can cause the up-regulation of GSDME expression, and produce reactive oxygen species (ROS) by UTMD to cleave up-regulated GSDME, thereby inducing pyroptosis. HYD-NDs combined with ultrasound (US) group had the strongest tumor inhibition effect, and the tumor inhibition rate was 87.15% (HYD-NDs group: 51.41 ± 3.61%, NDs + US group: 32.73%±7.72%), indicating that the strategy had a more significant synergistic anti-tumor effect. In addition, as a new drug delivery carrier, HYD-NDs have great biosafety, tumor targeting, and ultrasound imaging performance. According to the results, the combined therapy reasonably regulated the process of tumor cell pyroptosis, which offered a new strategy for optimizing the therapy of GSDME-silenced solid tumors.

The individuals' awareness and adoption of electronic health records in China: a questionnaire survey of 1,337 individuals.

BACKGROUND: Electronic health records (EHRs) are digital records of individual health information. However, their adoption and utilization remain low. This study explores the factors influencing the implementation of EHRs through a questionnaire survey to enhance individual awareness and adoption of EHRs. METHODS: A questionnaire and an expert rating scale were developed sequentially, and the consistency of the scores from five experts was calculated using Kendall's W to generate a final questionnaire. A non-parametric test was utilized to analyze differences in continuous data that did not follow a normal distribution. Categorical variables were expressed as percentages (%), the chi-square test was employed for group comparisons, and multiple logistic regression was implemented to assess individuals' awareness and adoption of EHRs. RESULTS: In total, 1,341 survey questionnaires were distributed between January and December 2022, with 1,337 valid responses (99.7%). The results indicated that the proportion of participants who were aware of EHRs and had a bachelor's degree or higher education, an income of ≥$700 per month, residence in urban areas, possessed self-care abilities, and underwent annual physical examinations was significantly higher than that without awareness of EHRs (P < 0.05), while in hearing problems and walking abilities was markedly lower than that of participants without awareness of EHRs (P < 0.05). Additionally, the proportion of individuals willing to self-manage EHRs was significantly higher than those reluctant to do so (P < 0.05) among participants with a bachelor's degree or higher education, an income of ≥$700 per month, residence in urban areas, possession of self-care abilities, annual physical examinations, hearing problems, and poor walking abilities. Age (Odds Ratio [OR] = 1.104, 95% Confidence Interval [CI] 1.001-1.028, P = 0.033), hearing problems (OR = 0.604, 95% CI 0.377-0.967, P = 0.036), self-care ability (OR = 5.881, 95% CI 1.867-18.529, P = 0.002), and annual physical examinations (OR = 3.167, 95% CI 2.31-4.34, P < 0.001) were independently associated with willingness to self-manage EHRs. Annual physical examination (OR = 2.507, 95%CI 1.585-2.669, P < 0.001) also independently made a difference to the awareness of EHRs. CONCLUSIONS: Our findings suggest that annual physical examinations, age, hearing problems, and self-care abilities are significant factors in assessing individuals' awareness and adoption of EHRs. Understanding the characteristics of individuals who are aware of or are willing to take advantage of EHRs plays a positive role in promoting their popularization and application.

The management pattern and outcomes of chronic thromboembolic pulmonary hypertension: rationale and design for a Chinese real-world study.

BACKGROUND: Chronic thromboembolic pulmonary hypertension (CTEPH) is a progressive pulmonary vascular disorder with substantial morbidity and mortality, also a disease underdiagnosed and undertreated. It is potentially curable by pulmonary endarterectomy (PEA) in patients with surgically accessible thrombi. Balloon pulmonary angioplasty (BPA) and targeted medical therapy are options for patients with distal lesions or persistent/recurrent pulmonary hypertension after PEA. There is an urgent need to increase the awareness of CTEPH. Qualified CTEPH centers are still quite limited. Baseline characteristics, management pattern and clinical outcome of CTEPH in China needs to be reported. METHODS AND DESIGN: The CHinese reAl-world study to iNvestigate the manaGEment pattern and outcomes of chronic thromboembolic pulmonary hypertension (CHANGE) study is designed to provide the multimodality treatment pattern and clinical outcomes of CTEPH in China. Consecutive patients who are ≥ 14 year-old and diagnosed with CTEPH are enrolled. The diagnosis of CTEPH is confirmed in right heart catheterization and imaging examinations. The multimodality therapeutic strategy, which consists of PEA, BPA and targeted medical therapy, is made by a multidisciplinary team. The blood sample and tissue from PEA are stored in the central biobank for further research. The patients receive regular follow-up every 3 or 6 months for at least 3 years. The primary outcomes include all-cause mortality and changes in functional and hemodynamic parameters from baseline. The secondary outcomes include the proportion of patients experiencing lung transplantation, the proportion of patients experiencing heart and lung transplantation, and changes in health-related quality of life. Up to 31 December 2023, the study has enrolled 1500 eligible patients from 18 expert centers. CONCLUSIONS: As a real-world study, the CHANGE study is expected to increase our understanding of CTEPH, and to fill the gap between guidelines and the clinical practice in the diagnosis, assessment and treatment of patients with CTEPH. REGISTRATION NUMBER IN CLINICALTRIALS.GOV: NCT05311072.

Serine deficiency exacerbates psoriatic skin inflammation by regulating S-adenosyl methionine-dependent DNA methylation and NF-κB signalling activation in keratinocytes.

BACKGROUND: Serine metabolism is crucial for tumour oncogenesis and immune responses. S-adenosyl methionine (SAM), a methyl donor, is typically derived from serine-driven one-carbon metabolism. However, the involvement of serine metabolism in psoriatic skin inflammation remains unclear. OBJECTIVES: To investigate the association between serine metabolism and psoriatic skin inflammation. METHODS: Clinical samples were collected from patients with psoriasis and the expression of serine biosynthesis enzymes was evaluated. The HaCaT human keratinocyte cell line was transfected with small interfering RNA (siRNA) of key enzyme or treated with inhibitors. RNA sequencing and DNA methylation assays were performed to elucidate the mechanisms underlying serine metabolism-regulated psoriatic keratinocyte inflammation. An imiquimod (IMQ)-induced psoriasis mouse model was established to determine the effect of the SAM administration on psoriatic skin inflammation. RESULTS: The expression of serine synthesis pathway enzymes, including the first rate-limiting enzyme in serine biosynthesis, phosphoglycerate dehydrogenase (PHGDH), was downregulated in the epidermal lesions of patients with psoriasis compared with that in healthy controls. Suppressing PHGDH in keratinocytes promoted the production of proinflammatory cytokines and enrichment of psoriatic-related signalling pathways, including the tumour necrosis factor-alpha (TNF-α) signalling pathway, interleukin (IL)-17 signalling pathway and NF-κB signalling pathway. In particular, PHGDH inhibition markedly promoted the secretion of IL-6 in keratinocytes with or without IL-17A, IL-22, IL-1α, oncostatin M and TNF-α (mix) stimulation. Mechanistically, PHGDH inhibition upregulated the expression of IL-6 by inhibiting SAM-dependent DNA methylation at the promoter and increasing the binding of myocyte enhancer factor 2A. Furthermore, PHGDH inhibition increased the secretion of IL-6 by increasing the activation of NF-κB via SAM inhibition. SAM treatment effectively alleviated IMQ-induced psoriasis-like skin inflammation in mice. CONCLUSIONS: Our study revealed the crucial role of PHGDH in antagonising psoriatic skin inflammation and indicated that targeting serine metabolism may represent a novel therapeutic strategy for treating psoriasis.

Biochemical insights into Paf1 complex-induced stimulation of Rad6/Bre1-mediated H2B monoubiquitination.

The highly conserved multifunctional polymerase-associated factor 1 (Paf1) complex (PAF1C), composed of five core subunits Paf1, Leo1, Ctr9, Cdc73, and Rtf1, participates in all stages of transcription and is required for the Rad6/Bre1-mediated monoubiquitination of histone H2B (H2Bub). However, the molecular mechanisms underlying the contributions of the PAF1C subunits to H2Bub are not fully understood. Here, we report that Ctr9, acting as a hub, interacts with the carboxyl-terminal acidic tail of Rad6, which is required for PAF1C-induced stimulation of H2Bub. Importantly, we found that the Ras-like domain of Cdc73 has the potential to accelerate ubiquitin discharge from Rad6 and thus facilitates H2Bub, a process that might be conserved from yeast to humans. Moreover, we found that Rtf1 HMD stimulates H2Bub, probably through accelerating ubiquitin discharge from Rad6 alone or in cooperation with Cdc73 and Bre1, and that the Paf1/Leo1 heterodimer in PAF1C specifically recognizes the histone H3 tail of nucleosomal substrates, stimulating H2Bub. Collectively, our biochemical results indicate that intact PAF1C is required to efficiently stimulate Rad6/Bre1-mediated H2Bub.

Dynamic mechanisms of exercise to improve body satisfaction: Perceived or actual fat loss?

We examined the dynamic mechanisms of aerobic training (AT) and strength training (ST) to improve body satisfaction. Sixty-six participants were randomised to either the AT or ST condition and completed an 8-week intervention. Participants completed 3 weekly, 30-minute sessions of moderate intensity AT (65-75% VO2 max) or ST (65-75% 1-RM). The energy consumption of each session was approximately equivalent under both conditions. Body satisfaction, body composition, perceived fitness and exercise self-efficacy were measured at baseline and biweekly during the intervention. Exercise improved individuals' body satisfaction (p < .05). When the energy expenditures of AT and ST were equal, there was no difference in body satisfaction improvement. There were dynamic mechanisms underlying the effects of exercise on body satisfaction. Specifically, perceived fitness influenced body satisfaction improvements during the early stages of the exercise program, while changes in body composition influenced body satisfaction toward the end of the exercise program. There were sex differences in the mechanisms underlying body satisfaction. For women, perceived fat was more important in the early intervention period; for men, actual body fat was more valuable in the late intervention period. An effective strategy to improve body satisfaction is to initially target perceived fitness before focusing on changing body composition.

Reference-based genome compression using the longest matched substrings with parallelization consideration.

BACKGROUND: A large number of researchers have devoted to accelerating the speed of genome sequencing and reducing the cost of genome sequencing for decades, and they have made great strides in both areas, making it easier for researchers to study and analyze genome data. However, how to efficiently store and transmit the vast amount of genome data generated by high-throughput sequencing technologies has become a challenge for data compression researchers. Therefore, the research of genome data compression algorithms to facilitate the efficient representation of genome data has gradually attracted the attention of these researchers. Meanwhile, considering that the current computing devices have multiple cores, how to make full use of the advantages of the computing devices and improve the efficiency of parallel processing is also an important direction for designing genome compression algorithms. RESULTS: We proposed an algorithm (LMSRGC) based on reference genome sequences, which uses the suffix array (SA) and the longest common prefix (LCP) array to find the longest matched substrings (LMS) for the compression of genome data in FASTA format. The proposed algorithm utilizes the characteristics of SA and the LCP array to select all appropriate LMSs between the genome sequence to be compressed and the reference genome sequence and then utilizes LMSs to compress the target genome sequence. To speed up the operation of the algorithm, we use GPUs to parallelize the construction of SA, while using multiple threads to parallelize the creation of the LCP array and the filtering of LMSs. CONCLUSIONS: Experiment results demonstrate that our algorithm is competitive with the current state-of-the-art algorithms in compression ratio and compression time.

Ligand Modulation of the Structures and Magnetic Relaxation in Triangular Dy3 Complexes Based on a Tricompartmental Scaffold.

Using a novel tricompartmental hydrazone ligand, a set of trinuclear Dy3 complexes has been isolated and structurally characterized. Complexes Dy3 ⋅ Cl, Dy3 ⋅ Br, and Dy3 ⋅ ClO4 feature a similar overall topology but different anions (Cl- , Br- , or ClO4 - ) in combination with exogenous OH- and solvent co-ligands, which is found to translate into very different magnetic properties. Complex Dy3 ⋅ Cl shows a double relaxation process with fast quantum tunneling of the magnetization, probably related to the structural disorder of µ2 -OH- and µ2 -Cl- co-ligands. Relaxation of the magnetization is slowed down for Dy3 ⋅ Br and Dy3 ⋅ ClO4 , which do not show any structural disorder. In particular, fast quantum tunneling is suppressed in case of Dy3 ⋅ ClO4 , resulting in an energy barrier of 341 K and magnetic hysteresis up to 3.5 K; this makes Dy3 ⋅ ClO4 one of the most robust air-stable trinuclear SMMs. Magneto-structural relationships of the three complexes are analyzed and rationalized with the help of CASSCF/RASSI-SO calculations.

X-Ray Triggered Coordination-Bond Breakage in Dysprosium-Organic Framework and its Impact on Magnetic Properties.

Photosensitive lanthanide-based single-molecule magnets (Ln-SMM) are very attractive for their potential applications in information storage, switching, and sensors. However, the light-driven structural transformation in Ln-SMMs hardly changes the coordination number of the lanthanide ion. Herein, for the first time it is reported that X-ray (λ=0.71073 Å) irradiation can break the coordination bond of Dy-OH2 in the three-dimensional (3D) metal-organic framework Dy2 (amp2 H2 )3 (H2 O)6 ⋅ 4H2 O (MDAF-5), in which the {Dy2 (OPO)2 } dimers are cross-linked by dianthracene-phosphonate ligands. The structural transformation proceeds in a single-crystal-to-single-crystal (SC-SC) fashion, forming the new phase Dy2 (amp2 H2 )3 (H2 O)4 ⋅ 4H2 O (MDAF-5-X). The phase transition is accompanied by a significant change in magnetic properties due to the alteration in coordination geometry of the DyIII ion from a distorted pentagonal bipyramid in MDAF-5 to a distorted octahedron in MDAF-5-X.

Expression and significance of cathepsin C and cathepsin D during pregnancy and Preeclampsia.

BACKGROUND: Cathepsin C (Cat C) is involved in the inflammatory-immune system and can be degraded by cathepsin D (Cat D). Preeclampsia (PE) and the inflammation-immunity relationship is currently a hot research topic, but there are still few studies. The aim was to investigate the expression and significance of Cat C and D in the serum of nonpregnant women, patients in various stages of pregnancy and patients with PE, and in the placenta of patients with normal pregnancy and PE. METHODS: Sixty young healthy nonpregnant women were selected: 180 normal pregnant women, including 60 each in the first, second, and third trimesters, and 100 women with PE, including 39 women with severe preeclampsia. The levels of Cat C and D in serum were detected by enzyme-linked immunosorbent assay (ELISA), and the expression levels of Cat C and D in placentas were detected by immunohistochemistry (IHC). RESULTS: The serum of Cat C in the first trimester was significantly lower than that in the nonpregnant group (P < 0.001), whereas Cat D was significantly higher than that in the nonpregnant group (P < 0.01). The levels of Cat C and D in the second trimester and third trimester were significantly higher than those in the first trimester (P < 0.05), but there was no significant difference in Cat C and D between the second trimester and third trimester. The levels of Cat C in the serum and placentas of patients with PE were significantly higher than those in the third trimester (P < 0.001) and positively correlated with the severity of PE (P < 0.001), whereas the levels of Cat D in the serum and placentas of patients with PE were significantly lower than those in the third trimester (P < 0.001) and negatively correlated with the severity of PE (P < 0.001). Age, primigravida proportion, and body mass index were significantly higher in the PE group than in the control group (P < 0.05), which were high-risk factors for PE. CONCLUSIONS: Cat C and D are associated with the maintenance of normal pregnancy. In patients with preeclampsia, a significant increase in Cat C and a significant decrease in Cat D levels may lead to the occurrence and development of preeclampsia.