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Single-cell proteomics sequencing technology sheds light on protein-protein interactions, posttranslational modifications and proteoform dynamics in the cell. However, the uncertainty estimation for peptide quantification, data missingness, batch effects and high noise hinder the analysis of single-cell proteomic data. It is important to solve this set of tangled problems together, but the existing methods tailored for single-cell transcriptomes cannot fully address this task. Here we propose a versatile framework designed for single-cell proteomics data analysis called scPROTEIN, which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering, batch correction, cell type annotation, clinical analysis and spatially resolved proteomic data exploration.
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Aprendizagem , Proteômica , Análise por Conglomerados , Processamento de Proteína Pós-Traducional , PeptídeosRESUMO
MOTIVATION: Early cancer detection is significant for patient mortality rate reduction. Although machine learning has been widely employed in that context, there are still deficiencies. In this work, we studied different machine learning algorithms for early cancer detection and proposed an Adaptive Support Vector Machine (ASVM) method by synergizing Shuffled Frog Leaping Algorithm and Support Vector Machine (SVM) in this study. RESULTS: Since ASVM regulates SVM for parameter adaption based on data characteristics, the experimental results reflected the robust generalization capability of ASVM on different datasets under different settings; for instance, ASVM can enhance the sensitivity by over 10% for early cancer detection compared with SVM. Besides, our proposed ASVM outperformed Grid Search + SVM and Random Search + SVM by significant margins in terms of the area under the ROC curve (AUC) (0.938 versus 0.922 versus 0.921). AVAILABILITY AND IMPLEMENTATION: The proposed algorithm and dataset are available at https://github.com/ElaineLIU-920/ASVM-for-Early-Cancer-Detection. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Current studies in early cancer detection based on liquid biopsy data often rely on off-the-shelf models and face challenges with heterogeneous data, as well as manually designed data preprocessing pipelines with different parameter settings. To address those challenges, we present AutoCancer, an automated, multimodal, and interpretable transformer-based framework. This framework integrates feature selection, neural architecture search, and hyperparameter optimization into a unified optimization problem with Bayesian optimization. Comprehensive experiments demonstrate that AutoCancer achieves accurate performance in specific cancer types and pan-cancer analysis, outperforming existing methods across three cohorts. We further demonstrated the interpretability of AutoCancer by identifying key gene mutations associated with non-small cell lung cancer to pinpoint crucial factors at different stages and subtypes. The robustness of AutoCancer, coupled with its strong interpretability, underscores its potential for clinical applications in early cancer detection.
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Both ecological regime shifts and carbon cycling in lakes have been the subject of global debates in recent years. However, the direct linkage between them is poorly understood. Lake Baiyangdian, a representative large shallow lake with the coexistence of a macrophyte-dominated area (MDA) and an algae-dominated area (ADA) in eastern China, allowing better understanding of the relationship between regime shifts and organic carbon (OC) burial in lakes. On the basis of Bayesian isotopic mixing modelling of C/N ratios and δ13C values, the sediment OC is primarily of autochthonous origin. The mean OC burial rate (OCBR) was 39 g C m-2 yr-1 before eutrophication occurred in 1990 and increased approximately 2.7-fold to 106 g C m-2 yr-1 after eutrophication. Partial least squares path modelling revealed that this change can be largely attributed to enhanced primary productivity and rapid burial as a result of intensified human perturbation. In terms of spatial patterns, the OCBR was greater in the MDA than in the ADA, which may be related to the different burial and mineralization processes of debris from macrophytes and algae. It then deduced that a decrease in the OCBR and an increase in the mineralization rate might have occurred after a shift from a macrophyte-dominated state to an algae-dominated state. Our findings highlight that eutrophication generally increases OC burial by enhancing lake primary productivity. However, once nutrient levels reach a critical range, lake ecosystems may shift from a macrophyte-dominated state to an algae-dominated state, which can lead to a significant reduction in the carbon burial capacity of lakes. Therefore, more attention should be given to avoiding shifts in eutrophic lakes, as such shifts can alter carbon cycling.
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Alterations in the inflammatory and immune systems have been documented to occur from the earliest stages of schizophrenia, and have been associated with neurodevelopmental changes. Cognitive impairment is a core feature in the pathology of schizophrenia, and recent studies showed a significant increase in serum IL-18 in schizophrenia, and a putative role of IL-18 in neuroprogression and thus neurocognitive defects. The purpose of this study was to examine the association of IL-18 with cognitive deficits in schizophrenia. We recruited 77 first episode and drug naïve schizophrenic patients and 75 healthy control subjects and examined the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and serum IL-18 in both groups. Schizophrenic symptoms were assessed using the positive and negative syndrome scale (PANSS). We found that IL-18 levels were non-significantly higher in patients than controls (206.0±92.9 pg/ml vs 193.2±41.8 pg/ml, p=0.28). Cognitive scores on the RBANS and nearly all of its five subscales (all p<0.05) except for the Visuospatial/Constructional index (p>0.05) were significantly lower in schizophrenic patients than normal controls. For the patients, IL-18 was positively associated with the Visuospatial/Constructional domain of cognitive deficits in schizophrenia. Our findings suggest that cognitive deficits occur during the acute stage of a schizophrenic episode, and IL-18 may be involved in Visuospatial/Constructional deficits of these patients.
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Transtornos Cognitivos/complicações , Transtornos Cognitivos/psicologia , Interleucina-18/metabolismo , Esquizofrenia/metabolismo , Adolescente , Adulto , Idade de Início , Cognição/fisiologia , Manual Diagnóstico e Estatístico de Transtornos Mentais , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Testes Neuropsicológicos , Desempenho Psicomotor/fisiologia , Psicologia do Esquizofrênico , Adulto JovemRESUMO
As synthetic fibers with superior performances, nylon 6 fibers are widely used in many fields. Due to the potential fire hazard caused by flammability, the study of the flame retardancy of nylon 6 fibers has been attracting more and more attention. The review has summarized the present research status of flame-retarded nylon 6 fibers from three aspects: intrinsic flame-retarded nylon 6, nylon 6 composites, and surface strategies of nylon 6 fibers/fabrics. The current main focus is still how to balance the application performances, flame retardancy, and production cost. Moreover, melt dripping during combustion remains a key challenge for nylon 6 fibers, and the further developing trend is to study novel flame retardants and new flame-retardancy technologies for nylon 6 fibers.
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Lead-free ceramics with superior piezoelectric performance are highly desirable in various electromechanical applications. Unfortunately, it is still challenging to achieve significantly enhanced piezoelectricity without sacrificing the Curie temperature (Tc) in current BaTiO3-based ceramics. To address this issue, a synergistic design strategy of integrating crystallographic texture, multiphase coexistence, and doping engineering is proposed here. Highly [001]c-textured (Ba0.95Ca0.05)(Ti0.92Zr0.06Sn0.02)O3 ceramics are synthesized through Li-related liquid-phase-assisted templated grain growth, with improved grain orientation quality (f of â¼96% and r of â¼0.16) achieved at substantially reduced texture temperatures. Encouragingly, ultrahigh comprehensive piezoelectric properties, i.e., piezoelectric coefficient d33 of â¼820 pC N-1, electrostrain Smax/Emax of â¼2040 pm V-1, and figure of merit d33 × g33 of â¼23.5 × 10-12 m2 N-1, are simultaneously obtained without sacrificing Tc, which are also about 2.3, 2.4, and 4.3 times as high as those of non-textured counterpart, respectively. On the basis of the experiments and theoretical modeling, the outstanding piezoelectric performance is attributed to more effective exploration of property anisotropy and easier polarization rotation/extension, owing to improved grain orientation quality, dissolution of templates into oriented grains, coexisting R + O + T phases, and domain miniaturization. This work provides important guidelines for developing novel ceramics with outstanding piezoelectric properties and can largely expand application fields of textured BaTiO3-based ceramics into high-performance and multilayer electronic devices.
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The piezoelectric properties of lead zirconate titanate [Pb(Zr,Ti)O3 or PZT] ceramics could be enhanced by fabricating textured ceramics that would align the crystal grains along specific orientations. We present a seed-passivated texturing process to fabricate textured PZT ceramics by using newly developed Ba(Zr,Ti)O3 microplatelet templates. This process not only ensures the template-induced grain growth in titanium-rich PZT layers but also facilitates desired composition through interlayer diffusion of zirconium and titanium. We successfully prepared textured PZT ceramics with outstanding properties, including Curie temperatures of 360°C, piezoelectric coefficients d33 of 760 picocoulombs per newton and g33 of 100 millivolt meters per newton, and electromechanical couplings k33 of 0.85. This study addresses the challenge of fabricating textured rhombohedral PZT ceramics by suppressing the otherwise severe chemical reaction between PZT powder and titanate templates.
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Although the MPB composition 0.42PNN-0.21PZ-0.37PT ceramic has high piezoelectric properties, its temperature stability at room temperature is rather poor due to the low phase-transition temperature. By texture engineering using BaTiO3 (BT) as the template, the temperature stability of this material can be greatly improved. In the temperature range from room temperature up to 140 °C, the high effective piezoelectric strain constant d33* of 0.42PNN-0.21PZ-0.37PT-3BT only changed by 4.9% from 1278 to 1215 pm/V, while the d33* of the nontextured counterpart changed by 46.7% from the room temperature value of 920 pm/V with the maximum deviation to 1350 pm/V at 80 °C. In addition, the textured ceramic has higher piezoelectric properties, lower dielectric loss, and slightly higher coercive field. The room-temperature figure-of-merit d33 × g33 for PNN-PZT-2BT is increased by as much as 42% compared with the nontextured counterpart. Our results demonstrated that texture engineering is an effective way to improve the temperature stability of the MPB composition piezoceramics.
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Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.
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Complex disease is different from Mendelian disorders. Its development usually involves the interaction of multiple genes or the interaction between genes and the environment (i.e. epistasis). Although the high-throughput sequencing technologies for complex diseases have produced a large amount of data, it is extremely difficult to analyze the data due to the high feature dimension and the combination in the epistasis analysis. In this work, we introduce machine learning methods to effectively reduce the gene dimensionality, retain the key epistatic effects, and effectively characterize the relationship between epistatic effects and complex diseases.
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Epistasia Genética , Aprendizado de Máquina , Modelos Genéticos , Herança Multifatorial/genética , Polimorfismo de Nucleotídeo Único , Biologia Computacional/métodos , Conjuntos de Dados como Assunto , Humanos , Redução Dimensional com Múltiplos Fatores , SoftwareRESUMO
BACKGROUND: This study aimed to develop and validate an individualized nomogram to predict the risk of positive hidden blood loss (HBL) in patients with single-level thoracolumbar burst fracture (TBF) during the perioperative period. METHODS: We conducted a retrospective investigation including 150 consecutive patients with TBL, and the corresponding patient data was extracted from March 2013 to March 2019. The independent risk factors for positive HBL were screened using univariate and multivariate logistic regression analyses. According to published literature and clinical experience, a series of variables were selected to develop a nomogram prediction model for positive HBL. The area under the receiver operating characteristic curves (AUC), C-index, calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the prediction model. Bootstrapping validation was performed to evaluate the performance of the model. RESULTS: Among the 150 consecutive patients, 62 patients were positive for HBL (38.0%). The multivariate logistic regression analysis showed that the six risk factors of age, length of surgical incision, duration of operation, percentage of vertebral height restoration (P1%), preoperative total cholesterol, and preoperative fibrinogen were independent risk factors of positive HBL. The C-index was 0.831 (95% CI 0.740-0.889) and 0.845 in bootstrapping validation, respectively. The calibration curve showed that the predicted probability of the model was consistent with the actual probability. Decision curve analysis (DCA) showed that the nomogram had clinical utility. CONCLUSION: Overall, we explored the relationship between the positive HBL requirement and predictors. The individualized prediction model for patients with single-level TBF can accurately assess the risk of positive HBL and facilitate clinical decision making. However, external validation will be needed in the future.
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Nomogramas , Humanos , Período Perioperatório , Curva ROC , Estudos Retrospectivos , Fatores de RiscoRESUMO
With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.
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Research on light modulation has typically examined the wavelength, intensity, and exposure time of light, and measured rhythm, sleep, and cognitive ability to evaluate the regulatory effects of light variables on physiological and cognitive functions. Although the frequency of light is one of the main dimensions of light, few studies have attempted to manipulate it to test the effect on brain activation and performance. Recently, 40-Hz light stimulation has been proven to significantly alleviate deficits in gamma oscillation of the hippocampus caused by Alzheimer's disease. Although this oscillation is one of the key functional characteristics of performing memory tasks in healthy people, there is no evidence that 40-Hz blue light exposure can effectively regulate brain activities related to complex cognitive tasks. In the current study, we examined the difference in the effects of 40-Hz light or 0-Hz light exposure on brain activation and functional connectivity during a recognition memory task. Through joint augmentation of visual area activation, 40-Hz light enhanced brain areas mostly in the limbic system that are related to memory, such as the hippocampus and thalamus. Conversely, 0-Hz light enhanced brain areas mostly in the prefrontal cortex. Additionally, functional connection analysis, with the hippocampus as the seed point, showed that 40-Hz light enhanced connection with the superior parietal lobe and reduced the connection with the default network. These results indicate that light at a frequency of 40 Hz can change the activity and functional connection of memory-related core brain areas. They also indicate that in the use of light to regulate cognitive functions, its frequency characteristics merit attention.
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Depression is one of the most common mental disorders with an increasing incidence. However, factors involved in depression are so complex, thus it is difficult to find effective strategies to reverse the impairments. This study aims to verify the role of regulator of G protein signaling 2 (RGS2) in the mouse mode of unpredictable mild stress-induced depression-like behaviors. Knockdown of RGS2 was achieved by transfection of siRNA-RGS2 in mouse hippocampal (HT-22) cells in vitro and injection of recombinant adenovirus expressing siRNA-RGS2 in mice in vivo. An aberrant high expression of RGS2 was found in mice with depression-like behaviors through immunohistochemical analysis. Silencing of RGS2 or Forskolin (activator of cAMP pathway) developed sweet water consumption, reduced inflammation and oxidative stress injury, and attenuated cognitive impairment and neuronal damage in mice with depression-like behaviors. Furthermore, regeneration was enhanced and apoptosis was repressed in mouse hippocampal neurons in the presence of RGS2 knockdown and Forskolin. Mechanistic studies indicated that silencing of RGS2 promoted the activation of cAMP pathway, thus rescuing depression-like behaviors of mice. Collectively, our study uncovered the role of RGS2-dependent cAMP pathway in regulation of cognitive impairment and hippocampal neuron regeneration in depression-like behaviors of mice, which may be a potential therapeutic target for impairments and symptoms associated with depression.
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AMP Cíclico/metabolismo , Depressão , Regeneração Nervosa/fisiologia , Neurônios , Proteínas RGS/metabolismo , Animais , Depressão/metabolismo , Depressão/patologia , Técnicas de Silenciamento de Genes , Hipocampo/metabolismo , Hipocampo/patologia , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Neurônios/metabolismo , Neurônios/patologia , Transdução de Sinais/fisiologiaRESUMO
High-performance piezoelectrics are pivotal to various electronic applications including multilayer actuators, sensors, and energy harvesters. Despite the presence of high Lotgering factor F001, two key limitations to today's relaxor-PbTiO3 textured ceramics are low piezoelectric properties relative to single crystals and high texture temperature. In this work, Pb(Yb1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3 (PYN-PMN-PT) textured ceramics with F001 â¼ 99% were synthesized at only 975 °C through liquid-phase-assisted templated grain growth, where of particular significance is that single-crystal properties, i.e., very large electrostrain Smax/Emax â¼ 1830 pm V-1, giant piezoelectric figure of merit d33 × g33 â¼ 61.3 × 10-12 m2 N-1, high electromechanical coupling k33 â¼ 0.90, and Curie temperature Tc â¼ 205 °C, were simultaneously achieved. Especially, the Smax/Emax and d33 × g33 values correspond to â¼180% enhancement as compared to the regularly 1200 °C-textured ceramics with F001 â¼ 96%, representing the highest values ever reported on piezoceramics. Phase-field simulation revealed that grain misorientation has a stronger influence on piezoelectricity than texture fraction. The ultrahigh piezoelectric response achieved here is mainly attributed to effective control of grain orientation features and domain miniaturization. This work provides important guidelines for developing novel ceramics with significantly enhanced functional properties and low synthesis temperature in the future and can also greatly expand application fields of piezoceramics to high-performance, miniaturized electronic devices with multilayer structures.
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Effects of adlay starch on the rheological and conformational changes in wheat gluten gel were investigated in this study. Rheological measurement showed that adlay starch-gluten composite gels exhibited higher storage modulus G' and loss modulus Gâ³ compared with pure gluten gels. This result was also confirmed through morphological analysis. As the addition of adlay starch increased from 0% to 40%, the surface hydrophobicity of gluten protein gel decreased from 16,660 to 11,931 and the free thiol content increased from 3.11 to 4.30⯵mol/g. In addition, the ß-sheet structure in the gluten protein gel increased at the expense of the α-helical structure with increasing adlay starch fraction. This study revealed that besides acting as an inert filler, adlay starch could participate in hydrogen bonding and induce the enhancement of hydrophobic interaction to modify gluten protein association, altering the rheological and structural properties of gluten protein gels.
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Coix/metabolismo , Glutens/química , Amido/química , Géis/química , Ligação de Hidrogênio , Interações Hidrofóbicas e Hidrofílicas , Reologia , Solubilidade , Análise Espectral Raman , TemperaturaRESUMO
OBJECTIVE: This study aimed to investigate the correlations of three common single nucleotide polymorphisms (SNPs) in the PTEN gene (rs701848 T>C, rs2735343 G>C and rs112025902 A>T) with the risk of depression and depressive symptoms in a Chinese population. METHODS: From July 2011 to June 2013, a total of 384 patients with depression and 400 healthy individuals were included in this study. These SNPs in the PTEN gene were detected by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and direct sequencing. The Hamilton Depression Rating Scale (HAMD) was used to evaluate the severity of depression. RESULTS: The C allele of rs701848, the C allele of rs2735343 and the T allele of rs112025902 were associated with an increased risk of depression (odds ratio [OR]=3.814, 95% CI: 3.093-4.703, P<0.001; OR=2.642, 95% CI: 2.152-3.242, P<0.001; OR=2.882, 95% CI: 2.347-3.539, P<0.001; respectively). Depression patients carrying C allele (TC+CC) of rs701848 and carrying T allele (AT+TT) of rs112025902 had higher HAMD total scores and HAMD anxiety factor scores than those carrying TT genotype of rs701848 and carrying AA genotype of rs112025902 (all P<0.05). Furthermore, depression patients carrying C allele (GC+CC) of rs2735343 had lower HAMD total scores and HAMD factors associated with depression scores than those carrying GG genotype (both P<0.05). Logistic regression analysis revealed that rs701848, rs2735343 and rs112025902 polymorphisms in the PTEN gene may be independent risk factors of depression (relative risk [RR]=1.807, 95% CI=1.023-3.193, P=0.042; RR=1.759, 95% CI=1.033-2.995, P=0.038; RR=1.646, 95% CI=1.018-2.663, P=0.042; respectively). CONCLUSION: Our findings provide evidence that rs701848, rs2735343 and rs112025902 polymorphisms in the PTEN gene may be correlated with the risk of depression and depressive symptoms in the Chinese population.
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Alelos , Depressão/genética , Genótipo , PTEN Fosfo-Hidrolase/genética , Polimorfismo de Fragmento de Restrição , Polimorfismo de Nucleotídeo Único , Adulto , China , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fatores de RiscoRESUMO
This study aimed to examine the prevalence and clinical associated variables of tardive dyskinesia (TD) in a large sample of Chinese inpatients with schizophrenia on long-term treatment with clozapine versus typical antipsychotics. A total of 584 male inpatients with schizophrenia on long-term clozapine (n=341) or typical antipsychotic (n=243) treatment were evaluated using the Abnormal Involuntary Movement Scale (AIMS). The patient's psychopathology was assessed using the Positive and Negative Syndrome Scale. The overall prevalence of TD was 44.5%, with rates of 48.7% in the clozapine group and 38.7% in the typical antipsychotic group (P=0.017). The AIMS score was significantly lower in typical than in clozapine groups (P<0.005). A multiple regression analysis showed that the following variables were significantly associated with the AIMS score: clozapine versus typical medication (P=0.008), Positive and Negative Syndrome Scale negative subscore (P=0.017), and age (P=0.04). There are significant differences in the prevalence and clinical correlates of TD in schizophrenia treated with clozapine versus typical antipsychotics.