DDN3.0: determining significant rewiring of biological network structure with differential dependency networks.

MOTIVATION: Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent. RESULTS: We previously developed an accurate differential network analysis method-differential dependency networks (DDN), that enables joint learning of common and rewired network structures under different conditions. We now introduce the DDN3.0 tool that improves this framework with three new and highly efficient algorithms, namely, unbiased model estimation with a weighted error measure applicable to imbalance sample groups, multiple acceleration strategies to improve learning efficiency, and data-driven determination of proper hyperparameters. The comparative experimental results obtained from both realistic simulations and case studies show that DDN3.0 can help biologists more accurately identify, in a study-specific and often unknown conserved regulatory circuitry, a network of significantly rewired molecular players potentially responsible for phenotypic transitions. AVAILABILITY AND IMPLEMENTATION: The Python package of DDN3.0 is freely available at https://github.com/cbil-vt/DDN3. A user's guide and a vignette are provided at https://ddn-30.readthedocs.io/.

ABDS: a bioinformatics tool suite for analyzing biologically diverse samples.

Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel. We developed the ABDS tool suite specifically for analyzing biologically diverse samples. Collectively, a mechanism-integrated group-wise pre-imputation scheme is proposed to retain informative missingness associated with signature genes, a cosine-based one-sample test is extended to detect group-silenced signature genes, and a unified heatmap is designed to display multiple sample groups. We describe the methodological principles and demonstrate the effectiveness of three analytics tools under targeted scenarios, supported by comparative evaluations and biomedical showcases. As an open-source R package, ABDS tool suite complements rather than replaces existing tools and will allow biologists to more accurately detect interpretable molecular signals among phenotypically diverse sample groups.

Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals.

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce Activity Quantification and Analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine learning techniques. It decomposes complex live imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, and imaging modalities. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, and distinct sensorimotor signal propagation patterns in the mouse spinal cord.

Outcomes of vaginal repair and vaginal repair combined with GnRHa administration in the treatment of cesarean section scar defects: A randomized clinical trial.

STUDY OBJECTIVE: To prospectively investigate whether the application of vaginal repair (VR) of cesarean section scar defect (CSD) combined with a gonadotropin-releasing hormone agonist (GnRHa) achieve better clinical outcomes than VR alone. DESIGN: A randomized clinical trial. SETTING: University hospital. PATIENTS: A total of 124 women with CSD were undergoing expectant management from December 2016 to September 2021. 61 were randomised to VR+ GnRHa and 63 to VR alone. INTERVENTION: Vaginal repair combined with GnRHa and vaginal repair alone. MEASURES AND MAIN RESULTS: The primary outcome was the duration of menstruation and thickness of the remaining muscular layer (TRM) at 6 months after surgery. Secondary outcomes included the length, width and depth of the CSD; operation time; estimated blood loss; hospitalization time; and operative complications. Women were treated with either VR (n = 63) or VR + GnRHa (n = 61). Menstruation and TRM in patients pre. vs. post comparisons either with VR or VR + GnRHa are significant improved (P < .05). Significant differences in menstruation duration and TRM occurred in patients treated with VR + GnRHa compared with those treated with VR (P < .05). Moreover, the rate of CSD after surgery in the VR group was significantly higher than that in the VR + GnRHa group (P = .033), and CSD patients in the VR + GnRHa group achieved better therapeutic effects than those in the VR group (P = .017). Patients who received VR + GnRHa had a shorter menstruation duration and a greater increment of TRM postoperatively than did patients treated with VR alone (P = .021; P = .002, respectively). CONCLUSION: VR + GnRHa therapy has a greater potential to improve scar healing and reduce the number of menstruation days than VR alone for symptomatic women with CSD. PRéCIS: Vaginal Repair Combined with GnRHa Creates Better Therapeutic Effects of CSD. TRIAL REGISTRATION: Date of registration: October 13, 2016, Date of initial participant enrollment: December 20, 2016, Clinical trial identification number: NCT02932761, URL of the registration site: ClinicalTrials.gov, Figshare DOI: 10.6084/m9.figshare.24117114 LINK TO THE CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT02932761.

Identification of fibronectin type III domain containing 3B as a potential prognostic and therapeutic target for pancreatic cancer: a preliminary analysis.

BACKGROUND: Fibronectin type III domain containing 3B (FNDC3B), a member of the fibronectin type III domain-containing protein family, has been indicated in various malignancies. However, the precise role of FNDC3B in the progression of pancreatic cancer (PC) still remains to be elucidated. METHODS: In this study, we integrated data from the National Center for Biotechnology Information, the Cancer Genome Atlas, Genotype-Tissue Expression database, and Gene Expression Omnibus datasets to analyze FNDC3B expression and its association with various clinicopathological parameters. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, along with Gene Set Enrichment Analysis (GSEA), single sample Gene Set Enrichment Analysis (ssGSEA) and estimate analysis were recruited to delve into the biological function and immune infiltration based on FNDC3B expression. Additionally, the prognostic estimation was conducted using Cox analysis and Kaplan-Meier analysis. Subsequently, a nomogram was constructed according to the result of Cox analysis to enhance the prognostic ability of FNDC3B. Finally, the preliminary biological function of FNDC3B in PC cells was explored. RESULTS: The study demonstrated a significantly higher expression of FNDC3B in tumor tissues compared to normal pancreatic tissues, and this expression was significantly associated with various clinicopathological parameters. GSEA revealed the involvement of FNDC3B in biological processes and signaling pathways related to integrin signaling pathway and cell adhesion. Additionally, ssGSEA analysis indicated a positive correlation between FNDC3B expression and infiltration of Th2 cells and neutrophils, while showing a negative correlation with plasmacytoid dendritic cells and Th17 cells infiltration. Kaplan-Meier analysis further supported that high FNDC3B expression in PC patients was linked to shorter overall survival, disease-specific survival, and progression-free interval. However, although univariate analysis demonstrated a significant correlation between FNDC3B expression and prognosis in PC patients, this association did not hold true in multivariate analysis. Finally, our findings highlight the crucial role of FNDC3B expression in regulating proliferation, migration, and invasion abilities of PC cells. CONCLUSION: Despite limitations, the findings of this study underscored the potential of FNDC3B as a prognostic biomarker and its pivotal role in driving the progression of PC, particularly in orchestrating immune responses.

Neuron type-specific proteomics reveals distinct Shank3 proteoforms in iSPNs and dSPNs lead to striatal synaptopathy in Shank3B-/- mice.

Combinatorial expression of postsynaptic proteins underlies synapse diversity within and between neuron types. Thus, characterization of neuron-type-specific postsynaptic proteomes is key to obtaining a deeper understanding of discrete synaptic properties and how selective dysfunction manifests in synaptopathies. To overcome the limitations associated with bulk measures of synaptic protein abundance, we developed a biotin proximity protein tagging probe to characterize neuron-type-specific postsynaptic proteomes in vivo. We found Shank3 protein isoforms are differentially expressed by direct and indirect pathway spiny projection neurons (dSPNs and iSPNs). Investigation of Shank3B-/- mice lacking exons 13-16 within the Shank3 gene, reveal distinct Shank3 protein isoform expression in iSPNs and dSPNs. In Shank3B-/- striatum, Shank3E and Shank3NT are expressed by dSPNs but are undetectable in iSPNs. Proteomic analysis indicates significant and selective alterations in the postsynaptic proteome of Shank3B-/- iSPNs. Correspondingly, the deletion of exons 13-16 diminishes dendritic spine density, reduces spine head diameter, and hampers corticostriatal synaptic transmission in iSPNs. Remarkably, reintroducing Shank3E in adult Shank3B-/- iSPNs significantly rectifies the observed dendritic spine morphological and corticostriatal synaptic transmission deficits. We report unexpected cell-type specific synaptic protein isoform expression which could play a key causal role in specifying synapse diversity and selective synapse dysfunction in synaptopathies.

Local mitochondrial replication in the periphery of neurons requires the eEF1A1 protein and thetranslation of nuclear-encoded proteins.

In neurons, it is commonly assumed that mitochondrial replication only occurs in the cell body, after which the mitochondria must travel to the neuron's periphery. However, while mitochondrial DNA replication has been observed to occur away from the cell body, the specific mechanisms involved remain elusive. Using EdU-labelling in mouse primary neurons, we developed a tool to determine the mitochondrial replication rate. Taking of advantage of microfluidic devices, we confirmed that mitochondrial replication also occurs locally in the periphery of neurons. To achieve this, mitochondria require de novo nuclear-encoded, but not mitochondrial-encoded protein translation. Following a proteomic screen comparing synaptic with non-synaptic mitochondria, we identified two elongation factors - eEF1A1 and TUFM - that were upregulated in synaptic mitochondria. We found that mitochondrial replication is impaired upon the downregulation of eEF1A1, and this is particularly relevant in the periphery of neurons.

Tight regulation of a nuclear HAPSTR1-HUWE1 pathway essential for mammalian life.

The recently discovered HAPSTR1 protein broadly oversees cellular stress responses. This function requires HUWE1, a ubiquitin ligase that paradoxically marks HAPSTR1 for degradation, but much about this pathway remains unclear. Here, leveraging multiplexed proteomics, we find that HAPSTR1 enables nuclear localization of HUWE1 with implications for nuclear protein quality control. We show that HAPSTR1 is tightly regulated and identify ubiquitin ligase TRIP12 and deubiquitinase USP7 as upstream regulators titrating HAPSTR1 stability. Finally, we generate conditional Hapstr1 knockout mice, finding that Hapstr1-null mice are perinatal lethal, adult mice depleted of Hapstr1 have reduced fitness, and primary cells explanted from Hapstr1-null animals falter in culture coincident with HUWE1 mislocalization and broadly remodeled signaling. Notably, although HAPSTR1 potently suppresses p53, we find that Hapstr1 is essential for life even in mice lacking p53. Altogether, we identify novel components and functional insights into the conserved HAPSTR1-HUWE1 pathway and demonstrate its requirement for mammalian life.

Survival benefit of adjuvant chemotherapy in patients with resected gallbladder adenocarcinoma: An updated retrospective cohort analysis.

BACKGROUND: The rarity yet high malignancy of gallbladder adenocarcinoma (GBA) endows it with a distinctive nature. Radical resection remains the foremost therapeutic approach for GBA, while the impact of early recurrence and metastasis on patient prognosis necessitates the utilization of adjuvant chemotherapy (AC). Despite numerous previous studies on this topic, a consensus regarding the authentic efficacy of AC has yet to be reached. METHODS: We conducted an updated retrospective cohort analysis utilizing data from the Surveillance, Epidemiology, and End Results (SEER) database spanning from 2010 to 2020 to explore the association between AC and survival outcomes in patients with resected GBA. RESULTS: Our study included 2782 patients from the SEER database, with further evaluation of 843 patients in each cohort following meticulous execution of a 1:1 propensity score matching. Remarkably, the AC cohort exhibited a significant survival advantage when juxtaposed against the non-AC cohort. Multivariable Cox regression analysis identified age at diagnosis, year at diagnosis, grade, AJCC T stage, AJCC N stage as well as AC as independent prognostic factors. Furthermore, our findings unveiled that poor/undifferentiated tumor histology, pathological T2 or higher category and pathological N1 category were significantly associated with improved survival when treated with AC while simultaneously observing improved survival across all age categories. CONCLUSION: These results provide additional evidence supporting the survival benefits of AC and offer guidance for personalized therapy in patients with resected GBA.

Notch receptor-ligand binding facilitates extracellular vesicle-mediated neuron-to-neuron communication.

Extracellular vesicles (EVs) facilitate intercellular communication by transferring cargo between cells in a variety of tissues. However, how EVs achieve cell-type-specific intercellular communication is still largely unknown. We found that Notch1 and Notch2 proteins are expressed on the surface of neuronal EVs that have been generated in response to neuronal excitatory synaptic activity. Notch ligands bind these EVs on the neuronal plasma membrane, trigger their internalization, activate the Notch signaling pathway, and drive the expression of Notch target genes. The generation of these neuronal EVs requires the endosomal sorting complex required for transport-associated protein Alix. Adult Alix conditional knockout mice have reduced hippocampal Notch signaling activation and glutamatergic synaptic protein expression. Thus, EVs facilitate neuron-to-neuron communication via the Notch receptor-ligand system in the brain.

Immune checkpoints targeting dendritic cells for antibody-based modulation in cancer.

Dendritic cells (DC) are professional antigen-presenting cells which link innate to adaptive immunity. DC play a central role in regulating antitumor T-cell responses in both tumor-draining lymph nodes (TDLN) and the tumor microenvironment (TME). They modulate effector T-cell responses via immune checkpoint proteins (ICPs) that can be either stimulatory or inhibitory. Functions of DC are often impaired by the suppressive TME leading to tumor immune escape. Therefore, better understanding of the mechanisms of action of ICPs expressed by (tumor-infiltrating) DC will lead to potential new treatment strategies. Genetic manipulation and high-dimensional analyses have provided insight in the interactions between DC and T-cells in TDLN and the TME upon ICP targeting. In this review, we discuss (tumor-infiltrating) DC lineage cells and tumor tissue specific "mature" DC states and their gene signatures in relation to anti-tumor immunity. We also review a number of ICPs expressed by DC regarding their functions in phagocytosis, DC activation, or inhibition and outline position in, or promise for clinical trials in cancer immunotherapy. Collectively, we highlight the critical role of DC and their exact status in the TME for the induction and propagation of T-cell immunity to cancer.

SynBot: An open-source image analysis software for automated quantification of synapses.

The formation of precise numbers of neuronal connections, known as synapses, is crucial for brain function. Therefore, synaptogenesis mechanisms have been one of the main focuses of cellular and molecular neuroscience. Immunohistochemistry is a common tool for labeling and visualization of synapses. Thus, quantifying the numbers of synapses from light microscopy images enables screening the impacts of experimental manipulations on synapse development. Despite its utility, this approach is paired with low throughput image analysis methods that are challenging to learn, and results are variable between experimenters. We developed a new open-source ImageJ-based software, SynBot, to address these technical bottlenecks by automating several stages of the analysis. SynBot incorporates the advanced algorithms ilastik and SynQuant for accurate thresholding for synaptic puncta identification, and the code can easily be modified by users. The use of this software will allow for rapid and reproducible screening of synaptic phenotypes in healthy and diseased nervous systems. Motivation: Light microscopy imaging of pre- and post-synaptic proteins from neurons in tissue or in vitro allows for the effective identification of synaptic structures. Previous methods for quantitative analysis of these images were time-consuming, required extensive user training, and the source code could not be easily modified. Here, we describe SynBot, a new open-source tool that automates the synapse quantification process, decreases the requirement for user training, and allows for easy modifications to the code.

A Prediction Model for the Efficacy of Transvaginal Repair in Patients With Cesarean Scar Defect: An Evidence-Based Proposal for Patient Selection.

STUDY OBJECTIVE: To establish a prediction model to help doctors determine which patients with cesarean scar defect are more suitable for transvaginal repair. DESIGN: Retrospective analysis. SETTING: Xinhua Hospital and Shanghai First Maternity & Infant Hospital between June 2014 and May 2021. PATIENTS: 1015 women who underwent transvaginal repair of cesarean scar defect (CSD). INTERVENTIONS: All enrolled patients underwent CSD repair performed by the same gynecologist and his team. And followed up a clinic visit at 6 months to record their menstruation and measure multiple parameters of the CSD by Magnetic Resonance Imaging. MAIN OUTCOMES AND MEASURES: CSD patients are categorized as optimal healing group when the menstruation duration is no more than 7 days, meanwhile the thickness of residual myometrium is no less than 5.39 mm after vaginal repair. The final nomogram is constructed to predict surgical outcomes based on preoperative variables. RESULTS: The key factors that determine optimal healing are the timing of cesarean section (elective or emergency), menstrual cycle, CSD length, width, depth, and the thickness of the lower uterine segment. With the prediction model, scores are given to each parameter according to the statistics. Total scores range from 0 to 25 points, with a cutoff point of 16.5. When a score is greater than 16.5, the transvaginal repair can achieve optimal healing. Uterine position (anteflexion or retroflexion) and preoperative thickness of residual myometrium are the key factors affecting postoperative thickness of residual myometrium. The width of the CSD and the thickness of the lower uterine segment are the key factors affecting abnormal uterine bleeding symptoms (p < 0.01). CONCLUSIONS: For the first time, we established a prediction model system that may predict the repair effect of CSD and can potentially be useful in future clinical trials to determine which patients are more suitable for surgery or other treatment options.

Comparison of Intergrowth-21st and Fenton growth standards to evaluate and predict the postnatal growth in eastern Chinese preterm infants.

Objectives: The aim of this article was to compare the differences between Intergrowth-21st (IG-21) and Fenton growth standards in the classification of intrauterine and extrauterine growth restriction (EUGR) in eastern Chinese preterm infants, and detect which one can better relate to neonatal diseases and predict the physical growth outcomes at 3-5 years old. Methods: Premature infants admitted to a tertiary pediatric hospital in Shanghai, China, from 2016 to 2018 were enrolled. Prenatal information, neonatal diseases during hospitalization, and anthropometric data (weight, height, and head circumference) at birth and at discharge were collected and analyzed. Physical growth outcomes (short stature, thinness, and overweight) were examined by telephone investigations in 2021 at age 3-5 years. Results: The medium gestational age and birth weight of the included 1,065 preterm newborns were 33.6 weeks and 1,900 g, respectively. The IG-21 curves diagnosed more newborns with small for gestational age (SGA) (19% vs. 14.7%) and fewer newborns with longitudinal EUGR on height (25.5% vs. 27.9%) and head circumference (17.9% vs. 24.7%) compared to Fenton curves. Concordances between Fenton and IG-21 standards were substantial or almost perfect in the classification of SGA and longitudinal EUGR, but minor in cross-sectional EUGR. EUGR identified by Fenton curves was better related to neonatal diseases than IG-21 curves. There were no statistical significances in the prediction of short stature, thinness, and overweight at 3-5 years old between the two charts. Conclusions: IG-21 growth standards are not superior to Fenton in assessing preterm growth and development in the eastern Chinese population.

Dissecting climate change risk and financial market instability: Implications for ecological risk management.

This research investigates the impact of climate challenges on financial markets by introducing an innovative approach to measure climate risk, specifically the aggregate climate change concern (ACCC) index. The study aims to assess and quantify the potential influence of climate change and risk-related factors on the performance and dynamics of financial markets. In this paper, concern is defined as the attention paid to the risk of climate change and the associated negative consequences. The findings demonstrate that the aggregate index exhibits robust predictability of market risk premiums, both within the sample and out-of-sample. By comparison, the index contains additional information beyond 14 economic predictors and 12 risk/uncertainty indexes in forecasting stock market return. In addition, the index proves valuable for mean-variance investors in asset allocation, leading to significant economic gains. The study identifies the index's ability to capture the reversal of temporary price crashes caused by overreactions to climate change risk. Furthermore, it exhibits stronger return forecasting capability for green stocks, non-state-owned enterprise (non-SOE) stocks, and stocks in regions with low air pollution. Particularly during periods of low air pollution and relaxed regulation, the index displays an enhanced ability to forecast returns. The study's findings provide valuable insights for policymakers and financial institutions as they address 21st-century environmental challenges. Moreover, these findings can inform the design of adaptive measures and interventions aimed at mitigating ecological risks and promoting sustainable economic growth.

A Pathogenic Missense Mutation in Kainate Receptors Elevates Dendritic Excitability and Synaptic Integration through Dysregulation of SK Channels.

Numerous rare variants that cause neurodevelopmental disorders (NDDs) occur within genes encoding synaptic proteins, including ionotropic glutamate receptors. However, in many cases, it remains unclear how damaging missense variants affect brain function. We determined the physiological consequences of an NDD causing missense mutation in the GRIK2 kainate receptor (KAR) gene, that results in a single amino acid change p.Ala657Thr in the GluK2 receptor subunit. We engineered this mutation in the mouse Grik2 gene, yielding a GluK2(A657T) mouse, and studied mice of both sexes to determine how hippocampal neuronal function is disrupted. Synaptic KAR currents in hippocampal CA3 pyramidal neurons from heterozygous A657T mice exhibited slow decay kinetics, consistent with incorporation of the mutant subunit into functional receptors. Unexpectedly, CA3 neurons demonstrated elevated action potential spiking because of downregulation of the small-conductance Ca2+ activated K+ channel (SK), which mediates the post-spike afterhyperpolarization. The reduction in SK activity resulted in increased CA3 dendritic excitability, increased EPSP-spike coupling, and lowered the threshold for the induction of LTP of the associational-commissural synapses in CA3 neurons. Pharmacological inhibition of SK channels in WT mice increased dendritic excitability and EPSP-spike coupling, mimicking the phenotype in A657T mice and suggesting a causative role for attenuated SK activity in aberrant excitability observed in the mutant mice. These findings demonstrate that a disease-associated missense mutation in GRIK2 leads to altered signaling through neuronal KARs, pleiotropic effects on neuronal and dendritic excitability, and implicate these processes in neuropathology in patients with genetic NDDs.SIGNIFICANCE STATEMENT Damaging mutations in genes encoding synaptic proteins have been identified in various neurodevelopmental disorders, but the functional consequences at the cellular and circuit level remain elusive. By generating a novel knock-in mutant mouse, this study examined the role of a pathogenic mutation in the GluK2 kainate receptor (KAR) subunit, a subclass of ionotropic glutamate receptors. Analyses of hippocampal CA3 pyramidal neurons determined elevated action potential firing because of an increase in dendritic excitability. Increased dendritic excitability was attributable to reduced activity of a Ca2+ activated K+ channel. These results indicate that a pathogenic KAR mutation results in dysregulation of dendritic K+ channels, which leads to an increase in synaptic integration and backpropagation of action potentials into distal dendrites.

Quantum generative adversarial learning in photonics.

Quantum generative adversarial networks (QGANs), an intersection of quantum computing and machine learning, have attracted widespread attention due to their potential advantages over classical analogs. However, in the current era of noisy intermediate-scale quantum (NISQ) computing, it is essential to investigate whether QGANs can perform learning tasks on near-term quantum devices usually affected by noise and even defects. In this Letter, using a programmable silicon quantum photonic chip, we experimentally demonstrate the QGAN model in photonics for the first time to our knowledge and investigate the effects of noise and defects on its performance. Our results show that QGANs can generate high-quality quantum data with a fidelity higher than 90%, even under conditions where up to half of the generator's phase shifters are damaged, or all of the generator and discriminator's phase shifters are subjected to phase noise up to 0.04π. Our work sheds light on the feasibility of implementing QGANs on the NISQ-era quantum hardware.

Mixtures of strategies underlie rodent behavior during reversal learning.

In reversal learning tasks, the behavior of humans and animals is often assumed to be uniform within single experimental sessions to facilitate data analysis and model fitting. However, behavior of agents can display substantial variability in single experimental sessions, as they execute different blocks of trials with different transition dynamics. Here, we observed that in a deterministic reversal learning task, mice display noisy and sub-optimal choice transitions even at the expert stages of learning. We investigated two sources of the sub-optimality in the behavior. First, we found that mice exhibit a high lapse rate during task execution, as they reverted to unrewarded directions after choice transitions. Second, we unexpectedly found that a majority of mice did not execute a uniform strategy, but rather mixed between several behavioral modes with different transition dynamics. We quantified the use of such mixtures with a state-space model, block Hidden Markov Model (block HMM), to dissociate the mixtures of dynamic choice transitions in individual blocks of trials. Additionally, we found that blockHMM transition modes in rodent behavior can be accounted for by two different types of behavioral algorithms, model-free or inference-based learning, that might be used to solve the task. Combining these approaches, we found that mice used a mixture of both exploratory, model-free strategies and deterministic, inference-based behavior in the task, explaining their overall noisy choice sequences. Together, our combined computational approach highlights intrinsic sources of noise in rodent reversal learning behavior and provides a richer description of behavior than conventional techniques, while uncovering the hidden states that underlie the block-by-block transitions.

A novel signature for predicting prognosis and immune landscape in cutaneous melanoma based on anoikis-related long non-coding RNAs.

Anoikis is a unique form of apoptosis associated with vascularization and distant metastasis in cancer. Eliminating anoikis resistance in tumor cells could be a promising target for improving the prognosis of terminal cancer patients. However, current studies have not elaborated on the prognosis effect of anoikis-related long non-coding RNAs (lncRNAs) in cutaneous melanoma. Pre-processed data, including RNA sequences and clinical information, were retrieved from TCGA and GTEx databases. After a series of statistical analyses, anoikis-related lncRNAs with prognostic significance were identified, and a unique risk signature was constructed. Risk scores were further analyzed in relation to the tumor microenvironment, tumor immune dysfunction and exclusion, immune checkpoint genes, and RNA methylation genes. The indicators were also used to predict the potentially sensitive anti-cancer drugs. An anoikis-related lncRNAs risk signature consisting of LINC01711, POLH-AS1, MIR205HG, and LINC02416 was successfully established in cutaneous melanoma. Overall survival and progression-free survival of patients were strongly linked with the risk score, independently of other clinical factors. The low-risk group exhibited a more beneficial immunological profile, was less affected by RNA methylation, and was more sensitive to the majority of anti-cancer drugs, all of which indicated a better prognostic outcome. The 4 hub lncRNAs may be fundamental to studying the mechanism of anoikis in cutaneous melanoma and provide personalized therapy for salvaging drug resistance.