Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-Term Plasticity Regulates Both Divisive Normalization and Adaptive Responses in Drosophila Olfactory System

In Drosophila, olfactory information received by olfactory receptor neurons (ORNs) is first processed by an incoherent feed forward neural circuit in the antennal lobe (AL) that consists of ORNs (input), inhibitory local neurons (LNs), and projection neurons (PNs). This “early” olfactory information processing has two important characteristics. First, response of a PN to its cognate ORN is normalized by the overall activity of other ORNs, a phenomenon termed “divisive normalization. ” Second, PNs respond strongly to the onset of ORN activities, but they adapt to prolonged or continuously varying inputs. Despite the importance of these characteristics for learning and memory, their underlying mechanisms are not fully understood. Here, we develop a circuit model for describing the ORN-LN-PN dynamics by including key neuron-neuron interactions such as short-term plasticity (STP) and presynaptic inhibition (PI). By fitting our model to experimental data quantitatively, we show that a strong STP balanced between short-term facilitation (STF) and short-term depression (STD) is responsible for the observed nonlinear divisive normalization in Drosophila. Our circuit model suggests that either STP or PI alone can lead to adaptive response. However, by comparing our model results with experimental data, we find that both STP and PI work together to achieve a strong and robust adaptive response. Our model not only helps reveal the mechanisms underlying two main characteristics of the early olfactory process, it can also be used to predict PN responses to arbitrary time-dependent signals and to infer microscopic properties of the circuit (such as the strengths of STF and STD) from the measured input-output relation. Our circuit model may be useful for understanding the role of STP in other sensory systems.
Pancreatic Ductal Adenocarcinoma: Preclinical in vitro and ex vivo Models

Pancreatic ductal adenocarcinoma (PDAC) is one of the most overlooked cancers despite its dismal median survival time of 6 months. The biggest challenges in improving patient survival are late diagnosis due to lack of diagnostic markers, and limited treatment options due to almost complete therapy resistance. The past decades of research identified the dense stroma and the complex interplay/crosstalk between the cancer- and the different stromal cells as the main culprits for the slow progress in improving patient outcome. For better ex vivo simulation of this complex tumor microenvironment the models used in PDAC research likewise need to become more diverse. Depending on the focus of the investigation, several in vitro and in vivo models for PDAC have been established in the past years. Particularly, 3D cell culture such as spheroids and organoids have become more frequently used. This review aims to examine current PDAC in vitro models, their inherent limitations, and their successful implementations in research.
Association Between Infections and Risk of Ankylosing Spondylitis: A Systematic Review and Meta-Analysis

Background Previous literature on the association between infections and the risk of developing ankylosing spondylitis (AS) presented controversial results. This meta-analysis aimed to quantitatively investigate the effect of infections on the risk of AS. Methods We searched the PubMed, Embase, and Web of Science databases until March 26, 2021 for analytical epidemiological studies on the association between infections and the risk of AS. Fixed or random effect models were used to calculate total risk estimates based on study heterogeneity. Subgroup analysis, and sensitivity analysis were also performed. Publication bias was estimated using funnel plots and Begg’s test. Results Six case-control articles (n=1,296,239) and seven cohort articles (n=7,618,524) were incorporated into our meta-analysis. The pooled odds ratio (OR) from these case-control studies showed that infections were associated with an increased risk of AS (OR=1.46, 95% confidence interval [CI], 1.23–1.73), and the pooled relative risk (RR) from the cohort studies showed the same findings (RR=1.35, 95% CI, 1.12–1.63). Subgroup analysis showed that infections in participants with unadjusted comorbidities (OR=1.66, 95% CI, 1.35–2.03), other types of infection (OR=1.40, 95% CI, 1.15–1.70), and infection of the immune system (OR=1.46, 95% CI, 1.42–1.49) were associated with the risk of AS in case-control studies. In cohort studies, infections with adjusted comorbidities (RR=1.39, 95% CI, 1.15–1.68), viral infection (RR=1.43, 95% CI, 1.22–1.66), other types of infection (RR=1.44, 95% CI, 1.12–1.86), and other sites of infection (RR=1.36, 95% CI, 1.11–1.67) were associated with an increased risk of AS. Conclusions The findings of this meta-analysis confirm that infections significantly increase the risks of AS. This is helpful in providing an essential basis for the prevention of AS via the avoidance of infections.
Combination of Pseudo-LC-NMR and HRMS/MS-Based Molecular Networking for the Rapid Identification of Antimicrobial Metabolites From Fusarium petroliphilum

An endophytic fungal strain isolated from a seagrass endemic to the Mediterranean Sea (Posidonia oceanica) was studied in order to identify its antimicrobial constituents and further characterize the composition of its metabolome. It was identified as Fusarium petroliphilum by in-depth phylogenetic analyses. The ethyl acetate extract of that strain exhibited antimicrobial activities and an ability to inhibit quorum sensing of Staphylococcus aureus. To perform this study with a few tens of mg of extract, an innovative one-step generic strategy was devised. On one side, the extract was analyzed by UHPLC-HRMS/MS molecular networking for dereplication. On the other side, semi-preparative HPLC using a similar gradient profile was used for a single-step high-resolution fractionation. All fractions were systematically profiled by 1H-NMR. The data were assembled into a 2D contour map, which we call “pseudo-LC-NMR,” and combined with those of UHPLC-HRMS/MS. This further highlighted the connection within structurally related compounds, facilitated data interpretation, and provided an unbiased quantitative profiling of the main extract constituents. This innovative strategy led to an unambiguous characterization of all major specialized metabolites of that extract and to the localization of its bioactive compounds. Altogether, this approach identified 22 compounds, 13 of them being new natural products and six being inhibitors of the quorum sensing mechanism of S. aureus and Pseudomonas aeruginosa. Minor analogues were also identified by annotation propagation through the corresponding HRMS/MS molecular network, which enabled a consistent annotation of 27 additional metabolites. This approach was designed to be generic and applicable to natural extracts of the same polarity range.
Childhood Maltreatment Predicts Specific Types of Dysfunctional Attitudes in Participants With and Without Depression

Background: Studies have shown a strong association between childhood maltreatment (CM) and major depressive disorder (MDD). Dysfunctional attitudes (DAs) play a crucial role in the development of MDD. In this study, we aimed to investigate whether (1) DAs are associated with CM, (2) specific CM types predict specific types of DAs, and (3) higher childhood trauma counts (CTCs) predict more DAs. Methods: One hundred seventy-one MDD participants and 156 healthy controls (HCs) were enrolled for the study. CM was assessed retrospectively with the Childhood Trauma Questionnaire. DAs were evaluated using the Chinese version of the Dysfunctional Attitude Scale–Form A (C-DAS-A). A series of analyses, including multiple analyses of covariance and hierarchical regression analyses, were used in this study to examine the hypotheses. Results: The proportion of CM was 60.2% in the MDD group and 44.2% in the HC group. The 2 × 2 analysis of covariance results showed no interaction effect between CM and MDD on C-DAS-A total score. When the factor scores replaced the C-DAS-A total score, a similar trend was observed. Within the MDD group, emotional abuse (EA) predicted two forms of DAs: self-determination type and overall DAs; physical neglect (PN) was predictive of attraction and repulsion-type DAs. Higher childhood trauma counts significantly predicted more types of DAs in the MDD group. Conclusion: DAs are a trait feature of CM. EA and PN predict specific types of DAs in MDD patients. Higher CTCs predict more DAs in MDD patients.
Unwanted Exacerbation of the Immune Response in Neurodegenerative Disease: A Time to Review the Impact

The COVID-19 pandemic imposed a series of behavioral changes that resulted in increased social isolation and a more sedentary life for many across all age groups, but, above all, for the elderly population who are the most vulnerable to infections and chronic neurodegenerative diseases. Systemic inflammatory responses are known to accelerate neurodegenerative disease progression, which leads to permanent damage, loss of brain function, and the loss of autonomy for many aged people. During the COVID-19 pandemic, a spectrum of inflammatory responses was generated in affected individuals, and it is expected that the elderly patients with chronic neurodegenerative diseases who survived SARSCoV-2 infection, it will be found, sooner or later, that there is a worsening of their neurodegenerative conditions. Using mouse prion disease as a model for chronic neurodegeneration, we review the effects of social isolation, sedentary living, and viral infection on the disease progression with a focus on sickness behavior and on the responses of microglia and astrocytes. Focusing on aging, we discuss the cellular and molecular mechanisms related to immunosenescence in chronic neurodegenerative diseases and how infections may accelerate their progression.
Investigation of Specific Proteins Related to Different Types of Coronary Atherosclerosis

Objective: Coronary heart disease (CHD) is a complex disease caused by multifaceted interaction between genetic and environmental factors, which makes identification of the most likely disease candidate proteins and their associated risk markers a big challenge. Atherosclerosis is presented by a broad spectrum of heart diseases, including stable coronary artery disease (SCAD) and acute myocardial infarction (AMI), which is the progressive stage of SCAD. As such, the correct and prompt diagnosis of atherosclerosis turns into imperative for precise and prompt disease diagnosis, treatment and prognosis. Methods: The current work aims to look for specific protein markers for differential diagnosis of coronary atherosclerosis. Thirty male patients between 45 and 55 years diagnosed with atherosclerosis were analyzed by tandem mass tag (TMT) mass spectrometry. The study excluded those who were additionally diagnosed with hypertension and type 1 and 2 diabetes. The Mufuzz analysis was applied to select target proteins for precise and prompt diagnosis of atherosclerosis, most of which were most related to high lipid metabolism. The parallel reaction monitoring (PRM) was used to verify the selected target proteins. Finally, The receiver operating characteristic curve (ROC) was calculated by a random forest experiment. Results: One thousand one hundred and forty seven proteins were identified in the TMT mass spectrometry, 907 of which were quantifiable. In the PRM study, six proteins related to lipid metabolism pathway were selected for verification and they were ALB, SHBG, APOC2, APOC3, APOC4, SAA4. Conclusion: Through the detected specific changes in these six proteins, our results provide accuracy in atherosclerosis patients' diagnosis, especially in cases with varying types of the disease.
Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series

The detection of causal effects among simultaneous observations provides knowledge about the underlying network, and is a topic of interests in many scientific areas. Over the years different causality measures have been developed, each with their own advantages and disadvantages. However, an extensive evaluation study is missing. In this work we consider some of the best-known causality measures i.e., cross-correlation, (conditional) Granger causality index (CGCI), partial directed coherence (PDC), directed transfer function (DTF), and partial mutual information on mixed embedding (PMIME). To correct for noise-related spurious connections, each measure (except PMIME) is tested for statistical significance based on surrogate data. The performance of the causality metrics is evaluated on a set of simulation models with distinct characteristics, to assess how well they work in- as well as outside of their “comfort zone. ” PDC and DTF perform best on systems with frequency-specific connections, while PMIME is the only one able to detect non-linear interactions. The varying performance depending on the system characteristics warrants the use of multiple measures and comparing their results to avoid errors. Furthermore, lags between coupled variables are inherent to real-world systems and could hold essential information on the network dynamics. They are however often not taken into account and we lack proper tools to estimate them. We propose three new methods for lag estimation in multivariate time series, based on autoregressive modelling and information theory. One of the autoregressive methods and the one based on information theory were able to reliably identify the correct lag value in different simulated systems. However, only the latter was able to maintain its performance in the case of non-linear interactions. As a clinical application, the same methods are also applied on an intracranial recording of an epileptic seizure. The combined knowledge from the causality measures and insights from the simulations, on how these measures perform under different circumstances and when to use which one, allow us to recreate a plausible network of the seizure propagation that supports previous observations of desynchronisation and synchronisation during seizure progression. The lag estimation results show absence of a relationship between connectivity strength and estimated lag values, which contradicts the line of thinking in connectivity shaped by the neuron doctrine.
Development and Validation of an E2F-Related Gene Signature to Predict Prognosis of Patients With Lung Squamous Cell Carcinoma

Background Lung squamous cell carcinoma (LUSC) generally correlates with poor clinical prognoses due to the lack of available prognostic biomarkers. This study is designed to identify a potential biomarker significant for the prognosis and treatment of LUSC, so as to provide a scientific basis for clinical treatment decisions. Methods Genomic changes in LUSC samples before and after radiation were firstly discussed to identify E2 factor (E2F) pathway of prognostic significance. A series of bioinformatics analyses and statistical methods were combined to construct a robust E2F-related prognostic gene signature. Furthermore, a decision tree and a nomogram were established according to the gene signature and multiple clinicopathological characteristics to improve risk stratification and quantify risk assessment for individual patients. Results In our investigated cohorts, the E2F-related gene signature we identified was capable of predicting clinical outcomes and therapeutic responses in LUSC patients, besides, discriminative to identify high-risk patients. Survival analysis suggested that the gene signature was independently prognostic for adverse overall survival of LUSC patients. The decision tree identified the strong discriminative performance of the gene signature in risk stractification for overall survival while the nomogram demonstrated a high accuracy. Conclusion The E2F-related gene signature may help distinguish high-risk patients so as to formulate personalized treatment strategy in LUSC patients.
The Prognostic Value of Bone Morphogenetic Proteins and Their Receptors in Lung Adenocarcinoma

Background Bone morphogenetic proteins (BMPs) regulate tumor progression via binding to their receptors (BMPRs). However, the expression and clinical significance of BMPs/BMPRs in lung adenocarcinoma remain unclear due to a lack of systematic studies. Methods This study screened differentially expressed BMPs/BMPRs (deBMPs/BMPRs) in a training dataset combining TCGA-LUAD and GTEx-LUNG and verified them in four GEO datasets. Their prognostic value was evaluated via univariate and multivariate Cox regression analyses. LASSO was performed to construct an initial risk model. Subsequently, after weighted gene co-expression network analysis (WGCNA), differential expression analysis, and univariate Cox regression analysis, hub genes co-expressed with differentially expressed BMPs/BMPRs were filtered out to improve the risk model and explore potential mechanisms. The improved risk model was re-established via LASSO combining hub genes with differentially expressed BMPs/BMPRs as the core. In the testing cohort including 93 lung adenocarcinoma patients, immunohistochemistry (IHC) was performed to verify BMP5 protein expression and its association with prognosis. Results BMP2, BMP5, BMP6, GDF10, and ACVRL1 were verified as downregulated in lung adenocarcinoma. Survival analysis identified BMP5 as an independent protective prognostic factor. We also found that BMP5 was significantly correlated with EGFR expression and mutations, suggesting that BMP5 may play a role in targeted therapy. The initial risk model containing only BMP5 showed a significant correlation (HR: 1.71, 95% CI: 1.28−2.28, p: 3e-04) but low prognostic accuracy (AUC of 1-year survival: 0.6, 3-year survival: 0.6, 5-year survival: 0.63). Seventy-nine hub genes co-expressed with BMP5 were identified, and their functions were enriched in cell migration and tumor metastasis. The re-established risk model showed greater prognostic correlation (HR: 2.58, 95% CI: 1.92–3.46, p: 0) and value (AUC of 1-year survival: 0.72, 3-year survival: 0.69, and 5-year survival: 0.68). IHC results revealed that BMP5 protein was also downregulated in lung adenocarcinoma and higher expression was markedly associated with better prognosis (HR: 0.44, 95% CI: 0.23–0.85, p: 0.0145). Conclusion BMP5 is a potential crucial target for lung adenocarcinoma treatment based on significant differential expression and superior prognostic value.
Power Consumption Predicting and Anomaly Detection Based on Transformer and K-Means

With the advancement of technology and science, the power system is getting more intelligent and flexible, and the way people use electric energy in their daily lives is changing. Monitoring the condition of electrical energy loads, particularly in the early detection of aberrant loads and behaviors, is critical for power grid maintenance and power theft detection. In this paper, we combine the widely used deep learning model Transformer with the clustering approach K-means to estimate power consumption over time and detect anomalies. The Transformer model is used to forecast the following hour’s power usage, and the K-means clustering method is utilized to optimize the prediction results, finally, the anomalies is detected by comparing the predicted value and the test value. On real hourly electric energy consumption data, we test the proposed model, and the results show that our method outperforms the most commonly used LSTM time series model.
Multimodal Exercise Effects in Older Adults Depend on Sleep, Movement Biography, and Habitual Physical Activity: A Randomized Controlled Trial

Background: The promotion of healthy aging is one of the major challenges for healthcare systems in current times. The present study investigates the effects of a standardized physical activity intervention for older adults on cognitive capacity, self-reported health, fear of falls, balance, leg strength and gait under consideration of movement biography, sleep duration, and current activity behavior. Methods: This single-blinded, randomized controlled trial included 49 community-dwelling older adults (36 women; 82.9 ± 4.5 years of age (Mean [M] ± SD); intervention group = 25; control group = 24). Movement biography, sleep duration, cognitive capacity, self-reported health status, and fear of falls were assessed by means of questionnaires. Leg strength, gait, and current activity levels were captured using a pressure plate, accelerometers, and conducting the functional-reach and chair-rising-test. The multicomponent intervention took place twice a week for 45 min and lasted 16 weeks. Sub-cohorts of different sleep duration were formed to distinguish between intervention effects and benefits of healthy sleep durations. Change scores were evaluated in univariate analyses of covariances (ANCOVAs) between groups and sub-cohorts of different sleep duration in both groups. Changes in cognitive capacity, self-reported health, fear of falls, balance, leg strength, and gait were investigated using the respective baseline values, movement biography, and current activity levels as covariates. Analysis was by intention-to-treat (ITT). Results: We found sub-cohort differences in cognitive capacity change scores [F(3,48) = 5.498, p = 0.003, ηp2 = 0.287]. Effects on fear of falls [F(1,48) = 12.961, p = 0.001, ηp2 = 0.240] and balance change scores F(1,48) = 4.521, p = 0.040, ηp2 = (0.099) were modified by the level of current activity. Effects on gait cadence were modified by the movement biography [F(1,48) = 4.545; p = 0.039, ηp2 = 0.100]. Conclusions: Unlike for functional outcomes, our multicomponent intervention in combination with adequate sleep duration appears to provide combinable beneficial effects for cognitive capacity in older adults. Trainability of gait, fear of falls, and flexibility seems to be affected by movement biography and current physical activity levels. Trial registration: This study was registered at the DRKS (German Clinical Trials Register) on November 11, 2020 with the corresponding trial number: DRKS00020472.
Exercise in the Park or Gym? The Physiological and Mental Responses of Obese People Walking in Different Settings at Different Speeds: A Parallel Group Randomized Trial

Evidence shows that physical activity has multiple health benefits for the body and mind of oneself, but little is known about the impacts of the setting and the intensity on exercise experience, especially for obese people. This study investigated the physiological and psychological effects of four walking conditions with different settings (park vs. gym) and intensity (slow vs. fast) on young obese adults. Subjects were 18–21 years old Chinese university students (N = 77), who were diagnosed as obese. They were randomly assigned to participate in one of the four activities in the field: slow walk in the park (2.8 km/h), fast walk in the park (5.5 km/h), slow walk in the gym, and fast walk in the gym. Physiological indices, including blood pressure and heart rate, were measured before and after the walk. Psychological responses were measured by the Symbol Digit Modalities Test, the mood states scale, and the semantic differential scale. This study of obese people aged 18–21 years confirmed the previous findings that exercising in natural environments better relieved stress and restored attentional level than indoor activities. The results suggested that the mood states of the participants and their environmental perceptions may be influenced by the walking conditions with different setting and speed. The findings can be used in planning and designing urban green spaces for promoting physical activity and making exercise plans for obese people.