UNASSIGNED: In this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics.
UNASSIGNED: Spatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male-female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis.
UNASSIGNED: The proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients.
UNASSIGNED: The proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.

This research analyzed the real-world NOx and particle number (PN) emissions of 21 China VI heavy-duty diesel trucks (HDDTs). On-road emission conformity was first evaluated with portable emission measurement system (PEMS). Only 76.19 %, 71.43 % and 61.90 % of the vehicles passed the NOx test, PN test and both tests, respectively. The impacts of vehicle features including exhaust gas recirculation (EGR) equipment, mileage and tractive tonnage were then assessed. Results demonstrated that EGR helped reducing NOx emission factors (EFs) while increased PN EFs. Larger mileages and tractive tonnages corresponded to higher NOx and PN EFs, respectively. In-depth analyses regarding the influences of operating conditions on emissions were conducted with both numerical comparisons and statistical tests. Results proved that HDDTs generated higher NOx EFs under low speeds or large vehicle specific powers (VSPs), and higher PN EFs under high speeds or small VSPs in general. In addition, unqualified vehicles generated significantly higher NOx EFs than qualified vehicles on freeways or under speed≥40 km/h, while significant higher PN EFs were generated on suburban roads, freeways or under operating modes with positive VSPs by unqualified vehicles. The reliability and accuracy of on-board diagnostic (OBD) NOx data were finally investigated. Results revealed that 43 % of the test vehicles did not report reliable OBD data. Correlation analyses between OBD NOx and PEMS measurements further demonstrated that the consistency of instantaneous concentrations were generally low. However, sliding window averaged concentrations show better correlations, e.g., the Pearson correlation coefficients on 20s-window averaged concentrations exceeded 0.85 for most vehicles. The research results provide valuable insights into emission regulation, e.g., focusing more on medium- to high-speed operations to identify unqualified vehicles, setting higher standards to improve the quality of OBD data, and adopting window averaged OBD NOx concentrations in evaluating vehicle emission performance.

Local associations refer to spatial-temporal correlations that emerge from the biological realm, such as time-dependent gene co-expression or seasonal interactions between microbes. One can reveal the intricate dynamics and inherent interactions of biological systems by examining the biological time series data for these associations. To accomplish this goal, local similarity analysis algorithms and statistical methods that facilitate the local alignment of time series and assess the significance of the resulting alignments have been developed. Although these algorithms were initially devised for gene expression analysis from microarrays, they have been adapted and accelerated for multi-omics next generation sequencing datasets, achieving high scientific impact. In this review, we present an overview of the historical developments and recent advances for local similarity analysis algorithms, their statistical properties, and real applications in analyzing biological time series data. The benchmark data and analysis scripts used in this review are freely available at http://github.com/labxscut/lsareview.

The fragility index has been increasingly used to assess the robustness of the results of clinical trials since 2014. It aims at finding the smallest number of event changes that could alter originally statistically significant results. Despite its popularity, some researchers have expressed several concerns about the validity and usefulness of the fragility index. It offers a comprehensive review of the fragility index\'s rationale, calculation, software, and interpretation, with emphasis on application to studies in obstetrics and gynecology. This article presents the fragility index in the settings of individual clinical trials, standard pairwise meta-analyses, and network meta-analyses. Moreover, this article provides worked examples to demonstrate how the fragility index can be appropriately calculated and interpreted. In addition, the limitations of the traditional fragility index and some solutions proposed in the literature to address these limitations were reviewed. In summary, the fragility index is recommended to be used as a supplemental measure in the reporting of clinical trials and a tool to communicate the robustness of trial results to clinicians. Other considerations that can aid in the fragility index\'s interpretation include the loss to follow-up and the likelihood of data modifications that achieve the loss of statistical significance.

Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidance to rate the certainty domain of imprecision is presently not fully operationalized for rating down by two levels and when different baseline risk or uncertainty in these risks are considered. In addition, there are scenarios in which lowering the certainty of evidence by three levels for imprecision is more appropriate than lowering it by two levels. In this article, we conceptualize and operationalize rating down for imprecision by one, two and three levels for imprecision using the contextualized GRADE approaches and making decisions.
Through iterative discussions and refinement in online meetings and through email communication, we developed draft guidance to rating the certainty of evidence down by up to three levels based on examples. The lead authors revised the approach according to the feedback and the comments received during these meetings and developed GRADE guidance for how to apply it. We presented a summary of the results to all attendees of the GRADE Working Group meeting for feedback in October 2021 (approximately 80 people) where the approach was formally approved.
This guidance provides GRADE\'s novel approach for the considerations about rating down for imprecision by one, two and three levels based on serious, very serious and extremely serious concerns. The approach includes identifying or defining thresholds for health outcomes that correspond to trivial or none, small, moderate or large effects and using them to rate imprecision. It facilitates the use of evidence to decision frameworks and also provides guidance for how to address imprecision about implausible large effects and trivial or no effects using the concept of the \'review information size\' and for varying baseline risks. The approach is illustrated using practical examples, an online calculator and graphical displays and can be applied to dichotomous and continuous outcomes.
In this GRADE guidance article, we provide updated guidance for how to rate imprecision using the partially and fully contextualized GRADE approaches for making recommendations or decisions, considering alternate baseline risks and for both dichotomous and continuous outcomes.

Biological time series data plays an important role in exploring the dynamic changes of biological systems, while the determinate patterns of association between various biological factors can further deepen the understanding of biological system functions and the interactions between them. At present, local trend analysis (LTA) has been commonly conducted in many biological fields, where the biological time series data can be the sequence at either the level of gene expression or OTU abundance, etc., A local trend score can be obtained by taking the similarity degree of the upward, constant or downward trend of time series data as an indicator of the correlation between different biological factors. However, a major limitation facing local trend analysis is that the permutation test conducted to calculate its statistical significance requires a time-consuming process. Therefore, the problem attracting much attention from bioinformatics scientists is to develop a method of evaluating the statistical significance of local trend scores quickly and effectively. In this paper, a new approach is proposed to evaluate the efficient approximation of statistical significance in the local trend analysis of dependent time series, and the effectiveness of the new method is demonstrated through simulation and real data set analysis.

There has been a long-standing controversy among scientists regarding the appropriate use of P-values and statistical significance in clinical research. This debate has resurfaced through recent calls to modify the threshold of P-value required to declare significance, or to retire statistical significance entirely. In this article, we revisit the issue by discussing: i) the connection between statistical thinking and evidence-based practice; ii) some history of statistical significance and P-values; iii) some practical challenges with statistical significance or P-value thresholds in clinical research; iv) the on-going debate on what to do with statistical significance; v) suggestions to shift the focus away from binary thinking of statistical significance and towards education for key stakeholders on research essentials including statistical thinking, critical thinking, good reporting, basic clinical research concepts and methods, and more. We then conclude with remarks and illustrations of the potential deleterious public health consequences of poor methods including selective choice of analysis approach and misguided reliance on binary use of P-values to report and interpret scientific findings.

A standard pathway/gene-set enrichment analysis, the over-representation analysis, is based on four values: the size of two gene-sets, size of their overlap, and size of the gene universe from which the gene-sets are chosen. The standard result of such an analysis is based on the p-value of a statistical test. We supplement this standard pipeline by six cautions: (1) any p-value threshold to distinguish enriched gene-sets from not-enriched ones is to certain degree arbitrary; (2) genes in a gene-set may be correlated, which potentially overcount the gene-set size; (3) any attempt to impose multiple testing correction will increase the false negative rate; (4) gene-sets in a gene-set database may be correlated, potentially overcount the factor for multiple testing correction; (5) the discrete nature of the data make it possible that a minimum change in counts may lead to a quantum change in the p-value threshold-based conclusion; (6) the two gene-sets may not be chosen from the universe of all human genes, but in fact from a subset of that universe, or even two different subsets of all genes. Careful reconsideration of these issues can have an impact on an enrichment analysis conclusion. Part of our cautions mirror the call from statistician that reaching conclusion from data is not a simple matter of p-value smaller than 0.05, but a thoughtful process with due diligences.

Drugs are very important for human life because they can provide treatment, cure, prevention, or diagnosis of different diseases. However, they also cause side effects, which can increase the risks for humans and pharmaceuticals companies. It is essential to identify drug side effects in drug discovery. To date, lots of computational methods have been proposed to predict the side effects of drugs and most of them used the fact that similar drugs always have similar side effects. However, previous studies did not analyze which substructures are highly related to which kind of side effect.
In this study, we conducted a computational investigation. In this regard, we extracted a drug set for each side effect, which consisted of drugs having the side effect. Also, for each substructure, a set was constructed by picking up drugs owing such substructure. The relationship between one side effect and one substructure was evaluated based on linkages between drugs in their corresponding drug sets, resulting in an Es value. Then, the statistical significance of Es value was measured by a permutation test.
A number of highly related pairs of side effects and substructures were obtained and some were extensively analyzed to confirm the reliability of the results reported in this study.

BACKGROUND: Local similarity analysis (LSA) of time series data has been extensively used to investigate the dynamics of biological systems in a wide range of environments. Recently, a theoretical method was proposed to approximately calculate the statistical significance of local similarity (LS) scores. However, the method assumes that the time series data are independent identically distributed, which can be violated in many problems.
RESULTS: In this paper, we develop a novel approach to accurately approximate statistical significance of LSA for dependent time series data using nonparametric kernel estimated long-run variance. We also investigate an alternative method for LSA statistical significance approximation by computing the local similarity score of the residuals based on a predefined statistical model. We show by simulations that both methods have controllable type I errors for dependent time series, while other approaches for statistical significance can be grossly oversized. We apply both methods to human and marine microbial datasets, where most of possible significant associations are captured and false positives are efficiently controlled.
CONCLUSIONS: Our methods provide fast and effective approaches for evaluating statistical significance of dependent time series data with controllable type I error. They can be applied to a variety of time series data to reveal inherent relationships among the different factors.