Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors

The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed acyclic graph (DAG) on the decision-distribution interplay, we investigate the possibility of achieving long-term fairness from a dynamic perspective. We propose Tier Balancing, a technically more challenging but more natural notion to achieve in the context of long-term, dynamic fairness analysis. Different from previous fairness notions that are defined purely on observed variables, our notion goes one step further, capturing behind-the-scenes situation changes on the unobserved latent causal factors that directly carry out the influence from the current decision to the future data distribution. Under the specified dynamics, we prove that in general one cannot achieve the long-term fairness goal only through one-step interventions. Furthermore, in the effort of approaching long-term fairness, we consider the mission of "getting closer to" the long-term fairness goal and present possibility and impossibility results accordingly

Efficacy and safety of consolidation durvalumab after chemoradiation therapy for stage III non-small-cell lung cancer: a systematic review, meta-analysis, and meta-regression of real-world studies

Background: The current review aimed to pool real-world evidence on the efficacy and toxicity of consolidation durvalumab for stage III unresectable non-small cell lung cancer (NSCLC) after curative chemoradiotherapy.Methods: PubMed, CENTRAL, ScienceDirect, Embase, and Google Scholar were searched for observational studies reporting the use of durvalumab for NSCLC till 12th April 2022. Twenty-three studies with 4,400 patients were included.Results: The pooled 1-year overall survival (OS) and progression-free survival rates (PFS) were 85% (95% CI: 81%–89%) and 60% (95% CI: 56%–64%) respectively. Pooled incidence of all-grade pneumonitis, grade ≥3 pneumonitis and discontinuation of durvalumab due to pneumonitis were 27% (95% CI: 19%–36%), 8% (95% CI: 6%–10%) and 17% (95% CI: 12%–23%) respectively. The pooled proportion of patients experiencing endocrine, cutaneous, musculoskeletal, and gastrointestinal adverse events was 11% (95% CI: 7%–18%), 8% (95% CI: 3%–17%), 5% (95% CI: 3%–6%), and 6% (95% CI: 3%–12%), respectively.Conclusion: Meta-regression indicated that performance status significantly influenced PFS, while age, time to durvalumab, and programmed death-ligand 1 status significantly affected pneumonitis rates. Real-world evidence suggests that the short-term efficacy and safety of durvalumab are consistent with that of the PACIFIC trial. The congruence of results lends support to durvalumab use in improving outcomes of unresectable stage III NSCLC.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022324663, identifier CRD42022324663

Integrative analysis based on survival associated co-expression gene modules for predicting Neuroblastoma patients' survival time

BACKGROUND: More than 90% of neuroblastoma patients are cured in the low-risk group while only less than 50% for those with high-risk disease can be cured. Since the high-risk patients still have poor outcomes, we need more accurate stratification to establish an individualized precise treatment plan for the patients to improve the long-term survival rate. RESULTS: We focus on extracting features and providing a workflow to improve survival prediction for neuroblastoma patients. With a workflow for gene co-expression network (GCN) mining in microarray and RNA-Seq datasets, we extracted molecular features from each co-expressed module and summarized them into eigengenes. Then we adopted the lasso-regularized Cox proportional hazards model to select the most informative eigengene features regarding association to the risk of metastasis. Nine eigengenes were selected which show strong association with patient survival prognosis. All of the nine corresponding gene modules also have highly enriched biological functions or cytoband locations. Three of them are unique modules to RNA-Seq data, which complement the modules from microarray data in terms of survival prognosis. We then merged all eigengenes from these unique modules and used an integrative method called Similarity Network Fusion to test the prognostic power of these eigengenes for prognosis. The prognostic accuracies are significantly improved as compared to using all eigengenes, and a subgroup of patients with very poor survival rate was identified. CONCLUSIONS: We first compared GCNs mined from microarray and RNA-seq data. We discovered that each data modality yields unique GCNs, which are enriched with clear biological functions. Then we do module unique analysis and use lasso-cox model to select survival-associated eigengenes. Integration of unique and survival-associated eigengenes from both data types provides complementary information that leads to more accurate survival prognosis

Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation

SGK (sequential generalization of K-means) dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA) noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1) The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2) The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3) Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance

Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers

Improving the expression of recombinant pullulanase by increasing mRNA stability in Escherichia coli

Background: Pullulanase production in both wild-type strains and recombinantly engineered strains remains low. The Shine-Dalgarno (SD) sequence and stem-loop structure in the 5\u2032 or 3\u2032 untranslated region (UTR) are well-known determinants of mRNA stability. This study investigated the effect of mRNA stability on pullulanase heterologous expression. Results: We constructed four DNA fragments, pulA, SD-pulA, pulA-3t, and SD-pulA-3t,whichwere cloned into the expression vector pHT43 to generate four pullulanase expression plasmids. The DNA fragment pulA was the coding sequence (CDS) of pulA in Klebsiella variicola Z-13. SD-pulA was constructed by the addition of the 5\u2032 SD sequence at the 5\u2032 UTR of pulA. pulA-3t was constructed by the addition of a 3\u2032 stem-loop structure at the 3\u2032 UTR of pulA. SD-pulA-3t was constructed by the addition of the 5\u2032 SD sequence at the 5\u2032 UTR and a 3\u2032 stem-loop structure at the 3\u2032 UTR of pulA. The four vectors were transformed into Escherichia coli BL21(DE3). The pulA mRNA transcription of the transformant harboring pHT43-SD-pulA-3t was 338.6%, 34.9%, and 79.9% higher than that of the other three transformants, whereas the fermentation enzyme activities in culture broth and intracellularly were 107.0 and 584.1 times, 1.2 and 2.0 times, and 62.0 and 531.5 times the amount of the other three transformants (pulA, SD-pulA, and pulA-3 t), respectively. Conclusion: The addition of the 5\u2032 SD sequence at the 5\u2032 UTR and a 3\u2032 stem-loop structure at the 3\u2032 UTR of the pulA gene is an effective approach to increase pulA gene expression and fermentation enzyme activity

Metabolomic and transcriptomice analyses of flavonoid biosynthesis in apricot fruits

IntroductionFlavonoids, as secondary metabolites in plants, play important roles in many biological processes and responses to environmental factors.MethodsApricot fruits are rich in flavonoid compounds, and in this study, we performed a combined metabolomic and transcriptomic analysis of orange flesh (JN) and white flesh (ZS) apricot fruits.Results and discussionA total of 222 differentially accumulated flavonoids (DAFs) and 15855 differentially expressed genes (DEGs) involved in flavonoid biosynthesis were identified. The biosynthesis of flavonoids in apricot fruit may be regulated by 17 enzyme-encoding genes, namely PAL (2), 4CL (9), C4H (1), HCT (15), C3’H (4), CHS (2), CHI (3), F3H (1), F3’H (CYP75B1) (2), F3’5’H (4), DFR (4), LAR (1), FLS (3), ANS (9), ANR (2), UGT79B1 (6) and CYP81E (2). A structural gene-transcription factor (TF) correlation analysis yielded 3 TFs (2 bHLH, 1 MYB) highly correlated with 2 structural genes. In addition, we obtained 26 candidate genes involved in the biosynthesis of 8 differentially accumulated flavonoids metabolites in ZS by weighted gene coexpression network analysis. The candidate genes and transcription factors identified in this study will provide a highly valuable molecular basis for the in-depth study of flavonoid biosynthesis in apricot fruits

Stress-Activated Kinase MKK7 Governs Epigenetics of Cardiac Repolarization for Arrhythmia Prevention

BACKGROUND: Ventricular arrhythmia is a leading cause of cardiac mortality. Most antiarrhythmics present paradoxical proarrhythmic side effects, culminating in a greater risk of sudden death. METHODS: We describe a new regulatory mechanism linking mitogen-activated kinase kinase-7 deficiency with increased arrhythmia vulnerability in hypertrophied and failing hearts using mouse models harboring mitogen-activated kinase kinase-7 knockout or overexpression. The human relevance of this arrhythmogenic mechanism is evaluated in human-induced pluripotent stem cell-derived cardiomyocytes. Therapeutic potentials by targeting this mechanism are explored in the mouse models and human-induced pluripotent stem cell-derived cardiomyocytes. RESULTS: Mechanistically, hypertrophic stress dampens expression and phosphorylation of mitogen-activated kinase kinase-7. Such mitogen-activated kinase kinase-7 deficiency leaves histone deacetylase-2 unphosphorylated and filamin-A accumulated in the nucleus to form a complex with Kruppel-like factor-4. This complex leads to Kruppel-like factor-4 disassociation from the promoter regions of multiple key potassium channel genes (Kv4.2, KChIP2, Kv1.5, ERG1, and Kir6.2) and reduction of their transcript levels. Consequent repolarization delays result in ventricular arrhythmias. Therapeutically, targeting the repressive function of the Kruppel-like factor-4/histone deacetylase-2/filamin-A complex with the histone deacetylase-2 inhibitor valproic acid restores K+ channel expression and alleviates ventricular arrhythmias in pathologically remodeled hearts. CONCLUSIONS: Our findings unveil this new gene regulatory avenue as a new antiarrhythmic target where repurposing of the antiepileptic drug valproic acid as an antiarrhythmic is supported.British Heart Foundation [PG/09/052/27833, PG/14/71/31063, PG/12/76/29852, FS/15/16/31477]; Medical Research Council [G1002082, MC_PC_13070]; American Heart Association National Scientist Development Grants [12SDG12070077]; National Basic Research Program of China [2012CB518000]SCI(E)ARTICLE7683-69913

Visualization 5: High-resolution, real-time simultaneous 3D surface geometry and temperature measurement

Visualization 5 Originally published in Optics Express on 27 June 2016 (oe-24-13-14552