Identification of microRNA Biogenesis Regulators and Activity Modulators

MicroRNAs play a key role in post-transcriptional gene regulation. They regulate target gene expression with mRNA degradation or translation repression. Each miRNA is estimated to regulate dozens of genes in human, and dysregulation of miRNA leads to various diseases, such as cancer, heart disease and depression. Therefore, it is critical to understand the mechanism of miRNA biogenesis and targeting. This work integrated gene and miRNA expression profile from various cancer projects to screen for potential miRNA biogenesis regulators and activity modulators. In this analysis, we identified several genes that regulate miRNA pathway and found their association with tumor progression and clinical outcome

A Fast Mental Poker Protocol

Abstract. We present a fast and secure mental poker protocol. It is twice as fast as Barnett-Smart\u27s and Castellà-Roca\u27s protocols. This protocol is provably secure under DDH assumption

The Effect of Wavelength Conversions on Broiler Growth and Leg Disorders

Abstract: The wavelength of light affects the behavior and psychology of chickens. In this study, the effects of several wavelengths and daily wavelength conversions on chickens were examined

Antidepressant Effects on Insulin Sensitivity and Proinflammatory Cytokines in the Depressed Males

Growing evidence suggests that mood disorder is associated with insulin resistance and inflammation. Thus the effects of antidepressants on insulin sensitivity and proinflammatory responses will be a crucial issue for depression treatment. In this study, we enrolled 43 non-diabetic young depressed males and adapted standard testing procedures to assess glucose metabolism during 4-week hospitalization. Before and after the 4-week antidepressant treatment, participants underwent oral glucose tolerance test (OGTT) and frequently sampled intravenous glucose tolerance test (FSIGT). Insulin sensitivity (SI), glucose effectiveness (SG), acute insulin response, and disposition index (DI) were estimated using the minimal model method. The plasma levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and adiponectin were measured. The Hamilton depression rating scale (HAM-D) total scores were reduced significantly during the course of treatment. There were no significant changes in the parameters of SI, SG, and DI. Compared to drug naïve status, the level of plasma IL-6 was significantly elevated (0.77 to 1.30 pg/ml; P = .001) after antidepressant therapy. However, the concentrations of CRP, TNF-α, and adiponectin showed no differences during the course of treatment. The results suggest that antidepressants may promote stimulatory effect on the IL-6 production in the early stage of antidepressant treatment

Toward Fairness Through Fair Multi-Exit Framework for Dermatological Disease Diagnosis

Fairness has become increasingly pivotal in medical image recognition. However, without mitigating bias, deploying unfair medical AI systems could harm the interests of underprivileged populations. In this paper, we observe that while features extracted from the deeper layers of neural networks generally offer higher accuracy, fairness conditions deteriorate as we extract features from deeper layers. This phenomenon motivates us to extend the concept of multi-exit frameworks. Unlike existing works mainly focusing on accuracy, our multi-exit framework is fairness-oriented; the internal classifiers are trained to be more accurate and fairer, with high extensibility to apply to most existing fairness-aware frameworks. During inference, any instance with high confidence from an internal classifier is allowed to exit early. Experimental results show that the proposed framework can improve the fairness condition over the state-of-the-art in two dermatological disease datasets.Comment: MICCAI202

Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space

Fairness has become increasingly pivotal in machine learning for high-risk applications such as machine learning in healthcare and facial recognition. However, we see the deficiency in the previous logits space constraint methods. Therefore, we propose a novel framework, Logits-MMD, that achieves the fairness condition by imposing constraints on output logits with Maximum Mean Discrepancy. Moreover, quantitative analysis and experimental results show that our framework has a better property that outperforms previous methods and achieves state-of-the-art on two facial recognition datasets and one animal dataset. Finally, we show experimental results and demonstrate that our debias approach achieves the fairness condition effectively

Genome-wide gene-based association study

Genome-wide association studies, which analyzes hundreds of thousands of single-nucleotide polymorphisms to identify disease susceptibility genes, are challenging because the work involves intensive computation and complex modeling. We propose a two-stage genome-wide association scanning procedure, consisting of a single-locus association scan for the first stage and a gene-based association scan for the second stage. Marginal effects of single-nucleotide polymorphisms are examined by using the exact Armitage trend test or logistic regression, and gene effects are examined by using a p-value combination method. Compared with some existing single-locus and multilocus methods, the proposed method has the following merits: 1) convenient for definition of biologically meaningful regions, 2) powerful for detection of minor-effect genes, 3) helpful for alleviation of a multiple-testing problem, and 4) convenient for result interpretation. The method was applied to study Genetic Analysis Workshop 16 Problem 1 rheumatoid arthritis data, and strong association signals were found. The results show that the human major histocompatibility complex region is the most important genomic region associated with rheumatoid arthritis. Moreover, previously reported genes including PTPN22, C5, and IL2RB were confirmed; novel genes including HLA-DRA, BTNL2, C6orf10, NOTCH4, TAP2, and TNXB were identified by our analysis

Plasma-cell type Castleman’s disease of the neck and lymphocyte-depletion Hodgkin lymphoma associated with intestinal intussusception in an AIDS patient

A 36-year-old man was diagnosed with plasma-cell type Castleman’s disease with the presentation of recurrent lymphadenpathy of the neck. HIV infection was not suspected or confirmed until esophageal candidiasis developed one year later. Meanwhile, surgery was performed for intestinal intussusception and obstruction caused by lymphocyte-depletion Hodgkin lymphoma. However, he died of rapidly progressive pneumonia and disseminated intravascular coagulation associated with intracerebral hemorrhage, which occurred 6 months later during the course of chemotherapy. This case suggests that HIV infection should be considered in patients who present with plasma-cell type Castleman’s disease or lymphocyte-depletion Hodgkin lymphoma with extra-nodal involvement in order to conduct appropriate diagnosis and initiate treatment for HIV infection

The role of reactive monomer in Pl-free technology for the alignment ability and image sticking performance

Polyimide-free technology is a technology in which an additive can replace polyimide film to align LC molecules. In this technology, the additive added in liquid crystal (LC) host can not only affect the alignment behavior, but also affect the reliability of the panel, such as image sticking. Because the polymer is polymerized from the additive in LC, the system of the additive is very important. In this paper, we studied the alignment and image sticking performance fabricated by two different additive systems: 1. the mixed system of additive and reactive monomer; 2. the single additive system. From the results of cell and 28” panel, we can conclude that the mixed system has similar alignment ability and voltage holding ratio to the single additive system, however, has better image sticking performance than the single additive system