A Euclidean Distance Matrix Model for Convex Clustering

Clustering has been one of the most basic and essential problems in unsupervised learning due to various applications in many critical fields. The recently proposed sum-of-nums (SON) model by Pelckmans et al. (2005), Lindsten et al. (2011) and Hocking et al. (2011) has received a lot of attention. The advantage of the SON model is the theoretical guarantee in terms of perfect recovery, established by Sun et al. (2018). It also provides great opportunities for designing efficient algorithms for solving the SON model. The semismooth Newton based augmented Lagrangian method by Sun et al. (2018) has demonstrated its superior performance over the alternating direction method of multipliers (ADMM) and the alternating minimization algorithm (AMA). In this paper, we propose a Euclidean distance matrix model based on the SON model. An efficient majorization penalty algorithm is proposed to solve the resulting model. Extensive numerical experiments are conducted to demonstrate the efficiency of the proposed model and the majorization penalty algorithm.Comment: 32 pages, 3 figures, 3 table

Tumor elimination by clustered microRNAs miR-306 and miR-79 via noncanonical activation of JNK signaling

JNK signaling plays a critical role in both tumor promotion and tumor suppression. Here, we identified clustered microRNAs (miRNAs) miR-306 and miR-79 as novel tumor-suppressor miRNAs that specifically eliminate JNK-activated tumors in Drosophila. While showing only a slight effect on normal tissue growth, miR-306 and miR-79 strongly suppressed growth of multiple tumor models, including malignant tumors caused by Ras activation and cell polarity defects. Mechanistically, these miRNAs commonly target the mRNA of an E3 ubiquitin ligase ring finger protein 146 (RNF146). We found that RNF146 promotes degradation of tankyrase (Tnks), an ADP-ribose polymerase that promotes JNK activation in a noncanonical manner. Thus, downregulation of RNF146 by miR-306 and miR-79 leads to hyper-enhancement of JNK activation. Our data show that, while JNK activity is essential for tumor growth, elevation of miR-306 or miR-79 overactivate JNK signaling to the lethal level via noncanonical JNK pathway and thus eliminate tumors, providing a new miRNA-based strategy against cancer

Bis[μ-2-(2-carboxyl­atophen­yl)acetato]-κ3 O 1,O 1′:O 2;κ3 O 2:O 1,O 1′-bis­[aqua­(1,10-phenanthroline-κ2 N,N′)nickel(II)]

The title compound, [Ni2(C9H6O4)2(C12H8N2)2(H2O)2], is isostructural with the ZnII analogue. Each NiII atom is coordinated in a distorted octa­hedral geometry by three O atoms from two homophthalate anions, one aqua O atom and two 1,10-phenanthroline N atoms. The two NiII atoms are linked by two bridging homophthalate dianions into a centrosymmetric dinuclear unit. The dinuclear units are linked into one-dimensional ladder-like chains along [100] by O—H⋯O hydrogen bonds between the coordinated water mol­ecules and one of the O atoms of the carboxyl­atomethyl group

Evaluating Fairness Without Sensitive Attributes: A Framework Using Only Auxiliary Models

Although the volume of literature and public attention on machine learning fairness has been growing significantly, in practice some tasks as basic as measuring fairness, which is the first step in studying and promoting fairness, can be challenging. This is because sensitive attributes are often unavailable due to privacy regulations. The straightforward solution is to use auxiliary models to predict the missing sensitive attributes. However, our theoretical analyses show that the estimation error of the directly measured fairness metrics is proportional to the error rates of auxiliary models' predictions. Existing works that attempt to reduce the estimation error often require strong assumptions, e.g. access to the ground-truth sensitive attributes or some form of conditional independence. In this paper, we drop those assumptions and propose a framework that uses only off-the-shelf auxiliary models. The main challenge is how to reduce the negative impact of imperfectly predicted sensitive attributes on the fairness metrics without knowing the ground-truth sensitive attributes. Inspired by the noisy label learning literature, we first derive a closed-form relationship between the directly measured fairness metrics and their corresponding ground-truth metrics. And then we estimate some key statistics (most importantly transition matrix in the noisy label literature), which we use, together with the derived relationship, to calibrate the fairness metrics. In addition, we theoretically prove the upper bound of the estimation error in our calibrated metrics and show our method can substantially decrease the estimation error especially when auxiliary models are inaccurate or the target model is highly biased. Experiments on COMPAS and CelebA validate our theoretical analyses and show our method can measure fairness significantly more accurately than baselines under favorable circumstances

Hesperetin protects SH-SY5Y cells against 6- hydroxydopamine-induced neurotoxicity via activation of NRF2/ARE signaling pathways

Purpose: To investigation the protective effects of hesperetin against 6-hydroxydopamine (6-OHDA)- induced neurotoxicity. Methods: SH-SY5Y cells were incubated with 6-OHDA to create an in vitro model of neurotoxicity. This model was used to test the neuroprotective effects of hesperetin. Cell viability was assessed by MTT and lactate dehydrogenase (LDH) release assays. Flow cytometry and western blot were used to quantify apoptosis. Oxidative stress was evaluated by determining intracellular glutathione (GSH), malondialdehyde (MDA), superoxide dismutase (SOD), and reactive oxygen species (ROS). Results: In SH-SY5Y cells, treatment with 6-OHDA decreased cell viability and promoted LDH release. However, exogenous hesperetin protected against 6-OHDA-mediated toxicity. Similarly, although incubation with 6-OHDA induced apoptosis and increased cleaved caspase-3 and -9 levels, treatment with hesperetin protected against these effects. Treatment with 6-OHDA also led to significant oxidative stress, as indicated by reduced GSH and SOD levels and increased MDA and ROS levels in SH-SY5Y cells. However, these changes were reversed by pre-treatment with hesperetin. Of interest, hesperetin led to changes in 6-OHDA-induced expression of NRF2, heme oxygenase-1 (HO-1), glutamate-cysteine ligase (GCL) catalytic subunit (GCLC), and GCL modulatory (GCLM). Conclusion: Hesperetin protects against cell toxicity, apoptosis, and oxidative stress via activation of NRF2 pathway in a 6-OHDA-induced model of neurotoxicity. Future studies should investigate the use of hesperetin as a potential therapeutic approach for prevention or management of Parkinson’s disease. Keywords: Hesperetin, 6-OHDA, Neurotoxicity, NRF2, Parkinson’s diseas