The geometry of quantum learning

Concept learning provides a natural framework in which to place the problems solved by the quantum algorithms of Bernstein-Vazirani and Grover. By combining the tools used in these algorithms--quantum fast transforms and amplitude amplification--with a novel (in this context) tool--a solution method for geometrical optimization problems--we derive a general technique for quantum concept learning. We name this technique "Amplified Impatient Learning" and apply it to construct quantum algorithms solving two new problems: BATTLESHIP and MAJORITY, more efficiently than is possible classically.Comment: 20 pages, plain TeX with amssym.tex, related work at http://www.math.uga.edu/~hunziker/ and http://math.ucsd.edu/~dmeyer

Toll-like receptor 4 signalling mediates inflammation in skeletal muscle of patients with chronic kidney disease

Background: Inflammation in skeletal muscle is implicated in the pathogenesis of insulin resistance and cachexia but why uremia up-regulates pro-inflammatory cytokines is unknown. Toll-like receptors (TLRs) regulate locally the innate immune responses, but it is unknown whether in chronic kidney disease (CKD) TLR4 muscle signalling is altered. The aim of the study is to investigate whether in CKD muscle, TLRs had abnormal function and may be involved in transcription of pro-inflammatory cytokine. Methods: TLR4, phospho-p65, phospho-ikB\u3b1, tumour necrosis factor (TNF)-\u3b1, phospho p38, Murf 1, and atrogin were studied in skeletal muscle from nondiabetic CKD stage 5 patients (n\u2009=\u200929) and controls (n\u2009=\u200914) by immunohistochemistry, western blot, and RT\u2013PCR. Muscle cell cultures (C2C12) exposed to uremic serum were employed to study TLR4 expression (western blot and RT\u2013PCR) and TLR-driven signalling. TLR4 signalling was abrogated by a small molecule chemical inhibitor or TLR4 siRNA. Phospho AKT and phospho p38 were evaluated by western blot. Results: CKD subjects had elevated TLR4 gene and protein expression. Also expression of NFkB, p38 MAPK and the NFkB-regulated gene TNF-\u3b1 was increased. At multivariate analysis, TLR4 protein content was predicted by eGFR and Subjective Global Assessment, suggesting that the progressive decline in renal function and wasting mediate TLR4 activation. In C2C12, uremic serum increased TLR4 as well as TNF-\u3b1 and down-regulated pAkt. These effects were prevented by blockade of TLR4. Conclusions: CKD promotes muscle inflammation through an up-regulation of TLR4, which may activate downward inflammatory signals such as TNF-\u3b1 and NFkB-regulated genes

Protein-Energy Wasting and Mortality in Chronic Kidney Disease

Protein-energy wasting (PEW) is common in patients with chronic kidney disease (CKD) and is associated with an increased death risk from cardiovascular diseases. However, while even minor renal dysfunction is an independent predictor of adverse cardiovascular prognosis, PEW becomes clinically manifest at an advanced stage, early before or during the dialytic stage. Mechanisms causing loss of muscle protein and fat are complex and not always associated with anorexia, but are linked to several abnormalities that stimulate protein degradation and/or decrease protein synthesis. In addition, data from experimental CKD indicate that uremia specifically blunts the regenerative potential in skeletal muscle, by acting on muscle stem cells. In this discussion recent findings regarding the mechanisms responsible for malnutrition and the increase in cardiovascular risk in CKD patients are discussed. During the course of CKD, the loss of kidney excretory and metabolic functions proceed together with the activation of pathways of endothelial damage, inflammation, acidosis, alterations in insulin signaling and anorexia which are likely to orchestrate net protein catabolism and the PEW syndrome

The geometry of quantum learning

Concept learning provides a natural framework in which to place the problems solved by the quantum algorithms of Bernstein-Vazirani and Grover. By combining the tools used in these algorithms—quantum fast transforms and amplitude amplification—with a novel (in this context) tool—a solution method for geometrical optimization problems—we derive a general technique for quantum concept learning. We name this technique “Amplified Impatient Learning” and apply it to construct quantum algorithms solving two new problems: Battleship and Majority, more efficiently than is possible classically