Methylseleninic Acid Elevates REDD1 and Inhibits Prostate Cancer Cell Growth Despite AKT Activation and mTOR Dysregulation in Hypoxia

Methylseleninic acid (MSeA) is a monomethylated selenium metabolite theoretically derived from subsequent β-lyase or transamination reactions of dietary Se-methylselenocysteine that has potent antitumor activity by inhibiting cell proliferation of several cancers. Our previous studies showed that MSeA promotes apoptosis in invasive prostate cancer cells in part by downregulating hypoxia-inducible factor HIF-1α. We have now extended these studies to evaluate the impact of MSeA on REDD1 (an mTOR inhibitor) in inducing cell death of invasive prostate cancer cells in hypoxia. In both PTEN+ and PTEN- prostate cancer cells we show that MSeA elevates REDD1 and phosphorylation of AKT along with p70S6K in hypoxia. Furthermore, REDD1 induction by MSeA is independent of AKT and the mTOR inhibition in prostate cancer cells causes partial resistance to MSeA-induced growth reduction in hypoxia. Our data suggest that MSeA induces REDD1 and inhibits prostate cancer cell growth in hypoxia despite activation of AKT and dysregulation of mTOR

Osteopontin is a potential target gene in mouse mammary cancer chemoprevention by Se-methylselenocysteine

BACKGROUND: Se-methylselenocysteine (MSC) is a naturally occurring organoselenium compound that inhibits mammary tumorigenesis in laboratory animals and in cell culture models. Previously we have documented that MSC inhibits DNA synthesis, total protein kinase C and cyclin-dependent kinase 2 kinase activities, leading to prolonged S-phase arrest and elevation of growth-arrested DNA damage genes, followed by caspase activation and apoptosis in a synchronized TM6 mouse mammary tumor model. The aim of the present study was to examine the efficacy of MSC against TM6 mouse mammary hyperplastic outgrowth (TM6-HOG) and to determine in vivo targets of MSC in this model system. METHODS: Twenty mammary fat pads each from female Balb/c mice transplanted with TM6-HOG and fed with 0.1 ppm selenium and with 3 ppm selenium respectively, were evaluated at 4 and 12 weeks after transplantation for growth spread, proliferative index and caspase-3 activity. Thirteen mice transplanted with TM6-HOG in each selenium group were observed for tumor formation over 23 weeks. Tumors from mice in both groups were compared by cDNA array analysis and data were confirmed by reverse transcription–polymerase chain reaction. To determine the effect of MSC on the expression of the novel target gene and on cell migration, experiments were performed in triplicate. RESULTS: A dietary dose of 3 ppm selenium significantly reduced the growth spread and induced caspase-3 activity in mammary fat pads in comparison with mice fed with the basal diet (0.1 ppm selenium). The extended administration (23 weeks) of 3 ppm selenium in the diet resulted in a tumor incidence of 77% in comparison with 100% tumor incidence in 0.1 ppm selenium-fed animals. The size of TM6 tumors in the supplemented group was smaller (mean 0.69 cm(2)) than in the mice fed with the basal diet (mean 0.93 cm(2)). cDNA array analysis showed a reduced expression of osteopontin (OPN) in mammary tumors of mice fed with the 3 ppm selenium diet in comparison with OPN expression in tumors arising in 0.1 ppm selenium-fed mice. A 24-hour treatment of TM6 cells with MSC significantly inhibited their migration and also reduced their OPN expression in comparison with untreated cells. CONCLUSIONS: OPN is a potential target gene in the inhibition of mammary tumorigenesis by selenium

Online Discrepancy Minimization for Stochastic Arrivals

In the stochastic online vector balancing problem, vectors $v_1,v_2,\ldots,v_T$ chosen independently from an arbitrary distribution in $\mathbb{R}^n$ arrive one-by-one and must be immediately given a $\pm$ sign. The goal is to keep the norm of the discrepancy vector, i.e., the signed prefix-sum, as small as possible for a given target norm. We consider some of the most well-known problems in discrepancy theory in the above online stochastic setting, and give algorithms that match the known offline bounds up to $\mathsf{polylog}(nT)$ factors. This substantially generalizes and improves upon the previous results of Bansal, Jiang, Singla, and Sinha (STOC' 20). In particular, for the Koml\'{o}s problem where $\|v_t\|_2\leq 1$ for each $t$, our algorithm achieves $\tilde{O}(1)$ discrepancy with high probability, improving upon the previous $\tilde{O}(n^{3/2})$ bound. For Tusn\'{a}dy's problem of minimizing the discrepancy of axis-aligned boxes, we obtain an $O(\log^{d+4} T)$ bound for arbitrary distribution over points. Previous techniques only worked for product distributions and gave a weaker $O(\log^{2d+1} T)$ bound. We also consider the Banaszczyk setting, where given a symmetric convex body $K$ with Gaussian measure at least $1/2$, our algorithm achieves $\tilde{O}(1)$ discrepancy with respect to the norm given by $K$ for input distributions with sub-exponential tails. Our key idea is to introduce a potential that also enforces constraints on how the discrepancy vector evolves, allowing us to maintain certain anti-concentration properties. For the Banaszczyk setting, we further enhance this potential by combining it with ideas from generic chaining. Finally, we also extend these results to the setting of online multi-color discrepancy

Software Piracy in the Presence of Open Source Alternatives

We develop a model to investigate the manner in which the pricing, profitability, and protection strategies of a seller of a proprietary digital good respond to changing market conditions. Specifically, we investigate how product piracy and the presence of open source software alternatives (such as Open Office) impact the optimal strategy of a seller of proprietary software (such as Microsoft Office). In contrast to previous literature, we show that firms may make more (rather than less) effort to control piracy when network externalities are strong. In addition, we show that the level of network externalities amplifies losses incurred by an incumbent due to high-quality pirated goods. Therefore, for products characterized by high network externalities (such as software), sellers need to try to maintain a large perceived quality gap between their product and illegal copies. Further, we demonstrate that the appearance of an OSS alternative leads the incumbent to reduce both price and the level of piracy control. Although high-quality pirated goods are detrimental to profits in the absence of OSS, they may actually limit the incumbent’s losses and the need to adjust price and protection strategies due to the introduction of an OSS alternative. Thus, an incumbent may find it easier to compete with OSS in the presence of product piracy. Finally, highly correlated intrinsic valuation between an incumbent and OSS products require smaller adjustments to price and piracy controls and leads to muted impact on incumbent profit

Prefix Discrepancy, Smoothed Analysis, and Combinatorial Vector Balancing

A well-known result of Banaszczyk in discrepancy theory concerns the prefix discrepancy problem (also known as the signed series problem): given a sequence of $T$ unit vectors in $\mathbb{R}^d$, find $\pm$ signs for each of them such that the signed sum vector along any prefix has a small $\ell_\infty$-norm? This problem is central to proving upper bounds for the Steinitz problem, and the popular Koml\'os problem is a special case where one is only concerned with the final signed sum vector instead of all prefixes. Banaszczyk gave an $O(\sqrt{\log d+ \log T})$ bound for the prefix discrepancy problem. We investigate the tightness of Banaszczyk's bound and consider natural generalizations of prefix discrepancy: We first consider a smoothed analysis setting, where a small amount of additive noise perturbs the input vectors. We show an exponential improvement in $T$ compared to Banaszczyk's bound. Using a primal-dual approach and a careful chaining argument, we show that one can achieve a bound of $O(\sqrt{\log d+ \log\!\log T})$ with high probability in the smoothed setting. Moreover, this smoothed analysis bound is the best possible without further improvement on Banaszczyk's bound in the worst case. We also introduce a generalization of the prefix discrepancy problem where the discrepancy constraints correspond to paths on a DAG on $T$ vertices. We show that an analog of Banaszczyk's $O(\sqrt{\log d+ \log T})$ bound continues to hold in this setting for adversarially given unit vectors and that the $\sqrt{\log T}$ factor is unavoidable for DAGs. We also show that the dependence on $T$ cannot be improved significantly in the smoothed case for DAGs. We conclude by exploring a more general notion of vector balancing, which we call combinatorial vector balancing. We obtain near-optimal bounds in this setting, up to poly-logarithmic factors.Comment: 22 pages. Appear in ITCS 202

Pediatric Crohn disease: A case series from a tertiary care center

Crohn disease (CD) is an inflammatory bowel disease that causes transmural inflammation of the gastrointestinal tract. Growth failure is a major complication and can occur before gastrointestinal manifestations in children with CD. We present here four such cases where short stature and undernutrition constituted major symptomatology, along with enteric features. Median age of presentation was 13 years. Intermittent abdominal pain and growth failure were the predominant clinical manifestations. Features of imaging and colonoscopy were suggestive of CD. A poor response was noted to corticosteroids and azathioprine in three children. Clinical remission was not achieved as per abbreviated pediatric CD activity index score. One child succumbed to illness secondary to dilated cardiomyopathy. A good therapeutic response with significant weight gain was observed in two children after starting biologicals. Only one child responded to oral corticosteroids and mesalamine. Considering biologicals early in chronically active moderate to severe CD can help in altering the prognosis

Online discrepancy minimization for stochastic arrivals

In the stochastic online vector balancing problem, vectors v1,v2,…,vT chosen independently from an arbitrary distribution in Rn arrive one-by-one and must be immediately given a ± sign. The goal is to keep the norm of the discrepancy vector, i.e., the signed prefix-sum, as small as possible for a given target norm. We consider some of the most well-known problems in discrepancy theory in the above online stochastic setting, and give algorithms that match the known offline bounds up to polylog(nT) factors. This substantially generalizes and improves upon the previous results of Bansal, Jiang, Singla, and Sinha (STOC' 20). In particular, for the Komlos problem where ∥v_t∥_2≤1 for each t, our algorithm achieves ˜O(1) discrepancy with high probability, improving upon the previous ˜O(n3/2) bound. For Tusnády's problem of minimizing the discrepancy of axis-aligned boxes, we obtain an O(log^{d+4}T) bound for arbitrary distribution over points. Previous techniques only worked for product distributions and gave a weaker O(log^{2d+1}T) bound. We also consider the Banaszczyk setting, where given a symmetric convex body K with Gaussian measure at least 1/2, our algorithm achieves \tilde{O}(1) discrepancy with respect to the norm given by K for input distributions with sub-exponential tails. Our results are based on a new potential function approach. Previous techniques consider a potential that penalizes large discrepancy, and greedily chooses the next color to minimize the increase in potential. Our key idea is to introduce a potential that also enforces constraints on how the discrepancy vector evolves, allowing us to maintain certain anti-concentration properties. We believe that our techniques to control the evolution of states could find other applications in stochastic processes and online algorithms. For the Banaszczyk setting, we further enhance this potential by combining it with ideas from generic chaining. Finally, we also extend these results to the setting of online multi-color discrepancy.</p

Online discrepancy minimization for stochastic arrivals

In the stochastic online vector balancing problem, vectors v1, v2,..., vT chosen independently from an arbitrary distribution in Rn arrive one-by-one and must be immediately given a ± sign. The goal is to keep the norm of the discrepancy vector, i.e., the signed prefix-sum, as small as possible for a given target norm. We consider some of the most well-known problems in discrepancy theory in the above online stochastic setting, and give algorithms that match the known offline bounds up to polylog(nT) factors. This substantially generalizes and improves upon the previous results of Bansal, Jiang, Singla, and Sinha (STOC' 20). In particular, for the Komlós problem where kvtk2 ≤ 1 for each t, our algorithm achieves Oe(1) discrepancy with high probability, improving upon the previous Oe(n3/2) bound. For Tusnády's problem of minimizing the discrepancy of axis-aligned boxes, we obtain an O(logd+4 T) bound for arbitrary distribution over points. Previous techniques only worked for product distributions and gave a weaker O(log2d+1 T) bound. We also consider the Banaszczyk setting, where given a symmetric convex body K with Gaussian measure at least 1/2, our algorithm achieves Oe(1) discrepancy with respect to the norm given by K for input distributions with sub-exponential tails. Our results are based on a new potential function approach. Previous techniques consider a potential that penalizes large discrepancy, and greedily chooses the next color to minimize the increase in potential. Our key idea is to introduce a potential that also enforces constraints on how the discrepancy vector evolves, allowing us to maintain certain anti-concentration properties. We believe that our techniques to control the evolution of states could find other applications in stochastic processes and online algorithms. For the Banaszczyk setting, we further enhance this potential by combining it with ideas from generic chaining. Finally, we also extend these results to the setting of online multicolor discrepancy