VGF changes during the estrous cycle: a novel endocrine role for TLQP peptides?

Although the VGF derived peptide TLQP-21 stimulates gonadotropin-releasing hormone (GnRH) and gonadotropin secretion, available data on VGF peptides and reproduction are limited. We used antibodies specific for the two ends of the VGF precursor, and for two VGF derived peptides namely TLQP and PGH, to be used in immunohistochemistry and enzyme-linked immunosorbent assay complemented with gel chromatography. In cycling female rats, VGF C-/N-terminus and PGH peptide antibodies selectively labelled neurones containing either GnRH, or kisspeptin (VGF N-terminus only), pituitary gonadotrophs and lactotrophs, or oocytes (PGH peptides only). Conversely, TLQP peptides were restricted to somatostatin neurones, gonadotrophs, and ovarian granulosa, interstitial and theca cells. TLQP levels were highest, especially in plasma and ovary, with several molecular forms shown in chromatography including one compatible with TLQP-21. Among the cycle phases, TLQP levels were higher during metestrus-diestrus in median eminence and pituitary, while increased in the ovary and decreased in plasma during proestrus. VGF N- and C-terminus peptides also showed modulations over the estrous cycle, in median eminence, pituitary and plasma, while PGH peptides did not. In ovariectomised rats, plasmatic TLQP peptide levels showed distinct reduction suggestive of a major origin from the ovary, while the estrogen-progesterone treatment modulated VGF C-terminus and TLQP peptides in the hypothalamus-pituitary complex. In in vitro hypothalamus, TLQP-21 stimulated release of growth hormone releasing hormone but not of somatostatin. In conclusion, various VGF peptides may regulate the hypothalamus-pituitary complex via specific neuroendocrine mechanisms while TLQP peptides may act at further, multiple levels via endocrine mechanisms involving the ovary

Strategic Effects of Investment and Private Information: The Incumbent's Curse

We study a two-period entry model where the incumbent, privately informed about his cost of production, makes a long run investment choice along with a pricing decision. Investment is cost-reducing and its effects are assumed to differ across incumbent's types, as a result investment plays a double role as a commitment variable and, along with price, as a signal. We ask whether and how investment decisions allow the incumbent to limit entry into the market. We find that the incumbent will never undertake strategic investment to deter profitable entry, because when incumbent's costs are private information the signaling role of investment cancels out its value of commitment

Investment in early education and job market signaling

We consider a signaling model of the job market in which workers, before choosing their level of education, have the opportunity to undertake an unobservable investment in activities aimed at saving on future education costs. Suciently high levels of investments allow a low productivity worker to cut the marginal costs of signaling below the high productivity worker's. In contrast to standard results, we nd that the equilibrium outcome will depend on the relative magnitude of workers' average productivity. If average productivity exceeds a certain threshold the most plausible solution is a rened pooling equilibrium in which all workers attain the same level of over-education and are paid the same wage. Otherwise, the most plausible outcome is the standard least cost separating equilibrium in which only high ability workers are over-educated

Private Information and the Commitment Value of Unobservable Investment

The commitment value of unobservable investment with cost-reducing effects is examined in an entry model where the incumbent is privately informed about his costs of production. We show that when the price signals incumbent's costs, unobservable investment can not have any commitment value and the limit price does not limit entry. By contrast, if the price does not reveal costs, which is the more likely outcome, unobservable investment has a magnified value of commitment and a less aggressive limit price deters profitable entry

Limit Pricing and Strategic Investment

We study an entry model where an incumbent privately informed about costs can make a cost-reducing investment choice, along with a pricing decision, in order to prevent a competing rm from entering the market. We show that if limit pricing per se can not deter pro table entry, the opportunity to undertake a strategic investment does not provide an additional instrument for the achievement of this goal to the incumbent

Holocene slip rate variability along the Pernicana fault system (Mt. Etna, Italy): Evidence from offset lava flows

The eastern flank of the Mount Etna stratovolcano is affected by extension and is slowly sliding eastward into the Ionian Sea. The Pernicana fault system forms the border of the northern part of this sliding area. It consists of three E-W−oriented fault sectors that are seismically active and characterized by earthquakes up to 4.7 in magnitude (M) capable of producing ground rupture and damage located mainly along the western and central sectors, and by continuous creep on the eastern sector. A new topographic study of the central sector of the Pernicana fault system shows an overall bell-shaped profile, with maximum scarp height of 35 m in the center of the sector, and two local minima that are probably due to the complex morphological relation between fault scarp and lava flows. We determined the ages of lava flows cut by the Pernicana fault system at 12 sites using cosmogenic 3He and 40Ar/39Ar techniques in order to determine the recent slip history of the fault. From the displacement-age relations, we estimate an average throw rate of ∼2.5 mm/yr over the last 15 k.y. The slip rate appears to have accelerated during the last 3.5 k.y., with displacement rates of up to ∼15 mm/yr, whereas between 3.5 and 15 ka, the throw rate averaged ∼1 mm/yr. This increase in slip rate resulted in significant changes in seismicity rates, for instance, decreasing the mean recurrence time of M ≥ 4.7 earthquakes from ∼200 to ∼20 yr. Based on empirical relationships, we attribute the variation in seismic activity on the Pernicana fault system to factors intrinsic to the system that are likely related to changes in the volcanic system. These internal factors could be fault interdependencies (such as those across the Taupo Rift, New Zealand) or they could represent interactions among magmatic, tectonic, and gravitational processes (e.g., Kīlauea volcano, Hawaii). Given their effect on earthquake recurrence intervals, these interactions need to be fully assessed in seismic hazard evaluations

High performance polyethylene nanocomposite fibers

A high density polyethylene (HDPE) matrix was melt compounded with 2 vol% of dimethyldichlorosilane treated fumed silica nanoparticles. Nanocomposite fibers were prepared by melt spinning through a co-rotating twin screw extruder and drawing at 125°C in air. Thermo-mechanical and morphological properties of the resulting fibers were then investigated. The introduction of nanosilica improved the drawability of the fibers, allowing the achievement of higher draw ratios with respect to the neat matrix. The elastic modulus and creep stability of the fibers were remarkably improved upon nanofiller addition, with a retention of the pristine tensile properties at break. Transmission electronic microscope (TEM) images evidenced that the original morphology of the silica aggregates was disrupted by the applied drawing

Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing

The increasing complexity and amount of data available in modern applications strongly demand Trustworthy Learning algorithms that can be fed directly with complex and large graphs data. In fact, on one hand, machine learning models must meet high technical standards (e.g., high accuracy with limited computational requirements), but, at the same time, they must be sure not to discriminate against subgroups of the population (e.g., based on gender or ethnicity). Graph Neural Networks (GNNs) are currently the most effective solution to meet the technical requirements, even if it has been demonstrated that they inherit and amplify the biases contained in the data as a reflection of societal inequities. In fact, when dealing with graph data, these biases can be hidden not only in the node attributes but also in the connections between entities. Several Fair GNNs have been proposed in the literature, with uNIfying Fairness and stabiliTY (NIFTY) (Agarwal et al., 2021) being one of the most effective. In this paper, we will empower NIFTY's fairness with two new strategies. The first one is a Biased Edge Dropout, namely, we drop graph edges to balance homophilous and heterophilous sensitive connections, mitigating the bias induced by subgroup node cardinality. The second one is Attributes Preprocessing, which is the process of learning a fair transformation of the original node attributes. The effectiveness of our proposal will be tested on a series of datasets with increasingly challenging scenarios. These scenarios will deal with different levels of knowledge about the entire graph, i.e., how many portions of the graph are known and which sub-portion is labelled at the training and forward phases