Roles of Crustacean Female Sex Hormone 1a in a Protandric Simultaneous Hermaphrodite Shrimp

Crustacean female sex hormone (CFSH) plays a pivotal role in the development of secondary sex characteristics in dioecious crustaceans. However, until now the knowledge concerning its functions in hermaphroditic species is scanty. Herein, we explored the function of CFSH (Lvit-CFSH1a) in the peppermint shrimp Lysmata vittata, a species characterized by a rare reproductive system of protandric simultaneous hermaphroditism (PSH). Lvit-CFSH1a cDNA was 1,220-bp in length with a 720-bp ORF encoded a polypeptide of 239-aa. RT-PCR showed that Lvit-CFSH1a was exclusively expressed in the eyestalk ganglion. For female physiology, it was found that Lvit-CFSH1a was indispensable for the development of female gonopores, but it might not involve vitellogenesis of the species. For male physiology, Lvit-CFSH1a suppressed Lvit-IAG2 expression in short-term silencing experiment and recombinant protein injection experiment, but did not affect male sexual differentiation in long-term silencing experiment. In addition, silencing the Lvit-CFSH1a gene impeded individual growth in L. vittata.

Liquid Film Behavior of Bottoming Liquid Jet in a Shallow Pool Measured by 3D-LIF

When the core meltdown accident occurs in the nuclear plant, molten corium falls into a coolant pool of the lower plenum. It is considered that the molten corium jet is broken up, cooled, and solidified with fuel-coolant interaction (FCI). However, the coolant pool could be a shallow condition by the leakage and evaporation of the coolant. In this situation, it is considered that the corium jet bottoms and spreads without the jet breakup. From the viewpoint of safety, understanding a jet behavior and estimating a cooling behavior are needed. The purpose of this study is to clarify the mechanism of the liquid jet behavior in a shallow pool as the fundamental process for estimating the cooling behavior in the real machine. In this paper, we discuss the spreading behavior of the liquid jet after bottoming. The jet injection experiment was conducted using test fluids. By using the 3D-LIF method, the 3D visualization of the liquid jet was Successfully implemented. From the visualization result, the following behaviors were seen. After bottoming, the jet spread radially with the liquid film. As the jet spreading behavior, the liquid film was rolled up to the inside, and the vortex was formed. After a certain time, the vortex was broken. Then the flow and the number density of the fragment were changed.

Amylin Inhibits the Feeding of the Siberian Sturgeon

Amylin is a 37-amino acid polypeptide that has been shown to be involved in feeding regulation in a few mammals, birds and goldfish. To study whether amylin regulates the feeding of Siberian sturgeon, this study cloned amylin of Siberian sturgeon and detected the distribution pattern of amylin in 15 tissues, the expression level in periprandial (pre- and post-feeding), and the changes of food intake and related appetite factors after intraperitonal injection experiment. The results showed that the expression of amylin was highest in hypothalamus, followed by duodenum, telencephalon, forebrain, midbrain, heart and liver, and low in cerebellum, pancreas, valvular intestine and other detection tissues. Compared with 1h pre-feeding, the expression level of amylin in hypothalamus was significantly increased at 1h post-feeding (P < 0.05), and the expression level of amylin in duodenum was extremely significantly increased at 1h post-feeding (P < 0.01). Compared with the control group (normal saline), the food intake at 0-1h and 1-3h after intraperitoneal injection of 100 ng/g BW and 200 ng/g BW was significantly decreased (P < 0.05), and the food intake at 3-6h in 100 ng/g BW group was extremely significantly decreased (P < 0.01), and the food intake at 3-6h in 200 ng/g BW group was significantly decreased (P < 0.05). After 1h of injection of 100 ng/g amylin, MC4R in hypothalamus was significantly increased (P < 0.05) and somatostatin was extremely significantly increased (P < 0.01), while amylin and NPY were significantly decreased (P < 0.05). CCK in valvular intestine was extremely significantly increased (P < 0.01), insulin in duodenum was significantly increased (P < 0.05), but ghrelin in duodenum had no significant change (P > 0.05). These results showed that Amylin inhibited feeding in Siberian sturgeon by down-regulating appetite stimulating factor NPY and up-regulating appetite suppressing factors somatostatin , MC4R , CCK and insulin .

Coupled HM modeling assists in designing CO2 long-term periodic injection experiment (CO2LPIE) in Mont Terri rock laboratory

&lt;p&gt;We are performing a series of coupled hydro-mechanical (HM) simulations to model CO&lt;sub&gt;2&lt;/sub&gt; flow through Opalinus Clay at the Mont Terri rock laboratory in the CO&lt;sub&gt;2&lt;/sub&gt; Long-term Periodic Injection Experiment (CO&lt;sub&gt;2&lt;/sub&gt;LPIE). CO&lt;sub&gt;2&lt;/sub&gt;LPIE aims at inter-disciplinary investigations of the caprock sealing capacity in geologic CO&lt;sub&gt;2&lt;/sub&gt; storage in a highly monitored environment at the underground laboratory scale. Numerical modeling allows us to gain knowledge on the dynamic processes resulting from CO&lt;sub&gt;2&lt;/sub&gt; periodic injection and to assist the experimental design. The cyclic injection parameters (i.e., the period and the amplitude) have to be optimized for the field experiment and therefore different values are taken into account. Opalinus Clay is a claystone with nanoDarcy permeability that contains well developed bedding planes responsible for its anisotropic HM behavior. The hydraulic anisotropy is defined by a permeability parallel to the bedding planes being three times the one perpendicular to it. Additionally, the drained Young&amp;#8217;s modulus is measured to be 1.7&amp;#160;GPa parallel and 2.1&amp;#160;GPa perpendicular to bedding. Excavation reports by swisstopo document a SSE-dip of 45&amp;#176; for the bedding planes at the experiment location. CO&lt;sub&gt;2&lt;/sub&gt; injection generates a mean overpressure of 1 MPa into the brine that propagates into the formation. The differential pressure between CO&lt;sub&gt;2&lt;/sub&gt; and formation water, i.e., capillary pressure, is lower than the entry pressure and thus, CO&lt;sub&gt;2&lt;/sub&gt; diffuses through the pores but does not advect in free phase. The liquid overpressure distribution is distorted by the hydraulic anisotropy, preferentially advancing along the bedding planes, as the associated permeability is higher than the one perpendicular to the bedding. The pore pressure buildup induces a poromechanical stress increase and an expansion of the rock that leads to a permeability enhancement of up to two orders of magnitude. The cyclic stimulation propagates trough the domain faster and with a lag time and an attenuation, both of which increase with distance from the source with, their values being dependent on permeability, porosity and stiffness of the rock. As a result of the model orthotropy, the attenuation and the lag time change with direction, i.e. they are higher in the direction perpendicular to the bedding and lower in the direction parallel to the bedding. Given the very low permeability of Opalinus Clay, the overpressure generated requires a long time to diffuse into the rock. Furthermore, the amplitude attenuation dissipates quite rapidly, so monitoring wells should be placed as close to the injection well as possible. The study of amplitude attenuation and time lag is necessary to determine how they can be utilized to evaluate the evolution of the HM properties as the rock is altered by the acidic nature of CO&lt;sub&gt;2&lt;/sub&gt;-brine mixture Comparison between field data and numerical simulations will be a useful asset to fill the gap.&lt;/p&gt;

Fault sealing and caprock integrity for CO₂ storage: an in situ injection experiment

The success of geological carbon storage depends on the assurance of permanent containment for injected carbon dioxide (CO2) in the storage formation at depth. One of the critical elements of the safekeeping of CO2 is the sealing capacity of the caprock overlying the storage formation despite faults and/or fractures, which may occur in it. In this work, we present an ongoing injection experiment performed in a fault hosted in clay at the Mont Terri underground rock laboratory (NW Switzerland). The experiment aims to improve our understanding of the main physical and chemical mechanisms controlling (i) the migration of CO2 through a fault damage zone, (ii) the interaction of the CO2 with the neighboring intact rock, and (iii) the impact of the injection on the transmissivity in the fault. To this end, we inject CO2-saturated saline water in the top of a 3 m thick fault in the Opalinus Clay, a clay formation that is a good analog of common caprock for CO2 storage at depth. The mobility of the CO2 within the fault is studied at the decameter scale by using a comprehensive monitoring system. Our experiment aims to close the knowledge gap between laboratory and reservoir scales. Therefore, an important aspect of the experiment is the decameter scale and the prolonged duration of observations over many months. We collect observations and data from a wide range of monitoring systems, such as a seismic network, pressure temperature and electrical conductivity sensors, fiber optics, extensometers, and an in situ mass spectrometer for dissolved gas monitoring. The observations are complemented by laboratory data on collected fluids and rock samples. Here we show the details of the experimental concept and installed instrumentation, as well as the first results of the preliminary characterization. An analysis of borehole logging allows for identifying potential hydraulic transmissive structures within the fault zone. A preliminary analysis of the injection tests helped estimate the transmissivity of such structures within the fault zone and the pressure required to mechanically open such features. The preliminary tests did not record any induced microseismic events. Active seismic tomography enabled sharp imaging the fault zone.

Extracting microphysical fault friction parameters from laboratory and field injection experiments

Human subsurface activities induce significant hazard by (re-)activating slip on faults, which are ubiquitous in geological reservoirs. Laboratory and field (decametric-scale) fluid injection experiments provide insights into the response of faults subjected to fluid pressure perturbations, but assessing the long-term stability of fault slip remains challenging. Numerical models offer means to investigate a range of fluid injection scenarios and fault zone complexities and require frictional parameters (and their uncertainties) constrained by experiments as an input. In this contribution, we propose a robust approach to extract relevant microphysical parameters that govern the deformation behaviour of laboratory samples. We apply this Bayesian approach to the fluid injection experiment of Cappa et al. (2019) and examine the uncertainties and trade-offs between parameters. We then continue to analyse the field injection experiment reported by Cappa et al. (2019), from which we conclude that the fault-normal displacement is much larger than expected from the adopted microphysical model (the Chen–Niemeijer–Spiers model), indicating that fault structure and poro-elastic effects dominate the observed signal. This demonstrates the importance of using a microphysical model with physically meaningful constitutive parameters, as it clearly delineates scenarios where additional mechanisms need to be considered.

A machine learning approach for socialbot targets detection on twitter

In online social networks (OSNs), socialbots are responsible for various malicious activities, and they are mainly programmed to imitate human-behavior to bypass the existing detection systems. The socialbots are generally successful in their malicious intent due to the existence of OSN users who follow them and thereby increase their reputation in the network. Analysis of the socialbot networks and their users is vital to comprehend the socialbot problem from target users’ perspective. In this paper, we present a machine learning-based approach for characterizing and detecting socialbot targets, i.e., users who are susceptible to be trapped by the socialbots. We model OSN users based on their identity and behavior information, representing the static and dynamic components of their personality. The proposed approach classifies socialbot targets into three categories viz. active, reactive, and inactive users. We evaluate the proposed approach using three classifiers over a dataset collected from a live socialbot injection experiment conducted on Twitter. We also present a comparative evaluation of the proposed approach with a state-of-the-art method and show that it performs significantly better. On feature ablation analysis, we found that network structure and user intention and personality related dynamic features are most discriminative, whereas static features show the least impact on the classification. Additionally, following rate, multimedia ratio, and follower rate are most relevant to segregate different categories of the socialbot targets. We also perform a detailed topical and behavioral analysis of socialbot targets and found active users to be suspicious. Further, joy and agreeableness are the most dominating personality traits among the three categories of the users.