Single-molecule sensing electrode embedded in-plane nanopore

Electrode-embedded nanopore is considered as a promising device structure for label-free single-molecule sequencing, the principle of which is based on nucleotide identification via transverse electron tunnelling current flowing through a DNA translocating through the pore. Yet, fabrication of a molecular-scale electrode-nanopore detector has been a formidable task that requires atomic-level alignment of a few nanometer sized pore and an electrode gap. Here, we report single-molecule detection using a nucleotide-sized sensing electrode embedded in-plane nanopore. We developed a self-alignment technique to form a nanopore-nanoelectrode solid-state device consisting of a sub-nanometer scale electrode gap in a 15 nm-sized SiO2 pore. We demonstrate single-molecule counting of nucleotide-sized metal-encapsulated fullerenes in a liquid using the electrode-integrated nanopore sensor. We also performed electrical identification of nucleobases in a DNA oligomer, thereby suggesting the potential use of this synthetic electrode-in-nanopore as a platform for electrical DNA sequencing

Oxytocin-enhanced group therapy for methamphetamine use disorder: Randomized controlled trial.

BackgroundMethamphetamine (METH) use is a public health crisis that disproportionately affects men who have sex with men (MSM). There are currently no FDA-approved pharmacological interventions to treat methamphetamine use disorder (MUD). MUD is associated with social impairments and extremely high treatment attrition rates. Administration of oxytocin, a neuropeptide involved in social attachment, may be a novel approach to addressing these issues. Moreover, oxytocin administration has shown promise for reducing METH-related addictive behavior in animal models, but has not yet been investigated in clinical trials for MUD. Last, oxytocin is known to modulate stress responsivity via regulation of the autonomic nervous system, which is dysregulated in METH users. We hypothesize that oxytocin, in combination with group psychotherapy, will increase treatment engagement, reduce addiction behavior, and mitigate stress hyperreactivity.MethodsThis is a randomized, double blind trial of oxytocin 40-IU (n = 24) or placebo (n = 24) administered intranasally prior to each of six weekly motivational interviewing group therapy (MIGT) sessions for MUD in MSM.Primary outcome(a) session attendance.Secondary outcomes(b) group cohesion, (c) anxiety, (d) METH craving, (e) METH use, and (f) in-session cardiac physiology.ResultsParticipants receiving oxytocin had significantly higher group therapy attendance than those receiving placebo, OR 3.26, 95% CI [1.27-8.41], p = .014. There was a small effect of oxytocin on group cohension, but not anxiety or craving. METH use did not change over the six-week MIGT course in either treatment arm. Participants receiving oxytocin had lower average heart rates during MIGT sessions and higher heart rate variability. There were positive main effects of MIGT over Time regardless of study drug.ConclusionsThis evidence, and the lack of any serious adverse events, suggests that oxytocin may safely increase treatment attendance. One possible mechanism by which it may do so is its modulation of the autonomic nervous system. Further investigation is warranted

Neuro-Genetic Adaptive Attitude Control

It has previously been demonstrated that for smooth dynamic systems, using relatively few sample points from a single trajectory, a neural network can be trained to perform very accurate short-term prediction over a large part of the phase space. In this paper, we exploit the capability of a Locally Predictive Network (LPN) to derive an adaptive control architecture for a satellite equipped with controllable, bidirectional thrusters on each of the three principal axes. It is assumed that a hardware implementation of the neural network is available. The inputs for the network are a small history of system states up to the present time and a set of current control inputs, the outputs are the next system state. Once the LPN has been trained successfully, at each time step a genetic algorithm searches the space of hypothetical control inputs. Given a set of control signals, the LPN is used to predict the state of the system at the next sample point. This enables the ‘fitness’ of each set of hypothetical control torques to be evaluated very rapidly. In effect, the genetic algorithm determines a satisfactory solution to the inverse kinematic problem in time to apply the solution (set of control torques) at the next control point. With the exception of the neuromodelling (which is repeated only when the system dynamics change), the whole process is then repeated. The results presented indicate that such an architecture is easily able to master the attitude control problem for arbitrary slew angles, with arbitrary a priori unknowndynamics and noise in the sensor system