MethControl

Transcranial electrical stimulation (tES) is a promising therapeutic tool for a range of neurological diseases. Understanding how the small currents used in tES spread across the scalp and penetrate the brain will be important for the rational design of tES therapies. Alternating currents applied tr …

[EXCERPT] Journalist Edward Hunter was the first to sound the alarm. “Brain-washing Tactics Force Chinese Into Ranks of Communist Party,” blared his headline in the Miami Daily News in September 19…

Cross and Co-Occurring Addictions Individuals who are cross-addicted are people who switch from one addiction

The door to demonic possession opens when we knowingly and willingly choose to do wrong and violate our conscience. Ask for God’s help to obtain freedom.

N-Methylamphetamine (also known as Methamphetamine,[3] Meth,[3] Glass,[3] Ice,[3] Shard,[3] Crank,[3] Tina,[3] Tweak,[3] Yaba,[3] and Crystal[3]) is a potent stimulant substance of the amphetamine class. Along with heroin and cocaine, it has a notorious reputation as a dangerous and highly addictive "street drug".[citation needed] It is structurally related to amphetamine; the addition of the methyl group is thought to increase its ability to cross the blood-brain barrier, significantly enhancing its potency.[citation needed] It produces its effects by increasing levels of the neurotransmitters serotonin, dopamine, and norepinephrine in the brain.

The remarkable endurance of German and Allied soldiers during World War II had a secret ingredient.

Unless you understand how reality is manifested, your destiny and the destiny of the planet will be in the hands of the ruling elite who do understand it.

In what may signal a seismic shift in the treatment of methamphetamine addiction, a pair of prescription drugs appears to help patients significantly reduce their stimulant use, or quit altogether.

A 9,000 foot gap in the middle of the Morganza to the Gulf storm protection system near Cocodrie should be completed this summer.

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Mass spectrometry imaging (MSI) is a technique that provides comprehensive molecular information with high spatial resolution from tissue. Today, there is a strong push toward sharing data sets through public repositories in many research fields where MSI is commonly applied; yet, there is no standardized protocol for analyzing these data sets in a reproducible manner. Shifts in the mass-to-charge ratio (m/z) of molecular peaks present a major obstacle that can make it impossible to distinguish one compound from another. Here, we present a label-free m/z alignment approach that is compatible with multiple instrument types and makes no assumptions on the sample’s molecular composition. Our approach, MSIWarp (https://github.com/horvatovichlab/MSIWarp), finds an m/z recalibration function by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra. MSIWarp requires only centroid spectra to find the recalibration function and is thereby readily applicable to almost any MSI data set. To deal with particularly misaligned or peak-sparse spectra, we provide an option to detect and exclude spurious peak matches with a tailored random sample consensus (RANSAC) procedure. We evaluate our approach with four publicly available data sets from both time-of-flight (TOF) and Orbitrap instruments and demonstrate up to 88% improvement in m/z alignment.

This paper presents a WiFi-aided magnetic matching (MM) algorithm for indoor pedestrian navigation with consumer portable devices. This algorithm reduces both the mismatching rate (i.e., the rate of matching to an incorrect point that is more than 20 m away from the true value) and computational load of MM by using WiFi positioning solutions to limit the MM search space. Walking tests with Samsung Galaxy S3 and S4 smartphones in two different indoor environments (i.e., Environment #1 with abundant WiFi APs and significant magnetic features, and Environment #2 with less WiFi and magnetic information) were conducted to evaluate the proposed algorithm. It was found that WiFi fingerprinting accuracy is related to the signal distributions. MM provided results with small fluctuations but had a significant mismatch rate; when aided by WiFi, MM’s robustness was significantly improved. The outcome of this research indicates that WiFi and MM have complementary characteristics as the former is a point-by-point matching approach and the latter is based on profile-matching. Furthermore, performance improvement through integrating WiFi and MM depends on the environment (e.g., the signal distributions of magnetic intensity and WiFi RSS): In Environment #1 tests, WiFi-aided MM and WiFi provided similar results; in Environment #2 tests, the former was approximately 41.6% better. Our results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features.

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FatCAT: Functional And Tractographic Connectivity Analysis Toolbox Tools for combining FC and AC