Threshold Procedure Research Articles

Biological sex modulates the efficacy of 2,5-dimethoxy-4-iodoamphetamine (DOI) to mitigate fentanyl demand

BackgroundOverdose deaths remain high for opioid use disorder, emphasizing the need to pursue innovative therapeutics. Classic psychedelic drugs that engage many monoamine receptors mitigate opioid use. Here, we tested the hypothesis that the preferential serotonin 5-HT2AR agonist, 2,5-dimethoxy-4-iodoamphetamine (DOI) could reduce the demand for fentanyl in a preclinical model of fentanyl self-administration. MethodsMale and female Sprague-Dawley rats (n = 25–29) were implanted with indwelling jugular catheters and allowed to self-administer fentanyl (3.2μg/kg/infusion). Rats progressed to a novel low price twice within-session threshold procedure where rats sampled the lowest price twice before decreasing the dose of fentanyl by a ¼ log every 10minutes across 11 doses. Once stable, rats were pretreated with saline or DOI (0.01, 0.03, 1mg/kg). Fentanyl consumption was analyzed using an exponentiated demand function to extract the dependent variables, Q0 and α. ResultsMale and female rats acquired fentanyl self-administration in the lowest price twice within-session threshold procedure. DOI dose-dependently altered fentanyl intake such that 5-HT2AR activation decreased Q0 in female rats but increased Q0 in male rats. For demand elasticity, DOI increased α in male rats but did not alter α in female rats. DOI did not alter inactive lever presses or latency. ConclusionDOI reduces consumption at minimally constrained costs but did not affect the reinforcement value of fentanyl in female rats. Alternatively, DOI significantly reduced the reinforcement value of fentanyl in male rats. Biological sex alters the therapeutic efficacy of DOI and 5-HT2AR activation sex-dependently alters opioid reinforcement.

On the short-term response of entrained air bubbles in the upper ocean: a case study in the north Adriatic Sea

Abstract. Air bubbles in the upper ocean are generated mainly by waves breaking at the air–sea interface. As such, after the waves break, entrained air bubbles evolve in the form of plumes in the turbulent flow, exchange gas with the surrounding water, and may eventually rise to the surface. To shed light on the short-term response of entrained bubbles in different stormy conditions and to assess the link between bubble plume penetration depth, mechanical and thermal forcings, and the air–sea transfer velocity of CO2, a field experiment was conducted from an oceanographic research tower in the north Adriatic Sea. Air bubble plumes were observed using high-resolution echosounder data from an upward-looking 1000 kHz sonar. The backscatter signal strength was sampled at a high resolution, 0.5 s in time and 2.5 cm along the vertical direction. Time series profiles of the bubble plume depth were established using a variable threshold procedure applied to the backscatter strength. The data show the occurrence of bubbles organized into vertical plume-like structures, drawn downwards by wave-generated turbulence and other near-surface circulations, and reaching the seabed at 17 m depth under strong forcing. We verify that bubble plumes adapt rapidly to wind and wave conditions and have depths that scale approximately linearly with wind speed. Scaling with the wind–wave Reynolds number is also proposed to account for the sea-state severity in the plume depth prediction. Results show a correlation between measured bubble depths and theoretical air–sea CO2 transfer velocity parametrized with wind-only and wind/wave formulations. Further, our measurements corroborate previous results suggesting that the sinking of newly formed cold-water masses helps bring bubbles to greater depths than those reached in stable conditions for the water column. The temperature difference between air and sea seems sufficient for describing this intensification at the leading order of magnitude. The results presented in this study are relevant for air–sea interaction studies and pave the way for progress in CO2 gas exchange formulations.

Assessing and Improving the Robustness of Bayesian Evidential Learning in One Dimension for Inverting Time-Domain Electromagnetic Data: Introducing a New Threshold Procedure

Understanding the subsurface is of prime importance for many geological and hydrogeological applications. Geophysical methods offer an economical alternative for investigating the subsurface compared to costly borehole investigations. However, geophysical results are commonly obtained through deterministic inversion of data whose solution is non-unique. Alternatively, stochastic inversions investigate the full uncertainty range of the obtained models, yet are computationally more expensive. In this research, we investigate the robustness of the recently introduced Bayesian evidential learning in one dimension (BEL1D) for the stochastic inversion of time-domain electromagnetic data (TDEM). First, we analyse the impact of the accuracy of the numerical forward solver on the posterior distribution, and derive a compromise between accuracy and computational time. We also introduce a threshold-rejection method based on the data misfit after the first iteration, circumventing the need for further BEL1D iterations. Moreover, we analyse the impact of the prior-model space on the results. We apply the new BEL1D with a threshold approach on field data collected in the Luy River catchment (Vietnam) to delineate saltwater intrusions. Our results show that the proper selection of time and space discretization is essential for limiting the computational cost while maintaining the accuracy of the posterior estimation. The selection of the prior distribution has a direct impact on fitting the observed data and is crucial for a realistic uncertainty quantification. The application of BEL1D for stochastic TDEM inversion is an efficient approach, as it allows us to estimate the uncertainty at a limited cost.

Backdraft experiments and large eddy simulations in a scaled compartment

An extensive set of backdraft experiments has been performed at the NIST National Fire Research Laboratory. These experiments were conducted in a reduced scale compartment, and are part of defining the conditions conducive to backdraft. Further, the detailed chemistry and heat measurements are intended to evaluate computational fire models. In this article, we describe the modeling effort employing the Fire Dynamics Simulator (FDS) in simulations involving different fuels and ignition source locations mirroring a subset of the named experiments. We focus on using default simulation parameters and study their effect on the backdraft outcomes. In particular, the ignition model’s temperature threshold and ignition procedure play a primary role in developing a backdraft.

A robust hybrid machine learning model for Bengali cyber bullying detection in social media

Social networking platforms give users countless opportunities to share information, collaborate, and communicate positively. The same platform can be extended to a fabricated and poisonous atmosphere that gives an impersonal, harmful platform for online misuse and assault. Cyberstalking is when someone uses an internet system to ridicule, torment, insult, criticize, slander, and discredit a victim while never seeing them. With the growth of social networks, Facebook has become the online arena for bullying. Since the effects could result in a widespread contagion, it is vital to have models and mechanisms in place for the automatic identification and removal of internet cyberbullying data. This paper presents a robust hybrid ML model for cyberbullying detection in the Bengali language on social media. The Bengalibullying proposal involves an effective text preprocessing to make the Bengali text data into a useful text format, feature extraction using the TfidfVectorizer (TFID) to get the beneficial information of text data and resampling by Instance Hardness Threshold (IHT) procedure to balance the dataset to avoid overfitting or underfitting problems. In our experiment, we used the publicly available Bangla text dataset (44,001 comments) and got the highest performance ever published works on it. The model achieved the most elevated accuracy rate of 98.57% and 98.82% in binary and multilabel classification to detect cyberbullying on social media in the Bengali language. Our best performance findings are more effective than any previous effort in identifying and categorizing bullying in the Bengali language. As a result, we might use our model to correctly classify Bengali bullying in online bullying detection systems, protecting people from being the targets of social bullying.

A new psychophysical paradigm for measuring distortion products

A unique contribution of this work is the demonstration that distortion product (i.e., Cubic difference tone (CDT)) effects can be systematically investigated with a discrimination task. Much of our knowledge about the perceptual effects of distortion products has been obtained with probe-tone, cancellation experiments, which are cumbersome and require highly trained, knowledgeable listeners. In this 2IFC discrimination study, the standard interval consists of two primaries, f1 and f2, having the same amplitude and phase. For the two experimental conditions, the signal interval consists of a change in the phase of f2 to either π/2 or π. A third tone, f3, was added to both intervals at the frequency of cubic distortion tone (CDT). The amplitude of f3 is titrated with an adaptive threshold procedure. In order to acquire data relevant to the interaction between f3 and the CDT, the phase of f3 was varied across conditions. As predicted, the phase difference of π between the two primaries lowered the threshold by as much as 10 dB compared to a phase difference of π/2. This gain in sensitivity improves the precision of the methodology in estimating the phase and the amplitude of CDT in psychophysical tasks.

Cocaine-induced plasticity, motivation, and cue responsivity do not differ in obesity-prone vs obesity-resistant rats; implications for food addiction

RationaleCompared to obesity-resistant rats, obesity-prone rats consume more food, work harder to obtain food, show greater motivational responses to food-cues, and show greater striatal plasticity in response to eating sugary/fatty foods. Therefore, it is possible that obesity-prone rats may also be more sensitive to the motivational properties of cocaine and cocaine-paired cues, and to plasticity induced by cocaine.ObjectiveTo examine baseline differences in motivation for cocaine and effects of intermittent access (IntA) cocaine self-administration on cocaine motivation, neurobehavioral responsivity to cocaine-paired cues, and locomotor sensitization in male obesity-prone vs obesity-resistant rats.MethodsIntravenous cocaine self-administration was used to examine drug-taking and drug-seeking in males. Motivation for cocaine was measured using a within session threshold procedure. Cue-induced c-Fos expression in mesocorticolimbic regions was measured.ResultsDrug-taking and drug-seeking, cue-induced c-Fos, locomotor sensitization, and preferred level of cocaine consumption (Q0) were similar between obesity-prone and obesity-resistant groups. Maximal responding during demand testing (Rmax) was lower in obesity-prone rats. IntA experience enhanced motivation for cocaine (Pmax) in obesity-prone rats.ConclusionsThe results do not support robust inherent differences in motivation for cocaine, cue-induced cocaine seeking, or neurobehavioral plasticity induced by IntA in obesity-prone vs obesity-resistant rats. This contrasts with previously established differences seen for food and food cues in these populations and shows that inherent enhancements in motivation for food and food-paired cues do not necessarily transfer to drugs and drug-paired cues.

Gene-based whole genome sequencing meta-analysis of 250 circulating proteins in three isolated European populations

ObjectiveDeep sequencing offers unparalleled access to rare variants in human populations. Understanding their role in disease is a priority, yet prohibitive sequencing costs mean that many cohorts lack the sample size to discover these effects on their own. Meta-analysis of individual variant scores allows the combination of rare variants across cohorts and study of their aggregated effect at the gene level, boosting discovery power. However, the methods involved have largely not been field-tested. In this study, we aim to perform the first meta-analysis of gene-based rare variant aggregation optimal tests, applied to the human cardiometabolic proteome. MethodsHere, we carry out this analysis across MANOLIS, Pomak and ORCADES, three isolated European cohorts with whole-genome sequencing (total N = 4,422). We examine the genetic architecture of 250 proteomic traits of cardiometabolic relevance. We use a containerised pipeline to harmonise variant lists across cohorts and define four sets of qualifying variants. For every gene, we interrogate protein-damaging variants, exonic variants, exonic and regulatory variants, and regulatory only variants, using the CADD and Eigen scores to weigh variants according to their predicted functional consequence. We perform single-cohort rare variant analysis and meta-analyse variant scores using the SMMAT package. ResultsWe describe 5 rare variant pQTLs (RV-pQTL) which pass our stringent significance threshold (7.45 × 10−11) and quality control procedure. These were split between four cis signals for MARCO, TEK, MMP2 and MPO, and one trans association for GDF2 in the SERPINA11 gene. We show that the cis-MPO association, which was not detectable using the single-point data alone, is driven by 5 missense and frameshift variants. These include rs140636390 and rs119468010, which are specific to MANOLIS and ORCADES, respectively. We show how this kind of signal could improve the predictive accuracy of genetic factors in common complex disease such as stroke and cardiovascular disease. ConclusionsOur proof-of-concept study demonstrates the power of gene-based meta-analyses for discovering disease-relevant associations complementing common-variant signals by incorporating population-specific rare variation.

Effects of NMDA receptor antagonists on behavioral economic indices of cocaine self-administration

BackgroundCurrently, there are no FDA-approved medications for the treatment of psychostimulant (e.g., cocaine) use disorders. Because the GluN2B subunit of the glutamate N-methyl-D-aspartate (NMDA) receptor is an important mediator of addiction-like behaviors, the goal of the current study was to determine if the GluN2B-selective antagonist Ro 63–1908 is efficacious in attenuating cocaine self-administration. MethodsAdult Sprague Dawley rats (24 males and 11 females) were implanted with indwelling catheters and were trained to self-administer cocaine (0.75 mg/kg/inf). Rats were then trained in a threshold procedure, in which the dose of cocaine decreased across six 6-min blocks (0.75, 0.27, 0.08, 0.03, 0.01, 0.003 mg/kg/inf). This procedure allowed for the quantification of behavioral economic indices of drug self-administration. Following training in the threshold procedure, rats were treated with the GluN2B-selective antagonist Ro 63–1908 (0, 0.1, 0.3, 1.0 mg/kg; s.c.). Rats also received treatments of the NMDA receptor channel blocker MK-801 (0, 0.01, 0.03, 0.06 mg/kg; s.c.). ResultsBlocking NMDA receptors decreased initial intake (i.e., consumption during the first block), although Ro 63–1908 and MK-801 increased area under the curve (global measure of demand) and decreased demand elasticity, an effect observed primarily in males. Neither drug affected demand intensity (i.e., consumption of cocaine at a minimally constrained price). ConclusionsWhile blocking the NMDA receptor decreases initial intake of cocaine, NMDA receptor antagonists make cocaine more inelastic with increasing price. These results suggest that NMDA receptor antagonists can exacerbate addiction-like behaviors during self-administration during extinction-like conditions that are observed in later blocks of the threshold procedure.

Does the choice of response time threshold procedure substantially affect inferences concerning the identification and exclusion of rapid guessing responses?\xa0A meta-analysis

BackgroundIn testing contexts that are predominately concerned with power, rapid guessing (RG) has the potential to undermine the validity of inferences made from educational assessments, as such responses are unreflective of the knowledge, skills, and abilities assessed. Given this concern, practitioners/researchers have utilized a multitude of response time threshold procedures that classify RG responses in these contexts based on either the use of no empirical data (e.g., an arbitrary time limit), response time distributions, and the combination of response time and accuracy information. As there is little understanding of how these procedures compare to each other, this meta-analysis sought to investigate whether threshold typology is related to differences in descriptive, measurement property, and performance outcomes in these contexts.MethodsStudies were sampled that: (a) employed two or more response time (RT) threshold procedures to identify and exclude RG responses on the same computer-administered low-stakes power test; and (b) evaluated differences between procedures on the proportion of RG responses and responders, measurement properties, and test performance.ResultsBased on as many as 86 effect sizes, our findings indicated non-negligible differences between RT threshold procedures in the proportion of RG responses and responders. The largest differences for these outcomes were observed between procedures using no empirical data and those relying on response time and accuracy information. However, these differences were not related to variability in aggregate-level measurement properties and test performance.ConclusionsWhen filtering RG responses to improve inferences concerning item properties and group score outcomes, the actual threshold procedure chosen may be of less importance than the act of identifying such deleterious responses. However, given the conservative nature of RT thresholds that use no empirical data, practitioners may look to avoid the use of these procedures when making inferences at the individual-level, given their potential for underclassifying RG.

Extraction and Evaluation of Corpus Callosum from 2D Brain MRI Slice: A Study with Cuckoo Search Algorithm.

The work proposes a computer-based diagnosis method (CBDM) to delineate and assess the corpus callosum (CC) segment from the 2-dimensional (2D) brain magnetic resonance images (MRI). The proposed CBDM consists of two parts: (1) preprocessing and (2) postprocessing sections. The preprocessing tools have a multithreshold technique with the chaotic cuckoo search (CCS) algorithm and a preferred threshold procedure. The postprocessing employs a delineation process for extracting the CC section. The proposed CBDM finally extracts the vital CC parameters, such as total brain area (TBA) and CC area (CCA) to classify the considered 2D MRI slices into the control and autism spectrum disorder (ASD) groups. This attempt considers the benchmark brain MRI database which includes ABIDE and MIDAS for the experimental investigation. The results obtained with ABIDE dataset are further confirmed against the fuzzy C-means driven level set (FCM + LS) and multiphase level set (MLS) technique and the proposed CBDM with Shannon entropy along with active contour (SE + AC) presented improved result in comparison to the existing methodologies. Further, the performance of CBDM is confirmed on MIDAS and clinical dataset. The experimental outcomes approve that the proposed CBDM extracts the CC section from the 2D MR brain images that have higher accuracy compared to alternative techniques.

Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings.

Periods of sleep and wakefulness can be estimated from wrist-locomotor activity recordings via algorithms that identify periods of relative activity and inactivity. Here, we evaluated the performance of our Munich Actimetry Sleep Detection Algorithm. The Munich Actimetry Sleep Detection Algorithm uses a moving 24-h threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. The Munich Actimetry Sleep Detection Algorithm was validated against sleep logs and polysomnography. Sleep-log validation was performed on two field samples collected over 54 and 34days (median) in 34 adolescents and 28 young adults. Polysomnographic validation was performed on a clinical sample of 23 individuals undergoing one night of polysomnography. Epoch-by-epoch analyses were conducted and comparisons of sleep measures carried out via Bland-Altman plots and correlations. Compared with sleep logs, the Munich Actimetry Sleep Detection Algorithm classified sleep with a median sensitivity of 80% (interquartile range [IQR]=75%-86%) and specificity of 91% (87%-92%). Mean onset and offset times were highly correlated (r=.86-.91). Compared with polysomnography, the Munich Actimetry Sleep Detection Algorithm reached a median sensitivity of 92% (85%-100%) but low specificity of 33% (10%-98%), owing to the low frequency of wake episodes in the night-time polysomnographic recordings. The Munich Actimetry Sleep Detection Algorithm overestimated sleep onset (~21min) and underestimated wake after sleep onset (~26min), while not performing systematically differently from polysomnography in other sleep parameters. These results demonstrate the validity of the Munich Actimetry Sleep Detection Algorithm in faithfully estimating sleep-wake patterns in field studies. With its good performance across daytime and night-time, it enables analyses of sleep-wake patterns in long recordings performed to assess circadian and sleep regularity and is therefore an excellent objective alternative to sleep logs in field settings.

Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image

The segmentation of a single leaf from an image with overlapping leaves is an important step towards the realization of effective precision agricultural systems. A popular approach used for this segmentation task is the hybridization of the Chan-Vese model and the Sobel operator CV-SO. This hybridized approach is popular because of its simplicity and effectiveness in segmenting a single leaf of interest from a complex background of overlapping leaves. However, the manual threshold and parameter tuning procedure of the CV-SO algorithm often degrades its detection performance. In this paper, we address this problem by introducing a dynamic iterative model to determine the optimal parameters for the CV-SO algorithm, which we dubbed the Dynamic CV-SO (DCV-SO) algorithm. This is a new hybrid automatic segmentation technique that attempts to improve the detection performance of the original hybrid CV-SO algorithm by reducing its mean error rate. The results obtained via simulation indicate that the proposed method yielded a 1.23% reduction in the mean error rate against the original CV-SO method.

Endogenous thresholds for the determinants of FDI inflows: evidence from the MENA countries

PurposeThis paper investigates the main drivers of foreign direct investment (FDI) inflows for a balanced panel of 11 Middle East and North Africa (MENA) economies over the 1995–2017 annual period. The author postulates that the impacts of the main pull (growth) and push (global financial conditions, GFC) factors may not be invariant to endogenously estimated thresholds for structural domestic conditions (SDCs) including trade and capital account openness, financial development, human capital (HC) and natural resource endowments.Design/methodology/approachThe author investigates whether the main SDC provide endogenous thresholds for the impacts of basic pull and push factors on FDI inflows for the MENA sample by employing panel fixed effects threshold procedure of Hansen (1999). As a robustness check, the author also present the results of the dynamic panel data two-step system generalized method of moments (GMM) estimation, which explicitly consider the potential endogeneity of SDC along with main pull factor for the evolution of FDI inflows.FindingsGrowth, GFC and SDC are important drivers of FDI inflows. The impacts of SDC tend to be higher in countries with higher financial depth, openness to international trade and finance and lower natural resource and HC endowments. The sensitivities of FDI inflows to GFC are substantially higher in the countries which are more open to international trade and capital flows and higher levels of financial depth. FDI inflows are found to be pro-cyclical and this pro-cyclicality tends to be much higher for the episodes exceeding the SDC thresholds.Practical implicationsImproving SDC including higher openness to international trade and finance and financial development may be effective in encouraging FDI inflows. The findings support an argument that, better SDC are crucially important not only for attracting FDI but also achieving the growth benefits of FDI inflows. Therefore, improving SDC appears to be an important growth-oriented policy agenda for emerging market and developing economies (EMDEs) including MENA.Originality/valueThe impacts of the main push and pull factors on FDI (and capital) inflows may be nonlinear. The literature often tackles the nonlinearity issue either by some interaction specifications or imposing exogenous thresholds. The literature, however, is yet to comprehensively investigate whether the main SDC provide endogenous thresholds for the impacts of basic pull and push factors. The author aims to contribute to the literature by estimating endogenous SDC threshold levels for the impacts of the main determinants of FDI flows for MENA.

Sequence submodular maximization meets streaming

In this paper, we study the problem of maximizing a sequence submodular function in the streaming setting, where the utility function is defined on sequences instead of sets of elements. We encode the sequence submodular maximization with a weighted digraph, in which the weight of a vertex reveals the utility value in selecting a single element and the weight of an edge reveals the additional profit with respect to a certain selection sequence. The edges are visited in a streaming fashion and the aim is to sieve a sequence of at most k elements from the stream, such that the utility is maximized. In this work, we present an edge-based threshold procedure, which makes one pass over the stream, attains an approximation ratio of $$(1/(2\varDelta +1)- O(\epsilon ))$$ , consumes $$O(k\varDelta /\epsilon )$$ memory source in total and $$O(\log (k\varDelta )/\epsilon )$$ update time per edge, where $$\varDelta $$ is the minimum of the maximal outdegree and indegree of the directed graph.

Behavioral economics of cocaine self-administration in male and female rats.

There are sex differences in the development of cocaine addiction. For example, the time that it takes for women from initial use to addiction is significantly shorter than for men. Thus, understanding why females are more vulnerable to cocaine addiction will provide insights into sex differences in the mechanisms underlying cocaine addiction. This study aimed to determine how cocaine demand intensity and elasticity might differ between sexes. In addition, the impact of estrous cycle and cocaine intake on demand was investigated. Male and female rats were trained to self-administer 0.125 mg of cocaine intravenously under a chained schedule in daily 2-h sessions for 2 weeks, and then, the cocaine demand function was determined with a modified within-session threshold procedure. Following the test, the rats began to self-administer a higher dose of cocaine (0.25 mg) to increase the cocaine intake. The demand function was then similarly determined in the same rats after 2 weeks of cocaine self-administration of the higher dose. No sex differences were found in either demand intensity or elasticity. Neither did the level of cocaine intake have an impact on demand. The demand elasticity, but not intensity, was significantly lower during proestrus/estrus compared with diestrus. These data suggest that the faster transition to cocaine addiction in women cannot be explained by sex differences in the demand for cocaine and such a demand may change during different phases of estrus cycle.

Mo1283 LEARNING CURVE OF TRANSORAL INCISIONLESS FUNDOPLICATION: A SINGLE ENDOSCOPIST EXPERIENCE

Transoral Incisionless Fundoplication (TIF) is a minimally invasive endoscopic technique proven to be safe and effective for treating Gastroesophageal Reflux Disease (GERD) in selected patients. The learning curve of this technique has not been studied. (1) To report the learning curve for TIF, by identifying a breakpoint or threshold number of procedures at which there is a significant decrease in procedure time, time taken per 2-set fastener placement and an increase in the valve circumference. Prospectively collected data on patients who had a TIF procedure at an academic medical center between September 2017 and November 2019 were analyzed. Routine pre-TIF work-up included upper endoscopy (EGD) with biopsies, cine-esophagram, high resolution esophageal manometry (HREM) and pH-monitoring. Patients who had prior per-oral endoscopic myotomy (POEM) were excluded. Learning curve analysis was done using STATA software v 15.1 and Power BI to calculate the threshold for learning assessed by time to reach specific endpoints of the TIF procedure. 51 patients (71% male, mean age 56.5±13.2 yr.) had TIF after testing confirmed appropriate criteria (ph-positive test, < 2cm hiatal hernia, Hill grade <=2, BMI< 35). Indications for TIF were refractory GERD (51%), laryngopharyngeal reflux disease (9%), failed Nissen fundoplication (9%) and GERD in PPI-averse patients (31%) (Table 1). Six (12%) patients had prior surgical fundoplication. All TIF procedures were performed by a single endoscopist after hands-on and supervised training. TIF was successfully completed in 50/51 (1 aborted due to technical failure) with no serious adverse events. Overall, mean procedure time was 56±15 minutes. Break point analysis revealed that the threshold procedure time was reached at 23 procedures, where time declined from 67±11 to 47±13 minutes (p<0.0001). The mean time to deploy set of 2 fasteners was 4.3±1.9 minutes and mean valve circumference was 285±24 degrees. The breakpoint analysis revealed that a threshold was reached at 18 procedures for both fastener deployment and valve wrap, with a decrease in the mean procedure time to deploy a set of 2 fasteners from 6.5±1.4 to 3.1± 0.8 minutes (p<0.0001) and an improvement in the mean valve circumference from 261±20 to 297±14 degrees (p<0.0001). There was a significant decrease in total procedure time and time to deploy 2-set fasteners as well as an improvement of the valve circumference with increased number of procedures (Figure 1). Our single-center analysis showed that the number of procedures needed for an endoscopist to achieve procedure times under 1-hour is 23 procedures. Furthermore, the number of procedures needed to achieve times under 4 minutes to deploy 2-set of fasteners and to achieve tighter valves is 18 procedures. Future larger studies are needed to validate these results.TIF learning curve breakpoint analysisView Large Image Figure ViewerDownload Hi-res image Download (PPT)

Effects of 5-HT2A receptor stimulation on economic demand for fentanyl after intermittent and continuous access self-administration in male rats.

The relative value of and motivation for abused drugs often increases with drug experience and differs based on drug availability. Here, we determined how different intake patterns of fentanyl, a μ-opioid agonist, alter economic demand for fentanyl and how 5-HT2A receptor stimulation affects economic demand for fentanyl. We used a within-session demand threshold procedure to characterize changes in economic demand for fentanyl before and after intermittent or continuous access schedules. We subsequently tested the acute effects of 5-HT2A receptor stimulation with psychedelic 2,5-dimethoxy-4-iodoamphetamine (DOI) on economic demand for fentanyl. Extended fentanyl experience with both intermittent and continuous schedules increased fentanyl consumption at low cost (Q0 ), increased total fentanyl consumption, and decreased demand elasticity (α), indicating both schedules elevated motivation to self-administer fentanyl. Overall, the two schedules produced similar alterations in economic demand for fentanyl, although low-cost consumption (Q0 ) increased more in the continuous access group. Systemic injections of DOI (0.0-0.4 mg/kg, i.p.) dose-dependently produced economic demand changes in the opposite direction produced by fentanyl experience. DOI decreased motivation (increased "α"), decreased Q0 , and decreased total fentanyl consumption. The selective 5-HT2A antagonist, M100907 (0.3 mg/kg, i.p.), blocked the effects of DOI, indicating that DOI is acting through 5-HT2A receptors to alter economic demand for fentanyl. In an economic food demand experiment, DOI (0.4 mg/kg) also increased demand elasticity and reduced food consumption. These results demonstrate that both intermittent and continuous fentanyl experience raise the economic demand for fentanyl, and acute 5-HT2A receptor activation reduces economic demand for fentanyl and food.

Biorthogonal multiwavelets on the interval for solving multidimensional fractional optimal control problems with inequality constraint

SummaryThis article proposes a new numerical approach for solving fractional optimal control problems including state and control inequality constraints using new biorthogonal multiwavelets. The properties of biorthogonal multiwavelets are first given. The Riemann‐Liouville fractional integral operator for biorthogonal multiwavelets is utilized to reduce the solution of optimal control problems to a nonlinear programming one, to which existing, well‐developed algorithms may be applied. In order to save the memory requirement and computation time, a threshold procedure is applied to obtain algebraic equations. The method is computationally very attractive and gives very accurate results.

Nicotine reduction does not alter essential value of nicotine or reduce cue-induced reinstatement of nicotine seeking

Reduction of nicotine content in tobacco products is a regulatory control strategy intended to decrease smoking dependence, and is hypothesized to produce gradual reductions of nicotine intake. Rats were initially trained to self-administer 0.06 mg/kg/infusion nicotine (Phase 1), which was followed by a threshold procedure to determine nicotine demand via a behavioral economics (BE) paradigm (Phase 2). Rats then either self-administered the training dose (high dose group), or were switched to a low dose of nicotine (0.001 mg/kg/infusion; low dose group) in Phase 3. Both groups then underwent a second threshold procedure and demand curves were re-determined (Phase 4). In Phase 5, responding for nicotine was extinguished over the course of 21 sessions. Cue-induced reinstatement was then evaluated (Phase 6). Rats in the low dose group maintained a steady amount of infusions, and thus, did not compensate for nicotine reduction. Rats in the low dose group also showed similar demand elasticity and nicotine seeking (Phase 6) compared to the high dose group, indicating that nicotine reduction did not decrease nicotine demand or seeking. Further, both groups displayed resistance to extinction, indicating that nicotine reduction did not facilitate extinction learning. These results suggest that although compensation of intake does not occur, decreasing the dose of nicotine does not alter nicotine reinforcement value or relapse vulnerability. Further, these results indicate persistence of nicotine-motivated behavior after self-administration of a low nicotine dose. Translationally, these results suggest that alternative strategies may be needed to achieve positive smoking cessation outcomes.