Stochastic Syncing in Sinusoidally Driven Atomic Orbital Memory.

Stochastically fluctuating multiwell systems are a promising route toward physical implementations of energy-based machine learning and neuromorphic hardware. One of the challenges is finding tunable material platforms that exhibit such multiwell behavior and understanding how complex dynamic input signals influence their stochastic response. One such platform is the recently discovered atomic Boltzmann machine, where each stochastic unit is represented by a binary orbital memory state of an individual atom. Here, we investigate the stochastic response of binary orbital memory states to sinusoidal input voltages. Using scanning tunneling microscopy, we investigated orbital memory derived from individual Fe and Co atoms on black phosphorus. We quantify the state residence times as a function of various input parameters such as frequency, amplitude, and offset voltage. The state residence times for both species, when driven by a sinusoidal signal, exhibit synchronization that can be quantitatively modeled by a Poisson process based on the switching rates in the absence of a sinusoidal signal. For individual Fe atoms, we also observe a frequency-dependent response of the state favorability, which can be tuned by the input parameters. In contrast to Fe, there is no significant frequency dependence in the state favorability for individual Co atoms. Based on the Poisson model, the difference in the response of the state favorability can be traced to the difference in the voltage-dependent switching rates of the two different species. This platform provides a tunable way to induce population changes in stochastic systems and provides a foundation toward understanding driven stochastic multiwell systems.

Biocatalytic Regioselective O-acylation of Sesquiterpene Lactones from Chicory: A Pathway to Novel Ester Derivatives.

We report the first biocatalytic modification of sesquiterpene lactones (STLs) found in the chicory plants, specifically lactucin (Lc), 11ß,13-dihydrolactucin (DHLc), lactucopicrin (Lp), and 11ß,13-dihydrolactucopicrin (DHLp). The selective O-acylation of their primary alcohol group was carried out by the lipase B from Candida antarctica (CAL-B) using various aliphatic vinyl esters as acyl donors. Perillyl alcohol, a simpler monoterpenoid, served as a model to set up the desired O-acetylation reaction by comparing the use of acetic acid and vinyl acetate as acyl donors. Similar conditions were then applied to DHLc, where five novel ester chains were selectively introduced onto the primary alcohol group, with conversions going from >99 % (acetate and propionate) to 69 % (octanoate). The synthesis of the corresponding O-acetyl esters of Lc, Lp, and DHLp was also successfully achieved with near-quantitative conversion. Molecular docking simulations were then performed to elucidate the preferred enzyme-substrate binding modes in the acylation reactions with STLs, as well as to understand their interactions with crucial amino acid residues at the active site. Our methodology enables the selective O-acylation of the primary alcohol group in four different STLs, offering possibilities for synthesizing novel derivatives with significant potential applications in pharmaceuticals or as biocontrol agents.

Postoperative Delirium After Intracranial Surgery: A Retrospective Cohort Study.

BACKGROUND: The clinical relevance of postoperative delirium (POD) in neurosurgery remains unclear and should be investigated because these patients are vulnerable. Hence, we investigated the impact of POD, by means of incidence and health outcomes, and identified independent risk factors. METHODS: Adult patients undergoing an intracranial surgical procedure in the Erasmus Medical Center Rotterdam between June 2017 and September 2020 were retrospectively included. POD incidence, defined by a Delirium Observation Screening Scale (DOSS) ≥3 or antipsychotic treatment for delirium within 5 days after surgery, was calculated. Logistic regression analysis on the full data set was conducted for the multivariable risk factor and health outcome analyses. RESULTS: After including 2901 intracranial surgical procedures, POD was present in 19.4% with a mean onset in days of 2.62 (standard deviation, 1.22) and associated with more intensive care unit admissions and more discharge toward residential care. Onset of POD was not associated with increased length of hospitalization or mortality. We identified several independent nonmodifiable risk factors such as age, preexisting memory problems, emergency operations, craniotomy compared with burr-hole surgery, and severe blood loss. Moreover, we identified modifiable risk factors such as low preoperative potassium and opioid and dexamethasone administration. CONCLUSIONS: Our POD incidence rates and correlation with more intensive care unit admission and discharge toward residential care suggest a significant impact of POD on neurosurgical patients. We identified several modifiable and nonmodifiable risk factors, which shed light on the pathophysiologic mechanisms of POD in this cohort and could be targeted for future intervention studies.

Phase I, first-in-human study of MSC-1 (AZD0171), a humanized anti-leukemia inhibitory factor monoclonal antibody, for advanced solid tumors.

BACKGROUND: Activation of leukemia inhibitory factor (LIF) is linked to an immunosuppressive tumor microenvironment (TME), with a strong association between LIF expression and tumor-associated macrophages (TAMs). MSC-1 (AZD0171) is a humanized monoclonal antibody that binds with high affinity to LIF, promoting antitumor inflammation through TAM modulation and cancer stem cell inhibition, slowing tumor growth. In this phase I, first-in-human, open-label, dose-escalation study, MSC-1 monotherapy was assessed in patients with advanced, unresectable solid tumors. MATERIALS AND METHODS: Using accelerated-titration dose escalation followed by a 3 + 3 design, MSC-1 doses of 75-1500 mg were administered intravenously every 3 weeks (Q3W) until progression or unmanageable toxicity. Additional patients were enrolled in selected cohorts to further evaluate safety, pharmacokinetics (PK), and pharmacodynamics after escalation to the next dose had been approved. The primary objective was characterizing safety and determining the recommended phase II dose (RP2D). Evaluating antitumor activity and progression-free survival (PFS) by RECIST v1.1, PK and immunogenicity were secondary objectives. Exploratory objectives included pharmacodynamic effects on circulating LIF and TME immune markers. RESULTS: Forty-one patients received treatment. MSC-1 monotherapy was safe and well tolerated at all doses, with no dose-limiting toxicities. The maximum tolerated dose was not reached and the RP2D was determined to be 1500 mg Q3W. Almost half of the patients had treatment-related adverse events (TRAEs), with no apparent trends across doses; no patients withdrew due to TRAEs. There were no objective responses; 23.7% had stable disease for ≥2 consecutive tumor assessments. Median PFS was 5.9 weeks; 23.7% had PFS >16 weeks. On-treatment changes in circulating LIF and TME signal transducers and activators of transcription 3 signaling, M1:M2 macrophage populations, and CD8+ T-cell infiltration were consistent with the hypothesized mechanism of action. CONCLUSIONS: MSC-1 was very well tolerated across doses, with prolonged PFS in some patients. Biomarker and preclinical data suggest potential synergy with checkpoint inhibitors.

An atomic Boltzmann machine capable of self-adaption.

The quest to implement machine learning algorithms in hardware has focused on combining various materials, each mimicking a computational primitive, to create device functionality. Ultimately, these piecewise approaches limit functionality and efficiency, while complicating scaling and on-chip learning, necessitating new approaches linking physical phenomena to machine learning models. Here, we create an atomic spin system that emulates a Boltzmann machine directly in the orbital dynamics of one well-defined material system. Utilizing the concept of orbital memory based on individual cobalt atoms on black phosphorus, we fabricate the prerequisite tuneable multi-well energy landscape by gating patterned atomic ensembles using scanning tunnelling microscopy. Exploiting the anisotropic behaviour of black phosphorus, we realize plasticity with multi-valued and interlinking synapses that lead to tuneable probability distributions. Furthermore, we observe an autonomous reorganization of the synaptic weights in response to external electrical stimuli, which evolves at a different time scale compared to neural dynamics. This self-adaptive architecture paves the way for autonomous learning directly in atomic-scale machine learning hardware.

Bayesian neural networks with variable selection for prediction of genotypic values.

BACKGROUND: Estimating the genetic component of a complex phenotype is a complicated problem, mainly because there are many allele effects to estimate from a limited number of phenotypes. In spite of this difficulty, linear methods with variable selection have been able to give good predictions of additive effects of individuals. However, prediction of non-additive genetic effects is challenging with the usual prediction methods. In machine learning, non-additive relations between inputs can be modeled with neural networks. We developed a novel method (NetSparse) that uses Bayesian neural networks with variable selection for the prediction of genotypic values of individuals, including non-additive genetic effects. RESULTS: We simulated several populations with different phenotypic models and compared NetSparse to genomic best linear unbiased prediction (GBLUP), BayesB, their dominance variants, and an additive by additive method. We found that when the number of QTL was relatively small (10 or 100), NetSparse had 2 to 28 percentage points higher accuracy than the reference methods. For scenarios that included dominance or epistatic effects, NetSparse had 0.0 to 3.9 percentage points higher accuracy for predicting phenotypes than the reference methods, except in scenarios with extreme overdominance, for which reference methods that explicitly model dominance had 6 percentage points higher accuracy than NetSparse. CONCLUSIONS: Bayesian neural networks with variable selection are promising for prediction of the genetic component of complex traits in animal breeding, and their performance is robust across different genetic models. However, their large computational costs can hinder their use in practice.

Sexual reproduction of Zymoseptoria tritici on durum wheat in Tunisia revealed by presence of airborne inoculum, fruiting bodies and high levels of genetic diversity.

Septoria tritici blotch (STB) caused by the heterothallic ascomycete Zymoseptoria tritici is currently one of the most devastating diseases of wheat worldwide. The extent of sexual reproduction of this pathogen is well documented on bread wheat, but not on durum wheat. The objective of the present study was to quantify the occurrence of Z. tritici sexual reproduction on durum wheat in the Tunisian environment. The assessment was undertaken using a triple approach combining fruiting body assessment, ascospore trapping and population genetic analyses. The results highlighted the formation of pseudothecia on leaves and stubble from the autumn until the end of the growing season. Likewise, qPCR monitoring highlighted a constant release of Z. tritici airborne inoculum during the wheat-growing season, with a peak of production at the end of the season. Genetic investigations using microsatellites revealed high levels of gene and genotypic diversities, an equal distribution of mating types, and a lack of genetic clustering within and between growing seasons. Taken together, these findings indicate that Z. tritici undergoes sexual reproduction on durum wheat in Tunisia at least to the same extent than on bread wheat in Western Europe, and that the dry and warm climate does not affect the mating process of the fungus. Frequent occurrence of sexual reproduction is a valuable knowledge to take into account in STB control strategies on durum wheat.

Survival patterns and cancer determinants in families with myotonic dystrophy type 1.

BACKGROUND AND PURPOSE: Research indicates that patients with myotonic dystrophy type 1 (DM1) are at increased risk of cancer and early death. Family data may provide insights given DM1 phenotypic heterogeneity, the broad range of non-muscular manifestations and the usual delays in the diagnosis of DM1. METHOD: Family history data were collected from 397 genetically and/or clinically confirmed DM1 patients (respondents) enrolled in the US or UK myotonic dystrophy registries. Standardized mortality ratios were calculated for DM1 first-degree relatives (parents, siblings and offspring) by their reported DM1 status (affected, unaffected or unknown). For cancer-related analyses, mixed effects logistic regression models were used to evaluate factors associated with cancer development in DM1 families, including familial clustering. RESULTS: A total of 467 deaths and 337 cancers were reported amongst 1737 first-degree DM1 relatives. Mortality risk amongst relatives reported as DM1-unaffected was comparable to that of the general population [standardized mortality ratio (SMR) 0.82, P = 0.06], whilst significantly higher mortality risks were noted in DM1-affected relatives (SMR = 2.47, P < 0.0001) and in those whose DM1 status was unknown (SMR = 1.60, P < 0.0001). In cancer risk analyses, risk was higher amongst families in which the DM1 respondent had cancer (odds ratio 1.95, P = 0.0001). Unknown DM1 status in the siblings (odds ratio 2.59, P = 0.004) was associated with higher cancer risk. CONCLUSION: There is an increased risk of death, and probably cancer, in relatives with DM1 and in those whose DM1 status is unknown. This suggests a need to perform a careful history and physical examination, supplemented by genetic testing, to identify family members at risk for DM1 and who might benefit from disease-specific clinical care and surveillance.

Role of Synaptic Stochasticity in Training Low-Precision Neural Networks.

Stochasticity and limited precision of synaptic weights in neural network models are key aspects of both biological and hardware modeling of learning processes. Here we show that a neural network model with stochastic binary weights naturally gives prominence to exponentially rare dense regions of solutions with a number of desirable properties such as robustness and good generalization performance, while typical solutions are isolated and hard to find. Binary solutions of the standard perceptron problem are obtained from a simple gradient descent procedure on a set of real values parametrizing a probability distribution over the binary synapses. Both analytical and numerical results are presented. An algorithmic extension that allows to train discrete deep neural networks is also investigated.

Knowledge, attitudes and practices related to tuberculosis in pharmacy workers in a cross-sectional survey in El Agustino, Peru.

INTRODUCTION: Although the worldwide incidence of tuberculosis (TB) is slowly decreasing, annual infection rates in Peru remain among the highest in the Americas. Pharmacies could play an important role in facilitating early detection of TB. However, the awareness, expertise and cooperation of pharmacy workers is fundamental. This study explored the TB-related knowledge, attitudes and practices of pharmacy workers in a district with one of the highest incidences of TB in Peru. METHODS: This cross-sectional study applied a questionnaire that was administered face to face using smartphones with one pharmacy worker at each of 45 randomly selected pharmacies in the El Agustino district of Lima, Peru. RESULTS: Participants were primarily female (78%) and had an average age of 31.3 years old (range 18-57 years old). Only 11% of participants were pharmacists with complete university training. The pharmacy workers' knowledge was adequate; however, workers had important knowledge gaps and myths regarding prevention of TB transmission. Most pharmacy workers (77%) reported they would send a client with a history of cough for more than two weeks to a healthcare center, while 23% reported they would offer them antitussive medication or antibiotics. Almost all workers reported talking with clients about diseases and reported respiratory symptoms as one of the most common causes for consultation (60%). Most participants expressed interest in learning more about TB and expanding their involvement in the fight against TB in their community. CONCLUSION: Pharmacy workers have adequate knowledge about TB. However, we identified gaps in knowledge with respect to prevention of TB transmission. Pharmacy workers commonly see patients with respiratory symptoms and some offer recommendations, including for treatment. Pharmacy workers are willing to learn more and contribute to TB control and could be a valuable asset in the control and prevention of TB in Peru. To achieve this integration of pharmacy workers into TB control and prevention, more research is needed.

Pigmentation phenotype, photosensitivity and skin neoplasms in patients with myotonic dystrophy.

BACKGROUND AND PURPOSE: Recent studies have suggested a possible excess risk of skin neoplasms in patients with myotonic dystrophy (DM). Risk factors related to this observation have not been defined. METHOD: Information regarding personal history of skin tumors, pigmentation phenotype, and skin reaction to sun exposure were collected from 266 DM patients who were enrolled in the US National Institutes of Health National Registry of Myotonic Dystrophy and Facioscapulohumeral Muscular Dystrophy Patients and Family Members. RESULTS: Seventy-seven subjects reported having skin tumors that were either benign (n = 31), malignant (n = 32) or both (n = 14). Female gender [odds ratio (OR) = 2.27, 95% confidence interval (CI) 1.02-5.05, P = 0.04], older age (OR = 1.10, 95% CI 1.05-1.16, P < 0.001) and DM1 subtype (OR = 3.42, 95% CI 1.27-9.26, P = 0.02) were associated with a malignant skin tumor. The associations between malignant skin tumors and known risk factors [light eye color (OR = 1.62, 95% CI 0.78-3.39, P = 0.20), light skin complexion (OR = 1.31, 95% CI 0.63-2.73, P = 0.48) and moderate/extensive face freckles (OR = 1.47, 95% CI 0.50-4.34, P = 0.49)] were modest. Strong, but not statistically significant, associations were noted with sunburn reactions when exposed to sunlight (OR = 4.28, 95% CI 0.91-19.95, P = 0.06, and OR = 2.19, 95% CI 0.67-7.09, P = 0.19, for sunburn with and without blistering, respectively). CONCLUSIONS: Although our study was limited by small sample size, the risk factors for malignant skin tumors in DM strongly resemble the general population. It is recommended that DM patients adhere to sun exposure protective behavior.

Learning Universal Computations with Spikes.

Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them.

Utilization of mixtures of aromatic N-donor ligands of different coordination ability for the solvothermal synthesis of thiostannate containing molecules.

Utilization of mixtures of differently coordinating aromatic N-donor ligands leads to the formation of the two new compounds {[Ni(phen)2]2Sn2S6}·4,4'-bipy·½H2O I and {[Ni(phen)2]2Sn2S6}·2,2'-bipy II that could be prepared under solvothermal conditions (4,4'-bipy = 4,4'-bipyridine, C10H8N2; phen = 1,10-phenanthroline, C12H8N2; 2,2'-bipy = 2,2'-bipyridine, C10H8N2). In the structures of both compounds Ni-S bond formation is observed which is highly unusual when only bidentate N-donor ligands are applied in the reaction mixture. The detailed analysis of the crystal structure indicates that the presence of 4,4'-bipy and 2,2'-bipy molecules are essential for the stabilization of the arrangement of the constituents. The main structural motif {[Ni(phen)2]2Sn2S6} is arranged generating off center parallel stacking of the phen ligands. The empty spaces between the {[Ni(phen)2]2Sn2S6} moieties are occupied by either 2,2'-bipy (I) or 4,4'-bipy (II) molecules which are oriented towards the phen ligands to form intermolecular π-π interactions.

Quantitative analysis of task selection for brain-computer interfaces.

OBJECTIVE: To assess quantitatively the impact of task selection in the performance of brain-computer interfaces (BCI). APPROACH: We consider the task-pairs derived from multi-class BCI imagery movement tasks in three different datasets. We analyze for the first time the benefits of task selection on a large-scale basis (109 users) and evaluate the possibility of transferring task-pair information across days for a given subject. MAIN RESULTS: Selecting the subject-dependent optimal task-pair among three different imagery movement tasks results in approximately 20% potential increase in the number of users that can be expected to control a binary BCI. The improvement is observed with respect to the best task-pair fixed across subjects. The best task-pair selected for each subject individually during a first day of recordings is generally a good task-pair in subsequent days. In general, task learning from the user side has a positive influence in the generalization of the optimal task-pair, but special attention should be given to inexperienced subjects. SIGNIFICANCE: These results add significant evidence to existing literature that advocates task selection as a necessary step towards usable BCIs. This contribution motivates further research focused on deriving adaptive methods for task selection on larger sets of mental tasks in practical online scenarios.

Emerging phenomena in neural networks with dynamic synapses and their computational implications.

In this paper we review our research on the effect and computational role of dynamical synapses on feed-forward and recurrent neural networks. Among others, we report on the appearance of a new class of dynamical memories which result from the destabilization of learned memory attractors. This has important consequences for dynamic information processing allowing the system to sequentially access the information stored in the memories under changing stimuli. Although storage capacity of stable memories also decreases, our study demonstrated the positive effect of synaptic facilitation to recover maximum storage capacity and to enlarge the capacity of the system for memory recall in noisy conditions. Possibly, the new dynamical behavior can be associated with the voltage transitions between up and down states observed in cortical areas in the brain. We investigated the conditions for which the permanence times in the up state are power-law distributed, which is a sign for criticality, and concluded that the experimentally observed large variability of permanence times could be explained as the result of noisy dynamic synapses with large recovery times. Finally, we report how short-term synaptic processes can transmit weak signals throughout more than one frequency range in noisy neural networks, displaying a kind of stochastic multi-resonance. This effect is due to competition between activity-dependent synaptic fluctuations (due to dynamic synapses) and the existence of neuron firing threshold which adapts to the incoming mean synaptic input.

Time-integrated position error accounts for sensorimotor behavior in time-constrained tasks.

Several studies have shown that human motor behavior can be successfully described using optimal control theory, which describes behavior by optimizing the trade-off between the subject's effort and performance. This approach predicts that subjects reach the goal exactly at the final time. However, another strategy might be that subjects try to reach the target position well before the final time to avoid the risk of missing the target. To test this, we have investigated whether minimizing the control effort and maximizing the performance is sufficient to describe human motor behavior in time-constrained motor tasks. In addition to the standard model, we postulate a new model which includes an additional cost criterion which penalizes deviations between the position of the effector and the target throughout the trial, forcing arrival on target before the final time. To investigate which model gives the best fit to the data and to see whether that model is generic, we tested both models in two different tasks where subjects used a joystick to steer a ball on a screen to hit a target (first task) or one of two targets (second task) before a final time. Noise of different amplitudes was superimposed on the ball position to investigate the ability of the models to predict motor behavior for different levels of uncertainty. The results show that a cost function representing only a trade-off between effort and accuracy at the end time is insufficient to describe the observed behavior. The new model correctly predicts that subjects steer the ball to the target position well before the final time is reached, which is in agreement with the observed behavior. This result is consistent for all noise amplitudes and for both tasks.

Irregular dynamics in up and down cortical states.

Complex coherent dynamics is present in a wide variety of neural systems. A typical example is the voltage transitions between up and down states observed in cortical areas in the brain. In this work, we study this phenomenon via a biologically motivated stochastic model of up and down transitions. The model is constituted by a simple bistable rate dynamics, where the synaptic current is modulated by short-term synaptic processes which introduce stochasticity and temporal correlations. A complete analysis of our model, both with mean-field approaches and numerical simulations, shows the appearance of complex transitions between high (up) and low (down) neural activity states, driven by the synaptic noise, with permanence times in the up state distributed according to a power-law. We show that the experimentally observed large fluctuation in up and down permanence times can be explained as the result of sufficiently noisy dynamical synapses with sufficiently large recovery times. Static synapses cannot account for this behavior, nor can dynamical synapses in the absence of noise.

Influence of the Duffy antigen on pharmacokinetics and pharmacodynamics of recombinant monocyte chemoattractant protein (MCP-1, CCL-2) in vivo.

Monocyte chemoattractant protein-1 (MCP-1, CCL-2) binds to the Duffy antigen (DARC) on red blood cells, which act as a sink for several chemokines including MCP-1. In this study it is hypothesized that DARC may alter the pharmacokinetics of infused recombinant human MCP-1 (rhMCP-1). The primary aim of this first in man trial is to compare the pharmacokinetics of rhMCP-1 in Duffy positive and negative individuals. A randomized, double-blinded, placebo-controlled dose escalation trial was conducted on 36 healthy volunteers. Subjects received infusions of 0.02-2.0 microg/kg rhMCP-1 or placebo for one hour. RhMCP-1 displayed linear pharmacokinetics. Duffy negative individuals reached maximal plasma levels significantly earlier, but overall plasma concentration profiles were not altered. rhMCP-1 markedly increased monocyte counts, and estimated EC50 values were 10-fold higher in Duffy positive than in Duffy negative subjects. Increased monocyte counts were associated with decreased surface expression of intercellular adhesion molecule 1 (ICAM-1, CD54). In contrast, neither CCR-2 or CD11b expression, nor markers of platelet or endothelial activation, inflammation and coagulation were altered. RhMCP-1 is a highly selective chemoattractant for monocytes in humans. The Duffy antigen only minimally alters the pharmacokinetics of rhMCP-1 for doses up to 2 microg/kg.

Multipoint approximations of identity-by-descent probabilities for accurate linkage analysis of distantly related individuals.

We propose an analytical approximation method for the estimation of multipoint identity by descent (IBD) probabilities in pedigrees containing a moderate number of distantly related individuals. We show that in large pedigrees where cases are related through untyped ancestors only, it is possible to formulate the hidden Markov model of the Lander-Green algorithm in terms of the IBD configurations of the cases. We use a first-order Markov approximation to model the changes in this IBD-configuration variable along the chromosome. In simulated and real data sets, we demonstrate that estimates of parametric and nonparametric linkage statistics based on the first-order Markov approximation are accurate. The computation time is exponential in the number of cases instead of in the number of meioses separating the cases. We have implemented our approach in the computer program ALADIN (accurate linkage analysis of distantly related individuals). ALADIN can be applied to general pedigrees and marker types and has the ability to model marker-marker linkage disequilibrium with a clustered-markers approach. Using ALADIN is straightforward: It requires no parameters to be specified and accepts standard input files.

The hypocretin neurotransmission system in myotonic dystrophy type 1.

BACKGROUND: Patients with myotonic dystrophy type 1 (DM1) frequently have symptoms of excessive daytime sleepiness (EDS). Some patients with DM1 show sleep-onset REM, similar to that observed in narcolepsy. Narcolepsy is characterized by impaired hypocretin (Hcrt) neurotransmission. OBJECTIVE: To test for dysregulation of Hcrt neurotransmission in a prospective cohort of patients with DM1. METHODS: Hcrt levels in CSF were measured by radioimmunoassay. Sleep physiology was assessed by overnight polysomnography (PSG) and a multiple sleep latency test (MSLT). Splicing of Hcrt receptor 1 and 2 (HcrtR1 and HcrtR2) mRNA was examined in postmortem samples of temporal cortex. RESULTS: Seventeen of 38 patients with DM1 reported symptoms of EDS. Among patients with DM1 with EDS who underwent PSG/MSLT, 7 of 13 showed reduced sleep latency, sleep-onset REM, or both. However, CSF Hcrt levels in DM1 (mean 277 pg/mL, n = 38) were not different from controls (mean 277 pg/mL, n = 33). Also, splicing of HcrtR1 and HcrtR2 mRNA in patients with DM1 was similar to controls. CONCLUSIONS: Excessive daytime sleepiness and dysregulation of REM sleep occur frequently in patients with myotonic dystrophy type 1 (DM1). However, the pathophysiologic basis is distinct from narcolepsy, as patients with DM1 do not have a consistent defect of Hcrt release or receptor splicing.