Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation of Alcohol Extinction and Cue-Induced Reinstatement by Specific Projections among Medial Prefrontal Cortex, Nucleus Accumbens, and Basolateral Amygdala

The ability to inhibit drinking is a significant challenge for recovering alcoholics, especially in the presence of alcohol-associated cues. Previous studies have demonstrated that the regulation of cue-guided alcohol seeking is mediated by the basolateral amygdala (BLA), nucleus accumbens (NAc), and medial prefrontal cortex (mPFC). However, given the high interconnectivity between these structures, it is unclear how mPFC projections to each subcortical structure, as well as projections between BLA and NAc, mediate alcohol-seeking behaviors. Here, we evaluate how cortico-striatal, cortico-amygdalar, and amygdalo-striatal projections control extinction and relapse in a rat model of alcohol seeking. Specifically, we used a combinatorial viral technique to express diphtheria toxin receptors in specific neuron populations based on their projection targets. We then used this strategy to create directionally selective ablations of three distinct pathways after acquisition of ethanol self-administration but before extinction and reinstatement. We demonstrate that ablation of mPFC neurons projecting to NAc, but not BLA, blocks cue-induced reinstatement of alcohol seeking and neither pathway is necessary for extinction of responding. Further, we show that ablating BLA neurons that project to NAc disrupts extinction of alcohol approach behaviors and attenuates reinstatement. Together, these data provide evidence that the mPFC→NAc pathway is necessary for cue-induced reinstatement of alcohol seeking, expand our understanding of how the BLA→NAc pathway regulates alcohol behavior, and introduce a new methodology for the manipulation of target-specific neural projections. SIGNIFICANCE STATEMENT The vast majority of recovering alcoholics will relapse at least once and understanding how the brain regulates relapse will be key to developing more effective behavior and pharmacological therapies for alcoholism. Given the high interconnectivity of cortical, striatal, and limbic structures that regulate alcohol intake, it has been difficult to disentangle how separate projections between them may control different aspects of these complex behaviors. Here, we demonstrate a new approach for noninvasively ablating each of these pathways and testing their necessity for both extinction and relapse. We show that inputs to the nucleus accumbens from medial prefrontal cortex and amygdala regulate alcohol-seeking behaviors differentially, adding to our understanding of the neural control of alcoholism.
Large time behavior of solutions to a nonlinear hyperbolic relaxation system with slowly decaying data

We consider the large time asymptotic behavior of the global solutions to the initial value problem for the nonlinear damped wave equation with slowly decaying initial data. When the initial data decay fast enough, it is known that the solution to this problem converges to the self‐similar solution to the Burgers equation called a nonlinear diffusion wave, and its optimal asymptotic rate is obtained. In this paper, we focus on the case that the initial data decay more slowly than previous works and derive the corresponding asymptotic profile. Moreover, we investigate how the change of the decay rate of the initial values affect its asymptotic rate.
Progressive Differentiable Architecture Search: Bridging the Depth Gap Between Search and Evaluation

Recently, differentiable search methods have made major progress in reducing the computational costs of neural architecture search. However, these approaches often report lower accuracy in evaluating the searched architecture or transferring it to another dataset. This is arguably due to the large gap between the architecture depths in search and evaluation scenarios. In this paper, we present an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure. This brings two issues, namely, heavier computational overheads and weaker search stability, which we solve using search space approximation and regularization, respectively. With a significantly reduced search time (~7 hours on a single GPU), our approach achieves state-of-the-art performance on both the proxy dataset (CIFAR10 or CIFAR100) and the target dataset (ImageNet). Code is available at https://github.com/chenxin061/pdarts
Validation of Nafamostat Mesilate as an Anticoagulant in Extracorporeal Membrane Oxygenation: A Large-Animal Experiment

Background Unfractionated heparin is commonly used for anticoagulation in extracorporeal membrane oxygenation (ECMO). Several studies have shown that nafamostat mesilate (NM) has comparable clinical outcomes to unfractionated heparin. This study compared anticoagulation with NM and heparin in a large-animal model. Methods Beagle dogs (n=8; weight, 6.5–9 kg) were placed on venovenous ECMO. Blood samples were taken every hour and the following parameters were compared: hemoglobin level, activated partial thromboplastin time (aPTT), thromboelastography (TEG) data, platelet function, and inflammatory cytokine levels. Results In both groups, the aPTT was longer than the baseline value. Although the aPTT in the NM group was shorter than in the heparin group, the TEG parameters were similar between the 2 groups. Hemoglobin levels decreased in both groups, but the decrease was less with NM than with heparin (p=0.049). Interleukin (IL)-1β levels significantly decreased in the NM group (p=0.01), but there was no difference in the levels of tumor necrosis factor alpha or IL-10 between the 2 groups. Conclusion NM showed a similar anticoagulant effect to that of unfractionated heparin, with fewer bleeding complications. NM also had anti-inflammatory properties during ECMO. Based on this preclinical study, NM may be a good alternative candidate for anticoagulation in ECMO.
Ragusana donkey milk as a source of lactic acid bacteria and yeast strains of dairy technological interest

Donkey milk is considered an interesting substitute to cow’s milk thanks to its nutritional properties. The chemical composition of donkey milk has been extensively considered essential to prevent the growth of undesirable microorganisms, and few data are available on its microbiological characterization. In the present study raw and cultured donkey milk samples were analyzed in order to characterize lactic acid bacteria and yeast populations. Both lactic acid bacteria and yeast isolated strains were identified using a combination of different mo lecular techniques and results were confirmed by sequence analysis of the 16S rRNA gene and ITS regions, respectively. Furthermore, technological traits of the strains were investigated. The results indicated that dominant lactic acid bacteria strains were identified mainly as Lactococcus lactis subsp. lactis/cremoris and Lactobacillus paracasei . The strains showed both good coagulant and acidifying activities and 50% of them was able to grow in presence of 5.0 g/l of lysozyme. Among isolated yeast strains, the 70% was ascribed to the Kluyveromyces lactis species. The results of present study indicated that Ragusana donkey milk is an interesting source of lactic acid bacteria and yeast strains of technological interest. & streptococci; Sabouraud Dextrose Agar (SDA), plus chloramphenicol (100 mg/l) (Fluka), incubated at 25°C for 24-48h, for yeasts; Violet Red Bile Glucose Agar (VRBGA), incubated at 37°C for 24-48 h, for Enterobacteriaceae. Microbial counts were performed in triplicate for each sample and results were reported as log10 cfu/ml.
Working Memory Load-related Theta Power Decreases in Dorsolateral Prefrontal Cortex Predict Individual Differences in Performance

Holding information in working memory (WM) is an active and effortful process that is accompanied by sustained load-dependent changes in oscillatory brain activity. These proportional power increases are often reported in EEG studies recording theta over frontal midline sites. Intracranial recordings, however, yield mixed results, depending on the brain area being recorded from. We recorded intracranial EEG with depth electrodes in 13 patients with epilepsy who were performing a Sternberg WM task. Here, we investigated patterns of theta power changes as a function of memory load during maintenance in three areas critical for WM: dorsolateral prefrontal cortex (DLPFC), dorsal ACC (dACC), and hippocampus. Theta frequency power in both hippocampus and dACC increased during maintenance. In contrast, theta frequency power in the DLPFC decreased during maintenance, and this decrease was proportional to memory load. Only the power decreases in DLPFC, but not the power increases in hippocampus and dACC, were predictive of behavior in a given trial. The extent of the load-related theta power decreases in the DLPFC in a given participant predicted a participant's RTs, revealing that DLPFC theta explains individual differences in WM ability between participants. Together, these data reveal a pattern of theta power decreases in the DLPFC that is predictive of behavior and that is opposite of that in other brain areas. This result suggests that theta band power changes serve different cognitive functions in different brain areas and specifically that theta power decreases in DLPFC have an important role in maintenance of information.
Quantitative Analysis of Gametic Incompatibility Between Closely Related Species of Neotropical Sea Urchins

Species of the sea urchin genus Echinometra found on the two coasts of Panamá are recently diverged and only partially isolated by incomplete barriers to interspecific fertilization. This study confirms previous work that revealed incompatibility between the eggs of the Atlantic E. lucunter and the sperm of the other two neotropical species, whereas eggs of its sympatric congener E. viridis and allopatric E. vanbrunti are largely compatible with heterospecific sperm. Here we quantify fertilization using a range of sperm dilutions. We demonstrate a much stronger block to cross-species fertilization of E. lucunter eggs than was previously shown at fixed sperm concentrations, and mild incompatibility of the other two species’ eggs where previous crosses between species were not distinguishable from within-species controls. Additionally, we present evidence for intraspecific variation in egg receptivity towards heterospecific sperm. Our findings here again discount the “reinforcement model” as a viable explanation for the pattern of prezygotic isolation. Gamete incompatibility in these Echinometra has appeared recently—within the last 1.5 million years—but is weaker in sympatry than in allopatry. Accidents of history may help explain why incompatibility of eggs emerged in one species and not in others. Compensatory sexual selection on sperm in this species could follow, and promote divergence of proteins mediating sperm-egg recognition.
Wave physics as an analog recurrent neural network

Analog machine learning computations are performed passively by propagating light and sound waves through programmed materials. Analog machine learning hardware platforms promise to be faster and more energy efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate for building analog processors for time-varying signals. Here, we identify a mapping between the dynamics of wave physics and the computation in recurrent neural networks. This mapping indicates that physical wave systems can be trained to learn complex features in temporal data, using standard training techniques for neural networks. As a demonstration, we show that an inverse-designed inhomogeneous medium can perform vowel classification on raw audio signals as their waveforms scatter and propagate through it, achieving performance comparable to a standard digital implementation of a recurrent neural network. These findings pave the way for a new class of analog machine learning platforms, capable of fast and efficient processing of information in its native domain.
SEALion: a Framework for Neural Network Inference on Encrypted Data

We present SEALion: an extensible framework for privacy-preserving machine learning with homomorphic encryption. It allows one to learn deep neural networks that can be seamlessly utilized for prediction on encrypted data. The framework consists of two layers: the first is built upon TensorFlow and SEAL and exposes standard algebra and deep learning primitives; the second implements a Keras-like syntax for training and inference with neural networks. Given a required level of security, a user is abstracted from the details of the encoding and the encryption scheme, allowing quick prototyping. We present two applications that exemplifying the extensibility of our proposal, which are also of independent interest: i) improving efficiency of neural network inference by an activity sparsifier and ii) transfer learning by querying a server-side Variational AutoEncoder that can handle encrypted data.
AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization Systems

With widely deployed WiFi network and the uniqueness feature (fingerprint) of wireless channel information, fingerprinting based WiFi positioning is currently the mainstream indoor positioning method, in which fingerprint database construction is crucial. However, for accuracy, this approach requires enough data to be sampled at many reference points, which consumes excessive efforts and time. In this paper, we collect Channel State Information (CSI) data at reference points by the method of device-free localization, then we convert collected CSI data into amplitude feature maps and extend the fingerprint database using the proposed Amplitude-Feature Deep Convolutional Generative Adversarial Network (AF-DCGAN) model. The use of AF-DCGAN accelerates convergence during the training phase, and substantially increases the diversity of the CSI amplitude feature map. The extended fingerprint database both reduces the human effort involved in fingerprint database construction and the accuracy of an indoor localization system, as demonstrated in the experiments.
Kidney Injury and Repair Biomarkers in Marathon Runners.

BACKGROUND Investigation into strenuous activity and kidney function has gained interest given increasing marathon participation. STUDY DESIGN Prospective observational study. SETTING & PARTICIPANTS Runners participating in the 2015 Hartford Marathon. PREDICTOR Completing a marathon. OUTCOMES Acute kidney injury (AKI) as defined by AKI Network (AKIN) criteria. Stage 1 AKI was defined as 1.5- to 2-fold or 0.3-mg/dL increase in serum creatinine level within 48 hours of day 0 and stage 2 was defined as a more than 2- to 3-fold increase in creatinine level. Microscopy score was defined by the number of granular casts and renal tubular epithelial cells. MEASUREMENTS Samples were collected 24 hours premarathon (day 0), immediately postmarathon (day 1), and 24 hours postmarathon (day 2). Measurements of serum creatinine, creatine kinase, and urine albumin were completed, as well as urine microscopy analysis. 6 injury urine biomarkers (IL-6, IL-8, IL-18, kidney injury molecule 1, neutrophil gelatinase-associated lipocalin, and tumor necrosis factor α) and 2 repair urine biomarkers (YKL-40 and monocyte chemoattractant protein 1) were measured. RESULTS 22 marathon runners were included. Mean age was 44 years and 41% were men. 82% of runners developed an increase in creatinine level equivalent to AKIN-defined AKI stages 1 and 2. 73% had microscopy diagnoses of tubular injury. Serum creatinine, urine albumin, and injury and repair biomarker levels peaked on day 1 and were significantly elevated compared to day 0 and day 2. Serum creatine kinase levels continued to significantly increase from day 0 to day 2. LIMITATIONS Small sample size and limited clinical data available at all time points. CONCLUSIONS Marathon runners developed AKI and urine sediment diagnostic of tubular injury. An increase in injury and repair biomarker levels suggests structural damage to renal tubules occurring after marathon. The results of our study should be validated in larger cohorts with longer follow-up of kidney function.
Prenatal Stress as a Risk—and an Opportunity—Factor

Two separate lines of research indicate (a) that prenatal stress is associated with heightened behavioral and physiological reactivity and (b) that these postnatal phenotypes are associated with increased susceptibility to both positive and negative developmental experiences. Therefore, prenatal stress may increase sensitivity to the rearing environment. We tested this hypothesis by manipulating prenatal stress and rearing-environment quality, using a cross-fostering paradigm, in prairie voles. Results showed that prenatally stressed voles, as adults, displayed the highest behavioral and physiological reactivity when cross-fostered to low-contact (i.e., low-quality) rearing but the lowest behavioral and physiological reactivity when cross-fostered to high-contact (i.e., high-quality) rearing; non-prenatally stressed voles showed no effect of rearing condition. Additionally, while neither prenatal stress nor rearing condition affected oxytocin receptor binding, prenatally stressed voles cross-fostered to high-contact rearing showed the highest vasopressin-1a receptor binding in the amygdala. Results indicate that prenatal stress induces greater environmental sensitivity, making it both a risk and an opportunity factor.
Sex Differences in Mechanisms of Recovery after Isometric and Dynamic Fatiguing Tasks

Purpose The purpose of this study was to determine whether supraspinal mechanisms contribute to the sex difference in fatigability during and recovery from a dynamic and isometric fatiguing task with the knee extensors. Methods Transcranial magnetic stimulation and electrical stimulation were used to determine voluntary activation and contractile properties of the knee extensors in 14 men and 17 women (20.8 ± 1.9 yr) after a 1) 60-s sustained, maximal voluntary isometric contraction (MVIC), and 2) dynamic fatiguing task involving 120 maximal voluntary concentric contractions with a 20% MVIC load. Results There were no differences between men and women in the reduction of maximal torque during the sustained MVIC (54.4% ± 18.9% vs 55.9% ± 11.2%, P = 0.49) or in the decrease in power during the dynamic fatiguing task (14.7% ± 20.1% vs 14.2% ± 18.5%, P = 0.92). However, MVIC torque recovered more quickly for women than men after the sustained MVIC and the dynamic task (P < 0.05). The transcranial magnetic stimulation–elicited superimposed twitch was larger for men than for women during the sustained MVIC and in recovery (immediately post, R0.1: 4.7% ± 3.3% vs 2.4% ± 1.9% MVIC; P = 0.02), with no sex difference after the dynamic task (P = 0.35). The reduction in resting twitch amplitude was larger for men than for women immediately after the dynamic task (37% ± 22% vs 23% ± 18%; P = 0.016) with no sex difference after the sustained MVIC (64% ± 16% vs 67% ± 11%; P = 0.46). Conclusions Supraspinal fatigue contributed to fatigability of the knee extensors more for men than for women after a maximal isometric task, whereas contractile mechanisms explained the sex difference in torque recovery after the fast-velocity dynamic task. The mechanisms for the sex difference in fatigability are task dependent.
Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome – the MADDEC study

Abstract Objective Investigation of the clinical potential of extensive phenotype data and machine learning (ML) in the prediction of mortality in acute coronary syndrome (ACS). Methods The value of ML and extensive clinical data was analyzed in a retrospective registry study of 9066 consecutive ACS patients (January 2007 to October 2017). Main outcome was six-month mortality. Prediction models were developed using two ML methods, logistic regression and extreme gradient boosting (xgboost). The models were fitted in training set of patients treated in 2007–2014 and 2017 (81%, n = 7344) and validated in a separate validation set of patients treated in 2015–2016 with full GRACE score data available for comparison of model accuracy (19%, n = 1722). Results Overall, six-month mortality was 7.3% (n = 660). Several variables were found to be significantly associated with six-month mortality by both ML methods. The xgboost scored the best performance: AUC 0.890 (0.864–0.916). The AUC values for logistic regression and GRACE score were 0.867(0.837–0.897) and 0.822 (0.785–0.859), respectively. The AUC value of xgboost was better when compared to logistic regression (p = .012) and GRACE score (p < .00001). Conclusions The use of extensive phenotype data and novel machine learning improves prediction of mortality in ACS over traditional GRACE score. KEY MESSAGES The collection of extensive cardiovascular phenotype data from electronic health records as well as from data recorded by physicians can be used highly effectively in prediction of mortality after acute coronary syndrome. Supervised machine learning methods such as logistic regression and extreme gradient boosting using extensive phenotype data significantly outperform conventional risk assessment by the current golden standard GRACE score. Integration of electronic health records and the use of supervised machine learning methods can be easily applied in a single centre level to model the risk of mortality.
Psychiatric Symptoms in Patients With Dementia

Purpose: Neuropsychiatric symptoms (NPS) are common in Alzheimer Disease (AD). Treatment could be optimized by supplementing the clinician’s impression of a patient with information from the caregiver. Yet the agreement between caregivers and physicians on the presence of NPS in patients with AD is understudied. Methods: Data were obtained from a 2-staged survey in neurology outpatient offices. At stage 1, patients (n=403) were documented by their physicians, including an assessment on the presence of NPS. At stage 2, patients’ CGs (n=171) were asked about the presence of NPS in the patients, based on questions from the Neuropsychiatric Inventory. Caregivers were screened for depression with the Depression Screening Questionnaire. Patients: The study sample comprised patients with mild or moderate AD. Results: NPS frequency varied between 52.6% [95% confidence interval (CI), 44.9%-60.3%] and 67.2% (95% CI, 59.7%-74.2%, reported by CGs) and 34.2% (95% CI, 26.8%-42.1%) and 50.9% (95% CI, 42.9%-58.9%, reported by physicians). Apathy, depression, aggression, and irritability occurred most frequently according to both sources. κ values were lowest for euphoria (κ=0.03; 95% CI, −0.08 to 0.25), and highest for depression (κ=0.26; 95% CI, 0.11-0.43). CG depression was associated with an increased probability (odds ratio=2.9; 95% CI, 1.2-6.7) of disagreement between caregivers and physicians on the patient’s mental status. Conclusion: NPS, though very prevalent in dementia patients, are perceived differently by caregivers and physicians. This divergence increases depending on the psychological health of caregivers.