Miracle-dz/UniFAR-FTUs huggingface dataset:
aytime Prazosin Reduces Psychological Distress to rauma Specific Cues in Civilian Trauma Posttraumatic tress Disorder letcher

ackground: Persons with posttraumatic stress disorder (PTSD) whose trauma-related nightmares improve or resolve with bedtime dministration of the alpha-1 adrenergic antagonist prazosin often continue to experience PTSD symptoms during the day. This study ddressed whether daytime prazosin compared to placebo would alleviate psychological distress provoked experimentally by a rauma-related word list included in the emotional Stroop (E-Stroop) paradigm. ethods: Eleven persons with civilian trauma PTSD who continued to experience daytime PTSD symptoms despite a stable bedtime razosin dose that suppressed trauma-related nightmares were studied. Prazosin and placebo were administered on two different ccasions in the early afternoon followed two hours later by the E-Stroop. Effects of drug on psychological distress were assessed by the rofile of Mood States (POMS). esults: POMS total score and an “emotional distress” POMS subscale score following trauma-related words were significantly lower n the prazosin than placebo condition. There were no treatment effects on E-Stroop completion time. In 10 subjects who continued pen label daytime prazosin, there was a reduction in global PTSD illness severity at 2-week follow-up. onclusions: Daytime prazosin pretreatment reduced psychological distress specifically to trauma cues. Adding daytime prazosin to edtime prazosin may further reduce overall PTSD illness severity and distress.
Biochemical , Histochemical and Enzymatic Studies in Relation to Sorghum Downy Mildew Infection of Resistant and Susceptible Genotypes of Maize

Biochemical, histochemical and enzymatic studies were conducted on indigenous susceptible, CM-500, NAI-127 and resistant, NAI-129, SKV-10 maize leaves infected with Peronosclerospora sorghi and sampling was done at 15, 30, 45, 60 and 75 days after infection with appropriate controls. Biochemical observations revealed high total sugars, starch and amino-acid content in resistant lines compared to susceptible in both inoculated and uninoculated leaves at all times whereas the total phenol and ortho dihydroxy phenols decreased during the first sampling, then later increased consistently in inoculated tissues at all the days. Further evidence, from leaf histochemical analysis draws the same trend as described in the above biochemical analysis. Upon pathogen attack, peroxidase (POX) and phenylalanine ammonia-lyase (PAL) activity increased drastically and high levels were maintained at all times. Higher levels of carbohydrates, aminoacids, different phenols and also elevated activity of POX and PAL in NAI-129, SKV-10 may play a role in inhibiting P. sorghi. _____________________________________________________________________________________________________________
Im/migrant Children’s Education Experiences and Families’ Sacrifices in a Global Pandemic

Family separation policies’ impacts on children’s education and well-being are critical issues of our time. This paper argues through ethnographic study that although im/migrant parents believed in the promise of a better life for their children as they migrated, COVID-19 and remote schooling contributed to a breakdown in structures of care once they were in the United States. Thus, the experience of remote schooling during 2020 was a difficult task for parents and children who were already dealing with the trauma of detention or separation at the border. Ultimately, we argue that to understand the educational experiences of im/migrant parents and children in the United States, we must consider a multiple disruptions framework. The findings in this article reveal the layered consequences that broader immigration policy has on the everyday educational lives of im/migrant children and their parents.
Spin-exchange-induced exotic superfluids in a Bose-Fermi spinor mixture

We consider a mixture of spin-1/2 bosons and fermions, where only the bosons are subjected to the spin-orbit coupling induced by Raman beams. The fermions, although not directly coupled to the Raman lasers, acquire an effective spin-orbit coupling through the spin-exchange interaction between the two species. Our calculation shows that this is a promising way of obtaining spin-orbit coupled Fermi gas without Raman-induced heating, where the long-sought topological Fermi superfluids and topological bands can be realized. Conversely, we find that the presence of fermions not only provides a new way to create the supersolid stripe phase of the bosons, but more strikingly it can also greatly increase the spatial period of the bosonic density stripes, and hence makes this phase directly observable in the experiment. This system provides a new and practical platform to explore the physics of spin-orbit coupling, which possesses a dynamic nature through the interaction between the two species.
The Effects of PANI Concentration on the Mechanical Properties of PP/PANI Composites

In this study, the effect of PANI additive with different concentrations (0.3-3.0 wt.%) on mechanical properties of Polypropylene/Polyaniline (PP/PANI) film composites have been investigated. The composites films obtained from the hot pressing technique have been characterized to determine their surface properties and structural morphology (SEM and FT-IR), thermal properties (TG). The effects of PANI additive on mechanical properties of polypropylene have been examined by tensile tests. In particular, tensile strength, Young's modulus and percentage strain at break of the composites have been tested. According to our results, it has been observed that the PANI additive in polypropylene had a significant impact on its mechanical properties. The tensile strength has been reached highest value in PP/0.6 wt. % PANI composites.
Options and Market Expectations: Implied Probability Density Functions on the Polish Foreign Exchange Market

An overview of methods used for estimation of option-implied risk-neutral probability density functions (PDFs) is presented in the study, and one of such methods, double lognormal approach, is used for the analysis of the information content of the EUR/PLN currency options on the Polish market. Estimated PDFs have proven to provide superior information concerning future volatility than historical volatility, yet their forecasting power is comparable to that of the Black-Scholes model. There are no strong grounds for using PDFs as a predictor of the future EUR/PLN exchange rate. Low informative content does not directly follow, as PDFs can be used as an indicator of markets conditions. The issues that could be addressed more thoroughly in the future studies concern the assumption of risk neutrality and the impact of the estimation method on the higher moments of the distribution.
Fuel choice and the demand for natural gas in Western European households

The paper presents an econometric model for analysing natural gas demand for space heating in the residential sector in Weste rn Europe. A discrete-continuous dynamic choice model is specified. Households' decisions on energy consumption are • viewed as carried out in two steps: In the first step they choose between a limited number of fuel systems. Given this choice a decision is made on how intensively the energy equipment should be utilised. The model is estimated from data on energy use and dwelling stock in seven European countries, organised in a database at Lawrence Berkeley Laboratory, Berkeley, USA. The results yield rather reasonable estimates on price and income elasticities of gas demand, but the effect on demand from conversions are found to be rather moderate. 'Berits Bartlett is a staff scientist at International Energy Studies, Lawrence Berkeley Laboratory, Berkeley, John K. Dagsvik and Øyetein Olsen are senior research officers at the Research Department, Central Bureau of Statistics, Oslo. Steinar Strom is Professor at the Institute of Economics, University of Oslo. Not to be quoted without permission from author(s). Comments welcome.
COMMENTARY: Data Quality in Sampling, Analysis, and Compilation

The focus of this paper is on Data Quality as it applies to Nutritional Compositional Databases. Quality implies fitness for the purposes for which the data will be used. An appreciation of the requirements of the users of the database is thus a primary determinant of the standards that should be applied during the generation of the data. Most users expect that the compositional data will be representative of the foods consumed by the population being studied and thus Sampling is the first critical element. It is also expected that the data will be analytically sound so the second critical element is Analytical Quality. Finally, the Compilation process involves making judgements about the data to be included and their presentation and in this the users expect that the compilation process will adopt to focus on the quality of the data that will meet their needs. The integration of these elements during the production of data for inclusion in the database will be advocated. Finally, there follows a discussion of how best to present data quality in a database so that the users can use the database appropriately and effectively.
A feedforward inhibitory premotor circuit for auditory–vocal interactions in zebra finches

Significance During conversations, we frequently alternate between listening and speaking. This involves withholding responses while the other person is vocalizing and rapidly initiating a reply once they stop. Similar exchanges also occur in other animals, such as songbirds, yet little is known about how brain areas responsible for vocal production are influenced by areas dedicated to listening. Here, we combined neural recordings and mathematical modeling of a sensorimotor circuit to show that input-dependent inhibition can both suppress vocal responses and regulate the onset latencies of vocalizations. Our resulting model provides a simple generalizable circuit mechanism by which inhibition precisely times vocal output and integrates auditory input within a premotor nucleus.
Temperature Dependence of the Characteristics of an Electrohydrodynamic Pump With Plate–Bar Electrodes

When an electrohydrodynamic (EHD) pump is used as a power source to drive a refrigerant liquid, the temperature dependence of the EHD pump characteristics must be made clear; however, this topic has hardly been investigated. Therefore, this study measures the pump characteristics under different temperatures ranging from 3 to 45 °C using the fluorine solvents Vertrel XF (C5H2F10) and Novec 7100 (C4F9OC2H5) as the working fluids. The pressure developed by the EHD pump at zero flow rate increases with decreasing temperature. The slope of the pressure–flow rate characteristics is steeper with lower temperature. The numerical simulation results obtained under the assumption that the injected charge density increases with decreasing temperature agree relatively well with the experimental results and reveal the effect of the fluid temperature.
Influence of Menstrual Cycle Length and Age at Menarche on Symptoms, Cognition, Social Cognition, and Metacognition in Patients with First-Episode Psychosis

A protective effect has traditionally been attributed to estrogen in psychotic disorders. The aim of this study was to investigate cumulative lifetime estrogen by assessing the menstrual cycle length, age at menarche, and years of difference between the onset of psychotic symptoms and the age of menarche, measuring their effects on symptoms, cognition, social cognition, and metacognition. As it was not possible to directly measure cumulative estrogen levels over the lifetime of a patient, the study sample was composed of 42 women with first-episode psychosis; estrogen levels were inferred by the menstrual cycle length, age at menarche, and years of difference between the onset of psychotic symptoms and menarche. All patients were assessed with a battery of questionnaires using the BDI, PSYRATS, PANSS, STROOP, TAVEC, WSCT, IPSAQ, and BCIS questionnaires. The results related to menstrual cycle length showed a relationship with memory; specifically, shorter cycles with semantic strategies (p = 0.046) and longer cycles with serial strategies in the short term (p = 0.005) as well as in the long term (p = 0.031). The results also showed a relationship with perseverative errors (p = 0.035) and self-certainty (p = 0.049). Only personalized bias (p = 0.030) was found to be significant in relation to the age at menarche. When analyzing the differences in years of difference between the age at menarche and the onset of psychotic symptoms, the results indicated lower scores in women with a smaller difference between both events in memory (short-term (p = 0.050), long-term (p = 0.024), intrusions (p = 0.013), and recognition (p = 0.043)) and non-perseverative errors (p = 0.024). No relationship was found between symptoms and menstrual characteristics. The investigatory outcomes seem to indicate a relationship between estrogen cumulative effects and the memory domain. More in-depth investigations in the field are necessary in order to improve personalized treatment in women with psychosis.
Study on a Novel Hybrid Thermal Network of Homopolar Inductor Machine

The thermal analysis is necessary for the design and safe operation of the homopolar inductor machine (HIM) applied in the flywheel energy storage system (FESS). However, due to its unique three-dimensional (3-D) structure, the thermal analysis of HIM relies on the 3-D finite element analysis (FEA) or computational fluid dynamics (CFD), which is time-consuming and computationally inefficient. To facilitate the thermal analysis and design of HIM, a fast and accurate analytical model is essential. In this article, the novel 2-D and 3-D hybrid thermal networks combined with a coupled iterative analysis method are proposed to predict the temperature of HIM. First, the structure, operation principle, and material properties of HIM are illustrated. Second, the heat sources of HIM, including iron loss, rotor eddy current loss, rotor air friction loss, copper loss, and bearing loss, are fully analyzed and investigated. Third, the proposed hybrid thermal model (HTM) and coupled iterative analysis method are demonstrated and applied to predict its temperature. The proposed thermal model and analysis methods are explained in detail. The influences of different speeds and operation conditions on the temperature rise of HIM are analyzed based on the proposed thermal model and FEA. Finally, the temperature of an HIM prototype is tested, which proves the accuracy of FEA and the proposed method. The proposed HTM can quickly and accurately predict the temperature of HIM, which provides a new idea for the temperature prediction of this kind of machine.
Prolongation of the effect of a single dose of rocuronium in a patient with postpolio syndrome under desflurane anesthesia: a case report

Postpolio syndrome (PPS) is widely known to manifest as muscle weakness in patients affected by poliomyelitis in early childhood. This is caused by the long-term overwork of motor nerves regenerated from surviving nerve cells. We report a characteristic delay in recovery from muscle relaxation after administering rocuronium to a patient with PPS under general anesthesia with desflurane. A 59-year-old woman was scheduled to undergo surgical debridement for jaw osteonecrosis. She had a history of poliomyelitis at the age of 2 years, and was diagnosed with PPS at the age of 51 years. General anesthesia was induced with 80 mg propofol, 50 µg fentanyl, and 30 mg (0.69 mg/kg) rocuronium, and maintained with desflurane and remifentanil. The durations of train-of-four (TOF) count 0 and 1 were 96 and 37 min, respectively. Five minutes after discontinuing desflurane, the TOF count was 4. Three minutes after administering 200 mg sugammadex, the TOF ratio was 0.83, and the tracheal tube was subsequently removed. In summary, the effect of a single dose of rocuronium on twitch in TOF monitoring was significantly prolonged in a patient with PPS, which may have been exacerbated by desflurane.
Exploring the Disaggregated Memory Interface Design Space

Rapid increases in network performance, coupled with the dynamic nature of modern cloud workloads, has led to calls for memory disaggregation within the datacenter. In a memorydisaggregated machine, compute nodes contain only a limited amount of local memory, but are able to utilize free memory elsewhere in the machine over a network connection. This broad definition of memory disaggregation allows for a great diversity of techniques to actually provide that interface. In this paper, we present an overview of these techniques and lay out a few dimensions of the disaggregated memory interface design space. We conclude with a discussion of future research directions and challenges.
A 35 dBm PSAT and 41% PAE GaAs Power Amplifier With Series Distributed-Balun

This brief presents a high-power Sub-6 GHz two-way differential power amplifier (PA) with a series distributed-balun output network in $0.5{\mu }\text{m}$ GaAs pHEMT process. The proposed series distributed-balun output network consists of two coupled-line-based distributed-baluns connected in series to achieve low-loss matching and power combining. The general three-port distributed-balun unit and its extended two-way series distributed-balun have been theoretically analyzed in detail. In addition, the multi-layer transformer structure is introduced to enhance the coupling factor of the inter-stage matching network. The measurement results of this three-stage PA demonstrate a peak gain of 32.6dB, with 3-dB bandwidth (BW−3dB) of 4.2-6 GHz. The saturated output power (Psat) is 34–35 dBm covers 4.8-6 GHz, and the peak power-added efficiency (PAE) is 41%. The measured root mean square error vector magnitude (EVMrms) with 160MHz of 64-QAM/256-QAM is 2.4%/1.93% with 28.7/26.5 dBm average output power (Pave) and −36.8/−38.7 dBc adjacent channel leakage ratio (ACLR) without digital pre-distortion (DPD).
Measuring the local gravitational field using survival resonances in a dissipatively driven atom-optics system

We do a proof-of-principle demonstration of an atomic gravimeter based on survival resonances of dissipatively driven atoms. Exposing laser-cooled atoms to a sequence of near-resonant standing-wave light pulses reveals survival resonances when the standing-wave interference pattern accelerates. The resonant accelerations determine the local gravitational acceleration and we achieve a precision of 5 ppm with a drop distance less than 1 mm. The incisiveness of the resonances scales with the square of the drop time. Present results indicate that an appropriately designed atomic gravimeter based on survival resonances might be able to reach a precision of 1μGal with a 10-cm-high fountain. The relatively simple experimental construction of this technique may be of interest for a compact absolute atomic gravimeter.
Testing the effect of the submerged macrophyte Ceratophyllum demersum (L.) on heterotrophic bacterioplankton densities under different levels of nitrogen and phosphorus concentrations in shallow lake mesocosms

Abstract To investigate the effect of submerged macrophytes on heterotrophic bacterioplankton communities in response to nutrient enrichment, we simulated mesocosms to test two factors, namely, the presence of Ceratophyllum demersum (L.) and the level of nutrients (slight and medium nutrient enrichment) under four possible system combinations for a duration of more than 3 months. The results show that C. demersum can affect the temporal dynamics of heterotrophic bacterioplankton density (HBD) and cause it to decrease. However, the effect of C. demersum on HBD was more pronounced under medium nutrient enrichment. The mean values of HBD in the treatment and control systems under slight nutrient enrichment were 1.30 × 105 cells mL−1 and 1.34 × 105 cells mL−1, respectively; whereas for medium nutrient enrichment, they were 1.78 × 105 cells mL−1 and 2.65 × 105 cells mL−1, respectively. The total nitrogen (TN) and total phosphorus (TP) concentrations were maintained throughout the experiment, and no significant differences were observed in the pH value, chlorophyll a (Chl. a) concentrations or dissolved organic carbon (DOC) levels between the systems with and without macrophytes, regardless of the nutrient level. Furthermore, linear mixed models revealed that environmental variables had a limited impact on HBD and that C. demersum had no significant direct effect on the environmental variables in the systems. A likely explanation is higher predation on bacterioplankton in the mesocosms, although allelopathic effects exerted by C. demersum cannot be excluded.
Synthesis and electrochemical properties of LiNi 0 . 87 Co 0 . 10 Mg 0 . 03 O 2 cathode materials

A Co−Mg co-substituted LiNi0.87Co0. 10Mg0.03O2 cathode material was prepared by a co-precipitation method. The prepared LiNi0.87Co0. 10Mg0.03O2 exhibits excellent electrochemical properties, such as initial discharge capacities of 202.6 mA·h/g and 190.5 mA·h/g at 0.2C and 1C rate, respectively, in operating voltage range of 3.0−4.3 V (versus Li/Li). The capacity retentions are 96.1% and 93.4% at 0.2C and 1C, respectively, after 50 cycles. Moreover, the cycle performance of the sample was investigated in a 053048-type square Li ion battery. This type of battery can keep 81.7% of initial capacity after 500 charge−discharge cycles at 1C rate, which is close to that of commercial LiCoO2 battery. Therefore, the as-prepared material is capable of such high energy applications as portable product power.
3 D Tooth Segmentation and Labeling using Deep Convolutional Neural Networks

In this paper, we present a novel approach for 3D dental model segmentation via deep Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to receive undesirable results due to the complex appearance of human teeth (e.g., missing/rotten teeth, feature-less regions, crowding teeth, extra medical attachments, etc.). Furthermore, labeling of individual tooth is hardly enabled in traditional tooth segmentation methods. To address these issues, we propose to learn a generic and robust segmentation model by exploiting deep Neural Networks, namely NNs. The segmentation task is achieved by labeling each mesh face. We extract a set of geometry features as face feature representations. In the training step, the network is fed with those features, and produces a probability vector, of which each element indicates the probability a face belonging to the corresponding model part. To this end, we extensively experiment with various network structures, and eventually arrive at a 2-level hierarchical CNNs structure for tooth segmentation: one for teeth-gingiva labeling and the other for inter-teeth labeling. Further, we propose a novel boundary-aware tooth simplification method to significantly improve efficiency in the stage of feature extraction. After CNNs prediction, we do graph-based label optimization and further refine the boundary with an improved version of fuzzy clustering. The accuracy of our mesh labeling method exceeds that of the state-of-art geometry-based methods, reaching 99.06% measured by area which is directly applicable in orthodontic CAD systems. It is also robust to any possible foreign matters on model surface, e.g., air bubbles, dental accessories, and