Miracle-dz/UniFAR-FTUs huggingface dataset:
The Treatment of Primary Insomnia

There are a several therapies available for the treatment of primary insomnia, most notably cognitive-behavioral therapy (CBT) and pharmacologic treatment. The benzodiazepines are a group of chemically related compounds represented by medications such as triazolam, temazepam, and lorazepam. Non-benzodiazepines, such as zaleplon, zolpidem extended-release (ER), eszopiclone and zopiclone, act via a mechanism similar to the benzodiazepines but have a different chemical structure. Also available is the melatonin receptor agonist, ramelteon, as well as several antihistamines such as diphenhydramine, doxylamine, hydroxyzine. Medications, such as the antidepressants trazodone, mirtazapine, doxepin, and amitriptyline as well as the antipsychotics, quetiapine, olanzapine, and risperidone, have been used off-label for the treatment of insomnia. Patients often turn to alcohol, over-the-counter agents, and melatonin for relief of their sleep problems (Slide 1). Given these treatment options, there are several questions faced in clinical practice concerning when to treat patients for insomnia, what treatments should be used, and how long treatment should continue. CBT is typically continued for a fixed length of time. Generally, the greater the functional impairment a person experiences, the more there is to be gained from treatment. The decision about whether to treat and which treatments should be used, should consider the risks and expected benefits of all treatment options. CBT for insomnia has relatively minimal risks, with the only downsides being cost, access, and convenience. CBT should always be a therapeutic option.
Misophonia: physiological investigations and case descriptions

Misophonia is a relatively unexplored chronic condition in which a person experiences autonomic arousal (analogous to an involuntary “fight-or-flight” response) to certain innocuous or repetitive sounds such as chewing, pen clicking, and lip smacking. Misophonics report anxiety, panic, and rage when exposed to trigger sounds, compromising their ability to complete everyday tasks and engage in healthy and normal social interactions. Across two experiments, we measured behavioral and physiological characteristics of the condition. Interviews (Experiment 1) with misophonics showed that the most problematic sounds are generally related to other people's behavior (pen clicking, chewing sounds). Misophonics are however not bothered when they produce these “trigger” sounds themselves, and some report mimicry as a coping strategy. Next, (Experiment 2) we tested the hypothesis that misophonics' subjective experiences evoke an anomalous physiological response to certain auditory stimuli. Misophonic individuals showed heightened ratings and skin conductance responses (SCRs) to auditory, but not visual stimuli, relative to a group of typically developed controls, supporting this general viewpoint and indicating that misophonia is a disorder that produces distinct autonomic effects not seen in typically developed individuals.
Preparation of microparticles of naproxen with Eudragit RS and Talc by spherical crystallization technique

Microparticles of naproxen with Eudragit RS and talc were prepared by the spherical crystallization technique, i.e. quasi-emulsion solvent diffusion method. The obtained microparticles were evaluated by micromeritic properties, yield, encapsulation efficiency, drug physical state and dissolution rate of drug. The influence of formulation factors and preparation condition (drug: polymer ratio, talc: polymer ratio, SLS concentration, stirring speed) on the properties of the microparticles were also examined. The resultant microparticles were finely spherical and uniform with high incorporation efficiency and yield. Greater encapsulation efficiency was obtained by increasing the drug: polymer ratio and talc: polymer ratio and by reducing the SLS %. The dissolution rate of naproxen from microparticles was enhanced significantly with increasing the ratio of drug: polymer and stirring rate, and sustained by increasing SLS % in crystallization medium. The results of X-ray diffraction and differential scanning calorimeter analysis indicated that naproxen was highly dispersed in microparticles, so as amorphous state. Studies carried out to characterize the micromeritic properties of formulations, such as flowability and packability showed that microparticles were suitable for further pharmaceutical manipulation (e.g. capsule filling). Hence, the spherical crystallization technique can be successfully used for obtaining spherical microparticles, generating a heterogeneous matrix system and providing sustained drug release.
p53 status identifies two subgroups of triple-negative breast cancers with distinct biological features.

OBJECTIVE Despite the clinical similarities triple-negative and basal-like breast cancer are not synonymous. Indeed, not all basal-like cancers are negative for estrogen receptor, progesterone receptor and HER2 expression while triple-negative also encompasses other cancer types. P53 protein appears heterogeneously expressed in triple-negative breast cancers, suggesting that it may be associated with specific biological subgroups with a different outcome. METHODS We comparatively analyzed p53 expression in triple-negative tumors from two independent breast cancer case series (633 cases from the University of Ferrara and 1076 cases from the University of Nottingham). RESULTS In both case series, p53 protein expression was able to subdivide the triple-negative cases into two distinct subsets consistent with a different outcome. In fact, triple-negative patients with a p53 expressing tumor showed worse overall and event-free survival. CONCLUSIONS The immunohistochemical evaluation of p53 expression may help in taming the currently stormy relationship between pathological (triple-negative tumors) and biological (basal breast cancers) classifications and in selecting patient subgroups with different biological features providing a potentially powerful prognostic contribution in triple-negative breast cancers.
Near-Data Processing for Machine Learning

In computer architecture, near-data processing (NDP) refers to augmenting the memory or the storage with processing power so that it can process the data stored therein. By offloading the computational burden of CPU and saving the need for transferring raw data in its entirety, NDP exhibits a great potential for acceleration and power reduction. Despite this potential, specific research activities on NDP have witnessed only limited success until recently, often owing to performance mismatches between logic and memory process technologies that put a limit on the processing capability of memory. Recently, there have been two major changes in the game, igniting the resurgence of NDP with renewed interest. The first is the success of machine learning (ML), which often demands a great deal of computation for training, requiring frequent transfers of big data. The second is the advent of NAND flash-based solid-state drives (SSDs) containing multicore processors that can accommodate extra computation for data processing. Sparked by these application needs and technological support, we evaluate the potential of NDP for ML using a new SSD platform that allows us to simulate in-storage processing (ISP) of ML workloads. Our platform (named ISP-ML) is a full-fledged simulator of a realistic multi-channel SSD that can execute various ML algorithms using the data stored in the SSD. For thorough performance analysis and in-depth comparison with alternatives, we focus on a specific algorithm: stochastic gradient decent (SGD), which is the de facto standard for training differentiable learning machines including deep neural networks. We implement and compare three variants of SGD (synchronous, Downpour, and elastic averaging) using ISP-ML, exploiting the multiple NAND channels for parallelizing SGD. In addition, we compare the performance of ISP and that of conventional in-host processing, revealing the advantages of ISP. Based on the advantages and limitations identified through our experiments, we further discuss directions for future research on ISP for accelerating ML.
Adaptive laser-induced damage detection

Laser-Induced Damage Threshold (LIDT) measurements are typically performed in order to characterize the optical resistance of laser components. However the sensitivity of online damage detection techniques is often limiting factor for the accuracy and reproducibility of so called S-on-1 and 1-on-1 measurements. In fact the sensitivity of damage detection has the biggest impact to precision of these experiments. In this paper we describe the idea of making improvements on scattered light registration based damage detection. It was learned, that scattered light intensity is linearly proportional to incident energy of laser pulses while material is not damaged. However in case of induced damage the linear proportionality becomes nonlinear: this feature is used in order to detect optically induced surface and bulk changes in material. According to the base of those theoretical considerations the adaptive scattering detector was proposed and made up. It was put into practice by projecting it on discrete element schematics. The improved sensitivity of laserinduced damage detection was reached. This technique helps to avoid degradation of damaged site and pollution of surrounding area due to laser ablation during the S-on-1 tests since it allows blocking the repetitive irradiation immediately when damage appears. Numerous tests were made, that shows, that adaptive scattering detector can precisely detect damages in their initiation state, independently from material ability of light scattering. Calibration of this detector can be automated, therefore the influence of human factor is minimized. This fact opens up the possibility to run whole damage threshold measurement procedure automatically.
Using recurrent networks for non-temporal classification tasks

In recent years, deep neural networks have led to considerable advances in the performance of neural network architectures. However, deep architectures tend to have a large numbers of parameters, leading to long training times and the need for huge amounts of training data and regularization. In addition, biological neural networks make extensive use of recurrent and feedback connections, which are absent for most commonly used deep architectures. In this paper, we investigate the use of recurrent neural networks as an alternative to deep architectures. The approach replaces depth with recurrent computations through time. It can also be seen as a deep architecture with parameter tying. We show that for a comparable numbers of parameters or complexity, replacing depth with recurrency can result in improved performance.
Patentssearcher: a novel portal to search and explore patents

There is an abundance of systems today to search for relevant patents, ranging from free ones like Google Patents (google.com/patents) to subscription ones like Delphion (delphion.com). After studying many existing systems, we found that they all apply general-purpose Information Retrieval (IR) techniques to rank patents. We argue that the quality of search can be significantly improved by exploiting the domain semantics: e.g., patents are organized into classes and subclasses, and have links to external publication and to other patents. Also patents' text is organized into various sections and uses specific legal wording. We present the PatentsSearcher system, available at PatentsSearcher.com, whose key contribution is to leverage the domain semantics to improve the quality of discovery and ranking. PatentsSearcher also offers other novel functionalities to help users locate and navigate relevant and important patents or applications.
Knowledge-Based Sales Advisory: Experiences and Future Directions

This paper summarizes our experiences gained from several industrial advisory applications that were devel- oped with the knowledge-based ADVISOR SUITE framework over the last years and gives an outlook on future extensions of the presented system. In the 'experiences' section of the paper, we first address aspects related to the development of such appli- cations, such as knowledge engineering, software maintenance, or testing. In addition, we describe the main requirements for such an advisory application to be perceived as an intelligent, value-adding service by the end users and finally summarize the results of an industrial study on how advisory applications are able to influence the buying behavior of online shoppers. The second part of the paper discusses current and future extensions of our system. The main lines of research addressed in this section are 'Extended debugging support', 'Automated extraction of product data from web sources', 'Log mining and advanced data analysis', and 'Community-adapted advisory systems'.
A hybrid H component histogram threshold and sparse field level set algorithm for litchi image automatic segmentation

To recognize litchi image under natural condition correctly, and support three-dimension information to robot for complete automatic fruit picking. A method based on H component histogram threshold and level set is developed for automatic litchi image segmentation. In this paper, sixty-three images with natural scenes were analyzed using the color properties of target objects. Hue component of HSV color space is selected, and the rotation of Hue is used as the feature for image segmentation; And then, fuzzy rule are utilized to find the optimal threshold, and obtain the appropriate initial evolution curve contour automatically. Second, the sparse field level set method is adopted to extract the target region precisely. This algorithm, not only overcomes the influence of random noise, but also maintains the integrity of the segmentation area. The result shows that the proposed algorithm can keep the integrity of object area on litchi image segmentation, and the correct segmentation rate is up to 90.48%.
Minimum Semantic Error Cost Training of Deep Long Short-Term Memory Networks for Topic Spotting on Conversational Speech

The topic spotting performance on spontaneous conversational speech can be significantly improved by operating a support vector machine with a latent semantic rational kernel (LSRK) on the decoded word lattices (i.e., weighted finite-state transducers) of the speech [1]. In this work, we propose the minimum semantic error cost (MSEC) training of a deep bidirectional long short-term memory (BLSTM)-hidden Markov model acoustic model for generating lattices that are semantically accurate and are better suited for topic spotting with LSRK. With the MSEC training, the expected semantic error cost of all possible word sequences on the lattices is minimized given the reference. The word-word semantic error cost is first computed from either the latent semantic analysis or distributed vector-space word representations learned from the recurrent neural networks and is then accumulated to form the expected semantic error cost of the hypothesized word sequences. The proposed method achieves 3.5% 4.5% absolute topic classification accuracy improvement over the baseline BLSTM trained with cross-entropy on Switchboard-1 Release 2 dataset.
Polarizing metal stripe gratings for a micro-optical polarimeter

Several applications in science and industry require a precise measurement of the polarization of the incident light, e.g. the online characterization of textile-fiber birefringency in spinning machines. A promising concept to realize these polarimeters is based on structured polarizers that consist of three polarizing metal stripe gratings, whose spatial orientations are tilted against each other with an angle of 60deg. With such an approach the state of polarization can be determined with a very small device without any moving parts. Here we report on the fabrication of a micro optical polarimeter based on three polarizing metal stripe gratings with a grating period of 300nm and a size of 0.3 mm x 0.3 mm each. To achieve the necessary high aspect ratio we combined electron-beam direct writing with dry etching and metal coating under oblique incidence. The measured polarization contrast of the fabricated micropolarizers is more than 300:1 over a large spectral range (wavelength between 450nm and 1000nm), moreover they have a contrast of even more than 1000:1 if light with a wavelength between 500nm and 600nm is used. Thus, they are well suited for microsensors to detect birefringency.
Endoscopic root canal treatment.

OBJECTIVE To describe an innovative endoscopic technique for root canal treatment. MATERIALS AND METHODS Root canal treatment was performed on 12 patients (15 teeth), using a newly developed endoscope (Sialotechnology), which combines an endoscope, irrigation, and a surgical microinstrument channel. RESULTS Endoscopic root canal treatment of all 15 teeth was successful with complete resolution of all symptoms (6-month follow-up). CONCLUSION The novel endoscope used in this study accurately identified all microstructures and simplified root canal treatment. The endoscope may be considered for use not only for preoperative observation and diagnosis but also for active endodontic treatment.
Unified Error Performance Analysis of Space Shift Keying Modulation for MISO and MIMO Systems Under Generalized Fading

The performance of space shift keying modulation is strongly affected by the signal envelopes as well as by the channel induced phase variations. Motivated by this fact, this letter has a twofold motivation: 1) to propose a Hankel transform approach to transform the average error-rate integral for a Multiple-Input-Single-Output system with Maximum-Likelihood detection and perfect Channel State Information into the Hankel transform domain using Parseval theorem; 2) to derive simple analytical expressions for the Average Bit Error Probability of a generic Multiple-Input-Multiple-Output wireless system employing space shift keying modulation, by using an accurate exponential bound for the Gaussian Q-function along with the theory of Hankel transforms; Both methods, require only the knowledge of the Hankel transform of the probability density function of the signal envelope, which is readily available. The proposed analysis is tested and verified by numerically evaluated results accompanied with Monte-Carlo simulations.
A Slowly Accreting ~10 Myr-old Transitional Disk in Orion OB1a

Here we present the Spitzer IRS spectrum of CVSO 224, the sole transitional disk located within the ~10 Myr old 25 Orionis group in Orion OB1a. A model fit to the spectral energy distribution of this object indicates a ~7 AU inner disk hole that contains a small amount of optically thin dust. In previous studies, CVSO 224 had been classified as a weak-line T Tauri star based on its Hα equivalent width, but here we find an accretion rate of 7 × 10−11 M☉ yr−1 based on high-resolution Hectochelle data. CVSO 224’s low Ṁ is in line with photoevaporative clearing theories. However, the Spitzer IRS spectrum of CVSO 224 has a substantial mid-infrared excess beyond 20 μm which indicates that it is surrounded by a massive outer disk. Millimeter measurements are necessary to constrain the mass of the outer disk around CVSO 224 in order to confirm that photoevaporation is not the mechanism behind creating its inner disk hole.
PathExpress update: the enzyme neighbourhood method of associating gene-expression data with metabolic pathways

The post-genomic era presents us with the challenge of linking the vast amount of raw data obtained with transcriptomic and proteomic techniques to relevant biological pathways. We present an update of PathExpress, a web-based tool to interpret gene-expression data and explore the metabolic network without being restricted to predefined pathways. We define the Enzyme Neighbourhood (EN) as a sub-network of linked enzymes with a limited path length to identify the most relevant sub-networks affected in gene-expression experiments. PathExpress is freely available at: http://bioinfoserver.rsbs. anu.edu. au/utils/PathExpress/.
Forecast Confidence Measures for Deterministic Storm-scale Aviation Forecasts

Deterministic storm-scale weather forecasts, such as those generated by the FAA’s 0-8 hour CoSPA system (Wolfson et al. 2008; Pinto et al. 2009; Iskenderian et al., 2011) and shown in Figure 1, are highly valuable to aviation traffic managers. They provide forecasted characteristics of storm structure, strength, orientation, and coverage that are helpful for strategic planning purposes in the National Airspace System (NAS). However, these deterministic weather forecasts contain inherent uncertainty that varies with the general weather scenario at the forecast issue time, the predicted storm type, and the forecast time horizon. This uncertainty can cause changes in the forecast from update to update, thereby eroding user confidence and ultimately reducing the forecast’s effectiveness in the decision-making process. Deterministic forecasts generally lack objective measures of this uncertainty, making it difficult for users of the forecast to know a priori how much weight to give the forecast in their decision making process.
Pseudo-Spin-Valve Trilayer Using Amorphous CoNbZr Layer： Giant Magnetoresistance, Domain Structures and Potentials for Spin-Electronic Devices

We propose a pseudo-spin-valve (PSV) trilayer using amorphous CoNbZr Mloy for soft magnetic layers. The giant magnetoresistance (GMR), domain structures and their variation upon thermal annealing are investigated. The GMR effect is not only stable up to 300℃ but also enhanced due to the improvement of the interfaces between Cu and magnetic layers. With high annealing temperature, the magnetoresistance (MR) ratio decreases rapidly as a result of serious layer interdiffusion. Dense stripe domains, which disappear after annealing at 300℃ for 1h, are observed in the sandwiched films. It is found that after patterning to elliptic stripe with aspect ratio of 6:1, the trilayers have a single domain and their MR ratio increases. The dynamic MR behaviour under an ac magnetic field indicates that the patterned stripes have good linear MR responses. Therefore, it is believed that the CoNbZr/Cu/Co PSV trilayers have strong potentials for spin-electronic devices including magnetic random access memory.
Upregulated glial cell line‐derived neurotrophic factor through cyclooxygenase‐2 activation in the muscle is required for mechanical hyperalgesia after exercise in rats

• Unaccustomed strenuous exercise that includes lengthening contraction often causes delayed onset muscle soreness (DOMS), characterised as muscular mechanical hyperalgesia. • It has been reported that bradykinin triggers upregulation of nerve growth factor in exercised muscle, sensitizing nociceptors and resulting in DOMS, but additional mechanism(s) may be involved. • We showed that pretreatment with cyclooxygenase (COX)‐2 inhibitors completely suppressed the development of DOMS, but treatment 2 days after lengthening contraction failed to reverse existing mechanical hyperalgesia. • We demonstrated that COX‐2 induced upregulation of glial cell line‐derived neurotrophic factor (GDNF) and that intramuscularly injected anti‐GDNF antibody reduced muscle mechanical hyperalgesia after exercise. • These results suggest that upregulation of GDNF through COX‐2 activation is essential to mechanical hyperalgesia after exercise, and is another pathway alongside the bradykinin–nerve growth factor pathway that is involved in DOMS development.