Miracle-dz/UniFAR-FTUs huggingface dataset:
Implications of prescription drug monitoring and medical cannabis legislation on opioid overdose mortality.

OBJECTIVES To determine whether specific state legislation has an effect on opioid overdose mortality rates compared to states without those types of legislation. DESIGN Ecological study estimating opioid-related mortality in states with and without a prescription drug monitoring program (PDMP) and/or medical cannabis legislation. SETTING AND PARTICIPANTS Opioid-related mortality rates for 50 states and Washington DC from 2011 to 2014 were obtained from CDC WONDER. PDMP data were obtained from the National Alliance for Model State Drug Laws, and data on medical cannabis legislation from the National Organization for the Reform of Marijuana Laws. MAIN OUTCOMES AND MEASURES The relationship between PDMPs with mandatory access provisions, medical cannabis legislation, and opioid-related mortality rates. METHODS Multivariate repeated measures analysis performed with software and services. RESULTS Medical cannabis laws were associated with an increase of 21.7 percent in mean age-adjusted opioid-related mortality (p < 0.0001). PDMPs were associated with an increase of 11.4 percent in mean age-adjusted opioid-related mortality (p = 0.005). For every additional year since enactment, mean age-adjusted opioid-related mortality rate increased by 1.7 percent in states with medical cannabis (p = 0.049) and 5.8 percent for states with a PDMP (p = 0.005). Interaction between both types of legislation produced a borderline significant decrease of 10.1 percent (p = 0.055). For every year states had both types of legislation, interaction resulted in a 0.6 percent decrease in rate (p = 0.013). CONCLUSION When combined with the availability of medical cannabis as an alternative analgesic therapy, PDMPs may be more effective at decreasing opioid-related mortality.
A goodness-of-fit test for the random-effects distribution in mixed models

In this paper, we develop a simple diagnostic test for the random-effects distribution in mixed models. The test is based on the gradient function, a graphical tool proposed by Verbeke and Molenberghs to check the impact of assumptions about the random-effects distribution in mixed models on inferences. Inference is conducted through the bootstrap. The proposed test is easy to implement and applicable in a general class of mixed models. The operating characteristics of the test are evaluated in a simulation study, and the method is further illustrated using two real data analyses.
Deep Neural Networks with Confidence Sampling for Electrical Anomaly Detection

The increase in electrical metering has created tremendous quantities of data and, as a result, possibilities for deep insights into energy usage, better energy management, and new ways of energy conservation. As buildings are responsible for a significant portion of overall energy consumption, conservation efforts targeting buildings can provide tremendous effect on energy savings. Building energy monitoring enables identification of anomalous or unexpected behaviors which, when corrected, can lead to energy savings. Although the available data is large, the limited availability of labels makes anomaly detection difficult. This research proposes a deep semi-supervised convolutional neural network with confidence sampling for electrical anomaly detection. To achieve semi-supervised learning, two sub-networks are used: the first performs reconstruction and uses unlabelled data, while the second performs classification with labelled data. The two sub-networks overlap: the encoder parameters are shared between the two. To quantify anomaly detection confidence, a valuable metric in anomaly detection, the network uses a dropout sampling method. The proposed approach has been evaluated with real-world electrical data from systems such as HVAC, lighting, and heat pumps. The results demonstrated the accuracy of the proposed anomaly detection solution.
The inositol pyrophosphate pathway in health and diseases

Inositol pyrophosphates (IPPs) are present in organisms ranging from plants, slime moulds and fungi to mammals. Distinct classes of kinases generate different forms of energetic diphosphate‐containing IPPs from inositol phosphates (IPs). Conversely, polyphosphate phosphohydrolase enzymes dephosphorylate IPPs to regenerate the respective IPs. IPPs and/or their metabolizing enzymes regulate various cell biological processes by modulating many proteins via diverse mechanisms. In the last decade, extensive research has been conducted in mammalian systems, particularly in knockout mouse models of relevant enzymes. Results obtained from these studies suggest impacts of the IPP pathway on organ development, especially of brain and testis. Conversely, deletion of specific enzymes in the pathway protects mice from various diseases such as diet‐induced obesity (DIO), type‐2 diabetes (T2D), fatty liver, bacterial infection, thromboembolism, cancer metastasis and aging. Furthermore, pharmacological inhibition of the same class of enzymes in mice validates the therapeutic importance of this pathway in cardio‐metabolic diseases. This review critically analyses these findings and summarizes the significance of the IPP pathway in mammalian health and diseases. It also evaluates benefits and risks of targeting this pathway in disease therapies. Finally, future directions of mammalian IPP research are discussed.
Prediction of drug abuse recurrence: a Swedish National Study

Abstract Background Relapse from drug abuse (DA) is common, but has rarely been studied in general population samples using a wide range of objective predictors. Method Using nationwide registries, we ascertained 44 523 subjects first registered for DA between the ages of 15 and 40 in 1998 to 2004 and followed for 8 years. We predicted relapse in subjects defined as a second DA registration. We also predicted DA relapse in relative pairs concordant for DA but discordant for relapse. Results In multivariate regression analyses, the strongest predictors for relapse were prior criminal behavior, male sex, being on social welfare, low school achievement, prior alcoholism, and a high-risk father. A risk index trained from these analyses on random split-halves demonstrated a risk ratio of 1.11 [95% confidence intervals (CIs) 1.10–1.11] per decile and an ROC value of 0.70 (0.69–0.71). Co-relative analyses indicated that a modest proportion of this association was causal, with the remainder arising from familial confounders. A developmental structural equation model revealed a complex interviewing of risk pathways to DA with three key mediational hubs: low educational attainment, early age at first registration, and being on social welfare. Conclusions In a general population sample, using objective registry information, DA relapse is substantially predictable. However, the identified risk factors may not be valid targets for interventions because many index familial risk and may not impact causally on probability of relapse. Risk for DA relapse may reflect an inter-weaving, over developmental time, of genetic–temperamental vulnerability, indices of externalizing behaviors and social factors reflecting deprivation.
AutoTiering: Automatic data placement manager in multi-tier all-flash datacenter

In the year of 2017, the capital expenditure of Flash-based Solid State Drivers (SSDs) keeps declining and the storage capacity of SSDs keeps increasing. As a result, the “selling point” of traditional spinning Hard Disk Drives (HDDs) as a backend storage — low cost and large capacity — is no longer unique, and eventually they will be replaced by low-end SSDs which have large capacity but perform orders of magnitude better than HDDs. Thus, it is widely believed that all-flash multi-tier storage systems will be adopted in the enterprise datacenters in the near future. However, existing caching or tiering solutions for SSD-HDD hybrid storage systems are not suitable for all-flash storage systems. This is because that all-flash storage systems do not have a large speed difference (e.g., 10x) among each tier. Instead, different specialties (such as high performance, high capacity, etc.) of each tier should be taken into consideration. Motivated by this, we develop an automatic data placement manager called “AutoTiering” to handle virtual machine disk files (VMDK) allocation and migration in an all-flash multitier datacenter to best utilize the storage resource, optimize the performance, and reduce the migration overhead. AutoTiering is based on an optimization framework, whose core technique is to predict VM's performance change on different tiers with different specialties without conducting real migration. As far as we know, AutoTiering is the first optimization solution designed for all-flash multi-tier datacenters. We implement AutoTiering on VMware ESXi [1], and experimental results show that it can significantly improve the I/O performance compared to existing solutions.
Emotional distress and burden among caregivers of children with oncological/hematological disorders.

INTRODUCTION Caring for children with oncological and hematological disorders may lead to caregiver emotional distress and caregiver burden; however, little work has examined the relationship between children's symptoms and caregiver's distress and burden. METHOD This study used self-report survey data from caregivers (N = 96) and a cross-sectional design to examine correlates of caregiver emotional distress and burden. Data collected included caregiver and child demographic data, child symptoms (i.e., sleep problems, pain, and emotional/behavioral problems), caregiver emotional distress, and caregiver burden. RESULTS Multiple linear regression found that parent reported financial difficulty (β = 0.29, t = 3.13, p = .003), greater child sleep problems (β = 0.29 t = 2.81, p = .007), greater child pain (β = 0.33 t = 3.48, p = .001), and greater child emotional/behavioral problems (β = 0.27, t = 2.71, p = .009) were all related to higher levels of caregiver emotional distress. Only financial difficulties (β = -0.35, t = -2.03, p = .04) and child pain (β = -0.30, t = -2.33, p = .02) were related to caregiver burden. DISCUSSION Child symptoms may play an important role in the development of caregiver distress and caregiver burden; future research should utilize longitudinal designs to examine temporal and casual relationships. (PsycINFO Database Record
cBAD: ICDAR2017 Competition on Baseline Detection

The cBAD competition aims at benchmarking state-of-the-art baseline detection algorithms. It is in line with previous competitions such as the ICDAR 2013 Handwriting Segmentation Contest. A new, challenging, dataset was created to test the behavior of state-of-the-art systems on real world data. Since traditional evaluation schemes are not applicable to the size and modality of this dataset, we present a new one that introduces baselines to measure performance. We received submissions from five different teams.
A discrete-time equivalent system approach to the periodic response of nonlinear autonomous circuits

The problem of computing the steady state response of nonlinear autonomous circuits is solved making use of a discrete-time equivalent system approach. With the application of an s-plane to z-plane mapping, the circuit equations are discretized and written in vector form. Using this technique, it is not necessary to repeatedly compute transforms between the time and the frequency domain. An efficient scheme to build the Jacobian matrix with exact partial derivatives with respect to the oscillation period and with respect to the samples of the unknown variables is described. Application examples on two widely studied circuits are provided to validate the proposed technique. <<ETX>>
Requirements Engineering Challenges in Large-Scale Agile System Development

Motivated by their success in software development, companies implement agile methods and their practices increasingly for software-intense, large products, such as cars, telecommunication infrastructure, and embedded systems. Such systems are usually subject to safety and regulative concerns as well as different development cycles of hardware and software. Consequently, requirements engineering involves upfront and detailed analysis, which can be at odds with agile (software) development. In this paper, we present results from a multiple case study with two car manufacturers, a telecommunications company, and a technology company that are on the journey to introduce organization wide continuous integration and continuous delivery to customers. Based on 20 qualitative interviews, 5 focus groups, and 2 cross-company workshops, we discuss possible scopes of agile methods within system development, the consequences this has on the role of requirements, and the challenges that arise from the interplay of requirements engineering and agile methods in large-scale system development. These relate in particular to communicating and managing knowledge about a) customer value and b) the system under development. We conclude that better alignment of a holistic requirements model with agile development practices promises rich gains in development speed, flexibility, and overall quality of software and systems.
Discovery of novel, orally bioavailable, antileishmanial compounds using phenotypic screening

Leishmaniasis is a parasitic infection that afflicts approximately 12 million people worldwide. There are several limitations to the approved drug therapies for leishmaniasis, including moderate to severe toxicity, growing drug resistance, and the need for extended dosing. Moreover, miltefosine is currently the only orally available drug therapy for this infection. We addressed the pressing need for new therapies by pursuing a two-step phenotypic screen to discover novel, potent, and orally bioavailable antileishmanials. First, we conducted a high-throughput screen (HTS) of roughly 600,000 small molecules for growth inhibition against the promastigote form of the parasite life cycle using the nucleic acid binding dye SYBR Green I. This screen identified approximately 2,700 compounds that inhibited growth by over 65% at a single point concentration of 10 μM. We next used this 2700 compound focused library to identify compounds that were highly potent against the disease-causing intra-macrophage amastigote form and exhibited limited toxicity toward the host macrophages. This two-step screening strategy uncovered nine unique chemical scaffolds within our collection, including two previously described antileishmanials. We further profiled two of the novel compounds for in vitro absorption, distribution, metabolism, excretion, and in vivo pharmacokinetics. Both compounds proved orally bioavailable, affording plasma exposures above the half-maximal effective concentration (EC50) concentration for at least 12 hours. Both compounds were efficacious when administered orally in a murine model of cutaneous leishmaniasis. One of the two compounds exerted potent activity against trypanosomes, which are kinetoplastid parasites related to Leishmania species. Therefore, this compound could help control multiple parasitic diseases. The promising pharmacokinetic profile and significant in vivo efficacy observed from our HTS hits highlight the utility of our two-step phenotypic screening strategy and strongly suggest that medicinal chemistry optimization of these newly identified scaffolds will lead to promising candidates for an orally available anti-parasitic drug.
The Kent Face Matching Test

This study presents the Kent Face Matching Test (KFMT), which comprises 200 same-identity and 20 different-identity pairs of unfamiliar faces. Each face pair consists of a photograph from a student ID card and a high-quality portrait that was taken at least three months later. The test is designed to complement existing resources for face-matching research, by providing a more ecologically valid stimulus set that captures the natural variability that can arise in a person's appearance over time. Two experiments are presented to demonstrate that the KFMT provides a challenging measure of face matching but correlates with established tests. Experiment 1 compares a short version of this test with the optimized Glasgow Face Matching Test (GFMT). In Experiment 2, a longer version of the KFMT, with infrequent identity mismatches, is correlated with performance on the Cambridge Face Memory Test (CFMT) and the Cambridge Face Perception Test (CFPT). The KFMT is freely available for use in face-matching research.
Food Cravings and the Effects of Left Prefrontal Repetitive Transcranial Magnetic Stimulation Using an Improved Sham Condition

This study examined whether a single session of repetitive transcranial magnetic stimulation (rTMS) of the left prefrontal cortex (PFC) would inhibit food cravings in healthy women who endorsed frequent food cravings. Ten participants viewed images of food and completed ratings for food cravings before and after receiving either real or sham rTMS over the left PFC (10 Hz, 100% resting motor threshold, 10 s-on, 20 s-off for 15 min; 3000 pulses). Sham-TMS was matched with real TMS with respect to perceived painfulness of the stimulation. Each participant received both real and sham rTMS in random order and were blind to the condition in a within-subject cross-over design. With an improved sham control condition, prefrontal rTMS inhibited food cravings no better than sham rTMS. The mild pain from the real and sham rTMS may distract or inhibit food craving, and the decreased craving may not be caused by the effect of rTMS itself. Further studies are needed to elucidate whether rTMS has any true effects on food craving and whether painful stimuli inhibit food or other cravings. A sham condition which matches the painfulness is important to understand the true effects of TMS on behaviors and diseases.
MICROPOWDER INJECTION MOLDING

Micro powder injection molding(μ-PM)is an economical fabrication technology for mass production of precise small parts at low cost.This technology is a promising route to produce the parts for micro electro-mechanical systems(MEMS). The production of carbonyl iron micro gear by μ-PIM was studied.The suitable parameters were obtained.The results show that carbonyl iron micro gears could be successfully produced by μ-PIM.The parts could be successfully debound and sintered.
A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution

Twitter is one of the most popular social networks. Previous research found that users employ Twitter to communicate about software applications via short messages, commonly referred to as tweets, and that these tweets can be useful for requirements engineering and software evolution. However, due to their large number---in the range of thousands per day for popular applications---a manual analysis is unfeasible.In this work we present ALERTme, an approach to automatically classify, group and rank tweets about software applications. We apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to specific attributes, such as content category, sentiment and number of retweets. We ran our approach on 68,108 collected tweets from three software applications and compared its results against software practitioners' judgement. Our results show that ALERTme is an effective approach for filtering, summarizing and ranking tweets about software applications. ALERTme enables the exploitation of Twitter as a feedback channel for information relevant to software evolution, including end-user requirements.
PLA enhanced via plasma technology: a review

In the last 50 years, it has numerously been evidenced that plasma treatments can effectively change the surface properties of conventional polymers (PP, PET, PE, PA…). Only recently the influence of plasmas on biodegradable polymers, such as polylactic acid (PLA) has been reported. PLA can have important biomedical applications in tissue engineering as three-dimensional porous structures (scaffolds). But their low surface energy leads to poor cell attachment and proliferation and this limits the success of these biodegradable scaffolds. The response it elicits from the surrounding biological environment is crucial and this response is mainly governed by the scaffold surface properties. Therefore, PLA surface properties need to be modified to introduce additional functional groups, which can be recognized as adhesion sites for surrounding cells. Different methods have been developed to obtain the wanted surface properties, however, in the past decade, the use of non-equilibrium plasmas for selective surface modification has been a rapidly growing research field. This chapter therefore presents a critical overview on recent advances in plasma-assisted surface modification of PLA.
Limit analysis-based approach to determine the material plastic properties with conical indentation

Representative strain plays an important role in indentation analysis; by using the representative strain and stress, the normalized indentation load becomes a function of one variable, which facilitates the reverse analysis of obtaining the material plastic properties. The accuracy of such function is critical to indentation analysis. Traditionally, polynomial functions are used to fit the function, which does not incorporate correct elastic/plastic limits and has no physical basis. In this paper, we have proposed a new limit analysis-based functional formulation based on the theoretical solutions of conical/wedge indentation on elastic and rigid plastic solids. It is found that both limits agree well with numerical results, and the new approach involves no—or at most one—fitting parameter, which can be obtained with much less effort compare with the traditional polynomial approach. Reverse analyses on five different materials have shown that the new and simple limit analysis-based formulation works better than the traditional polynomial fit. The new technique may be used to quickly and effectively measure material plastic properties for any conical indenter if the elastic modulus is known a priori.
[POSTER] Composite Realism: Effects of Object Knowledge and Mismatched Feature Type on Observer Gaze and Subjective Quality

We report on the results of the first visual search and rating study (N60) evaluating human gaze when assessing the realism of image composites. The effects of object identity knowledge and mismatched feature type on observers' gaze and subjective realism scores are studied. Gaze metrics used include: fixation count, fixation duration, time and duration of first fixation on target object, as well as area of interest similarity and inter-observer consistency. Monte-Carlo-based techniques are used for analysis of the data obtained. Results indicate that while knowledge of object identity impacts gaze allocation and response times, it leaves subjective realism ratings unchanged. We show that the type of mismatched feature (correlated colour temperature vs exposure) has a significant impact on fixation counts and durations. This study provides a first step to utilising objective gaze metrics to better understand subjective assessment processes and leads towards the development of gaze-inspired compositing methods.