Miracle-dz/UniFAR-FTUs huggingface dataset:
The occurrence of ‘Sleeping Beauty’ publications in medical research: Their scientific impact and technological relevance

We investigate publications in medical research that have gone unnoticed for a number of years after being published and then suddenly become cited to a significant degree. Such publications are called Sleeping Beauties (SBs). This study focuses on SBs that are cited in patents. We find that the increasing trend of the relative number of SBs comes to an end around 1998. However, still a constant fraction of publications becomes an SB. Many SBs become highly cited publications, they even belong to the top-10 to 20% most cited publications in their field. We measured the scaling of the number of SBs in relation to the sleeping period length, during-sleep citation-intensity, and with awakening citation-intensity. We determined the Grand Sleeping Beauty Equation for these medical SBs which shows that the probability of awakening after a period of deep sleep is becoming rapidly smaller for longer sleeping periods and that the probability for higher awakening intensities decreases extremely rapidly. The exponents of the scaling functions show a time-dependent behavior which suggests a decreasing occurrence of SBs with longer sleeping periods. We demonstrate that the fraction of SBs cited by patents before scientific awakening exponentially increases. This finding shows that the technological time lag is becoming shorter than the sleeping time. Inventor-author self-citations may result in shorter technological time lags, but this effect is small. Finally, we discuss characteristics of an SBs that became one of the highest cited medical papers ever.
Predicting diabetes second-line therapy initiation in the Australian population via time span-guided neural attention network

Introduction The first line of treatment for people with diabetes is metformin. However, over the course of the disease metformin may fail to achieve appropriate glycemic control, and a second-line therapy becomes necessary. In this paper we introduce Tangle, a timespan-guided neural attention model that can accurately and timely predict the upcoming need for a second-line diabetes therapy from administrative data in the Australian adult population. The method could be used to design automatic therapy review recommendations for patients and their providers without the need to collect clinical measures. Data We analyzed seven years of deidentified records (2008-2014) of the 10% publicly available linked sample of Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) electronic databases of Australia. Methods By design, Tangle can inherit the representational power of pre-trained word embedding, such as GloVe, to encode sequences of claims with the related MBS codes. The proposed attention mechanism can also natively exploit the information hidden in the timespan between two successive claims (measured in number of days). We compared the proposed method against state-of-the-art sequence classification methods. Results Tangle outperforms state-of-the-art recurrent neural networks, including attention-based models. In particular, when the proposed timespan-guided attention strategy is coupled with pre-trained embedding methods, the model performance reaches an Area Under the ROC Curve of 90%, an improvement of almost 10 percentage points over an attentionless recurrent architecture. Implementation Tangle is implemented in Python using Keras and it is hosted on GitHub at https://github.com/samuelefiorini/tangle.
Fixed-Time Observer Based Prescribed-Time Containment Control of Unmanned Underwater Vehicles with Faults and Uncertainties

The problem of prescribed-time containment control of unmanned underwater vehicles (UUVs) with faults and uncertainties is considered. Different from both regular finite-time control and fixed-time control, the proposed prescribed-time control strategy is built upon a novel coordinate transformation function and the block decomposition technique, resulting in the followers being able to move into the convex hull spanned by the leaders in prespecifiable convergence time. Moreover, intermediate variables and the control input terms are also shown to remain uniformly bounded at the prescribed-time. To reduce the magnitude of the bounds, a novel fixed-time observer for the fault is proposed. Two numerical examples are provided to verify the effectiveness of the proposed prescribed-time control strategy.
Transient hyperthermia during oxygenated rewarming of isolated rat livers

Pretransplant machine perfusion of donor grafts has gained clinical appreciation to improve graft function and survival after transplantation. This study was aimed as pilot investigation to evaluate the additive potential of a transient ex vivo heat shock treatment of the isolated organ during machine perfusion to further protect the graft from subsequent reperfusion injury. Rat livers were retrieved after 20 min of cardiac arrest and preserved for 18 h by cold storage in HTK solution. Prior to reperfusion, livers were subjected to 2 h of reconditioning machine perfusion with gradual increase in perfusion temperature up to 35 °C. In half of the livers (n = 7), a brief hyperthermic impulse (10 min perfusion at 42 °C) was implemented in the machine perfusion period. Functional recovery of the grafts was observed upon normothermic reperfusion in vitro. Induction of heat shock protein 70 was followed on the mRNA and protein level. Chaperone induction by transient hyperthermia was associated with a significant improvement of bile production upon reperfusion and significantly reduced enzyme loss of mitochondrial GLDH. Heat shock treatment further affected pro‐inflammatory upregulation in the graft in significantly reducing gene expression as wells as protein release of TNF‐alpha. It is concluded, that graft conditioning by controlled hyperthermia ex vivo may represent a feasible and useful tool to improve liver recovery after preservation.
Effects of Reablement on the Independence of Community-Dwelling Older Adults with Mild Disability: A Randomized Controlled Trial

We aimed to assess the efficacy of a reablement program in improving the independence from long-term care services of older adults with mild disability. This parallel, two-arm, randomized controlled, superiority trial was conducted in Neyagawa, a local government area in Osaka, Japan. Eligible participants were community-dwelling individuals aged ≥65 years certified as support-required level. They were assigned in a 1:1 ratio to receive either a community-based, multicomponent, multidisciplinary, individualized goal-directed, and time-limited intervention (the CoMMIT program) plus standard care or standard care alone. The primary outcome was independence, that is, the nonuse of long-term care services during the three-month follow-up period. The study was terminated early due to slow enrollment. A total of 375 participants were enrolled and randomized to either the intervention (n = 190) or control (n = 185) group. The proportions of independence were 11.1% and 3.8% in the intervention and control groups, respectively (absolute difference: 7.3; 95% confidence interval: 2.0–12.5). There was no difference in the risk of serious adverse events between the groups. The CoMMIT program plus standard care was found superior to standard care alone in enhancing the independence from long-term care services of older adults with mild disability.
Chemical Modification of Microcin J25 Reveals New Insights on the Stereospecific Requirements for Antimicrobial Activity

In this study, microcin J25, a potent antimicrobial lasso peptide that acts on Gram-negative bacteria, was subjected to a harsh treatment with a base in order to interrogate its stability and mechanism of action and explore its structure-activity relationship. Despite the high stability reported for this lasso peptide, the chemical treatment led to the detection of a new product. Structural studies revealed that this product retained the lasso topology, but showed no antimicrobial activity due to the epimerization of a key residue for the activity. Further microbiological assays also demonstrated that it showed a high synergistic effect with colistin.
Uncommon EGFR Mutations in Non-Small Cell Lung Cancer and Their Impact on the Treatment.

BACKGROUND Epidermal growth factor receptor (EGFR) mutations play an important role in the pathogenesis of non-small cell lung cancer. Because these alterations are so-called targetable mutations, their identification is important in daily clinical practice. The diagnostic standard of EGFR mutations is currently based on polymerase chain reaction methods, particularly the quantitative real-time polymerase chain reaction. In recent years, new generation sequencing has become increasingly important. In patients with EGFR mutations, a significant improvement in therapeutic outcomes was achieved with the administration of targeted therapy using tyrosine kinase inhibitors. EGFR is composed of four domains: extracellular with a ligand binding site, a transmembrane domain, a cytoplasmic tyrosine kinase catalytic domain, and a C-terminal domain. The key structures of the tyrosine kinase domain responsible for signal activation and transmission are encoded within exons 18-21 on chromosome 7. EGFR mutations are highly heterogeneous. About 90% of EGFR mutations are deletions of exon 19 and point mutation L858R in exon 21. These are referred to as classic mutations. Approximately 10% of the total number of EGFR mutations is attributable to less frequent alterations in the EGFR gene. Due to the low incidence of non-small cell lung cancer with less frequent EGFR mutations, information on their predictive significance is still incomplete. Most of the data for the treatment of cases with uncommon mutations were gathered from retrospective analyses and evaluations of small cohorts. PURPOSE The aim of this review is to summarise the current options for diagnosing and treating non-small cell lung cancer patients with uncommon EGFR mutations. This work was supported by the MEYS - NPS I - LO1413 and MH CR - DRO (MMCI, 00209805). The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study. The Editorial Board declares that the manuscript met the ICMJE recommendation for biomedical papers. Submitted: 2. 6. 2019 Accepted: 26. 8. 2019.
Cannabis Use Is Associated With Increased Risk for Diabetic Ketoacidosis in Adults With Type 1 Diabetes: Findings From the T1D Exchange Clinic Registry

OBJECTIVE We examined the frequency of diabetic ketoacidosis (DKA) in cannabis users compared with nonusers in the T1D Exchange clinic registry (T1DX). RESEARCH DESIGN AND METHODS The association between cannabis use by total substance score for cannabis (TSC) and DKA in the past 12 months was examined using a logistic regression model adjusted for potential confounders among adults in the T1DX. RESULTS Of 932 adults with type 1 diabetes, 61 had a TSC >4, which classified them as moderate cannabis users. Adjusting for sex, age at study visit, and HbA1c, cannabis use was associated with a twofold increase in risk for DKA among adults with type 1 diabetes (odds ratio 2.5 [95% CI 1.0–5.9]). CONCLUSIONS Cannabis use was associated with an increased risk for DKA among adults in the T1DX. Providers should inform their patients of the potential risk of DKA with cannabis use.
Strategic Exercise Prescription for Baseball: Bridging the Gap Between Injury Prevention and Power Production

RESISTANCE TRAINING AND ANAEROBIC CONDITIONING STILL REMAIN THE FOCAL POINT IN BASEBALL ATHLETIC DEVELOPMENT. WHEN DESIGNING ANY BASEBALL ATHLETIC DEVELOPMENT PROGRAM, SPECIAL ATTENTION NEEDS TO BE PLACED ON ACQUIRING SPECIFIC ADAPTATIONS THROUGH DIVERSE MUSCLE STIMULATION AT MANY ANGLES AND SPEEDS, PERFORMED EFFICIENTLY AND APPLIED REPETITIVELY. CREATING LONG-LASTING DURABILITY FOR A BASEBALL PLAYER REQUIRES ADVANCED, STRATEGIC, AND INDIVIDUALIZED EXERCISE PRESCRIPTION CENTERING ON MOVEMENTS THAT INCORPORATE THE HIPS, SPINE, AND SCAPULA.
Goat Dairy Product Assortment in Different Sales Channels in Northwestern Italy

Simple Summary Goat dairy product manufacturing in Piedmont has increased in recent years, along with consumption. This research highlights the characteristics of goat cheese, milk, and yogurt available in three categories of sales channels, large-scale retail, retail sales, and farmers’ markets, in a metropolitan area in Northwestern Italy. We reveal clear differences between the offered products’ characteristics in the various distribution channels, both in terms of the reference numbers and assortment, and in terms of price, quality, and typicality. In particular, the analysis of product origins shows a correlation between the share of local product presence and the distribution channel type. Given the importance of product origin for consumers, as well as consumer acceptance of goat cheeses, the increase of local goat dairy products in farmers’ markets or retail outlets could represent an opportunity for growth and differentiation of this sector in the regional market, allowing operators in the sector to benefit from direct sales advantages. Abstract An analysis of goat dairy-based product assortment was carried out in the metropolitan area of Turin (Northwestern Italy), considering three different sales channels: large-scale retail chains, retail stores, and direct sales in farmers’ markets (FMs). The survey results show a widespread presence of goat products in the Turin market. In each type of selected distribution channel, characterized by its own peculiarities, products differed both in terms of reference numbers and assortment; they were better in large-scale retail distribution, both in terms of quality and typicality, whereas specialized retail and direct sales were better equipped. Furthermore, given the importance of the sector at the regional level, we also focused, through the analysis of product origin, on the fact that local provenience increases from large-scale distribution to fully regional farmers’ markets. The mean price was different, being lower in direct sales at FMs, and medium to high and high at retail sales, as they are considered high quality niche products. The offer is correlated and in agreement with consumer targets (modern, ethical, and traditional), finding the "ideal" product in the different types offered, however, even if the results underline the potential of this sector, the fragmentation of the goat sector in Piedmont still represents a limit to expansion, and to the positioning of products in the market.
Generalized Mixed Modeling in Massive Electronic Health Record Databases: What is a Healthy Serum Potassium?

Background: Converting electronic health record (EHR) entries to useful clinical inferences requires one to address the poor scalability of existing implementations of Generalized Linear Mixed Models (GLMM) for repeated measures. The major computational bottleneck concerns the numerical evaluation of multivariable integrals, which even for the simplest EHR analyses may involve millions of dimensions (one for each patient). The hierarchical likelihood (h-lik) approach to GLMMs is a methodologically rigorous framework for the estimation of GLMMs that is based on the Laplace Approximation (LA), which replaces integration with numerical optimization, and thus scales very well with dimensionality. Methods: We present a high-performance implementation of the h-lik for GLMMs in the R package TMB. Using this approach, we examined the relation of repeated serum potassium measurements and survival in the Cerner Real World Data (CRWD) EHR database. Analyzing this data requires the evaluation of an integral in over 3 million dimensions, putting this problem beyond the reach of conventional approaches. We also assessed the scalability and accuracy of LA in smaller samples of 1 and 10% size of the full dataset that were analyzed via the a) original, interconnected Generalized Linear Models (iGLM), approach to h-lik, b) Adaptive Gaussian Hermite (AGH) and c) the gold standard of Markov Chain Monte Carlo (MCMC) for multivariate integration. Results: Random effects estimates generated by the LA were within 10% of the values obtained by the iGLMs, AGH and MCMC techniques. The H-lik approach was 4-30 times faster than AGH and nearly 800 times faster than MCMC. The major clinical inferences in this problem are the establishment of the non-linear relationship between the potassium level and the risk of mortality, as well as estimates of the individual and health care facility sources of variations for mortality risk in CRWD. Conclusions: We found that the combination of the LA and AD offers a computationally efficient, numerically accurate approach for the analysis of extremely large, real world repeated measures data via the h-lik approach to GLMMs. The clinical inference from our analysis may guide choices of threatment thresholds for treating potassium disorders in the clinic.
Mapper Based Classifier

Topological data analysis aims to extract topological quantities from data, which tend to focus on the broader global structure of the data rather than local information. The Mapper method, specifically, generalizes clustering methods to identify significant global mathematical structures, which are out of reach of many other approaches. We propose a classifier based on applying the Mapper algorithm to data projected onto a latent space. We obtain the latent space by using PCA or autoencoders. Notably, a classifier based on the Mapper method is immune to any gradient based attack, and improves robustness over traditional CNNs. We report theoretical justification and some numerical experiments that confirm our claims.
Single and Cross-Dimensional Feature Detection and Description: An Evaluation

Three-dimensional local feature detection and description techniques are widely used for object registration and recognition applications. Although several evaluations of 3D local feature detection and description methods have already been published, these are constrained in a single dimensional scheme, i.e. either 3D or 2D methods that are applied onto multiple projections of the 3D data. However, cross-dimensional (mixed 2D and 3D) feature detection and description has yet to be investigated. Here, we evaluated the performance of both single and cross-dimensional feature detection and description methods on several 3D datasets and demonstrated the superiority of cross-dimensional over single-dimensional schemes.
Cooperative Beamforming in Cognitive Radio Relay Networks Using Amplify-and-Forward Relaying Technique

In this work, we study the problem of relay beamforming for an underlay cognitive radio relay network using amplify-and-forward (AF) relaying technique. We consider a cognitive radio network consisting of single primary and multiple secondary transmitters/receivers. In addition, there are several relay nodes that help both primary and secondary networks. We propose a new beamforming method for relays' transmission, in which the beamforming weights at the relays are the solution of an optimization problem. The objective of the optimization problem is maximizing the worst case signal to interference plus noise ratio (SINR) at the secondary receivers (SU-RX) and the constraints are the interference power on the primary receiver (PU-RX) and the total transmitted power of the relay nodes. We show that the beamforming problem can be reformulated as a second order cone programming (SOCP) problem and solved by the bisection search method. Our simulation results show the performance improvement as a function of the size and the power of the relay network.
Effective neutrino masses in KATRIN and future tritium beta-decay experiments

Past and current direct neutrino mass experiments set limits on the so-called effective neutrino mass, which is an incoherent sum of neutrino masses and lepton mixing matrix elements. The electron energy spectrum which neglects the relativistic and nuclear recoil effects is often assumed. Alternative definitions of effective masses exist, and an exact relativistic spectrum is calculable. We quantitatively compare the validity of those different approximations as function of energy resolution and exposure in view of tritium beta decays in the KATRIN, Project 8 and PTOLEMY experiments. Furthermore, adopting the Bayesian approach, we present the posterior distributions of the effective neutrino mass by including current experimental information from neutrino oscillations, beta decay, neutrinoless double-beta decay and cosmological observations. Both linear and logarithmic priors for the smallest neutrino mass are assumed.
Follow Alice into the Rabbit Hole: Giving Dialogue Agents Understanding of Human Level Attributes

For conversational AI and virtual assistants to communicate with humans in a realistic way, they must exhibit human characteristics such as expression of emotion and personality. Current attempts toward constructing human-like dialogue agents have presented signiﬁcant difﬁculties. We propose Human Level Attributes (HLAs) based on tropes as the basis of a method for learning dialogue agents that can imitate the per-sonalities of ﬁctional characters. Tropes are characteristics of ﬁctional personalities that are observed recurrently and determined by viewers’ impressions. By combining detailed HLA data with dialogue data for speciﬁc characters, we present a dataset that models character proﬁles and gives dialogue agents the ability to learn characters’ language styles through their HLAs. We then introduce a three-component system, ALOHA (which stands for Artiﬁcial Learning On Human At-tributes), that combines character space mapping, character community detection, and language style retrieval to build a character (or personality) speciﬁc language model. Our pre-liminary experiments demonstrate that ALOHA, combined with our proposed dataset, can outperform baseline models at identifying correct dialogue responses of any chosen target character, and is stable regardless of the character’s identity, genre of the show, and context of the dialogue.
Habitual physical activity, peripheral neuropathy, foot deformities and lower limb function: characterising prevalence and interlinks in patients with type 2 diabetes mellitus

Background: Patients with type 2 diabetes mellitus (T2DM) may have diverse foot problems, but how these problems are linked with physical activity is not clear. This study investigated the prevalence of foot problems among patients with T2DM and investigated how the problems were related to physical activity. Methods: Habitual physical activity, peripheral neuropathy, lower limb functions and foot deformities of 246 T2DM patients were respectively assessed with the Baecke Physical Activity Questionnaire, Michigan Neuropathy Screening Instrument, Lower Limb Function Scale, and a self-designed foot deformity audit form. Results: Habitual physical activity index (3.2 ± 0.83) was highest in work-related activities; 69 (26.1 %) patients presented with peripheral neuropathy and 52 (19. 7%) had the lowest limb function. Pes planus was the most prevalent foot deformity (20.1%). Significant differences existed in physical activity indices across deformity groups (p < 0.05) and total activity index was related to neuropathic and lower limb function scores (p < 0.05). Conclusion: A higher work-related but reduced participation in sports and leisure time physical activity among the patients was observed. Habitual physical activity was lowest in patients with a forefoot deformity, higher neuropathic scores and lesser lower limb function scores. Patients with T2DM in these categories may be a target for special physical activity intervention programmes.