Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibitory interneurons with differential plasticities at their connections tune excitatory/inhibitory balance in the spinal nociceptive system.

ABSTRACT Networks of the dorsal-horn of the spinal-cord process nociceptive information from the periphery. In these networks, the excitation/inhibition balance is critical to shape this nociceptive information and to gate it to the brain where it is interpreted as pain. Our aim was to define whether short-term plasticity of inhibitory connections could tune this inhibition/excitation balance by differentially controlling excitatory and inhibitory microcircuits. To this end, we used spinal-cord slices from adult mice expressing enhanced green fluorescent protein (eGFP) under the GAD65 promoter and recorded from both eGFP+ (putative inhibitory) and eGFP- (putative excitatory) neurons of lamina II while stimulating single presynaptic GABAergic interneurons at various frequencies. Our results indicate that GABAergic neurons of lamina II simultaneously contact eGFP- and eGFP+ neurons, but these connections display very different frequency-dependent short-term plasticities. Connections onto eGFP- interneurons displayed limited frequency-dependent changes, and strong time-dependent summation of inhibitory synaptic currents that was however subjected to a tonic activity-dependent inhibition involving A1 adenosine receptors. In contrast, GABAergic connections onto eGFP+ interneurons expressed pronounced frequency-dependent depression, thus favoring disinhibition at these synapses by a mechanism involving the activation of GABAB autoreceptors at low frequency. Interestingly, the balance favors inhibition at frequencies associated with intense pain whether it favors excitation at frequencies associated with low pain. Therefore, these target- and frequency-specific plasticities allow to tune the balance between inhibition and disinhibition while processing frequency-coded information from primary afferents. These short-term plasticities and their modulation by A1 and GABAB receptors might represent an interesting target in pain-alleviating strategies.
Modelling of metallic inclusions on non-homogeneous dielectric substrates

ABSTRACT In this article, modelling is done for Square Split Ring Resonators (SSRR) as a quasi-static equivalent circuit in a non-homogeneous environment. The authors propose a new relationship for the relative permittivity for a metallic inclusion etched on top of a dual substrate in a non-homogeneous medium. The stacking sequence of the substrates plays a vital role in the response of the SSRR. This paper highlights the change brought about by the dielectric materials’ stacking order on resonant frequency and quality factor. The analytical and numerical study of SSRR etched on the dual substrates shows a good correlation of values. A comparative study indicates a better fit of the proposed formula for the effective permittivity over the conventional averaging method. This article highlights the advantages of using a dual substrate material for fabricating metamaterial structures for different applications. The advantage of the proposed permittivity equation is its applicability to all kinds of geometries. Three different substrate materials were used for the performance study of the SSRR (FR4, Rogers RT/Duroid 5880 and Alumina).
COVID-19 Concerns in US: Topic Detection in Twitter

COVID-19 pandemic is affecting the lives of the citizens worldwide. Epidemiologists, policy makers and clinicians need to understand public concerns and sentiment to make informed decisions and adopt preventive and corrective measures to avoid critical situations. In the last few years, social media become a tool for spreading the news, discussing ideas and comments on world events. In this context, social media plays a key role since represents one of the main source to extract insight into public opinion and sentiment. In particular, Twitter has been already recognized as an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. However, it is a challenging issue identifying interesting and useful content from large and noisy text-streams. The study proposed in the paper aims to extract insight from Twitter by detecting the most discussed topics regarding COVID-19. The proposed approach combines peak detection and clustering techniques. Tweets features are first modeled as time series. After that, peaks are detected from the time series, and peaks of textual features are clustered based on the co-occurrence in the tweets. Results, performed over real-world datasets of tweets related to COVID-19 in US, show that the proposed approach is able to accurately detect several relevant topics of interest, spanning from health status and symptoms, to government policy, economic crisis, COVID-19-related updates, prevention, vaccines and treatments.
Incorporating Glucose Variability into Glucose Forecasting Accuracy Assessment Using the New Glucose Variability Impact Index and the Prediction Consistency Index: An LSTM Case Example

Background: In this work, we developed glucose forecasting algorithms trained and evaluated on a large dataset of free-living people with type 1 diabetes (T1D) using closed-loop (CL) and sensor-augmented pump (SAP) therapies; and we demonstrate how glucose variability impacts accuracy. We introduce the glucose variability impact index (GVII) and the glucose prediction consistency index (GPCI) to assess the accuracy of prediction algorithms. Methods: A long-short-term-memory (LSTM) neural network was designed to predict glucose up to 60 minutes in the future using continuous glucose measurements and insulin data collected from 175 people with T1D (41,318 days) and evaluated on 75 people (11,333 days) from the Tidepool Big Data Donation Dataset. LSTM was compared with two naïve forecasting algorithms as well as Ridge linear regression and a random forest using root-mean-square error (RMSE). Parkes error grid quantified clinical accuracy. Regression analysis was used to derive the GVII and GPCI. Results: The LSTM had highest accuracy and best GVII and GPCI. RMSE for CL was 19.8 ± 3.2 and 33.2 ± 5.4 mg/dL for 30- and 60-minute prediction horizons, respectively. RMSE for SAP was 19.6 ± 3.8 and 33.1 ± 7.3 mg/dL for 30- and 60-minute prediction horizons, respectively; 99.6% and 97.6% of predictions were within zones A+B of the Parkes error grid at 30- and 60-minute prediction horizons, respectively. Glucose variability was strongly correlated with RMSE (R≥0.64, P < 0.001); GVII and GPCI demonstrated a means to compare algorithms across datasets with different glucose variability. Conclusions: The LSTM model was accurate on a large real-world free-living dataset. Glucose variability should be considered when assessing prediction accuracy using indices such as GVII and GPCI.
Quantification and modeling of inorganic carbon processing in scleractinian coral

Scleractinian corals build extensive reef frameworks that house extraordinary biodiversity and provide numerous ecosystem services to local human populations. Corals depend upon endosymbiotic dinoflagellates to provide them energy via photosynthesis, which supports both tissue and skeletal growth. Unfortunately, corals, and consequently reefs, are under threat from local and global stressors, including climate change. Increased anthropogenic CO2 emissions causing CO2 to invade the surface ocean, lowering pH levels and CO3 2concentrations; this is ocean acidification (OA). Changes to the dissolved inorganic carbon (DIC) system have the potential to alter both photosynthesis (CO2 dependent) and calcification (CO3 2dependent). Scleractinian corals are faced with the possibility of impacts to both of these vital processes, yet studies have shown no clear picture of what is likely to occur as pH continues to decline over the course of this century. Much of how corals process DIC is still unquantified, making it difficult to understand the scale of potential changes and what the drivers might be. In order to address these knowledge gaps, membrane inlet mass spectrometry methods, which have been used to describe carbon concentrating mechanisms and photosynthetic DIC kinetics in phytoplankton, were adapted for use with corals. Three different species of Caribbean coral were found to have high levels of carbonic anhydrase (CA) activity on the coral surface, within the coral tissues and in their symbionts. This enzyme is a key component of carbon concentrating mechanisms, and the surface CA provided CO2 to support half of net photosynthesis. Taxanomic variation was found at the coral level with regard to DIC saturation for photosynthesis, but all symbionts had a high affinity for carbon, suggestive of host controlled DIC delivery. This information more completely quantified photosynthesis with regard to DIC, and allowed a model of DIC flow through a coral to be developed. Running the model under projected OA conditions showed no change to photosynthesis, but slight reductions in both calcification and calcifying fluid pH. Investigating the drivers of these changes indicated that increased fluxes of CO2 into the calcifying fluid led to the declines, but biological modification of the calcifying fluid mitigated the effect. INDEX WORDS: Coral, inorganic carbon, photosynthesis, carbonic anhydrase, Membrane Inlet Mass Spectrometry, modeling, ocean acidification QUANTIFICATION AND MODELING OF INORGANIC CARBON PROCESSING IN
Getting the Story Right: Reflecting on an Indigenous Rubric to Guide the Interpretation of Mortality Data

The Family Violence Death Review Committee (FVDRC) is one of five Mortality Review Committees (MRCs) that sit within the Health Quality & Safety Commission, Aotearoa, New Zealand. A key goal of the work of these committees is the reduction of the unequal burden of disparities shouldered by Māori (Indigenous peoples). Guidance to the committees on interpreting and reporting Māori mortality comes from Te Pou (the pillar/post), a Māori responsiveness rubric published in 2019 by Ngā Pou Arawhenua (the caucus of Māori MRC members). This guidance was called upon by the FVDRC in the preparation of its sixth report, “Men who use violence,” published in 2020. In this article, the FVDRC reflects on how it strove to uphold responsibilities toward Te Titiriti o Waitangi 1 in its sixth report to get the story right (Tika—to be correct or true), be culturally and socially responsive (Manaakitanga—hospitability, kindness, support), advance equity, self-determination and social justice (Mana—prestige, authority, spiritual power), and establish relationship for positive change (Mahi Tahi—working together). Opportunities for improved responsiveness in FVDRC reporting are identified, alongside suggestions for extending the guidance in Te Pou. Reflective practice on responsiveness to Māori/Indigenous peoples is recommended more generally for MRCs.
Predictive constitutive modelling of arteries by deep learning

The constitutive modelling of soft biological tissues has rapidly gained attention over the last 20 years. Current constitutive models can describe the mechanical properties of arterial tissue. Predicting these properties from microstructural information, however, remains an elusive goal. To address this challenge, we are introducing a novel hybrid modelling framework that combines advanced theoretical concepts with deep learning. It uses data from mechanical tests, histological analysis and images from second-harmonic generation. In this first proof of concept study, our hybrid modelling framework is trained with data from 27 tissue samples only. Even such a small amount of data is sufficient to be able to predict the stress–stretch curves of tissue samples with a median coefficient of determination of R2 = 0.97 from microstructural information, as long as one limits the scope to tissue samples whose mechanical properties remain in the range commonly encountered. This finding suggests that deep learning could have a transformative impact on the way we model the constitutive properties of soft biological tissues.
Limited plasticity in thermally tolerant ectotherm populations: evidence for a trade-off

Many species face extinction risks owing to climate change, and there is an urgent need to identify which species' populations will be most vulnerable. Plasticity in heat tolerance, which includes acclimation or hardening, occurs when prior exposure to a warmer temperature changes an organism's upper thermal limit. The capacity for thermal acclimation could provide protection against warming, but prior work has found few generalizable patterns to explain variation in this trait. Here, we report the results of, to our knowledge, the first meta-analysis to examine within-species variation in thermal plasticity, using results from 20 studies (19 species) that quantified thermal acclimation capacities across 78 populations. We used meta-regression to evaluate two leading hypotheses. The climate variability hypothesis predicts that populations from more thermally variable habitats will have greater plasticity, while the trade-off hypothesis predicts that populations with the lowest heat tolerance will have the greatest plasticity. Our analysis indicates strong support for the trade-off hypothesis because populations with greater thermal tolerance had reduced plasticity. These results advance our understanding of variation in populations' susceptibility to climate change and imply that populations with the highest thermal tolerance may have limited phenotypic plasticity to adjust to ongoing climate warming.
A randomized controlled trial comparing the efficacy, tolerability, and cost of oral iron preparations in iron‐deficiency anemia in pregnancy

To evaluate the efficacy, tolerability, and cost of four commonly prescribed oral iron preparations: ferrous sulfate (FS), ferrous fumarate (FF), ferrous ascorbate (FA), and carbonyl iron (CI) in the treatment of iron‐deficiency anemia (IDA) in pregnant women.
Six-month versus nine-month therapy for intestinal tuberculosis: a protocol for a randomized controlled study.

BACKGROUND The optimal duration of treatment for intestinal tuberculosis (TB), which remains a common disease worldwide, has not yet been established. The proposed randomized controlled study will aim to compare the efficacy of short-term six-month with nine-month anti-TB therapy for treating intestinal TB. METHODS This multicenter, open-label, double-blinded, randomized controlled trial conducted in the Affiliated Hangzhou Chest Hospital of Zhejiang University will include a total of 80 patients. Patients who meet the inclusion criteria will be randomly assigned to either the six-month (n=40) or nine-month (n=40) treatment group. The primary outcome will be complete response, which is defined as endoscopy displaying active lesion healing at the end of treatment. Participants will be scheduled for follow-up visits once a month in the first three months, then once every three months until the end of the treatment. The last follow-up will be one year after the treatment. Recurrence will be assessed one year after the end of treatment, which is defined as endoscopy displaying recurrent lesions after complete response. DISCUSSION In addition to the reports of tuberculous lymphadenitis and spinal TB, there are few appropriate randomized trials for the treatment of extrapulmonary TB with appropriate clinical endpoints. We believe that the proposed randomized controlled trial will provide further data on the efficacy of short-term six-month anti-TB therapy in intestinal TB patients. TRIAL REGISTRATION This trial will be registered on ClinicalTrial.gov.
Germinación de semillas de Quercus humboldtii Bonpl. (Fagaceae): especie vulnerable del bosque Altoandino Germination of Quercus humboldtii Bonpl. (Fagaceae) seeds: vulnerable species of the high Andean forest

The Colombian oak (Quercus humdoldtii) is an endemic dominant and vulnerable species of the high Andean forest. The wood and fruits have traditionally been used on a small scale by rural populations. A greater synchrony of the germination process might facilitate the production of seedlings for conservation plans. In a forest nursery, the emergence was evaluated according to immersion of seeds in four solutions of gibberellic acid (GA3) during 24 h (0, 150, 300 and 600 mg L), at 4 °C. A completely randomized design was adopted, with four replications of 25 seeds. The mean emergence time was 32 days, without differences between doses. There was no effect of GA3 for the emergence of seedlings up to a dose of 300 mg L (mean= 92 %); however, there was a negative effect with 600 mg L (20 %). Fresh seeds of the Andean oak have a high moisture content (38 %), perhaps indicative of a recalcitrant behavior. This study confirms, as in most white oaks in the tropic, the absence of dormancy and the high viability of their seeds. The use of GA3 is not a useful pre-germinative treatment for accelerating and synchronizing seedling emergency in this species.
Interpretation of Electrocardiogram Heartbeat by CNN and GRU

The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the medical field. In this paper, a classification method is proposed to classify ECG signals. Firstly, wavelet transform is used to denoise the original data, and data enhancement technology is used to overcome the problem of an unbalanced dataset. Secondly, an integrated convolutional neural network (CNN) and gated recurrent unit (GRU) classifier is proposed. The proposed network consists of a convolution layer, followed by 6 local feature extraction modules (LFEM), a GRU, and a Dense layer and a Softmax layer. Finally, the processed data were input into the CNN-GRU network into five categories: nonectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. The MIT-BIH arrhythmia database was used to evaluate the approach, and the average sensitivity, accuracy, and F1-score of the network for 5 types of ECG were 99.33%, 99.61%, and 99.42%. The evaluation criteria of the proposed method are superior to other state-of-the-art methods, and this model can be applied to wearable devices to achieve high-precision monitoring of ECG.
Introduction to the special issue on COMPLEX NETWORKS 2019

This special issue of Network Science contains a collection of extended papers from the 8th International Conference on Complex Networks & their Applications (COMPLEX NETWORKS 2019) . This major international event in network science brings together every year researchers from around the globe. The great diversity of the participants’ scientiﬁc backgrounds ranges from Finance and Economics, Medicine and Neuroscience, Biology and Earth Sciences, Sociology and Political Science to Mathematics and Computer Science, Physics, and many others, making it a special opportunity to review the current state of the ﬁeld and formulate new directions. This edition of the conference took place at the Calouste Gulbenkian Foundation in Lisbon (Portugal) from December 10 to December 12, 2019. It attracted 470 submissions with authors from 58 countries all over the world. After thorough review, 161 papers were selected to be included in the proceedings Cheriﬁ et al. (2020a,b). The conference program also included keynote presentations from Lada Adamic (Facebook, Inc., USA), Reka Albert (Pennsylvania State University, USA), Ulrik Brandes (ETH Zurich, Switzerland), Stefan Thurner (Medical University of Vienna, Austria), Jari Saramki (Aalto University, Finland), and Michalis Vazirgiannis (LIX, cole Polytechnique, France). Papers invited for this special issue have been selected from the accepted contributions based on relevance to the journal and excellent reviews of the conference version of the papers. The authors were asked to submit an extended version of their conference submission for journal publication in accordance with the customary practice of adding 30% new material. These submissions went through the standard double-blind review process dictated by the journal guidelines. The seven papers accepted to this special issue provide a remarkable sample illustrating the diversity of issues studied in network science research.
Biobased PLA/PA11 Blends and the Incorporation of Cellulose Nanocrystal

Polymer blending and plasticization are known as two major strategies in poly(lactic acid) (PLA) toughening. A combination of these two methods, however, is lacking in the literature. The blending of PLA with other bio-plastics is a strategy that allows for tuning the mechanical properties while still maintains a bio-material. PLA plasticization on the other hand has shown the greatest promise in enhancing PLA chain mobility. The main drawback of PLA plasticization, however, is the drop in the modulus and tensile strength compared to neat PLA. Incorporation of solid nano-inclusions into polymer-polymer blend is a well-known pathway for engineering materials with a good toughness/stiffness balance. In this dissertation, the morphology and mechanical properties of PLA/PA11 blends and the localization of crystalline nanocellulose in the blend are studied. In the first part of this work, a systematic study of the interfacial tension and morphology development of PLA/PA11 is reported. It is shown that the interfacial tension in PLA/PA11 blends under dynamic conditions measured by the Neuman Triangle technique (3.2 mN/m) is significantly lower than that measured under static conditions by the breaking thread (5.8 mN/m) and imbedded fiber retraction (5.4 mN/m) methods. Under dynamic mixing conditions, morphological and thermal analysis unambiguously indicate that the PLA/PA11 system is behaving as a highly interacting interfacially compatibilized system. It is shown that the limited PLA chain mobility is the main reason for the observed discrepancy between the static and dynamic behavior of PLA/PA11 blends. Enhancing the PLA chain mobility through the addition of 5% PEO plasticizer results in a significantly lower interfacial tension and even further suppressed coalescence under dynamic mixing at 30 and 40 vol% minor phase. These results are important since they clearly indicate that even if polymers may have the potential for interfacial interactions, sufficient chain mobility is necessary in order for those interactions to come into play. In the second part of this project, the profound impact of controlled PLA chain mobility on the mechanical properties of PLA/bio-PA11 blends, is studied. It is shown that enhanced PLA ductility due to the addition of an optimal amount of polyethylene oxide (PEO) significantly affects the mechanical properties of PLA/PA11 blends. In the (PLA-15PEO)/PA11 50/50 vol% blend a remarkable improvement in mechanical properties is observed with specimens