Miracle-dz/UniFAR-FTUs huggingface dataset:
Economic Complexity and the Emergence of New Ideas

Recent research has shown a strong link between the economic complexity of a country and important economic indicators such as GDP per capita and lower inequality. However, the mechanisms underpinning this relationship remain poorly understood. While it is often argued that complex ecosystems provide a fertile ground for novel combinations of ideas leading to the emergence of impactful new technologies and industries, this process is hard to measure with aggregate data structured around industrial codes offering a lagging view of the economy. We seek to overcome this challenge by combining official data about industrial activity in UK Local Authority Districts (LADs) with a novel dataset containing the content and meta-data from almost a million UK business websites. We measure economic complexity using ECI and a Fitness based measure, which respectively capture a location’s specialization in unique, knowledge intensive sectors, and a weighted measure of economic diversity. We use a complex networks approach to topic modelling to detect ‘topics’ in the websites of businesses and analyse their relation to economic complexity and industrial sectors before going on to measure emergence by looking for novel words in the websites of businesses in different locations and sectors. We show that LADs with high ECI scores have a stronger share of companies active in emerging technologies, even after we control for their industrial composition. Further, and contrary to our initial expectations, emergent companies are more industrially diversified in locations with high ECI scores than locations with high Fitness scores. One potential reason is that the innovative sectors that high ECI locations specialize on are lead adopters of emerging technologies that are subsequently diffused into other parts of the local economy.
pyparty: Blob Detection, Drawing and Manipulation in Python

Introduction Open-source image processing tools are rapidly spreading across programming languages and scientific domains. The widely-used ImageJ[1] platform, along with its distributions such as Fiji[2], offer an excellent Java-based suite. In the last few years, image processing and computer vision in Python has rapidly matured1 with the emergence of libraries like scikit-image[3], Mahotas[4], and SimpleCV[5]. scikit-image and Mahotas utilize 2d NumPy[6] arrays for their image data structures, thus providing a familiar API to users of NumPy and the other core SciPy libraries, especially Matplotlib[7], IPython[8]. The motivation for pyparty arose from difficulties encountered when quantifying nanoparticle distributions on silica substrates. We had found that after acquiring an image, preprocessing, segmentation and labelling techniques were easily applied; however, separating and measuring the various particle species in post-processing was difficult. This is primarily because the objects of interest were stored as boolean or integer-labeled arrays, which become unwieldy under heavy manipulation. pyparty emerged as a means to abstract the concept of particles (i.e. image blobs) into custom data structures for intuitive manipulation and characterization whilst preserving the image API. In addition to integrating new particle constructs with the existing array and image processing functions, pyparty extends scikit-image’s rasterization toolbox with new particle types, patterning, and an interface to Matplotlib patch objects for vectorized particle renderings. Thus, pyparty leverages the conventional imaging pipeline at both ends; it provides a tool set for artificial image composition, and a framework for particle post-processing.
Reliability of the Modified TICI Score among Endovascular Neurosurgeons

BACKGROUND AND PURPOSE: The modified TICI score is the benchmark for quantifying reperfusion after mechanical thrombectomy. There has been limited investigation into the reliability of this score. We aim to identify intra-rater and inter-rater reliability of the mTICI score among endovascular neurosurgeons. MATERIALS AND METHODS: Four independent endovascular neurosurgeons (raters) reviewed angiograms of 67 patients at 2 time points. κ statistics assessed inter- and intrarater reliability and compared raters’-versus-proceduralists’ scores. Reliability was also assessed for occlusion location and by dichotomizing modified TICI scores (0–2a versus 2b–3). RESULTS: Interrater reliability was moderate-to-substantial, weighted κ = 0.417–0.703, overall κ = 0.374 (P < .001). The dichotomized modified TICI score had moderate-to-substantial interrater agreement, κ statistics = 0.468–0.715, overall κ = 0.582 (P < .001). Intrarater reliability was moderate-to-almost perfect, weighted κ = 0.594–0.81. The dichotomized modified TICI score had substantial-to-almost perfect reliability, κ = 0.632–0.82. Proceduralists had fair-to-moderate agreement with raters, weighted κ = 0.348–0.574, and the dichotomized modified TICI score had fair-to-moderate agreement, κ = 0.365–0.544. When proceduralists and raters disagreed, proceduralists’ scores were higher in 79.6% of cases. M1 followed by ICA occlusions had the highest agreement. CONCLUSIONS: The modified TICI score is a practical metric for assessing reperfusion after mechanical thrombectomy, though not without limitations. Agreement improved when scores were dichotomized around the clinically relevant threshold of successful revascularization. Interrater reliability improved with time, suggesting that formal training of interventionalists may improve reporting reliability. Agreement of the modified TICI scale is best with M1 and ICA occlusion and becomes less reliable with more distal or posterior circulation occlusions. These findings should be considered when developing research trials.
New agents in acute myeloid leukemia (AML)

Despite expanding knowledge in the molecular landscape of acute myeloid leukemia (AML) and an increasing understanding of leukemogenic pathways, little has changed in the treatment of AML in the last 40 years. Since introduction in the 1970s, combination chemotherapy consisting of anthracycline and cytarabine has been the mainstay of treatment, with major therapeutic advances based on improving supportive care rather than the introduction of novel therapeutics. Over the last decades, there have been extensive efforts to identify specific target mutations or pathways with the aim of improving clinical outcomes. Finally, after a prolonged wait, we are witnessing the next wave of AML treatment, characterized by a more “precise” and “personalized” understanding of the unique molecular or genetic mapping of individual patients. This new trend has since been further facilitated, with four new FDA approvals granted in 2017 in AML therapeutics. Currently, a total of eight targeted agents have been approved since 2017 (as of Jan. 2020). In this review, we will briefly discuss these newer agents in the context of their indication and the basis of their approval.
Cerebral Microbleeds Are Associated with Loss of White Matter Integrity

BACKGROUND AND PURPOSE: Previous studies have shown that diffusion tensor imaging suggests a diffuse loss of white matter integrity in people with white matter hyperintensities or lacunes. The purpose of this study was to investigate whether the presence of cerebral microbleeds and their distribution are related to the integrity of white matter microstructures. MATERIALS AND METHODS: The study comprised 982 participants who underwent brain MR imaging to determine microbleed status. The cross-sectional relation between microbleeds and the microstructural integrity of the white matter was assessed by 2 statistical methods: a multilinear regression model based on the average DTI parameters of normal-appearing white matter and Tract-Based Spatial Statistics analysis, a tract-based voxelwise analysis. Fiber tractography was used to spatially describe the microstructural abnormalities along WM tracts containing a cerebral microbleed. RESULTS: The presence of cerebral microbleeds was associated with lower mean fractional anisotropy and higher mean diffusivity, axial diffusivity, and radial diffusivity, and the association remained when cardiovascular risk factors and cerebral small-vessel disease markers were further adjusted. Tract-Based Spatial Statistics analysis indicated strictly lobar cerebral microbleeds associated with lower fractional anisotropy, higher mean diffusivity, and higher radial diffusivity in the internal capsule and corpus callosum after adjusting other cerebral small-vessel disease markers, while only a few voxels remained associated with deep cerebral microbleeds. Diffusion abnormalities gradients along WM tracts containing a cerebral microbleed were not found in fiber tractography analysis. CONCLUSIONS: Cerebral microbleeds are associated with widely distributed changes in white matter, despite their focal appearance on SWI.
Sexual differences in the foraging behaviour of Magellanic Penguins related to stage of breeding

Abstract Understanding the foraging behaviour of seabirds and its plasticity is vital to establish their role in marine food webs and their use as indicators of change in the availability of prey. The foraging behaviour of penguins is known to differ with locality, sex, stage of breeding and between years. We studied the diving behaviour of breeding Magellanic Penguins (Spheniscus magellanicus), using time-depth recorders, during incubation and brooding in the 2003–04 and 2004–05 breeding seasons at Isla Martillo, Tierra del Fuego, Argentina. Foraging trips during the incubation period were longer than those during the brooding period for both sexes in both years of the study. Sex-related differences in foraging behaviour were observed during the incubation stage. During the incubation stage females performed longer foraging trips than males, foraging effort was lower, and did not dive as deep as males in both years. Foraging success was lower for females than males during incubation only in 2003. Our results suggest that sexual differences, expressed as differences in the foraging parameters of males and females, only develop when Fuegian Sprat (Sprattus fuegensis), the main prey in this locality, is not abundant close to the colony. Females may be extending the volume of water they can exploit by extending the duration of trips (horizontal distance), whereas males do so by diving deeper (vertical distance). Our results show the fundamental differences in foraging strategies between the sexes in Magellanic Penguin are a consequence of environmental conditions not morphological differences between sexes.
Absence of a Context-General Behavioural Syndrome in a Solitary Predator

The correlation of seemingly unrelated behaviours into behavioural syndromes has been established in a wide range of species and taxa. However, most studies report on short-term behavioural correlations without insight into individual consistency or temporal stability of the behavioural syndrome. Here, we examine the individual repeatability of single behaviours, and the presence and temporal stability of a context-general behavioural syndrome in a solitary piscivorous predator, the pike (Esox lucius). Behavioural measurements on the same individuals were quantified independently through time and across three contexts: activity in the presence of a competitor, exploration of a novel environment and boldness under predation risk. There was no indication of a temporally stable behavioural syndrome, consisting of boldness, activity and exploration, nor were individuals consistent in the separate behaviours, contradicting the general assertion of its taxonomic prevalence. Furthermore, the study did not provide support for size or growth-dependent behaviour in this size-dimorphic species in conditions of limited food availability. The study highlights the importance of independent multiple observations of individual behaviour across time or contexts when measuring behavioural repeatability and covariation.
Bryobilimbia, a new generic name for Lecidea hypnorum and closely related species

Abstract The new generic name Bryobilimbia is proposed for Lecidea hypnorum and the closely related taxa Lecidea ahlesii var. ahlesii, L. ahlesii var. nemoralis, L. diapensiae, L. sanguineoatra and Mycobilimbia australis. A phylogenetic analysis based on five genes shows that Lecidea berengeriana does not belong to this group but is more closely related to Romjularia. Both groups of species have been included in Mycobilimbia by some authors but are instead shown to be most closely related to a group of genera (including Clauzadea, Farnoldia, Lecidoma and Romjularia) that do not belong to Lecideaceae s. str. A neotype is selected for Lichen sanguineoater Wulfen and the new combinations Bryobilimbia ahlesii var. ahlesii, B. ahlesii var. nemoralis, B. australis, B. diapensiae, B. hypnorum and B. sanguineoatra are proposed. A lectotype is also selected for L. templetonii Taylor.
Securing Deep Learning Based Edge Finger Vein Biometrics With Binary Decision Diagram

With built-in artificial intelligence (AI), edge devices, e.g., smart cameras, can perform tasks like detecting and tracking individuals, which is referred to as edge biometrics. As a driving force for AI, machine/deep learning plays a critical role in edge biometrics. Machine/deep learning based edge biometric systems outperform their nonmachine learning counterpart. However, research shows that artificial neural networks, e.g., convolutional neural networks, are invertible such that adversaries can obtain a certain amount of information about the original inputs/templates. This information leakage is not tolerable for biometric systems because biometric data in the original (raw) templates cannot be reset or replaced. Once compromised, they are lost forever. Therefore, how to prevent original biometric templates from being attacked through inverting deep neural networks is a pressing, but unsolved issue, for deep learning based biometric recognition. To address the issue, in this paper, we develop a novel biometric template protection algorithm using the binary decision diagram (BDD) for deep learning based finger-vein biometric systems. The proposed algorithm is capable of creating a new noninvertible version of the original finger-vein template, which is stacked with an artificial neural network–the multilayer extreme learning machine (ML-ELM) to generate a privacy-preserving finger-vein recognition system, named BDD-ML-ELM. The proposed BDD-ML-ELM ensures the safety of the original finger-vein template even if its transformed version is compromised. The transformed template, if compromised, can be revoked and replaced with another new version by simply changing the user-specific keys. Therefore, the BDD-ML-ELM has a clear advantage over the existing machine/deep learning based biometric systems, whose raw biometric templates are vulnerable when the artificial neural network suffers an inversion attack.
Expression of foreign aquaporin genes in lily pollen protoplasts

Aquaporins facilitate water flux across biomembranes in cells and are involved in various physiological phenomena in several plant tissues. Generally, the water-flux activity of exogenously expressed aquaporins is measured in Xenopus laevis oocytes or yeast. However, heterogeneous systems are not likely to be optimal for plant aquaporin analysis. Thus, we created a new experimental system for functional analysis of plant aquaporins using lily (Lilium longiflorum) pollen protoplasts. Large protoplasts with uniform diameters of approximately 95 mm were isolated from lily pollen grains. No plasma membrane intrinsic protein (PIP) aquaporin was detected in lily pollen. For ectopic expression of PIPs in the lily pollen protoplasts, we constructed plasmids in which Arabidopsis AtPIP1;1 or AtPIP2;1 was under the control of a strong pollen-specific promoter (maize Zm13). The PCR-amplified DNA fragments were transformed into the pollen protoplasts by electroporation. Between 45 and 60% of protoplasts were successfully transformed. The protoplasts expressing AtPIP2;1 significantly increased in volume in a hypotonic solution (350 mM mannitol), compared with the vector control. In contrast, the changes in volume of the protoplasts expressing AtPIP1;1 were similar to that of the vector control. This result suggests that PIP2 induces higher water-flux activity in plant cells, whereas PIP1 does not. Thus, we propose the lily pollen protoplast as a simple and useful experimental system to analyze the function of plant aquaporins.
Enzymatic Protein Biopolymers as a Tool to Synthetize Eukaryotic Messenger Ribonucleic Acid (mRNA) with Uses in Vaccination, Immunotherapy and Nanotechnology

Multi-subunit enzymes are protein biopolymers that are involved in many cellular processes. The enzyme that carries out the process of transcription of mRNAs is RNA polymerase II (RNAPII), which is a multi-subunit enzyme in eukaryotes. This protein biopolymer starts the transcription from specific sites and is positioned by transcription factors, which form a preinitiation complex (PIC) on gene promoters. To recognize and position the RNAPII and the transcription factors on the gene promoters are needed specific DNA sequences in the gene promoters, which are named promoter elements. Those gene promoter elements can vary and therefore several kinds of promoters exist, however, it appears that all promoters can use a similar pathway for PIC formation. Those pathways are discussed in this review. The in vitro transcribed mRNA can be used as vaccines to fight infectious diseases, e.g., in immunotherapy against cancer and in nanotechnology to deliver mRNA for a missing protein into the cell. We have outlined a procedure to produce an mRNA vaccine against the SARS-CoV-2 virus, which is the causing agent of the big pandemic, COVID-19, affecting human beings all over the world. The potential advantages of using eukaryotic RNAPII to synthetize large transcripts are outlined and discussed. In addition, we suggest a method to cap the mRNA at the 5′ terminus by using enzymes, which might be more effective than cap analogs. Finally, we suggest the construction of a future multi-talented RNAPII, which would be able to synthetize large mRNA and cap them in the test tube.
The Background K+ Channel TRESK in Sensory Physiology and Pain

TRESK belongs to the K2P family of potassium channels, also known as background or leak potassium channels due to their biophysical properties and their role regulating membrane potential of cells. Several studies to date have highlighted the role of TRESK in regulating the excitability of specific subtypes of sensory neurons. These findings suggest TRESK could be involved in pain sensitivity. Here, we review the different evidence available that involves the channel in pain and sensory perception, from studies knocking out the channel or overexpressing it to identified mutations that link the channel to migraine pain. In addition, the therapeutic possibilities are discussed, as targeting the channel seems an interesting therapeutic approach to reduce nociceptor activation and to decrease pain.
4-(N-Alkyl- and -Acyl-amino)-1,2,4-triazole-3-thione Analogs as Metallo-β-Lactamase Inhibitors: Impact of 4-Linker on Potency and Spectrum of Inhibition

To fight the increasingly worrying bacterial resistance to antibiotics, the discovery and development of new therapeutics is urgently needed. Here, we report on a new series of 1,2,4-triazole-3-thione compounds as inhibitors of metallo-β-lactamases (MBLs), which represent major resistance determinants to β-lactams, and especially carbapenems, in Gram-negative bacteria. These molecules are stable analogs of 4-amino-1,2,4-triazole-derived Schiff bases, where the hydrazone-like bond has been reduced (hydrazine series) or the 4-amino group has been acylated (hydrazide series); the synthesis and physicochemical properties thereof are described. The inhibitory potency was determined on the most clinically relevant acquired MBLs (IMP-, VIM-, and NDM-types subclass B1 MBLs). When compared with the previously reported hydrazone series, hydrazine but not hydrazide analogs showed similarly potent inhibitory activity on VIM-type enzymes, especially VIM-2 and VIM-4, with Ki values in the micromolar to submicromolar range. One of these showed broad-spectrum inhibition as it also significantly inhibited VIM-1 and NDM-1. Restoration of β-lactam activity in microbiological assays was observed for one selected compound. Finally, the binding to the VIM-2 active site was evaluated by isothermal titration calorimetry and a modeling study explored the effect of the linker structure on the mode of binding with this MBL.
Cost-Effective Approaches Based on Machine Learning to Predict Dynamic Modulus of Warm Mix Asphalt with High Reclaimed Asphalt Pavement

Warm mix asphalt (WMA) technology, taking advantage of reclaimed asphalt pavements, has gained increasing attention from the scientific community. The determination of technical specifications of such a type of asphalt concrete is crucial for pavement design, in which the asphalt concrete dynamic modulus (E*) of elasticity is amongst the most critical parameters. However, the latter could only be determined by complicated, costly, and time-consuming experiments. This paper presents an alternative cost-effective approach to determine the dynamic elastic modulus (E*) of WMA based on various machine learning-based algorithms, namely the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR), and ensemble boosted trees (Boosted). For this, a total of 300 samples were fabricated by warm mix asphalt technology. The mixtures were prepared with 0%, 20%, 30%, 40%, and 50% content of reclaimed asphalt pavement (RAP) and modified bitumen binder using Sasobit and Zycotherm additives. The dynamic elastic modulus tests were conducted by varying the temperature from 10 °C to 50 °C at different frequencies from 0.1 Hz to 25 Hz. Various common quantitative indications, such as root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) were used to validate and compare the prediction capability of different models. The results showed that machine learning models could accurately predict the dynamic elastic modulus of WMA using up to 50% RAP and fabricated by warm mix asphalt technology. Out of these models, the Boosted algorithm (R = 0.9956) was found as the best predictor compared with those obtained by ANN-LMN (R = 0.9954), SVM (R = 0.9654), and GPR (R= 0.9865). Thus, it could be concluded that Boosted is a promising cost-effective tool for the prediction of the dynamic elastic modulus (E*) of WMA. This study might help in reducing the cost of laboratory experiments for the determination of the dynamic modulus (E*).
Isolation and characterization of polymorphic microsatellite markers from Primula nutans (Primulaceae)

Seven polymorphic microsatellite loci were developed for a perennial seashore plant, Primula nutans . Degenerate oligonucleotide-primed (DOP)–polymerase chain reaction (PCR)amplified DNA was ligated to TOPO TA vector and screened with radioactively labelled dinucleotide repeat probes. A sample of 378 individuals from Finland, Norway and Russia were used to characterize those loci, which exhibited two to four alleles per locus with observed heterozygosity of 0.003–0.229 and expected heterozygosity of 0.016–0.527. No linkage disequilibrium was found between these seven loci. These are the first microsatellite markers reported for P. nutans .