Miracle-dz/UniFAR-FTUs huggingface dataset:
A specific reverse complement sequence for distinguishing Brucella canis from other Brucella species

Canine brucellosis is primarily caused by Brucella canis, but other Brucella species can also cause the disease. Identifying sequences specific to B. canis and establishing PCR assays that can distinguish between B. canis and other Brucella species is essential to determine the etiology of canine brucellosis and the source of infection and to achieve effective control. We analyzed the gaps and SNPs of genomes I and II from B. canis strain RM6/66 and B. melitensis strain 16M using the Mauve genome alignment software, and the specificity of each of these differential regions was analyzed by BLAST. A 132 bp specific sequence was found between the DK60_915 (glycosyl hydrolase 108 family protein) and DK60_917 (aldose 1-epimerase) loci in B. canis chromosome 1. Further comparative analysis revealed that this is a reverse complement sequence between B. canis and other Brucella species. Then, three primers were designed based on the sequence that could detect B. canis with a 310 bp amplification product or other Brucella species with a 413 bp product. The PCR based on these primers had reasonable specificity and a sensitivity of 100 copies of Brucella DNA. The detection results for the blood samples of the aborted dogs showed a favorable accordance with the Bruce-ladder multiplex PCR assay. In conclusion, we found a specific reverse complement sequence between B. canis and other Brucella and developed a PCR method that allows a more comprehensive identification of the pathogen involved in canine brucellosis. These findings provide an effective means for preventing and controlling brucellosis.
Reality bites: An analysis of corona deniers in Germany over time

The COVID-19 pandemic resulted in unprecedented government interventions in many people's lives. Opposition to these measures was not only based on policy disagreements but for some founded in an outright denial of basic facts surrounding the pandemic, challenging social cohesion. Conspiracy beliefs have been prolific within various protest groups and require attention, as such attitudes have been shown to be associated with lower rule compliance. Several studies have shown that the characteristics linked to holding COVID-19 conspiracy beliefs are complex and manifold; however, those insights usually rest on cross-sectional studies only. We have less knowledge on whether these cross-sectional correlates also reveal which parts of the population have been newly convinced by conspiracy theories or have dropped their support for them as the pandemic evolved. Using a unique panel data set from Germany, this paper explores a wide range of characteristics and compares the insights gained from cross-sectional associations on the one hand and links to the ways in which people change their views on the other hand. The findings show that cross-sectional analyses miss out on nuanced differences between different groups of temporary and more consistent conspiracy supporters. Specifically, this paper identifies major differences in the profiles of people who have been denying COVID-19 consistently compared to those who changed their minds on the question and those who assessed the reality correctly throughout. In doing so, socio-political and perception-based dimensions are differentiated and distinctions between respondents from East and West Germany explored.
Reduced vitamin D-induced cathelicidin production and killing of Mycobacterium tuberculosis in macrophages from a patient with a non-functional vitamin D receptor: A case report

Tuberculosis (TB) presents a serious health problem with approximately a quarter of the world’s population infected with Mycobacterium tuberculosis (M. tuberculosis) in an asymptomatic latent state of which 5–10% develops active TB at some point in their lives. The antimicrobial protein cathelicidin has broad antimicrobial activity towards viruses and bacteria including M. tuberculosis. Vitamin D increases the expression of cathelicidin in many cell types including macrophages, and it has been suggested that the vitamin D-mediated antimicrobial activity against M. tuberculosis is dependent on the induction of cathelicidin. However, unraveling the immunoregulatory effects of vitamin D in humans is hampered by the lack of suitable experimental models. We have previously described a family in which members suffer from hereditary vitamin D-resistant rickets (HVDRR). The family carry a mutation in the DNA-binding domain of the vitamin D receptor (VDR). This mutation leads to a non-functional VDR, meaning that vitamin D cannot exert its effect in family members homozygous for the mutation. Studies of HVDRR patients open unique possibilities to gain insight in the immunoregulatory roles of vitamin D in humans. Here we describe the impaired ability of macrophages to produce cathelicidin in a HVDRR patient, who in her adolescence suffered from extrapulmonary TB. The present case is a rare experiment of nature, which illustrates the importance of vitamin D in the pathophysiology of combating M. tuberculosis.
Insights into melatonin-induced photosynthetic electron transport under low-temperature stress in cucumber

In this study, the differences in chlorophyll fluorescence transient (OJIP) and modulated 820 nm reflection (MR820) of cucumber leaves were probed to demonstrate an insight into the precise influence of melatonin (MT) on cucumber photosystems under low temperature stress. We pre-treated cucumber seedlings with different levels of MT (0, 25, 50, 100, 200, and 400 μmol · L-1) before imposing low temperature stress (10 °C/6 °C). The results indicated that moderate concentrations of MT had a positive effect on the growth of low temperature-stressed cucumber seedlings. Under low temperature stress conditions, 100 μmol · L-1 (MT 100) improved the performance of the active photosystem II (PSII) reaction centers (PIabs), the oxygen evolving complex activity (OEC centers) and electron transport between PSII and PSI, mainly by decreasing the L-band, K-band, and G-band, but showed differences with different duration of low temperature stress. In addition, these indicators related to quantum yield and energy flux of PSII regulated by MT indicated that MT (MT 100) effectively protected the electron transport and energy distribution in the photosystem. According to the results of WO-I ≥ 1 and MR820 signals, MT also affected PSI activity. MT 100 decreased the minimal value of MR/MRO and the oxidation rate of plastocyanin (PC) and PSI reaction center (P700) (Vox ), while increased △MRslow/MRO and deoxidation rates of PC+ and P700 + (Vred ). The loss of the slow phase of MT 200 and MT 400-treated plants in the MR820 kinetics was due to the complete prevention of electron movement from PSII to re-reduce the PC+ and P700 +. These results suggest that appropriate MT concentration (100 μmol · L-1) can improve the photosynthetic performance of PS II and electron transport from primary quinone electron acceptor (QA) to secondary quinone electron acceptor (QB), promote the balance of energy distribution, strengthen the connectivity of PSI and PSII, improve the electron flow of PSII via QA to PC+ and P700 + from reaching PSI by regulating multiple sites of electron transport chain in photosynthesis, and increase the pool size and reduction rates of PSI in low temperature-stressed cucumber plants, All these modifications by MT 100 treatment promoted the photosynthetic electron transfer smoothly, and further restored the cucumber plant growth under low temperature stress. Therefore, we conclude that spraying MT at an appropriate concentration is beneficial for protecting the photosynthetic electron transport chain, while spraying high concentrations of MT has a negative effect on regulating the low temperature tolerance in cucumber.
Predicting the tissue outcome of acute ischemic stroke from acute 4D computed tomography perfusion imaging using temporal features and deep learning

Predicting follow-up lesions from baseline CT perfusion (CTP) datasets in acute ischemic stroke patients is important for clinical decision making. Deep convolutional networks (DCNs) are assumed to be the current state-of-the-art for this task. However, many DCN classifiers have not been validated against the methods currently used in research (random decision forests, RDF) and clinical routine (Tmax thresholding). Specialized DCNs have even been designed to extract complex temporal features directly from spatiotemporal CTP data instead of using standard perfusion parameter maps. However, the benefits of applying deep learning to source or deconvolved CTP data compared to perfusion parameter maps have not been formally investigated so far. In this work, a modular UNet-based DCN is proposed that separates temporal feature extraction from tissue outcome prediction, allowing for both model validation using perfusion parameter maps as well as end-to-end learning from spatiotemporal CTP data. 145 retrospective datasets comprising baseline CTP imaging, perfusion parameter maps, and follow-up non-contrast CT with manual lesion segmentations were assembled from acute ischemic stroke patients treated with intravenous thrombolysis alone (IV; n = 43) or intra-arterial mechanical thrombectomy (IA; n = 102) with or without combined IV. Using the perfusion parameter maps as input, the proposed DCN (mean Dice: 0.287) outperformed the RDF (0.262) and simple Tmax-thresholding (0.249). The performance of the proposed DCN was approximately equal using features optimized from the deconvolved residual curves (0.286) compared to perfusion parameter maps (0.287), while using features optimized from the source concentration-time curves (0.296) provided the best tissue outcome predictions.
Autoimmunity: Are we asking the right question?

For decades, the main question immunologists have asked about autoimmunity is “what causes a break in self-tolerance? ” We have not found good answers to that question, and I believe we are still so ignorant because it’s the wrong question. Rather than a break in self-tolerance, I suggest that many autoimmune diseases might be due to defects in normal tissue physiology.
Radiomics features of DSC-PWI in time dimension may provide a new chance to identify ischemic stroke

Ischemic stroke has become a severe disease endangering human life. However, few studies have analyzed the radiomics features that are of great clinical significance for the diagnosis, treatment, and prognosis of patients with ischemic stroke. Due to sufficient cerebral blood flow information in dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) images, this study aims to find the critical features hidden in DSC-PWI images to characterize hypoperfusion areas (HA) and normal areas (NA). This study retrospectively analyzed 80 DSC-PWI data of 56 patients with ischemic stroke from 2013 to 2016. For exploring features in HA and NA,13 feature sets (Fmethod) were obtained from different feature selection algorithms. Furthermore, these 13 Fmethod were validated in identifying HA and NA and distinguishing the proportion of ischemic lesions in brain tissue. In identifying HA and NA, the composite score (CS) of the 13 Fmethod ranged from 0.624 to 0.925. FLasso in the 13 Fmethod achieved the best performance with mAcc of 0.958, mPre of 0.96, mAuc of 0.982, mF1 of 0.959, and mRecall of 0.96. As to classifying the proportion of the ischemic region, the best CS was 0.786, with Acc of 0.888 and Pre of 0.863. The classification ability was relatively stable when the reference threshold (RT) was <0.25. Otherwise, when RT was >0.25, the performance will gradually decrease as its increases. These results showed that radiomics features extracted from the Lasso algorithms could accurately reflect cerebral blood flow changes and classify HA and NA. Besides, In the event of ischemic stroke, the ability of radiomics features to distinguish the proportion of ischemic areas needs to be improved. Further research should be conducted on feature engineering, model optimization, and the universality of the algorithms in the future.
Resistance to aztreonam-avibactam due to CTX-M-15 in the presence of penicillin-binding protein 3 with extra amino acids in Escherichia coli

Aztreonam-avibactam is a promising combination to treat carbapenem-resistant Enterobacterales including coverage for metallo-β-lactamases. Escherichia coli strains resistant to aztreonam-avibactam have emerged but resistance mechanisms remain to be elucidated. We performed a study to investigate the mechanism for aztreonam-avibactam in a carbapenem-resistant Escherichia coli clinical strain. This strain was resistant to aztreonam-avibactam (aztreonam MIC, 16 mg/L in the presence of 4 mg/L avibactam). Whole genome sequencing revealed that the strain carried metallo-β-lactamase gene blaNDM-4 and the extended-spectrum β-lactamase (ESBL) gene blaCTX-M-15 and had a YRIK four amino acid insertion in penicillin-binding protein 3 (PBP3). blaCTX-M-15 was cloned into pET-28a(+), followed by the transformation, with the gene, of E. coli strain 035125∆pCMY42 possessing the YRIK insertion in PBP3 and strain BL21 with the wildtype PBP3. blaCTX-M-14, another common ESBL gene, and blaCTX-M-199, a hybrid of blaCTX-M-14 and blaCTX-M-15 were also individually cloned into both E. coli strains for comparison. Aztreonam-avibactam resistance was only observed in the E. coli strains with the YRIK insertion in PBP3 that produced CTX-M-15 or its hybrid enzyme CTX-M-199. Checkerboard titration assays were performed to determine the synergistic effects between aztreonam-avibactam and ceftazidime or meropenem. Doubling avibactam concentration in vitro reversed aztreonam-avibactam resistance, while the combination of aztreonam-avibactam and ceftazidime or meropenem did not. In conclusion, CTX-M enzymes with activity against aztreonam, (e.g., CTX-M-15 and CTX-M-199), can confer resistance in the combination of PBP3 with YRIK insertions in metallo-β-lactamase-producing carbapenem-resistant E. coli. Doubling the concentration of avibactam may overcome such resistance.
Culturable endophytic fungal assemblages from Styrax sumatrana and Stryax benzoin and their potential as antifungal, antioxidant, and alpha-glucosidase inhibitory resources

Benzoin resin, produced by the native Indonesian trees Styrax sumatrana and Styrax benzoin, has been incorporated into medical practices to treat wounds, erythema, and many other conditions for centuries. Endophytic fungi that reside within medicinal plants have antimicrobial, antioxidant, and α-glucosidase inhibitory capacities, contributing to plant health and derivative products. In this study, we determined the antifungal, antioxidant, and α-glucosidase inhibitory capacities of endophytic fungal isolates from three different tissues (leaves, bark, and stems) of S. sumatrana and S. benzoin trees. The genera of fungal isolates were determined by phylogenetic analysis of internal transcribed spacer sequences. A total of 58 fungal isolates were classified into 15 different fungal genera from eight taxonomic orders—Hypocreales, Botryosphaeriales, Glomerellales, Diaphortales, Pleosporales, Eurotiales, Xylariales, and Mucorales—with a pattern of host species specificity. Among these isolates, Trichoderma sp. 6407 consistently exhibited high inhibition of the growth of plant pathogens Fusarium sp. , Trichoderma viride, and Aspergillus niger. With respect to antioxidant activity, Phyllosticta sp. 6454 consistently showed 2,2-diphenyl-1-picrylhydrazyl inhibition (37.59 ± 0.05%), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid)-based antioxidant activity (25.04 ± 0.27 mgTE/g), and α-glucosidase inhibitory activity (52.15 ± 10.08%). Neopestalotiopsis sp. 6431 was notably potent in 2,2-diphenyl-1-picrylhydrazyl inhibition (49.65 ± 0.80%), ferric reducing antioxidant power-based antioxidant activity (197.49 ± 8.65 mgTE/g), and α-glucosidase inhibitory activity (52.88 ± 4.93%). This study revealed that Trichoderma sp. 6407, Phyllosticta sp. 6454, and Neopestalotiopsis sp. 6431 exhibited antifungal, antioxidant, and α-glucosidase inhibitory activities.
Stochastic Low-Rank Subspace Clustering by Auxiliary Variable Modeling

Low-Rank Representation (LRR) is a popular tool to identify the data that are generated by a union of subspaces. However, the size of the regularized matrix of LRR is proportional to n 2 , where n is the number of samples, which hinders LRR for large scale problems. In this paper, we study how to scale up the LRR method accurately and memory efficiently. In particular, we propose an online implementation that reduces the memory cost fromO(n 2 ) to O(pd), with p being the ambient dimension and d being some expected rank. There are two key techniques in our algorithm: one is to reformulate the nuclear norm to an equivalent matrix factorization form and the other is to introduce an auxiliary variable working as a basis dictionary of the underlying data, which combined together makes the problem amenable to stochastic optimization. We establish a theoretical guarantee that the sequence of solutions produced by our algorithm will converge to a stationary point asymptotically. Numerical experiments on subspace recovery and subspace clustering tasks demonstrate the efficacy and robu stness of the proposed algorithm.
Selected Readings in Economic Theory from Econometrica

The journal "Econometrica" is the locus of some of the most important and advanced contributions to modern economics, and a new series of books has been established, the better to preserve and disseminate the cream of these contributions. Each volume presents a selection of articles covering a broad aspect of the field. This second volume in the series closely follows publication of the first, "Selected Readings in Econometrics from" Econometrica. Joseph E. Stiglitz is General Editor of the series.The editor of the present volume has chosen the 22 articles reprinted here in such a way as to point up the general utility of economic theory and the widely distributed range of specific problems that can be analyzed and clarified with its application. Among the topics covered by these selected readings are the distribution of national income, entrepreneurial and consumer demand theories, the stability of equilibrium (including competitive equilibrium), the bargaining problem, resource utilization, the nature of the expectation and planning horizon, utility discrimination, social welfare functions, separable utility and aggregation, risk aversion, and two-level planning.The contributors are M. Kalecki; A. Wald; Louis M. Court; Paul A. Samuelson; Rene Roy; Wassily Leontief; George B. Dantzig; John F. Nash, Jr.; Gerard Debreu; Franco Modigliani and Franz E. Hohn; M. Boiteux; R. Duncan Luce; Julian H. Blau; Kenneth J. Arrow, H. D. Block, and Leonid Hurwicz; W. M. Gorman; Lionel W. McKenzie; Frank H. Hahn and Takashi Negishi; Robert J. Aumann; Tjalling C. Koopmans, Peter A. Diamond, and Richard E. Williamson; John H. Pratt; and J. Kornai and Th. Liptak.
STACEE Observations of Markarian 421 during an Extended Gamma-Ray Outburst

The active galaxy Markarian 421 underwent a substantial outburst in early 2001. Between January and May of that year, the STACEE detector was used to observe the source in γ-rays between the energies of 50 and 500 GeV. These observations represent the lowest energy γ-ray detection of this outburst by a ground-based experiment. Here we present results from these observations, which indicate an average integral γ-ray flux of (8.0 ± 0.7 ± 1.5) × 10-10 cm-2 s-1 above 140 GeV. We also present a light curve for Markarian 421 as observed by STACEE from March to May and compare our temporal, as well as spectral, measurements with those of other experiments.
Effects of implant annealing on the power conversion efficiency of vertical cavity top-surface emitting lasers

Annealing of implant damage in proton implant isolated, top-emitting VCSELs at temperatures up to 400 /spl deg/C has been shown to improve power conversion efficiency through threshold current reduction. Additionally, lower temperature (300 /spl deg/C) annealing substantially reduces the series resistance of some device geometries, specifically for larger devices without overlapping contacts. <<ETX>>
AIBO: An Embodied Emotionally Intelligent Artificial Intelligence Brainwave Opera

AIBO is an embodied, immersive, interactive love story about our infatuation and trust in artificial intelligence, and how AI hierarchies over our experienced emotions. The performance takes place between a human character Eva and AIBO (Artificial Intelligence Brainwave Opera), a custom built ‘sicko’ AI. Eva wears an EEG brainwave headset attached to a body suit of light that displays her emotions as different colors, akin to peeling off her skin to reveal her interior nervous system as light. She performs a spoken word libretto about their love affair. Her brainwaves trigger databanks of videos and audios of her emotionally themed memories. Eva’s libretto, uploaded to the computing cloud is processed by a custom built GPT-2 ‘sicko’ AIBO character, seeded with 47 ‘sicko’ or perverted texts. AIBO’s answers are analyzed by the Natural Language Processing Toolkit in the Google Cloud. The results of these analysis of AIBO’s sentiment emotional values launch different colored backgrounds: green for positive, red for negative and yellow for neutral. AIBO also tries but fails to recreate Eva’s previous emotional memory. It wants to learn how to be human but can only display Eva’s memories as glitchy videos. The opera raises issues about a time when humans and machines potentially merge bodies and consciousness, raising tensions about the embodied vs. the virtual, while also exploring if an AI can be fascist.
Fine-Grained Forgetting for the Description Logic ALC

Forgetting is an important ontology extraction technique. A variant of forgetting which has received significant attention in the literature is deductive forgetting (or uniform interpolation ). While deductive forgetting is attractive as it generates the forgetting view in a language with the same complexity as the language of the original ontology, it is known that with a slightly extended target language using definer symbols more information can be preserved. In this paper, we study deductive forgetting of concept names with the aim of understanding the unpreserved information. We present a system that performs deductive forgetting and produces a set Δ of axioms representing the unpreserved information in the forgetting view. Our system allows a new fine-grained ontology extraction process that gives the user the option to enhance the informativeness of the deductive forgetting view by appending to it axioms from Δ .
Modeling dynamic web polarization and proximity depolarization processes by compactness measures

In this paper, we deal with the possibility of simulating dynamic polarization and proximity depolarization processes in a software multi-agent community, modeling homophily and trust relationships usually present in human processes. Group polarization involves various disciplines, such as economics, social psychology, political science, sociology and many others, and it can be considered a critical process underlying relevant behaviors as, for instance, voting and conflictual intergroup relations in the society. Moreover, being a human social phenomenon, the polarization processes are subject to change over time or also effect overturning (i.e., depolarization). Our contribution consists of proposing a compactness-based model for equipping agents in order to simulate the complexity of such processes. We have simulated two case studies where polarization is ruled by compactness measures, combining similarity and trust with different percentages. We evaluated the results provided by compactness measures in order to verify the role of similarity and trust in the agent polarization processes.