Miracle-dz/UniFAR-FTUs huggingface dataset:
Punahomealttius ohra- ja kauralajikkeissa

Mykotoksiinit muodostavat turvallisuusriskin viljaa ravintona käyttäville ihmisille ja viljarehua syöville eläimille. Toksiineista johtuvat syömisja lisääntymishäiriöt voivat johtaa huomattaviin taloudellisiin menetyksiin kotieläintuotannossa. Viileän ilmaston maissa Fusarium-suvun sienet ovat yleisimpiä toksiinien muodostajia viljassa. Kasvukauden kuluessa saatu tartunta jyvissä voi johtaa sopivissa oloissa toksiinien muodostumiseen. Viljalajeista kauraa pi-detään alttiina Fusarium-tartunnalle ja sillä tavataan usein korkeita toksiinipitoisuuksia. Ohra on kuitenkin yhtä yhtä arka tartunnalle ja sillä tavataan myös runsaasti toksiineja. Punahomeita aiheuttavien Fusarium-sienten tartuntaa määritettiin MTT Ylistaron toimipaikan virallisissa lajikekokeissa 2008-2009 vertailtavina olevista kauraja ohralajikkeista. Ohrasta tutkittiin kumpanakin vuonna 31 lajiketta. Kauran lajikkeita ja jalostuslinjoja tutkittiin 2008 24 ja 2009 20. Tähkäja röyhynäytteitä otettiin koealueilta, joita ei käsitelty fungisideilla. Fusarium-tartunnan kehitystä seurattiin tähkälle tulosta aina valmiiseen satoon asti. Kahden kesän olosuhteet olivat erilaisia sadeja lämpöoloiltaan. Kesä 2008 oli viileä ja sateinen, 2009 kuivempi ja lämpimämpi. Ohrista monitahoiset lajikkeet olivat 2008 alttiimpia Fusariumtartunnalle kuin kaksitahoiset. Kesän 2009 havainnoissa Fusarium-tartunta runsastui nopeammin kuin edellisenä vuonna. T2/HT-2-toksiinien muodostajista F. langsethiaelaji tartutti silloin kauran lisäksi ohraa verrattain runsaasti jo heinäkuun jälkipuoliskolla. Myös deoksinivalenolin (DON)-muodostajien määrä lisääntyi jo ennen elokuuta eräillä lajikkeilla. Lajikkeiden välillä oli suuria eroja Fusarium-tartunnassa. Aikaiset lajikkeet säästyvät usein hometartunnoilta myöhäisiä paremmin, mutta kaikki lajikkeiden väliset erot eivät selity niiden valmistumisella. Kauralla DON-muodostajia havaittiin ennen sadonkorjuuta eniten ‘Aslak’-, ‘Fiia’-, ‘Ivory’ ‘Julius’‘Steinar’-, ja ‘Roope’-lajikkeilla. Ohrista eniten tartuntaa todettiin ‘Olavi’-, ‘Vilde’-, ‘Einar’-, ‘Edel’-, ‘Conchita’-, ‘Ingmar’-, ‘Afrodite’-, ‘Kunnari’ja ‘Malta-sia’-lajikkeilla. T2/HT-2 -toksiinien muodostajia oli valmistuvassa sadossa 2008 vähemmän kuin 2009. Kauralajikkeista ‘Eemeli’, ‘Roope’, ‘Aarre’, ‘Carron’ ja ‘Ivory’ saivat kohtalaisesti tartuntaa. Ohralla lajeja esiintyi 2008 vähän. Kuitenkin 2009 ohralajikkeilla F. langsethiae oli jopa yleisempi kuin kauroilla. Erityisesti ‘Umbrella’, ‘Maltasia’ ja ‘Olavi’ olivat saastuneita. Tutkimus täydentää lajikekokeista tehtäviä kasvitautihavaintoja, mutta ei pysty tuottamaan tietoa kaikista lajikkeista. Fusarium-alttius ennakoi sadon hävikkiä surkastuneina jyvinä ja laatua elintarvikkeeksi, rehuksi ja mallastukseen. Fusarium-alttiuden tunteminen auttaa arvioimaan myös tuotetun sadon toksiiniriskiä. SUOMEN MAATALOUSTIETEELLISEN SEURAN TIEDOTE NRO 26
Blood eosinophil count as a prognostic biomarker in COPD

Background High blood eosinophil count is a predictive biomarker for response to inhaled corticosteroids in prevention of acute exacerbation of COPD, and low blood eosinophil count is associated with pneumonia risk in COPD patients taking inhaled corticosteroids. However, the prognostic role of blood eosinophil count remains underexplored. Therefore, we investigated the associated factors and mortality based on blood eosinophil count in COPD. Methods Patients with COPD were recruited from 16 hospitals of the Korean Obstructive Lung Disease cohort (n=395) and COPD in Dusty Area cohort (n=234) of Kangwon University Hospital. The two merged cohorts were divided based on blood eosinophil count into three groups: high (≥5%), middle (2%–5%), and low (<2%). Results The high group had longer six-minute walk distance (high =445.8±81.4, middle =428.5±88.0, and low =414.7±86.3 m), higher body mass index (23.3±3.1, 23.1±3.1, and 22.5±3.2 kg/m2), lower emphysema index (18.5±14.1, 22.2±15.3, and 23.7±16.3), and higher inspiratory capacity/total lung capacity ratio (32.6±7.4, 32.4±9.2, and 29.9% ± 8.9%) (P<0.05). The survival period increased with increasing blood eosinophil count (high =9.52±0.23, middle =8.47±1.94, and low =7.42±0.27 years, P<0.05). Multivariate linear regression analysis revealed that the emphysema index was independently and negatively correlated with blood eosinophil count (P<0.05). Conclusion In COPD, the severity of emphysema was independently linked with low blood eosinophil count and the longer survival period was associated with increased blood eosinophil count, though it was not proven in the multivariate analysis.
Revisiting Pre-training: An Efficient Training Method for Image Classification.

The training method of repetitively feeding all samples into a pre-defined network for image classification has been widely adopted by current state-of-the-art. In this work, we provide a new method, which can be leveraged to train classification networks in a more efficient way. Starting with a warm-up step, we propose to continually repeat a Drop-and-Pick (DaP) learning strategy. In particular, we drop those easy samples to encourage the network to focus on studying hard ones. Meanwhile, by picking up all samples periodically during training, we aim to recall the memory of the networks to prevent catastrophic forgetting of previously learned knowledge. Our DaP learning method can recover 99.88%, 99.60%, 99.83% top-1 accuracy on ImageNet for ResNet-50, DenseNet-121, and MobileNet-V1 but only requires 75% computation in training compared to those using the classic training schedule. Furthermore, our pre-trained models are equipped with strong knowledge transferability when used for downstream tasks, especially for hard cases. Extensive experiments on object detection, instance segmentation and pose estimation can well demonstrate the effectiveness of our DaP training method.
Parental Education Attainment and Educational Upward Mobility; Role of Race and Gender

Background. The Minorities’ Diminished Return theory suggests that education attainment and other socioeconomic resources have smaller effects on the health and well-being of African Americans and other racial and ethnic minorities compared to Whites. Racial and ethnic differences in the processes involved with educational upward mobility may contribute to the diminished returns of education attainment for African Americans compared to Whites. Aim: This study compared African Americans and non-Hispanic Whites for the effect of parental education attainment on educational upward mobility and explored gender differences in these effects. Methods. The National Survey of American Life (NSAL 2003) is a nationally representative survey of American adults. Participants included 891 non-Hispanic White and 3570 African American adults. Gender, race/ethnicity, age, highest parental education attainment, and respondents’ educational attainment were measured. Data were analyzed using linear regression models. Results. Overall, higher parental education attainment was associated with higher educational upward mobility (b = 0.34, p < 0.001), however, this boosting effect was significantly smaller for African Americans compared to Whites (b = −0.13, p = 0.003). Our further analysis showed that race by parental education attainment can be found for females (b = −0.14, p = 0.013) but not males (p > 0.05). Conclusion. African American females are at a disadvantage compared to White females regarding the effect of parental education attainment on their educational upward mobility, a phenomenon which could not be observed when comparing African American and White males. These results advocate for taking intersectionality frameworks to study the effects of race, gender, and class in the US.
Rethinking dopamine as generalized prediction error

Midbrain dopamine neurons are commonly thought to report a reward prediction error, as hypothesized by reinforcement learning theory. While this theory has been highly successful, several lines of evidence suggest that dopamine activity also encodes sensory prediction errors unrelated to reward. Here we develop a new theory of dopamine function that embraces a broader conceptualization of prediction errors. By signaling errors in both sensory and reward predictions, dopamine supports a form of reinforcement learning that lies between model-based and model-free algorithms. This account remains consistent with current canon regarding the correspondence between dopamine transients and reward prediction errors, while also accounting for new data suggesting a role for these signals in phenomena such as sensory preconditioning and identity unblocking, which ostensibly draw upon knowledge beyond reward predictions.
Transoral endoscopic thyroidectomy by vestibular approach (TOETVA): initial experience in Brazil.

OBJECTIVE to present the initial experience in Brazil with transoral endoscopic thyroidectomy by vestibular approach (TOETVA). METHODS prospective study of patients undergoing TOETVA in the Department of Head and Neck Surgery of Hospital Regional do Oeste, Chapecó, Santa Catarina. Patients between 18 and 65 years of age, ASA I and II, with maximum glandular volume of 35cm3 and nodules up to 4cm were candidates for the study. Data of the patients, nodules, surgical time, complications, and length of hospital stay were recorded. RESULTS nine patients were operated between May 2017 and April 2018. All were women, aged 33-64 years. The size of the nodule ranged from 1cm to 4cm. Two patients had malignant neoplasia and total thyroidectomy was performed in eight cases. Seven patients were hospitalized for only one day. One patient had a minor complication on skin, but there were no lesions of the laryngeal recurrent nerves or definitive hypoparathyroidism. CONCLUSION TOETVA is a safe technique for well-selected patients, with favorable conditions and special concern for aesthetic results.
Extracting Interaction-Related Failure Indicators for Online Detection and Prediction of Content Failures

With the increasing complexity of software-intensive systems, software health management is proposed to assure their runtime dependability, in which online failure detection and prediction is one of the most significant components. Failure indicators are characteristics of internal states and behavior of a system which indicate potential failures. However, previous studies mostly focused on extracting failure indicators from network and hardware outside of a software system or operating system level, neglected the runtime dynamics on application level. Besides, most of these studies aimed at detecting and predicting performance-related failures. As a major category of software failures, content failures are often omitted. This paper proposes an experiment-based approach to extract interaction-related failure indicators on application level for content failures, composed of abnormal execution time of modules and abnormal interaction times between modules. Firstly, an experiment-based failure data generation method is proposed due to a lack of real-world failure data which can reflect the runtime states and behavior of a software system. Then a machine learning method is selected and applied on the failure dataset to construct classifiers for normal data and failure data, from which failure indicators are extracted. Finally, three open-source software were selected to show the validity of our extracting method and the effectiveness of the extracted failure indicators. Interaction-related failure indicators extracted by the proposed approach can be used for runtime failure detection and prediction of content failures, thus improving runtime dependability of complex software-intensive systems.
Indoleamine 2,3-dioxygenase as a novel therapeutic target for Huntington’s disease

ABSTRACT Introduction: Huntington’s disease (HD) is an autosomal dominant, neurodegenerative disorder. Despite the severe motor, psychiatric and cognitive symptoms and the great socioeconomic burden caused by the disease, available treatment is mainly symptomatic. The kynurenine pathway (KP) is the main metabolic route of tryptophan degradation, in the course of which several neuroactive compounds are generated. The imbalance of the neurotoxic and neuroprotectant metabolites can lead to excitotoxicity and overproduction of reactive oxygen species, which both contribute to the progression of HD. Indoleamine 2,3-dioxygenase 1 (IDO1) is a key enzyme of the KP that has various immune modulatory roles. Areas covered: Current knowledge of the involvement of KP in HD pathogenesis with a particular focus on IDO1. By reviewing the diverse roles of the enzyme in kynurenine production, immune modulation, and serotonin metabolism, we elucidate the factors that make this enzyme a therapeutic target. Expert opinion: Due to the complexity of HD and the various effects that IDO1 exerts, targeting this enzyme, while highly profitable, may be a great challenge. Through IDO1 activity, neurodegeneration, inflammatory processes and depressive symptoms, often related to HD, can be modulated. Ongoing trials of IDO1 inhibitors in other areas of medicine offer advantages for initiating approaches toward this enzyme as a therapeutic target. Trial registration: ClinicalTrials.gov identifier: NCT02855476. Trial registration: ClinicalTrials.gov identifier: NCT02519036.
Enhanced shortcut nitrogen removal and metagenomic analysis of functional microbial communities in a double sludge system treating ammonium-rich wastewater

ABSTRACT Biological nitrogen removal processes based on partial nitrification are promising for ammonium-rich wastewater treatment. In this study, a partial nitrification-denitrification double sludge system was applied to treat synthetic ammonium-rich wastewater. Metagenomic analysis of functional genes and metabolic pathways was conducted, also with the evaluation of system performance and nitrous oxide (N2O) emission. In the nitrifying sequencing batch reactor (SBRPN), the removal percentage of ammonium nitrogen reached to 99.98% with a high nitritation efficiency of 93.24%, and the N2O emission factor was 0.88%. In the denitrifying sequencing batch reactor (SBRDN), there was almost no nitrate nitrogen and nitrite nitrogen in the effluent, and the maximum N2O emission was 0.078 mg N/L. The dominant ammonia oxidizing bacteria was Nitrosomonas in SBRPN (13.6%), and the main potential denitrifiers in SBRDN were Thauera (14.6%), an uncultured genus in the Comamonadaceae family (4.0%), an uncultured genus in Rhodocyclaceae family (2.4%) and Comamonas (1.1%). Metagenomic analysis revealed that amo mainly distributed in Nitrosomonas eutropha (38.3%), Nitrosomonas europaea (27.1%), Nitrosomonas sp. GH22 (20.5%) and Nitrosomonas sp. TK794 (15.0%), and Bacteroidetes had the N2O reduction potential in SBRPN. GRAPHICAL ABSTRACT
The Quality of EBM Sources Perceived By Belgian Family Physicians

BACKGROUND: Belgian family physicians use several local and international sources for evidence-based medicine (EBM). AIM: This study aims to investigate the quality of these EBM sources according to the Belgian family physicians. METHODS: A sample of Belgian family physicians completed a digital survey on the quality of EBM sources. RESULTS: Respondents evaluated the quality of the information for the major part of the local and international EBM sources good to excellent. More than 50% of the respondents found in the major part of the sources an answer to the question. More than half of the respondents found the necessary information in less than 5 minutes in most of the sources. Younger participants self-evaluated their search skills better than older participants. CONCLUSION: The quality of most frequently used EBM sources in Belgium is evaluated as good and client-friendly. More than half of the respondents found an answer to their questions in most of the sources and this within 5 minutes.
mRNA Expression and Activity of Nucleoside Transporters in Human Hepatoma HepaRG Cells

The HepaRG cell line is a highly differentiated human hepatoma cell line, displaying the expression of various drug transporters. However, functional expression of nucleoside transporters remains poorly characterized in HepaRG cells, although these transporters play a key role in hepatic uptake of antiviral and anticancer drugs. The present study was, therefore, designed to characterize the expression, activity and regulation of equilibrative (ENT) and concentrative (CNT) nucleoside transporter isoforms in differentiated HepaRG cells. These cells were found to exhibit a profile of nucleoside transporter mRNAs similar to that found in human hepatocytes, i.e., notable expression of ENT1, ENT2 and CNT1, with very low or no expression of CNT2 and CNT3. ENT1 activity was, next, demonstrated to be the main uridine transport activity present in HepaRG cells, like in cultured human hepatocytes. Various physiological factors, such as protein kinase C (PKC) activation or treatment by inflammatory cytokines or hepatocyte growth factor (HGF), were additionally found to regulate expression of ENT1, ENT2 and CNT1; PKC activation and HGF notably concomitantly induced mRNA expression and activity of ENT1 in HepaRG cells. Overall, these data suggest that HepaRG cells may be useful for analyzing cellular pharmacokinetics of nucleoside-like drugs in human hepatic cells, especially of those handled by ENT1.
Psychometric properties of the symptom check list 90 (SCL-90) for Chinese undergraduate students

Abstract Background: Despite the widespread application of the Chinese version of Symptom Check List 90 (SCL-90) amongst undergraduate students, researchers have not thoroughly evaluated its psychometric features with representative samples of Chinese undergraduate students. Aim: To evaluate its psychometric properties based on a large, representative sample of Chinese undergraduate students. Methods: A total of 4456 Chinese undergraduate students (age range = 17–25 years) completed the SCL-90. Correlation analysis, confirmatory factor analysis and structural equation modeling were used to evaluate its reliability and validity. Results: The evidence for item-total correlations, internal consistency reliability, intraclass correlation coefficients and composite reliability, and the hypothesized factor structure was satisfactory. The evidence for convergent validity was acceptable; however, the evidence for discriminant validity was not satisfactory. Conclusion: Based on a large, representative sample of Chinese undergraduate students, the Chinese version of the SCL-90 revealed both strengths and limitations, suggesting the need for further research.
Pacing Equilibrium in First-Price Auction Markets

In the isolated auction of a single item, second price is often preferable to first price in properties of theoretical interest. Unfortunately, single items are rarely sold in true isolation, so considering the broader context is critical when adopting a pricing strategy. In this paper, we show that this context is important in a model centrally relevant to Internet advertising: when items (ad impressions) are individually auctioned within the context of a larger system that is managing budgets, theory offers surprising support for using a first price auction to sell each individual item. In particular, first price auctions offer theoretical guarantees of equilibrium uniqueness, monotonicity, and other desirable properties, as well as efficient computability as the solution to the well-studied Eisenberg-Gale convex program. We also use simulations to demonstrate that while there are incentives to misreport in thin markets (where budgets aren't constraining), a bidder's incentive to deviate vanishes in thick markets.
MUNet: Macro Unit-Based Convolutional Neural Network for Mobile Devices

Deep neural networks perform better than traditional machine learning methods on various classification problems by producing good quality feature maps through successive convolution operation(s). However, when implementing a deep neural network in an embedded system or SoC for mobile devices, its large parameter size can be a significant burden on the internal memory design. In this paper, we propose a new deep neural network that reduces computation and the number of model parameters but maintains reasonable performance. The configuration of the proposed network is as follows: First, we propose a macro unit (MU) to reduce heavy computations and to learn various feature maps. Second, we employ asymmetric convolution of the well-known Inception network to further efficiently manipulate feature maps within the MU. Third, all the feature maps produced from MU(s) of each layer are concatenated and then the grouped feature map is distributed to all the MUs of the next layer for transferring richer information. Experimental results show that the proposed network achieves about 10% higher performance than DenseNet-BC in case of extremely small parameter size for CIFAR-100. The proposed network also has very few learning parameters and smaller floating point operations per second (FLOPS) than the other networks optimized for mobile devices such as MobileNet.
An Innovative Osmotic Computing Framework for Self Adapting City Traffic in Autonomous Vehicle Environment

In recent years, autonomous driving is becoming a very hot topic for both researchers and car manufacturers. Indeed, around the world new discoveries have been published. In this work we present an innovative Osmotic Computing solution for self adapting city traffic in autonomous vehicle environment. The Vehicular-to-Vehicular (V2V) and Vehicular to Edge-Cloud (V2EC) interactions inside specific areas of the City are considered: the interconnections. The Framework we are creating is able to adapt on a Dynamic Environment where Vehicles, Pedestrians and Physical Infrastructures can interact each other, offering continuous information on interconnections status and city traffic in general.
CPR-Content 15-1

Methods: A PubMed search was completed in Clinical Queries using the key term "breath-holding spells". The search strategy included meta-analyses, randomized controlled trials, clinical trials, observational studies, and reviews. Only papers published in the English literature were included in this review. The information retrieved from the above search was used in the compilation of the present article.
TGANv2: Efficient Training of Large Models for Video Generation with Multiple Subsampling Layers

In this paper, we propose a novel method to efficiently train a Generative Adversarial Network (GAN) on high dimensional samples. The key idea is to introduce a differentiable subsampling layer which appropriately reduces the dimensionality of intermediate feature maps in the generator during training. In general, generators require large memory and computational costs in the latter stages of the network as the feature maps become larger, though the latter stages have relatively fewer parameters than the earlier stages. It makes training large models for video generation difficult due to the limited computational resource. We solve this problem by introducing a method that gradually reduces the dimensionality of feature maps in the generator with multiple subsampling layers. We also propose a network (Temporal GAN v2) with such layers and perform video generation experiments. As a consequence, our model trained on the UCF101 dataset at $192 \times 192$ pixels achieves an Inception Score (IS) of 24.34, which shows a significant improvement over the previous state-of-the-art score of 14.56.