Miracle-dz/UniFAR-FTUs huggingface dataset:
Low hedonic tone and attention-deficit hyperactivity disorder: risk factors for treatment resistance in depressed adults

Background The burdens imposed by treatment-resistant depression (TRD) necessitate the identification of predictive factors that may improve patient treatment and outcomes. Because depression and attention-deficit hyperactivity disorder (ADHD) are frequently comorbid and share a complex relationship, we hypothesized that ADHD may be a predictive factor for the diagnosis of TRD. This exploratory study aimed to determine the percentage of undetected ADHD in those with TRD and evaluate factors associated with treatment resistance and undetected ADHD in depressed patients. Subjects and methods Adults referred (n=160) for psychiatric consultation completed a structured interview (MINI Plus, Mini International Neuropsychiatric Interview Plus) to assess the presence of psychiatric disorders. Results TRD was significantly associated with the number of diagnoses (P<0.001), past (P<0.001) and present medications (P<0.001), chronic anhedonia (P=0.013), and suicide ideation (P=0.008). Undetected ADHD was present in 34% of TRD patients. The number of referral diagnoses (P<0.001), failed medications (P=0.002), and past selective serotonin reuptake inhibitor failures (P=0.035) were predictive of undetected ADHD in TRD. Conclusion Undetected ADHD may be more prevalent among TRD patients than previously thought. In addition, TRD patients are more likely to present with psychiatric comorbidity than non-TRD patients. Screening patients with depression for the presence of ADHD and chronic anhedonia/low hedonic tone may help identify patients with TRD and undetected ADHD and improve treatment outcomes.
MC1R CpG island regulates MC1R expression and is methylated in a subset of melanoma tumours

Melanocortin 1 receptor (MC1R) is a G protein‐coupled receptor expressed in melanocytes where it plays an important role in skin pigmentation and in the UV response, and has implications in melanoma development. Here we show that methylation of a CpG island (CGI) within the MC1R gene can control expression of MC1R in melanoma. This CGI overlaps with a potential enhancer region, and is unmethylated in normal melanocytes but highly methylated in other skin cells, suggesting a melanocyte specific function. Analysis showed that MC1R was the only gene significantly differentially expressed by methylation of this region. Within several data sets, this region is methylated in a subset of melanoma tumours (55%–74% of tumours) and results in reduced MC1R expression and significantly longer overall survival.
MFSC: Mean-Field-Theory and Spreading-Coefficient Based Degree Distribution Analysis in Social Network

Degree distribution can provide basic information for structural characteristics and internal relationship in social network. It is a critical procedure for social network topology analysis. In this paper, based on the mean-field theory, we study a special type of social network with exponential distribution of time intervals. First of all, in order to improve the accuracy of analysis, we propose a spreading coefficient algorithm based on intimate relationship, which determines the number of the joined members through the intimacy among members. Then, simulation show that the degree distribution of follows the power-law distribution and has small-world characteristics. Finally, we compare the performance of our algorithm with the existing algorithms, and find that our algorithm improves the accuracy of degree distribution as well as reducing the time complexity significantly, which can complete 29.04% higher precision and 40.94% lower implementation time.
Aldose Reductase Polymorphisms, Fasting Blood Glucose, and Age-Related Cortical Cataract.

Purpose To determine whether there is an association between polymorphisms of the AKR1B1 gene and cortical cataract in the presence of hyperglycemia. Methods In the second cross section of the Blue Mountains Eye Study (BMES), 3508 participants (2334 at 5-year follow-up and 1174 newly recruited participants) were examined during 1997 to 2000. Cataract was graded from lens photographs using the Wisconsin Cataract Grading System. Fasting blood glucose (FBG) was measured. Continuous imputed dosages of minor alleles of 17 AKR1B1 single nucleotide polymorphisms (SNPs) were assessed for associations with prevalent cortical cataract. Gene-environment interactions between SNPs and FBG were examined. Odds ratios (OR) and 95% confidence intervals (CI) for prevalent cortical cataract were estimated using logistic regression adjusting for age, sex, smoking, hypertension, education, and myopia. A P value of 0.005 was considered statistically significant after correction for 10 independent tests. Replication of significant associations found in the BMES sample was conducted in the Singapore Epidemiology of Eye Diseases (SEED) study (n = 10,033). Results No polymorphism was associated with prevalent cortical cataract. A significant interaction was observed between rs9640883 and FBG (Pinteraction = 0.004), with increased cortical cataract prevalence associated with rs9640883 minor allele dosage in those with FBG >6.0 mM (strata-specific OR 1.72, 95% CI 1.09-2.72). No similar association was found in participants with normal FBG (OR 0.85, 95% CI 0.69-1.04). This interaction was not evident in the SEED study. Conclusions The identified interaction between rs9640883 and FBG in relation to cortical cataract was not replicated but may warrant further investigation.
Methods for addressing data diversity in automatic speech recognition

The performance of speech recognition systems is known to degrade in mismatched conditions, where the acoustic environment and the speaker population significantly differ between the training and target test data. Performance degradation due to the mismatch is widely reported in the literature, particularly for diverse datasets. This thesis approaches the mismatch problem in diverse datasets with various strategies including data refinement, variability modelling and speech recognition model adaptation. These strategies are realised in six novel contributions. The first contribution is a data subset selection technique using likelihood ratio derived from a target test set quantifying mismatch. The second contribution is a multi-style training method using data augmentation. The existing training data is augmented using a distribution of variabilities learnt from a target dataset, resulting in a matched set. The third contribution is a new approach for genre identification in diverse media data with the aim of reducing the mismatch in an adaptation framework. The fourth contribution is a novel method which performs an unsupervised domain discovery using latent Dirichlet allocation. Since the latent domains have a high correlation with some subjective meta-data tags, such as genre labels of media data, features derived from the latent domains are successfully applied to the genre and broadcast show identification tasks. The fifth contribution extends the latent modelling technique for acoustic model adaptation, where latent-domain specific models are adapted from a base model. As the sixth contribution, an alternative adaptation approach is proposed where subspace adaptation of deep neural network acoustic models is performed using the proposed latent-domain aware training procedure. All of the proposed techniques for mismatch reduction are verified using diverse datasets. Using data selection, data augmentation and latent-domain model adaptation methods the mismatch between training and testing conditions of diverse ASR systems are reduced, resulting in more robust speech recognition systems.
An iterative biased-randomized heuristic for the fleet size and mix vehicle-routing problem with backhauls

This paper analyzes the fleet mixed vehicle-routing problem with backhauls, a rich and realistic variant of the popular vehicle-routing problem in which both delivery and pick-up customers are served from a central depot using a heterogeneous and configurable fleet of vehicles. After a literature review on the issue and a detailed description of the problem, a solution based on a multistart biased-randomized heuristic is proposed. Our algorithm uses an iterative method that relies on solving a series of smaller instances of the homogeneous-fleet version of the problem and then using these subsolutions as partial solutions for the original heterogeneous instance. In order to better guide the exploration of the solutions space, the algorithm employs several biased-randomized processes: a first one for selecting a vehicle type; a second one for sorting the savings list; and a third one to define the number of routes that must be selected from the homogenous-fleet subsolution. The computational experiments show that our approach is competitive and able to provide 20 new best-known solutions for a 36-instance benchmark recently proposed in the literature.
Text Mining and Sentiment Analysis for Predicting Box Office Success

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers’ sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.
Formal analysis of modern security protocols in current standards

While research has been done in the past on evaluating standardised security protocols, most notably TLS, there is still room for improvement. Modern security protocols need to be rigorously and thoroughly analysed, ideally before they are widely deployed, so as to minimise the impact of often creative, powerful adversaries. We explore the potential vulnerabilities of modern security protocols specified in current standards, including TLS 1.2, TLS 1.3, and SSH. We introduce and formalise the threat of Actor Key Compromise (AKC), and show how this threat can and cannot be avoided in the protocol design stage. We find AKC-related and other serious security flaws in protocols from the ISO/IEC 11770 standard, find realistic exploits, and harden the protocols to ensure strong security properties. Based on our work, the ISO/IEC 11770 working group is releasing an updated version of the standard that incorporates our suggested improvements. We analyse the unilaterally and mutually authenticated modes of the TLS 1.3 Handshake and Record protocols according to revision 06 of their specification draft. We verify session key secrecy and perfect forward secrecy in both modes with respect to a powerful symbolic attacker and an unbounded number of threads. Subsequently, we model and verify the standard authenticated key exchange requirements in revision 10. We analyse a proposal for its extension and uncover a flaw in it, which directly impacts the draft of revision 11.
Versican is differentially regulated in the adventitial and medial layers of human vein grafts

Changes in extracellular matrix proteins may contribute significantly to the adaptation of vein grafts to the arterial circulation. We examined the production and distribution of versican and hyaluronan in intact human vein rings cultured ex vivo, veins perfused ex vivo, and cultured venous adventitial and smooth muscle cells. Immunohistochemistry revealed higher levels of versican in the intima/media compared to the adventitia, and no differences in hyaluronan. In the vasa vasorum, versican and hyaluronan associated with CD34+ progenitor cells. Culturing the vein rings for 14 days revealed increased versican immunostaining of 30–40% in all layers, with no changes in hyaluronan. Changes in versican accumulation appear to result from increased synthesis in the intima/media and decreased degradation in the adventitia as versican transcripts were increased in the intima/media, but unchanged in the adventitia, and versikine (the ADAMTS-mediated cleavage product of versican) was increased in the intima/media, but decreased in the adventitia. In perfused human veins, versican was specifically increased in the intima/media in the presence of venous pressure, but not with arterial pressure. Unexpectedly, cultured adventitial cells express and accumulate more versican and hyaluronan than smooth muscle cells. These data demonstrate a differential regulation of versican and hyaluronan in human venous adventitia vs. intima/media and suggest distinct functions for these extracellular matrix macromolecules in these venous wall compartments during the adaptive response of vein grafts to the arterial circulation.
Impact of vegetation on the methane budget of a temperate forest.

Upland forest soils are known to be the main biological sink for methane, but studies have shown that net methane uptake of a forest ecosystem can be reduced when methane emissions by vegetation are considered. We estimated the methane budget of a young oak plantation by considering tree stems but also the understorey vegetation. Automated chambers connected to a laser-based gas analyser, on tree stems, bare soil and soil covered with understorey vegetation, recorded CH4 fluxes for 7 months at 3 h intervals. Tree stem emissions were low and equated to only 0.1% of the soil sink. Conversely, the presence of understorey vegetation increased soil methane uptake. This plant-driven enhancement of CH4 uptake occurred when the soil was consuming methane. At the stand level, the methane budget shifted from -1.4 ± 0.4 kg C ha-1 when we upscaled data obtained only on bare soil, to -2.9 ± 0.6 kg C ha-1 when we considered soil area that was covered with understorey vegetation. These results indicate that aerenchymatous plant species, which are known to reduce the methane sink in wetlands, actually increase soil methane uptake two-fold in an upland forest by enhancing methane and oxygen transport and/or by promoting growth of methanotrophic populations.
Dataset Enhancement in Hair Follicle Detection: ESENSEI Challenge

—In this paper, a solution to ESENSEI data mining challenge concerning the analysis of microscopic hair images is described. The task of the challenge was to detect locations of hair follicles in closeup images of a human scalp. The proposed solution is based on a convolutional neural network architecture. To improve generalization performance, we enhance training and test datasets using image transformations applied to both input and output. The chosen transformations are two axis symmetries and switching axes, all of which are possible to apply regardless of resolution without producing interpolation artifacts. Since these can be combined, 2 3 = 8 possible views of each image can be created to expand both training and test data. We demonstrate the effects of dataset enhancement in both training and classifying on results achievable on the competition dataset. The solution placed 2nd in the ﬁnal challenge evaluation.
WordNet based Semantic Similarity Measures for Process Model Matching

Process Model Matching (PMM) refers to the automatic identification of corresponding activities between a pair of process models. Due to the wider applicability of PMM techniques several semantic matching techniques have been proposed. However, these techniques focus on utilizing few word-to-word (word-level) similarity measures, without giving due consideration to activitylevel aggregation methods. The inadequate attention to the choice of activitylevel methods limit the effectiveness of the matching techniques. Furthermore, there are some WordNet-based semantic similarity measures that have shown promising results for various text matching tasks. However, the effectiveness of these measures has never been evaluated in the context of PMM. To that end, in this paper we have used five word-level semantic similarity measures and three sentence-level aggregation methods to experimentally evaluate the effectiveness of their 15 combinations for PMM. The experiments are performed on the three widely used PMMC’15 datasets. From the results we conclude that, a) Jiang similarity is more suitable than the mostly used Lin similarity, and b) QAP is the most suitable sentence-level aggregation method.
A new section and species of AgaricussubgenusPseudochitonia from Thailand

Abstract A large species diversity has recently been discovered in the genus Agaricus. Six subgenera and 23 sections are now recognised. In this study, three specimens collected from Thailand, formed a monophyletic clade in subgenus Pseudochitonia, based on analyses of ITS sequence data. Further analyses, based on multi-gene sequence data (ITS, LSU, tef1-α), using BEAST, revealed that this clade originated 26.7 Ma. According to their distinct morphological characteristics, phylogenetic position and relatively old divergence time, a new section Cymbiformes is proposed and this section is represented by a new species A.angusticystidiatus. This new section is characterised by the strong iodoform odour of basidiomes and cymbiform basidiospores. Descriptions, colour photographs and illustrations are presented.
Comparative Study of the Effect of Warming at Various Temperatures on Biochemical, Hematologic, and Hemodynamic Parameters During Spinal Fusion Surgery Under Intravenous Anesthesia

Background Perioperative inadvertent hypothermia (PIH) commonly occurs after major surgical procedures under local or general anesthesia and increases the risk of complications such as organ failure, hypoperfusion, and peripheral vasoconstriction, as well as adverse postoperative outcomes, such as wound infection and increased surgical bleeding. Objectives We hypothesized that the intra-operative warming may affect these complications and thus, we aimed to compare the most appropriate temperature of the warmer to decrease patients’ complications. Methods The present randomized clinical trial investigated 90 patients undergoing total intravenous anesthesia in posterior spinal fusion surgery, randomly divided into two groups of 45. The warmer was set at 38°C for group “A” during surgery and at 40°C for group “B. ” Patient’s demographic characteristics, the serum level of hemoglobin, hematocrit, and platelet counts, mean core temperature, systolic blood pressure (BP), heart rate, and respiratory rate were recorded before and after the surgery. Variables were compared between the two groups at three time intervals (during induction, during operation, and during recovery). Results The mean temperature was not different between the two groups at the three time intervals. Other laboratory serum tests, vital signs, and oxygen consumption were maintained within the normal range although they did not improve significantly in two groups at the three time intervals. Conclusions There was no significant difference in warming of the patients during operation at 38 or 40°C to prevent hypothermia-induced complications during induction, operation, and recovery.
Lithuanian Author Profiling with the Deep Learning

We address the Lithuanian author profiling task in two dimensions (AGE and GENDER) using two deep learning methods (i.e., Long Short-Term Memory - LSTM) and Convolutional Neural Network - CNN) applied on the top of Lithuanian neural word embeddings. We also investigate an impact of the training dataset size on the author profiling accuracy. The best results are achieved with the largest datasets, containing 5,000 instances in each class. Besides, LSTM was more effective on the smaller datasets, and CNN - on the larger ones. We compare the deep learning methods with the traditional machine learning methods (in particular, Naive Bayes Multinomial and Support Vector Machine), and frequencies of elements as the feature representation). The comparison revealed that the deep learning is not the best solution for our author profiling task.
Dependency of prepulse inhibition deficits on baseline startle reactivity in a mouse model of the human 22q11.2 microdeletion syndrome

Hemizygous microdeletion at the chromosomal locus 22q11.2 is a copy number variation with strong genetic linkage to schizophrenia and related disorders. This association, along with its phenotypic overlap with the 22q11.2 microdeletion syndrome, has motivated the establishment of Df[h22q11]/+ mice, in which the human 22q11.2 orthologous region is deleted. Previous investigations using this model showed the presence of reduced prepulse inhibition (PPI) of the acoustic startle reflex, a form of sensorimotor gating known to be impaired in a number of psychiatric disorders. Concomitantly to reduced PPI, however, Df[h22q11]/+ mice are also characterized by a robust increase in baseline startle reactivity, which may complicate or confound the interpretation of PPI. Therefore, the present study re‐examined the relationship between acoustic startle reactivity and PPI in this mouse model. We found that while PPI is reduced in Df[h22q11]/+ mice when using its relative indexation (ie, % PPI), this deficit is no longer apparent when using the absolute quantification, that is, the direct comparison between pulse‐alone and prepulse‐plus‐pulse conditions with successively increasing prepulse intensities. We further identified marked negative correlations between % PPI and startle reactivity in Df[h22q11]/+ mice. Moreover, when stratifying Df[h22q11]/+ mice into subgroups displaying low‐ and high‐startle reactivity, only the latter subgroup displayed a significant reduction in % PPI. Collectively, our data suggest that alterations in baseline startle reactivity can confound the outcomes and interpretation of PPI in this mouse model of the human 22q11.2 microdeletion syndrome.
Investigating the potential of novel non-woven fabrics for efficient pollination control in plant breeding

Plant breeding is achieved through the controlled self- or cross-pollination of individuals and typically involves isolation of floral parts from selected parental plants. Paper, cellulose or synthetic materials are used to avoid self pollination or cross contamination. Low seed set limits the rate of breeding progress and increases costs. We hypothesized that a novel ‘non-woven’ fabric optimal for both pollination and seed set in multiple plant species could be developed. After determining the baseline pollen characteristics and usage requirements we established iterative three phase development and biological testing. This determined (1) that white fabric gave superior seed return and informed the (2) development of three non-woven materials using different fibre and layering techniques. We tested their performance in selfing and hybridisation experiments recording differences in performance by material type within species. Finally we (3) developed further advanced fabrics with increased air permeability and tested biological performance. An interaction between material type and species was observed and environmental decoupling investigated, showing that the non-woven fabrics had superior water vapour transmission and temperature regulation compared to controls. Overall, non-woven fabrics outperformed existing materials for both pollination and seed set and we found that different materials can optimize species-specific, rather than species-generic performance.