Miracle-dz/UniFAR-FTUs huggingface dataset:
Discriminating stress from rest based on resting‐state connectivity of the human brain: A supervised machine learning study

Acute stress induces large‐scale neural reorganization with relevance to stress‐related psychopathology. Here, we applied a novel supervised machine learning method, combining the strengths of a priori theoretical insights with a data‐driven approach, to identify which connectivity changes are most prominently associated with a state of acute stress and individual differences therein. Resting‐state functional magnetic resonance imaging scans were taken from 334 healthy participants (79 females) before and after a formal stress induction. For each individual scan, mean time‐series were extracted from 46 functional parcels of three major brain networks previously shown to be potentially sensitive to stress effects (default mode network (DMN), salience network (SN), and executive control networks). A data‐driven approach was then used to obtain discriminative spatial linear filters that classified the pre‐ and post‐stress scans. To assess potential relevance for understanding individual differences, probability of classification using the most discriminative filters was linked to individual cortisol stress responses. Our model correctly classified pre‐ versus post‐stress states with highly significant accuracy (above 75%; leave‐one‐out validation relative to chance performance). Discrimination between pre‐ and post‐stress states was mainly based on connectivity changes in regions from the SN and DMN, including the dorsal anterior cingulate cortex, amygdala, posterior cingulate cortex, and precuneus. Interestingly, the probability of classification using these connectivity changes were associated with individual cortisol increases. Our results confirm the involvement of DMN and SN using a data‐driven approach, and specifically single out key regions that might receive additional attention in future studies for their relevance also for individual differences.
Transcription Factors in Plant Stress Responses: Challenges and Potential for Sugarcane Improvement

Increasing vulnerability of crops to a wide range of abiotic and biotic stresses can have a marked influence on the growth and yield of major crops, especially sugarcane (Saccharum spp.). In response to various stresses, plants have evolved a variety of complex defense systems of signal perception and transduction networks. Transcription factors (TFs) that are activated by different pathways of signal transduction and can directly or indirectly combine with cis-acting elements to modulate the transcription efficiency of target genes, which play key regulators for crop genetic improvement. Over the past decade, significant progresses have been made in deciphering the role of plant TFs as key regulators of environmental responses in particular important cereal crops; however, a limited amount of studies have focused on sugarcane. This review summarizes the potential functions of major TF families, such as WRKY, NAC, MYB and AP2/ERF, in regulating gene expression in the response of plants to abiotic and biotic stresses, which provides important clues for the engineering of stress-tolerant cultivars in sugarcane.
Effects of Novel Dual GIP and GLP-1 Receptor Agonist Tirzepatide on Biomarkers of Nonalcoholic Steatohepatitis in Patients With Type 2 Diabetes

OBJECTIVE To determine the effect of tirzepatide, a dual agonist of glucose-dependent insulinotropic polypeptide and glucagon-like peptide 1 receptors, on biomarkers of nonalcoholic steatohepatitis (NASH) and fibrosis in patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS Patients with T2DM received either once weekly tirzepatide (1, 5, 10, or 15 mg), dulaglutide (1.5 mg), or placebo for 26 weeks. Changes from baseline in alanine aminotransferase (ALT), aspartate aminotransferase (AST), keratin-18 (K-18), procollagen III (Pro-C3), and adiponectin were analyzed in a modified intention-to-treat population. RESULTS Significant (P < 0.05) reductions from baseline in ALT (all groups), AST (all groups except tirzepatide 10 mg), K-18 (tirzepatide 5, 10, 15 mg), and Pro-C3 (tirzepatide 15 mg) were observed at 26 weeks. Decreases with tirzepatide were significant compared with placebo for K-18 (10 mg) and Pro-C3 (15 mg) and with dulaglutide for ALT (10, 15 mg). Adiponectin significantly increased from baseline with tirzepatide compared with placebo (10, 15 mg). CONCLUSIONS In post hoc analyses, higher tirzepatide doses significantly decreased NASH-related biomarkers and increased adiponectin in patients with T2DM.
Emergence of a butterfly: the life experiences of type 1 diabetes Taiwanese patients during the 16–25 years old transition period

ABSTRACT Purpose: To explore the life experiences of patients with type 1 diabetes transition from adolescence into adulthood in Taiwan. Methods: Descriptive phenomenological design was used. Fourteen participants were individually interviewed using a semi-structured interview. Results: The life experiences of patients with type 1 diabetes transition from adolescence into adulthood experience a metamorphosis from awareness of responsibility to figuring out a way to care for themselves. Six themes emerged: (1) hibernation: awareness of responsibility; (2) emergence: attempts to take responsibility; (3) perseverance: encountering difficulties; (4) anxiety: multiple worries; (5) hesitation: back-and-forth,” and (6) exit: finding a way out. ” Conclusions: During the transition phase, the participants experienced the trials of various situations. Regardless of whether they are able to independently bear the responsibilities of self-management, they all hope to turn around the challenges of disease control and take ownership of their disease. Like a butterfly that emerges from a cocoon, they hoped to overcome the dangers of taking flight through trial and error and navigating the world. The results of this study can serve as a reference for clinical care and developing localized intervention strategies targeted to the transition period between adolescence and young adulthood.
Identification of male heterogametic sex‐determining regions on the Atlantic herring Clupea harengus genome

Abstract The sex determination system of Atlantic herring Clupea harengus L. , a commercially important fish, was investigated. Low coverage whole‐genome sequencing of 48 females and 55 males and a genome‐wide association study revealed two regions on chromosomes 8 and 21 associated with sex. The genotyping data of the single nucleotide polymorphisms associated with sex showed that 99.4% of the available female genotypes were homozygous, whereas 68.6% of the available male genotypes were heterozygous. This is close to the theoretical expectation of homo/heterozygous distribution at low sequencing coverage when the males are factually heterozygous. This suggested a male heterogametic sex determination system in C. harengus, consistent with other species within the Clupeiformes group. There were 76 protein coding genes on the sex regions but none of these genes were previously reported master sex regulation genes, or obviously related to sex determination. However, many of these genes are expressed in testis or ovary in other species, but the exact genes controlling sex determination in C. harengus could not be identified.
Reflections in Hilbert Space

In the final lecture, we focus on discrete time quantum walks and their connection to the reflection operations we’ve been considering so far in the series. Discrete time quantum walks were an outpouring of the study of quantum cellular automata [1, 2] and were first seriously recognized as useful for quantum speedup in the paper by Ambainis [3]. Ref. [3] was the precursor to Szegedy’s work [4, 5] that we’ve been discussing in the previous lectures. There are many different applications of discrete time quantum walks as discussed in reviews [6, 7, 8] and to close the lecture we will discuss a recent paper [9] on simulating Google’s PageRank algorithm using discrete time quantum walks. The lecture begins with discrete quantum walks on a line followed by the generalization to discrete quantum walks on general graphs, and, finally, the quantized Google PageRank algorithm. The aim of the presentation is to impart a broad understanding of discrete quantum walks as the products of reflection operators; just as we’ve been studying. Throughout the lecture, the Hilbert space will be partitioned into two spaces, e.g. HA ⊗ HB and the following two notations are used interchangeably for matrix elements (rank one projectors):
3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks

Training deep convolution neural network requires a large amount of data to obtain good performance and generalisable results. Transfer learning approaches from datasets such as ImageNet had become important in increasing accuracy and lowering training samples required. However, as of now, there has not been a popular dataset for training 3D volumetric medical images. This is mainly due to the time and expert knowledge required to accurately annotate medical images. In this study, we present a method in extracting labels from DICOM metadata that information on the appearance of the scans to train a medical domain 3D convolution neural network. The labels include imaging modalities and sequences, patient orientation and view, presence of contrast agent, scan target and coverage, and slice spacing. We applied our method and extracted labels from a large amount of cancer imaging dataset from TCIA to train a medical domain 3D deep convolution neural network. We evaluated the eﬀectiveness of using our proposed network in transfer learning a liver segmentation task and found that our network achieved superior segmentation performance (DICE=90.0%) compared to training from scratch (DICE=41.8%). Our proposed network shows promising results to be used as a backbone network for transfer learning to another task. Our approach along with the utilising our network, can potentially be used to extract features from large-scale unlabelled DICOM datasets.
Distinguishing Professional and Amateur Abstract Art Using Convolutional Neural Networks

In 2007, Freddie Linksky fooled the world into paying large numbers for his abstract paintings – people didn’t realize they were a two-year-old’s ketchup art [9]. Distinguishing between professional and amateur artwork is vital when evaluating its commercial value. In 2010, psychologists Hawley-Dolan and Winner found that even untrained adults can distinguish between professionals’ and children’s abstract works [4]. Can a machine learn to do the same? CNNs generally perform well on many image classification tasks that require distinguishing between the objects represented by images. The task of classifying professional and amateur abstract artwork may be challenging for humans, and research thus far has called into question the balance of content-based and style-based information necessary for models to perform well on such tasks [6][5][7]. In this paper, I demonstrate that by applying deep learning techniques to address issues of high variance, even standard CNN architectures are able to successfully distinguish between most professional and amateur artwork.
A Deep Learning Approach for Human Activity Recognition Project Category: Other (Time-Series Classiﬁcation)

The hardware and sensors in smartphones and wearable devices are becoming more powerful and precise every year; the valuable data these sensors collect can be used for more precise Human Activity Recognition. In this paper, we explore implementing both Convolutional Neural Networks and LSTMs to classify 18 unique activities ranging from eating chips to dribbling a ball from labelled, time-series gyroscope and accelerometer data collected from a smartwatch and smartphone. All of our work is located in our public repo here, and the processed data can be accessed here
Licensing and anaphora in Tenyidie

Tenyidie shows a typologically unusual pattern of φ-covarying agreement with anaphors, but not other arguments. We argue that this apparent agreement actually reveals the nature of anaphoric binding in the language, which involves an Agree relation between the anaphor and its antecedent mediated by a licensing head. e features on this mediating head are realized overtly in Tenyidie. is is not genuine φ-agreement, but rather a morphological reex of the binding relation itself. We provide supporting evidence from a range of constructions and also show how this analysis accounts for restrictions on anaphora in double object constructions. Tenyidie therefore suggests that there is a close link between binding and Agree.
Convolutional Neural Networks in Logarithmic Gradient Image Sensing

—This paper explores the application of residual learning and proposes a minimal residual network (mResNet) in the classification of log-gradient (LG) images. ResNet-10, 18, and 34 are also implemented as comparison. All ResNets are trained and tested on four categories: person, car, bicycle, and bus. The training and test sets are generated based on Pascal VOC, consisting of 6433 and 2388 images, respectively. To further reduce the data volume, some modifications of datasets are used, including resolution, numbers of RGB layers, LG filters. Adversarial images are also generated to examine the robustness of the proposed mResNet. Because of the insufficient accuracy, this paper also performs the error analysis and discusses future work in details.
THE FACTORS OF DIGITAL TRANSFORMATIONS LEAD TO DIGITAL MARKETING AND CUSTOMER VALUE THAT IMPACTED FOR MARKET POSITIONS AND ITS IMPLICATIONS THE FINANCIAL PERFORMANCE IN MANUFACTORY CORPORATIONS-LQ45

This decade an among multinational-corporate should not operate within the misguide to implemented the vision and missions in the heterogeneous-industrial competitiveness and the consumers engagement, tight competitions, data, customer-based value and an incremental innovation. This study would be inquiry of fulfillment the research gap with the competition-advantage within market positions from the digital transformation within multinational-firms. The novelty research model has the intervening variables-the powerful digital marketing and the focus creation in customer-value that could be more exploring the R-A theory. Furthermore, the research methods conducted with the confirmed strategy in structural equation modeling within an entire latent variable.
Real Time Beat Tracking: A Mixed Approach Category: Music

Beats are the fundamental unit of time in music. Finding the beats in music is usually quite easy for many people; many people will actually inadvertently do it as they tap their feet to music. Beat tracking is the term used to describe the task when given to a computer. In this paper I describe a beat tracking system built that works in real time to ﬁnd beats as they occur in an musical audio signal and predict the location of the next beat. The incoming audio gets converted to a spectrogram image where frequency and amplitude are plotted over a time domain. At a regular frequency, the image representing the last 3 seconds of audio gets passed to a convolutional neural net which predicts the location of the beats within the image. Given the predicted locations of the beat in the image a tempo is inferred as well as the temporal distance since the last beat. This information is passed to a Kalman ﬁlter which keeps track of the current belief about tempo and temporal distance from the last beat and updates its belief based on the new predictions.
The Prevalence of Low Weight in Children under 6 Years Old in Lordegan City in 2015-2016

Introduction: Human health is affected by heredity and environment. The issue of inheritance is of paramount importance, but the impacts of environmental factors and nutritional conditions sometimes go beyond the hereditary factors. Investigation of the status of child development is the most important criterion for identifying the health status. The aim of this study was to investigate the prevalence of underweight children in Lordegan City during 2015-2016. Methods: This descriptive-analytical cross-sectional study was carried out on 900 children under 6 years of age in Lordegan in 2015-2016. Sampling was performed using two-stage cluster sampling method. Data were collected using observation and interview by filling out a questionnaire. Collected data were analyzed using SPSS.V 22 software, independent T-test, and Chi-Square tests at a significant level of 0.05. Results: In this study, 900 children were studied; 49% (441 persons) were female and 51% (459) were male. Of this number, 700 (77.7%) were of normal weight, 155 (17.3%) were moderately underweight, and 45 (5%) were severely underweight. The highest prevalence of malnutrition in boys and girls was at the age of 2 and 3 years, respectively. Moreover, 91.1% of the population lived in the village, 8.4% lived in the city, and 0.5% were living in the suburbs and tribes. In this study, a significant relationship was found between illiteracy of parents, breakfast in the week, place of living, main meal per day, age of the child, number of children in the family, hospitalization of the child due to illness, maternal number of pregnancies, and low weight prevalence (p≤0.05). Conclusion: Regarding the results and their comparison with the standard weight, environmental factors can be effective in delaying weight gain. Therefore, educating health care and parental care for proper nutrition can lead to children's health at this stage of growth.
Online Donation Based Crowdfunding using Clustering and K-Nearest Neighbor Algorithm

: Charity is an organization setup to provide help and raise money for those in need. It is generosity and helpfulness especially toward the needy or suffering. Crowdfunding works through individual or organization that invests in (or donate to). Crowdfunding projects in return for a potential profit or reward. Investing this way can be risky, so make sure you know what you are doing. So, we are providing the trustworthy crowd funding through government, which is the funding of a project by a large number of supporters who can each contribute a small amount. only authenticated recipient and donor can request and donate money here . This system uses the clustering algorithm to filter the data from large scale of datasets and uses k-nearest neighbour algorithm for clustering the similar data from large datasets. These systems help in automatically notifying the donors according to their interest in donation on any particular day, for example, on their birthday, and appreciate the donors to further improve their sequence of donation.
Tennis Shot Recognition through Spatiotemporal Deep Neural Networks

Recognition of human action in videos has generated substantial attention from the deep learning community in recent years [1-4]. In this project, we aim apply the video classification problem to detect tennis shots. This work is also aimed at making progress towards a larger project that we are working on, where we envision a need to identify and correct player postures while playing various tennis shots. Consequently, there are two ML problems: to identify a particular shot, and to detect if the posture is correct. As a first step towards this goal, we aim to use deep neural networks which takes a video as input, and classifies it into one of 6 basic tennis shots.
Optimization of Crystallization Process of Methyl -2- (4- Hydroxyphenoxy) Propionate Based on Response Surface Methodology

Methyl-2-(4-hydroxyphenoxy) propionate is an important intermediate for the synthesis of aryloxyphenoxy propionate (APP) high efficiency herbicides. There is a certain gap between the existing products and the process demand, such as the particle size and the distribution of particle size. Central composite design (CCD), a kind of response surface methodology, was used to explore the effects of stirring rate, cooling rate, seeding quantity and aging time on the particle size of methyl-2-(4-hydroxyphenoxy) propionate and the coefficient of variation (C.V.) of methyl-2-(4-hydroxyphenoxy) propionate. Based on the regression analysis, regression equations related to response values and factors were established, respectively. For crystal size, the most significant parameter was aging time, then was seeding quantity, and last were stirring rate and cooling rate. For crystal size distribution, the most significant parameter was stirring rate, then was seeding quantity, and last were aging time and cooling rate. The interaction between stirring rate and seeding quantity, stirring rate and aging time, were significant, too. The crystallization process was optimized as follows: stirring rate of 400 rpm, cooling rate of 5C·h -1 , seeding quantity of 2.5g, and aging time of 20 min. In the above conditions, methyl-2-(4-hydroxyphenoxy) propionate were prepared and its purity reached above 98%, with an average particle size of 1054. 27μm and a more concentrated size distribution, with the C.V. in 0.8913. The errors between the predicted and experimental values were small, which showed that the established models were accurate and reliable for the analysis and prediction of the process of reactive crystallization.
The use of model checking and the COSMA environment in the design of reactive systems

The paper discusses how a bridge between the design practice and the formal methods could be maintained. The use of model checking seems to be the most promising approach. Then, the software environment COSMA is presented, implementated in the Institute of Computer Science,WUT. The conceptual framework of COSMA is based upon Concurrent State Machines (CSM) and Extended CSM, which are also briefly summarized and illustrated with a simple example.
PRODUCTION OF AMYLASE BY USING BACILLUS LICHENIFORMIS ISOLATED FROM VERMICOMPOST

Bacillus licheniformis strain isolated from a vermicompost was tested for its abilities to hydrolyze the structural polysaccharides. The effect of different production parameters such as pH, temperature, carbon source, nitrogen source (Organic and inorganic) incubation time inoculum sizes and surfactants on amylase production by the isolated bacterial strain was studied. The enzyme production was assayed in submerged fermentation (SmF) condition. The maximum amylase production was observed with maltose (200±2.6 U/ml), yeast extract (143±3.6U/ml), ammonium sulphate (65±3.0U/ml), pH 7.0 (233±4.1), temperature 40°C (125±9.1), Tween-80 (145±2.8U/ml), inoculum size level 5% (132±2.0U/ml) and incubation time 48 hours (162.01± 0.56U/ml) in the production medium.