Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Germination Receptors of Bacillus cereus ATCC 14579

ABSTRACT Specific amino acids, purine ribonucleosides, or a combination of the two is required for efficient germination of endospores of Bacillus cereus ATCC 14579. A survey including 20 different amino acids showed that l-alanine, l-cysteine, l-threonine, and l-glutamine are capable of initiating the germination of endospores of B. cereus ATCC 14579. In addition, the purine ribonucleosides inosine and adenosine can trigger germination of the spores. Advanced annotation of the B. cereus ATCC 14579 genome revealed the presence of seven putative germination (ger) operons, termed gerG, gerI, gerK, gerL, gerQ, gerR, and gerS. To determine the role of the encoded putative receptors in nutrient-induced germination, disruption mutants were constructed by the insertion of pMUTIN4 into each of the seven operons. Four of the seven mutants were affected in the germination response to amino acids or purine ribonucleosides, whereas no phenotype could be attributed to the mutants with disrupted gerK, gerL, and gerS loci. The strain with a disrupted gerR operon was severely hampered in the ability to germinate: germination occurred in response to l-glutamine but not in the presence of any of the other amino acids tested. The gerG mutant showed significantly reduced l-glutamine-induced germination, which points to a role of this receptor in the l-glutamine germination signaling pathway. gerR, gerI, and gerQ mutants showed reduced germination rates in the presence of inosine, suggesting a role for these operons in ribonucleoside signaling. Efficient germination by the combination of l-glutamine and inosine was shown to involve the gerG and gerI operons, since the germination of mutants lacking either one of these receptors was significantly reduced. Germination triggered by the combination of l-phenylalanine and inosine was lost in the gerI mutant, indicating that both molecules are effective at the GerI receptor.
A Computerized Recognition System for the Home-Based Physiotherapy Exercises Using an RGBD Camera

Computerized recognition of the home based physiotherapy exercises has many benefits and it has attracted considerable interest among the computer vision community. However, most methods in the literature view this task as a special case of motion recognition. In contrast, we propose to employ the three main components of a physiotherapy exercise (the motion patterns, the stance knowledge, and the exercise object) as different recognition tasks and embed them separately into the recognition system. The low level information about each component is gathered using machine learning methods. Then, we use a generative Bayesian network to recognize the exercise types by combining the information from these sources at an abstract level, which takes the advantage of domain knowledge for a more robust system. Finally, a novel postprocessing step is employed to estimate the exercise repetitions counts. The performance evaluation of the system is conducted with a new dataset which contains RGB (red, green, and blue) and depth videos of home-based exercise sessions for commonly applied shoulder and knee exercises. The proposed system works without any body-part segmentation, bodypart tracking, joint detection, and temporal segmentation methods. In the end, favorable exercise recognition rates and encouraging results on the estimation of repetition counts are obtained.
Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter

Since the shooting of Black teenager Michael Brown by White police officer Darren Wilson in Ferguson, Missouri, the protest hashtag #BlackLivesMatter has amplified critiques of extrajudicial killings of Black Americans. In response to #BlackLivesMatter, other Twitter users have adopted #AllLivesMatter, a counter-protest hashtag whose content argues that equal attention should be given to all lives regardless of race. Through a multi-level analysis of over 860,000 tweets, we study how these protests and counter-protests diverge by quantifying aspects of their discourse. We find that #AllLivesMatter facilitates opposition between #BlackLivesMatter and hashtags such as #PoliceLivesMatter and #BlueLivesMatter in such a way that historically echoes the tension between Black protesters and law enforcement. In addition, we show that a significant portion of #AllLivesMatter use stems from hijacking by #BlackLivesMatter advocates. Beyond simply injecting #AllLivesMatter with #BlackLivesMatter content, these hijackers use the hashtag to directly confront the counter-protest notion of “All lives matter. ” Our findings suggest that Black Lives Matter movement was able to grow, exhibit diverse conversations, and avoid derailment on social media by making discussion of counter-protest opinions a central topic of #AllLivesMatter, rather than the movement itself.
Phoneme State Posteriorgram Features for Speech Based Automatic Classification of Speakers in Cold and Healthy Condition

We consider the problem of automatically detecting if a speaker is suffering from common cold from his/her speech. When a speaker has symptoms of cold, his/her voice quality changes compared to the normal one. We hypothesize that such a change in voice quality could be reﬂected in lower likelihoods from a model built using normal speech. In order to capture this, we compute a 120-dimensional posteriorgram feature in each frame using Gaussian mixture model from 120 states of 40 three-states phonetic hidden Markov models trained on approximately 16.4 hours of normal English speech. Finally, a ﬁxed 5160-dimensional phoneme state posteriorgram (PSP) feature vector for each utterance is obtained by computing statistics from the posteriorgram feature trajectory. Experiments on the 2017-Cold sub-challenge data show that when the decisions from bag-of-audio-words (BoAW) and end-to-end (e2e) are combined with those from PSP features with unweighted majority rule, the UAR on the development set becomes 69% which is 2.9% (absolute) better than the best of the UARs obtained by the baseline schemes. When the decisions from ComParE, BoAW and PSP features are combined with simple majority rule, it re-sults in a UAR of 68.52% on the test set.
Domain dependence of statistical named entity recognition and classification in Croatian texts

Influence of text domain selection on statistical named entity recognition and classification in Croatian texts is investigated. Two datasets of Croatian newspaper texts of differing text domains were manually annotated for named entities and used for training and testing the Stanford NER system for named entity recognition based on sequence labeling with CRF. State of the art scores were observed in both domains. A strong preference for systems trained on mixed text domains is established by the experiment. The top-performing system was recorded with an overall F1-score of 0.876 on mixed-domain test sets, scoring 0.899 in one of the selected domains and 0.852 in the other. The single best domain F1-scores were recorded at 0.910 and 0.858.
Comparison of the Prognostic Utility of the Diverse Molecular Data among lncRNA, DNA Methylation, microRNA, and mRNA across Five Human Cancers

Introduction Advances in high-throughput technologies have generated diverse informative molecular markers for cancer outcome prediction. Long non-coding RNA (lncRNA) and DNA methylation as new classes of promising markers are emerging as key molecules in human cancers; however, the prognostic utility of such diverse molecular data remains to be explored. Materials and Methods We proposed a computational pipeline (IDFO) to predict patient survival by identifying prognosis-related biomarkers using multi-type molecular data (mRNA, microRNA, DNA methylation, and lncRNA) from 3198 samples of five cancer types. We assessed the predictive performance of both single molecular data and integrated multi-type molecular data in patient survival stratification, and compared their relative importance in each type of cancer, respectively. Survival analysis using multivariate Cox regression was performed to investigate the impact of the IDFO-identified markers and traditional variables on clinical outcome. Results Using the IDFO approach, we obtained good predictive performance of the molecular datasets (bootstrap accuracy: 0.71–0.97) in five cancer types. Impressively, lncRNA was identified as the best prognostic predictor in the validated cohorts of four cancer types, followed by DNA methylation, mRNA, and then microRNA. We found the incorporating of multi-type molecular data showed similar predictive power to single-type molecular data, but with the exception of the lncRNA + DNA methylation combinations in two cancers. Survival analysis of proportional hazard models confirmed a high robustness for lncRNA and DNA methylation as prognosis factors independent of traditional clinical variables. Conclusion Our study provides insight into systematically understanding the prognostic performance of diverse molecular data in both single and aggregate patterns, which may have specific reference to subsequent related studies.
Instance-Based Ontology Population Exploiting Named-Entity Substitution

We present an approach to ontology population based on a lexical substitution technique. It consists in estimating the plausibility of sentences where the named entity to be classified is substituted with the ones contained in the training data, in our case, a partially populated ontology. Plausibility is estimated by using Web data, while the classification algorithm is instance-based. We evaluated our method on two different ontology population tasks. Experiments show that our solution is effective, outperforming existing methods, and it can be applied to practical ontology population problems.
The Mosaic Test: Benchmarking Colour-based Image Retrieval Systems Using Image Mosaics

Evaluation and benchmarking in content-based image retrieval has always been a somewhat neglected research area, making it difficult to judge the efficacy of many presented approaches. In this paper we investigate the issue of benchmarking for colour-based image retrieval systems, which enable users to retrieve images from a database based on lowlevel colour content alone. We argue that current image retrieval evaluation methods are not suited to benchmarking colour-based image retrieval systems, due in main to not allowing users to reflect upon the suitability of retrieved images within the context of a creative project and their reliance on highly subjective ground-truths. As a solution to these issues, the research presented here introduces the Mosaic Test for evaluating colour-based image retrieval systems, in which test-users are asked to create an image mosaic of a predetermined target image, using the colour-based image retrieval system that is being evaluated. We report on our findings from a user study which suggests that the Mosaic Test overcomes the major drawbacks associated with existing image retrieval evaluation methods, by enabling users to reflect upon image selections and automatically measuring image relevance in a way that correlates with the perception of many human assessors. We therefore propose that the Mosaic Test be adopted as a standardised benchmark for evaluating and comparing colour-based image retrieval systems.
Malignant Solitary Fibrous Tumor with Heterologous Rhabdomyosarcomatous Differentiation: A Case Report

Malignant solitary fibrous tumor (MSFT) is a well-described entity, from which heterologous differentiation is extremely rare. We encountered a case of MSFT with rhabdomyosarcomatous differentiation in a 56-year-old man. This patient presented with a large mass in his posterior thigh. He had been treated with chemoradiation for sarcoma involving the cervical spine, right femoral head, and both lungs 6 months earlier. A wide excision was performed. The mass measured 10.6 cm and showed a fish-flesh cut surface with necrotic foci. Microscopically, the tumor showed heterogeneous cellularity with a hemangiopericytic vascular pattern. A hypercellular area showed spindle cells or epithelioid cells with high mitotic activity (63/10 high-power fields) and immunoreactivity for CD34 and CD99. A hypocellular area and a cystic area showed pleomorphic rhabdoid cells with immunoreactivity for desmin and myogenin. This is a report of a rare case of MSFT with rhabdomyosarcomatous differentiation and presents new histologic features of MSFT.
A performance comparison of compressed sensing algorithms for scatterer detection

A compressed sensing technique has been studied for accurate scatterer detection. It has many kinds of algorithms, and the features are different. In this paper, we evaluate the performance of compressed sensing algorithms for scatterer detection. We examine three algorithms, OMP, OIHT, and FISTA. We investigate the performance of the algorithms for a single-target and two-target scenarios. It is concluded that FISTA reveals the best performance among the three algorithms.
Desert Dust Is a Risk Factor for the Incidence of Acute Myocardial Infarction in Western Japan

Background—Recently, there has been increasing concern about adverse health effects of exposure to desert dust events. However, the association between dust and the incidence of ischemic heart diseases is unknown. The aim of the present study was to elucidate whether Asian dust (AD), a windblown sand dust originating from mineral soil in China and Mongolia, is associated with the incidence of acute myocardial infarction (AMI). Methods and Results—We investigated the data regarding hospitalization because of AMI among 3068 consecutive patients from 4 AMI centers in Fukuoka, Japan, and data for AD from April 2003 to December 2010. We applied a time-stratified case-crossover design to examine the association between AD and the incidence of AMI. Using a conditional logistic regression analysis, we estimated the odds ratios of AMI associated with AD after controlling for ambient temperature and relative humidity. The occurrence of AD events 0 to 4 days before the day of admission was significantly associated with the incidence of AMI. In particular, the occurrence of AD 4 days before admission was significantly associated with the onset of AMI. Conclusions—These data suggest that exposure to AD a few days before symptom onset is associated with the incidence of AMI.
Four- leg fault- tolerant indirect modulation strategy for three- phase matrix converter based drives

A new control method of fault-tolerant based on indirect modulation strategy for redundant leg based three-phase matrix converter drives to improve reliability of the converter system is proposed in this paper. To achieve the satisfactory matrix converter drive performance after open fault occurrence, the converter topology with help of auxiliary switch devices is modified, and the backup leg is connected to the neutral point of motor windings. Then, to guarantee continuous operation of the converter system under fault operating modes based on reconfigured converter, a new post-fault indirect modulation algorithm is developed to synthesize redefined output waveforms. Simulation results are presented to demonstrate the validity and feasibility of the proposed fault- tolerant modulation control applied to matrix converter drives.
Translational efficiency of polycistronic mRNAs and their utilization to express heterologous genes in mammalian cells.

The translation of polycistronic mRNAs in mammalian cells was studied. Transcription units, constructed to contain one, two or three open reading frames (ORFs), were introduced stably into Chinese hamster ovary cells and transiently into COS monkey cells. The analysis of mRNA levels and protein synthesis in these cells demonstrated that the mRNAs transcribed were translated to generate multiple proteins. The efficiency of translation was reduced approximately 40‐ to 300‐fold by the insertion of an upstream ORF. The results support a modified ‘scanning’ model for translation initiation which allows for translation initiation at internal AUG codons. High‐level expression of human granulocyte‐macrophage colony stimulating factor was achieved utilizing a vector that contains a polycistronic transcription unit encoding an amplifiable dihydrofolate reductase marker gene in its 3′ end. Thus, polycistronic expression vectors can be exploited to obtain high‐level expression of foreign genes in mammalian cells.
Usage of multiple prediction models based on defect categories

Background: Most of the defect prediction models are built for two purposes: 1) to detect defective and defect-free modules (binary classification), and 2) to estimate the number of defects (regression analysis). It would also be useful to give more information on the nature of defects so that software managers can plan their testing resources more effectively. Aims: In this paper, we propose a defect prediction model that is based on defect categories. Method: We mined the version history of a large-scale enterprise software product to extract churn and static code metrics. and grouped them into three defect categories according to different testing phases. We built a learning-based model for each defect category. We compared the performance of our proposed model with a general one. We conducted statistical techniques to evaluate the relationship between defect categories and software metrics. We also tested our hypothesis by replicating the empirical work on Eclipse data. Results: Our results show that building models that are sensitive to defect categories is cost-effective in the sense that it reveals more information and increases detection rates (pd) by 10% keeping the false alarms (pf) constant. Conclusions: We conclude that slicing defect data and categorizing it for use in a defect prediction model would enable practitioners to take immediate actions. Our results on Eclipse replication showed that haphazard categorization of defects is not worth the effort.
Towards Global Reinforcement Learning

There has been substantial progress in the field of Reinforcement Learning (RL) in recent years. Many RL applications have achieved remarkable success by taking advantage of as much prior knowledge (PK) as possible. However, they often do so through problem-specific heuristics, due to the lack of a general framework capable of incorporating broad PK. We take the first steps towards a global RL framework one that incorporates PK over multiple learning components, to learn about all of them simultaneously. Specifically, we consider PK over both model dynamics and policy. We construct a prior belief that reflects both components of PK, use this belief to select actions and update the belief upon new evidence.
Predicting time-to-event from Twitter messages

We describe a system that estimates when an event is going to happen from a stream of microtexts on Twitter referring to that event. Using a Twitter archive and 60 known football events, we train machine learning classifiers to map unseen tweets onto discrete time segments. The time period before the event is automatically segmented; the accuracy with which tweets can be classified into these segments determines the error (RMSE) of the time-to-event prediction. In a cross-validation experiment we observe that support vector machines with X2 feature selection attain the lowest prediction error of 52.3 hours off. In a comparison with human subjects, humans produce a larger error, but recognize more tweets as posted before the event; the machine-learning approach more often misclassifies a before tweet as posted during or after the event.
A general proof of convergence for adaptive distributed beamforming schemes

This work focuses on the convergence analysis of adaptive distributed beamforming schemes that can be reformulated as local random search algorithms via a random search framework. Once reformulated as local random search algorithms, it is proved that under two sufficient conditions: a) the objective function of the algorithm is continuous and all its local maxima are global maxima, and b) the origin is an interior point within the range of the considered transformation of the random perturbation, the corresponding adaptive distributed beamforming schemes converge both in probability and in mean. This proof of convergence is general since it can be applied to analyze randomized adaptive distributed beamforming schemes with any type of objective functions and probability measures as long as both the sufficient conditions are satisfied. Further, this framework can be generalized to analyze an asynchronous scheme where distributed transmitters can only update their beamforming coefficients asynchronously. Simulation results are also provided to validate our analyses.
Formononetin reverses established osteopenia in adult ovariectomized rats

ObjectiveFormononetin (Formo) prevents ovariectomy (Ovx)-induced bone loss in rats. However, there are no reports on the curative effects of Formo. The objective of this study was to investigate the ability of Formo in restoring trabecular microarchitecture and promoting new bone formation in osteopenic rats. MethodsAdult Sprague-Dawley rats were ovariectomized and left for 90 days for osteopenia to develop. After 90 days, Formo (10.0 mg kg−1 d−1) was given orally for the next 12 weeks to Ovx rats in a therapeutic protocol. Sham-operated, Ovx + vehicle, and Ovx + parathyroid hormone (PTH) groups served as controls. Trabecular microarchitecture, osteoid formation, bone turnover/resorption markers, and bone osteoprotegerin–to–receptor activator for nuclear &kgr;B ligand ratio were studied. One-way analysis of variance was used to test significance of effects. ResultsFormo treatment significantly restored the lost trabecular microarchitecture in the femurs and tibia of osteopenic Ovx rats and promoted new bone formation. Formo was devoid of any uterine estrogenicity. Serum levels of type I collagen N-terminal propeptide, which is a reliable marker of bone formation, were increased in Ovx rats treated with Formo compared with Ovx + vehicle group, and the levels were comparable with those in the sham group. Formo prevented the Ovx-induced increase in bone turnover markers, including serum osteocalcin and urinary type I collagen degradation product. Furthermore, Formo-treated Ovx rats had an increased bone osteoprotegerin–to–receptor activator for nuclear &kgr;B ligand ratio compared with the Ovx + vehicle group. ConclusionsDaily oral administration of Formo for 12 weeks has a substantial anabolic effect, thus raising the possibility of its use in postmenopausal osteoporosis.