Miracle-dz/UniFAR-FTUs huggingface dataset:
Anatomical Atlas of the Quail's Ear (Coturnix coturnix)

This study aims to enhance the anatomical knowledge of the ear of the adult quail (Coturnix coturnix) through the creation of a scaled 3D model utilizing data from micro‐CT images. In addition, 17 annotated histological sections of the quail's ear are aligned to their 3D position in the model. The resulting anatomical atlas provides an intuitive insight into the 3D anatomy and can be used for medical education. The model also allows measuring anatomical structures and can thus serve as reference for the quail's auricular anatomy and as a basis to evaluate clinical diagnostic imaging results.
An iterative algorithm for battery-aware task scheduling on portable computing platforms

We consider battery powered portable systems which either have field programmable gate arrays (FPGA) or voltage and frequency scalable processors as their main processing element. An application is modeled in the form of a precedence task graph at a coarse level of granularity. We assume that, for each task in the task graph, several unique design-points are available which correspond to different hardware implementations for FPGAs and different voltage-frequency combinations for processors. It is assumed that performance and total power consumption estimates for each design-point are available for any given portable platform, including the power usage of peripheral components, such as memory and display. We present an iterative heuristic algorithm which finds a sequence of tasks along with an appropriate design-point for each task, such that a deadline is met and the amount of battery energy used is as small as possible. A detailed illustrative example, along with a case study of a real-world application of a robotic arm controller which demonstrates the usefulness of our algorithm, is also presented.
Review of fire detection technologies based on video image

Because of high fire frequency and huge damage, the early fire detection is becoming more and more important. Due to the rapid development of image processing technology, video image in the application of detection technology has become more and more widely. Video based fire detection technology is becoming the focal point of research with its advantages of high intuitive, speed and anti-jamming capability. This paper reviewed the fire detection methods based on video images in recent years. Through the review, it is clear to see that video based fire detection technology can be divided into two main areas: the characteristics detection of flame and smoke. This fire detection method can improve the accuracy of the fire alarm, real-time and robustness. If the optimal algorithms can be adopted for each part of detecting motion area and extracting fire characteristics, the system performance will be further improved. KEYWORDS: Video Image; Video Based Fire Detection; Flame Detection Methods; Smoke Detection Methods Language: en
Unsupervised Lexical Acquisition for Part of Speech Tagging

It is known that POS tagging is not very accurate for unknown words (words which the POS tagger has not seen in the training corpora). Thus, a first step to improve the tagging accuracy would be to extend the coverage of the taggers learned lexicon. It turns out that, through the use of a simple procedure, one can extend this lexicon without using additional, hard to obtain, hand-validated training corpora. The basic idea consists of merely adding new words along with their (correct) POS tags to the lexicon and trying to estimate the lexical distribution of these words according to similar ambiguity classes already present in the lexicon. We present a method of automatically acquire high quality POS tagging lexicons based on morphologic analysis and generation. Currently, this procedure works on Romanian for which we have a required paradigmatic generation procedure but the architecture remains general in the sense that given the appropriate substitutes for the morphological generator and POS tagger, one should obtain similar results.
Correlates of HIV-1 Genital Shedding in Tanzanian Women

Background Understanding the correlates of HIV shedding is important to inform strategies to reduce HIV infectiousness. We examined correlates of genital HIV-1 RNA in women who were seropositive for both herpes simplex virus (HSV)-2 and HIV-1 and who were enrolled in a randomised controlled trial of HSV suppressive therapy (aciclovir 400 mg b.i.d vs. placebo) in Tanzania. Methodology Samples, including a cervico-vaginal lavage, were collected and tested for genital HIV-1 and HSV and reproductive tract infections (RTIs) at randomisation and 6, 12 and 24 months follow-up. Data from all women at randomisation and women in the placebo arm during follow-up were analysed using generalised estimating equations to determine the correlates of cervico-vaginal HIV-1 RNA detection and load. Principal Findings Cervico-vaginal HIV-1 RNA was detected at 52.0% of 971 visits among 482 women, and was independently associated with plasma viral load, presence of genital ulcers, pregnancy, bloody cervical or vaginal discharge, abnormal vaginal discharge, cervical ectopy, Neisseria gonorrhoeae, Chlamydia trachomatis, Trichomonas vaginalis, an intermediate bacterial vaginosis score and HSV DNA detection. Similar factors were associated with genital HIV-1 RNA load. Conclusions RTIs were associated with increased presence and quantity of genital HIV-1 RNA in this population. These results highlight the importance of integrating effective RTI treatment into HIV care services.
Kinematics of lower extremity for the young and elderly Chinese population during squatting

The purpose of this study was to investigate the kinematics of the lower extremities of Chinese population during normal squatting activity. 25 young subjects (average age of 23.6 years) and 25 elderly Chinese subjects ((average age of 62.7 years) were recruited. With each subject's six trials data collected, the means of the three-dimensional (3D) rotation angles of the ankle, knee, and hip joints of two groups were calculated and analyzed. The statistical analysis showed that there are significant differences for hip joints and no significant differences for the knee and ankle joints between the two groups. The maximal eccentric range of hip flexion/extension of 90.1° for the young group (P<;0.01) was compared to that of 160.9° for the elderly group. Thus, the elderly enjoys more hip flexion/extension angles than the young, with the squatting posture assumed. The mean range of motion (ROM) of knee flexion/extension was 136.4° for the young group and 137.7°(P>0.05) for the elderly group. The mean ROM of ankle flexion/extension was 33.0° for the young group and 33.1°(P>0.05) for the elderly group. The ROMs available for us to obtain in the experiment during squatting were greater than the reported ones achieved after joint arthroplasty. These data may be valuable in establishing a standard of designing lower extremity prosthesis and assessing effect of rehabilitation for the Chinese population.
Sparse Representation based Classification for mine hunting using Synthetic Aperture Sonar

In this paper, a Sparse Representation based Classification (SRC) approach is employed for mine hunting using Synthetic Aperture Sonar (SAS) images. Given a training database with enough samples, SRC exploits the properties of sparse signals and expresses a sample of unknown class as a sparse linear combination of the training samples. The class of the training samples with greater weight is likely to be the candidate sample class. The method was introduced for face recognition, where the face images are directly taken as feature sets. Due to the greater variability of sonar images, for mine hunting applications it is more convenient to transform the image samples into a different feature domain. Several feature sets are considered, and the results are compared with those provided by a linear discriminant analysis classifier. We have tested the method on an extensive SAS database with more than 400 mines.
Reference Ranges of Blood Flow in the Major Vessels of the Normal Human Fetal Circulation at Term by Phase-Contrast Magnetic Resonance Imaging

Background—Phase-contrast MRI with metric-optimized gating is a promising new technique for studying the distribution of the fetal circulation. However, mean and reference ranges for blood flow measurements made in the major fetal vessels using this technique are yet to be established. Methods and Results—We measured flow in the major vessels of the fetal circulation in 40 late-gestation normal human fetuses using phase-contrast MRI (mean gestational age, 37 [SD=1.1] weeks). Flows were indexed to the fetal weight, which was estimated from the fetal volume calculated by MRI segmentation. The following mean flows (in mL/min per kilogram; ±2SD) were obtained: combined ventricular output, 465 (351, 579); main pulmonary artery, 261 (169, 353); ascending aorta, 191 (121, 261); superior vena cava, 137 (77, 197); ductus arteriosus, 187 (109, 265); descending aorta, 252 (160, 344); pulmonary blood flow, 77 (0, 160); umbilical vein, 134 (62, 206); and foramen ovale, 135 (37, 233). Expressed as percentages of the combined ventricular output, the mean flows±2 SD were as follows: main pulmonary artery, 56 (44, 68); ascending aorta, 41 (29, 53); superior vena cava, 29 (15, 43); ductus arteriosus, 41 (25, 57); descending aorta, 55 (35, 75); pulmonary blood flow, 16 (0, 34); umbilical vein, 29 (11, 47); and foramen ovale, 29 (7, 51). A strong inverse relationship between foramen ovale shunt and pulmonary blood flow was noted (r=−0.64; P<0.0001). Conclusions—Although too small a sample size to provide normal ranges, these results are in keeping with those predicted in humans based on measurements made in fetal lambs using radioactive microspheres and provide preliminary reference ranges for the late-gestation human fetuses. The wide range we found in foramen ovale shunting suggests a degree of variability in the way blood is streamed through the fetal circulation.
Acute disseminated encephalomyelitis in children.

OBJECTIVE To describe the epidemiologic, clinical, neuroimaging, and laboratory features; treatment; and outcome in a cohort of children with acute disseminated encephalomyelitis (ADEM). METHODS A 6-year retrospective chart review of children with the diagnosis of ADEM was conducted. RESULTS Eighteen cases were identified. Sixteen patients (88%) presented in either winter or spring. Thirteen children (72%) had a recent upper respiratory tract illness. Patients presented most often with motor deficits (77%) and secondly with altered consciousness (45%). Spinal fluid abnormalities occurred in 70%. Despite rigorous microbiologic testing, a definite microbiologic diagnosis was established only in 1 child with Epstein-Barr virus disease and probable or possible diagnoses in 3 children with Bartonella henselae, Mycoplasma pneumoniae, or rotavirus disease. Brain magnetic resonance imaging identified lesions in the cerebral cortex in 80%, in subcortical white matter in 93%, in periventricular white matter in 60%, in deep gray matter in 47%, and in brainstem in 47% of patients. Eleven patients (61%) were treated with corticosteroids, and 2 were treated with intravenous immunoglobulins. All patients survived. Three patients (17%) had long-term neurologic sequelae. CONCLUSIONS Epidemiologic evidence from this study suggests an infectious cause for ADEM. The agent is most likely a difficult-to-diagnose winter/spring respiratory virus. Magnetic resonance imaging was the neuroimaging study of choice for establishing the diagnosis and for following the course of the disease. Prognosis for survival and outcome was excellent. Recurrent episodes of ADEM must be differentiated from multiple sclerosis.
Comparison of two different methods for physiologic dead space measurements in ventilated dogs in a clinical setting.

OBJECTIVE To compare physiologic dead space (V(D)) and physiologic dead space to tidal volume (V(T)) ratio (V(D)/V(T)) obtained by an automated single breath test for carbon dioxide (CO(2)) (method SBT) and a manual calculation (method MC) in ventilated healthy dogs. STUDY DESIGN Prospective clinical study. ANIMALS Twenty client-owned dogs, ASA I and II undergoing anaesthesia for clinical purposes. METHODS Following pre-medication, induction of anaesthesia, and intubation of the trachea, intermittent positive pressure ventilation was commenced. Mixed expired CO(2) partial pressure (PēCO(2)) was measured by two methods: method MC by analysis, using an infrared capnograph, of the expired gas collected in a mixing box and method SBT which calculated it automatically by a device consisting of a mainstream capnograph and a pneumotachograph. At four time points arterial partial pressure of CO(2) (PaCO(2)) was measured. Physiologic dead space variables (V(D) and V(D)/V(T)) were calculated manually (method MC) or automatically (method SBT) using the Bohr-Enghoff equation. Method MC and SBT were compared using Bland-Altman plots and linear regression. Intra-class correlation coefficient (ICC) was used to measure consistency of each method. RESULTS Four measurement pairs were obtained in all 20 dogs for method SBT and MC. The bias was -1.15 mmHg, 7.97 mL and 0.02 for PēCO(2), V(D) and V(D)/V(T), respectively. Linear regression analysis revealed a correlation coefficient (r(2)) of 0.79, 0.94, and 0.83 for PēCO(2), V(D) and V(D)/V(T), respectively. The ICC revealed an excellent consistency for both methods. CONCLUSIONS The single breath test (SBT) can be used for clinical evaluation of V(D) and V(D)/V(T) in anaesthetized ventilated dogs. CLINICAL RELEVANCE Through measuring V(D) and V(D)/V(T) important information about lung ventilation can be obtained and the SBT is an easy method to use for this purpose.
Transcription activation by Myc and Max: flanking sequences target activation to a subset of CACGTG motifs in vivo.

The Myc oncoprotein has been implicated in control of cell growth, division and differentiation. Although Myc contains a bHLH‐LZ motif, it fails to bind DNA alone but can do so by forming heterodimers with an unrelated bHLH‐LZ protein, Max. Max homodimers and Myc‐Max heterodimers share the ability to bind CACGTG or CATGTG elements. Current models, based on experimentally induced overexpression of Myc and Max in mammalian cells, propose that Max‐Max homodimers repress while Myc‐Max heterodimers activate transcription through CACGTG binding sites. The interpretation of the results using mammalian cells is complicated by the presence of numerous unrelated CACGTG binding transcription activators and the existence of two alternative Max dimerization partners, Mad and Mxi‐1. Thus, the mechanism whereby overexpression of Max leads to transcriptional repression remains to be established. Using a yeast system we show that Max homodimers have the potential to activate transcription through CACGTG motifs. Activation by Max requires DNA binding and amino acids outside the bHLH‐LZ domain but is reduced compared with activation by Myc‐Max heterodimers. Moreover, transcriptional activation by Myc‐Max heterodimers, but not Max‐Max homodimers, is strongly inhibited in vivo by specific sequences flanking the core CACGTG binding motif, presumably reflecting reduced DNA binding affinity. These results suggest a mechanism for directing the Myc‐Max complex to a specific subset of CACGTG‐containing target genes.
A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017

This paper proposes a deep-learning based native-language identification (NLI) using a latent semantic analysis (LSA) as a participant (ETRI-SLP) of the NLI Shared Task 2017 where the NLI Shared Task 2017 aims to detect the native language of an essay or speech response of a standardized assessment of English proficiency for academic purposes. To this end, we use the six unit forms of a text data such as character 4/5/6-grams and word 1/2/3-grams. For each unit form of text data, we convert it into a count-based vector, extract a 2000-rank LSA feature, and perform a linear discriminant analysis (LDA) based dimension reduction. From the count-based vector or the LSA-LDA feature, we also obtain the output prediction values of a support vector machine (SVM) based classifier, the output prediction values of a deep neural network (DNN) based classifier, and the bottleneck values of a DNN based classifier. In order to incorporate the various kinds of text-based features and a speech-based i-vector feature, we design two DNN based ensemble classifiers for late fusion and early fusion, respectively. From the NLI experiments, the F1 (macro) scores are obtained as 0.8601, 0.8664, and 0.9220 for the essay track, the speech track, and the fusion track, respectively. The proposed method has comparable performance to the top-ranked teams for the speech and fusion tracks, although it has slightly lower performance for the essay track.
Comparison of Model-Based Learning Methods for Feature-Level Opinion Mining

The tasks of feature-level opinion mining usually include the extraction of product entities from product reviews, the identification of opinion words that are associated with the entities, and the determining of these opinions' polarities (e.g., positive, negative, or neutral). In recent years, several approaches have been proposed such as rule-based and statistical methods on this subject, but few attentions have been paid to applying more discriminative learning models to achieve the goal. On the other hand, little work has evaluated their algorithms' performance for identifying intensifiers, entity phrases and infrequent entities. In this paper, we in particular adopt the Conditional Random Fields (CRFs) model to perform the opinion mining tasks. Relative to related approaches, we have not only highlighted the algorithm's ability in mining intensifiers, phrases and infrequent entities, but also integrated more elements in the model so as to optimize its training and decoding process. Our method was compared to the lexicalized Hidden Markov Model (L-HMMs) based opinion mining method in the experiment, which proves its significantly better accuracy from several aspects.
Augmented Lung Inflammation Protects against Influenza A Pneumonia

Background Influenza pneumonia causes high mortality every year, and pandemic episodes kill millions of people. Influenza-related mortality has been variously ascribed to an ineffective host response that fails to limit viral replication, an excessive host inflammatory response that results in lung injury and impairment of gas exchange, or to bacterial superinfection. We sought to determine whether lung inflammation promoted or impaired host survival in influenza pneumonia. Methods and Findings To distinguish among these possible causes of influenza-related death, we induced robust lung inflammation by exposing mice to an aerosolized bacterial lysate prior to challenge with live virus. The treatment induced expression of the inflammatory cytokines IL-6 and TNF in bronchoalveolar lavage fluid 8- and 40-fold greater, respectively, than that caused by lethal influenza infection. Yet, this augmented inflammation was associated with striking resistance to host mortality (0% vs 90% survival, p = 0.0001) and reduced viral titers (p = 0.004). Bacterial superinfection of virus infected lungs was not observed. When mice were repeatedly exposed to the bacterial lysate, as would be clinically desirable during an influenza epidemic, there was no tachyphylaxis of the induced viral resistance. When the bacterial lysate was administered after the viral challenge, there was still some mortality benefit, and when ribavirin was added to the aerosolized bacterial lysate, host survival was synergistically improved (0% vs 93.3% survival, p<0.0001). Conclusions Together, these data indicate that innate immune resistance to influenza can be effectively stimulated, and suggest that ineffective rather than excessive inflammation is the major cause of mortality in influenza pneumonia.
Molecular genetics and genomics of abiotic stress responses

Abiotic stresses are the major causes that limit productivity of crop plants worldwide. Plants have developed intricate machinery to respond and adapt over these adverse environmental conditions both at physiological and molecular levels. Due to increasing abiotic stress constraints, plant biotechnologists and breeders need to devise and employ new approaches to improve abiotic stress tolerance in crop plants. Although the current research has divulged several key genes, gene regulatory networks and quantitative trait loci (QTLs) that mediate plant responses to various abiotic stresses, the comprehensive understanding of this complex trait is still not available. With an objective to understand the plant response/adaptation to various abiotic stresses, a special issue was planned for the journal. The current research topic “Abiotic Stress: Molecular Genetics and Genomics” has a combination of primary research articles, perspective, opinion and review work, written by authorities in their respective fields. These articles provide novel insights and detailed overviews on the current knowledge into different aspects of plant responses and adaptation to abiotic stresses.
A Survey on Deployment Algorithms in Underwater Acoustic Sensor Networks

Node deployment is one of the fundamental tasks for underwater acoustic sensor networks (UASNs) where the deployment strategy supports many fundamental network services, such as network topology control, routing, and boundary detection. Due to the complex deployment environment in three-dimensional (3D) space and unique characteristics of underwater acoustic channel, many factors need to be considered specifically during the deployment of UASNs. Thus, deployment issues in UASNs are significantly different from those of wireless sensor networks (WSNs). Node deployment for UASNs is an attractive research topic upon which a large number of algorithms have been proposed recently. This paper seeks to provide an overview of the most recent advances of deployment algorithms in UASNs while pointing out the open issues. In this paper, the deployment algorithms are classified into three categories based on the mobility of sensor nodes, namely, (I) static deployment, (II) self-adjustment deployment, and (III) movement-assisted deployment. The differences of the representative algorithms in aspects of sensor node types, computation complexity, energy consumption, deployment objectives, and so forth, are discussed and investigated in detail.