Miracle-dz/UniFAR-FTUs huggingface dataset:
LUNG FUNCTIONS AFTER CORRECTION OF SCOLIOSIS ANGLE

Background: In this study we evaluated lung volumes, volume changes relative to Cobb angle and correlation of volume changes with Cobb angle changes before and after the surgery. Materials and Methods: Eighteen non-smoker patients with idiopathic scoliosis were included in this descriptive observational study. Cobb angle, lung volume and flow were measured before and after the surgery. To assess height and weight changes during the follow-up period, we used the percent relative to normal (percent predicted) instead of absolute volumes. Results: Eighteen of 30 selected patients were included. The mean follow-up period was 34.5±19.6 months. Dynamic volume changes of lung were: VC= -13.4 SD=8.6 (p<0.005); FVC=-9.22 SD=14 (p<0.001); FEV1=9.8 SD=15 (p<0.001). There was a weak correlation between the mean value of dynamic volume changes and the mean changes in Cobb angle after the surgery. There was a weak correlation between Cobb angle and dynamic volume of lung before the surgery. Conclusion: In this study there was a significant decrement of dynamic lung volumes after corrective surgery for thoracic curve scoliosis. (Tanaffos 2008; 7(4): 27-31)
Correlations between cervical spine posture and low back pain.

Introduction In the last decades there has been a significant increase of the incidence of the lumbar diseases in all its clinical aspects: low back pain and pain associated with sciatic nerve compression. Researches showed a high percentages of these symptoms, 7080% in adult population (Pope et al 1993). The causes are especially related to faulty postures, which has increased due to a sedentary life. Office jobs, driving cars for long periods of time, watching television in supine posture, lacking of regular physical activities lead to changes in posture, in cervical spine biomechanics , all these being reflected on the other spine curves. Some studies demonstrated the amount of stress induced in lumbar spine at the professional drivers. The researchers establised that the physiological vibrations of the lumbar spine are 4-5 Hz, and the professional drivers values are higher than normal. A different pattern of faulty posture is one determined by certain professions (surgeons, desk workers, students, etc.). These professions lead to the onset of low back pain. The prevalence of low back pain at the teenagers is 46.8% (18.2% chronic and 28.6% acute) (Pope et al 1993; Alsiddiky et al 2015). These biomechanical changes lead to a premature degeneration mainly of the spine structures, but also of the other joints, muscular imbalances, inflammations and faulty movement patterns which compromise the whole body dynamic (Calvosa et al 2008). Modifications of the head posture lead to changing of the levers, perturbation of the muscles force, loss of the scapula thoracic girdle balance, modification of the body gravity-center, which will overuse the lumbar spine. In these conditions the lumbar spine won’t be able to balance the body on the pelvis girdle and lower limbs. Among the physical therapy protocols used in low back pain treatment, we mention Williams, McKenzie, Pilates and lumbar spine stretching exercises. This study started from the presumption that the improvement of the cervical spine biomechanics and posture will improve the lumbar spine biomechanics and also will decrease the low back symptoms. The study assesses the posture modifications due to the experimental physical therapy protocol versus the classic Williams one.
Compliance with occupational post-exposure protocol for injuries among dental surgeons.

OBJECTIVE To analyze the compliance with occupational post-exposure protocol for accidents and factors associated with compliance among dental surgeons. METHODS A cross-sectional study was performed in the municipality of Montes Claros, Southeastern Brazil, from 2007 to 2008, with accident reports from needles and sharp instruments among practicing dental surgeons. Variables describing the dentists' characteristics, work conditions, clients, injuries from needles and sharp instruments and post-accident actions were evaluated by means of a previously tested, structured questionnaire. The data underwent descriptive analysis and chi-square testing (p<0.05). RESULTS A total of 241 dentists (89.2%) answered the questionnaire. Compliance with occupational post-exposure protocol was reported by 51.5%. The majority of the professionals reported seeing blood at the time of the accident. Burs were the instrument most often involved in accidents, and the finger was the most injured body part. Compliance with a post-exposure protocol was more prevalent among those with greater monthly income (OR=2.42; 95% CI=1.03;5.71), continuing education in the last two years (OR=2.16; 95% CI=1.09;4.27) and who took regular breaks every four hours (OR=1.23; 95% CI=1.23;4.92). Dental surgeons who attended to children (OR=0.50; 95%CI=0.27;0.93) and to individuals from the middle, middle-high and high socioeconomic classes (OR=0.54, 95% CI=0.31;0.95) showed less compliance with the occupational post-exposure protocol. The frequency of compliance to a post-exposure protocol was significantly greater among individuals who followed the actions described in post-exposure protocols. CONCLUSIONS There is low compliance with occupational post-exposure protocols among dentists, which is influenced by the knowledge and monthly income of professionals, by taking regular breaks, by age group and the socioeconomic class of the clientele.
Limited engraftment capacity of bone marrow-derived mesenchymal cells following T-cell-depleted hematopoietic stem cell transplantation

The engraftment capacity of bone marrow–derived mesenchymal cells was investigated in 41 patients who had received a sex-mismatched, T-cell–depleted allograft from human leukocyte antigen (HLA)–matched or –mismatched family donors. Polymerase chain reaction (PCR) analysis of the human androgen receptor (HUMARA) or the amelogenin genes was used to detect donor-derived mesenchymal cells. Only 14 marrow samples (34%) from 41 consenting patients generated a marrow stromal layer adequate for PCR analysis. Monocyte-macrophage contamination of marrow stromal layers was reduced below the levels of sensitivity of HUMARA and amelogenin assays (5% and 3%, respectively) by repeated trypsinizations and treatment with the leucyl-leucine (leu-leu) methyl ester. Patients who received allografts from 12 female donors were analyzed by means of the HUMARA assay, and in 5 of 12 cases a partial female origin of stromal cells was demonstrated. Two patients who received allografts from male donors were analyzed by amplifying the amelogenin gene, and in both cases a partial male origin of stromal cells was shown. Fluorescent in situ hybridization analysis using a Y probe confirmed the results of PCR analysis and demonstrated in 2 cases the existence of a mixed chimerism at the stromal cell level. There was no statistical difference detected between the dose of fibroblast progenitors (colony-forming unit–F [CFU-F]) infused to patients with donor- or host-derived stromal cells (1.18 ± 0.13 × 104/kg vs 1.19 ± 0.19 × 104/kg; P ≥ .97). In conclusion, marrow stromal progenitors reinfused in patients receiving a T-cell–depleted allograft have a limited capacity of reconstituting marrow mesenchymal cells.
Gerência e competências gerais do enfermeiro

As politicas de educacao por meio das Diretrizes Curriculares Nacionais visam direcionar as instituicoes de ensino superior para a formacao das seguintes competencias e habilidades gerais dos profissionais de saude: atencao a saude, tomada de decisoes, comunicacao, lideranca, administracao e gerenciamento, educacao permanente. Como a maioria das competencias apontadas podem ser caracterizadas como competencias gerenciais, o presente artigo busca descreve-las e relacionar os conhecimentos necessarios para a formacao dessas competencias. Essa classificacao trouxe algumas reflexoes conceituais que permitem analisar o trabalho do enfermeiro e as relacoes entre gerencia e assistencia.
Contextual Reinforcement Learning of Visuo-tactile Multi-fingered Grasping Policies

Using simulation to train robot manipulation policies holds the promise of an almost unlimited amount of training data, generated safely out of harm's way. One of the key challenges of using simulation, to date, has been to bridge the reality gap, so that policies trained in simulation can be deployed in the real world. We explore the reality gap in the context of learning a contextual policy for multi-fingered robotic grasping. We propose a Grasping Objects Approach for Tactile (GOAT) robotic hands, learning to overcome the reality gap problem. In our approach we use human hand motion demonstration to initialize and reduce the search space for learning. We contextualize our policy with the bounding cuboid dimensions of the object of interest, which allows the policy to work on a more flexible representation than directly using an image or point cloud. Leveraging fingertip touch sensors in the hand allows the policy to overcome the reduction in geometric information introduced by the coarse bounding box, as well as pose estimation uncertainty. We show our learned policy successfully runs on a real robot without any fine tuning, thus bridging the reality gap.
SolidityCheck : Quickly Detecting Smart Contract Problems Through Regular Expressions

As a blockchain platform that has developed vigorously in recent years, Ethereum is different from Bitcoin in that it introduces smart contracts into blockchain.Solidity is one of the most mature and widely used smart contract programming language,which is used to write smart contracts and deploy them on blockchain. However, once the data in the blockchain is written, it cannot be modified. Ethereum smart contract is stored in the block chain, which makes the smart contract can no longer repair the code problems such as re-entrancy vulnerabilities or integer overflow problems. Currently, there still lacks of an efficient and effective approach for detecting these problems in Solidity. In this paper, we first classify all the possible problems in Solidity, then propose a smart contract problem detection approach for Solidity, namely SolidityCheck. The approach uses regular expressions to define the characteristics of problematic statements and uses regular matching and program instrumentation to prevent or detect problems. Finally, a large number of experiments is performed to show that SolidityCheck is superior to existing approaches.
The Power and Pitfalls of Transparent Privacy Policies in Social Networking Service Platforms

Users disclose ever-increasing amounts of personal data on Social Network Service platforms (SNS). Unless SNSs' policies are privacy friendly, this leaves them vulnerable to privacy risks because they ignore the privacy policies. Designers and regulators have pushed for shorter, simpler and more prominent privacy policies, however the evidence that transparent policies increase informed consent is lacking. To answer this question, we conducted an online experiment with 214 regular Facebook users asked to join a fictitious SNS. We experimentally manipulated the privacy-friendliness of SNS's policy and varied threats of secondary data use and data visibility. Half of our participants incorrectly recalled even the most formally "perfect" and easy-to-read privacy policies. Mostly, users recalled policies as more privacy friendly than they were. Moreover, participants self-censored their disclosures when aware that visibility threats were present, but were less sensitive to threats of secondary data use. We present design recommendations to increase informed consent.
Scalable methods for computing state similarity in deterministic Markov Decision Processes

We present new algorithms for computing and approximating bisimulation metrics in Markov Decision Processes (MDPs). Bisimulation metrics are an elegant formalism that capture behavioral equivalence between states and provide strong theoretical guarantees on differences in optimal behaviour. Unfortunately, their computation is expensive and requires a tabular representation of the states, which has thus far rendered them impractical for large problems. In this paper we present a new version of the metric that is tied to a behavior policy in an MDP, along with an analysis of its theoretical properties. We then present two new algorithms for approximating bisimulation metrics in large, deterministic MDPs. The first does so via sampling and is guaranteed to converge to the true metric. The second is a differentiable loss which allows us to learn an approximation even for continuous state MDPs, which prior to this work had not been possible.
DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functions

From fully connected neural networks to convolutional neural networks, the learned parameters within a neural network have been primarily relegated to the linear parameters (e.g., convolutional filters). The non-linear functions (e.g., activation functions) have largely remained, with few exceptions in recent years, parameter-less, static throughout training, and seen limited variation in design. Largely ignored by the deep learning community, radial basis function (RBF) networks provide an interesting mechanism for learning more complex non-linear activation functions in addition to the linear parameters in a network. However, the interest in RBF networks has waned over time due to the difficulty of integrating RBFs into more complex deep neural network architectures in a tractable and stable manner. In this work, we present a novel approach that enables end-to-end learning of deep RBF networks with fully learnable activation basis functions in an automatic and tractable manner. We demonstrate that our approach for enabling the use of learnable activation basis functions in deep neural networks, which we will refer to as DeepLABNet, is an effective tool for automated activation function learning within complex network architectures.
Towards FAIR protocols and workflows: The OpenPREDICT case study

It is essential for the advancement of science that scientists and researchers share, reuse and reproduce workflows and protocols used by others. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize a number of important points regarding the means by which digital objects are found and reused by others. The question of how to apply these principles not just to the static input and output data but also to the dynamic workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe our inclusive and overarching approach to apply the FAIR principles to workflows and protocols and demonstrate its benefits. We apply and evaluate our approach on a case study that consists of making the PREDICT workflow, a highly cited drug repurposing workflow, open and FAIR. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. A semantic model was proposed to better address these specific requirements and were evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.
Chemical-protein Interaction Extraction via Gaussian Probability Distribution and External Biomedical Knowledge

MOTIVATION The biomedical literature contains a wealth of chemical-protein interactions (CPIs). Automatically extracting CPIs described in biomedical literature is essential for drug discovery, precision medicine, as well as basic biomedical research. Most existing methods focus only on the sentence sequence to identify these CPIs. However, the local structure of sentences and external biomedical knowledge also contain valuable information. Effective use of such information may improve the performance of CPI extraction. RESULTS In this paper, we propose a novel neural network-based approach to improve CPI extraction. Specifically, the approach first employs BERT to generate high-quality contextual representations of the title sequence, instance sequence, and knowledge sequence. Then, the Gaussian probability distribution is introduced to capture the local structure of the instance. Meanwhile, the attention mechanism is applied to fuse the title information and biomedical knowledge, respectively. Finally, the related representations are concatenated and fed into the softmax function to extract CPIs. We evaluate our proposed model on the CHEMPROT corpus. Our proposed model is superior in performance as compared with other state-of-the-art models. The experimental results show that the Gaussian probability distribution and external knowledge are complementary to each other. Integrating them can effectively improve the CPI extraction performance. Furthermore, the Gaussian probability distribution can effectively improve the extraction performance of sentences with overlapping relations in biomedical relation extraction tasks. AVAILABILITY Data and code are available at https://github.com/CongSun-dlut/CPI_extraction. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning

There is an increasing number of pre-trained deep neural network models. However, it is still unclear how to effectively use these models for a new task. Transfer learning, which aims to transfer knowledge from source tasks to a target task, is an effective solution to this problem. Fine-tuning is a popular transfer learning technique for deep neural networks where a few rounds of training are applied to the parameters of a pre-trained model to adapt them to a new task. Despite its popularity, in this paper we show that fine-tuning suffers from several drawbacks. We propose an adaptive fine-tuning approach, called AdaFilter, which selects only a part of the convolutional filters in the pre-trained model to optimize on a per-example basis. We use a recurrent gated network to selectively fine-tune convolutional filters based on the activations of the previous layer. We experiment with 7 public image classification datasets and the results show that AdaFilter can reduce the average classification error of the standard fine-tuning by 2.54%.
An analysis of observation length requirements in spoken language for machine understanding of human behaviors

Automatic quantification of human interaction behaviors based on language information has been shown to be effective in psychotherapy research domains such as marital therapy and cancer care. Existing systems typically use a moving-window approach where the target behavior construct is first quantified based on observations inside a window, such as a fixed number of words or turns, and then integrated over all the windows in that interaction. Given a behavior of interest, it is important to employ the appropriate length of observation, since too short a window might not contain sufficient information. Unfortunately, the link between behavior and observation length for lexical cues has not been well studied and it is not clear how these requirements relate to the characteristics of the target behavior construct. Therefore, in this paper, we investigate how the choice of window length affects the efficacy of language-based behavior quantification, by analyzing (a) the similarity between system predictions and human expert assessments for the same behavior construct and (b) the consistency in relations between predictions of related behavior constructs. We apply our analysis to a large and diverse set of behavior codes that are used to annotate real-life interactions and find that behaviors related to negative affect can be quantified from just a few words whereas those related to positive traits and problem solving require much longer observation windows. On the other hand, constructs that describe dysphoric affect do not appear to be quantifiable from language information alone, regardless of how long they are observed. We compare our findings with related work on behavior quantification based on acoustic vocal cues as well as with prior work on thin slices and human personality predictions and find that, in general, they are in agreement.
Nitric oxide Bhramari Pranayam and deafness

© 2016 Indian Journal of Otology | Published by Wolters Kluwer Medknow | 1 Nitric oxide is a gas molecule which has become an exceptionally important molecule in biophysics. It is a key biological messenger playing an important role in various biological processes at the cellular level. Nitric oxide is known as endothelium‐derived relaxing factor and is biosynthesized endogenously from semi‐essential amino acid, L‐argenine, molecular oxygen, and various nitric oxide synthase enzymes.
Incorporating Textual Evidence in Visual Storytelling

Previous work on visual storytelling mainly focused on exploring image sequence as evidence for storytelling and neglected textual evidence for guiding story generation. Motivated by human storytelling process which recalls stories for familiar images, we exploit textual evidence from similar images to help generate coherent and meaningful stories. To pick the images which may provide textual experience, we propose a two-step ranking method based on image object recognition techniques. To utilize textual information, we design an extended Seq2Seq model with two-channel encoder and attention. Experiments on the VIST dataset show that our method outperforms state-of-the-art baseline models without heavy engineering.
Heart Segmentation From MRI Scans Using Convolutional Neural Network

Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the recent past, various computer assisted medical imaging systems have been proposed for the segmentation of the organ of interest. However, for the segmentation of heart using MRI, only few methods have been proposed each with its own merits and demerits. For further advancement in this area of research, we analyze automated heart segmentation methods for magnetic resonance images. The analysis are based on deep learning methods that processes a full MR scan in a slice by slice fashion to predict desired mask for heart region. We design two encoder decoder type fully convolutional neural network models
Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech

In this paper, we present a method for learning discrete linguistic units by incorporating vector quantization layers into neural models of visually grounded speech. We show that our method is capable of capturing both word-level and sub-word units, depending on how it is configured. What differentiates this paper from prior work on speech unit learning is the choice of training objective. Rather than using a reconstruction-based loss, we use a discriminative, multimodal grounding objective which forces the learned units to be useful for semantic image retrieval. We evaluate the sub-word units on the ZeroSpeech 2019 challenge, achieving a 27.3% reduction in ABX error rate over the top-performing submission, while keeping the bitrate approximately the same. We also present experiments demonstrating the noise robustness of these units. Finally, we show that a model with multiple quantizers can simultaneously learn phone-like detectors at a lower layer and word-like detectors at a higher layer. We show that these detectors are highly accurate, discovering 279 words with an F1 score of greater than 0.5.