Miracle-dz/UniFAR-FTUs huggingface dataset:
Degenerative Changes in the Knee 2 Years After Anterior Cruciate Ligament Rupture and Related Risk Factors

Background: Anterior cruciate ligament (ACL) rupture is a well-known risk factor for development of knee osteoarthritis. Early identification of those patients at risk and early identification of the process of ACL rupture leading to osteoarthritis may aid in preventing the onset or progression of osteoarthritis. Purpose: To identify early degenerative changes as assessed on magnetic resonance imaging (MRI) after 2-year follow-up in patients with a recent ACL rupture and to evaluate which determinants are related to these changes. Study Design: Cohort study; Level of evidence, 2. Methods: Included in this study were 154 adults aged between 18 and 45 years with acute ACL rupture diagnosed by physical examination and MRI, without previous knee trauma or surgery, and without osteoarthritic changes on radiographs. A total of 143 patients completed the 2-year follow-up, and the results in this study apply to these 143 patients. All patients were treated according to the Dutch guideline on ACL injury. Of the 143 patients, 50 patients were treated nonoperatively during the 2-year follow-up period. Main outcome was early degenerative changes assessed on MRI defined as progression of cartilage defects and osteophytes in tibiofemoral and patellofemoral compartments. Patient characteristics, activity level, functional instability, treatment type, and trauma-related variables were evaluated as determinants. Results: The median time between MRI at baseline and MRI at 2-year follow-up was 25.9 months (interquartile range, 24.7-26.9 months). Progression of cartilage defects in the medial and lateral tibiofemoral compartments was present in 12% and 27% of patients, and progression of osteophytes in tibiofemoral and patellofemoral compartments was present in 10% and 8% of patients, respectively. The following determinants were positively significantly associated with early degenerative changes: male sex (odds ratio [OR], 4.43; 95% CI, 1.43-13.66; P = .010), cartilage defect in the medial tibiofemoral compartment at baseline (OR, 3.66; 95% CI, 1.04-12.95; P = .044), presence of bone marrow lesions in the medial tibiofemoral compartment 1 year after trauma (OR, 5.19; 95% CI, 1.56-17.25; P = .007), joint effusion 1 year after trauma (OR, 4.19; 95% CI, 1.05-16.72; P = .042), and presence of meniscal tears (OR, 6.37; 95% CI, 1.94-20.88; P = .002). When the patients were categorized into 3 treatment groups (nonoperative, reconstruction <6 months after ACL rupture, and reconstruction ≥6 months after ACL rupture), there was no significant relationship between the treatment options and the development of early degenerative changes. Conclusion: Two years after ACL rupture, early degenerative changes were assessed on MRI. Concomitant medial cartilage defect and meniscal injury, male sex, persistent bone marrow lesions in the medial tibiofemoral compartment, and joint effusion are risk factors for degenerative changes.
Reef Odor: A Wake Up Call for Navigation in Reef Fish Larvae

The behavior of reef fish larvae, equipped with a complex toolbox of sensory apparatus, has become a central issue in understanding their transport in the ocean. In this study pelagic reef fish larvae were monitored using an unmanned open-ocean tracking device, the drifting in-situ chamber (DISC), deployed sequentially in oceanic waters and in reef-born odor plumes propagating offshore with the ebb flow. A total of 83 larvae of two taxonomic groups of the families Pomacentridae and Apogonidae were observed in the two water masses around One Tree Island, southern Great Barrier Reef. The study provides the first in-situ evidence that pelagic reef fish larvae discriminate reef odor and respond by changing their swimming speed and direction. It concludes that reef fish larvae smell the presence of coral reefs from several kilometers offshore and this odor is a primary component of their navigational system and activates other directional sensory cues. The two families expressed differences in their response that could be adapted to maintain a position close to the reef. In particular, damselfish larvae embedded in the odor plume detected the location of the reef crest and swam westward and parallel to shore on both sides of the island. This study underlines the critical importance of in situ Lagrangian observations to provide unique information on larval fish behavioral decisions. From an ecological perspective the central role of olfactory signals in marine population connectivity raises concerns about the effects of pollution and acidification of oceans, which can alter chemical cues and olfactory responses.
SNAREpin Assembly by Munc18-1 Requires Previous Vesicle Docking by Synaptotagmin 1*

Background: The cascade of reactions and proteins conferring regulated exocytosis needs to be characterized. Results: Synaptotagmin 1 is a primary vesicle-docking factor, and Munc18-1 accelerates subsequent v-/t-SNARE assembly/zippering. Conclusion: Synaptotagmin 1, PI(4,5)P2, complexin II, and Munc18-1 function in a sequential and concerted manner to mediate vesicle docking, SNAREpin assembly, and fast Ca2+-triggered exocytosis. Significance: Efficient Ca2+-regulated membrane fusion was reconstituted from a minimal set of components. Regulated exocytosis requires the general membrane fusion machinery–soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) and Sec1/Munc18 (SM) proteins. Using reconstituted giant unilamellar vesicles containing preassembled t-SNARE proteins (syntaxin 1·SNAP-25), we determined how Munc18-1 controls the docking, priming, and fusion of small unilamellar vesicles containing the v-SNARE VAMP2 and the Ca2+ sensor synaptotagmin 1. In vitro assays allowed us to position Munc18-1 in the center of a sequential reaction cascade; vesicle docking by synaptotagmin 1 is a prerequisite for Munc18-1 to accelerate trans-SNARE complex (SNAREpin) assembly and membrane fusion. Complexin II stalls SNAREpin zippering at a late stage and, hence, contributes to synchronize membrane fusion in a Ca2+- and synaptotagmin 1-dependent manner. Thus, at the neuronal synapse, the priming factor Munc18-1 may accelerate the conversion of docked synaptic vesicles into a readily releasable pool by activating SNAREs for efficient membrane fusion.
Back to the mouth

In this paper, we focus on breathe and propose interactive system Back to the mouth. There are many studies related to respiration of breath, but our emphasis is on mouth odor. Study of smell using input is not progressing in comparison with study of smell using output. This system uses mouth odor strength as a trigger in addition to breathing action. By using mouth odor, there is possibility that a better breath device would be developed.
Automatic Classification of Tweets for Analyzing Communication Behavior of Museums

In this paper, we present a study on tweet classification which aims to define the communication behavior of the 103 French museums that participated in 2014 in the Twitter operation: MuseumWeek. The tweets were automatically classified in four communication categories: sharing experience, promoting participation, interacting with the community, and promoting-informing about the institution. Our classification is multi-class. It combines Support Vector Machines and Naive Bayes methods and is supported by a selection of eighteen subtypes of features of four different kinds: metadata information, punctuation marks, tweet-specific and lexical features. It was tested against a corpus of 1,095 tweets manually annotated by two experts in Natural Language Processing and Information Communication and twelve Community Managers of French museums. We obtained an state-of-the-art result of F1-score of 72% by 10-fold cross-validation. This result is very encouraging since is even better than some state-of-the-art results found in the tweet classification literature.
L'altruismo: atteggiamento irrazionale, strategia vincente o amore per il prossimo?

L’altruismo: atteggiamento irrazionale, strategia vincente o amore per il prossimo? (di Stefania Ottone) - ABSTRACT: Results from sociobiological and psychological studies have called into question the classical model of Homo Oeconomicus underling the multifaceted nature of both human nature and the consequences of people’s actions. The present paper gives an overview of the various theories developed over time by economists, sociobiologists and psychologists concerning the model of man and of his nature. In particular, it studies how opting for dense regulation, when instead the morality of the subjects would be strong enough to positively condition their behaviour, might turn out to be a disadvantageous choice. In fact, a trade-offexists between ethics and regulation. Where the moral sense of the subjects is particularly well-developed, excessive development of the institutional apparatus would give birth to crowding-outeffects. From this point, the importance of a change in the way of creating policy in both private and public institutions. They might realize that it would be a profitable investment to use resources to transmit values to the subjects through the channel of education, since such a choice would create a stronger equilibrium and continuous control and regulation would be unnecessary.
Coordinate Expression of CC Chemokine Ligand 5, Granulysin, and Perforin in CD8+ T Cells Provides a Host Defense Mechanism against Mycobacterium tuberculosis1

The ability of CD8+ T cells to kill intracellular pathogens depends upon their capacity to attract infected cells as well as their secretion of cytolytic and antimicrobial effector molecules. We examined the Ag-induced expression of three immune effector molecules contained within cytoplasmic granules of human CD8+ T cells: the chemokine CCL5, the cytolytic molecule perforin, and the antimicrobial protein granulysin. Macrophages infected with virulent Mycobacterium tuberculosis triggered the expression of CCL5 in CD8+ T cells only in donors with previous exposure to the tuberculosis bacteria, not in naive donors. Functionally, CCL5 efficiently attracted M. tuberculosis-infected macrophages, but failed to exert direct antibacterial activity. Infected macrophages also triggered the expression of granulysin in CD8+ T cells, and granulysin was found to be highly active against drug-susceptible and drug-resistant M. tuberculosis clinical isolates. The vast majority of CCL5-positive cells coexpressed granulysin and perforin. Taken together, this report provides evidence that a subset of CD8+ T cells coordinately expresses CCL5, perforin and granulysin, thereby providing a host mechanism to attract M. tuberculosis-infected macrophages and kill the intracellular pathogen.
Interplanetary magnetic field during the past 9300 years inferred from cosmogenic radionuclides

[1] We have reconstructed the interplanetary magnetic field (IMF), its radial component, and the open solar magnetic flux using the solar modulation potential derived from cosmogenic 10Be radionuclide data for a period covering the past 9300 years. Reconstructions using the assumption of both constant and variable solar wind speeds yielded closely similar results. During the Maunder Minimum, the strength of the IMF was approximately 2 nT compared to a mean value of 6.6 nT for the past 40 years, corresponding to an increase of the open solar magnetic flux of about 350%. We examine four cycles of the Hallstatt periodicity in the IMF with a mean period of ∼2250 years and an amplitude of ∼0.75 nT. Grand solar minima have largely occurred in clusters during the Hallstatt cycle minima around the years −5300, −3400, −1100, and +1500 A.D. The last cluster includes the Dalton, Maunder, and Sporer minima. We predict that the next such cluster will occur in about 1500 years. The long-term IMF has varied between ∼2 nT and ∼8 nT and does not confirm a proposed floor (lower limit). There is a slowly changing long-term trend of amplitude 1.5 nT, with a minimum around the year −4600 and a maximum around 0 A.D. that may be of solar origin but which also may be due to unknown long-term changes in the atmospheric effects or geomagnetic field intensity.
Open/closed eye recognition by local binary increasing intensity patterns

Open/closed eye recognition is an open topic in many intelligent systems. This paper proposes a novel appearance-based method to solve the problem. It is considered as a two-class classification task, and two important components are: feature descriptor and classifier learning. Originally, this work introduces a distinct local feature named Local Binary Increasing Intensity Patterns (LBIIP), which uses one decimal label to specify the intensity increasing tendency of the local region around each pixel. It inherits the merits of both Local Binary Patterns (LBP) and gradient features. Given an eye image, numerous sub-windows are obtained by scanning at various scales and locations. Then the LBIIP-Histograms (LBIIPHs) are extracted from the sub-windows, and concatenated into a feature vector (descriptor). On the other hand, Discrete AdaBoost is applied to selecte a few most discriminative features and learn the open/closed eye classifier. Experimental results show that the proposed approach is very fast and efficient.
Using Feature Weights to Improve Performance of Neural Networks

Different features have different relevance to a particular learning problem. Some features are less relevant; while some very important. Instead of selecting the most relevant features using feature selection, an algorithm can be given this knowledge of feature importance based on expert opinion or prior learning. Learning can be faster and more accurate if learners take feature importance into account. Correlation aided Neural Networks (CANN) is presented which is such an algorithm. CANN treats feature importance as the correlation coefficient between the target attribute and the features. CANN modifies normal feed-forward Neural Network to fit both correlation values and training data. Empirical evaluation shows that CANN is faster and more accurate than applying the two step approach of feature selection and then using normal learning algorithms.
Building surface texture segmentation in urban remote sensing image using improved ORTSEG algorithm

Texture segmentation is a critical step in building-based analysis in urban remote sensing images to obtain more detail information for further applications. Most existing segmentation algorithms rely on region or edge information to segment, which failed to segment building surfaces with almost same texture and unclear edge between the surfaces. Therefore, in order to solve this challenging task, based on the ORTSEG algorithm in Michael T. McCann's paper, an improved ORTSEG algorithm is proposed, in which, a sparse non-negative matrix factorization (SNMF) is used for the optimization. The final segmentation results show the superiority of this improved ORTSEG algorithm.
A 3D self-positioning method for wireless sensor nodes based on linear FMCW and TFDA

In wireless sensor networks (WSN), location information acquisition is critical to guarantee their performance. This paper presents a new positioning method in WSN with high precision at reasonable implementation cost for 3D case. Reference nodes with known locations transmit linear frequency modulation continuous wave (FMCW), while other sensor nodes estimate the range difference to them based on the received signals' frequency difference, called time frequency difference arrival (TFDA). The location information can be obtained by solving a set of hyperbolic equations. Two different positioning methods: Taylor iterative method and Chan's method are inspected and compared in terms of accuracy, constraints and computational complexity. This proposed technique is cost-effective, scalable and easy to implement. The simulation results show that the new method enjoys high precision.
POSTER: Accuracy vs. Time Cost: Detecting Android Malware through Pareto Ensemble Pruning

This paper proposes Begonia, a malware detection system through Pareto ensemble pruning. We convert the malware detection problem into the bi-objective Pareto optimization, aiming to trade off the classification accuracy and the size of classifiers as two objectives. We automatically generate several groups of base classifiers using SVM and generate solutions through bi-objective Pareto optimization. We then select the ensembles with highest accuracy of each group to form the final solutions, among which we hit the optimal solution where the combined loss function is minimal considering the trade-off between accuracy and time cost. We expect users to provide different trade-off levels to their different requirements to select the best solution. Experimental results show that Begonia can achieve higher accuracy with relatively lower overhead compared to the ensemble containing all the classifiers and can make a good trade-off to different requirements.
Sexism as a Predictor of Attitudes Toward Women in the Military and in Combat

This study examined four forms of sexist beliefs as predictors of attitudes toward women in the military and in combat. Survey data revealed that military-affiliated college students (n = 62) held more negative attitudes than civilian students (n = 254) toward women in combat, but the groups did not differ on attitudes toward women in the military. All forms of sexism contributed uniquely to the prediction of attitudes toward women in the military, and 3 forms were associated with approval of women in combat. Sexist beliefs mediated the relationship between military-affiliation status and approval of women in combat.
Building a Language‐Independent Discourse Parser using Universal Networking Language

Discourse parsing has become an inevitable task to process information in the natural language processing arena. Parsing complex discourse structures beyond the sentence level is a significant challenge. This article proposes a discourse parser that constructs rhetorical structure (RS) trees to identify such complex discourse structures. Unlike previous parsers that construct RS trees using lexical features, syntactic features and cue phrases, the proposed discourse parser constructs RS trees using high‐level semantic features inherited from the Universal Networking Language (UNL). The UNL also adds a language‐independent quality to the parser, because the UNL represents texts in a language‐independent manner. The parser uses a naive Bayes probabilistic classifier to label discourse relations. It has been tested using 500 Tamil‐language documents and the Rhetorical Structure Theory Discourse Treebank, which comprises 21 English‐language documents. The performance of the naive Bayes classifier has been compared with that of the support vector machine (SVM) classifier, which has been used in the earlier approaches to build a discourse parser. It is seen that the naive Bayes probabilistic classifier is better suited for discourse relation labeling when compared with the SVM classifier, in terms of training time, testing time, and accuracy.
Using a learning journal to improve professional practice: a journey of personal and professional self‐discovery

This paper provides an account of how I improved my professional practice by keeping a reflective learning journal. It begins with the recognition that to improve my practice I first needed to understand its nature. This resulted in an evolving journey of personal and professional self‐discovery that led to a greater understanding of my role as a practitioner–researcher. I describe how engaging in double‐loop reflection (as distinct from single‐loop reflection) became my real reflective learning, and how that process helped me analyse my theories‐in‐use, leading to an understanding of where and how my practice needed to improve. I acknowledge the importance of recording my practice honestly, and illustrate how writing openly and appreciatively led to a richer reflective and reflexive journal that helped me better understand and manage the change taking place in my organization.
Power Series Classification: A Hybrid of LSTM and a Novel Advancing Dynamic Time Warping

As many applications organize data into temporal sequences, the problem of time series data classification has been widely studied. Recent studies show that the 1-nearest neighbor with dynamic time warping (1NN-DTW) and the long short term memory (LSTM) neural network can achieve a better performance than other machine learning algorithms. In this paper, we build a novel time series classification algorithm hybridizing 1NN-DTW and LSTM, and apply it to a practical data center power monitoring problem. Firstly, we define a new distance measurement for the 1NN-DTW classifier, termed as Advancing Dynamic Time Warping (ADTW), which is non-commutative and non-dynamic programming. Secondly, we hybridize the 1NN-ADTW and LSTM together. In particular, a series of auxiliary test samples generated by the linear combination of the original test sample and its nearest neighbor with ADTW are utilized to detect which classifier to trust in the hybrid algorithm. Finally, using the power consumption data from a real data center, we show that the proposed ADTW can improve the classification accuracy from about 84\% to 89\%. Furthermore, with the hybrid algorithm, the accuracy can be further improved and we achieve an accuracy up to about 92\%. Our research can inspire more studies on non-commutative distance measurement and the hybrid of the deep learning models with other traditional models.
Open Cirrus TM cloud computing testbed: federated data centers for open source systems and services research

There are a number of important and useful testbeds, such as PlanetLab, EmuLab, IBM/Google cluster, and Amazon EC2/S3, that enable researchers to study different aspects of distributed computing. However, no single testbed supports research spanning systems, applications, services, open-source development, and datacenters. Towards this end, we have developed Open Cirrus, a cloud computing testbed for the research community that federates heterogeneous distributed data centers. Open Cirrus offers a cloud stack consisting of physical and virtual machines, and global services, such as sign-on, monitoring, storage, and job submission. By developing the testbed and making it available to the research community, we hope to help spur innovation in cloud computing and catalyze the development of an open source stack for the cloud.
Noncovalent Bonding of RGD and YIGSR to an Electrospun Poly(ε‐Caprolactone) Conduit through Peptide Self‐Assembly to Synergistically Promote Sciatic Nerve Regeneration in Rats

The nerve conduit with biofunctionalities can regulate neurite outgrowth, as well as the migration, proliferation, and myelination activity of Schwann cells. In the present study, polycaprolactone (PCL) conduits are coated with Naphthalene‐phenylalanine‐phenylalanine‐glycine‐arginine‐glycine‐aspartic (Nap‐FFGRGD) and Naphthalene‐phenylalanine‐phenylalanine‐glycine‐cysteine‐aspartic‐proline‐glycine‐tyrosine‐isoleucine‐glycine‐serine‐arginine (Nap‐FFGCDPGYIGSR) by self‐assembly. In vitro studies demonstrate that arginine‐glycine‐aspartic (RGD) and tyrosine‐isoleucine‐glycine‐serine‐arginine (YIGSR) are capable of synergistically enhancing the ability of PCL to support the adhesion and proliferation of Schwann cells, as well as increasing neurite outgrowth from dorsal root ganglions explants. This synergistic effect may occur via the activation of both the phosphoinositide 3‐kinase/protein kinase B and mitogen‐activated protein kinase/extracellular signal‐regulated protein kinase pathways. RGD/YIGSR modifications demonstrate beneficial effects across a 15 mm sciatic nerve gap in axonal regeneration and functional recovery. In addition, increased vascularization is observed in the RGD/YIGSR‐PCL group, which might contribute to their beneficial effects on nerve regeneration. These findings indicate the potential of the RGD/YIGSR‐PCL conduit to promote axonal regeneration and functional recovery, making the RGD/YIGSR‐PCL conduit an attractive candidate for the treatment of a critical nerve defect.