Miracle-dz/UniFAR-FTUs huggingface dataset:
Relations between emotions, display rules, social motives, and facial behaviour

We report research on the relations between emotions, display rules, social motives, and facial behaviour. In Study 1 we used a questionnaire methodology to examine how respondents would react to a funny or a not funny joke told to them by a close friend or a stranger. We assessed display rules and motivations for smiling and/or laughing. Display rules and social motives (partly) mediated the relationship between the experimental manipulations and self‐reported facial behaviour. Study 2 was a laboratory experiment in which funny or not funny jokes were told to participants by a male or female stranger. Consistent with hypotheses, hearing a funny joke evoked a stronger motivation to share positive affect by showing longer Duchenne smiling. Contrary to hypotheses, a not funny joke did not elicit greater prosocial motivation by showing longer “polite” smiling, although such a smiling pattern did occur. Rated funniness of the joke and the motivation to share positive affect mediated the relationship between the joke manipulation and facial behaviour. Path analysis was used to explore this mediating process in greater detail.
Unveiling the Role of the Magnetic Field at the Smallest Scales of Star Formation

We report Atacama Large Millimeter/submillimeter Array (ALMA) observations of polarized dust emission from the protostellar source Ser-emb 8 at a linear resolution of 140 au. Assuming models of dust-grain alignment hold, the observed polarization pattern gives a projected view of the magnetic field structure in this source. Contrary to expectations based on models of strongly magnetized star formation, the magnetic field in Ser-emb 8 does not exhibit an hourglass morphology. Combining the new ALMA data with previous observational studies, we can connect magnetic field structure from protostellar core (∼80,000 au) to disk (∼100 au) scales. We compare our observations with four magnetohydrodynamic gravo-turbulence simulations made with the AREPO code that have initial conditions ranging from super-Alfvénic (weakly magnetized) to sub-Alfvénic (strongly magnetized). These simulations achieve the spatial dynamic range necessary to resolve the collapse of protostars from the parsec scale of star-forming clouds down to the ∼100 au scale probed by ALMA. Only in the very strongly magnetized simulation do we see both the preservation of the field direction from cloud to disk scales and an hourglass-shaped field at <1000 au scales. We conduct an analysis of the relative orientation of the magnetic field and the density structure in both the Ser-emb 8 ALMA observations and the synthetic observations of the four AREPO simulations. We conclude that the Ser-emb 8 data are most similar to the weakly magnetized simulations, which exhibit random alignment, in contrast to the strongly magnetized simulation, where the magnetic field plays a role in shaping the density structure in the source. In the weak-field case, it is turbulence—not the magnetic field—that shapes the material that forms the protostar, highlighting the dominant role that turbulence can play across many orders of magnitude in spatial scale.
Interferon regulatory factor 8 regulates caspase-1 expression to facilitate Epstein-Barr virus reactivation in response to B cell receptor stimulation and chemical induction

Interferon regulatory factor 8 (IRF8), also known as interferon consensus sequence-binding protein (ICSBP), is a transcription factor of the IRF family. IRF8 plays a key role in normal B cell differentiation, a cellular process that is intrinsically associated with Epstein-Barr virus (EBV) reactivation. However, whether IRF8 regulates EBV lytic replication remains unknown. In this study, we utilized a CRISPR/Cas9 genomic editing approach to deplete IRF8 and found that IRF8 depletion dramatically inhibits the reactivation of EBV upon lytic induction. We demonstrated that IRF8 depletion suppresses the expression of a group of genes involved in apoptosis and thus inhibits apoptosis induction upon lytic induction by B cell receptor (BCR) stimulation or chemical induction. The protein levels of caspase-1, caspase-3 and caspase-8 all dramatically decreased in IRF8-depleted cells, which led to reduced caspase activation and the stabilization of KAP1, PAX5 and DNMT3A upon BCR stimulation. Interestingly, caspase inhibition blocked the degradation of KAP1, PAX5 and DNMT3A, suppressed EBV lytic gene expression and viral DNA replication upon lytic induction, suggesting that the reduced caspase expression in IRF8-depleted cells contributes to the suppression of EBV lytic replication. We further demonstrated that IRF8 directly regulates CASP1 (caspase-1) gene expression through targeting its gene promoter and knockdown of caspase-1 abrogates EBV reactivation upon lytic induction, partially through the stabilization of KAP1. Together our study suggested that, by modulating the activation of caspases and the subsequent cleavage of KAP1 upon lytic induction, IRF8 plays a critical role in EBV lytic reactivation.
Nicorandil Attenuates Monocrotaline-Induced Vascular Endothelial Damage and Pulmonary Arterial Hypertension

Background An antianginal KATP channel opener nicorandil has various beneficial effects on cardiovascular systems; however, its effects on pulmonary vasculature under pulmonary arterial hypertension (PAH) have not yet been elucidated. Therefore, we attempted to determine whether nicorandil can attenuate monocrotaline (MCT)-induced PAH in rats. Materials and Methods Sprague-Dawley rats injected intraperitoneally with 60 mg/kg MCT were randomized to receive either vehicle; nicorandil (5.0 mg·kg−1·day−1) alone; or nicorandil as well as either a KATP channel blocker glibenclamide or a nitric oxide synthase (NOS) inhibitor N ω-nitro-l-arginine methyl ester (l-NAME), from immediately or 21 days after MCT injection. Four or five weeks later, right ventricular systolic pressure (RVSP) was measured, and lung tissue was harvested. Also, we evaluated the nicorandil-induced anti-apoptotic effects and activation status of several molecules in cell survival signaling pathway in vitro using human umbilical vein endothelial cells (HUVECs). Results Four weeks after MCT injection, RVSP was significantly increased in the vehicle-treated group (51.0±4.7 mm Hg), whereas it was attenuated by nicorandil treatment (33.2±3.9 mm Hg; P<0.01). Nicorandil protected pulmonary endothelium from the MCT-induced thromboemboli formation and induction of apoptosis, accompanied with both upregulation of endothelial NOS (eNOS) expression and downregulation of cleaved caspase-3 expression. Late treatment with nicorandil for the established PAH was also effective in suppressing the additional progression of PAH. These beneficial effects of nicorandil were blocked similarly by glibenclamide and l-NAME. Next, HUVECs were incubated in serum-free medium and then exhibited apoptotic morphology, while these changes were significantly attenuated by nicorandil administration. Nicorandil activated the phosphatidylinositol 3-kinase (PI3K)/Akt and extracellular signal-regulated kinase (ERK) pathways in HUVECs, accompanied with the upregulation of both eNOS and Bcl-2 expression. Conclusions Nicorandil attenuated MCT-induced vascular endothelial damage and PAH through production of eNOS and anti-apoptotic factors, suggesting that nicorandil might have a promising therapeutic potential for PAH.
Surgeon Experience and Complications with Transoral Robotic Surgery (TORS)

Objective To investigate surgeon preferences for perioperative management of transoral robotic surgery (TORS) and explore the frequency of postoperative complications. Study Design Retrospective survey. Setting Multi-institutional. Subjects and Methods An electronic survey was sent to over 300 TORS-trained surgeons in the United States identified by Intuitive Surgical, Inc. Participation was voluntary and solicited by email invitations to participate 3 times over a 1-month period. Results A total of 2015 procedures were reported by 45 respondent TORS-trained surgeons: 67% academic, 33% nonacademic. A minority of TORS procedures (n = 214, 10.6%) were performed on previously irradiated patients. Neck dissections were performed concurrently (58%) or staged (42%). Fewer than 6% of TORS procedures required tracheotomy or reconstruction. Most surgeons (62%) initiated oral intake on postoperative day 0-1. Of the patients who required readmission, bleeding (n = 62, 3.1%) was the most common cause followed by dehydration (n = 26, 1.3%). Other complications of surgery included tooth injury (n = 29, 1.4%), percutaneous endoscopic gastrostomy (PEG) dependency >6 months (n = 21, 1.0%), temporary hypoglossal nerve injury (n = 18, 0.9%), and lingual nerve injury (n = 11, 0.6%). A total of 6 deaths (0.3%) were reported within 30 days of TORS. All reported deaths were due to postoperative hemorrhage. The complication rate decreased significantly with higher surgeon case volume (>50 cases). Conclusions TORS is associated with a low major complication rate, early initiation of oral intake, and a low rate of long-term PEG dependency. Postoperative hemorrhage was the most common cause of hospital readmission and postoperative mortality.
Ambulatory Gait Behavior in Patients With Dementia: A Comparison With Parkinson’s Disease

Accelerometry-based gait analysis is a promising approach in obtaining insightful information on the gait characteristics of patients with neurological disorders such as dementia and Parkinson's disease (PD). In order to improve its practical use outside the laboratory or hospital, it is required to design new metrics capable of quantifying ambulatory gait and their extraction procedures from long-term acceleration data. This paper presents a gait analysis method developed for such a purpose. Our system is based on a single trunk-mounted accelerometer and analytical algorithm for the assessment of gait behavior that may be context dependent. The algorithm consists of the detection of gait peaks from acceleration data and the analysis of multimodal patterns in the relationship between gait cycle and vertical gait acceleration. A set of six new measures can be obtained by applying the algorithm to a 24-h motion signal. To examine the performance and utility of our method, we recorded acceleration data from 13 healthy, 26 PD, and 26 mild cognitive impairment or dementia subjects. Each patient group was further classified into two, comprising 13 members each, according to the severity of the disease, and the gait behavior of the five groups was compared. We found that the normal, PD, and MCI/dementia groups show characteristic walking patterns which can be distinguished from one another by the developed gait measure set. We also examined conventional parameters such as gait acceleration, gait cycle, and gait variability, but failed to reproduce the distinct differences among the five groups. These findings suggest that the proposed gait analysis may be useful in capturing disease-specific gait features in a community setting.
Micromechanical approach to transformation toughening in zirconia-enriched multiphase composites

A micromechanical model based on the mean-field approach was developed to investigate the effect of the mismatch in material properties of the constituents on the transformation toughening in zirconia enriched multiphase composites. Important results have been obtained for SiC/Al composites, which have a large potential for application in lightweight automotive structures.
Common Hydraulic Fracturing Fluid Additives Alter the Structure and Function of Anaerobic Microbial Communities

ABSTRACT The development of unconventional oil and gas (UOG) resources results in the production of large volumes of wastewater containing a complex mixture of hydraulic fracturing chemical additives and components from the formation. The release of these wastewaters into the environment poses potential risks that are poorly understood. Microbial communities in stream sediments form the base of the food chain and may serve as sentinels for changes in stream health. Iron-reducing organisms have been shown to play a role in the biodegradation of a wide range of organic compounds, and so to evaluate their response to UOG wastewater, we enriched anaerobic microbial communities from sediments collected upstream (background) and downstream (impacted) of an UOG wastewater injection disposal facility in the presence of hydraulic fracturing fluid (HFF) additives: guar gum, ethylene glycol, and two biocides, 2,2-dibromo-3-nitrilopropionamide (DBNPA) and bronopol (C3H6BrNO4). Iron reduction was significantly inhibited early in the incubations with the addition of biocides, whereas amendment with guar gum and ethylene glycol stimulated iron reduction relative to levels in the unamended controls. Changes in the microbial community structure were observed across all treatments, indicating the potential for even small amounts of UOG wastewater components to influence natural microbial processes. The microbial community structure differed between enrichments with background and impacted sediments, suggesting that impacted sediments may have been preconditioned by exposure to wastewater. These experiments demonstrated the potential for biocides to significantly decrease iron reduction rates immediately following a spill and demonstrated how microbial communities previously exposed to UOG wastewater may be more resilient to additional spills. IMPORTANCE Organic components of UOG wastewater can alter microbial communities and biogeochemical processes, which could alter the rates of essential natural attenuation processes. These findings provide new insights into microbial responses following a release of UOG wastewaters and are critical for identifying strategies for the remediation and natural attenuation of impacted environments.
An efficient design and implementation of LSM-tree based key-value store on open-channel SSD

Various key-value (KV) stores are widely employed for data management to support Internet services as they offer higher efficiency, scalability, and availability than relational database systems. The log-structured merge tree (LSM-tree) based KV stores have attracted growing attention because they can eliminate random writes and maintain acceptable read performance. Recently, as the price per unit capacity of NAND flash decreases, solid state disks (SSDs) have been extensively adopted in enterprise-scale data centers to provide high I/O bandwidth and low access latency. However, it is inefficient to naively combine LSM-tree-based KV stores with SSDs, as the high parallelism enabled within the SSD cannot be fully exploited. Current LSM-tree-based KV stores are designed without assuming SSD's multi-channel architecture. To address this inadequacy, we propose LOCS, a system equipped with a customized SSD design, which exposes its internal flash channels to applications, to work with the LSM-tree-based KV store, specifically LevelDB in this work. We extend LevelDB to explicitly leverage the multiple channels of an SSD to exploit its abundant parallelism. In addition, we optimize scheduling and dispatching polices for concurrent I/O requests to further improve the efficiency of data access. Compared with the scenario where a stock LevelDB runs on a conventional SSD, the throughput of storage system can be improved by more than 4X after applying all proposed optimization techniques.
Identification of Operating Parameters Most Strongly Influencing the Jetting Performance in a Piezoelectric Actuator-Driven Dispenser

This work identifies crucial operating parameters which most significantly influence the jetting performances of piezostack-driven non-contact dispensers. This is achieved through experimental investigation and statistical analysis. After introducing the configuration and operating principle of the piezoelectric jetting dispenser, an experimental setup is constructed in order to test the jetting performance, such as the dispensed amount. After selecting four significant operating parameters for the light-emitting diode (LED)-packaging process, two levels for each parameter are considered. Subsequently, the weight of a single dispensed dot is measured 100 times, and the average weight and standard deviation are calculated for each experimental set. The results are then statistically analyzed using a commercial software package. Finally, the crucial operating parameters which provide a low average weight and a minimum variation in the weight of a single dispensed dot are identified.
Cytomorphologic Spectrum of Giant Cell Tumor of Tendon Sheath

OBJECTIVE To correlate the cytomorphologic spectrum of giant cell tumor of tendon sheath (GCTTS) with clinical and histologic findings and determine key features helpful in preoperative diagnosis. STUDY DESIGN Retrospective analysis was done on 48 cases diagnosed cytologically over 9 years. Cases were divided into 2 groups: in group 1 cytology and histology were available (12), and in group 2 cytology alone was available (36). Cytomorphologic features were correlated with clinical and histologic findings. RESULTS Patients ranged in age from 11 to 60 years, with more women. Small joint involvement was seen in all cases except 1, with upper limb involvement in most cases. Recurrence occurred in 3 cases. Aspiration smears in all cases showed high cellularity, multinucleated osteoclastic type of giant cells and stromal cells. Other features seen less frequently were cytoplasmic granules and vacuoles, nuclear grooves, inclusions, budding, focal mild pleomorphism, hemosiderin-laden macrophages and foam cells. Mitosis and necrosis were absent. Cytologic features were classified as constant when present in all cases and variable when present occasionally. CONCLUSION The constant cytologic features when combined with clinical and radiologic details are sufficiently distinctive of GCTTS. Fine needle aspiration cytology can be used in early, accurate preoperative diagnosis.
Longitudinal analyses of adoptive parents’ expectations and depressive symptoms

Grounded in a theoretical model specific to adoptive parents, we examined the relationship between parental expectations and depressive symptoms across time. Assessments of 129 adoptive parents of 64 children were performed at three time points before and after placement of an adopted child with the family: 4-6 weeks pre-placement and 4-6 weeks and 5-6 months post-placement. Expectations were assessed in four dimensions: expectations of self as parents, of the child, of family and friends, and of society. Depressive symptoms were assessed with the Center for Epidemiologic Studies-Depression scale. Associations between parental expectations and depressive symptoms were analyzed, and longitudinal multilevel modeling was conducted to explore influences on expectations over time. Parental expectations changed from pre- to post-placement. With the exception of expectations of self as parent, adoptive parents' pre-adoption expectations were affirmed in the post-adoption time periods. In each expectation dimension, higher affirmation of expectations was correlated with decreased depressive symptoms before and after placement of a child. While parental expectations are not unique to adoptive parents, the essence and characteristics of certain expectations are unique to these parents. When working with adoptive parents, nurses who care for families should assess expectations both pre- and post-placement with awareness of their relationship to depressive symptoms.
Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist’s decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN) technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC) images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information—for example, the Local Binary Pattern (LBP) represents the effective textural information, Histogram represent the pixel strength distribution, Contourlet Transform (CT) gives much detailed information about the smoothness about the edges, and Discrete Fourier Transform (DFT) derives frequency-domain information from the image. Utilizing these advantages, along with our proposed novel CNN model, we have examined the performance of the novel CNN model as Histopathological image classifier. To do so, we have introduced five cases: (a) Convolutional Neural Network Raw Image (CNN-I); (b) Convolutional Neural Network CT Histogram (CNN-CH); (c) Convolutional Neural Network CT LBP (CNN-CL); (d) Convolutional Neural Network Discrete Fourier Transform (CNN-DF); (e) Convolutional Neural Network Discrete Cosine Transform (CNN-DC). We have performed our experiments on the BreakHis image dataset. The best performance is achieved when we utilize the CNN-CH model on a 200× dataset that provides Accuracy, Sensitivity, False Positive Rate, False Negative Rate, Recall Value, Precision and F-measure of 92.19%, 94.94%, 5.07%, 1.70%, 98.20%, 98.00% and 98.00%, respectively.
Are Generative Classifiers More Robust to Adversarial Attacks?

There is a rising interest in studying the robustness of deep neural network classifiers against adversaries, with both advanced attack and defence techniques being actively developed. However, most recent work focuses on discriminative classifiers which only models the conditional distribution of the labels given the inputs. In this abstract we propose deep Bayes classifier that improves the classical naive Bayes with conditional deep generative models, and verifies its robustness against a number of existing attacks. We further developed a detection method for adversarial examples based on conditional deep generative models. Our initial results on MNIST suggest that deep Bayes classifiers might be more robust when compared with deep discriminative classifiers, and the proposed detection method achieves high detection rates against two commonly used attacks.