Miracle-dz/UniFAR-FTUs huggingface dataset:
Chaotic Phenomena and Nonlinear Responses in a Vibroacoustic System

This study addresses the chaotic phenomena and nonlinear responses in a vibroacoustic system. It is the first study about the chaotic phenomena in a vibroacoustic system, which is formed by a flexible panel coupled with a cavity. A multimode formulation is developed from the acoustic governing equation and nonlinear structural governing equation. The chaotic and various nonlinear responses are computed from the multimode formulation using a numerical integration method. The results obtained from the proposed method and classical harmonic balance method are generally consistent. A set of modal convergence studies is performed to check the proposed method. The effects of various parameters on triggering the nonchaotic responses to chaotic responses in a vibroacoustic system are studied in detail.
Multiclonal Expansion and High Prevalence of β-Lactamase-Negative Haemophilus influenzae with High-Level Ampicillin Resistance in Japan and Susceptibility to Quinolones

β-Lactam-resistant Haemophilus influenzae is a clinical concern. A high prevalence (>40%) of β-lactamase-negative high-level ampicillin-resistant H. influenzae (high-BLNAR) isolates in Japan has been reported. ABSTRACT β-Lactam-resistant Haemophilus influenzae is a clinical concern. A high prevalence (>40%) of β-lactamase-negative high-level ampicillin-resistant H. influenzae (high-BLNAR) isolates in Japan has been reported. However, the reasons for the expansion are unknown. High-BLNAR strains possess an amino acid substitution, either Asn526Lys (group III) or Arg517His (group III-like) in addition to Ser385Thr, in penicillin-binding protein 3 (PBP3). To determine the current prevalence of high-BLNAR strains and the mechanisms behind their expansion in Japan, their prevalence, PBP3 types, multilocus sequence types, and susceptibilities to quinolones approved in Japan as alternatives were determined. Sixty percent of H. influenzae clinical isolates (62/104 isolates) were β-lactamase-negative ampicillin-resistant H. influenzae (BLNAR) strains. Among BLNAR isolates, 92% (57/62 isolates) were high-BLNAR strains. Most isolates were classified as belonging to group III, which contained many genotypes (11 PBP3 types and 25 sequence types). These results indicated that the expansion of high-BLNAR isolates was multiclonal and such strains are still predominant in Japanese clinical settings. One high-BLNAR isolate harbored the novel amino acid substitution Asn526Met in addition to Ser385Thr in PBP3, suggesting a new group (group IV). No quinolone-resistant H. influenzae isolates were identified. The MICs for the quinolones (moxifloxacin, garenoxacin, and tosufloxacin) were similar to that for levofloxacin, whereas sitafloxacin exhibited a lower MIC. However, we obtained 4 H. influenzae isolates with decreased quinolone susceptibility with the amino acid substitution Ser84Leu in GyrA, and 3 of those isolates were high-BLNAR isolates. In summary, this study shows that multiclonal high-BLNAR strains predominate in a Japanese university hospital. Isolates remain sensitive to quinolones, but vigilance is required to prevent the development of fluoroquinolone resistance in high-BLNAR strains.
DyNMF: Role Analytics in Dynamic Social Networks

Roles of nodes in a social network (SN) represent their functions, responsibilities or behaviors within the SN. Roles typically evolve over time, making role analytics a challenging problem. Previous studies either neglect role transition analysis or perform role discovery and role transition learning separately, leading to inefficiencies and limited transition analysis. We propose a novel dynamic non-negative matrix factorization (DyNMF) approach to simultaneously discover roles and learn role transitions. DyNMF explicitly models temporal information by introducing a role transition matrix and clusters nodes in SNs from two views: the current view and the historical view. The current view captures structural information from the current SN snapshot and the historical view captures role transitions by looking at roles in past SN snapshots. DyNMF efficiently provides more effective analytics capabilities, regularizing roles by temporal smoothness of role transitions and reducing uncertainties and inconsistencies between snapshots. Experiments on both synthetic and real-world SNs demonstrate the advantages of DyNMF in discovering and predicting roles and role transitions.
Renal expression of trefoil factor 3 mRNA in association with tubulointerstitial fibrosis in IgA nephropathy

Trefoil factor 3 (TFF3) is a small peptide that is involved in mucosal protection. TFF3 is widely expressed in multiple tissues including kidney tissue. Previous studies have reported that the levels of urinary TFF3 are significantly increased in patients with chronic kidney disease. The aim of this study is to detect the TFF3 mRNA in kidney and elucidate the relationship between renal TFF3 mRNA and tubulointerstitial fibrosis in IgA nephropathy (IgAN).
Analysis of Implicit Type Nonlinear Dynamical Problem of Impulsive Fractional Differential Equations

We study the existence, uniqueness, and various kinds of Ulam–Hyers stability of the solutions to a nonlinear implicit type dynamical problem of impulsive fractional differential equations with nonlocal boundary conditions involving Caputo derivative. We develop conditions for uniqueness and existence by using the classical fixed point theorems such as Banach fixed point theorem and Krasnoselskii’s fixed point theorem. For stability, we utilized classical functional analysis. Also, an example is given to demonstrate our main theoretical results.
Periprocedural Cardiopulmonary Bypass or Venoarterial Extracorporeal Membrane Oxygenation During Transcatheter Aortic Valve Replacement: A Systematic Review

Background There are limited data on the use of venoarterial extracorporeal membrane oxygenation (VA‐ECMO) or cardiopulmonary bypass (CPB) to provide hemodynamic support periprocedurally during transcatheter aortic valve replacement. This study sought to evaluate patients receiving transcatheter aortic valve replacement with concomitant use of CPB/VA‐ECMO. Methods and Results We systematically reviewed the published literature from 2000 to 2018 for studies evaluating adult patients requiring CPB/VA‐ECMO periprocedurally during transcatheter aortic valve replacement. Studies reporting short‐term and long‐term mortality were included. Given the significant methodological and statistical differences between published studies, meta‐analysis of the association of CPB/VA‐ECMO with mortality was not performed. Of the 537 studies identified, 9 studies representing 5191 patients met our inclusion criteria. Median ages were between 75 and 87 years with 33% to 75% male patients. Where reported, the Edwards SAPIEN™ transcatheter heart valve was the most frequently used. A total of 203 (3.9%) patients received periprocedural hemodynamic support with CPB/VA‐ECMO. Common indications for CPB/VA‐ECMO included left ventricular or aortic annular rupture, rapid hemodynamic deterioration, aortic regurgitation, cardiac arrest, and left main coronary artery obstruction. The use of CPB/VA‐ECMO was predominantly an emergent strategy and was used for durations of 1 to 2 hours. Short‐term mortality (in‐hospital and 30‐day) was 29.8%, and 1‐year mortality was 52.4%. Major complications such as bleeding, vascular injury, tamponade, stroke, and renal failure were noted in 10% to 50% of patients. Conclusions CPB/VA‐ECMO was used in 4% in the early experience of patients undergoing transcatheter aortic valve replacement, most commonly for periprocedural complications. There are limited data on preprocedural planned use of VA‐ECMO, and the characteristics of this population remain poorly defined.
Reallocating Time from Sedentary Behavior to Light and Moderate-to-Vigorous Physical Activity: What Has a Stronger Association with Adiposity in Older Adult Women?

This study is the first to use compositional data analysis to investigate movement behaviors of elderly women and their relationships with fat mass percentage (FM%). The focus of the study is on the associations of time reallocations from sedentary behavior (SB) to light physical activity (LIPA) or moderate-to-vigorous physical activity (MVPA) with adiposity. Over 400 older adult women were recruited as part of the cross-sectionally conducted measurements of older adults aged 60+ in Central European countries. An accelerometer was used to assess daily movement behaviors. Body mass index (BMI) and fat mass percentage (FM%) were assessed as adiposity indicators using InBody 720 MFBIA. Using LS-regression, we found positive relationships of BMI and FM% with SB (relative to remaining movement behaviors) (p < 0.001 for both), while their relationship with MVPA (relative to remaining movement behaviors) were negative (p < 0.001 for both). The estimated BMI and FM% associated with a 30-min SB-to-MVPA reallocation were reduced by 1.5 kg/m2 and 2.2 percentage points, respectively, whereas they were not reduced significantly with the reallocation of 30 min from SB to LIPA. The findings highlight that SB and MVPA, but not LIPA, are significantly associated with adiposity in elderly women. The reallocation of time from SB to MVPA could be advocated in weight loss interventions in older women.
Associative Prediction of Visual Shape in the Hippocampus

Perception can be cast as a process of inference, in which bottom-up signals are combined with top-down predictions in sensory systems. In line with this, neural activity in sensory cortex is strongly modulated by prior expectations. Such top-down predictions often arise from cross-modal associations, such as when a sound (e.g., bell or bark) leads to an expectation of the visual appearance of the corresponding object (e.g., bicycle or dog). We hypothesized that the hippocampus, which rapidly learns arbitrary relationships between stimuli over space and time, may be involved in forming such associative predictions. We exposed male and female human participants to auditory cues predicting visual shapes, while measuring high-resolution fMRI signals in visual cortex and the hippocampus. Using multivariate reconstruction methods, we discovered a dissociation between these regions: representations in visual cortex were dominated by whichever shape was presented, whereas representations in the hippocampus reflected only which shape was predicted by the cue. The strength of hippocampal predictions correlated across participants with the amount of expectation-related facilitation in visual cortex. These findings help bridge the gap between memory and sensory systems in the human brain. SIGNIFICANCE STATEMENT The way we perceive the world is to a great extent determined by our prior knowledge. Despite this intimate link between perception and memory, these two aspects of cognition have mostly been studied in isolation. Here we investigate their interaction by asking how memory systems that encode and retrieve associations can inform perception. We find that upon hearing a familiar auditory cue, the hippocampus represents visual information that had previously co-occurred with the cue, even when this expectation differs from what is currently visible. Furthermore, the strength of this hippocampal expectation correlates with facilitation of perceptual processing in visual cortex. These findings help bridge the gap between memory and sensory systems in the human brain.
Generating High Resolution Climate Change Projections through Single Image Super-Resolution: An Abridged Version

The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework with multi-scale input channels for statistical downscaling of climate variables. A comparison of DeepSD to four state-of-the-art methods downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios.
Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach

Millions of news articles emerge every day. How to provide personalized news recommendations has become a critical task for service providers. In the past few decades, latent factor models has been widely used for building recommender systems (RSs). With the remarkable success of deep learning techniques especially in visual computing and natural language understanding, more and more researchers have been trying to leverage deep neural networks to learn latent representations for advanced RSs. Following mainstream deep learning-based RSs, we propose a novel deep fusion model (DFM), which aims to improve the representation learning abilities in deep RSs and can be used for both candidate retrieval and item re-ranking. There are two key components in our DFM approach, namely an inception module and an attention mechanism. The inception module improves the plain multi-layer network via leveraging of various levels of interaction simultaneously, while the attention mechanism merges latent representations learnt from different channels in a customized fashion. We conduct extensive experiments on a commercial news reading dataset, and the results demonstrate that the proposed DFM is superior to several state-of-the-art models.
The lysosomal membrane protein LAMP2A promotes autophagic flux and prevents SNCA-induced Parkinson disease-like symptoms in the Drosophila brain

ABSTRACT The autophagy-lysosome pathway plays a fundamental role in the clearance of aggregated proteins and protection against cellular stress and neurodegenerative conditions. Alterations in autophagy processes, including macroautophagy and chaperone-mediated autophagy (CMA), have been described in Parkinson disease (PD). CMA is a selective autophagic process that depends on LAMP2A (lysosomal-associated membrane protein 2A), a mammal and bird-specific membrane glycoprotein that translocates cytosolic proteins containing a KFERQ-like peptide motif across the lysosomal membrane. Drosophila reportedly lack CMA and use endosomal microautophagy (eMI) as an alternative selective autophagic process. Here we report that neuronal expression of human LAMP2A protected Drosophila against starvation and oxidative stress, and delayed locomotor decline in aging flies without extending their lifespan. LAMP2A also prevented the progressive locomotor and oxidative defects induced by neuronal expression of PD-associated human SNCA (synuclein alpha) with alanine-to-proline mutation at position 30 (SNCAA30P). Using KFERQ-tagged fluorescent biosensors, we observed that LAMP2A expression stimulated selective autophagy in the adult brain and not in the larval fat body, but did not increase this process under starvation conditions. Noteworthy, we found that neurally expressed LAMP2A markedly upregulated levels of Drosophila Atg5, a key macroautophagy initiation protein, and that it increased the density of Atg8a/LC3-positive puncta, which reflects the formation of autophagosomes. Furthermore, LAMP2A efficiently prevented accumulation of the autophagy defect marker Ref(2)P/p62 in the adult brain under acute oxidative stress. These results indicate that LAMP2A can potentiate autophagic flux in the Drosophila brain, leading to enhanced stress resistance and neuroprotection. Abbreviations: Act5C: actin 5C; a.E. : after eclosion; Atg5: autophagy-related 5; Atg8a/LC3: autophagy-related 8a; CMA: chaperone-mediated autophagy; DHE: dihydroethidium; elav: embryonic lethal abnormal vision; eMI: endosomal microautophagy; ESCRT: endosomal sorting complexes required for transport; GABARAP: GABA typeA receptor-associated protein; Hsc70-4: heat shock protein cognate 4; HSPA8/Hsc70: heat shock protein family A (Hsp70) member 8; LAMP2: lysosomal associated membrane protein 2; MDA: malondialdehyde; PA-mCherry: photoactivable mCherry; PBS: phosphate-buffered saline; PCR: polymerase chain reaction; PD: Parkinson disease; Ref(2)P/p62: refractory to sigma P; ROS: reactive oxygen species; RpL32/rp49: ribosomal protein L32; RT-PCR: reverse transcription polymerase chain reaction; SING: startle-induced negative geotaxis; SNCA/α-synuclein: synuclein alpha; SQSTM1/p62: sequestosome 1; TBS: Tris-buffered saline; UAS: upstream activating sequence.
Learning Tag Dependencies for Sequence Tagging

Sequence tagging is the basis for multiple applications in natural language processing. Despite successes in learning long term token sequence dependencies with neural network, tag dependencies are rarely considered previously. Sequence tagging actually possesses complex dependencies and interactions among the input tokens and the output tags. We propose a novel multi-channel model, which handles different ranges of token-tag dependencies and their interactions simultaneously. A tag LSTM is augmented to manage the output tag dependencies and word-tag interactions, while three mechanisms are presented to efficiently incorporate token context representation and tag dependency. Extensive experiments on part-of-speech tagging and named entity recognition tasks show that the proposed model outperforms the BiLSTM-CRF baseline by effectively incorporating the tag dependency feature.
Fertility-related knowledge and perceptions of fertility education among adolescents and emerging adults: a qualitative study

Abstract Research shows that young people do not know much about their fertility. In the present study, we examined fertility knowledge and perceptions of a fertility educational brochure (i.e. ‘A Guide to Fertility’) in five focus groups with adolescents (16–18 years, n = 19) and emerging adults (21–24 years, n = 14) who were childless, not currently pregnant (or for men partner not pregnant) or trying to conceive but intending to have a child in the future. Participants (n = 33) reported having poor knowledge of a range of fertility topics and feelings of surprise, fear and concern in response to the brochure, despite perceiving benefits of the provision of fertility education and feasibility of ‘A Guide to Fertility’. Comparison between age groups showed that adolescents lacked confidence in their fertility knowledge and emerging adults more frequently referred to gender and family planning issues when considering the fertility information. The findings show the need and importance of ensuring fertility education is tailored to different age groups for it to be integrated at specific stages of the life course and optimize its benefits over costs. Results point to educators and researchers working together to determine how best to disseminate fertility information to relevant age groups.
Accelerating Convolutional Networks via Global & Dynamic Filter Pruning

Accelerating convolutional neural networks has recently received ever-increasing research focus. Among various approaches proposed in the literature, filter pruning has been regarded as a promising solution, which is due to its advantage in significant speedup and memory reduction of both network model and intermediate feature maps. To this end, most approaches tend to prune filters in a layer-wise fixed manner, which is incapable to dynamically recover the previously removed filter, as well as jointly optimize the pruned network across layers. In this paper, we propose a novel global & dynamic pruning (GDP) scheme to prune redundant filters for CNN acceleration. In particular, GDP first globally prunes the unsalient filters across all layers by proposing a global discriminative function based on prior knowledge of filters. Second, it dynamically updates the filter saliency all over the pruned sparse network, and then recover the mistakenly pruned filter, followed by a retraining phase to improve the model accuracy. Specially, we effectively solve the corresponding non-convex optimization problem of the proposed GDP via stochastic gradient descent with greedy alternative updating. Extensive experiments show that, comparing to the state-of-the-art filter pruning methods, the proposed approach achieves superior performance to accelerate several cutting-edge CNNs on the ILSVRC 2012 benchmark.
BCL11B mutations in patients affected by a neurodevelopmental disorder with reduced type 2 innate lymphoid cells

The transcription factor BCL11B is essential for development of the nervous and the immune system, and Bcl11b deficiency results in structural brain defects, reduced learning capacity, and impaired immune cell development in mice. However, the precise role of BCL11B in humans is largely unexplored, except for a single patient with a BCL11B missense mutation, affected by multisystem anomalies and profound immune deficiency. Using massively parallel sequencing we identified 13 patients bearing heterozygous germline alterations in BCL11B. Notably, all of them are affected by global developmental delay with speech impairment and intellectual disability; however, none displayed overt clinical signs of immune deficiency. Six frameshift mutations, two nonsense mutations, one missense mutation, and two chromosomal rearrangements resulting in diminished BCL11B expression, arose de novo. A further frameshift mutation was transmitted from a similarly affected mother. Interestingly, the most severely affected patient harbours a missense mutation within a zinc-finger domain of BCL11B, probably affecting the DNA-binding structural interface, similar to the recently published patient. Furthermore, the most C-terminally located premature termination codon mutation fails to rescue the progenitor cell proliferation defect in hippocampal slice cultures from Bcl11b-deficient mice. Concerning the role of BCL11B in the immune system, extensive immune phenotyping of our patients revealed alterations in the T cell compartment and lack of peripheral type 2 innate lymphoid cells (ILC2s), consistent with the findings described in Bcl11b-deficient mice. Unsupervised analysis of 102 T lymphocyte subpopulations showed that the patients clearly cluster apart from healthy children, further supporting the common aetiology of the disorder. Taken together, we show here that mutations leading either to BCL11B haploinsufficiency or to a truncated BCL11B protein clinically cause a non-syndromic neurodevelopmental delay. In addition, we suggest that missense mutations affecting specific sites within zinc-finger domains might result in distinct and more severe clinical outcomes.