Miracle-dz/UniFAR-FTUs huggingface dataset:
Linking Sequences of Active Appearance Sub-Models via Constraints: An Application in Automated Vertebral Morphometry

Statistical models of shape and appearance are powerful tools for interpreting medical and other images. However there can remain problems with under-trained models being too constrained. We have combined a global model with a sequence of partially overlapping sub-models, in a manner that exploits all the statistical information, whilst mitigating the under-training problem. Instead of applying one global model, we use a global model to apply iteratively-updated soft constraints on a sequence of sub-models. These sub-models may also partially overlap, and thus previously fit sub-models can also impose soft constraints on the next iteration. The algorithm has been applied to dual x-ray absorptiometry scans of the spine in order to automate vertebral morphometry measurements, using overlapping triplets of vertebrae as the sub-models, together with a global model of the entire spine. Combining a global model in this way with a sequence of sub-models gives substantially better results than using the former alone.
A noise coupled ΣΔ architecture using a Non Uniform Quantizer

A low power Discrete Time Sigma Delta modulator (DT SDM) for sensor applications is described in this paper. A third order noise shaping is reached with a second order loop filter due to the use of a noise coupling technique at the input of the quantizer. Using a Non Uniform Quantizer (NUQ) the hardware complexity is reduced. This architecture is suitable for high dynamic range sensor applications with some compression. Conventional mismatch shaping techniques can still be used to enhance linearity if a segmented DAC is used. At low amplitudes the NUQ is linearized by the noise coupling loop. A simulation example is presented, achieving a dynamic range of 103dB and a peak SNDR of 93dB over 25KHz bandwidth, and using a 1.6MHz clock.
Alkyl Amine Bevirimat Derivatives Are Potent and Broadly Active HIV-1 Maturation Inhibitors

ABSTRACT Concomitant with the release of human immunodeficiency virus type 1 (HIV-1) particles from the infected cell, the viral protease cleaves the Gag polyprotein precursor at a number of sites to trigger virus maturation. We previously reported that a betulinic acid-derived compound, bevirimat (BVM), blocks HIV-1 maturation by disrupting a late step in protease-mediated Gag processing: the cleavage of the capsid-spacer peptide 1 (CA-SP1) intermediate to mature CA. BVM was shown in multiple clinical trials to be safe and effective in reducing viral loads in HIV-1-infected patients. However, naturally occurring polymorphisms in the SP1 region of Gag (e.g., SP1-V7A) led to a variable response in some BVM-treated patients. The reduced susceptibility of SP1-polymorphic HIV-1 to BVM resulted in the discontinuation of its clinical development. To overcome the loss of BVM activity induced by polymorphisms in SP1, we carried out an extensive medicinal chemistry campaign to develop novel maturation inhibitors. In this study, we focused on alkyl amine derivatives modified at the C-28 position of the BVM scaffold. We identified a set of derivatives that are markedly more potent than BVM against an HIV-1 clade B clone (NL4-3) and show robust antiviral activity against a variant of NL4-3 containing the V7A polymorphism in SP1. One of the most potent of these compounds also strongly inhibited a multiclade panel of primary HIV-1 isolates. These data demonstrate that C-28 alkyl amine derivatives of BVM can, to a large extent, overcome the loss of susceptibility imposed by polymorphisms in SP1.
The Effect of Acute L-carnitine and Carbohydrate Intake on Cycling Performance

Chronic supplementation of L-carnitine and carbohydrate has been reported to increase L-carnitine content in skeletal muscle and have positive influences on exercise variables and performance. This study investigated the acute intake of L-carnitine and carbohydrate on the exercise parameters of cycling. A total of 10 males (27.0 ± 4 years) completed two exercise sessions consisting of 40 min of cycling at 65% of VO2peak, followed by cycling to exhaustion at 85% of VO2peak. L-carnitine or a placebo was consumed 3 hours prior to exercise, and beverages consisting of 94 g of carbohydrate were consumed at both 2 hours, and 30 minutes prior to exercise. Repeated measures ANOVAs were used to compare respiratory exchange ratio (RER), blood lactate, and power output across experimental trials and time. A repeated measures t-test was used to analyze differences between conditions and time to exhaustion. RER was significantly lower (p=0.01) at baseline with L-carnitine ingestion (.83 ± .05) compared to the placebo ingestion (.86 ± .06). Blood lactate was significantly lower (p=0.02) after 10 minutes of cycling at 65% of VO2peak with ingestion of L-carnitine (35% change from baseline) compared to placebo ingestion (53% change from baseline). No differences were found for power output or time to exhaustion at 85% of VO2peak. Despite mentioned differences, acute intake of L-carnitine and carbohydrate does not appear to influence exercise parameters, likely due to a lack of sufficient change in the content of L-carnitine in skeletal muscle.
LncRNA SNHG1 regulates cerebrovascular pathologies as a competing endogenous RNA through HIF‐1α/VEGF signaling in ischemic stroke

Studies have shown that long noncoding ribonucleic acids (lncRNAs) play critical roles in multiple biologic processes. However, the Small Nucleolar RNA Host Gene 1 (SNHG1) function and underlying molecular mechanisms in ischemic stroke have not yet been reported. In the present study, we found that SNHG1 expression was remarkably increased both in isolated cerebral micro‐vessels of a middle cerebral artery occlusion (MCAO) mice model, and in oxygen‐glucose deprivation (OGD)‐cultured mice brain micro‐vascular endothelial cells (BMECs), meanwhile, the SNHG1 level was negatively correlated with miR‐18a in MCAO mice. Mechanistically, SNHG1 inhibition presents larger brain infarct size and worsens neurological scores in MCAO mice. Consistent with the in vivo findings, SNHG1 inhibition also significantly increased caspase‐3 activity and cell apoptosis in OGD‐cultured BMECs. Furthermore, we found that SNHG1 functions as a competing endogenous RNA (ceRNA) for miR‐18a, thereby regulating the de‐repression of its endogenous target HIF‐1α and promoting BMEC survival through HIF‐1α/VEGF signaling. This study found a neuroprotective effect of SNHG1 mediated by HIF‐1α/VEGF signaling through acting as a ceRNA for miR‐18a. These findings reveal a novel function of SNHG1, which contributes to an extensive understanding of ischemic stroke and provides novel therapeutic options for this disease.
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models

We propose a nonparametric method for detecting nonlinear causal relationship within a set of multidimensional discrete time series, by using sparse additive models (SpAMs). We show that, when the input to the SpAM is a $\beta$-mixing time series, the model can be fitted by first approximating each unknown function with a linear combination of a set of B-spline bases, and then solving a group-lasso-type optimization problem with nonconvex regularization. Theoretically, we characterize the oracle statistical properties of the proposed sparse estimator in function estimation and model selection. Numerically, we propose an efficient pathwise iterative shrinkage thresholding algorithm (PISTA), which tames the nonconvexity and guarantees linear convergence towards the desired sparse estimator with high probability.
Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods

The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k-Nearest Neighbors (k-NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.
A survey of test methods for scale adhesion measurement

Abstract The spallation of oxide scales from the steam side of tubes in power generation boilers can severely limit component life time. The process involves through scale cracking and crack propagation at or parallel to the scale/alloy interface. Many techniques have been developed in the past two decades to assess the interfacial fracture toughness of brittle coatings on ductile substrates. Analyses from fracture mechanics approaches also allow for quantitative determination of the fracture toughness, which can be used for life time prediction modelling. This paper gives an overview on techniques that have been used on thermally grown oxides, such as Ni, Fe, Ti, Al, and Cr oxides and spinels, and compare the results obtained on each type of oxide system from different techniques. In doing so, the limitations of each technique are illustrated. The methods discussed are flexure, scratch, indentation, tensile and compression tests and those that perform direct analysis of the spallation process. The data on steam grown oxide is sparse, but the test methods discussed could all be applied to these scale types.
EFL Speaking Anxiety among Senior High School Students and Policy Recommendations

This report drew on a larger study which was to describe and understand the sources of senior high school students’ English language speaking anxiety at senior high schools in Jambi, Indonesia. The purpose of this paper was to report some of findings from the qualitative interview data on the sources of senior high school students’ English language speaking anxiety at one senior high school in Jambi, Indonesia. Data were collected through demographic profiles and semi-structured interview with senior high school students. The demographic data were analysed descriptively while the interview data were transcribed and analysed line by line to generate and develop codes and themes. An analysis of the interview data revealed that five major themes were related to students’ English language speaking anxiety, including (1) low speaking skill due to lack of vocabulary and grammar, (2) fear of negative responses from others, (3) low self-esteem to speak in English, (4) fear of being evaluated by teachers, and (5) cultural influences to speak English due to a more teacher-centred style. Suggestions and policy implications are also discussed.
Phytophagous Arthropods and a Pathogen Sharing a Host Plant: Evidence for Indirect Plant-Mediated Interactions

In ecological systems, indirect interactions between plant pathogens and phytophagous arthropods can arise when infestation by a first attacker alters the common host plant so that although a second attacker could be spatially or temporally separated from the first one, the former could be affected. The induction of plant defense reactions leading to the production of secondary metabolites is thought to have an important role since it involves antagonistic and/or synergistic cross-talks that may determine the outcome of such interactions. We carried out experiments under controlled conditions on young rose plants in order to assess the impact of these indirect interactions on life history traits of three pests: the necrotrophic fungus Botrytis cinerea Pers. : Fr. (Helotiales: Sclerotiniaceae), the aphid Rhodobium porosum Sanderson (Hemiptera: Aphididae) and the thrips Frankliniella occidentalis Pergande (Thysanoptera: Thripidae). Our results indicated (i) a bi-directional negative interaction between B. cinerea and R. porosum, which is conveyed by decreased aphid growth rate and reduced fungal lesion area, as well as (ii) an indirect negative effect of B. cinerea on insect behavior. No indirect effect was observed between thrips and aphids. This research highlights several complex interactions that may be involved in structuring herbivore and plant pathogen communities within natural and managed ecosystems.
Xylem traits, leaf longevity and growth phenology predict growth and mortality response to defoliation in northern temperate forests

Defoliation outbreaks are biological disturbances that alter tree growth and mortality in temperate forests. Trees respond to defoliation in many ways; some recover rapidly, while others decline gradually or die. Functional traits such as xylem anatomy, growth phenology or non-structural carbohydrate (NSC) storage could explain these responses, but idiosyncratic measures used by defoliation studies have frustrated efforts to generalize among species. Here, I test for functional differences with published growth and mortality data from 37 studies, including 24 tree species and 11 defoliators from North America and Eurasia. I synthesized data into standardized variables suitable for numerical models and used linear mixed-effects models to test the hypotheses that responses to defoliation vary among species and functional groups. Standardized data show that defoliation responses vary in shape and degree. Growth decreased linearly or curvilinearly, least in ring-porous Quercus and deciduous conifers (by 10-40% per 100% defoliation), whereas growth of diffuse-porous hardwoods and evergreen conifers declined by 40-100%. Mortality increased exponentially with defoliation, most rapidly for evergreen conifers, then diffuse-porous, then ring-porous species and deciduous conifers (Larix). Goodness-of-fit for functional-group models was strong (R2c = 0.61-0.88), if lower than species-specific mixed-models (R2c = 0.77-0.93), providing useful alternatives when species data are lacking. These responses are consistent with functional differences in leaf longevity, wood growth phenology and NSC storage. When defoliator activity lags behind wood-growth, either because xylem-growth precedes budburst (Quercus) or defoliator activity peaks later (sawflies on Larix), impacts on annual wood-growth will always be lower. Wood-growth phenology of diffuse-porous species and evergreen conifers coincides with defoliation and responds more drastically, and lower axial NSC storage makes them more vulnerable to mortality as stress accumulates. These functional differences in response apply in general to disturbances that cause spring defoliation and provide a framework that should be incorporated into forest growth and vegetation models.
Flame retardant properties of EVA‐nanocomposites and improvements by combination of nanofillers with aluminium trihydrate

Flame retardant nanocomposites are synthesized by melt‐blending ethylene–vinyl acetate copolymers (EVA) with modified layered silicates (montmorillonites). Thermogravimetric analysis performed under different atmospheres (nitrogen and air) demonstrated a clear increase in the thermal stability of the layered silicate‐based nanocomposites. The use of the cone calorimeter to investigate the fire properties of the materials indicated that the nanocomposites caused a large decrease in heat release. The char‐formation is the main factor important for improvement and its function is outlined. Further improvements of the flame retardancy by combinations of nanofillers and traditional FR‐additives on the basis of metal hydroxides were also studied. Copyright © 2002 John Wiley & Sons, Ltd.
Striatal cholinergic receptor activation causes a rapid, selective, & state-dependent rise in corticostriatal β activity

Cortico-basal ganglia-thalamic (CBT) β oscillations (15–30 Hz) are elevated in Parkinson’s disease and correlated with movement disability. To date, no experimental paradigm outside of loss of dopamine has been able to specifically elevate β oscillations in the CBT loop. Here, we show that activation of striatal cholinergic receptors selectively increases β oscillations in mouse striatum. Furthermore, β expression in primary motor cortex (M1) distinguishes two BG dynamical states. In one, β oscillations increase in both striatum and M1, with partial directed coherence occurring primarily from striatum to M1 and distinguishing between β subbands. In another, M1 is characterized by persistent β-HFO phase-amplitude coupling, and shows no β increase. This suggests: (1) striatal cholinergic tone exerts state-dependent and frequency-selective control over CBT β power and coordination; (2) ongoing rhythmic dynamics can determine whether elevated striatal β oscillations are expressed in M1; (3) altered striatal cholinergic tone differentially modulates distinct β subbands.
HIV and smoking: associated risks and prevention strategies

High rates of smoking among persons living with HIV (PLWH) may reduce the effectiveness of HIV treatment and contribute to significant morbidity and mortality. Factors associated with smoking in PLWH include mental health comorbidity, alcohol and drug use, health-related quality of life, smoking among social networks and supports, and lack of access to care. PLWH smokers are at a higher risk of numerous HIV-associated infections and non-HIV related morbidity, including a decreased response to antiretroviral treatment, impaired immune functioning, reduced cognitive functioning, decreased lung functioning, and cardiovascular disease. Seventeen smoking cessation interventions were identified, of which seven were randomized controlled trials. The most effective studies combined behavioral and pharmacotherapy treatments that incorporated comprehensive assessments, multiple sessions, and cognitive-behavioral and motivational strategies. Smoking cessation interventions that are tailored to the unique needs of diverse samples and incorporate strategies to reduce the risk of relapse are essential to advancing health outcomes in PLWH.
Diversity and Ecology of Viruses in Hyperarid Desert Soils

ABSTRACT In recent years, remarkable progress has been made in the field of virus environmental ecology. In marine ecosystems, for example, viruses are now thought to play pivotal roles in the biogeochemical cycling of nutrients and to be mediators of microbial evolution through horizontal gene transfer. The diversity and ecology of viruses in soils are poorly understood, but evidence supports the view that the diversity and ecology of viruses in soils differ substantially from those in aquatic systems. Desert biomes cover ∼33% of global land masses, and yet the diversity and roles of viruses in these dominant ecosystems remain poorly understood. There is evidence that hot hyperarid desert soils are characterized by high levels of bacterial lysogens and low extracellular virus counts. In contrast, cold desert soils contain high extracellular virus titers. We suggest that the prevalence of microbial biofilms in hyperarid soils, combined with extreme thermal regimens, exerts strong selection pressures on both temperate and virulent viruses. Many desert soil virus sequences show low values of identity to virus genomes in public databases, suggesting the existence of distinct and as-yet-uncharacterized soil phylogenetic lineages (e.g., cyanophages). We strongly advocate for amplification-free metavirome analyses while encouraging the classical isolation of phages from dominant and culturable microbial isolates in order to populate sequence databases. This review provides an overview of recent advances in the study of viruses in hyperarid soils and of the factors that contribute to viral abundance and diversity in hot and cold deserts and offers technical recommendations for future studies.
Deep Cross-Media Knowledge Transfer

Cross-media retrieval is a research hotspot in multimedia area, which aims to perform retrieval across different media types such as image and text. The performance of existing methods usually relies on labeled data for model training. However, cross-media data is very labor consuming to collect and label, so how to transfer valuable knowledge in existing data to new data is a key problem towards application. For achieving the goal, this paper proposes deep cross-media knowledge transfer (DCKT) approach, which transfers knowledge from a large-scale cross-media dataset to promote the model training on another small-scale cross-media dataset. The main contributions of DCKT are: (1) Two-level transfer architecture is proposed to jointly minimize the media-level and correlation-level domain discrepancies, which allows two important and complementary aspects of knowledge to be transferred: intramedia semantic and inter-media correlation knowledge. It can enrich the training information and boost the retrieval accuracy. (2) Progressive transfer mechanism is proposed to iteratively select training samples with ascending transfer difficulties, via the metric of cross-media domain consistency with adaptive feedback. It can drive the transfer process to gradually reduce vast cross-media domain discrepancy, so as to enhance the robustness of model training. For verifying the effectiveness of DCKT, we take the large-scale dataset XMediaNet as source domain, and 3 widely-used datasets as target domain for cross-media retrieval. Experimental results show that DCKT achieves promising improvement on retrieval accuracy.
The Alliance Hypothesis for Human Friendship

Background Exploration of the cognitive systems underlying human friendship will be advanced by identifying the evolved functions these systems perform. Here we propose that human friendship is caused, in part, by cognitive mechanisms designed to assemble support groups for potential conflicts. We use game theory to identify computations about friends that can increase performance in multi-agent conflicts. This analysis suggests that people would benefit from: 1) ranking friends, 2) hiding friend-ranking, and 3) ranking friends according to their own position in partners' rankings. These possible tactics motivate the hypotheses that people possess egocentric and allocentric representations of the social world, that people are motivated to conceal this information, and that egocentric friend-ranking is determined by allocentric representations of partners' friend-rankings (more than others' traits). Methodology/Principal Findings We report results from three studies that confirm predictions derived from the alliance hypothesis. Our main empirical finding, replicated in three studies, was that people's rankings of their ten closest friends were predicted by their own perceived rank among their partners' other friends. This relationship remained strong after controlling for a variety of factors such as perceived similarity, familiarity, and benefits. Conclusions/Significance Our results suggest that the alliance hypothesis merits further attention as a candidate explanation for human friendship.