The association of molecular typing, vancomycin MIC, and clinical outcome for patients with methicillin-resistant Staphylococcus aureus infections

AbstractBackground/PurposeThere are reports of an increase in vancomycin minimum inhibitory concentration (MIC) against methicillin-resistant Staphylococcus aureus (MRSA) over time, a phenomenon referred to as “MIC creep”, but some studies have conflicting results. The aim of this study is to evaluate the association of molecular typing, vancomycin MIC, and clinical outcome for patients with MRSA infections.MethodsThirty-two MRSA isolates from Taichung Veterans General Hospital (TCVGH), Taichung, Taiwan during the period of 2003 to 2008 were analyzed for the association of sequence typing, vancomycin MIC, and the correlated clinical outcome for patients with MRSA infections. The vancomycin MICs of 28 additional isolates from 2014 were used for the detection of MIC creep.ResultsAmong the genotypes of 32 isolates, there were 17 (53.1%) isolates with ST239-SCCmecIII, seven (21.9%) isolates with ST5-SCCmecII, six (18.8%) isolates with ST59-SCCmecIV, and two (6.2%) isolates with ST59-SCCmecVT. Two isolates had an MIC of 2 μg/mL and were identified as ST239-SCCmecIII. No statistically significant change in the distribution of MICs of all isolates was observed between 2003 and 2014 (p = 0.263). There was no significant difference in the mortality rates between two groups of patients with vancomycin MICs < 2 μg/mL and ≥ 2 μg/mL (p = > 0.99).ConclusionThere was no vancomycin MIC creep in the period from 2003 to 2014 in this study. Appropriate prognostic models for assessment of the association among sequence types, vancomycin MICs, and clinical outcome warrant further investigation

On the Adversarial Robustness of Vision Transformers

Following the success in advancing natural language processing and understanding, transformers are expected to bring revolutionary changes to computer vision. This work provides the first and comprehensive study on the robustness of vision transformers (ViTs) against adversarial perturbations. Tested on various white-box and transfer attack settings, we find that ViTs possess better adversarial robustness when compared with convolutional neural networks (CNNs). This observation also holds for certified robustness. We summarize the following main observations contributing to the improved robustness of ViTs: 1) Features learned by ViTs contain less low-level information and are more generalizable, which contributes to superior robustness against adversarial perturbations. 2) Introducing convolutional or tokens-to-token blocks for learning low-level features in ViTs can improve classification accuracy but at the cost of adversarial robustness. 3) Increasing the proportion of transformers in the model structure (when the model consists of both transformer and CNN blocks) leads to better robustness. But for a pure transformer model, simply increasing the size or adding layers cannot guarantee a similar effect. 4) Pre-training on larger datasets does not significantly improve adversarial robustness though it is critical for training ViTs. 5) Adversarial training is also applicable to ViT for training robust models. Furthermore, feature visualization and frequency analysis are conducted for explanation. The results show that ViTs are less sensitive to high-frequency perturbations than CNNs and there is a high correlation between how well the model learns low-level features and its robustness against different frequency-based perturbations

Complex Pathways to Cooperation Emergent from Asymmetry in Heterogeneous Populations

Cooperation within asymmetric populations has garnered significant attention in evolutionary games. This paper explores cooperation evolution in populations with weak and strong players, using a game model where players choose between cooperation and defection. Asymmetry stems from different benefits for strong and weak cooperators, with their benefit ratio indicating the degree of asymmetry. Varied rankings of parameters including the asymmetry degree, cooperation costs, and benefits brought by weak players give rise to scenarios including the prisoner's dilemma (PDG) for both player types, the snowdrift game (SDG), and mixed PDG-SDG interactions. Our results indicate that in an infinite well-mixed population, defection remains the dominant strategy when strong players engage in the prisoner's dilemma game. However, if strong players play snowdrift games, global cooperation increases with the proportion of strong players. In this scenario, strong cooperators can prevail over strong defectors when the proportion of strong players is low, but the prevalence of cooperation among strong players decreases as their proportion increases. In contrast, within a square lattice, the optimum global cooperation emerges at intermediate proportions of strong players with moderate degrees of asymmetry. Additionally, weak players protect cooperative clusters from exploitation by strong defectors. This study highlights the complex dynamics of cooperation in asymmetric interactions, contributing to the theory of cooperation in asymmetric games.Comment: 10 pages, 8 figure

VILAS: Exploring the Effects of Vision and Language Context in Automatic Speech Recognition

Enhancing automatic speech recognition (ASR) performance by leveraging additional multimodal information has shown promising results in previous studies. However, most of these works have primarily focused on utilizing visual cues derived from human lip motions. In fact, context-dependent visual and linguistic cues can also benefit in many scenarios. In this paper, we first propose ViLaS (Vision and Language into Automatic Speech Recognition), a novel multimodal ASR model based on the continuous integrate-and-fire (CIF) mechanism, which can integrate visual and textual context simultaneously or separately, to facilitate speech recognition. Next, we introduce an effective training strategy that improves performance in modal-incomplete test scenarios. Then, to explore the effects of integrating vision and language, we create VSDial, a multimodal ASR dataset with multimodal context cues in both Chinese and English versions. Finally, empirical results are reported on the public Flickr8K and self-constructed VSDial datasets. We explore various cross-modal fusion schemes, analyze fine-grained crossmodal alignment on VSDial, and provide insights into the effects of integrating multimodal information on speech recognition.Comment: Accepted to ICASSP 202

Vertically-aligned graphene nanowalls grown via plasma-enhanced chemical vapor deposition as a binder-free cathode in Li-O_2 batteries

In the present report, vertically-aligned graphene nanowalls are grown on Ni foam (VA-G/NF) using plasma-enhanced chemical vapor deposition method at room temperature. Optimization of the growth conditions provides graphene sheets with controlled defect sites. The unique architecture of the vertically-aligned graphene sheets allows sufficient space for the ionic movement within the sheets and hence enhancing the catalytic activity. Further modification with ruthenium nanoparticles (Ru NPs) drop-casted on VA-G/NF improves the charge overpotential for lithium–oxygen (Li–O_2) battery cycles. Such reduction we believe is due to the easier passage of ions between the perpendicularly standing graphene sheets thereby providing ionic channels

Vertically-aligned graphene nanowalls grown via plasma-enhanced chemical vapor deposition as a binder-free cathode in Li-O_2 batteries

In the present report, vertically-aligned graphene nanowalls are grown on Ni foam (VA-G/NF) using plasma-enhanced chemical vapor deposition method at room temperature. Optimization of the growth conditions provides graphene sheets with controlled defect sites. The unique architecture of the vertically-aligned graphene sheets allows sufficient space for the ionic movement within the sheets and hence enhancing the catalytic activity. Further modification with ruthenium nanoparticles (Ru NPs) drop-casted on VA-G/NF improves the charge overpotential for lithium–oxygen (Li–O_2) battery cycles. Such reduction we believe is due to the easier passage of ions between the perpendicularly standing graphene sheets thereby providing ionic channels

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike natural language process (NLP) and computer vision (CV), where a single large model can tackle multiple tasks, models for time series forecasting are often specialized, necessitating distinct designs for different tasks and applications. While pre-trained foundation models have made impressive strides in NLP and CV, their development in time series domains has been constrained by data sparsity. Recent studies have revealed that large language models (LLMs) possess robust pattern recognition and reasoning abilities over complex sequences of tokens. However, the challenge remains in effectively aligning the modalities of time series data and natural language to leverage these capabilities. In this work, we present Time-LLM, a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact. We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities. To augment the LLM's ability to reason with time series data, we propose Prompt-as-Prefix (PaP), which enriches the input context and directs the transformation of reprogrammed input patches. The transformed time series patches from the LLM are finally projected to obtain the forecasts. Our comprehensive evaluations demonstrate that Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models. Moreover, Time-LLM excels in both few-shot and zero-shot learning scenarios.Comment: Accepted by the 12th International Conference on Learning Representations (ICLR 2024

Pressure-Controlled Chemical Vapor Deposition of Graphene as Catalyst for Solar Hydrogen Evolution Reaction

In the present report, graphene-based catalysts on silicon substrate have been examined as the photocathode for solar hydrogen evolution reaction (HER). Mono-layered graphene has been synthesized through low-pressure chemical vapor deposition (LPCVD), whereas multi-layered graphene has been synthesized by atmospheric pressure chemical vapor deposition (APCVD). Copper foil is used as the substrate. The graphene layer on Cu foil subsequently transferred on to silicon photoabsorber using poly(methyl-2-methylpropenoate) (PMMA). At the initial linear sweep voltammetry (LSV) scan, LPCVD-synthesized graphene-Si (LPCVD-Si) electrode showed an onset potential of −0.65 V and photocurrent of −4.31 mA cm^(−2) (at −0.385 V). On the contrary, the onset potential and photocurrent of APCVD-prepared graphene-Si (APCVD-Si) photocathode are −0.36 V and −28.28 mA cm^(−2) (at −0.385 V), respectively. After the 130th LSV scan, the onset potential and photocurrent of LPCVD-Si improved to −0.39 V and −13.28 mA cm^(−2) (at −0.385 V), respectively. In addition, the onset potential and photocurrent of APCVD-Si photocathode at the LSV 130th scan are enhanced to −0.36 V and −28.28 mA cm^(−2) (at −0.385 V), respectively. The graphene sample grown via LPCVD-Si show stable performance whereas, the graphene obtained via APCVD-Si have higher photocurrent poor stability

Liposomal irinotecan in metastatic pancreatic adenocarcinoma in Asian patients: Subgroup analysis of the NAPOLI-1 study

The global, randomized NAPOLI-1 phase 3 trial reported a survival benefit with liposomal irinotecan (nal-IRI) plus 5-fluorouracil/leucovorin (nal-IRI+5-FU/LV) in patients with metastatic pancreatic ductal adenocarcinoma (mPDAC) after previous gemcitabine-based therapy. Median overall survival (OS) with nal-IRI+5-FU/LV was 6.1 vs 4.2 months with 5-FU/LV alone (unstratified hazard ratio [HR] = 0.67, P =.012). Herein, we report efficacy and safety results from a post-hoc subgroup analysis of Asian patients treated at Asian centers. Primary study endpoint was OS; secondary endpoints included progression-free survival (PFS), objective response rate (ORR), and safety. Patients receiving nal-IRI+5-FU/LV (n = 34) had significantly longer median OS versus 5-FU/LV (n = 35) (8.9 vs 3.7 months; unstratified HR = 0.51, P =.025). Patients had significantly increased median PFS with nal-IRI+5-FU/LV versus 5-FU/LV (4.0 vs 1.4; unstratified HR = 0.48, P =.011), and increased ORR (8.8% vs 0; P =.114). nal-IRI monotherapy (n = 50) numerically improved efficacy endpoints versus 5-FU/ LV (n = 48): median OS was 5.8 versus 4.3 months (HR = 0.83, P =.423) a nd m edian PFS was 2.8 versus 1.4 months (HR = 0.69, P =.155). Grade =3 neutropenia was reported more frequently with nal-IRI+5-FU/LV versus 5-FU/LV (54.5% vs 3.4%), and incidence of grade =3 diarrhea was comparable between the two arms (3.0% vs 6.9%). This subgroup analysis confirms nal-IRI+5-FU/LV as an efficacious treatment option that improves survival in Asian patients with mPDAC that progressed after gemcitabine-based therapy, with a safety profile agreeing with previous findings. The nal-IRI+5-FU/LV regimen should represent a new standard of care for these patients in Asia. (Clinicaltrials. gov: NCT01494506)

Defect-modulated thermal transport behavior of BAs under high pressure

Boron arsenide (BAs) is a covalent semiconductor with a theoretical intrinsic thermal conductivity approaching 1300 W/m K. The existence of defects not only limits the thermal conductivity of BAs significantly but also changes its pressure-dependent thermal transport behavior. Using both picosecond transient thermoreflectance and femtosecond time-domain thermoreflectance techniques, we observed a non-monotonic dependence of thermal conductivity on pressure. This trend is not caused by the pressure-modulated phonon–phonon scattering, which was predicted to only change the thermal conductivity by 10%–20%, but a result of several competing effects, including defect–phonon scattering and modification of structural defects under high pressure. Our findings reveal the complexity of the defect-modulated thermal behavior under pressure.The authors are grateful for the support from the National Science Foundation (NASCENT, Grant No. EEC-1160494; Center for Dynamics and Control of Materials DMR-1720595; CBET- 2211660); F.T., Z.R., and L.S. were supported by the Office of Naval Research under Multidisciplinary University Research Initiative (Grant No. N00014-16-1-2436).Center for Dynamics and Control of Material