Death Caused by Disseminated Herpes Zoster in a Patient with Multiple Myeloma: A Case Report and Literature Review.

We report a fatal case of disseminated herpes zoster in a patient with multiple myeloma, illustrating the severe risks immunocompromised individuals face from viral infections. By combining a detailed case report with an extensive literature review, the paper seeks to shed light on the underlying susceptibility factors for varicella-zoster virus infection in multiple myeloma patients. We further evaluate effective prophylactic protocols for herpes zoster, aiming to equip clinicians with improved therapeutic strategies. The case underscores the critical need for vigilant clinical assessments and tailored patient management to mitigate infection risks and enhance patient outcomes.

Invariant feature based label correction for DNN when Learning with Noisy Labels.

Learning with Noisy Labels (LNL) methods have been widely studied in recent years, which aims to improve the performance of Deep Neural Networks (DNNs) when the training dataset contains incorrectly annotated labels. Popular existing LNL methods rely on semantic features extracted by the DNN to detect and mitigate label noise. However, these extracted features are often spurious and contain unstable correlations with the label across different environments (domains), which can occasionally lead to incorrect prediction and compromise the efficacy of LNL methods. To mitigate this insufficiency, we propose Invariant Feature based Label Correction (IFLC), which reduces spurious features and accurately utilizes the learned invariant features that contain stable correlation to correct label noise. To the best of our knowledge, this is the first attempt to mitigate the issue of spurious features for LNL methods. IFLC consists of two critical processes: The Label Disturbing (LD) process and the Representation Decorrelation (RD) process. The LD process aims to encourage DNN to attain stable performance across different environments, thus reducing the captured spurious features. The RD process strengthens independence between each dimension of the representation vector, thus enabling accurate utilization of the learned invariant features for label correction. We then utilize robust linear regression for the feature representation to conduct label correction. We evaluated the effectiveness of our proposed method and compared it with state-of-the-art (sota) LNL methods on four benchmark datasets, CIFAR-10, CIFAR-100, Animal-10N, and Clothing1M. The experimental results show that our proposed method achieved comparable or even better performance than the existing sota methods. The source codes are available at https://github.com/yangbo1973/IFLC.

Highly Efficient One-Step Conversion of Fructose to Biofuel 5-Ethoxymethylfurfural Using a UIO-66-SO3H Catalyst.

In this study, a novel sulfonic acid-modified catalyst for MOFs (UIO-66-SO3H) was synthesized using chlorosulfonic acid as a sulfonating reagent and first used as efficient heterogeneous catalysts for the one-pot conversion of fructose into biofuel 5-ethoxymethylfurfural (EMF) in a cosolvent free system. The physicochemical properties of this catalyst were characterized by Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), and powder X-ray diffraction (XRD). The characterization demonstrated that the sulfonic acid group was successfully grafted onto the MOF material and did not cause significant changes to its morphology and structure. Furthermore, the effects of catalyst acid amount, reaction temperature, reaction time, and catalyst dosage on reaction results were investigated. The results showed that the conversion of fructose was 99.7% within 1 h at 140°C, while the EMF yield reached 80.4%. This work provides a viable strategy by application of sulfonic acid-based MOFs for the efficient synthesis of potential liquid fuel EMF from renewable biomass.

χ2-BidLSTM: A Feature Driven Intrusion Detection System Based on χ2 Statistical Model and Bidirectional LSTM.

In a network architecture, an intrusion detection system (IDS) is one of the most commonly used approaches to secure the integrity and availability of critical assets in protected systems. Many existing network intrusion detection systems (NIDS) utilize stand-alone classifier models to classify network traffic as an attack or as normal. Due to the vast data volume, these stand-alone models struggle to reach higher intrusion detection rates with low false alarm rates( FAR). Additionally, irrelevant features in datasets can also increase the running time required to develop a model. However, data can be reduced effectively to an optimal feature set without information loss by employing a dimensionality reduction method, which a classification model then uses for accurate predictions of the various network intrusions. In this study, we propose a novel feature-driven intrusion detection system, namely χ2-BidLSTM, that integrates a χ2 statistical model and bidirectional long short-term memory (BidLSTM). The NSL-KDD dataset is used to train and evaluate the proposed approach. In the first phase, the χ2-BidLSTM system uses a χ2 model to rank all the features, then searches an optimal subset using a forward best search algorithm. In next phase, the optimal set is fed to the BidLSTM model for classification purposes. The experimental results indicate that our proposed χ2-BidLSTM approach achieves a detection accuracy of 95.62% and an F-score of 95.65%, with a low FAR of 2.11% on NSL-KDDTest+. Furthermore, our model obtains an accuracy of 89.55%, an F-score of 89.77%, and an FAR of 2.71% on NSL-KDDTest-21, indicating the superiority of the proposed approach over the standard LSTM method and other existing feature-selection-based NIDS methods.

Efficient synthesis of 5-ethoxymethylfurfural from biomass-derived 5-hydroxymethylfurfural over sulfonated organic polymer catalyst.

Herein, we investigated catalytic potential of a functionalized porous organic polymer bearing sulfonic acid groups (PDVTA-SO3H) to the etherification of 5-hydroxymethylfurfural (HMF) to 5-ethoxymethylfurfural (EMF) under solvent-free conditions. The PDVTA-SO3H material was synthesized via post-synthetic sulfonation of the porous co-polymer poly-divinylbenzene-co-triallylamine by chlorosulfonic acid. The physicochemical properties of the PDVTA-SO3H were characterized by FT-IR, SEM, TG-DTG, and N2 adsorption isotherm techniques. PDVTA-SO3H had high specific surface area (591 m2 g-1) and high density of -SO3H group (2.1 mmol g-1). The reaction conditions were optimized via Box-Behnken response surface methodology. Under the optimized conditions, the PDVTA-SO3H catalyst exhibited efficient catalytic activity with 99.8% HMF conversion and 87.5% EMF yield within 30 min at 110 °C. The used PDVTA-SO3H catalyst was readily recovered by filtration and remained active in recycle runs.

Broad-Spectrum Profiling of Drug Safety via Learning Complex Network.

Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug-gene-adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene-ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlert-gene at http://www.bio-add.org/ADRAlert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage.

Visual and efficient immunosensor technique for advancing biomedical applications of quantum dots on Salmonella detection and isolation.

It is a great challenge in nanotechnology for fluorescent nanobioprobes to be applied to visually detect and directly isolate pathogens in situ. A novel and visual immunosensor technique for efficient detection and isolation of Salmonella was established here by applying fluorescent nanobioprobes on a specially-designed cellulose-based swab (a solid-phase enrichment system). The selective and chromogenic medium used on this swab can achieve the ultrasensitive amplification of target bacteria and form chromogenic colonies in situ based on a simple biochemical reaction. More importantly, because this swab can serve as an attachment site for the targeted pathogens to immobilize and immunologically capture nanobioprobes, our mAb-conjugated QD bioprobes were successfully applied on the solid-phase enrichment system to capture the fluorescence of targeted colonies under a designed excitation light instrument based on blue light-emitting diodes combined with stereomicroscopy or laser scanning confocal microscopy. Compared with the traditional methods using 4-7 days to isolate Salmonella from the bacterial mixture, this method took only 2 days to do this, and the process of initial screening and preliminary diagnosis can be completed in only one and a half days. Furthermore, the limit of detection can reach as low as 10(1) cells per mL Salmonella on the background of 10(5) cells per mL non-Salmonella (Escherichia coli, Proteus mirabilis or Citrobacter freundii, respectively) in experimental samples, and even in human anal ones. The visual and efficient immunosensor technique may be proved to be a favorable alternative for screening and isolating Salmonella in a large number of samples related to public health surveillance.

Rapid Assessment of Adverse Drug Reactions by Statistical Solution of Gene Association Network.

Adverse drug reaction (ADR) is a common clinical problem, sometimes accompanying with high risk of mortality and morbidity. It is also one of the major factors that lead to failure in new drug development. Unfortunately, most of current experimental and computational methods are unable to evaluate clinical safety of drug candidates in early drug discovery stage due to the very limited knowledge of molecular mechanisms underlying ADRs. Therefore, in this study, we proposed a novel na€ive Bayesian model for rapid assessment of clinical ADRs with frequency estimation. This model was constructed on a gene-ADR association network, which covered 611 US FDA approved drugs, 14,251 genes, and 1,254 distinct ADR terms. An average detection rate of 99.86 and 99.73 percent were achieved eventually in identification of known ADRs in internal test data set and external case analyses respectively. Moreover, a comparative analysis between the estimated frequencies of ADRs and their observed frequencies was undertaken. It is observed that these two frequencies have the similar distribution trend. These results suggest that the naive Bayesian model based on gene-ADR association network can serve as an efficient and economic tool in rapid ADRs assessment.

[Application of niche theory in evaluation of main tourism scenic areas in Zhangjiajie City].

Five tourism scenic areas in Zhangjiajie City were selected as research objects, and fifty kinds of resource conditions affecting the development of tourism scenic area were taken as evaluation indices. Through disposing and consolidating the indices level by level, an analysis was made on the niche breadth and niche overlap of the five tourism scenic areas at three levels (I, II, and III). In the five scenic areas, index level had significant effects on the niche breadth (F = 10.278, P = 0.006), but less effects on the relative niche breadth, suggesting that in the evaluation of the development potential of tourism scenic area, relative niche breadth was more reasonable than absolute niche breadth. From level III to level I, the niche overlap of the five scenic areas was increasing, indicating that level choice would affect the evaluation of the actual niche overlap of the scenic areas. With the progressive refinement of the indices to certain level, and when the difference between observed and Monte Carlo-simulated Pianka indices achieved to significant level, this index level could be used as the minimum standard of the refinement, and the simulated niche overlap could be taken as an important reference in the competition evaluation of tourism scenic area.

Time-critical dynamic decision modeling in medicine.

Many real-world medical applications require timely actions to be taken in time pressured situations. Existing approaches to dynamic decision modeling have provided relatively efficient methods for representing and reasoning, but the process of computing the optimal solution has remained intractable. A major reason for this difficulty is the lack of models that are capable of modeling temporal processes and dealing with time-critical situations. This paper presents a formalism called the time-critical dynamic influence diagram that provide the capability for both temporal and space abstraction. To deal with the time criticality, we exploit the concept of space and temporal abstraction to reduce the computational complexity and propose an anytime algorithm for the solution process. We illustrate through out the paper, the various approaches with the use of a medical problem on the treatment of cardiac arrest.