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Over the past decade, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry has revolutionized the practice of clinical microbiology and infectious disease diagnostics. Rapid advancement has occurred through the development and implementation of mass spectrometric protein profiling technologies that are widely available. Ease of sample preparation, rapid turnaround times, and high throughput accuracy have accelerated acceptance within the clinical laboratory. New mass spectrometric technologies centered on multiple microbial diagnostic markers are in development. Such new applications, reviewed in this article and on the near horizon, stand to greatly enhance the capabilities and utility for improved mass spectrometric microbial identification and patient care.Many studies have shown successful performance of matrix-assisted laser desorption ionization time-of-flight mass spectrometry for rapid yeast and mold identification, yet few laboratories have chosen to apply this technology into their routine clinical mycology workflow. This review provides an overview of the current status of matrix-assisted laser desorption ionization time-of-flight mass spectrometry for fungal identification, including key findings in the literature, processing and database considerations, updates in technology, and exciting future prospects. Significant advances toward standardization have taken place recently; thus, accurate species-level identification of yeasts and molds should be highly attainable, achievable, and practical in most clinical laboratories.N-glycan imaging mass spectrometry (IMS) can rapidly and reproducibly identify changes in disease-associated N-linked glycosylation that are linked with histopathology features in standard formalin-fixed paraffin-embedded tissue samples. It can detect multiple N-glycans simultaneously and has been used to identify specific N-glycans and carbohydrate structural motifs as possible cancer biomarkers. Recent advancements in instrumentation and sample preparation are also discussed. The tissue N-glycan IMS workflow has been adapted to new glass slide-based assays for effective and rapid analysis of clinical biofluids, cultured cells, and immunoarray-captured glycoproteins for detection of changes in glycosylation associated with disease.Rapid characterization of tissue disorder using ambient mass spectrometry (MS) techniques, requiring little to no preanalytical preparations of sampled tissues, has been shown using a variety of ion sources and with many disease classes. A brief overview of ambient MS in clinical applications, the state of the art in regulatory affairs, and recommendations to facilitate adoption for use at the bedside are presented. Unique challenges in the validation of untargeted MS methods and additional safety and compliance requirements for deployment within a clinical setting are further discussed. Development of a harmonized validation strategy for ambient MS methods is emphasized.The diagnosis of myeloma and other plasma cell disorders has traditionally been done with the aid of electrophoretic methods, whereas amyloidosis has been characterized by immunohistochemistry. Mass spectrometry has recently been established as an alternative to these traditional methods and has been proved to bring added benefit for patient care. These newer mass spectrometry-based methods highlight some of the key advantages of modern proteomic methods and how they can be applied to the routine care of patients.Various analytical methods can be applied to concentrate, separate, and examine trace volatile organic metabolites in the breath, with the potential for noninvasive, rapid, real-time identification of various disease processes, including an array of microbial infections. Although biomarker discovery and validation in microbial infections can be technically challenging, it is an approach that has shown great promise, especially for infections that are particularly difficult to identify with standard culture and molecular amplification-based approaches. This article discusses the current state of breath analysis for the diagnosis of infectious diseases.Mass spectrometry imaging (MSI) combines the excellence in molecular characterization of mass spectrometry with microscopic imaging capabilities of hematoxylin- and eosin-stained samples, enabling the precise location of several analytes in the tissue. Especially in the field of pathology, MSI may have an impactful role in tumor diagnosis, biomarker identification, prognostic prediction, and characterization of tumor margins during tumor resection procedures. This article discusses the recent developments in the field that are paving the way for this technology to become accepted as an analytical tool in the clinical setting, its current limitations, and future directions.The drastic changes of the space environment at the tunnel entrance can lead to frequent accidents with higher levels. The connected vehicle environment provides drivers with surrounding traffic information and improve their driving behavior by helping them make safe decisions efficiently. As such, this study is to examine the effects of the connected vehicle environment on driving behavior and safety at the tunnel entrance zone. To this end, this research simulates a connected vehicle environment and provides driving aids through the Human-Machine Interface (HMI). Secondly, 40 participants with diverse backgrounds drove the simulator under two different driving conditions HMI-OFF (traditional driving environment) and HMI-ON (connected vehicle environment). Finally, indicators are selected from speed control, stability and urgency to analyze the impact of the connected vehicle environment on drivers' behaviors and safety at the warning zone and tunnel entrance zone. The results show that in the connected vehinnel entrance.The Cox proportional hazard model is one of the most widely used methods in modeling time-to-event data in the health sciences. Due to the simplicity of the Cox partial likelihood function, many machine learning algorithms use it for survival data. However, due to the nature of censored data, the optimization problem becomes intractable when more complicated regularization is employed, which is necessary when dealing with high dimensional omic data. check details In this paper, we show that a convex conjugate function of the Cox loss function based on Fenchel duality exists, and provide an alternative framework to optimization based on the primal form. Furthermore, the dual form suggests an efficient algorithm for solving the kernel learning problem with censored survival outcomes. We illustrate performance and properties of the derived duality form of Cox partial likelihood loss in multiple kernel learning problems with simulated and the Skin Cutaneous Melanoma TCGA datasets.