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Speech emotion recognition predicts the emotional state of a speaker based on the person’s speech. It brings an additional element for creating more natural human–computer interactions. Earlier studies on emotional recognition have been primarily based on handcrafted features and manual labels. With the advent of deep learning, there have been some efforts in applying the deep-network-based approach to the problem of emotion recognition. As deep learning automatically extracts salient features correlated to speaker emotion, it brings certain advantages over the handcrafted-feature-based methods. There are, however, some challenges in applying them to the emotion recognition problem, because data required for properly training deep networks are often lacking. Therefore, there is a need for a new deep-learning-based approach which can exploit available information from given speech signals to the maximum extent possible. Our proposed method, called “Fusion-ConvBERT”, is a parallel fusion model consisting of bidirectional encoder representations from transformers and convolutional neural networks. Extensive experiments were conducted on the proposed model using the EMO-DB and Interactive Emotional Dyadic Motion Capture Database emotion corpus, and it was shown that the proposed method outperformed state-of-the-art techniques in most of the test configurations.

Vascular diseases are becoming an epidemic with an increasing aging population and increases in obesity and type II diabetes. Point-of-care (POC) diagnosis and monitoring of vascular diseases is an unmet medical need. Photoacoustic imaging (PAI) provides label-free multiparametric information of deep vasculature based on strong absorption of light photons by hemoglobin molecules. However, conventional PAI systems use bulky nanosecond lasers which hinders POC applications. Recently, light-emitting diodes (LEDs) have emerged as cost-effective and portable optical sources for the PAI of living subjects. However, state-of-art LED arrays carry significantly lower optical energy (<0.5 mJ/pulse) and high pulse repetition frequencies (PRFs) (4 KHz) compared to the high-power laser sources (100 mJ/pulse) with low PRFs of 10 Hz. Given these tradeoffs between portability, cost, optical energy and frame rate, this work systematically studies the deep tissue PAI performance of LED and laser illuminations to help select a suitable source for a given biomedical application. To draw a fair comparison, we developed a fiberoptic array that delivers laser illumination similar to the LED array and uses the same ultrasound transducer and data acquisition platform for PAI with these two illuminations. Several controlled studies on tissue phantoms demonstrated that portable LED arrays with high frame averaging show higher signal-to-noise ratios (SNRs) of up to 30 mm depth, and the high-energy laser source was found to be more effective for imaging depths greater than 30 mm at similar frame rates. Label-free in vivo imaging of human hand vasculature studies further confirmed that the vascular contrast from LED-PAI is similar to laser-PAI for up to 2 cm depths. Therefore, LED-PAI systems have strong potential to be a mobile health care technology for diagnosing vascular diseases such as peripheral arterial disease and stroke in POC and resource poor settings.

The last five years marked a surge in interest for and use of smart robots, which operate in dynamic and unstructured environments and might interact with humans. We posit that well-validated computer simulation can provide a virtual proving ground that in many cases is instrumental in understanding safely, faster, at lower costs, and more thoroughly how the robots of the future should be designed and controlled for safe operation and improved performance. Against this backdrop, we discuss how simulation can help in robotics, barriers that currently prevent its broad adoption, and potential steps that can eliminate some of these barriers. The points and recommendations made concern the following simulation-in-robotics aspects: simulation of the dynamics of the robot; simulation of the virtual world; simulation of the sensing of this virtual world; simulation of the interaction between the human and the robot; and, in less depth, simulation of the communication between robots. This Perspectives contribution summarizes the points of view that coalesced during a 2018 National Science Foundation/Department of Defense/National Institute for Standards and Technology workshop dedicated to the topic at hand. The meeting brought together participants from a range of organizations, disciplines, and application fields, with expertise at the intersection of robotics, machine learning, and physics-based simulation.

Patient-reported outcome measures (PROMs) studies for H-Wave device stimulation (HWDS) for chronic low back and neck pain (cLBP, cNP) have been promising, so a retrospective statistical analysis of chronic shoulder pain (cSP) patients was conducted. Surveys from a cohort of 34,192 pain patients, filtered for chronicity of 3–36 months and device use of 22–365 days, resulted in 1496 with “all shoulder” diagnoses, including 772 rotator cuff disorder patients. Reported shoulder pain dropped 3.17 points (0–10 pain scale), with significant (≥ 20%) relief in 89.73%. Reported function/activities of daily living (ADL) improved in 96.40% and work performance in 84.86%. Medications decreased in 74.61% and sleep improved in 59.89%. Over 96% patient satisfaction and no adverse events were reported. Subgroup analyses found benefit with longer device use and shorter pain chronicity, while rotator cuff outcomes were equivalent to all shoulder conditions. Similarly positive outcomes were self-reported by cSP patients as for previously published cLBP and cNP patients, suggesting device appropriateness beyond spinal conditions. HWDS may have contributed to effective cSP relief and improvements in function and ADL, while also improving sleep and work performance. Medication reduction (e.g., opioids) in 3 of 4 shoulder patients was higher than reported for low back and neck conditions.

Understanding the neuropathology of multiple sclerosis (MS) is essential for improved therapies. Therefore, identification of targets specific to pathological types of MS may have therapeutic benefits. Here we identify, by laser-capture microdissection and proteomics, proteins unique to three major types of MS lesions: acute plaque, chronic active plaque and chronic plaque. Comparative proteomic profiles identified tissue factor and protein C inhibitor within chronic active plaque samples, suggesting dysregulation of molecules associated with coagulation. In vivo administration of hirudin or recombinant activated protein C reduced disease severity in experimental autoimmune encephalomyelitis and suppressed Th1 and Th17 cytokines in astrocytes and immune cells. Administration of mutant forms of recombinant activated protein C showed that both its anticoagulant and its signalling functions were essential for optimal amelioration of experimental autoimmune encephalomyelitis. A proteomic approach illuminated potential therapeutic targets selective for specific pathological stages of MS and implicated participation of the coagulation cascade. Multiple sclerosis targets A large-scale proteomic analysis of tissue samples from multiple sclerosis (MS) lesions from different stages of the disease has identified proteins peculiar to brain lesions associated with different disease stages. Several new potential therapeutic targets were found. Two proteins in particular showed signs of damage during the chronic active period of the disease. One, called tissue factor, is involved in the initiation of blood clotting and the other, protein C inhibitor, in anti-inflammatory pathways. Administration of activated protein C or the anticoagulant hirudin slowed disease progression in a mouse model of multiple sclerosis, suggesting that the coagulation cascade has a previously unsuspected role in MS pathogenesis.

GPCR inhibitors are highly prevalent in modern therapeutics. However, interference with complex GPCR regulatory mechanisms leads to both therapeutic efficacy and adverse effects. Recently, the sphingosine-1-phosphate (S1P) receptor inhibitor FTY720 (also known as Fingolimod), which induces lymphopenia and prevents neuroinflammation, was adopted as a disease-modifying therapeutic in multiple sclerosis. Although highly efficacious, dose-dependent increases in adverse events have tempered its utility. We show here that FTY720P induces phosphorylation of the C-terminal domain of S1P receptor 1 (S1P₁) at multiple sites, resulting in GPCR internalization, polyubiquitinylation, and degradation. We also identified the ubiquitin E3 ligase WWP2 in the GPCR complex and demonstrated its requirement in FTY720-induced receptor degradation. GPCR degradation was not essential for the induction of lymphopenia, but was critical for pulmonary vascular leak in vivo. Prevention of receptor phosphorylation, internalization, and degradation inhibited vascular leak, which suggests that discrete mechanisms of S1P receptor regulation are responsible for the efficacy and adverse events associated with this class of therapeutics.

Spectral count, defined as the total number of spectra identified for a protein, has gained acceptance as a practical, label-free, semiquantitative measure of protein abundance in proteomic studies. In this review, we discuss issues affecting the performance of spectral counting relative to other label-free methods, as well as its limitations. Possible consequences of modifications, which are commonly applied to raw spectral counts to improve abundance estimations, are considered. The use of spectral counting for different types of quantitation studies is explored and critiqued. Different statistical methods and underlying frameworks that have been applied to spectral count analysis are described and compared, and problem areas that undermine confident statistical analysis are considered. Finally, the issue of accurate estimation of false-discovery rates is addressed and identified as a major current challenge in quantitative proteomics.

An approach to the systematic identification and quantification of the proteins contained in the microsomal fraction of cells is described. It consists of three steps: (1) preparation of microsomal fractions from cells or tissues representing different states; (2) covalent tagging of the proteins with isotope-coded affinity tag (ICAT) reagents followed by proteolysis of the combined labeled protein samples; and (3) isolation, identification, and quantification of the tagged peptides by multidimensional chromatography, automated tandem mass spectrometry, and computational analysis of the obtained data. The method was used to identify and determine the ratios of abundance of each of 491 proteins contained in the microsomal fractions of naïve and in vitro – differentiated human myeloid leukemia (HL-60) cells. The method and the new software tools to support it are well suited to the large-scale, quantitative analysis of membrane proteins and other classes of proteins that have been refractory to standard proteomics technology.

Yellow and knotroot foxtail are two common weed species infesting turfgrass and pastures in the southeastern region of the United States. Yellow and knotroot foxtail share morphological similarities and are frequently misidentified by weed managers, thus leading to confusion in herbicide selection. Greenhouse research was conducted to evaluate the response of yellow and knotroot foxtail to several turfgrass herbicides: pinoxaden (35 and 70 g ai ha–1), sethoxydim (316 and 520 g ai ha–1), thiencarbazone + dicamba + iodosulfuron (230 g ai ha–1), nicosulfuron + rimsulfuron (562.8 g ai ha–1), metribuzin (395 g ha–1), sulfentrazone (330 g ai ha–1), sulfentrazone + imazethapyr (504 g ai ha–1), and imazaquin (550 g ai ha–1). All treatments controlled yellow foxtail >87% with more than 90% reduction of the biomass. By comparison, only sulfentrazone alone controlled knotroot foxtail 90% and completely reduced aboveground biomass. Sethoxydim (520 g ai ha–1), metribuzin, and imazaquin controlled knotroot foxtail >70% at 28 d after application. In a rate response evaluation, nonlinear regression showed that yellow foxtail was approximately 8 times more susceptible to pinoxaden and 2 times more susceptible to sethoxydim than knotroot foxtail based on log (WR50) values, which were 50% reduction in fresh weight. Our research indicates that knotroot foxtail is more difficult to control across a range of herbicides, making differentiation of these two species important before herbicides are applied. Nomenclature: dicamba; imazaquin; imazethapyr; iodosulfuron; knotroot foxtail; metribuzin; nicosulfuron; Pinoxaden; rimsulfuron; Setaria parviflora (Poir.) Kerguélen; Setaria pumila (Poir.) Roem. & Schult.; sethoxydim; sulfentrazone; thiencarbazone; yellow foxtail

Since the 1830s, Holiness and Pentecostal movements have had a significant influence on many Christian churches, and they have been a central force in producing what is known today as World Christianity. This book demonstrates the advantages of analyzing them in relation to one another. The Salvation Army, the Church of the Nazarene, the Wesleyan Church, and the Free Methodist Church identify strongly with the Holiness Movement. The Assemblies of God and the Pentecostal Assemblies of the World identify just as strongly with the Pentecostal Movement. Complicating matters, denominations such as the Church of God (Cleveland), the International Holiness Pentecostal Church, and the Church of God in Christ have harmonized Holiness and Pentecostalism. This book, the first in the new series Studies in the Holiness and Pentecostal Movements, examines these complex relationships in a multidisciplinary fashion. Building on previous scholarship, the contributors provide new ways of understanding the relationships, influences, and circulation of ideas among these movements in the United States, the United Kingdom, India, and Southeast and East Asia. In addition to the editors, the contributors are Kimberly Ervin Alexander, Insik Choi, Robert A. Danielson, Chris E. W. Green, Henry H. Knight III, Frank D. Macchia, Luther Oconer, Cheryl J. Sanders, and Daniel Woods.