Search

Panoramic X-ray images are the major source used in field of dental image segmentation. However, such images suffers from the disturbances like low contrast, presence of jaw bones, nose bones, spinal bone, and artifacts. Thus, to observe these images manually is a tedious task, requires expertise of dentist and is time consuming. Hence, there is need to develop an automated tool for teeth segmentation. Recently, few deep models have been developed for dental image segmentation. But, such models possess large number of training parameters, thus making the segmentation a very complex task. Also, these models are based only on conventional CNN and lacks in exploiting multimodal CNN features for dental image segmentation. Thus, to address these issues, a novel encoder-decoder model based on multimodal-feature extraction for automatic segmentation of teeth area is proposed. The encoder has three different CNN based architectures: conventional CNN, atrous-CNN, and separable CNN to encode rich contextual information. Whereas decoder contains a single stream of deconvolutional layers for segmentation. The proposed model is tested on 1500 panoramic X-ray images and uses very less parameters when compared to state-of-the-art methods. Besides this, the precision and recall are 95.01% and 94.06%, which out performs the state-of-the art methods.

Here, we report the draft genome sequence of Alkalihalobacillus clausii strain AXA-BCL3, which was isolated from a soil sample from the Sahastradhara springs of Uttarakhand, India. The genome was assembled in 125 contigs with a total length of 4,428,477 bp and a GC content of 44.5%. Genome annotation predicted 4,278 protein-coding genes and 75 tRNA genes.

Viral infections affecting the lower respiratory tract place enormous burdens on hospitals. As neither vaccines nor antiviral agents exist for many viruses, understanding risk factors and outcomes in each patient using minimally invasive analysis, such as blood, can lead to improved health care delivery. A cohort of PAXgene RNA sequencing of infants admitted with moderate or severe acute bronchiolitis and respiratory syncytial virus were compared with case-control statistical analysis and cohort-based outlier mapping for precision transcriptomics. Patients with severe bronchiolitis had signatures connected to the immune system, interferon signaling, and cytokine signaling, with marked sex differences in XIST, RPS4Y1, KDM5D, and LINC00278 for severity. Several patients had unique secondary infections, cytokine activation, immune responses, biological pathways, and immune cell activation, highlighting the need for defining patient-level transcriptomic signatures. Balancing relative contributions of cohort-based biomarker discoveries with patient’s biological responses is needed to understand the totality of mechanisms of adverse outcomes in viral bronchiolitis.

Free-riding is a major problem in peer to peer networks. One of the ways to resolve this problem is by using a reputation management system. Each node provides resources to nodes with higher reputation, as he himself gains in reputation in proportion to the receiver’s reputation. So, free-riding becomes a non-optimal strategy as there is no resulting reputation gain, and hence no available resources over time. An efficient and accurate trust estimation method in the reputation management system is needed for effectiveness of this strategy. The existing trust estimation methods generally do not consider the uncertainties in the inputs. In this paper, we propose a new trust estimation method using BLUE (Best Linear Unbiased Estimator) that also takes into account the uncertainties in the input variables. The results show that using the proposed estimation method, effective trust estimation is achieved in the peer-to-peer resource sharing systems. Thus the free-riding can be countered more effectively.

Smoothed functional (SF) algorithm estimates the gradient of the stochastic optimization problem by convolution with a smoothening kernel. This process helps the algorithm to converge to a global minimum or a point close to it. We study a two-time scale SF based gradient search algorithm with Nesterov’s acceleration for stochastic optimization problems. The main contribution of our work is to prove the convergence of this algorithm using the stochastic approximation theory. We propose a novel Lyapunov function to show the associated second-order ordinary differential equations’ (o.d.e.) stability for a non-autonomous system. We compare our algorithm with other smoothed functional algorithms such as Quasi-Newton SF, Gradient SF and Jacobi Variant of Newton SF on two different optimization problems: first, on a simple stochastic function minimization problem, and second, on the problem of optimal routing in a queueing network. Additionally, we compared the algorithms on real weather data in a weather prediction task. Experimental results show that our algorithm performs significantly better than these baseline algorithms.

A wireless system communicating in the millimeter-wave (mmWave) band suffers from severe propagation loss and thus, employs a highly directional beam to mitigate the effect of propagation losses. However, such beams need to be aligned between the transmitter and receiver for any reliable communication to occur. In literature, this problem has been modeled as a multi-armed bandit. Most of the existing solutions for this problem use stationary bandit algorithms, which suit for time-invariant channels, whereas wireless channels are time-variant in nature. Thus, we propose two non-stationary multi-armed bandit based beam alignment BA algorithms, especially designed for time-varying channels. These algorithms perform a distributed BA between the transmitter and the receiver requiring minimal feedback. Using simulations, we have shown that the proposed BA algorithms minimize the outage probabilities and the regret compared to those of the exponential-weights algorithm.

SARS-CoV-2 (COVID-19) has infected millions of people worldwide, with lethality in hundreds of thousands. The rapid publication of information, both regarding the clinical course and the viral biology, has yielded incredible knowledge of the virus. In this review, we address the insights gained for the SARS-CoV-2 proteome, which we have integrated into the Viral Integrated Structural Evolution Dynamic Database, a publicly available resource. Integrating evolutionary, structural, and interaction data with human proteins, we present how the SARS-CoV-2 proteome interacts with human disorders and risk factors ranging from cytokine storm, hyperferritinemic septic, coagulopathic, cardiac, immune, and rare disease-based genetics. The most noteworthy human genetic potential of SARS-CoV-2 is that of the nucleocapsid protein, where it is known to contribute to the inhibition of the biological process known as nonsense-mediated decay. This inhibition has the potential to not only regulate about 10% of all biological transcripts through altered ribosomal biology but also associate with viral-induced genetics, where suppressed human variants are activated to drive dominant, negative outcomes within cells. As we understand more of the dynamic and complex biological pathways that the proteome of SARS-CoV-2 utilizes for entry into cells, for replication, and for release from human cells, we can understand more risk factors for severe/lethal outcomes in patients and novel pharmaceutical interventions that may mitigate future pandemics.

Residual neuromuscular blockade after surgery is associated with airway obstruction, hypoxia, and respiratory complications. Compared with neostigmine, sugammadex reverses neuromuscular blockade to a train-of-four ratio > 0.9 more rapidly. It is unknown, however, whether the superior reversal profile of sugammadex improves clinically relevant measures of strength in the early postoperative period. Patients undergoing general, gynecological, or urologic surgery were randomized to receive either neostigmine (70 µg·kg−1, maximum 5 mg) or sugammadex (2 or 4 mg·kg−1) to reverse neuromuscular blockade. The primary outcome was the ability to breathe deeply measured by incentive spirometry at 30, 60, and 120 min after reversal. We randomized 62 patients to either a neostigmine (n = 31) or sugammadex (n = 31) group. The incentive spirometry volume recovery trajectory was not different between the two groups (P = 0.35). Median spirometry volumes at baseline, 30, 60, and 120 min postoperatively were 2650 vs 2500 mL, 1775 vs 1750 mL, 1375 vs 2000 mL, and 1800 vs 1950 mL for the sugammadex and neostigmine groups, respectively. Postoperative incentive spirometry decrease from baseline was not different between the two groups. Hand grip strength, the ability to sit unaided, train-of-four ratio on postanesthesia care unit (PACU) admission, time to extubation, time to PACU discharge readiness, and Quality of Recovery-15 scores were also not different between the groups. Measures of postoperative strength, such as incentive spirometry, hand group strength, and the ability to sit up in the early postoperative period were not different in patients who received neostigmine or sugammadex for the reversal of neuromuscular blockade. www.clinicaltrials.gov (NCT02909439); registered: 21 September, 2016.

Precision medicine requires the translation of basic biological understanding to medical insights, mainly applied to characterization of each unique patient. In many clinical settings, this requires tools that can be broadly used to identify pathology and risks. Patients often present to the intensive care unit with broad phenotypes, including multiple organ dysfunction syndrome (MODS) resulting from infection, trauma, or other disease processes. Etiology and outcomes are unique to individuals, making it difficult to cohort patients with MODS, but presenting a prime target for testing/developing tools for precision medicine. Using multitime point whole blood (cellular/acellular) total transcriptomics in 27 patients, we highlight the promise of simultaneously mapping viral/bacterial load, cell composition, tissue damage biomarkers, balance between syndromic biology versus environmental response, and unique biological insights in each patient using a single platform measurement. Integration of a transcriptome workflow yielded unexpected insights into the complex interplay between host genetics and viral/bacterial specific mechanisms, highlighted by a unique case of virally induced genetics (VIG) within one of these 27 patients. The power of RNA-Seq to study unique patient biology while investigating environmental contributions can be a critical tool moving forward for translational sciences applied to precision medicine.

Users in a cooperative computing environment always have a tendency to free-ride. One can employ incentive mechanisms to prevent such behavior. Some of the cooperative computing scenarios have the same access link shared between upload and download. In such a situation, increasing upload capacity decreases the download capacity and vice versa. Optimal partitioning of link capacity between upload and download needs to be done by each user to maximize its gain (i.e., download) from the network. We model this link capacity partitioning problem as a feedback control system, where feedback (resources received) decides the number of resources to be uploaded by a user. The resulting algorithm called adaptive step size (ASZ) dynamically adjusts the partitioning of link capacity to an optimal value. To compare this approach with others, a metric “level of optimality (U)”is introduced. U achieved by the ASZ is closer to the optimal level than the reputation-based resource allocation policy (existing scheme), thus resulting in its better performance. The ASZ is also integrated with BitTorrent, and the simulation results show that it increases the resources received by the users. The ASZ can provide an efficient solution to the problem of optimal partitioning in real-life distributed networks due to its distributed implementation, robustness to changes in network dynamics, and compatibility with the existing partitioning schemes.

Surgical care episodes place opioid-naïve patients at risk for transitioning to new persistent postoperative opioid use. With one of the central principles being the application of multimodal pain interventions to reduce the reliance on opioid-based medications, enhanced recovery pathways provide a framework that decreases perioperative opioid use. The fourth Perioperative Quality Initiative brought together a group of international experts representing anesthesiology, surgery, and nursing with the objective of providing consensus recommendations on this important topic. Fourth Perioperative Quality Initiative was a consensus-building conference designed around a modified Delphi process in which the group alternately convened for plenary discussion sessions in between small group discussions. The process included several iterative steps including a literature review of the topics, building consensus around the important questions related to the topic, and sequential steps of content building and refinement until agreement was achieved and a consensus document was produced. During the fourth Perioperative Quality Initiative conference and thereafter as a writing group, reference applicability to the topic was discussed in any area where there was disagreement. For this manuscript, the questions answered included (1) What are the potential strategies for preventing persistent postoperative opioid use? (2) Is opioid-free anesthesia and analgesia feasible and appropriate for routine operations? and (3) Is opioid-free (intraoperative) anesthesia associated with equivalent or superior outcomes compared to an opioid minimization in the perioperative period? We will discuss the relevant literature for each questions, emphasize what we do not know, and prioritize the areas for future research.

Persistent postoperative opioid use is thought to contribute to the ongoing opioid epidemic in the United States. However, efforts to study and address the issue have been stymied by the lack of a standard definition, which has also hampered efforts to measure the incidence of and risk factors for persistent postoperative opioid use. The objective of this systematic review is to (1) determine a clinically relevant definition of persistent postoperative opioid use, and (2) characterize its incidence and risk factors for several common surgeries. Our approach leveraged a group of international experts from the Perioperative Quality Initiative-4, a consensus-building conference that included representation from anesthesiology, surgery, and nursing. A search of the medical literature yielded 46 articles addressing persistent postoperative opioid use in adults after arthroplasty, abdominopelvic surgery, spine surgery, thoracic surgery, mastectomy, and thoracic surgery. In opioid-naive patients, the overall incidence ranged from 2% to 6% based on moderate-level evidence. However, patients who use opioids preoperatively had an incidence of >30%. Preoperative opioid use, depression, factors associated with the diagnosis of substance use disorder, preoperative pain, and tobacco use were reported risk factors. In addition, while anxiety, sex, and psychotropic prescription are associated with persistent postoperative opioid use, these reports are based on lower level evidence. While few articles addressed the health policy or prescriber characteristics that influence persistent postoperative opioid use, efforts to modify prescriber behaviors and health system characteristics are likely to have success in reducing persistent postoperative opioid use.