Nevin Manimala

Ear Nose Throat J. 2022 Jul 13:1455613221115143. doi: 10.1177/01455613221115143. Online ahead of print.

ABSTRACT

OBJECTIVES: This study was conducted to identify the risk factors for free flap outcomes in head and neck reconstruction.

METHODS: A retrospective review of 318 free flaps were used for head and neck reconstructions in 317 patients over seven years. The patient characteristics, surgical data, and flap outcomes were recorded. The impact of risk factors related on the outcomes of free flaps were analyzed using single and multivariate analysis.

RESULTS: For single factor analysis, 295 free flaps for the first reconstruction were included. Hypertension and the type of recipient vein are associated with venous thrombosis (P = .018, P = .047). Hypertension, type of free flap, recipient artery, and recipient vein were associated with the incidence of re-exploration (P = .009, P = .011, P = .017, P = .021). Hypertension had an obvious effect on the flap survival (P = .005). For multivariate analysis, hypertension (odds ratio = .166, 95% confidence interval: .043 – .636; P = .009) was a statistically significant risk factor for flap survival. For types of recipient artery and vein, selecting two venous anastomosis (one of IJVS and one of EJVS) had the minimum incidence of venous thrombosis (2.2%), and selecting facial artery, single vein (one of IJVS), and two veins (one of IJVS and one of EJVS) for anastomosis had lower incidence of re-exploration, which were 4.4%, 2.9%, and 6.0%, respectively (P < .05).

CONCLUSIONS: Risk factors as hypertension, type of free flap, recipient artery and vein should be paid more attention in the free flaps for head and neck reconstructions. We believe proper measures will lead to better results in head and neck reconstruction.

PMID:35830468 | DOI:10.1177/01455613221115143

PLoS One. 2022 Jul 13;17(7):e0271395. doi: 10.1371/journal.pone.0271395. eCollection 2022.

ABSTRACT

Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) that play important roles in the genetic heritability of traits and diseases. With most of these SNPs located on the non-coding part of the genome, it is currently assumed that these SNPs influence the expression of nearby genes on the genome. However, identifying which genes are targeted by these disease-associated SNPs remains challenging. In the past, protein knowledge graphs have often been used to identify genes that are associated with disease, also referred to as “disease genes”. Here, we explore whether protein knowledge graphs can be used to identify genes that are targeted by disease-associated non-coding SNPs by testing and comparing the performance of six existing methods for a protein knowledge graph, four of which were developed for disease gene identification. We compare our performance against two baselines: (1) an existing state-of-the-art method that is based on guilt-by-association, and (2) the leading assumption that SNPs target the nearest gene on the genome. We test these methods with four reference sets, three of which were obtained by different means. Furthermore, we combine methods to investigate whether their combination improves performance. We find that protein knowledge graphs that include predicate information perform comparable to the current state of the art, achieving an area under the receiver operating characteristic curve (AUC) of 79.6% on average across all four reference sets. Protein knowledge graphs that lack predicate information perform comparable to our other baseline (genetic distance) which achieved an AUC of 75.7% across all four reference sets. Combining multiple methods improved performance to 84.9% AUC. We conclude that methods for a protein knowledge graph can be used to identify which genes are targeted by disease-associated non-coding SNPs.

PMID:35830458 | DOI:10.1371/journal.pone.0271395

PLoS One. 2022 Jul 13;17(7):e0270358. doi: 10.1371/journal.pone.0270358. eCollection 2022.

ABSTRACT

Animals use odors in many natural contexts, for example, for finding mates or food, or signaling danger. Most analyses of natural odors search for either the most meaningful components of a natural odor mixture, or they use linear metrics to analyze the mixture compositions. However, we have recently shown that the physical space for complex mixtures is ‘hyperbolic’, meaning that there are certain combinations of variables that have a disproportionately large impact on perception and that these variables have specific interpretations in terms of metabolic processes taking place inside the flower and fruit that produce the odors. Here we show that the statistics of odorants and odorant mixtures produced by inflorescences (Brassica rapa) are also better described with a hyperbolic rather than a linear metric, and that combinations of odorants in the hyperbolic space are better predictors of the nectar and pollen resources sought by bee pollinators than the standard Euclidian combinations. We also show that honey bee and bumble bee antennae can detect most components of the B. rapa odor space that we tested, and the strength of responses correlates with positions of odorants in the hyperbolic space. In sum, a hyperbolic representation can be used to guide investigation of how information is represented at different levels of processing in the CNS.

PMID:35830455 | DOI:10.1371/journal.pone.0270358

PLoS One. 2022 Jul 13;17(7):e0271175. doi: 10.1371/journal.pone.0271175. eCollection 2022.

ABSTRACT

BACKGROUND: Fear of spiders, or Arachnophobia, is one of the most common specific phobias. The gold standard treatment, in vivo exposure therapy, is effective, but comes with significant limitations, including restricted availability, high costs, and high refusal rates. Novel technologies, such as augmented reality, may help to overcome these limitations and make Exposure Therapy more accessible by using mobile devices.

OBJECTIVE: This study will use a Randomized Controlled Trial design to investigate whether ZeroPhobia: Arachnophobia, a 6-week Augmented Reality Exposure Therapy smartphone self-help application, can effectively reduce spider phobia symptoms. Additionally, we will examine user-friendliness of the application and the effect of usage intensity and presence on treatment outcome.

METHODS: This study is registered in the Netherlands Trial Registry under NL70238.029.19 (Trial NL9221). Ethical approval was received on October 11, 2019. One-hundred-twelve participants (age 18-64, score ≥ 59) on the Fear of Spiders Questionnaire [FSQ] will be recruited from the general Dutch population and randomly assigned to a treatment or waitlist control group. The ZeroPhobia application can be accessed on users’ smartphone. Baseline, post-test (i.e., at six weeks), 3- and 12-month follow-up assessments will be done, each including the Fear of Spiders Questionnaire as the main outcome measure as well as additional measures of anxiety, depression, user-friendliness, and presence as secondary measures and covariates.

RESULTS: The study was funded on September 25, 2018. Data collection started in September 2021 and the study is expected to run until September 2022.

CONCLUSIONS: Our study will improve our understanding of the efficacy and feasibility of providing Exposure Therapy for spider phobia using an Augmented Reality self-help application, with the intention of making mental health care more accessible.

PMID:35830423 | DOI:10.1371/journal.pone.0271175

PLoS One. 2022 Jul 13;17(7):e0264598. doi: 10.1371/journal.pone.0264598. eCollection 2022.

ABSTRACT

Different levels of arterial occlusive disease (aortoiliac, femoropopliteal, multi-level disease) can produce claudication symptoms in different leg muscle groups (buttocks, thighs, calves) in patients with peripheral artery disease (PAD). We tested the hypothesis that different locations of occlusive disease uniquely affect the muscles of PAD legs and produce distinctive patterns in the way claudicating patients walk. Ninety-seven PAD patients and 35 healthy controls were recruited. PAD patients were categorized to aortoiliac, femoropopliteal and multi-level disease groups using computerized tomographic angiography. Subjects performed walking trials both pain-free and during claudication pain and joint kinematics, kinetics, and spatiotemporal parameters were calculated to evaluate the net contribution of the calf, thigh and buttock muscles. PAD patients with occlusive disease affecting different segments of the arterial tree (aortoiliac, femoropopliteal, multi-level disease) presented with symptoms affecting different muscle groups of the lower extremity (calves, thighs and buttocks alone or in combination). However, no significant biomechanical differences were found between PAD groups during the pain-free conditions with minimal differences between PAD groups in the claudicating state. All statistical differences in the pain-free condition occurred between healthy controls and one or more PAD groups. A discriminant analysis function was able to adequately predict if a subject was a control with over 70% accuracy, but the function was unable to differentiate between PAD groups. In-depth gait analyses of claudicating PAD patients indicate that different locations of arterial disease produce claudication symptoms that affect different muscle groups across the lower extremity but impact the function of the leg muscles in a diffuse manner generating similar walking impairments.

PMID:35830421 | DOI:10.1371/journal.pone.0264598

IEEE Trans Pattern Anal Mach Intell. 2022 Jul 13;PP. doi: 10.1109/TPAMI.2022.3190645. Online ahead of print.

ABSTRACT

As a fundamental manner for learning and cognition, transfer learning has attracted widespread attention in recent years. Typical transfer learning tasks include unsupervised domain adaptation (UDA) and few-shot learning (FSL), which both attempt to sufficiently transfer discriminative knowledge from the training environment to the test environment to improve the model’s generalization performance. Previous transfer learning methods usually ignore the potential conditional distribution shift between environments. This leads to the discriminability degradation in the test environments. Therefore, how to construct a learnable and interpretable metric to measure and then reduce the gap between conditional distributions is very important in the literature. In this work, we design the Conditional Kernel Bures (CKB) metric for characterizing conditional distribution discrepancy, and derive an empirical estimation with convergence guarantee. CKB provides a statistical and interpretable approach, under the optimal transportation framework, to understand the knowledge transfer mechanism. It is essentially an extension of optimal transportation from the marginal distributions to the conditional distributions. CKB can be used as a plug-and-play module and placed onto the loss layer in deep networks, thus, it plays the bottleneck role in representation learning. From this perspective, the new method with network architecture is abbreviated as BuresNet, and it can be used extract conditional invariant features for both UDA and FSL tasks. BuresNet can be trained in an end-to-end manner. Extensive experiment results on several benchmark datasets validate the effectiveness of BuresNet.

PMID:35830411 | DOI:10.1109/TPAMI.2022.3190645

IEEE/ACM Trans Comput Biol Bioinform. 2022 Jul 13;PP. doi: 10.1109/TCBB.2022.3190427. Online ahead of print.

ABSTRACT

The uidA gene codifies for a glucuronidase (GUS) enzyme which has been used as a biotechnological tool during the last years. When uidA gene is fused to a gene’s promotor region, it is possible to evaluate the activity of this one in response to a stimulus. Arabidopsis thaliana has served as the biological platform to elucidate molecular and regulatory signaling responses in plants. Transgenic lines of A. thaliana, tagged with the uidA gene, have allowed explaining how plants modify their hormonal pathways depending on the environmental conditions. Although the information extracted from microscopic images of these transgenic plants is often qualitative and in many publications is not subjected to quantification, in this paper we report the development of an informatics tool focused on computer vision for processing and analysis of digital images in order to analyze the expression of the GUS signal in A. thaliana roots, which is strongly correlated with the intensity of the grayscale images. This means that the presence of the GUS-induced color indicates where the gene has been actively expressed, such as our statistical analysis has demonstrated after treatment of A. thaliana DR5::GUS with naphtalen-acetic acid (0.0001 mM and 1 mM). GUSignal is a free informatics tool that aims to be fast and systematic during the image analysis since it executes specific and ordered instructions, to offer a segmented analysis by areas or regions of interest, providing quantitative results of the image intensity levels.

PMID:35830410 | DOI:10.1109/TCBB.2022.3190427

IEEE Trans Image Process. 2022 Jul 13;PP. doi: 10.1109/TIP.2022.3187285. Online ahead of print.

ABSTRACT

Compressive covariance estimation has arisen as a class of techniques whose aim is to obtain second-order statistics of stochastic processes from compressive measurements. Recently, these methods have been used in various image processing and communications applications, including denoising, spectrum sensing, and compression. Notice that estimating the covariance matrix from compressive samples leads to ill-posed minimizations with severe performance loss at high compression rates. In this regard, a regularization term is typically aggregated to the cost function to consider prior information about a particular property of the covariance matrix. Hence, this paper proposes an algorithm based on the projected gradient method to recover low-rank or Toeplitz approximations of the covariance matrix from compressive measurements. The proposed algorithm divides the compressive measurements into data subsets projected onto different subspaces and accurately estimates the covariance matrix by solving a single optimization problem assuming that each data subset contains an approximation of the signal statistics. Furthermore, gradient filtering is included at every iteration of the proposed algorithm to minimize the estimation error. The error induced by the proposed splitting approach is analytically derived along with the convergence guarantees of the proposed method. The proposed algorithm estimates the covariance matrix of hyperspectral images from synthetic and real compressive samples. Extensive simulations show that the proposed algorithm can effectively recover the covariance matrix of hyperspectral images from compressive measurements with high compression ratios (8-15% approx) in noisy scenarios. Moreover, simulations and theoretical results show that the filtering step reduces the recovery error up to twice the number of eigenvectors. Finally, an optical implementation is proposed, and real measurements are used to validate the theoretical findings.

PMID:35830408 | DOI:10.1109/TIP.2022.3187285

J Dent Educ. 2022 Jul 13. doi: 10.1002/jdd.13037. Online ahead of print.

ABSTRACT

OBJECTIVES: Substantial variations are seen among clinicians in the diagnosis and treatment planning of periodontal diseases. Accurate diagnosis and treatment planning are fundamental requirements for effective outcome-based patient care. The aim of this study was to evaluate the understanding of the American Academy of Periodontology and the European Federation of Periodontology 2017 periodontal disease classifications in diagnoses and treatment plans across four study groups.

METHODS: The study recruited at least 20 participants in each of the four study groups. These included 1) Periodontal faculty and residents at Indiana University School of Dentistry (IUSD-PF) 2) IUSD general practice faculty (IUSD-GPF), 3) private practice periodontists (PPP), and 4) general practitioners (GP). The participants were provided with 10 HIPPA de-identified case records and a link to a survey. The survey comprised five demographic questions and two questions on diagnosis and treatment plan for each case along with a fixed list of responses. The responses were then compared against gold standards that were determined by a group of three board-certified periodontists.

RESULTS: Overall, for diagnostic questions, GP (69%) were correct significantly less often than IUSD-PF (86%, p < 0.001), IUSD-GPF (79%, p = 0.002), and PPP (80%, p = 0.001). No significant differences (p > 0.05) in the overall correct treatment plan responses were found among the four groups (IUSD-PF: 69%, IUSD-GPF: 62%, PPP: 68%, and GP: 60%). The multi-rater kappas for with-in-group agreement on overall diagnosis ranged from 0.36 (GP) to 0.55 (IUSD-PF) and on overall treatment plan ranged from 0.32 (IUSD-GPF) to 0.42 (IUSD-PF). Overall agreement for diagnosis and treatment plans among the four groups was relatively low and none of the groups were statistically different from each other (p > 0.05).

CONCLUSION: Regular participation in calibration sessions may lead to more accurate adoption of the 2017 periodontal classification and thereby help provide consistent diagnosis and treatment.

PMID:35830257 | DOI:10.1002/jdd.13037

Health Promot Chronic Dis Prev Can. 2022 Jul;42(7):301-305. doi: 10.24095/hpcdp.42.7.04.

ABSTRACT

This paper highlights findings on cancer trends from the Canadian Cancer Statistics 2021 report. Trends were measured using annual percent change (APC) of age-standardized incidence rates. Overall, cancer incidence rates are declining (-1.1%) but the findings are specific to the type of cancer and patient sex. For example, in males, the largest decreases per year were for prostate (-4.4%), colorectal (-4.3%), lung (-3.8%), leukemia (-2.6%) and thyroid (-2.4%) cancers. In females, the largest decreases were for thyroid (-5.4%), colorectal (-3.4%) and ovarian (-3.1%) cancers.

PMID:35830219 | DOI:10.24095/hpcdp.42.7.04