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Abstract: Many energy conversion and other thermal-fluid systems exhibit unsteady convective heat exchange. In such systems, generic spatiotemporal variations in the flow give rise to variations in the heat flux for a given fluid–solid temperature difference, which can be interpreted as spatiotemporal fluctuations of the instantaneous heat transfer coefficient. These variations can lead to unsteady conjugate heat transfer, in which the exchanged heat flux arises from an interaction between the bulk fluid temperature and the temperature in the solid. Further, the nonlinear coupling between the fluctuating temperature differences and the heat transfer coefficient can lead to an effect we refer to as augmentation, which quantitatively describes the ability of a particular arrangement to have a different time-mean heat flux from the product between the mean heat transfer coefficient and the mean temperature difference across the fluid. It is important to be able to understand and to model in a simple framework the effects of the material properties, the geometry and the character of the heat transfer coefficient on the thermal response of the fluid–solid system, and consequently to predict the overall heat transfer performance of these systems. This paper, which follows on from its predecessor [1], is concerned with the phenomenon of augmentation in simple, one-dimensional, unsteady and conjugate fluid–solid systems. A simple semi-analytical one-dimensional model of heat transfer with a time-varying heat transfer coefficient, which was presented in Mathie and Markides [1], is applied herein to two different paradigm problems. Such a model can be an important tool in the design of improved heat exchangers and thermal insulation, through for example, the novel selection of materials to exploit these augmentation effects. The first flow considered is a thin, wavy fluid film flowing over a heated plate. This film flow exhibits a periodic fluctuation in the heat transfer coefficient, that is linked to the wavy interfacial deformations of free surface of the liquid film. The second flow considered concerns the heat transfer behind a backwards-facing step, which exhibits broadband fluctuations in the heat transfer coefficient due to the flow separation and turbulence behind the step. The model predictions of the augmentation ratio for these problems are also compared to direct measurements from each case. Good agreement is observed with the experimental results for the global heat transfer trends. In both cases the augmentation ratio was negative, reflecting a reduction in time-averaged heat transfer. For the backwards-facing step flow a low magnitude of augmentation ratio was observed, however, the thin film flows exhibited augmentation ratios of as high as 10%. [Copyright &y& Elsevier]

Deep learning networks has become an important tool for image classification applications. Distortions on images may cause the performance of a classifier to decrease significantly. In the present paper, a comparative investigation for binary classification performance of VGG16 network under corrupted inputs has been presented. For this purpose, images corrupted at various levels and fixed levels with Gaussian noise, Salt and Pepper noise and blur effect were used for testing. Convolutional layers of the VGG16 were frozen except the last three convolutional layers and a dense layer for binary classification was added. According to experimental results, as the effect of distortion is increased, performance of the deep learning classifier drops significantly. In the case of augmented training with distortion effects, the results were improved significantly.

Abstract: In this paper, we consider the augmentation problem of an undirected graph with partitions of its vertices. The main issue is how to add a set of edges with the smallest possible cardinality so that the resulting graph is 2-edge-connected, i.e., bridge-connected, while maintaining the original partition constraint. To solve the problem, we propose a simple linear-time algorithm. To the best of our knowledge, the most efficient sequential algorithm runs in time. However, we show that it can also run in parallel time on an EREW PRAM using a linear number of processors, where is the number of vertices in the input graph. If a simple graph exists, our main algorithm ensures that it is as simple as possible. [Copyright &y& Elsevier]

Abstract: Studies on mass transfer due to the presence of co-axially placed disc as insert promoter in circular conduit were presented in this paper. Limiting current data were obtained at various flow rates using promoters of varying disc diameter and location of the disc. Wide range of variables are covered, disc diameter varied as 0.02, 0.03 and 0.04m and location of the disc varied as 0, 0.25 and 0.5m. Augmentation achieved is sixfold when the diameter of the disc is 0.03m and location of the disc 0.25m. A semi-theoretical model is developed in terms of geometric parameters and presented. [Copyright &y& Elsevier]

Abstract: Computing the eigenvalues and eigenvectors of a large sparse nonsymmetric matrix arises in many applications and can be a very computationally challenging problem. In this paper we propose the Augmented Block Householder Arnoldi (ABHA) method that combines the advantages of a block routine with an augmented Krylov routine. A public domain MATLAB code ahbeigs has been developed and numerical experiments indicate that the code is competitive with other publicly available codes. [Copyright &y& Elsevier]

Abstract: This paper presents the results of an experimental investigation of heat transfer to the airflow in the rectangular duct of an aspect ratio 10:1. The top wall surface is made rough with metal ribs of circular cross section in staggered manner to form defined grid. The roughened wall is uniformly heated and the other walls are insulated. This geometry of duct closely corresponds to that used in solar air heaters. The effect of grit geometry [i.e., relative roughness height of grid (e/D h), relative roughness pitch of grit (p/e), relative length of grit (l/s)] on the heat transfer coefficient and friction factor is investigated. The range of variation of system parameters and operating parameters is investigated within the limits, as e/D h: 0.035 to 0.044, p/e: 12.5–36 and l/s: 1.72–1, against variation of Reynolds number: 4000–17,000. It is observed that the plate of roughness parameters l/s =1.72, e/D h =0.044, p/e =17.5 shows optimum performance. Correlations for Nusselt number and friction factor in terms of above parameters are developed which reasonably correlate the experimental data. [Copyright &y& Elsevier]

Abstract: The solution of large linear discrete ill-posed problems by iterative methods continues to receive considerable attention. This paper presents decomposition methods that split the solution space into a Krylov subspace that is determined by the iterative method and an auxiliary subspace that can be chosen to help represent pertinent features of the solution. Decomposition is well suited for use with the GMRES, RRGMRES, and LSQR iterative schemes. [Copyright &y& Elsevier]

If a credit scoring model is built using only applicants who have been previously accepted for credit such a non-random sample selection may produce bias in the estimated model parameters and accordingly the model's predictions of repayment performance may not be optimal. Previous empirical research suggests that omission of rejected applicants has a detrimental impact on model estimation and prediction. This paper explores the extent to which, given the previous cutoff score applied to decide on accepted applicants, the number of included variables influences the efficacy of a commonly used reject inference technique, reweighting. The analysis benefits from the availability of a rare sample, where virtually no applicant was denied credit. The general indication is that the efficacy of reject inference is little influenced by either model leanness or interaction between model leanness and the rejection rate that determined the sample. However, there remains some hint that very lean models may benefit from reject inference where modelling is conducted on data characterized by a very high rate of applicant rejection. [ABSTRACT FROM AUTHOR]

Background: Capsular contracture is common in breast augmentation and reconstruction surgery. The present systematic review discusses the safety of textured and smooth implants in the submuscular position. Methods: A literature search was conducted on PubMed and EMBASE from inception until June 2020. The primary outcome was reduction of capsular contracture (Baker grade); others included time of capsular contracture onset, implant position/rupture rate/surface, hematoma/seroma risk, surgeon assessment and patient satisfaction. A total of 7731 patients were included from six publications. Results: The overall risk rate for capsular contracture increased from 7.6 to 25% in 10 years. The capsular contracture rates in textured and smooth implants were 3-14% and 6-20%, respectively, and no significant difference between implants was observed when the implants were placed submuscularly. Submuscular placement and textured implants also reduced the incidence of other complications. Conclusion: This systematic review suggests that appropriate placement reduces capsular contracture rate, irrespective of implant surface. [ABSTRACT FROM AUTHOR]

Background: Load carriage is common in a wide range of professions, but prolonged load carriage is associated with increased fatigue and overuse injuries. Exoskeletons could improve the quality of life of these professionals by reducing metabolic cost to combat fatigue and reducing muscle activity to prevent injuries. Current exoskeletons have reduced the metabolic cost of loaded walking by up to 22% relative to walking in the device with no assistance when assisting one or two joints. Greater metabolic reductions may be possible with optimized assistance of the entire leg.Methods: We used human-in the-loop optimization to optimize hip-knee-ankle exoskeleton assistance with no additional load, a light load (15% of body weight), and a heavy load (30% of body weight) for three participants. All loads were applied through a weight vest with an attached waist belt. We measured metabolic cost, exoskeleton assistance, kinematics, and muscle activity. We performed Friedman's tests to analyze trends across worn loads and paired t-tests to determine whether changes from the unassisted conditions to the assisted conditions were significant.Results: Exoskeleton assistance reduced the metabolic cost of walking relative to walking in the device without assistance for all tested conditions. Exoskeleton assistance reduced the metabolic cost of walking by 48% with no load (p = 0.05), 41% with the light load (p = 0.01), and 43% with the heavy load (p = 0.04). The smaller metabolic reduction with the light load may be due to insufficient participant training or lack of optimizer convergence. The total applied positive power was similar for all tested conditions, and the positive knee power decreased slightly as load increased. Optimized torque timing parameters were consistent across participants and load conditions while optimized magnitude parameters varied.Conclusions: Whole-leg exoskeleton assistance can reduce the metabolic cost of walking while carrying a range of loads. The consistent optimized timing parameters across participants and conditions suggest that metabolic cost reductions are sensitive to torque timing. The variable torque magnitude parameters could imply that torque magnitude should be customized to the individual, or that there is a range of useful torque magnitudes. Future work should test whether applying the load to the exoskeleton rather than the person's torso results in larger benefits. [ABSTRACT FROM AUTHOR]

The detection of diabetic retinopathy (DR) in millions of diabetic patients across the globe is a challenging problem. Diagnosis of retinopathy is a lengthy and tedious process, requiring a medical professional to assess the individual fundus images of a patient's retina. This process can be automated by applying deep learning (DL) technology given a huge dataset. The problems associated with DL are the unavailability of a large dataset and their higher training time. The DL model's best performance is achieved using set of optimal hyperparameters (OHPs) obtained by performing costly iterations of hyperparameter optimization (HPO). These problems can be addressed by using transfer learning (TL) technique in both DL model training and HPO. TL in HP tuning is the focus of this work. The authors study the applicability of EyePACS DR dataset's OHPs to other DR datasets, forming the basis of the research question addressed in this work. The DR classification is performed using a ResNet model trained on the EyePACS (kaggle) and Indian diabetic retinopathy image dataset (IDRiD) datasets. Various HPs tuned in this work are data augmentation configuration, number of layers, optimizers, data samplers, learning rate, and momentum. The authors demonstrate that EyePACS dataset's OHPs are suitable for training with IDRiD dataset without needing to tune HPs for IDRiD dataset from scratch. The OHPs for a task and their reusability is poorly reported in the literature. Therefore, the EyePACS DR dataset's OHPs reported here can be used by other researchers. Moreover, the researchers working on other DR datasets can also apply the same OHPs since they are reusable and no iterations of HPO are required. The OHPs are provided for both EyePAC and IDRiD datasets after being tuned from scratch, which can be used as starting point for HPO by others. [ABSTRACT FROM AUTHOR]

Background: Autonomous exoskeletons will need to be useful at a variety of walking speeds, but it is unclear how optimal hip-knee-ankle exoskeleton assistance should change with speed. Biological joint moments tend to increase with speed, and in some cases, optimized ankle exoskeleton torques follow a similar trend. Ideal hip-knee-ankle exoskeleton torque may also increase with speed. The purpose of this study was to characterize the relationship between walking speed, optimal hip-knee-ankle exoskeleton assistance, and the benefits to metabolic energy cost.Methods: We optimized hip-knee-ankle exoskeleton assistance to reduce metabolic cost for three able-bodied participants walking at 1.0 m/s, 1.25 m/s and 1.5 m/s. We measured metabolic cost, muscle activity, exoskeleton assistance and kinematics. We performed Friedman's tests to analyze trends across walking speeds and paired t-tests to determine if changes from the unassisted conditions to the assisted conditions were significant.Results: Exoskeleton assistance reduced the metabolic cost of walking compared to wearing the exoskeleton with no torque applied by 26%, 47% and 50% at 1.0, 1.25 and 1.5 m/s, respectively. For all three participants, optimized exoskeleton ankle torque was the smallest for slow walking, while hip and knee torque changed slightly with speed in ways that varied across participants. Total applied positive power increased with speed for all three participants, largely due to increased joint velocities, which consistently increased with speed.Conclusions: Exoskeleton assistance is effective at a range of speeds and is most effective at medium and fast walking speeds. Exoskeleton assistance was less effective for slow walking, which may explain the limited success in reducing metabolic cost for patient populations through exoskeleton assistance. Exoskeleton designers may have more success when targeting activities and groups with faster walking speeds. Speed-related changes in optimized exoskeleton assistance varied by participant, indicating either the benefit of participant-specific tuning or that a wide variety of torque profiles are similarly effective. [ABSTRACT FROM AUTHOR]

Pedicle screws are commonly used for spinal procedures for fusion stability, which is particularly important in osteoporotic patients, who are at an increased risk of requiring revision procedures. A systematic review and meta-analysis were conducted to compare clinical effectiveness of conventional pedicle screws (CPS) vs fenestrated pedicle screws (FPS) in patients undergoing spinal surgery. Primary outcomes included screw loosening, revision surgeries (involving an implant) and reoperations (not involving intervention on an implant) in patients treated with CPS vs FPS, sub-stratified by with and without osteoporosis. Secondary outcomes included changes in pain scores. Forty-eight studies with 8,302 patients were included, with 1,565 (19.18%) treated with FPS and 6,710 (80.82%) treated with CPS. FPS was associated with a lower risk of screw loosening (p = 0.001) vs CPS. In the general population, there was a non-significant trend of lower revision rate, but no difference in reoperation rate, between patients treated with FPS vs CPS. In osteoporotic patients, revision rates were significantly lower for FPS vs CPS (p = 0.009). This review suggests that FPS are effective for surgical fixation and reduce rates of screw loosening, and in osteoporotic patients, revision surgeries, compared to CPS. [ABSTRACT FROM AUTHOR]

Abstract Background The objective of this systematic review and meta-analysis was to assess the effectiveness of hyoscine n-butylbromide in labor progress. Methods The databases including PubMed, the Cochrane Library, Science-Direct, Scopus and Web of Science were searched for studies published up to December 2019. Articles that published as randomized controlled trials (RCTs), and full-text articles published in English or other languages were included and participants were primi or multigravida women who were in active phase of labor. The intervention included HBB compared to placebo (normal saline) that was used during active phase of labor. Pooled estimates were measured using the fixed or random effect model, while the overall effect was reported in a mean difference (MD). All data were analyzed using Review Manager 5.3. Results Twenty studies involving 3108 women were included in meta-analysis. Based on subgroup analysis by parity, use of HBB significantly reduced the duration of the first stage of labor in primigravida women (MD = − 57.73; 95% CI: [− 61.48, − 53.60]) and in multigravida women (MD = − 90.74; 95% CI: [− 97.24, − 84.24]). Administering HBB could reduce the second stages of labor in primigravidas and multigravidas about 6 min and 4 min respectively. Also, HBB reduced the duration of the third stage of labor in multigravidas about 3 min. APGAR score at one and 5 min after birth was not affected. The main maternal adverse effect was tachycardia and dry mouth. Labor duration in studies in which the participants were primi-and multigravida was not presented based on separate parities except for four papers, and the route of HBB administration was not the same across all studies. Conclusions Although, the effect of HBB was minimal when multigravidas and primigravidas women were considered together, the HBB was clinically effective in primigravida and multigravida women for shortening the first and the second stages of labor. Also, HBB could reduce the length of the third stage of labor in multigravidas.

This paper provides a complete matrix analysis on equivalence problems of estimation and inference results under a true multivariate linear model Y=XΘ+Ψ and its misspecified form Y=XΘ+ZΓ+Ψ with an augmentation part ZΓ through the cogent use of various algebraic formulas and facts in matrix analysis. The coverage of this study includes the matrix derivations of the best linear unbiased estimators under the true and misspecified models, and the establishment of necessary and sufficient conditions for the different estimators to be equivalent under the model assumptions.

Learning good data representations for medical imaging tasks ensures the preservation of relevant information and the removal of irrelevant information from the data to improve the interpretability of the learned features. In this paper, we propose a semi-supervised model—namely, combine-all in semi-supervised learning (CqSL)—to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two important tasks in medical imaging: segmentation and reconstruction. Our work is motivated by the recent progress in image segmentation using semi-supervised learning (SSL), which has shown good results with limited labeled data and large amounts of unlabeled data. A disentanglement block decomposes an input image into a domain-invariant spatial factor and a domain-specific non-spatial factor. We assume that medical images acquired using multiple scanners (different domain information) share a common spatial space but differ in non-spatial space (intensities, contrast, etc.). Hence, we utilize our spatial information to generate segmentation masks from unlabeled datasets using a generative adversarial network (GAN). Finally, to reconstruct the original image, our conditioning layer-based reconstruction block recombines spatial information with random non-spatial information sampled from the generative models. Our ablation study demonstrates the benefits of disentanglement in holding domain-invariant (spatial) as well as domain-specific (non-spatial) information with high accuracy. We further apply a structured L2 similarity (SL2SIM) loss along with a mutual information minimizer (MIM) to improve the adversarially trained generative models for better reconstruction. Experimental results achieved on the STACOM 2017 ACDC cine cardiac magnetic resonance (MR) dataset suggest that our proposed (CqSL) model outperforms fully supervised and semi-supervised models, achieving an 83.2% performance accuracy even when using only 1% labeled data. We hypothesize that our proposed model has the potential to become an efficient semantic segmentation tool that may be used for domain adaptation in data-limited medical imaging scenarios, where annotations are expensive. Code, and experimental configurations will be made available publicly.

Background: Restless legs syndrome (RLS) is a common sleep-related movement disorder characterized by an urge to move the legs during inactivity, especially at evening-night. RLS is highly prevalent in patients with kidney failure and have an impact on quality of life, mood, sleep quality and overall on compliance to the dialysis. Alport syndrome (AS) is a rare inherited disease, predominantly X-linked, secondary to mutations in genes encoding α3, α4 or α5 chains of type IV collagen, and characterized by hematuria, chronic kidney disease, neurosensory deafness, and lenticonus.Case Presentation: Here we describe a family with a combination of X-linked AS and severe RLS accompanied by periodic limb movements during sleep (PLMS). In the first patient we identified, RLS was complicated by a paradoxical response to dopamine agonists named "augmentation", leading to sleep disruption, hallucinations and five peritoneal perforations during the peritoneal dialysis due to the difficulty to rest still. Therapeutic adjustments and renal transplantation improved RLS and PLMS. In two brothers, severe RLS prevented a compliance with hemodialysis. Female family members carrying the mutation were also affected by RLS, while those without the mutations were RLS-free.Conclusions: RLS has not been reported earlier in association with AS, but the peculiar combinations observed in this family will stimulate further clinical studies and motivate nephrologists to seek for RLS symptoms and sleep disturbances in AS patients. [ABSTRACT FROM AUTHOR]

Lumbar support exoskeletons with active and passive actuators are currently the cutting-edge technology for preventing back injuries in workers while lifting heavy objects. However, many challenges still exist in both types of exoskeletons, including rigid actuators, risks of human–robot interaction, high battery consumption, bulky design, and limited assistance. In this paper, the design of a compact, lightweight energy storage device combined with a rotary series elastic actuator (ES-RSEA) is proposed for use in a lumbar support exoskeleton to increase the level of assistance and exploit the human bioenergy during the two stages of the lifting task. The energy storage device takes the responsibility to store and release passive mechanical energy while RSEA provides excellent compliance and prevents injury from the human body’s undesired movement. The experimental tests on the spiral spring show excellent linear characteristics (above 99%) with an actual spring stiffness of 9.96 Nm/rad. The results demonstrate that ES-RSEA can provide maximum torque assistance in the ascent phase with 66.6 Nm while generating nearly 21 Nm of spring torque during descent without turning on the DC motor. Ultimately, the proposed design can maximize the energy storage of human energy, exploit the biomechanics of lifting tasks, and reduce the burden on human effort to perform lifting tasks.

The automatic speech recognition (ASR) model usually requires a large amount of training data to provide better results compared with the ASR models trained with a small amount of training data. It is difficult to apply the ASR model to non-standard speech such as that of cochlear implant (CI) patients, owing to privacy concerns or difficulty of access. In this paper, an effective finetuning and augmentation ASR model is proposed. Experiments compare the character error rate (CER) after training the ASR model with the basic and the proposed method. The proposed method achieved a CER of 36.03% on the CI patient's speech test dataset using only 2 h and 30 min of training data, which is a 62% improvement over the basic method. [ABSTRACT FROM AUTHOR]

For the secondary use of clinical documents, it is necessary to de-identify protected health information (PHI) in documents. However, the difficulty lies in the fact that there are few publicly annotated PHI documents. To solve this problem, in this study, we propose a filtered bidirectional encoder representation from transformers (BERT)-based method that predicts a masked word and validates the word again through a similarity filter to construct augmented sentences. The proposed method effectively performs data augmentation. The results show that the augmentation method based on filtered BERT improved the performance of the model. This suggests that our method can effectively improve the performance of the model in the limited data environment. [ABSTRACT FROM AUTHOR]

Surface quality measures such as roughness, and especially its uncertain character, affect most magnetic non-destructive testing methods and limits their performance in terms of an achievable signal-to-noise ratio and reliability. This paper is primarily focused on an experimental study targeting nuclear reactor materials manufactured from the milling process with various machining parameters to produce varying surface quality conditions to mimic the varying material surface qualities of in-field conditions. From energising a local area electromagnetically, a receiver coil is used to obtain the emitted Barkhausen noise, from which the condition of the material surface can be inspected. Investigations were carried out with the support of machine-learning algorithms, such as Neural Networks (NN) and Classification and Regression Trees (CART), to identify the differences in surface quality. Another challenge often faced is undertaking an analysis with limited experimental data. Other non-destructive methods such as Magnetic Adaptive Testing (MAT) were used to provide data imputation for missing data using other intelligent algorithms. For data reinforcement, data augmentation was used. With more data the problem of ‘the curse of data dimensionality’ is addressed. It demonstrated how both data imputation and augmentation can improve measurement datasets.

Background: Over the last years, the most relevant results in the context of polyp detection were achieved through deep learning techniques. However, the most common obstacles in this field are the small datasets with a reduced number of samples and the lack of data variability. This paper describes a method to reduce this limitation and improve polyp detection results using publicly available colonoscopic datasets.Methods: To address this issue, we increased the number and variety of images from the original dataset. Our method consists on adding polyps to the dataset images. The developed algorithm performs a rigorous selection of the best region within the image to receive the polyp. This procedure preserves the realistic features of the images while creating more diverse samples for training purposes. Our method allows copying existing polyps to new non-polypoid target regions. We also develop a strategy to generate new and more varied polyps through generative adversarial neural networks. Hence, the developed approach enriches the training data, creating automatically new samples with their appropriate labels.Results: We applied the proposed data enhancement over a colonic polyp dataset. Thus, we can assess the effectiveness of our approach through a Faster R-CNN detection model. Performance results show improvements over the polyp detections while reducing the false-negative rate. The experimental results also show better recall metrics in comparison with both the original training set and other studies in the literature.Conclusion: We demonstrate that our proposed method has the potential to increase the data variability and number of samples in a reduced polyp dataset, improving the polyp detection rate and recall values. These results open new possibilities for advancing the study and implementation of new methods to improve computer-assisted medical image analysis. [ABSTRACT FROM AUTHOR]

Transcrestal sinus floor augmentation applied for severely atrophic maxilla sinus floor augmentation is fre⁃ quently performed because of posterior maxilla resorption and low bone density. The traditional perspective is that the residual bone height is a main factor influencing the selection of the augmentation technique and the transcrestal tech⁃ nique can only be used when the residual bone height is more than 4⁃5 mm. However, many researchers have used this technique with lower bone heights because of the development of better surgical instruments and clinical skills. Based on the review of the literature in recent years and the author′s understanding of clinical operation, this paper reviews the improvement progress and operation skills of the application of transalveolar ridge top maxillary sinus lift in the se⁃ verely absorbed maxillary posterior tooth area.

Image captioning is an important task for improving human-computer interaction as well as for a deeper understanding of the mechanisms underlying the image description by human. In recent years, this research field has rapidly developed and a number of impressive results have been achieved. The typical models are based on a neural networks, including convolutional ones for encoding images and recurrent ones for decoding them into text. More than that, attention mechanism and transformers are actively used for boosting performance. However, even the best models have a limit in their quality with a lack of data. In order to generate a variety of descriptions of objects in different situations you need a large training set. The current commonly used datasets although rather large in terms of number of images are quite small in terms of the number of different captions per one image. We expanded the training dataset using text augmentation methods. Methods include augmentation with synonyms as a baseline and the state-of-the-art language model called Bidirectional Encoder Representations from Transformers (BERT). As a result, models that were trained on a datasets augmented show better results than that models trained on a dataset without augmentation. [ABSTRACT FROM AUTHOR]

Background Pharmacological augmentation is a recommended strategy for patients with treatment-resistant depression. A range of guidelines provide advice on treatment selection, prescription, monitoring and discontinuation, but variation in the content and quality of guidelines may limit the provision of objective, evidence-based care. This is of importance given the side effect burden and poorer long-term outcomes associated with polypharmacy and treatment-resistant depression. This review provides a definitive overview of pharmacological augmentation recommendations by assessing the quality of guidelines for depression and comparing the recommendations made. Methods A systematic literature search identified current treatment guidelines for depression published in English. Guidelines were quality assessed using the Appraisal of Guidelines for Research and Evaluation II tool. Data relating to the prescription of pharmacological augmenters were extracted from those developed with sufficient rigor, and the included recommendations compared. Results Total of 1696 records were identified, 19 guidelines were assessed for quality, and 10 were included. Guidelines differed in their quality, the stage at which augmentation was recommended, the agents included, and the evidence base cited. Lithium and atypical antipsychotics were recommended by all 10, though the specific advice was not consistent. Of the 15 augmenters identified, no others were universally recommended. Conclusions This review provides a comprehensive overview of current pharmacological augmentation recommendations for major depression and will support clinicians in selecting appropriate treatment guidance. Although some variation can be accounted for by date of guideline publication, and limited evidence from clinical trials, there is a clear need for greater consistency across guidelines to ensure patients receive consistent evidence-based care. [ABSTRACT FROM AUTHOR]

Radiation is considered to be a risk factor for developing capsular contracture in augmented women, but the studies reporting on this subject show conflicting results. In this systematic review we sought to understand the risk of capsular contracture in augmented patients with breast cancer treated with breast conserving surgery and radiotherapy. A search was conducted through PubMed for studies reporting on breast cancer, breast augmentation and radiotherapy, with capsular contracture as our primary outcome. To determine if specific risk factors were significant predictors of the development of capsular contracture, we performed uni- and multivariate analysis. Our search revealed 136 articles, of which 12 were deemed eligible. A total of 237 patients were included in the analysis. Univariate analysis revealed that whole breast irradiation (WBI) was significantly associated with a higher risk of contracture (p <.001), compared to treatment with accelerated partial breast irradiation (APBI). A higher radiotherapy dose regimen was also significantly associated with a risk of capsular contracture (p <.001). When performing the multivariate analysis only the effect of increasing radiation dose remained significant (p <.05). Neither the implant location nor the age of the implant had any significant effect on the development of contracture. In conclusion this review shows that WBI is associated with a higher risk of contracture compared to treatment with APBI. Because of the limitations of the studies included, further studies with larger patient cohorts are needed to establish this correlation and evaluate other potential risk factors. [ABSTRACT FROM AUTHOR]

Emotion is subjective which convey rich semantics based on an image that induces different emotion based on each individual. A novel method is proposed for emotion classification by using deep learning network with transfer learning method. Transfer learning techniques are the predictive model that reuses the model trained on related predictive problems. The purpose of the proposed work is to classify the emotion perception from images based on visual features. Image augmentation and segmentation is performed to build powerful classifier. The performance of deep convolution neural network (CNN) is improved with transfer learning techniques in large scale Image-Emotion-dataset effectively. The experiments conducted on this dataset and result shows that proposed method achieve promising significant effect on emotion classification with good accuracy and PDA value, when compared with other state-of-art methods. [ABSTRACT FROM AUTHOR]

We present SMILES-embeddings derived from the internal encoder state of a Transformer [1] model trained to canonize SMILES as a Seq2Seq problem. Using a CharNN [2] architecture upon the embeddings results in higher quality interpretable QSAR/QSPR models on diverse benchmark datasets including regression and classification tasks. The proposed Transformer-CNN method uses SMILES augmentation for training and inference, and thus the prognosis is based on an internal consensus. That both the augmentation and transfer learning are based on embeddings allows the method to provide good results for small datasets. We discuss the reasons for such effectiveness and draft future directions for the development of the method. The source code and the embeddings needed to train a QSAR model are available on https://github.com/bigchem/transformer-cnn. The repository also has a standalone program for QSAR prognosis which calculates individual atoms contributions, thus interpreting the model's result. OCHEM [3] environment (https://ochem.eu) hosts the on-line implementation of the method proposed. [ABSTRACT FROM AUTHOR]

The present investigation aimed to look at the effects of biotic and abiotic elicitors during Soybean seed development and cell suspension culture in isoflavones accumulation. The expression levels of four major genes viz., CHS7, CHS8, IFS2, and IFS1 involved on isoflavones biosynthesis during seed developmental stages from R5L–R7 was seen in both MAUS-2 and JS-335 Soybean varieties. The R7 stage showed 1.24-fold upregulation of IFS1transcript level and considered as the control for Soybean seed development. Both varieties during R6−R8 stages responded differently to the foliar application of 10 µM SA, 10 µM MJ and 0.1% Aspergillus niger. The IFS2 transcripts were upregulated by SA at the R7 stage with 5.21- and 4.68-fold in JS-335 and MAUS-2, respectively. IFS1 expression was significantly increased by A. niger treatment at R7 stage with 3.98- and 3.21-fold in MAUS-2 and JS-335, respectively. The expression of CHS7 and CHS8 by 10 μM SA at R7 level revealed maximum up-regulation of 0.51- and 1.01-fold in MAUS-2; 0.37- and 0.82-fold in JS-335, respectively. In the soybean callus suspension culture, biosynthetic genes were used to validate the effects of elicitor on isoflavones. Both biotic and abiotic treatments contribute to the upregulation of IFS1 and IFS2 expression, that in turn, leads to the accumulation of isoflavone in seed development as well as in suspension cultures. These data further suggested that the IFS2 is the key gene responsible for the isoflavone accumulation during elicitor treatment. [ABSTRACT FROM AUTHOR]