Suboptimal Method Research Articles

Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study

There is an increasing interest in machine learning (ML) algorithms for predicting patient outcomes, as these methods are designed to automatically discover complex data patterns. For example, the random forest (RF) algorithm is designed to identify relevant predictor variables out of a large set of candidates. In addition, researchers may also use external information for variable selection to improve model interpretability and variable selection accuracy, thereby prediction quality. However, it is unclear to which extent, if at all, RF and ML methods may benefit from external information. In this paper, we examine the usefulness of external information from prior variable selection studies that used traditional statistical modeling approaches such as the Lasso, or suboptimal methods such as univariate selection. We conducted a plasmode simulation study based on subsampling a data set from a pharmacoepidemiologic study with nearly 200,000 individuals, two binary outcomes and 1152 candidate predictor (mainly sparse binary) variables. When the scope of candidate predictors was reduced based on external knowledge RF models achieved better calibration, that is, better agreement of predictions and observed outcome rates. However, prediction quality measured by cross-entropy, AUROC or the Brier score did not improve. We recommend appraising the methodological quality of studies that serve as an external information source for future prediction model development.

Evaluation of parameters for efficient purification and long-term storage of herpes simplex virus-based vectors

Replication competent oncolytic herpes simplex virus (HSV) vectors have been used extensively to treat solid tumors with promising results. However, highly defective HSV vectors will be needed for applications that require sustained therapeutic gene expression in the absence of vector-related toxicity or inflammation. These vectors require complementing cell lines for their manufacture, creating significant challenges to achieve high yields of infectious virus particles. We recently described an improved upstream process for the production of a non-cytotoxic HSV vector for gene therapy applications. Here, we sought to optimize the downstream conditions for purification and long-term storage of the same vector, JΔNI5. We compared different methods to remove cellular impurities and concentrate the vector by monitoring both physical and biological titers, resulting in the establishment of optimal conditions for vector production. To optimize the long-term storage parameters for non-cytotoxic HSV vectors, we evaluated vector stability at low temperature and sensitivity to freeze-thaw cycles. We report that suboptimal purification and storage methods resulted in loss of vector viability. Our results describe effective and reproducible protocols for purification and storage of HSV vectors for pre-clinical studies.

Automatic Detection of Small- and Medium-Sized Targets in High-Resolution Images Based on Computer Vision and Deep Learning Energy

In order to solve the problem that it is difficult for traditional manual feature algorithms to deal with complex image features quickly and automatically in high-resolution images, an automatic detection method for small objects in high-resolution images based on computer vision and deep learning energy is proposed. Starting from the deep learning target detection system, this paper studies many problems in the system according to the characteristics of target spatial deformation, more small targets, easy confusion, and rough regional proposal in high-score remote sensing images. The results showed that a combination of spatial deformation strength with spatial deformation resistance and multiphase coupling were all proposed to be comparable with spatial deformation. The process of tracking international products in the system has been extended to study local channels. Pay attention to the operation of the machine, make full use of rich semantic data of local characteristics and the world of spatial points, which will help to improve the accuracy of the purpose of the chaw, and identify ways to create a shared database. The proposed method is evaluated on three attribute, classification joint datasets; ACCNN on the AC-AID dataset, the performance of ACCNN is suboptimal on AC-AID and is very close to the best method for DCA fusion. The average error rate of DCA fusion is 5.67%; on the AC-UCM dataset, the error rate of ACCNN is 2.11% lower than the suboptimal method DCA fusion, and the error rate of DCA fusion is 6.16%. On the AC-Sydney dataset, the error rate of ACCNN is 1.86% lower than the suboptimal method DCA fusion, and the error rate of DCA fusion is 5.99%, which obtains quite competitive results, experimenting with high-resolution images.

Transceiver Optimization for Two-Hop AF MIMO Relay Systems With DFE Receiver and Direct Link

In this paper, we consider precoding and receiving matrices optimization for a two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system with a decision feedback equalizer (DFE) at the destination node in the presence of the direct source-destination link. By adopting the minimum mean-squared error (MMSE) criterion, we develop two new transceiver design algorithms for such a system. The first one employs an iterative procedure to design the source, relay, feed-forward, and feedback matrices. The second algorithm is a non-iterative suboptimal approach which decomposes the optimization problem into two tractable subproblems and obtains the source and relay precoding matrices by solving the two subproblems sequentially. Simulation results validate the better MSE and bit-error-rate (BER) performance of the proposed algorithms and show that the non-iterative suboptimal method has a negligible performance loss when the ratio of the source node transmission power to the relay node transmission power is small. In addition, the computational complexity analysis suggests that the second algorithm and one iteration of the first algorithm have the same order of complexity. As the first algorithm typically converges within a few iterations, both proposed algorithms exhibit a low complexity order.

Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior

Bayesian compressive sensing (BCS) is an important sub-class of sparse signal reconstruction algorithms. In this paper, a modified complex multitask Bayesian compressive sensing (MCMBCS) algorithm using the Laplacian scale mixture (LSM) prior is proposed. The LSM prior is first introduced into the complex BCS framework by exploiting its better sparse characteristic and flexibility than traditional Laplacian prior. Furthermore, by integrating out the noise variance analytically, the MCMBCS algorithm significantly improves the signal recovery performance than the original CMBCS. More importantly, the authors not only present the iterative algorithm but also develop the sub-optimal fast implementation method based on the marginal likelihood maximisation, which dramatically reduce the computational complexity. Finally, sufficient numerical simulations validate the better performance of the proposed algorithm in reconstruction accuracy and computational effectiveness than existing work. It is revealed that the proposed algorithm has great potential in the complex-valued signal processing field.

Human intestinal spirochetosis, irritable bowel syndrome, and colonic polyps: A systematic review and meta-analysis.

Human colonic spirochetosis (CS) is usually due to Brachyspira pilosicoli or Brachyspira aalborgi infection. While traditionally considered to be commensal bacteria, there are scattered case reports and case series of gastrointestinal (GI) symptoms in CS and reports of colonic polyps with adherent spirochetes. We performed a systematic review and meta‐analysis investigating the association between CS and GI symptoms and conditions including the irritable bowel syndrome (IBS) and colonic polyps. Following PRISMA 2020 guidelines, a systematic search of Medline, CINAHL, EMBASE, and Web of Science was performed using specific keywords for CS and GI disease. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a random‐effects model. Of 75 studies identified in the search, 8 case–control studies met the inclusion criteria for meta‐analysis and 67 case series studies met the inclusion criteria for pooled prevalence analysis. CS was significantly associated with diarrhea (n = 141/127, cases/controls, OR: 4.19, 95% CI: 1.72–10.21, P = 0.002) and abdominal pain (n = 64/65, OR: 3.66, 95% CI: 1.43–9.35, P = 0.007). CS cases were significantly more likely to have Rome III‐diagnosed IBS (n = 79/48, OR: 3.84, 95% CI: 1.44–10.20, P = 0.007), but not colonic polyps (n = 127/843, OR: 8.78, 95% CI: 0.75–103.36, P = 0.084). In conclusion, we found evidence of associations between CS and both diarrhea and IBS, but not colonic polyps. CS is likely underestimated due to suboptimal diagnostic methods and may be an overlooked risk factor for a subset of IBS patients with diarrhea.

Missing data were poorly reported and handled in randomized controlled trials with repeatedly measured continuous outcomes: a cross-sectional survey

Background and ObjectivesMissing data are common in randomized controlled trials (RCTs) involving repeatedly measured continuous outcomes. Evidence on the reporting and handling of such outcome data is lacking, which has prevented further improvement in methods and reporting of RCTs. MethodsWe searched PubMed to identify RCTs published in the Core Clinical Journals in 2019 that reported a continuous primary outcome with repeated measures. A team of investigators conducted a study screening and collected data using pilot-tested, standardized questionnaires from a random sample of eligible RCTs. We thoroughly collected information about the reporting of missing data for the repeatedly measured continuous outcome and the methods used to handle the missing data. ResultsWe included 200 eligible trials, whose mean number of repeated measures for the continuous primary outcomes was 5.46 (SD = 3.4). Sixty-one (30.5%) trials explicitly reported missing data at both participant and outcome levels, 116 (58.0%) at the participant level only, and 2 (1.0%) at the outcome level only. Sixty (30.0%) trials reported missing data at the participant level by group and by time point, and 53 (26.5%) at the outcome level by group and by time point. Among 179 trials having reported missing data, 162 (90.5%) did not assess the balance of baseline characteristics, 143 (79.9%) did not assume missing mechanism; 65 (36.3%) used suboptimal methods for handling missing data (e.g., complete case analysis); 41 (22.9%) conducted sensitivity analyses, and 5 (11.9%) assumed alternative missing mechanisms for sensitivity analyses. ConclusionThe reporting of missing data for repeatedly measured continuous outcomes were inadequate and the use of statistical methods for handling missing data was far from optimal. Substantial efforts are warranted to improve the reporting and statistical handling of these outcome data.

Three-dimensional field of view and impact angle constrained guidance with terminal speed maximization

A three-dimensional strap-down seeker based multi-constrained guidance law for an interceptor is proposed in this paper to achieve near zero miss distance with minimal control effort (lateral acceleration) and interception time. When the interceptor is guided based on strap-down seeker measurements, the field of view limit throughout the flight trajectory is one of the major concerns to guarantee tracking of the target. To enhance the capability of warhead while approaching a target, the interceptor needs to ensure the desired impact angles and maximum speed at terminal time as well as various path inequality constraints throughout the trajectory, such as lateral acceleration limit and field of view limit. To meet these requirements, the proposed guidance is designed using a new extended version of the computationally efficient, suboptimal generalized model predictive static programming (GMPSP) technique in an unspecified final time framework. In addition, to make all the states available, to estimate parameter uncertainties and to filter out the sensor measurement noises, an extended Kalman filter based state and parameter estimation is incorporated with this proposed guidance law. Using simulation studies, the efficacy of the proposed GMPSP based suboptimal guidance method is shown and compared with that of the Chebyshev pseudo-spectral method as well. The extensive simulation results also demonstrate that the proposed robust suboptimal guidance design can perform well, in the presence of aerodynamic parameter uncertainties and sensor noises.

Assessing anxiety-linked impairment in attentional control without eye-tracking: The masked-target antisaccade task

Contemporary cognitive theories of anxiety and attention processing propose that heightened levels of anxiety vulnerability are associated with a decreasing ability to inhibit the allocation of attention towards task-irrelevant information. Existing performance-based research has most often used eye-movement assessment variants of the antisaccade paradigm to demonstrate such effects. Critically, however, eye-movement assessment methods are limited by expense, the need for expert training in administration, and limited mobility and scalability. These barriers have likely led to researchers’ use of suboptimal methods of assessing the relationship between attentional control and anxiety vulnerability. The present study examined the capacity for a non-eye-movement-based variant of the antisaccade task, the masked-target antisaccade task (Guitton et al., 1985), to detect anxiety-linked differences in attentional control. Participants (N = 342) completed an assessment of anxiety vulnerability and performed the masked-target antisaccade task in an online assessment session. Greater levels of anxiety vulnerability predicted poorer performance on the task, consistent with findings observed from eye-movement methods and with cognitive theories of anxiety and attention processing. Results also revealed the task to have high internal reliability. Our findings indicate that the masked-target antisaccade task provides a psychometrically reliable, low-cost, mobile, and scalable assessment of anxiety-linked differences in attentional control.

Learning-based joint optimization of mode selection and transmit power control for D2D communication underlaid cellular networks

In device-to-device (D2D) communication, each transceiver pair can choose to operate in either direct D2D mode or device-infrastructure-device (DID) mode. This technique is called mode selection, which is important for establishing a reliable and efficient communication link that can significantly improve the network performance. However, the coexistence of direct D2D and DID communications in the same frequency band creates interference and degrades the performance of the system. Therefore, in this study, we aim to jointly optimize the mode selection and transmit power allocation of D2D pairs in D2D communication underlaid cellular networks. For this purpose, we first formulate a joint optimization model considering both mode selection and transmit power control of D2D pairs, which is an NP-hard combinatorial optimization problem with linear and nonlinear constraints. To solve this optimization problem, we design a deep neural network (DNN) structure. The proposed DNN algorithm is trained by minimizing the loss function which is obtained from Lagrange duality function. The weighting factor in the loss function is designed to decrease (increase) the rate of the receiver with strong (weak) interference compared to the mean channel gain. Simulation results show that the proposed DNN algorithm achieves a near-global optimal solution with lower computational complexity compared with the exhaustive search (ES) approach, and outperforms the solution obtained by using sub-optimal methods for Lagrange duality function.

183 A Systematic Review of Sample Size Calculations in High-Profile Surgical Trials That Use Patient-Reported Outcome Measures.

Abstract Background Patient reported outcome measures (PROMs) are increasingly recognised as a measure of treatment efficacy in randomised controlled trials (RCTs). It can be difficult to determine meaningful target differences in PROM scores for sample size calculations and this can risk over-recruitment and/or erroneous trial conclusions. The Difference ELicitation in TriAls (DELTA2) statement sets recommendations for sample size calculations in RCTs including target difference determination. We aimed to evaluate sample size calculations from high-profile surgical RCTs that used PROMs as their primary outcome, against DELTA2 standards, with a focus on target differences. Method Pubmed was systematically searched for surgical RCTs published in the five highest ranking journals, by Thomson Reuters impact factor, for medicine and surgery. Studies were included if surgery was the intervention and/or comparator arm, and a PROM was the primary outcome. Surgery was defined as using instrumentation to change macro-anatomy with the aim of improving health. Data were extracted with a piloted data collection sheet that included the DELTA2 reporting recommendations. Results Most target differences used in sample size calculations were determined with suboptimal techniques and target difference justification was overall poor. In this sample, £28 million of UK public research spending supported trials with poor target difference justification. Conclusions In this sample of trials, sample size calculations were generally not reported to DELTA2 standards. There was frequent use of sub-optimal methods to determine the target difference. This risks over-recruitment and/or erroneous trial conclusions. Clinicians should be aware of these potential pitfalls when interpreting published trials.

Selecting the optimal sampling rate for the waveform generator with a variable clock

The broad sampling rate range of the Waveform Generator with Variable Clock (WGVC) allows unlimited choices. Selecting the sample rate only based on the Nyquist criterion might cause aliasing, when the mirror frequency is below the cut frequency of WGVC's reconstruction filter. Replaying signals using the maximum sample rate of the WGVC requires arbitrary sample rate conversion (ASRC), which has a high computational cost. Previous work proposed a suboptimal method to retrieve a fraction for converting the initial waveform in a WGVC with fractional sample rate conversion (FSRC), which guarantees anti-aliasing and a low computation cost. In this paper, an algorithm based on continued fractions is proposed to select a conversion fraction within a closed interval for FSRC, which not only ensures that the numerator and denominator are minimized theoretically but also avoids inversion errors caused by floating-point calculations. Experimental results demonstrate that the computational cost of signal conversion and waveform storage requirements are reduced when this algorithm is applied. In addition, the spectral purity of the output signal is better than that of the ASRC algorithm using the proposed algorithm.

Comments on “Novel Polarimetric Contrast Enhancement Method Based on Minimal Clutter to Signal Ratio Subspace”

A novel polarimetric contrast enhancement method based on minimal clutter to signal ratio (MCSR) subspace was proposed for target detection. The latter is a typical trace ratio (TR) problem. In this article, a more effective iterative method is proposed to solve this problem. Moreover, a very simple and suboptimal method is also suggested by the eigenvalue decomposition (EVD), which performs better in ship detection.

Recent progresses on two suboptimal methods for nonlinear filtering problems

Recent progresses on two suboptimal methods for nonlinear filtering problems

Surgery without Sufficient Indications: an Update from Russia

This review is an update and continuation of preceding papers on invasive procedures applied in Russia with questionable clinical indications. Certain methods have been applied according to instructions by healthcare authorities and publication of leading specialists, facilitated by lacking professional autonomy. Examples are discussed here and in preceding papers: the overuse of Halsted and Patey mastectomy, preventive electrocoagulation of cervical ectropions without cytotological examination, gastrectomy for peptic ulcers, thoracic and abdominal surgery for bronchial asthma and diabetes mellitus, overuse of surgery in tuberculosis and other pulmonary conditions, overuse of bronchoscopy e.g. in conscripts with supposed pneumonia. Among mechanisms contributing to the persistence of suboptimal methods has been the autocratic or military managerial style discouraging criticism and polemics. Other attributes of this style are the paternalistic approach to patients and insufficient adherence to the principle of informed consent. Some invasive procedures with questionable indications were advocated by first generation military surgeons. Personnel training could have been one of the motives. Note that military and medical ethics are not the same. The comparatively short life expectancy in Russia is a strategic advantage as it necessitates less healthcare investments and pensions. Actually, Russia needs foreign help in the matter of healthcare. In view of the current international tensions, the cooperation has been partly interrupted. Obstacles to the import of medical products have adverse consequences. Under these circumstances, the purpose of this article was to remind that, performing a surgical or another invasive procedure, the risk-to-benefit ratio must be kept as low as possible. Insufficient coordination of medical studies and partial isolation from the international scientific community can result in parallelism in research, unnecessary experimentation, and application of invasive methods without sufficient indications.

Optimized Deep Learning Model for Fire Semantic Segmentation

Recent convolutional neural networks (CNNs) based deep learning has significantly promoted fire detection. Existing fire detection methods can efficiently recognize and locate the fire. However, the accurate flame boundary and shape information is hard to obtain by them, which makes it difficult to conduct automated fire region analysis, prediction, and early warning. To this end, we propose a fire semantic segmentation method based on Global Position Guidance (GPG) and Multi-path explicit Edge information Interaction (MEI). Specifically, to solve the problem of local segmentation errors in low-level feature space, a top-down global position guidance module is used to restrain the offset of low-level features. Besides, an MEI module is proposed to explicitly extract and utilize the edge information to refine the coarse fire segmentation results. We compare the proposed method with existing advanced semantic segmentation and salient object detection methods. Experimental results demonstrate that the proposed method achieves 94.1%, 93.6%, 94.6%, 95.3%, and 95.9% Intersection over Union (IoU) on five test sets respectively which outperforms the suboptimal method by a large margin. In addition, in terms of accuracy, our approach also achieves the best score.

Delay Compensation for Distributed MIMO Radar With Non-Orthogonal Waveforms

Distributed multi-input multi-output (MIMO) radar with non-orthogonal waveforms has become a critical problem because waveform orthogonality may be lost as waveforms experience distinct delays and Doppler across different transmit-receive propagation paths. In such cases, the widely used matched filter (MF) cannot perfectly separate the waveforms, and it outputs the filtered echo of the desired waveform (auto term) as well as multiple undesired waveform residuals (cross terms). In this paper, a transmit delay compensation scheme is proposed by employing a set of transmit delay compensation variables to control the cross terms so that they can be utilized to enhance target detection. Specifically, the probability of detection is maximized by optimizing the delay parameters. To solve the resulting nonconvex problem, an optimum solution based on multi-dimensional search and a computationally efficient suboptimal method are proposed. Simulation results show that the proposed delay compensation approach can substantially improve the target detection performance.

Sample size calculations in high-profile surgical trials that use patient-reported outcome measures: systematic review.

Sample size calculations from high-profile surgical RCTs that used a patient-reported outcome measure as primary outcome were reviewed systematically against Difference ELicitation in TriAls (DELTA2) standards, with a focus on target differences. In this sample of trials, there was frequent use of suboptimal methods to determine the target difference, and sample size calculations were generally not reported to DELTA2 standards. This risks over-recruitment and/or erroneous trial conclusions, which clinicians should be aware of when interpreting published trials.

A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning

Traditional aircraft maintenance support work is mainly based on structured data. Unstructured data, such as text data, have not been fully used, which means there is a waste of resources. These unstructured data contain a great storehouse of fault knowledge, which could provide decision support for aircraft maintenance support work. Therefore, a text-based fault diagnosis model is proposed in this paper. The proposed method uses Word2vec to map text words into vector space, and the extracted text feature vectors are then input into the classifier based on a stacking ensemble learning scheme. Its performance has been validated using a real aircraft fault text dataset. The results show that the fault diagnosis accuracy of the proposed method is 97.35%, which is about 2% higher than that of the suboptimal method.

Theory of local k-mer selection with applications to long-read alignment.

MotivationSelecting a subset of k-mers in a string in a local manner is a common task in bioinformatics tools for speeding up computation. Arguably the most well-known and common method is the minimizer technique, which selects the ‘lowest-ordered’ k-mer in a sliding window. Recently, it has been shown that minimizers may be a sub-optimal method for selecting subsets of k-mers when mutations are present. There is, however, a lack of understanding behind the theory of why certain methods perform well.ResultsWe first theoretically investigate the conservation metric for k-mer selection methods. We derive an exact expression for calculating the conservation of a k-mer selection method. This turns out to be tractable enough for us to prove closed-form expressions for a variety of methods, including (open and closed) syncmers, (a, b, n)-words, and an upper bound for minimizers. As a demonstration of our results, we modified the minimap2 read aligner to use a more conserved k-mer selection method and demonstrate that there is up to an 8.2% relative increase in number of mapped reads. However, we found that the k-mers selected by more conserved methods are also more repetitive, leading to a runtime increase during alignment. We give new insight into how one might use new k-mer selection methods as a reparameterization to optimize for speed and alignment quality.Availability and implementationSimulations and supplementary methods are available at https://github.com/bluenote-1577/local-kmer-selection-results. os-minimap2 is a modified version of minimap2 and available at https://github.com/bluenote-1577/os-minimap2.Supplementary information Supplementary data are available at Bioinformatics online.