Discrete Target Research Articles

Highly Effective Biocides against Pseudomonas aeruginosa Reveal New Mechanistic Insights Across Gram-Negative Bacteria.

Pseudomonas aeruginosa is a major nosocomial pathogen that persists in healthcare settings despite rigorous disinfection protocols due to intrinsic mechanisms conferring resistance. We sought to systematically assess cationic biocide efficacy against this pathogen using a panel of multidrug-resistant P. aeruginosa clinical isolates. Our studies revealed widespread resistance to commercial cationic disinfectants that are the current standard of care, raising concerns about their efficacy. To address this shortcoming, we highlight a new class of quaternary phosphonium compounds that are highly effective against all members of the panel. To understand the difference in efficacy, mechanism of action studies were carried out, which identified a discrete inner-membrane selective target. Resistance selection studies implicated the SmvRA efflux system (a transcriptionally regulated, inner membrane-associated efflux system) as a major determinant of resistance. This system is also implicated in resistance to two commercial bolaamphiphile antiseptics, octenidine and chlorhexidine, which was further validated herein. In sum, this work highlights, for the first time, a discrete inner-membrane specific mechanism for the bolaamphiphile class of disinfectants that contrasts with the prevailing model of indiscriminate membrane interactions of commercial amphiphiles paving the way for future innovations in disinfectant research.

Spiral scanning improves subject fixation in widefield retinal imaging.

Point scanning retinal imaging modalities, including confocal scanning light ophthalmoscopy (cSLO) and optical coherence tomography, suffer from fixational motion artifacts. Fixation targets, though effective at reducing eye motion, are infeasible in some applications (e.g., handheld devices) due to their bulk and complexity. Here, we report on a cSLO device that scans the retina in a spiral pattern under pseudo-visible illumination, thus collecting image data while simultaneously projecting, into the subject's vision, the image of a bullseye, which acts as a virtual fixation target. An imaging study of 14 young adult volunteers was conducted to compare the fixational performance of this technique to that of raster scanning, with and without a discrete inline fixation target. Image registration was used to quantify subject eye motion; a strip-wise registration method was used for raster scans, and a novel, to the best of our knowledge, ring-based method was used for spiral scans. Results indicate a statistically significant reduction in eye motion by the use of spiral scanning as compared to raster scanning without a fixation target.

The Hypoechoic Triangle: A New Sonographic Landmark for Rectus Sheath Block.

Rectus sheath blocks can provide analgesia for upper abdominal midline incisions. These blocks can be placed on patients who are anticoagulated, supine, and under general anesthesia. However, block success rates remain low, presumably because of the difficulty of placing local anesthetic between the correct fascial layers. Here we characterize a hypoechoic triangle with sonography, an anatomic space between adjacent rectus abdominis segments that can be accessed for easier needle tip and catheter placement. This approach could reduce reliance on hydrodissection to correctly identify the potential space and instead improve block efficacy by offering providers a discrete target for local anesthesia.

Discrete Mechanistic Target of Rapamycin Signaling Pathways, Stem Cells, and Therapeutic Targets.

The mechanistic target of rapamycin (mTOR) is a serine/threonine kinase that functions via its discrete binding partners to form two multiprotein complexes, mTOR complex 1 and 2 (mTORC1 and mTORC2). Rapamycin-sensitive mTORC1, which regulates protein synthesis and cell growth, is tightly controlled by PI3K/Akt and is nutrient-/growth factor-sensitive. In the brain, mTORC1 is also sensitive to neurotransmitter signaling. mTORC2, which is modulated by growth factor signaling, is associated with ribosomes and is insensitive to rapamycin. mTOR regulates stem cell and cancer stem cell characteristics. Aberrant Akt/mTOR activation is involved in multistep tumorigenesis in a variety of cancers, thereby suggesting that the inhibition of mTOR may have therapeutic potential. Rapamycin and its analogues, known as rapalogues, suppress mTOR activity through an allosteric mechanism that only suppresses mTORC1, albeit incompletely. ATP-catalytic binding site inhibitors are designed to inhibit both complexes. This review describes the regulation of mTOR and the targeting of its complexes in the treatment of cancers, such as glioblastoma, and their stem cells.

Discrete-Target Prosthesis Control Using Uncertainty-Aware Classification for Smooth and Efficient Gross Arm Movement.

Current control approaches for gross prosthetic arm movement mainly regulate movement over a continuous range of target poses. However, these methods suffer from output fluctuation caused by input signal variations during gross arm movements. Prosthesis control approaches with a finite number of discrete target poses can address this issue and reduce the complexity of the pose control process. However, it remains under-explored in the literature and suffers from the consequences of misclassifying the target poses. Here, we propose a novel Uncertainty-Aware Discrete-Target Prosthesis Control (UA-DPC) approach. This approach consists of (1) an uncertainty-aware classification scheme to reduce unintended pose switches caused by misclassifications, and (2) real-time trajectory planning that adjusts motion to be rapid or conservative based on low or high quantified uncertainty, respectively. By addressing the impact of misclassification, this approach facilitates more efficient and smooth movements. Human-in-the-loop experiments were conducted in a virtual reality environment with 12 non-disabled participants. The participants controlled a transhumeral prosthesis using three approaches: the proposed UA-DPC, a discrete-target approach based on a traditional off-the-shelf classifier, and a continuous-target approach. The results demonstrate the superior performance of UA-DPC, which provides more efficient task completion with fewer misclassification instances as well as smoother residual limb and prosthesis movement.

Attentional blur and blink: Effects of adaptive attentional scaling on visual awareness

Attentional scaling is a crucial mechanism that enables us to flexibly allocate our attention to larger or smaller regions in the visual field. Although previous studies have demonstrated the critical role of attentional scaling in visual processing, its impact on modulating visual awareness is not yet fully understood. This study investigates the adaptive control of attentional scaling and its influence on visual awareness in an attentional blink paradigm. Participants were required to attend to the first target’s location, which was manipulated either session-wise, trial-wise, or such that it could be learned across a block of trials. Discrete, all-or-none, awareness was expected when attention was allocated to a narrow area, while gradual awareness was expected when attention was allocated to a larger area. We used mixture modeling to assess second target awareness across these different attentional scales. The results revealed that participants could adaptively control their attentional scale both across stable sessions, and through (implicit) statistical learning in blocks of successive trials. This produced gradual perceptual awareness when the participants adopted a broad attentional scale, causing an attentional “blur”. However, trial-wise cues did not allow for attentional scaling, resulting in more discrete target perception overall, and an attentional “blink”. We conclude that the attentional scale is to some extent under adaptive control during the attentional blink/blur, where it can produce qualitatively different modes of perceptual awareness.

Defining metric-aware size-shape measures to validate and optimize curved high-order meshes

We define a regularized size-shape distortion (quality) measure for curved high-order elements on a Riemannian space. To this end, we measure the deviation of a given element, straight-sided or curved, from the stretching, alignment, and sizing determined by a target metric. The defined distortion (quality) is suitable to check the validity and the quality of straight-sided and curved elements on Riemannian spaces determined by constant and point-wise varying metrics. The examples illustrate that the distortion can be minimized to curve (deform) the elements of a given high-order (linear) mesh and try to match with curved (linear) elements the point-wise stretching, alignment, and sizing of a discrete target metric tensor. In addition, the resulting meshes simultaneously match the curved features of the target metric and boundary. Finally, to verify if the minimization of the metric-aware size-shape distortion leads to meshes approximating the target metric, we compute the Riemannian measures for the element edges, faces, and cells. The results show that, when compared to anisotropic straight-sided meshes, the Riemannian measures of the curved high-order mesh entities are closer to unit. Furthermore, the optimized meshes illustrate the potential of curved r-adaptation to improve the accuracy of a function representation.

DEVELOPMENT OF AN ENHANCED C4.5 DECISION TREE ALGORITHM USING A MEMOIZED MAPREDUCE MODEL

Classification technique in data mining concentrates on the prediction of categorical or discrete target variables which is designed to be handled by the classical C4.5 decision tree algorithm, an algorithm whose aim is to produce a tree which accurately predicts the target variable for a new unseen data. However its recursive nature poses a limitation when huge volume of dataset is involved; making computation more complex and resulting in an inefficient implementation of the algorithm in terms of computing time, memory utilization and data complexity. Meanwhile, several researches have been done to control these limitations. One of such improvements is the parallelizing of the algorithm using the MapReduce model. This involves dividing the large dataset into smaller units and sharing them on multiple computers for parallel processing, but the recursive nature of the algorithm makes the cost of computing large number of repeated calculations quite high, which is our concern in this work. . This research is aimed at reducing computation time further, by using a memoized MapReduce model, which involves the saving of the result of previous calculations in a cache; hence, when same calculations are encountered again, the cached result is returned, thus re-computation is avoided. The cached result is considered a reduced cost compared to the computational cost of re-computation.

An Efficient Robot Exploration Method Based on Heuristics Biased Sampling

Autonomous robot exploration has received widespread attention as a crucial step in constructing maps for unknown environments. Among the research results in the field of robotic exploration, the boundary exploration algorithm based on Rapidly-exploring Random Tree (RRT) performs well in most scenarios, which guides the robot motion by taking the frontier with the maximum revenue value as the target point discretely. However, this discrete target selection scheme ignores the geometric continuity of the environment, and the robot often turns to another area before completing a geometrically continuous area (e.g. a complete room), which results the disorderly motion of the robot and a relatively low exploration efficiency. This work proposes a heuristics biased sampling based robot exploration strategy, which utilizes the semantic information of the environment as the heuristics to guide the robot exploration. Firstly, a lightweight network model is proposed for heuristic object recognition. Secondly, a geometrically continuous region is estimated based on the heuristic object, and frontiers are then extracted in this region by using a biased sampling method. Finally, the heuristic information gain model is designed to determine the target frontier for the exploration, which instructs the robot to select the frontiers in the heuristic region with a higher priority. In this way, the robot can effectively use the heuristic knowledge of the environment to improve the efficiency of exploration. Compared with the RRT based exploration methods in simulations and real-world environmental studies, and the experimental results prove the feasibility and effectiveness of our approach.

Using the Social Vulnerability Index to Examine Disparities in Surgical Pediatric Trauma Patients

IntroductionThe Social Vulnerability Index (SVI) is a composite measure geocoded at the census tract level that has the potential to identify target populations at risk for postoperative surgical morbidity. We applied the SVI to examine demographics and disparities in surgical outcomes in pediatric trauma patients. MethodsSurgical pediatric trauma patients (≤18-year-old) at our institution from 2010 to 2020 were included. Patients were geocoded to identify their census tract of residence and estimated SVI and were stratified into high (≥70th percentile) and low (<70th percentile) SVI groups. Demographics, clinical data, and outcomes were compared using Kruskal–Wallis and Fisher's exact tests. ResultsOf 355 patients included, 21.4% had high SVI percentiles while 78.6% had low SVI percentiles. Patients with high SVI were more likely to have government insurance (73.7% versus 37.2%, P < 0.001), be of minority race (49.8% versus 19.1%, P < 0.001), present with penetrating injuries (32.9% versus 19.7%, P = 0.007), and develop surgical site infections (3.9% versus 0.4%, P = 0.03) compared to the low SVI group. ConclusionsThe SVI has the potential to examine health care disparities in pediatric trauma patients and identify discrete at-risk target populations for preventative resources allocation and intervention. Future studies are necessary to determine the utility of this tool in additional pediatric cohorts.

Compute offloading solution to maximize server rewards

With the development of the Internet of Things, cloud computing can no longer meet the demand, and edge computing emerges as the times require. As one of the most critical technologies in edge computing, computing offloading has been extensively studied. The research does not aim to minimize computing delay and energy consumption, but to maximize the reward of edge computing servers. Considering the scenario of one MEC server and multiple terminal devices, the target problem obtained is the two-dimensional 0-1 knapsack problem. In view of basic sine cosine algorithm is difficult to solve the discrete target offloading problem, the basic SCA is improved to obtain ISCA, and the offloading problems with 6, 15 and 50 terminal devices are solved respectively, compared with other algorithms, the simulation results show that ISCA has better performance, better solving ability, it is not easy to fall into local optimum, and the convergence speed is also greatly improved compared with SCA.

Robust Estimation in Continuous–Discrete Cubature Kalman Filters for Bearings-Only Tracking

The model of bearings-only tracking is generally described by discrete–discrete filtering systems. Discrete robust methods are also frequently used to address measurement uncertainty problems in bearings-only tracking. The recently popular continuous–discrete filtering system considers the state model of the target to be continuous in time, and is more suitable for bearings-only tracking because of its higher mathematical solution accuracy. However, the sufficient evaluation of robust methods in continuous–discrete systems is not available. In addition, in the different continuous–discrete measurement environments, the choice of a robust algorithm also needs to be discussed. To fill this gap, this paper firstly establishes the continuous–discrete target tracking model, and then evaluates the performance of proposed robust square-root continuous–discrete cubature Kalman filter algorithms in the measurement of uncertainty problems. From the simulation results, the robust square-root continuous–discrete maximum correntropy cubature Kalman filter algorithm and the variational Bayesian square-root continuous–discrete cubature Kalman filter algorithm have better environmental adaptability, which provides a promising means for solving continuous–discrete robust problems.

Discretely shrinking targets in moduli space

We consider the discrete shrinking target problem for Teichmüller geodesic flow on the moduli space of abelian or quadratic differentials and prove that the discrete geodesic trajectory of almost every differential will hit a shrinking family of targets infinitely often provided the measures of the targets are not summable. This result applies to any ergodic \(\mathrm {SL}(2,\mathbb {R})\)–invariant measure and any nested family of spherical targets. Under stronger conditions on the targets, we moreover prove that almost every differential will eventually always hit the targets. As an application, we obtain a logarithm law describing the rate at which generic discrete trajectories accumulate on a given point in moduli space. These results build on work of Kelmer (Geom Funct Anal 27:1257–1287, 2017) and generalize theorems of Aimino, Nicol, and Todd (Ann Inst Henri Poincaré Probab Stat 53:1371–1401, 2017).

‘I feel the weather and you just know’. Narrating the dynamics of commuter mobility choices

Efforts to promote travel behaviour change have frequently deployed social marketing strategies that are based on characterising populations into discrete target groups through quantitative segmentation techniques. Such techniques provide an important basis for understanding behavioural choices and motivations, frequently using psychological constructs that can be used for planning interventions. However, there are limitations to what a solely quantitative approach can offer practitioners in terms of understanding the dynamics of travel behaviour and the meanings associated with personal mobility that can be used to design appropriate interventions. In this paper we provide evidence to argue for a mixed-methods approach, where insights from quantitative segmentation and qualitative data can be used to reveal the experiential nature of factors that influence travel decision making. To pursue this argument we present findings from research with commuters in the city of Exeter, South West England. Using data from five workshops, we illustrate the ways in which participants articulated and gave meaning to a series of travel mode influences identified using quantitative segmentation techniques for specific commuter groups (private car, public transport, walking, cycling and a combination of modes). We demonstrate how both understanding the dynamism of travel behaviour and revealing its meanings can present opportunities for designing interventions, offering pathways to promote shifts away from carbon intensive transport.

Feasibility of Broken Ore Flow Simulation in Block Caving Mining Method Using Attribute Stochastic Medium Theory

In this paper, to reveal the flow characteristics of broken mineral-rock of block caving, we present the Attribute Stochastic Medium Theory that combines an attribute block model with stochastic medium theory. An attribute block model was established to fit discrete target regions. A void transfer model and data structure were established based on stochastic medium theory. Our key contribution is that we propose Attribute Stochastic Medium Theory and present the concept of draw-out index and block fragmentation index. It can be used to analyze the flow characteristics of rock block under different drawing heights and different fragmentation conditions. We implement the flow algorithm and simulation with C++ programming. The results show that mineral elemental grade information of the ore-drawing body was obtained, and when the block fragmentation index remains unchanged, with the increase of the draw-out index, the ore-drawing ellipsoid develops gradually, the eccentricity increases gradually, and the average depth of the depression increases gradually. In the isolated extraction zone (IEZ), the length of the long half-axis of the IEZ increases linearly with the increase of the ore-drawing height. But the length of the short half-axis and the eccentricity of the IEZ increase nonlinearly with the increase of the ore-drawing height, showing a general power exponential relationship. Under the condition of constant draw-out index, with an increase of the block index, the eccentricity of the ellipsoid decreases. This method could obtain the properties and flow characteristics of caving ore and rock. Combined with in situ test of ore drawing. This method could provide guidance for the design of ore drawing bottom structure and ore drawing plan.

Systematic review and meta-analysis of MRI features for differentiating autoimmune pancreatitis from pancreatic adenocarcinoma.

To identify reliable MRI features for differentiating autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC) and to summarize their diagnostic accuracy. We conducted a systematic literature review and meta-analysis using PubMed, EMBASE, and the Cochrane Library to identify original articles published between January 2006 and July 2021. The pooled diagnostic accuracy, including the diagnostic odds ratios (DORs) with 95% confidence intervals (CIs) of the identified features, was calculated using a bivariate random effects model. Twelve studies were included, and 92 overlapping descriptors were subsumed under 16 MRI features. Ten features favoring AIP were diffuse enlargement (DOR, 75; 95% CI, 9-594), capsule-like rim (DOR, 52; 95% CI, 20-131), multiple main pancreatic duct (MPD) strictures (DOR, 47; 95% CI, 17-129), homogeneous delayed enhancement (DOR, 46; 95% CI, 21-104), low apparent diffusion coefficient value (DOR, 30), speckled enhancement (DOR, 30), multiple pancreatic masses (DOR, 29), tapered narrowing of MPD (DOR, 15), penetrating duct sign (DOR, 14), and delayed enhancement (DOR, 13). Six features favoring PDAC were target type enhancement (DOR, 41; 95% CI, 11-158), discrete pancreatic mass (DOR, 35; 95% CI, 15-80), upstream MPD dilatation (DOR, 13), peripancreatic fat infiltration (DOR, 10), upstream parenchymal atrophy (DOR, 5), and vascular involvement (DOR, 3). This study identified 16 informative MRI features to differentiate AIP from PDAC. Among them, diffuse enlargement, capsule-like rim, multiple MPD strictures, and homogeneous delayed enhancement favored AIP with the highest DORs, whereas discrete mass and target type enhancement favored PDAC. • The MRI features with the highest pooled diagnostic odds ratios (DORs) for autoimmune pancreatitis were diffuse enlargement of the pancreas (75), capsule-like rim (52), multiple strictures of the main pancreatic duct (47), and homogeneous delayed enhancement (46). • The MRI features with the highest pooled DORs for pancreatic ductal adenocarcinoma were target type enhancement (41) and discrete pancreatic mass (35).

Discrete Army Ant Search Optimizer-Based Target Coverage Enhancement in Directional Sensor Networks

Coverage of interest points is one of the most critical issues in directional sensor networks. However, considering the remote or inhospitable environment and the limitation of the perspective of directional sensors, it is easy to form perception blind after random deployment. The intension of our research is to deal with the bound-constrained optimization problem of maximizing the coverage of target points. A coverage enhancement strategy based on a discrete army ant search optimizer (DAASO) is proposed to solve the above problem, which is inspired by the biological habits of army ants. A set of experiments are conducted using different sensor parameters. Experimental results verify the effectiveness of the DAASO in coverage effect when compared to the existing methods.

Evaluation of discrete target detection with an acoustic Doppler current profiler

AbstractAcoustic Doppler current profilers (ADCPs) use the signal scattered back from small suspended drifting particles, such as suspended sediments and planktonic species, to measure water current velocity. Additionally, ADCPs detect signals scattered from fish and other pelagic organisms, but these signals are generally treated as noise and rejected during data processing. However, these rejected signals can contain information on fish movement, which presents an opportunity to extend the application of ADCP technology as a tool for monitoring fish activity. We compare discrete target counts made using an ADCP with those of a split‐beam echosounder in a region of high tidal currents in the Bay of Fundy. The comparison was achieved using a self‐contained bottom‐mounted frame equipped with both a 600‐kHz Teledyne RD Instruments Workhorse ADCP and a 120‐kHz BioSonics DTX Submersible split‐beam echosounder system. The discrete targets were identified using a combination of signal correlation and volume backscatter (SV) thresholds in the ADCP data and using a fish tracking algorithm in the split‐beam echosounder data. The resulting ADCP discrete target counts agreed with fish tracking counts made from the co‐located split‐beam echosounder. Notably, discrete targets recorded by the ADCP could be differentiated from entrained air using a high signal correlation threshold. This method can expand the application of ADCP data for biophysical assessments for fisheries management and could be of particular use in rivers and tidal channels.

Prediction of the Electron Density of States for Crystalline Compounds with Atomistic Line Graph Neural Networks (ALIGNN)

Machine learning (ML)-based models have greatly enhanced the traditional materials discovery and design pipeline. Specifically, in recent years, surrogate ML models for material property prediction have demonstrated success in predicting discrete scalar-valued target properties to within reasonable accuracy of their DFT-computed values. However, accurate prediction of spectral targets, such as the electron density of states (DOS), poses a much more challenging problem due to the complexity of the target, and the limited amount of available training data. In this study, we present an extension of the recently developed atomistic line graph neural network to accurately predict DOS of a large set of material unit cell structures, trained to the publicly available JARVIS-DFT dataset. Furthermore, we evaluate two methods of representation of the target quantity: a direct discretized spectrum, and a compressed low-dimensional representation obtained using an autoencoder. Through this work, we demonstrate the utility of graph-based featurization and modeling methods in the prediction of complex targets that depend on both chemistry and directional characteristics of material structures.

Optimizing Rehabilitation Strategy for Walking after Stroke： from Assisted Walking Training to Brain-Limb Cooperative Therapy

Recovery of walking function after stroke is the most urgent need of patients and their families. It is also the primary goal when planning rehabilitation treatment plans with patients. For a long time, experts in the field of neurological rehabilitation have been exploring effective methods to accelerate the recovery of walking function after stroke. From various traditional Chinese medicine such as acupuncture on the limbs of patient, and modern rehabilitation technologies for paralyzed limbs (such as neurodevelopmental therapy, functional electrical stimulation, walking bicycle, and walking robot, etc.) to non-invasive brain stimulation technologies for focal brain (such as transcranial magnetic stimulation, transcranial direct current stimulation, and head acupuncture, etc.). A large number of clinical evidence-based studies have proved that these technologies are effective methods at different levels of evidence. In recent years, with the help of the 10-year research findings of brain in the 1990s, the rehabilitation for improving walking function after stroke has also been changed significantly. It began to develop from discrete target organ (such as limb or brain) to multi-target cooperative or mode therapy combining brain and limb, which gave birth to a new treatment mode: brain-limb cooperative therapy mode. Focusing on the theme of optimizing rehabilitation strategy for walking after stroke, this paper provides a brief introduction on the concept of this brain-limb cooperative treatment mode, focuses on several optimized combinations of brain-lower limb cooperative treatment modes related to improving walking function after stroke, and also a few related research papers of clinical application on this mode were published in this issue as supporting data. Through this review and the papers published in this issue, we hope to attract the attention of domestic stroke rehabilitation professionals to the brain-limb cooperative treatment model, and actively apply it in the rehabilitation treatment of improving walking function after stroke. Similar related research could be duplicated to further clarify this mode. By analogy, this brain-limb cooperative treatment model is extended to the rehabilitation treatment of upper limb function recovery after stroke, and the clinical application and research of brain-limb cooperative treatment model are expanded. From the perspective of stroke rehabilitation, the strategy of lower limb rehabilitation after stroke in the future should be to more effectively improve or restore the original walking function of patients. Therefore, the early intervention of error free learning rehabilitation strategy with walking as the core should implement the whole process of stroke lower limb rehabilitation. Only when the walking mode input from the periphery to the center is correct, the walking command output from the center can be correct. Brain-limb cooperative therapy is an effective guarantee for realizing this closed loop.