Explore the vast range of titles available.

Human cancer genomes harbour a variety of alterations leading to the deregulation of key pathways in tumour cells. The genomic characterization of tumours has uncovered numerous genes recurrently mutated, deleted or amplified, but gene fusions have not been characterized as extensively. Here we develop heuristics for reliably detecting gene fusion events in RNA-seq data and apply them to nearly 7,000 samples from The Cancer Genome Atlas. We thereby are able to discover several novel and recurrent fusions involving kinases. These findings have immediate clinical implications and expand the therapeutic options for cancer patients, as approved or exploratory drugs exist for many of these kinases. Kinases activated by gene fusions represent potentially important targets for the development of cancer drugs. Here, the authors develop a method for detecting gene fusion events in RNA sequencing data from The Cancer Genome Atlas and identify several novel recurrent fusions involving kinases.

As the demand for efficient energy management increases, the need for extensive, high-quality energy data becomes critical. However, privacy concerns and insufficient data volume pose significant challenges. To address these issues, data synthesis techniques are employed to augment and replace real data. This paper introduces Doubly Structured Data Synthesis (DS2), a novel method to tackle privacy concerns in time-series energy-use data. DS2 synthesizes rate changes to maintain longitudinal information and uses calibration techniques to preserve the cross-sectional mean structure at each time point. Numerical analyses reveal that DS2 surpasses existing methods, such as Conditional Tabular GAN (CTGAN) and Transformer-based Time-Series Generative Adversarial Network (TTS-GAN), in capturing both time-series and cross-sectional characteristics. We evaluated our proposed method using metrics for data similarity, utility, and privacy. The results indicate that DS2 effectively retains the underlying characteristics of real datasets while ensuring adequate privacy protection. DS2 is a valuable tool for sharing and utilizing energy data, significantly enhancing energy demand prediction and management.

In real applications, it is necessary to classify new unseen classes that cannot be acquired in training datasets. To solve this problem, few-shot learning methods are usually adopted to recognize new categories with only a few (out-of-bag) labeled samples together with the known classes available in the (large-scale) training dataset. Unlike common scene classification images obtained by CCD (Charge-Coupled Device) cameras, remote sensing scene classification datasets tend to have plentiful texture features rather than shape features. Therefore, it is important to extract more valuable texture semantic features from a limited number of labeled input images. In this paper, a multi-scale feature fusion network for few-shot remote sensing scene classification is proposed by integrating a novel self-attention feature selection module, denoted as SAFFNet. Unlike a pyramidal feature hierarchy for object detection, the informative representations of the images with different receptive fields are automatically selected and re-weighted for feature fusion after refining network and global pooling operation for a few-shot remote sensing classification task. Here, the feature weighting value can be fine-tuned by the support set in the few-shot learning task. The proposed model is evaluated on three publicly available datasets for few shot remote sensing scene classification. Experimental results demonstrate the effectiveness of the proposed SAFFNet to improve the few-shot classification accuracy significantly compared to other few-shot methods and the typical multi-scale feature fusion network.

Estimating skewness and kurtosis from real-world data remains a long-standing challenge in actuarial science and financial risk management, where these higher-order moments are critical for capturing asymmetry and tail risk. Traditional moment-based estimators are known to be highly sensitive to outliers and often fail when the assumption of normality is violated. Despite numerous extensions—from robust moment-based methods to quantile-based measures—being proposed over the decades, no universally satisfactory solution has been reported, and many existing methods exhibit limited effectiveness, particularly under challenging distributional shapes. In this paper we propose a novel method that jointly estimates skewness and kurtosis based on a regression adaptation of the Cornish–Fisher expansion. By modeling the empirical quantiles as a cubic polynomial of the standard normal variable, the proposed approach produces a reliable and efficient estimator that better captures distributional shape without strong parametric assumptions. Our comprehensive simulation studies show that the proposed method performs much better than existing estimators across a wide range of distributions, especially when the data are skewed or heavy-tailed, as is typical in actuarial and financial applications.

To generate and evaluate synthesized postoperative OCT images of epiretinal membrane (ERM) based on preoperative OCT images using deep learning methodology. This study included a total 500 pairs of preoperative and postoperative optical coherence tomography (OCT) images for training a neural network. 60 preoperative OCT images were used to test the neural networks performance, and the corresponding postoperative OCT images were used to evaluate the synthesized images in terms of structural similarity index measure (SSIM). The SSIM was used to quantify how similar the synthesized postoperative OCT image was to the actual postoperative OCT image. The Pix2Pix GAN model was used to generate synthesized postoperative OCT images. Total 60 synthesized OCT images were generated with training values at 800 epochs. The mean SSIM of synthesized postoperative OCT to the actual postoperative OCT was 0.913. Pix2Pix GAN model has a possibility to generate predictive postoperative OCT images following ERM removal surgery.

The coronavirus family member, SARS-CoV-2 has been identified as the causal agent for the pandemic viral pneumonia disease, COVID-19. At this time, no vaccine is available to control further dissemination of the disease. We have previously engineered a synthetic DNA vaccine targeting the MERS coronavirus Spike (S) protein, the major surface antigen of coronaviruses, which is currently in clinical study. Here we build on this prior experience to generate a synthetic DNA-based vaccine candidate targeting SARS-CoV-2 S protein. The engineered construct, INO-4800, results in robust expression of the S protein in vitro. Following immunization of mice and guinea pigs with INO-4800 we measure antigen-specific T cell responses, functional antibodies which neutralize the SARS-CoV-2 infection and block Spike protein binding to the ACE2 receptor, and biodistribution of SARS-CoV-2 targeting antibodies to the lungs. This preliminary dataset identifies INO-4800 as a potential COVID-19 vaccine candidate, supporting further translational study. There is currently no licensed SARS-CoV-2 vaccine. Here, the authors generate an optimized DNA vaccine candidate encoding the SARS-CoV-2 spike antigen, demonstrating induction of specific T cells and neutralizing antibody responses in mice and guinea pigs. These initial results support further development of this vaccine candidate.

Airspace geofencing is a key capability for low-altitude Unmanned Aircraft System (UAS) Traffic Management (UTM). Geofenced airspace volumes can be allocated to safely contain compatible UAS flight operations within a fly-zone (keep-in geofence) and ensure the avoidance of no-fly zones (keep-out geofences). This paper presents the application of three-dimensional flight volumization algorithms to support airspace geofence management for UTM. Layered polygon geofence volumes enclose user-input waypoint-based 3-D flight trajectories, and a family of flight trajectory solutions designed to avoid keep-out geofence volumes is proposed using computational geometry. Geofencing and path planning solutions are analyzed in an accurately mapped urban environment. Urban map data processing algorithms are presented. Monte Carlo simulations statistically validate our algorithms, and runtime statistics are tabulated. Benchmark evaluation results in a Manhattan, New York City low-altitude environment compare our geofenced dynamic path planning solutions against a fixed airway corridor design. A case study with UAS route deconfliction is presented, illustrating how the proposed geofencing pipeline supports multi-vehicle deconfliction. This paper contributes to the nascent theory and the practice of dynamic airspace geofencing in support of UTM.

Automated telemedicine interventions could potentially improve adherence to continuous positive airway pressure (CPAP) therapy. Examining the effects of telemedicine-delivered obstructive sleep apnea (OSA) education and CPAP telemonitoring with automated patient feedback messaging on CPAP adherence. This four-arm, randomized, factorial design clinical trial enrolled 1,455 patients (51.0% women; age, 49.1 ± 12.5 yr [mean ± SD]) referred for suspected OSA. Nine hundred and fifty-six underwent home sleep apnea testing, and 556 were prescribed CPAP. Two telemedicine interventions were implemented: 1) web-based OSA education (Tel-Ed) and 2) CPAP telemonitoring with automated patient feedback (Tel-TM). Patients were randomized to 1) usual care, 2) Tel-Ed added, 3) Tel-TM added, or 4) Tel-Ed and Tel-TM added (Tel-both). The primary endpoint was 90-day CPAP usage. Secondary endpoints included attendance to OSA evaluation, and change in Epworth Sleepiness Scale score. CPAP average daily use at 90 days was 3.8 ± 2.5, 4.0 ± 2.4, 4.4 ± 2.2, and 4.8 ± 2.3 hours in usual care, Tel-Ed, Tel-TM, and Tel-both groups. Usage was significantly higher in the Tel-TM and Tel-both groups versus usual care (P = 0.0002 for both) but not for Tel-Ed (P = 0.10). Medicare adherence rates were 53.5, 61.0, 65.6, and 73.2% in usual care, Tel-Ed, Tel-TM, and Tel-both groups (Tel-both vs. usual care, P = 0.001; Tel-TM vs. usual care, P = 0.003; Tel-Ed vs. usual care, P = 0.07), respectively. Telemedicine education improved clinic attendance compared with no telemedicine education (show rate, 68.5 vs. 62.7%; P = 0.02). The use of CPAP telemonitoring with automated feedback messaging improved 90-day adherence in patients with OSA. Telemedicine-based education did not significantly improve CPAP adherence but did increase clinic attendance for OSA evaluation. Clinical trial registered with www.clinicaltrials.gov (NCT02279901).

Continuous attractors are an emergent property of neural population dynamics that have been hypothesized to encode continuous variables such as head direction and eye position 1 , 2 , 3 – 4 . In mammals, direct evidence of neural implementation of a continuous attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles 2 , 3 . Dynamical systems modelling has revealed that neurons in the hypothalamus exhibit approximate line-attractor dynamics in male mice during aggressive encounters 5 . We have previously hypothesized that these dynamics may encode the variable intensity and persistence of an aggressive internal state. Here we report that these neurons also showed line-attractor dynamics in head-fixed mice observing aggression 6 . This allowed us to identify and manipulate line-attractor-contributing neurons using two-photon calcium imaging and holographic optogenetic perturbations. On-manifold perturbations yielded integration of optogenetic stimulation pulses and persistent activity that drove the system along the line attractor, while transient off-manifold perturbations were followed by rapid relaxation back into the attractor. Furthermore, single-cell stimulation and imaging revealed selective functional connectivity among attractor-contributing neurons. Notably, individual differences among mice in line-attractor stability were correlated with the degree of functional connectivity among attractor-contributing neurons. Mechanistic recurrent neural network modelling indicated that dense subnetwork connectivity and slow neurotransmission 7 best recapitulate our empirical findings. Our work bridges circuit and manifold levels 3 , providing causal evidence of continuous attractor dynamics encoding an affective internal state in the mammalian hypothalamus. Single-cell optogenetic stimulation and calcium imaging experiments provide direct evidence of line-attractor dynamics, including functional network connectivity, in the mammalian hypothalamus.

After cystectomy, patients with muscle-invasive bladder cancer were randomly assigned to pembrolizumab or observation for 1 year. The pembrolizumab group had a median disease-free survival twice as long as the observation group.