Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanomaterials in tuberculosis DNA vaccine delivery: historical perspective and current landscape

Abstract Vaccinations, especially DNA vaccines that promote host immunity, are the most effective interventions for tuberculosis (TB) control. However, the vaccine delivery system exhibits a significant impact on the protective effects of the vaccine. Recently, effective nanomaterial-based delivery systems (including nanoparticles, nanogold, nanoliposomes, virus-like particles, and virus carriers) have been developed for DNA vaccines to control TB. This review highlights the historical development of various nanomaterial-based delivery systems for TB DNA vaccines, along with the emerging technologies. Nanomaterial-based vaccine delivery systems could enhance the efficacy of TB vaccination; therefore, this summary could guide nanomaterial selection for optimal and safe vaccine delivery, facilitating the design and development of highly effective TB vaccines.
Effectiveness of onabotulinumtoxinA (BOTOX®) for the preventive treatment of chronic migraine: A meta-analysis on 10 years of real-world data

Background This meta-analysis evaluated the real-world effectiveness of onabotulinumtoxinA (BOTOX®), the first preventive treatment FDA-approved specifically for chronic migraine in 2010. Methods We systematically reviewed onabotulinumtoxinA observational data in chronic migraine published between 1 January 2010 and 31 March 2021. Random-effects models evaluated available data for primary and secondary endpoints defined in onabotulinumtoxinA pivotal trials at approximately 24 weeks and 52 weeks. Results Of the 44 full-text eligible studies (29 prospective; 13 retrospective; 2 other), seven evaluated change from baseline (mean[confidence interval]) at ∼24 weeks and ∼52 weeks, respectively, for onabotulinumtoxinA in: number of headache days/month: (–10.64 [–12.31, –8.97]; –10.32 [−14.92, –5.73]); number of days of acute headache pain medication intake per month (–7.40 [–13.04, –1.77]; overlapping CIs at 52 weeks); total Headache Impact Test-6 score (–11.70 [–13.86, –9.54]); –11.80 [14.70, –8.90]); and Migraine-Specific Quality-of-Life v2.1 score (MSQ; 23.60 [CI: 21.56, 25.64]; 30.90 [CI: 28.29, 33.51]). At ∼24 weeks onabotulinumtoxinA showed total Migraine Disability Assessment score of 44.74 [28.50, 60.99] and ≥50% reduction in migraine days response rate of 46.57% [29.50%, 63.65%]. A sensitivity analysis at study-end suggested durability of onabotulinumtoxinA effectiveness on MSQ. Conclusion The meta-analysis reflecting real-world practice broadly corroborated with evidence from pivotal and long-term open-label studies of onabotulinumtoxinA in chronic migraine preventive treatment.
The reference genome of the Vernal Pool Tadpole Shrimp, Lepidurus packardi

Abstract In this paper, we report on the scaffold-level assembled genome for the federally endangered, California endemic crustacean Lepidurus packardi (the Vernal Pool Tadpole Shrimp). L. packardi is a key food source for other conserved California species including the California Tiger Salamander Ambystoma californiense. It faces significant habitat loss and fragmentation as vernal pools are threatened by urbanization, agricultural conversion, and climate change. This resource represents the first scaffold-level genome of any Lepidurus species. The assembled genome spans 108.6 Mbps, with 6 chromosome-length scaffolds comprising 71% of total genomic length and 444 total contigs. The BUSCO score for this genome is 97.3%, suggesting a high level of completeness. We produced a predicted gene set for this species trained on the Daphnia magna set of genes and predicted 17,650 genes. These tools can aid researchers in understanding the evolution and adaptive potential of alternative reproductive modes within this species.
Independent Set on graphs with maximum degree 3

Let G be an undirected graph with maximum degree at most 3 such that G does not contain either of the two graphs shown in Figure 1 as a subgraph. We prove that the independence number of G is at least n(G)/3 + nt(G)/63, where n(G) is the number of vertices in G and nt(G) is the number of nontriangle vertices in G. We show an application of the aforementioned combinatorial result to the area of parameterized complexity. We present a linear-time kernelization algorithm for the independent set problem on graphs with maximum degree at most 3 that computes a kernel of size at most 630k/211 < 3k, where k is the lower bound on the size of the independent set sought.
Star formation across the galaxy : observations and modelling of the spectral energy distributions of young stars

In the last few decades, the emergence of large-scale infrared surveys has led to a revolution in the study of star formation. In particular, NASA’s Spitzer Space Telescope has recently carried out midand far-infrared observations of numerous star formation regions with unprecedented resolution and sensitivity, and has uncovered thousands of forming stars. In combination with present and future large-scale near-infrared and sub-mm surveys, spectral energy distributions from near-infrared to mm wavelengths will be available for these thousands of young stars. Never before has there been such a wealth of multi-wavelength data for so many young stars. Traditional techniques for studying the physical properties of young stars through their spectral energy distributions have usually focused either on the analysis of many sources using simple observational diagnostics such as colours or spectral indices, or on the analysis of a few sources through the detailed modelling of their full spectral energy distributions. The work presented in the first part of this thesis aims to bridge these two techniques through the efficient modelling of the spectral energy distributions of many young stars. In particular, the technique developed for this work makes it straightforward to find out how well different physical parameters are constrained, whether any parameters are degenerate, and whether additional data would resolve the degeneracies. In the second part of this thesis, a census of intrinsically red sources observed by Spitzer in the Galactic plane is presented, including a catalogue of over 11,000 likely young stellar objects. This sample of sources is the largest uniformly selected sample of young stars to date, and effectively provides a map of the sites of star formation in the mid-plane of the Milky-Way. In parallel, this census has uncovered over 7,000 candidate asymptotic giant branch stars, of which over 1,000 are variable at 4.5 or 8. 0μm.
EEG-Based Focus of Attention Tracking and Regulation During Dual-Task Training for Neural Rehabilitation of Stroke Patients

Dual-task training under variable-priority instructions (DT-VP), during which subjects are required to vary their focus of attention (FOA) between two concurrent tasks, has shown a more significant improvement in neural rehabilitation than that under fixed-priority instructions. Failed FOA switching not only diminishes the recovery benefits, but also causes anxieties, which is detrimental to rehabilitation. Developing a strategy for tracking and regulating patients' FOA to achieve a better performance in task priority-following during DT-VP is thus imperative. In this study, fifteen stroke patients participated in DT-VP that comprised two tasks: a mathematical problem-solving task and a cycling task, during which their electroencephalograms were recorded simultaneously. The significantly differentiated power spectra of four brain regions extracted from single-task training were fed into a support vector machine to build a FOA tracking algorithm for patients' attention assessment during the DT-VP. Moreover, dual-task difficulty adaptation method was designed to regulate patients' FOA when their FOA and the high-priority task were not coincident. The comparison experimental results showed that the proposed method significantly improved patients' FOA distributed on the high-priority task (analysis of variance, ${p}<$0.05). Meanwhile, the absolute power spectral densities of the motor cortex and the frontal region could also be improved during DT-VP under high motor and cognitive task priority instructions, respectively. These phenomena demonstrated the feasibility of the proposed method in helping stroke patients better implement FOA switching and maintenance.
Solving the inverse kinematics of multiloop robots by structural and geometric decoupling

A unified approach is presented to the kinematics of robots and mechanisms with many loops. It is shown that proper use of the structural information defining a kinematic chain (i.e., the shape of its links, the type of its pairs, and their connections) can lead to the direct derivation of a set of decoupled congruence equations. An example is utilized to introduce concepts and operating rules of general validity. The basic steps for a computer implementation of the proposed approach are also discussed. <<ETX>>
Theoretical and computational issues for improving the performance of linear optimization methods

Linear optimization tools are used to solve many problems that arise in our day-to-day lives. The linear optimization models and methodologies help to nd, for example, the best amount of ingredients in our food, the most suitable routes and timetables for the buses and trains we take, and the right way to invest our savings. We would cite many other situations that involves linear optimization, since a large number of companies around the world base their decisions in solutions which are provided by the linear optimization methodologies. In this thesis, we propose theoretical and computational developments to improve the performance of important linear optimization methods. Namely, we address simplex type methods, interior point methods, the column generation technique and the branch-and-price method. In simplex-type methods, we investigate a variant which exploits special features of problems which are formulated in the general form. We present a novel theoretical description of the method and propose how to e ciently implement this method in practice. Furthermore, we propose how to use the primal-dual interior point method to improve the column generation technique. This results in the primal-dual column generation method, which is more stable in practice and has a better overall performance in relation to other column generation strategies. The primal-dual interior point method also o ers advantageous features which can be exploited in the context of the branch-and-price method. We show that these features improves the branching operation and the generation of columns and valid inequalities. For all the strategies which are proposed in this thesis, we present the results of computational experiments which involves publicly available, well-known instances from the literature. The results indicate that these strategies help to improve the performance of the linear optimization methodologies. In particular for a class of problems, namely the vehicle routing problem with time windows, the interior point branch-and-price method proposed in this study was up to 33 times faster than a state-of-the-art implementation available in the literature.
Localization of Cracks in Concrete Structures Using an Unmanned Aerial Vehicle

Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain location information. When such absolute position information is used, several studies confirmed that positioning errors of the UAVs were reflected and were in the order of a few meters. To address these limitations, in this study, without using the absolute position information, localization of cracks was defined using relative position between objects in UAV-captured images to significantly reduce the error level. Through aerial photography, a total of 97 images were acquired. Using the point cloud technique, image stitching, and homography matrix algorithm, 5 cracks and 3 reference objects were defined. Importantly, the comparative analysis of estimated relative position values and ground truth values through field measurement revealed that errors in the range 24–84 mm and 8–48 mm were obtained on the x- and y-directions, respectively. Also, RMSE errors of 37.95–91.24 mm were confirmed. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.
Automatic Segmentation of Standing Trees from Forest Images Based on Deep Learning

Semantic segmentation of standing trees is important to obtain factors of standing trees from images automatically and effectively. Aiming at the accurate segmentation of multiple standing trees in complex backgrounds, some traditional methods have shortcomings such as low segmentation accuracy and manual intervention. To achieve accurate segmentation of standing tree images effectively, SEMD, a lightweight network segmentation model based on deep learning, is proposed in this article. DeepLabV3+ is chosen as the base framework to perform multi-scale fusion of the convolutional features of the standing trees in images, so as to reduce the loss of image edge details during the standing tree segmentation and reduce the loss of feature information. MobileNet, a lightweight network, is integrated into the backbone network to reduce the computational complexity. Furthermore, SENet, an attention mechanism, is added to obtain the feature information efficiently and suppress the generation of useless feature information. The extensive experimental results show that using the SEMD model the MIoU of the semantic segmentation of standing tree images of different varieties and categories under simple and complex backgrounds reaches 91.78% and 86.90%, respectively. The lightweight network segmentation model SEMD based on deep learning proposed in this paper can solve the problem of multiple standing trees segmentation with high accuracy.
Application of Teager–Kaiser Energy Operator in the Early Fault Diagnosis of Rolling Bearings

Rolling bearings are key components that support the rotation of motor shafts, operating with a quite high failure rate among all the motor components. Early bearing fault diagnosis has great significance to the operation security of motors. The main contribution of this paper is to illustrate Gaussian white noise in bearing vibration signals seriously masks the weak fault characteristics in the diagnosis based on the Teager–Kaiser energy operator envelope, and to propose improved TKEO taking both accuracy and calculation speed into account. Improved TKEO can attenuate noise in consideration of computational efficiency while preserving information about the possible fault. The proposed method can be characterized as follows: a series of band-pass filters were set up to extract several component signals from the original vibration signals; then a denoised target signal including fault information was reconstructed by weighted summation of these component signals; finally, the Fourier spectrum of TKEO energy of the resulting target signal was used for bearing fault diagnosis. The improved TKEO was applied to a vibration signal dataset of run-to-failure rolling bearings and compared with two advanced diagnosis methods. The experimental results verify the effectiveness and superiority of the proposed method in early bearing fault detection.
Chip Pad Inspection Method Based on an Improved YOLOv5 Algorithm

Chip pad inspection is of great practical importance for chip alignment inspection and correction. It is one of the key technologies for automated chip inspection in semiconductor manufacturing. When applying deep learning methods for chip pad inspection, the main problem to be solved is how to ensure the accuracy of small target pad detection and, at the same time, achieve a lightweight inspection model. The attention mechanism is widely used to improve the accuracy of small target detection by finding the attention region of the network. However, conventional attention mechanisms capture feature information locally, which makes it difficult to effectively improve the detection efficiency of small targets from complex backgrounds in target detection tasks. In this paper, an OCAM (Object Convolution Attention Module) attention module is proposed to build long-range dependencies between channel features and position features by constructing feature contextual relationships to enhance the correlation between features. By adding the OCAM attention module to the feature extraction layer of the YOLOv5 network, the detection performance of chip pads is effectively improved. In addition, a design guideline for the attention layer is proposed in the paper. The attention layer is adjusted by network scaling to avoid network characterization bottlenecks, balance network parameters, and network detection performance, and reduce the hardware device requirements for the improved YOLOv5 network in practical scenarios. Extensive experiments on chip pad datasets, VOC datasets, and COCO datasets show that the approach in this paper is more general and superior to several state-of-the-art methods.
Resistance analyses in Highly Treatment-Experienced People with HIV Treated with the Novel Capsid HIV Inhibitor Lenacapavir.

BACKGROUND Lenacapavir is a first-in-class inhibitor of HIV-1 capsid function in clinical development for the treatment of heavily treatment-experienced (HTE) people with HIV (PWH) harboring multidrug resistance (MDR) in combination with an optimized background regimen (OBR). Here we describe resistance analyses conducted in the pivotal phase 2/3 CAPELLA study. METHODS CAPELLA enrolled viremic HTE PWH with resistance to ≥ 3 of 4 of the main antiretroviral classes and resistance to ≥ 2 ARV drugs per class. Baseline resistance analyses used commercial assays (HIV-1 protease, RT, integrase genotypic/phenotypic tests). Post-baseline resistance was evaluated in participants experiencing virologic failure. RESULTS At baseline, 46% of participants had resistance to the 4 main ARV drug classes, with one third of participants having exhausted all drugs from ≥ 3 of the 4 main ARV classes. Treatment with LEN + OBR for 26 weeks led to viral suppression in 81% of participants. Post-baseline resistance mutations to lenacapavir occurred in 8 participants (6 with M66I, 1 with K70H, 1 with Q67H + K70R) who were receiving unintended functional LEN monotherapy at the time of resistance selection. CONCLUSIONS LEN added to OBR led to high efficacy in this HTE patient population with MDR but could select for resistance when used unintentionally as functional monotherapy.
Increased risk of dialysis circuit clotting in hemodialysis patients with COVID‐19 is associated with elevated FVIII, fibrinogen and D‐dimers

Severe COVID‐19 infections increase the risk of thrombotic events and Intensive Care Units reported increased extracorporeal circuit clotting (ECC) in COVID‐19 patients with acute kidney injury. We wished to determine whether hemodialysis (HD) patients with COVID‐19 also have increased risk of circuit clotting.
An investigation of extinction diagnostics towards the Orion Nebula

We present for the Orion nebula long-slit spectroscopic observations of emission lines in the wavelength regions 10 000-11 000 A and 3700-7330 A. These wavelength regions contain the corresponding multiplet lines of common upper atomic level of hydrogen P 6 -H 6 (=P γ -H δ ) and P 7 -H 7 (=P δ -H e ) and of [S II] at 10 300 A and 4072 A. From these lines the extinction A V towards the Orion nebula is derived with minimal reliance on recombination line model calculations. We find good internal agreement for the extinction derived from the corresponding hydrogen and [S II] lines, except for deviations of the order of 20-30% for data based on H e and P δ , where deblending of the H e line may be uncertain by this amount and the flux of the P δ line contains a similar error
Thermoelectric properties of BaSi2, SrSi2, and LaSi

We studied the thermoelectric properties of BaSi2, SrSi2, and LaSi. The polycrystalline samples were prepared by spark plasma sintering (SPS). The electrical resistivity (ρ), Seebeck coefficient (S), and thermal conductivity (κ) were measured above room temperature. The power factor (S2∕ρ) is quite low (below 10−5 Wm−1 K−2 over the whole temperature range) for BaSi2 and LaSi, while relatively high (1.19×10−3 Wm−1 K−2 at 331 K) for SrSi2. BaSi2 exhibits quite low κ. The κ values at room temperature are 1.56, 5.25, and 6.71 Wm−1 K−1 for BaSi2, SrSi2, and LaSi, respectively. The maximum values of the dimensionless figure of merit, ZT=S2T∕ρ∕κ, are 0.01 at 954 K for BaSi2, 0.09 at 417 K for SrSi2, and 0.002 at 957 K for LaSi.
Can gravitational microlensing by vacuum fluctuations be observed

Although the prospect is more plausible than it might appear, the answer to the title question is, unfortunately, "probably not. " Quantum fluctuations of vacuum energy can focus light, and while the effect is tiny, the distribution of fluctuations is highly non-Gaussian, offering hope that relatively rare "large" fluctuations might be observable. I show that although gravitational microlensing by such fluctuations become important at scales much larger than the Planck length, the possibility of direct observation remains remote, although there is a small chance that cumulative effects over cosmological distances might be detectable. The effect is sensitive to the size of the Planck scale, however, and could offer a new test of TeV-scale gravity.
Einstein's cosmological considerations

The objective of this paper is not simply to present an historical overview of Einstein's cosmological considerations, but to discuss the central role they played in shaping the paradigm of relativistic cosmology. This, we'll show, was a result of both his actions and, perhaps more importantly, his inactions. Accordingly, discussion won't simply be restricted to Einstein's considerations, as we'll analyse relevant contributions to the relativistic expansion paradigm during the approximately twenty years following Slipher's first redshift measurements in 1912. Our aim is to shed some light on why we think some of the things we do, with the idea that a better understanding of the reasoning that fundamentally influenced the common idea of our expanding universe might help to resolve some of the significant problems that modern cosmology now faces; and we eventually use this knowledge to probe the foundations of the standard model. Much of the information we present, including many of the historical details, we expect will be news to modern practitioners.