Miracle-dz/UniFAR-FTUs huggingface dataset:
CodeBreaK 200: Sotorasib (AMG510) Has Broken the KRAS G12C+ NSCLC Enigma Code

Abstract Per the US FDA sotorasib approval summary, KRAS G12C mutation is found in approximately 14% of adenocarcinoma of the lung, primarily in patients with a history of smoking. Until recently, targeted therapies against KRAS G12C have been largely unsuccessful due to the small protein size of KRAS and thus lack of binding pockets in KRAS and rapid hydrolysis of GTP to GDP by KRAS enzymes from abundance of GTP in the cytoplasm. Sotorasib, a first-in-class covalent KRAS G12C inhibitor that binds to the switch pocket II in the KRAS G12C-GDP “off” state, received US FDA accelerated approval on May 21, 2021 in the US, based on a Phase II dose expansion cohort of CodeBreaK 100 trial. Sotorasib at 960 mg once daily achieved an ORR of 36% (95% CI: 28%, 45%), with a median response duration of 10 months (range 1.3+, 11.1) in 124 KRAS G12C+ NSCLC. At the European Society of Medical Oncology (ESMO) 2022 annual meeting, sotorasib achieved a statistically significant improved PFS over docetaxel (HR = 0.66; 95% CI: 0. 51–0.86; P = 0.002). The modest magnitude of PFS improvement of 1.1 months (from 4.5 months to 5.6 months) and the ORR of 28% led to a vigorous debate on whether sotorasib was indeed a true breakthrough. In this pros and cons debate, we argue thatsotorasib has achieved a true breakthrough.
Effects of angular range on image quality of chest digital tomosynthesis

Chest digital tomosynthesis (CDT) is a new 3D imaging technique that can be expected to improve clinical diagnosis over conventional chest radiography. We investigated the effect of the angular range of data acquisition on the image quality using newly developed CDT system. The four different acquisition sets were studied using ±15°, ±20°, ±30°, and ±35° angular ranges with 21 projection views (PVs). The point spread function (PSF), modulation transfer function (MTF), artifact spread function (ASF), and normalized contrast-to-noise ratio (CNR) were used to evaluate the image quality. We found that increasing angular ranges improved vertical resolution. The results indicated that there was the opposite relationship of the CNR with angular range for the two tissue types. While CNR for heart tissue increased with increasing angular range, CNR for spine bone decreased. The results showed that the angular range is an important parameter for the CDT exam.
Error estimates for second order Hamilton-Jacobi-Bellman equations.Approximation of probabilistic reachable sets

This work deals with numerical approximations of unbounded and discontinuous value functions associated to some stochastic control problems. We derive error estimates for monotone schemes based on a Semi-Lagrangian method (or more generally in the form of a Markov chain approximation). A motivation of this study consists in approximating chance-constrained reachability sets. The latters will be characterized as level sets of discontinuous value functions associated to adequate stochastic control problems. A precise analysis of the level-set approach is carried out and some numerical simulations are given to illustrate the approach.
A Survey on Reinforcement Learning Methods in Bionic Underwater Robots

Bionic robots possess inherent advantages for underwater operations, and research on motion control and intelligent decision making has expanded their application scope. In recent years, the application of reinforcement learning algorithms in the field of bionic underwater robots has gained considerable attention, and continues to grow. In this paper, we present a comprehensive survey of the accomplishments of reinforcement learning algorithms in the field of bionic underwater robots. Firstly, we classify existing reinforcement learning methods and introduce control tasks and decision making tasks based on the composition of bionic underwater robots. We further discuss the advantages and challenges of reinforcement learning for bionic robots in underwater environments. Secondly, we review the establishment of existing reinforcement learning algorithms for bionic underwater robots from different task perspectives. Thirdly, we explore the existing training and deployment solutions of reinforcement learning algorithms for bionic underwater robots, focusing on the challenges posed by complex underwater environments and underactuated bionic robots. Finally, the limitations and future development directions of reinforcement learning in the field of bionic underwater robots are discussed. This survey provides a foundation for exploring reinforcement learning control and decision making methods for bionic underwater robots, and provides insights for future research.
How Does Chronobiology Contribute to the Development of Diseases in Later Life

Abstract An increasingly older population is one of the major social and medical challenges we currently face. Between 2010 and 2050, it is estimated that the proportion of adults over 65 years of age will double from 8% to 16% of the global population. A major concern associated with aging is the changes in health that can lead to various diseases such as cancer and neurogenerative diseases, which are major burdens on individuals and societies. Thus, it is imperative to better understand changes in sleep and circadian rhythms that accompany aging to improve the health of an older population and target diseases associated with aging. Circadian rhythms play a role in most physiological processes and can contribute to age-related diseases. Interestingly, there is a relationship between circadian rhythms and aging. For example, many older adults have a shift in chronotype, which is an individual’s natural inclination to sleep certain times of the day. As adults age, most people tend to go to sleep earlier while also waking up earlier. Numerous studies also suggest that disrupted circadian rhythms may be indicative of developing age-related diseases, like neurodegenerative disorders and cancer. Better understanding the relationship between circadian rhythms and aging may allow us to improve current treatments or develop novel ones that target diseases commonly associated with aging.
Versatile LiDAR-Inertial Odometry With SE(2) Constraints for Ground Vehicles

LiDAR SLAM has become one of the major localization systems for ground vehicles since LiDAR Odometry And Mapping (LOAM). Many extension works on LOAM mainly leverage one specific constraint to improve the performance, e.g., information from on-board sensors such as loop closure and inertial state; prior conditions such as ground level and motion dynamics. In many robotic applications, these conditions are often known partially, hence a SLAM system can be a comprehensive problem due to the existence of numerous constraints. Therefore, we can achieve a better SLAM result by fusing them properly. In this letter, we propose a hybrid LiDAR-inertial SLAM framework that leverages both the on-board perception system and prior information such as motion dynamics to improve localization performance. In particular, we consider the case for ground vehicles, which are commonly used for autonomous driving and warehouse logistics. We present a computationally efficient LiDAR-inertial odometry method that directly parameterizes ground vehicle poses on SE(2). The out-of-SE(2) motion perturbations are not neglected but incorporated into an integrated noise term of a novel SE(2)-constraints model. For odometric measurement processing, we propose a versatile, tightly coupled LiDAR-inertial odometry to achieve better pose estimation than traditional LiDAR odometry. Thorough experiments are performed to evaluate our proposed method's performance in different scenarios, including localization for both indoor and outdoor environments. The proposed method achieves superior performance in accuracy and robustness.
Comparison of algorithms for contrast enhancement based on triangle orientation discrimination assessments by convolutional neural networks

Abstract. Within the last decades, a large number of techniques for contrast enhancement has been proposed. There are some comparisons of such algorithms for few images and figures of merit. However, many of these figures of merit cannot assess usability of altered image content for specific tasks, such as object recognition. In this work, the effect of contrast enhancement algorithms is evaluated by means of the triangle orientation discrimination (TOD), which is a current method for imager performance assessment. The conventional TOD approach requires observers to recognize equilateral triangles pointing in four different directions, whereas here convolutional neural network models are used for the classification task. These models are trained by artificial images with single triangles. Many methods for contrast enhancement highly depend on the content of the entire image. Therefore, the images are superimposed over natural backgrounds with varying standard deviations to provide different signal-to-background ratios. Then, these images are degraded by Gaussian blur and noise representing degradational camera effects and sensor noise. Different algorithms, such as the contrast-limited adaptive histogram equalization or local range modification, are applied. Then accuracies of the trained models on these images are compared for different contrast enhancement algorithms. Accuracy gains for low signal-to-background ratios and sufficiently large triangles are found, whereas impairments are found for high signal-to-background ratios and small triangles. A high generalization ability of our TOD model is found from the similar accuracies for several image databases used for backgrounds. Finally, implications of replacing triangles with real target signatures when using such advanced digital signal processing algorithms are discussed. The results are a step toward the assessment of those algorithms for generic target recognition.
An explicit upper bound for the mean busy period in a GI/G/1 queue

In this paper, an upper bound is derived for the mean busy cycle duration in GI/G/1 queues. The bound is of the form A/(1 – ρ), where ρ is the traffic intensity and A involves three moments of the basic random variables of the queue. The proof uses a well-known result of random walk theory.
Quantized friction force: Lindbladian model satisfying Ehrenfest theorems

We construct a quantum counterpart of classical friction, a dissipative force acting against the direction of motion with the magnitude proportional to particle's velocity. In particular, a Lindblad master equation is derived satisfying the appropriate dynamical relations for observables (i.e., the Ehrenfest theorems). These findings significantly advance a long search for a universal valid Lindbladian model of quantum friction.
Transcriptional Profiling of a Patient-Matched Cohort of Glioblastoma (IDH-Wildtype) for Therapeutic Target and Repurposing Drug Identification

Glioblastoma (GBM) is the most prevalent and aggressive adult brain tumor. Despite multi-modal therapies, GBM recurs, and patients have poor survival (~14 months). Resistance to therapy may originate from a subpopulation of tumor cells identified as glioma-stem cells (GSC), and new treatments are urgently needed to target these. The biology underpinning GBM recurrence was investigated using whole transcriptome profiling of patient-matched initial and recurrent GBM (recGBM). Differential expression analysis identified 147 significant probes. In total, 24 genes were validated using expression data from four public cohorts and the literature. Functional analyses revealed that transcriptional changes to recGBM were dominated by angiogenesis and immune-related processes. The role of MHC class II proteins in antigen presentation and the differentiation, proliferation, and infiltration of immune cells was enriched. These results suggest recGBM would benefit from immunotherapies. The altered gene signature was further analyzed in a connectivity mapping analysis with QUADrATiC software to identify FDA-approved repurposing drugs. Top-ranking target compounds that may be effective against GSC and GBM recurrence were rosiglitazone, nizatidine, pantoprazole, and tolmetin. Our translational bioinformatics pipeline provides an approach to identify target compounds for repurposing that may add clinical benefit in addition to standard therapies against resistant cancers such as GBM.
High-contrast planet imager for Kyoto 4m segmented telescope

We propose a new high contrast imager for Kyoto 4m segmented telescope called SEICA (Second-generation Exoplanet Imager with Coronagraphic Adaptive optics), aiming at detection and characterization of selfluminous gas giants within 10AU around nearby stars. SEICA is aggressively optimized for high performance at very small inner working angle, 10-6 detection contrast at 0". 1 in 1-hour integration. We start the on-sky commissioning test in 2016 and the science observations in 2017. Since it is the first time to realize the highcontrast imaging on the segmented telescope, SEICA is an important step toward future high contrast sciences on Extremely Large Telescopes (ELTs). This paper presents an overall of the SEICA program and the conceptual design for ultimate performance under given atmospheric conditions.
Entangling two separate photonic ququarts using linear optical elements

We report an experimental demonstration of entangling two separable photonic ququarts using linear optical elements. Each ququart is implemented by using the dichotomic spatial and polarization modes of a single photon. By interfering two independent ququarts at a polarizing beam splitter, we experimentally generate entangled ququarts.
Comparing Traditional Age Estimation at the Defense POW/MIA Accounting Agency to Age Estimation Using Random Forest Regression

Age estimation from developmental traits is typically assessed in isolation, where an age range is derived from known individuals that exhibit that degree of fusion. There are no objective means for incorporating developmental evidence from multiple areas of the skeleton into one cohesive age estimate. This limitation is obvious in the casework at the Defense POW/MIA Accounting Agency (DPAA), where subjectivity is introduced into age estimates based on multiple age indictors. This holds true even when age is derived from one source, The 1957 study by McKern and Stewart). This study uses 388 individuals from the McKern and Stewart study and 41 individuals from the Battle of Tarawa and uses Random Forest Regression (RFR) to estimate an age interval using multiple age indicators. These RFR estimates are compared to age estimates from the Forensic Anthropology Reports (FARs). Overall, FAR age estimates are more accurate (92.7%) than those from the two RFR models (80.5% and 76.6%). This increase in accuracy comes at the cost of some precision (FARs average age interval of 8.1 years and RFR average age intervals of 6.3 and 6.4 years). The RFR models prefer age indicators with late fusion, such as the medial clavicle, and the pubic symphysis, which exhibit a combination of developmental and degenerative ages in morphology. Some avenues for further research are discussed.
Discussion of \Sure Independence Screening for Ultra-High Dimensional Feature Space"

Screening. Consider a regression model, E(y|x) = η(x β), and assume that x has its characteristic function E(exp(itx) = exp(it μx)φ(t T Σxt). That is, x follows an elliptical distribution with mean μx, covariance matrix Σx and characteristic generator φ(·) (Fang, Kotz and Ng, 1990). Let z = Σ−1/2 x (x− μx). Then E(x− μx)y = E(x− μx)η(x β) = E{Σ1/2 x zη(β Σ x z + β Σ x μx)}. (1) Take Γ to be an orthogonal matrix whose first row is β Σ 1/2 x / √ β Σxβ, and let v = (v1, · · · , vp) = Γz. Then (1) becomes
On a Generalization of a Result of Waldspurger

Abstract We consider a generalization of a trace formula identity of Jacquet, in the context of the symmetric spaces GL(2n)/GL(/n) × GL(n) and G′/H′. Here G′ is an inner form of GL(2n) over F with a subgroup H′ isomorphic to GL(n, E) where E/F is a quadratic extension of number field attached to a quadratic idele class character η of F. A consequence of this identity would be the following conjecture: Let π be an automorphic cuspidal representation of GL(2n). If there exists an automorphic representation π′ of G′ which is related to π by the Jacquet-Langlands correspondence, and a vector ø in the space of π′ whose integral over H′ is nonzero, then both L(1/2, π) and L(1/2,π ⊗ η) are nonvanishing. Moreover, we have L(1/2, π)L(1/2, π ⊗ η) > 0. Here the nonvanishing part of the conjecture is a generalization of a result of Waldspurger for GL(2) and the nonnegativity of the product is predicted from the generalized Riemann Hypothesis. In this article, we study the corresponding local orbital integrals for the symmetric spaces. We prove the "fundamental lemma for the unit Hecke functions" which says that unit Hecke functions have "matching" orbital integrals. This serves as the first step toward establishing the trace formula identity and in the same time it provides strong evidence for what we proposed.
Representing the Appearances of Human Faces for Recognition

The images of a given object under varying conditions is commonly called an appearance manifold. This thesis presents methods for approximating the appearance manifold, implicitly or explicitly by linear subspaces. Based on this representation, a number of useful algorithms are presented for face recognition and tracking. First, this thesis shows that for any Lambertian object, the low-dimensional linear subspace spanned by a small set of corresponding images acquired under certain single light source directions can provide a good approximation of the image variation under all lighting conditions. This thesis also introduces a complementary approach for estimating directional lighting in uncalibrated frontal-pose face images. This approach applies the class-specific 3D shape and albedo to construct an illumination subspace to estimate the lighting direction. Then the result is used in a forward rendering step to “relight” input faces to a canonical form as needed for illumination-invariant face verification. Next, a novel integrated framework is presented to track and recognize human faces in video sequences. The appearance manifold representing each registered person is approximated by a collection of sub-manifolds and the connectivity between them. The integrated task of tracking and recognition is formulated as a maximum a posteriori estimation problem. Within this framework, the tracking and recognition modules are complementary to each other, and the capability and performance of one are enhanced by the other. Finally, this thesis presents an online learning algorithm to construct the aforementioned probabilistic appearance manifolds that is also useful for face recognition and tracking. For a class of human faces, a generic representation of the appearances of the class is learned off-line. From video of a particular person, an appearance model is incrementally learned on-line using the prior generic model and successive frames from the video. The online learning results are shown to be effective for face tracking, and its use in video-based face recognition compares favorably to the representation constructed with a batch technique. While the techniques have been applied to human faces, the presented approximations to the appearance manifold are general and can be applied to other classes of objects.
When Does Rank(A+B)=Rank(A)+Rank(B)?

In a recent note in the Bulletin, Murphy [5] gave a short proof that for complex m×n matrices A and B, r(A+B)=r(A)+r(B) if the rows of A are orthogonal to the rows of B and the columns of A are orthogonal to the columns of B. His proof was elegant and simple, an improvement on an earlier proof of the same result by Meyer [4].
Re‐evaluating the role of the Mahalanobis distance measure

It is shown that the sum of squares of the standardised scores of all non‐zero principal components (PCs) equals the squared Mahalanobis distance. A new distance measure, the reduced Mahalanobis distance, is explored in which the number of PCs retained is less than the full rank model. It is illustrated by both one‐class and two‐class classifiers. Linear discriminant analysis can be employed as a soft model, and principal component analysis using the pooled variance‐covariance matrix is introduced as an intermediate view between conjoint and disjoint models allowing linear discriminant analysis to be used on these reduced rank models. By choosing the most discriminatory PCs, it can be shown that the reduced Mahalanobis distance has superior performance over the full rank model for discriminating via soft models. Copyright © 2016 John Wiley & Sons, Ltd.
Measurement of the Heat Defect in Water and A-150 Plastic for High-Energy Protons, Deuterons, and alpha-Particles

An experimental set-up is described which allows the calorimetric determination of the heat defect of solids and fluids relative to that of gilded copper which is used as the reference material. The calorimeter operates in a quasi-adiabatic mode and is suitable for protons, deuterons, or particles and other heavy ions with energies above 5 MeV. Corrections have been made for secondary electron emission and also for heat losses due to the temperature gradient on the surface of the calorimeter core. Measurements over 26 months indicate a repeatability of the measured heat defect within 4 x 10 -3 (68% confidence level). Measurements show an LET dependence of the heat defect for two different samples of A-150 plastic, and for distilled water.
The effects of charismatic leadership and transformational leadership on job performance among health workers during the Covid-19 pandemic with the commitment as the mediator

This research aimed to understand the impacts of charismatic leadership on job performance moderated by a commitment among the health workers during the pandemic period. There were 367 health workers of private and state hospitals from several cities in Indonesia. This research used the convenience sampling data collection technique and was analyzed using three methods, namely the four-step model from Baron & Kenny, package MEDIAN, and package PROCESS-R. The result shows that (1) there is a positive significant influence between charismatic leadership and job performance, (2) there is a positive but not significant influence between job performance and commitment, (3) there is a positive significant influence between charismatic leadership and commitment, and (4) there is a positive significant influence between charismatics leadership and job performance mediated by a commitment among health workers during the Covid-19 pandemic. © 2023 Author(s).
A Genomic and Transcriptomic Analysis of the C-Type Lectin Gene Family Reveals Highly Expanded and Diversified Repertoires in Bivalves

C-type lectins belong to a widely conserved family of lectins characterized in Metazoa. They show important functional diversity and immune implications, mainly as pathogen recognition receptors. In this work, C-type lectin-like proteins (CTLs) of a set of metazoan species were analyzed, revealing an important expansion in bivalve mollusks, which contrasted with the reduced repertoires of other mollusks, such as cephalopods. Orthology relationships demonstrated that these expanded repertoires consisted of CTL subfamilies conserved within Mollusca or Bivalvia and of lineage-specific subfamilies with orthology only between closely related species. Transcriptomic analyses revealed the importance of the bivalve subfamilies in mucosal immunity, as they were mainly expressed in the digestive gland and gills and modulated with specific stimuli. CTL domain-containing proteins that had additional domains (CTLDcps) were also studied, revealing interesting gene families with different conservation degrees of the CTL domain across orthologs from different taxa. Unique bivalve CTLDcps with specific domain architectures were revealed, corresponding to uncharacterized bivalve proteins with putative immune function according to their transcriptomic modulation, which could constitute interesting targets for functional characterization.