Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating continuous maps of genetic diversity using moving windows

Genetic diversity plays a key role in maintaining population viability by preventing inbreeding depression and providing the building blocks for adaptation. Understanding how genetic diversity varies across space is, therefore, of key interest in conservation and population genetics. Here, we introduce wingen, an r package for calculating continuous maps of genetic diversity, including nucleotide diversity, allelic richness, and heterozygosity, from standard genotypic and spatial data using a spatial moving window approach. We provide functions to account for variation in sample size across space using rarefaction, to create kriging‐interpolated maps of genetic diversity, and to mask any areas that are outside the area of interest. Tests with simulated and empirical datasets demonstrate that wingen can successfully capture variation in genetic diversity across landscapes from both reduced‐representation and whole genome sequencing datasets. For reduced‐representation datasets, wingen's functions can be run easily on a standard laptop computer, and we provide options for parallelization to increase the efficiency of running larger whole genome datasets. wingen provides novel and computationally tractable tools for creating informative maps of genetic diversity with applications for conservation prioritization as well as population and landscape genetic analyses.
Diagnostic and Therapeutic Challenges in an Adolescent with Comorbid Oppositional Defiant Disorder and Bipolar Disorder

Oppositional defiant disorder (ODD) is often found to be comorbid with bipolar disorder (BD) in children and adolescents. Both disorders are characterized by chronic irritability, emotional dysregulation, and behavioral problems. These overlapping symptoms often lead to diagnostic difficulty. In addition, ODD-BD comorbidity also presents multiple therapeutic challenges. Here, we describe the case of an adolescent male, who presented with comorbid ODD and BD, along with multiple diagnostic and therapeutic challenges.
Variance Based Concentration and Moment Inequalities for Polynomials of Independent Random Variables: Multilinear Case

We show that the probability that a multilinear polynomial $f$ of independent random variables exceeds its mean by $\lambda$ is at most $e^{-\lambda^2 / (R^q Var(f))}$ for sufficiently small $\lambda$, where $R$ is an absolute constant. This matches (up to constants in the exponent) what one would expect from the central limit theorem. Our methods handle a variety of types of random variables including Gaussian, Boolean, exponential, and Poisson. Previous work by Kim-Vu and Schudy-Sviridenko gave bounds of the same form that involved less natural parameters in place of the variance.
Multiresolution Non-Local Mean Filter for Infrared Dim and Small Target Background Suppression

Structured clouds and ground building background suppression are difficult problems for dim and small target detection technique. In this paper, the dim and small target background suppression method based on combined curvelet transform with modified non-local means filter was presented to solve the problem. And local gradient statistics is introduced to boost ability of method which suppresses false by background structure. The innovation was that the curvelet transform was adopted to decompose the input infrared image, which extracts multi-scale and directional detail features of the image. Moreover non-local means filter improved by local gradient characteristic was introduced to suppress background details and enhance target information for suppression background. Compared with two-dimensional least mean square (TDLMS) and modified partial differential equation (MPDE) methods, through visual quality and value index, several groups of experimental results demonstrate that the presented method can suppress complicated background in dim and small target image effectively.
Komparasi karakteristik anatomi skelet ekstremitas kaki depan komodo (varanus komodoensis) dengan biawak air (Varanus salvator)

Komodo dan biawak air merupakan hewan yang memiliki kekerabatan yang dekat namun memiliki peran ekologi yang berbeda. Penelitian ini bertujuan untuk mengamati komparasi anatomi ekstremitas kaki depan komodo (Varanus komodoensis) dan biawak air (Varanus salvator) serta hubungan dengan perilaku alamiahnya. Hasil penelitian menunjukkan bahwa os scapula dan os coracoidea pada komodo menyatu menjadi os coracoscapula, sedangkan pada biawak air terpisah oleh syndesmosis. Pada komodo, terdapat crista deltiodea yang diduga berhubungan dengan perilaku menggali liang komodo yang lebih besar dibandingkan liang biawak air. Komodo memiliki dua buah crista pectoralis sedangkan biawak air hanya memiliki satu buah crista pectoralis. Perbedaan ini diduga berhubungan dengan sifat arboreal pada komodo muda yang tidak ditemukan pada biawak air. Komodo memiliki olecranon yang berkembang, sedangkan biawak air memiliki olecranon yang relatif kecil. Pada ossa carpi komodo, terdapat dua buah os pisiforme sedangkan biawak air hanya memiliki satu buah os pisiforme. Perbedaan ini diduga untuk meningkatkan fleksibilitas pergelangan kaki depan komodo saat berburu sedangkan biawak air tidak terlalu membutuhkan perilaku berlari untuk mengejar mangsanya.
A note on the effect of hub inertia and payload on the vibration of a flexible slewing link

The objective of this note is to retackle the issue of boundary conditions in the modelling of slewing beam systems, by taking account of those results given in some recent works. A rotating beam driven by a motor and carrying a payload is considered in this note. Analytical frequencies by virtue of the developed model are compared with those obtained experimentally
Hippocampal correlation coding

Hippocampal correlation coding is a putative neural mechanism underlying episodic memory. In this thesis, we look at two related phenomena: phase precession and reverse replay of behavioral sequences. Phase precession refers to the decrease of the firing phase of a place cell with respect to the local theta rhythm during the crossing of the place field. Reverse replay refers to reactivation of previously experienced place field sequences in reverse order during awake resting periods. First, we study properties of phase precession in single trials. Usually, phase precession is studied only on the basis of data in which many place field traversals are pooled together. We find that single-trial and pooledtrial phase precession are different with respect to phase-position correlation, phase-time correlation, and phase range. We demonstrate that phase precession exhibits a large trial-to-trial variability and that pooling trials changes basic measures of phase precession. These findings indicate that single trials may be better suited for encoding temporally structured events than is suggested by the pooled data. Second, we examine the coordination of phase precession among different subregions of the hippocampus. We find that the local theta rhythms in CA3 and CA1 are almost antiphasic. Still, phase precession in the two regions occurs with only a small phase shift, and CA3 cells tend to fire a few milliseconds before CA1 cells. These results suggest that phase precession in CA1 might be inherited from CA3. Finally, we present a model of reverse replay based on short-term facilitation. The model compresses temporal patterns from a behavioral time scale of seconds to shorter time scales relevant for synaptic plasticity. We demonstrate that the compressed patterns can be learned by the tempotron learning rule. The model provides testable predictions (synchronous activation of dentate gyrus during sharp wave-ripples) and functional interpretations of hippocampal activity (temporal pattern learning).
Semiparametric hierarchical selection models for bayesian meta analysis

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. Hierarchical models including selection models are introduced and shown to be useful in such Bayesian meta-analysis. Semiparametric hierarchical models are proposed using the Dirichlet process prior. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierarchical selection model with including unknown weight function and use Markov chain Monte Carlo methods to develop inference for the parameters of interest. Using Bayesian method, this model is used on a meta-analysis of twelve studies comparing the effectiveness of two different types of flouride, in preventing cavities. Clinical informative prior is assumed. Summaries and plots of model parameters are analyzed to address questions of interest.
The trend of normal vaginal delivery and cesarean sections before and after implementing the health system transformation plan based on ICD‐10 in the northeast of Iran: A cross‐sectional study

Concerning the growing rate of cesarean sections (CSs) worldwide, encouraging normal vaginal deliveries (NVDs) and mitigating CS rates is a necessity. This study investigated the status of delivery in hospitals affiliated with the Mashhad University of Medical Sciences (MUMS) before and after implementing health system transformation plan (HSTP).
Anti‐synthetase syndrome associated interstitial lung disease after combination dual immune checkpoint inhibition

Immune checkpoint inhibitors (ICIs) unleash potent anti‐tumour responses but with frequent off‐target immune‐mediated adverse events (irAE). ICIs can induce a spectrum of rheumatologic manifestations including inflammatory arthritis, Sjögren's syndrome, scleroderma and systemic lupus erythematosus. Here, we describe a case of antisynthetase syndrome associated interstitial lung disease (ILD) following dual Programmed Cell Death 1 and Cytotoxic T Lymphocyte‐Associated Protein 4 checkpoint inhibition in a patient with metastatic melanoma. Initial treatment course was complicated by a number of irAEs including pneumonitis, colitis and thyroiditis. Suspicion of an underlying systemic rheumatic disease was heightened by the severe, relapsing and fibrosing nature of the interstitial pneumonitis. A diagnosis of amyopathic antisynthetase syndrome was made upon detection of circulating aminoacyl‐tRNA synthetase (anti‐EJ) autoantibodies. Intensification of induction immunosuppression followed by maintenance mycophenolate, prednisone and monthly intravenous immunoglobulin achieved long‐term disease control. Detection of de novo ICI‐induced inflammatory myositis ILD requires a high index of suspicion and carries important prognostic and treatment implications.
Chromothripsis is correlated with reduced cytotoxic immune infiltration and diminished responsiveness to checkpoint blockade immunotherapy

Background: Chromothripsis caused massive, clustered genomic rearrangements is prevalent in cancer and is considered a new paradigm for tumorigenesis and progression. In this study, we investigated the association among chromothripsis, anti-tumor immune responses, and responsiveness to immune checkpoint blockade (ICB). Methods: Quantification of immune cell infiltration and functional enrichment of immune-related signaling pathways were performed in the discovery set (n = 9403) and the validation set (n = 1140). we investigated the association between chromothripsis and anti-tumor immune responses. In the immunotherapy cohort, copy number alteration-based chromothripsis scores (CPSs) were introduced to assess the extent of chromothripsis to evaluate its association with responsiveness to ICB. Results: In the discovery set and the validation set, the ratios of CD8+ T cells to Tregs, TAMs, and MDSCs were significantly lower in tumors with chromothripsis (P = 1.5 × 10-13, P = 5.4 × 10-8, and P = 1.2 × 10-4, respectively, TCGA; P = 1.0 × 10-13, P = 3.6 × 10-15, and P = 3.3 × 10-3, respectively, PCAWG). The relevant pathways underlying the antitumor immune effect were significantly enriched in tumors without chromothripsis. Chromothripsis can be used as an independent predictor, and patients with low-CPSs experienced longer overall survival (OS) after immunotherapy [HR, 1.90; 95% confidence interval, 1.10-3.28; P = 0.019]. Conclusions: Our findings highlight the reduced cytotoxic immune infiltration in tumors with chromothripsis and enhanced immunosuppression in the tumor microenvironment. Chromothripsis can thus be used as a potential indicator to help identify patients who will respond to ICB, which could complement established biomarkers.
Impact of dispersion media and carrier type on spray-dried proliposome powder formulations loaded with beclomethasone dipropionate for their pulmonary drug delivery via a next generation impactor

Drug delivery via aerosolization for localized and systemic effect is a non-invasive approach to achieving pulmonary targeting. The aim of this study was to prepare spray-dried proliposome (SDP) powder formulations to produce carrier particles for superior aerosolization performance, assessed via a next generation impactor (NGI) in combination with a dry powder inhaler. SDP powder formulations (F1-F10) were prepared using a spray dryer, employing five different types of lactose carriers (Lactose monohydrate (LMH), lactose microfine (LMF), lactose 003, lactose 220 and lactose 300) and two different dispersion media. The first dispersion medium was comprised of water and ethanol (50:50% v/v ratio), and the second dispersion medium comprised wholly of ethanol (100%). In the first dispersion medium, the lipid phase (consisting of Soya phosphatidylcholine (SPC as phospholipid) and Beclomethasone dipropionate (BDP; model drug) were dissolved in ethanol and the lactose carrier in water, followed by spray drying. Whereas in second dispersion medium, the lipid phase and lactose carrier were dispersed in ethanol only, post spray drying. SDP powder formulations (F1-F5) possessed significantly smaller particles (2.89 ± 1.24–4.48 ± 1.20 μm), when compared to SDP F6-F10 formulations (10.63 ± 3.71–19.27 ± 4.98 μm), irrespective of lactose carrier type via SEM (scanning electron microscopy). Crystallinity of the F6-F10 and amorphicity of F1-F15 formulations were confirmed by XRD (X-ray diffraction). Differences in size and crystallinity were further reflected in production yield, where significantly higher production yield was obtained for F1-F5 (74.87 ± 4.28–87.32 ± 2.42%) then F6-F10 formulations (40.08 ± 5.714–54.98 ± 5.82%), irrespective of carrier type. Negligible differences were noted in terms of entrapment efficiency, when comparing F1-F5 SDP formulations (94.67 ± 8.41–96.35 ± 7.93) to F6-F10 formulations (78.16 ± 9.35–82.95 ± 9.62). Moreover, formulations F1-F5 demonstrated significantly higher fine particle fraction (FPF), fine particle dose (FPD) and respirable fraction (RF) (on average of 30.35%, 890.12 μg and 85.90%) when compared to counterpart SDP powder formulations (F6-F10). This study has demonstrated that when a combination of water and ethanol was employed as dispersion medium (formulations F1-F5), superior formulation properties for pulmonary drug delivery were observed, irrespective of carrier type employed.
Transfer Learning for Real-time Deployment of a Screening Tool for Depression Detection Using Actigraphy

Automated depression screening and diagnosis is a highly relevant problem today. There are a number of limitations of the traditional depression detection methods, namely, high dependence on clinicians and biased self-reporting. In recent years, research has suggested strong potential in machine learning (ML) based methods that make use of the user's passive data collected via wearable devices. However, ML is data hungry. Especially in the healthcare domain primary data collection is challenging. In this work, we present an approach based on transfer learning, from a model trained on a secondary dataset, for the real time deployment of the depression screening tool based on the actigraphy data of users. This approach enables machine learning modelling even with limited primary data samples. A modified version of leave one out cross validation approach performed on the primary set resulted in mean accuracy of 0.96, where in each iteration one subject's data from the primary set was set aside for testing.
RE-MOVE: An Adaptive Policy Design for Robotic Navigation Tasks in Dynamic Environments via Language-Based Feedback

Reinforcement learning-based policies for continuous control robotic navigation tasks often fail to adapt to changes in the environment during real-time deployment, which may result in catastrophic failures. To address this limitation, we propose a novel approach called RE-MOVE (REquest help and MOVE on) to adapt already trained policy to real-time changes in the environment without re-training via utilizing a language-based feedback. The proposed approach essentially boils down to addressing two main challenges of (1) when to ask for feedback and, if received, (2) how to incorporate feedback into trained policies. RE-MOVE incorporates an epistemic uncertainty-based framework to determine the optimal time to request instructions-based feedback. For the second challenge, we employ a zero-shot learning natural language processing (NLP) paradigm with efficient, prompt design and leverage state-of-the-art GPT-3.5, Llama-2 language models. To show the efficacy of the proposed approach, we performed extensive synthetic and real-world evaluations in several test-time dynamic navigation scenarios. Utilizing RE-MOVE result in up to 80% enhancement in the attainment of successful goals, coupled with a reduction of 13.50% in the normalized trajectory length, as compared to alternative approaches, particularly in demanding real-world environments with perceptual challenges.
A Secondary Flow Model for the Planetary Boundary Layer

Abstract The equations of motion for a neutrally buoyant fluid are solved to produce an equilibrium flow consisting of a modified Ekman spiral mean flow plus a helical secondary flow. By considering the secondary flow to be a finite perturbation on a mean large-scale flow, approximate equations are obtained for the secondary flow and the modified mean flow an functions of the large-scale parameters. When the energetics of this system are considered, an equilibrium magnitude for the secondary flow can be obtained. The finite perturbations are assumed to preserve the structure of the infinitesimal perturbation solutions for the dynamic instability of the Ekman boundary layer. In particular, helical rolls occur as finite perturbation solutions. These finite disturbances are found to alter the mean Ekman velocity profile such that it becomes stable. The rolls, with characteristic depths of 5–7 times the Ekman characteristic length and corresponding wavelengths of 4π times this parameter, may be frequent occur.
PATS: Patch Area Transportation with Subdivision for Local Feature Matching

Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this paper, we propose Patch Area Transportation with Subdivision (PATS) to tackle this issue. Instead of building an expensive image pyramid, we start by splitting the original image pair into equal-sized patches and gradually resizing and subdividing them into smaller patches with the same scale. However, estimating scale differences between these patches is non-trivial since the scale differences are determined by both relative camera poses and scene structures, and thus spatially varying over image pairs. Moreover, it is hard to obtain the ground truth for real scenes. To this end, we propose patch area transportation, which enables learning scale differences in a self-supervised manner. In contrast to bipartite graph matching, which only handles one-to-one matching, our patch area transportation can deal with many-to-many relationships. PATS improves both matching accuracy and coverage, and shows superior performance in downstream tasks, such as relative pose estimation, visual localization, and optical flow estimation. The source code is available at https://zju3dv.github. io/pats/.
High-Dimensional Dynamic Pricing under Non-Stationarity: Learning and Earning with Change-Point Detection

We consider a high-dimensional dynamic pricing problem under non-stationarity, where a firm sells products to $T$ sequentially arriving consumers that behave according to an unknown demand model with potential changes at unknown times. The demand model is assumed to be a high-dimensional generalized linear model (GLM), allowing for a feature vector in $\mathbb R^d$ that encodes products and consumer information. To achieve optimal revenue (i.e., least regret), the firm needs to learn and exploit the unknown GLMs while monitoring for potential change-points. To tackle such a problem, we first design a novel penalized likelihood-based online change-point detection algorithm for high-dimensional GLMs, which is the first algorithm in the change-point literature that achieves optimal minimax localization error rate for high-dimensional GLMs. A change-point detection assisted dynamic pricing (CPDP) policy is further proposed and achieves a near-optimal regret of order $O(s\sqrt{\Upsilon_T T}\log(Td))$, where $s$ is the sparsity level and $\Upsilon_T$ is the number of change-points. This regret is accompanied with a minimax lower bound, demonstrating the optimality of CPDP (up to logarithmic factors). In particular, the optimality with respect to $\Upsilon_T$ is seen for the first time in the dynamic pricing literature, and is achieved via a novel accelerated exploration mechanism. Extensive simulation experiments and a real data application on online lending illustrate the efficiency of the proposed policy and the importance and practical value of handling non-stationarity in dynamic pricing.
Exotic energy injection in the early Universe. II. CMB spectral distortions and constraints on light dark matter

We calculate the post-recombination contribution to the Cosmic Microwave Background (CMB) spectral distortion due to general exotic energy injections, including dark matter (DM) decaying or annihilating to Standard Model particles. Upon subtracting the background distortion that would be present even without such energy injections, we find residual distortions that are still potentially large enough to be detectable by future experiments such as PIXIE. The distortions also have a high-energy spectral feature that is a unique signature of the injection of high-energy particles. We present a calculation of the global ionization history in the presence of decaying dark matter with sub-keV masses, and also show that previous calculations of the global ionization history in the presence of energy injection are not significantly modified by these additional spectral distortions. Our improved treatment of low-energy electrons allows us to extend calculations of the CMB anisotropy constraints for decaying DM down to arbitrarily low masses. We also recast these bounds as constraints on the coupling of axion-like particles to photons.
Photoelectric UBVRI sequences in the Magellanic Cloud clusters Lindsay 1, NGC 339, NGC 361, and NGC 1466

UBVRI sequences in three Small Magellanic Cloud (SMC) clusters Lindsay 1, NGC 339, NGC 361, and in NGC 1466, which lies between the two Magellanic Clouds, are presented. These sequences are appropriate for charge-coupled device (CCD) coverage. Only BV standards have been published in NGC 339 and UBV in NGC 1466; no sequences exist for the two other clusters. 15 refs.