Miracle-dz/UniFAR-FTUs huggingface dataset:
Fecal microbiome in dogs with lymphoid and nonlymphoid tumors

Abstract Background The association of gut microbiota with cancer etiology and prognosis has been demonstrated in humans and rodents but has not been studied in dogs with different types of tumors. Hypothesis/Objectives To analyze microbiome composition according to tumor progression based on metastasis, recurrence, and therapeutic response in canine tumors. Animals Thirty‐two client‐owned dogs were divided into 3 groups: healthy (n = 9), with lymphoma (n = 12), with nonlymphoid tumors (n = 11). Methods Retrospective case series included animals were divided into subgroups according to the nature and severity of their tumors. Feces were screened for the 16S rRNA gene. Results Overall, alpha diversity was significantly reduced in dogs with tumors (n = 23; 12 lymphoid and 11 nonlymphoid) compared to healthy dogs (n = 9). Bacteroides had lower abundance in canine tumors at genus level. Staphylococcus showed significantly reduced abundance in dogs with aggressive tumor progression. Higher white blood cell (WBC) and neutrophil counts and lower hematocrit were significant in dogs with aggressive tumor. Spearman's rank correlation coefficient analysis revealed several measurements that showed moderate to strong correlations, including Coprococcus with total WBC count, neutrophil count, and hematocrit in the aggressive tumor group, and Saccharimonas with serum albumin and sodium concentration in all tumor dogs. Conclusion and Clinical Importance The diversity of the gut microbiome was significantly reduced in dogs with tumors compared to healthy dogs. Correlations were found between changes in blood measurements and changes in microbiome composition in relation to paraneoplastic syndrome.
North Atlantic forcing of climate and its uncertainty from a multi‐model experiment

To understand recent climate change in the North Atlantic region and to produce better climate forecasts with uncertainty estimates it is important to determine the atmospheric ‘response’ to Atlantic sea‐surface temperature (SST) forcing. There have been conflicting results regarding the strength, character and tropical‐versus‐extratropical origin of this response. For model‐based studies, this may indicate differing sensitivities to Atlantic SST, but the comparison is complicated by changes in experimental design. Here, a highly controlled experiment with five atmospheric models is undertaken. The influence of realistic (if reasonably strong) and optimally chosen North Atlantic (equator to 70°N) SST anomalies is isolated. Unexpected global agreement between the models is found (e.g. the North Atlantic Oscillation (NAO), Eurasian temperatures, rainfall over the Americas and Africa, and the Asian monsoon). The extratropical North Atlantic region response appears to be associated with remote Caribbean and tropical Atlantic SST anomalies, and with local forcing. Some features such as the European winter‐temperature response would be stronger than atmospheric ‘noise’ if the prescribed SST anomalies persisted for just two years. More generally, Atlantic air–sea interaction appears to be important for climate variability on the 30‐year timescale and, thus, to be important in the climate‐change context. The multi‐model mean response patterns are in reasonable agreement with observational estimates, although the model response magnitudes may be too weak. The similarity between their responses helps to reconcile models. Inter‐model differences do still exist and these are discussed and quantified. © Crown copyright, 2004.
Microglia phagocytosis mediates the volume and function of the rat sexually dimorphic nucleus of the preoptic area

Significance Establishing the origins of brain sex differences is an essential goal for understanding phenotypic variance in behavior. The sexually dimorphic nucleus (SDN) is the oldest known and most robust anatomical sex difference in the mammalian brain. Its origins have long been assumed to be due to differential cell autonomous apoptosis, and the larger SDN correlated with preference for females as sexual partners, but these premises have been unproven. Data generated in rats establish that the mechanism establishing a sex difference in SDN neuron number involves an essential role for microglia as ultimate purveyors of cell death which may begin with the neurons. This early process, not the ultimate size of the SDN, is an arbiter of sexual partner preference in adulthood.
Electrical resistivity of bulk nanocrystalline nickel

The effect of grain boundaries on the electrical resistivity of bulk nickel with a nanocrystalline structure has been investigated. For this study, nanocrystalline nickel was produced using a pulse plating technique that yields equiaxed nanostructures with negligible porosity. Using the four probe technique, resistivity measurements were performed at various temperatures ranging from 77 K to room temperature. The values of the resistivity were found to increase with decreasing grain size. This is mainly due to the increased volume fraction of interfaces at smaller grain sizes and the associated electron scattering events at the grain boundaries. The temperature coefficient of resistivity was found to decrease with decreasing grain size.
Letter to the Editor: Influence of Tensioner’s Mobilization on the Centralization of Symptoms in Cervicobrachial Pain Syndrome: A Randomized Controlled Trial

Copyright C 2023 by Korean Society of Spine Surgery This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Asian Spine Journal • pISSN 1976-1902 eISSN 1976-7846 • www.asianspinejournal. org Received Dec 19, 2022; Revised Jan 9, 2023; Accepted Jan 9, 2023 Corresponding author: Sunita Sharma Department of Musculoskeletal Physiotherapy, Maharishi Markandeshwar Institute of Physiotherapy and Rehabilitation, Maharishi Markandeshwar (Deemed to be University), Mullana-133207, Haryana, India Tel: +91-9467633734, E-mail: drsunita.sharma@mmumullana.org
On the shock-induced variability of emission lines in M-type Mira variables - I. Observational data

We present time-resolved observations of metallic emission lines of Mg I, Mn I, Si I, Fe I and Fe II, including forbidden emission lines of [Fe II], for the six M-type Mira variables RR Sco, R Aql, R Car, R Leo, S Scl and R Hya, which range in period from 281 to 389 days. Data is also presented for the Balmer emission lines H γ , H δ , H ζ and H η . The observations were carried out in the optical wavelength region 3600-5700 A. Narrow-slit observations with dispersion of 1.53 km s -1 pixel -1 and a 3-pixel resolution of 4.6 km s -1 were made in some cases while most observations were done with a wide slit corresponding to a resolution of 15.3 km s -1 . The variation of line shape, flux and velocity with phase is discussed. The data presented in this paper will be used in subsequent papers for comparison with detailed models of the emission from shock waves in the upper atmospheric layers of Mira variables. It is in these important upper layers that dust formation occurs and mass loss is initiated.
Clinical correlates and prognostic implications of severe suicidal ideation in major depressive disorder

Suicidal ideation (SI) is a risk factor for suicidal behaviour. To ascertain the clinical correlates and prognostic impact of severe SI, we analysed 249 outpatients with major depressive disorder (MDD) and suicidal thoughts included in the COmbining Medications to Enhance Depression outcome (CO-MED) trial. Patients with severe SI (36%) were younger at disease onset (P = 0.0033), more severely depressed (P = 0.0029), had more lifetime suicidal behaviour (P < 0.0001) and psychiatric comorbidities (panic disorder: P = 0.0025; post-traumatic stress disorder: P = 0.0216), and a history of childhood maltreatment (neglect: P = 0.0054; emotional abuse: P = 0.0230; physical abuse: P = 0.0076; sexual abuse: P = 0.0016) than those experiencing low-moderate SI. After controlling for depression score, severe SI was positively correlated with lifetime suicidal behaviour (OR [95% CI]: 1.26 [1.12–1.41]), panic disorder (1.05 [1.00–1.12]), and childhood maltreatment (neglect: 1.93 [1.13–3.30]; physical abuse: 2.00 [1.11–3.69]; sexual abuse: 2.13 [1.17–3.88]), and inversely correlated with age of onset (0.97 [0.95–0.99]) and sleep-onset insomnia (0.76 [0.61–0.96]). Finally, the occurrence of serious lifetime suicidal behaviour was predicted by SI severity (2.18 [1.11-4.27]), bipolar score (1.36 [1.02–1.81]), and childhood sexual abuse (2.35 [1.09–5.05]). These results emphasise the importance of assessing childhood maltreatment and bipolar liability in MDD to estimate suicidal behaviour risk.
Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?

Decentralized learning has been advocated and widely deployed to make efficient use of distributed datasets, with an extensive focus on supervised learning (SL) problems. Unfortunately, the majority of real-world data are unlabeled and can be highly heterogeneous across sources. In this work, we carefully study decentralized learning with unlabeled data through the lens of self-supervised learning (SSL), specifically contrastive visual representation learning. We study the effectiveness of a range of contrastive learning algorithms under decentralized learning settings, on relatively large-scale datasets including ImageNet-100, MS-COCO, and a new real-world robotic warehouse dataset. Our experiments show that the decentralized SSL (Dec-SSL) approach is robust to the heterogeneity of decentralized datasets, and learns useful representation for object classification, detection, and segmentation tasks. This robustness makes it possible to significantly reduce communication and reduce the participation ratio of data sources with only minimal drops in performance. Interestingly, using the same amount of data, the representation learned by Dec-SSL can not only perform on par with that learned by centralized SSL which requires communication and excessive data storage costs, but also sometimes outperform representations extracted from decentralized SL which requires extra knowledge about the data labels. Finally, we provide theoretical insights into understanding why data heterogeneity is less of a concern for Dec-SSL objectives, and introduce feature alignment and clustering techniques to develop a new Dec-SSL algorithm that further improves the performance, in the face of highly non-IID data. Our study presents positive evidence to embrace unlabeled data in decentralized learning, and we hope to provide new insights into whether and why decentralized SSL is effective.
Prediction of Rain Attenuation and Impact of Rain in Wave Propagation at Microwave Frequency for Tropical Region (Uttarakhand, India)

The most classical approach of determining rain attenuation for radio-wave frequency has been to theoretically determine the specific attenuation. At frequency over 10 GHz, rain and precipitation can influence the attenuation a lot; the effect of atmospheric attenuation between the source and destination over wireless communication is of major concern and a proper site visit and proper method are required to control the attenuation level so that the performance can be increased. In this paper exponential model has been used to determine the attenuation level for k-region (India) which can be used for region having similar condition. The analyzed predicted attenuation data have been compared with ITU-R measured rain attenuation, and the results will provide useful estimation of rainfall attenuation on microwave links in tropical regions that have similar conditions as (Almora) Uttarakhand region.
A problem on odd unitary groups: The depth zero case

We study a problem concerning parabolic induction in certain $p$-adic unitary groups. More precisely, for $E/F$ a quadratic extension of $p$-adic fields the associated unitary group $G=\mathrm{U}(n,n+1)$ contains a parabolic subgroup $P$ with Levi component $L$ isomorphic to $\mathrm{GL}_n(E) \times \mathrm{U}_1(E)$. Let $\pi$ be an irreducible supercuspidal representation of $L$ of depth zero. We use Hecke algebra methods to determine when the parabolically induced representation $\iota_P^G \pi$ is reducible.
On Direct Estimation of Density Parameters Alleviating Hubble Tension for $\Lambda$CDM Universe using Hubble Measurements

The set of cosmological density parameters ($\Omega_{0m}h_{0}^{2}$, $\Omega_{0k}h_{0}^{2}$, $\Omega_{0\Lambda}h_{0}^{2}$) and Hubble constant ($h_{0}$) are useful for fundamental understanding of the universe from many perspectives. In this article, we propose a new procedure to estimate these parameters for $\Lambda$ cold dark matter ($\Lambda$CDM) universe in the Friedmann-Robertson-Walker (FRW) background. We generalize the two-point statistic first proposed by Sahni et al. (2008) to the three point case and estimate the parameters using currently available Hubble parameter ($H(z)$) values in the redshift range $0.07 \leq z \leq 2.36$ measured by differential age (DA) and baryon acoustic oscillation (BAO) techniques. All the parameters are estimated assuming the general case of non-flat universe. Using both DA and BAO data we obtain $\Omega_{0m}h_{0}^{2}=0.1485 \pm 0.0065$, $\Omega_{0k}h_{0}^{2}=-0.0137 \pm 0.017$, $\Omega_{0\Lambda}h_{0}^{2}=0.3126 \pm 0.0145$ and $h_{0}=0.6689 \pm 0.0021$. These results are in satisfactory agreement with the Planck results. An important advantage of our method is that to estimate value of any one of the independent cosmological parameters one does not need to use values for rest of them. Each parameter is obtained solely from the measured values of Hubble parameters at different redshifts without any need to use values of other parameters. Such a method is expected to be less susceptible to the undesired effects of degeneracy issues between cosmological parameters during their estimations. Moreover, there is no requirement of assuming spatial flatness in our method.
Particle-based Online Bayesian Sampling

Online optimization has gained increasing interest due to its capability of tracking real-world streaming data. Although online optimization methods have been widely studied in the setting of frequentist statistics, few works have considered online optimization with the Bayesian sampling problem. In this paper, we study an Online Particle-based Variational Inference (OPVI) algorithm that uses a set of particles to represent the approximating distribution. To reduce the gradient error caused by the use of stochastic approximation, we include a sublinear increasing batch-size method to reduce the variance. To track the performance of the OPVI algorithm with respect to a sequence of dynamically changing target posterior, we provide a detailed theoretical analysis from the perspective of Wasserstein gradient flow with a dynamic regret. Synthetic and Bayesian Neural Network experiments show that the proposed algorithm achieves better results than naively applying existing Bayesian sampling methods in the online setting.
Spectroscopic studies of the superconducting gap in the 12442 family of iron-based compounds (Review article)

The iron-based compounds of the so-called 12442 family are very peculiar in various respects. They originate from the intergrowth of 122 and 1111 building blocks, display a large in-plane vs out-of-plane anisotropy, possess double layers of FeAs separated by insulating layers, and are generally very similar to double-layer cuprates. Moreover, they are stoichiometric superconductors because of an intrinsic hole doping. Establishing their superconducting properties, and in particular the symmetry of the order parameter, is thus particularly relevant in order to understand to what extent these compounds can be considered as the iron-based counterpart of cuprates. In this work, we review the results of various techniques from the current literature and compare them with ours, obtained in Rb–12442 by combining point-contact Andreev reflection spectroscopy and coplanar waveguide resonator measurements of the superfluid density. It turns out that the compound possesses at least two gaps, one of which is certainly nodal. The compatibility of this result with the theoretically allowed gap structures, as well as with the other results in literature, is discussed in detail.
Least Information Principle for Quantum Mechanics

A least information principle is proposed here to demonstrate the information foundation of quantum mechanics. The principle is constructed by (1) introducing a novel method to calculate the information metrics due to vacuum ﬂuctuations, and (2) converting the classical action variable to an information metric through the Planck constant. The second step implies that the Planck constant is a basic discrete action unit to measure observable information of the dynamics behavior of a physical system. The least information principle not only recovers the formulations of non-relativistic quantum mechanics including the uncertainty relation and the Schr¨odinger equation in both position and momentum representations, but also brings in new results on two fronts. At the conceptual level, we found that the information metrics for vacuum ﬂuctuations are responsible for manifesting entanglement eﬀects without underlying physical interactions, implying that entanglement eﬀects are non-causal. At the mathematical level, deﬁning the information metrics for vacuum ﬂuctuations using more general deﬁnitions of relative entropy results in a generalized Schr¨odinger equation that depends on the order of relative entropy. We expect that the principle can be a mathematical tool to obtain other advanced quantum formulations.
The Average Energy of Disintegration of Radium E

The problem of the velocity of the particles emitted from the nuclei of dis integrating radioactive atoms has always attracted considerable attention. It was early established that in the case of the α-rays all the particles from one substance were emitted with the same velocity, and the latest experiments of Briggs have emphasised the high degree of homogeneity attained. This result, showing that each disintegration involves exactly the same emission of energy, is easily reconcilable with our general ideas of the radioactive processes, and, as is well known, there is undoubtedly some connection between this characteristic energy and the mean life of the body. The behaviour of the β-ray bodies is in sharp contrast to this. In place of the α-particles all emitted with the same energy, we find that the disintegration electrons coming from the nucleus have energies distributed over a wide range. For example, in the case of radium E this continuous energy spectrum formed by the disintegration electrons has an upper limit at 1,050,000 volts, rises to a maximum at 300,000 volts, and continues certainly as low as 40,000 volts, and similar results have been obtained for other β-ray bodies. If this result is interpreted as showing that different disintegrating nuclei of the same substance emit their disintegration electron with different energies, we must deduce that in this case the energy of disintegration is not a characteristic constant of the body, but can vary between wide limits. Many workers have considered this to be so contrary both to the ideas of the quantum theory and the definiteness shown by radioactive disintegration that they have asserted the inhomogeneity must be a result of some secondary process, such as collision with the extranuclear electrons or emission of general γ-radiation, and that although we cannot observe them before they become inhomogeneous, the disintegration electrons are actually emitted from the nucleus with a definite characteristic energy as in the case of the α-particles.
Attention-Based Point Cloud Edge Sampling

Point cloud sampling is a less explored research topic for this data representation. The most commonly used sampling methods are still classical random sampling and farthest point sampling. With the development of neural networks, various methods have been proposed to sample point clouds in a task-based learning manner. However, these methods are mostly generative-based, rather than selecting points directly using mathematical statistics. Inspired by the Canny edge detection algorithm for images and with the help of the attention mechanism, this paper proposes a non-generative Attention-based Point cloud Edge Sampling method (APES), which captures salient points in the point cloud outline. Both qualitative and quantitative experimental results show the superior performance of our sampling method on common benchmark tasks.
New approximate formulations for EM scattering by dielectric objects

The extended Born approximation (ExBorn) has been shown an efficient formulation in the electromagnetic (EM) scattering by dielectric objects in both free-space and air-Earth half-space problems. In most cases, ExBorn is much more accurate than the conventional Born approximation at low frequencies. When the frequency is high or the contrast of dielectric objects is large, however, the ExBorn approximation becomes inaccurate. In this paper, new approximations are proposed for the EM scattering by dielectric objects buried in a lossy Earth, which are also suitable for the case of free space. It has been shown that the zeroth-order form of new approximations is completely equivalent to ExBorn. Hence, high-order approximations can be regarded as high-order ExBorn. Closed-form formulations are derived for the new approximations. Using the fast Fourier transform (FFT), these formulations can be implemented efficiently at a cost of CNlogN, where N is the number of unknowns and C is a small number. Numerical simulations show that high-order ExBorn approximations are much more accurate than the ExBorn approximation.
GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgetting

Federated Learning (FL) has emerged as a de facto machine learning area and received rapid increasing research interests from the community. However, catastrophic forgetting caused by data heterogeneity and partial participation poses distinctive challenges for FL, which are detrimental to the performance. To tackle the problems, we propose a new FL approach (namely GradMA), which takes inspiration from continual learning to simultaneously correct the server-side and worker-side update directions as well as take full advantage of server's rich computing and memory resources. Furthermore, we elaborate a memory reduction strategy to enable GradMA to accommodate FL with a large scale of workers. We then analyze convergence of GradMA theoretically under the smooth non-convex setting and show that its convergence rate achieves a linear speed up w.r. t the increasing number of sampled active workers. At last, our extensive experiments on various image classification tasks show that GradMA achieves significant performance gains in accuracy and communication efficiency compared to SOTA baselines. We provide our code here: https://github.com/lkyddd/GradMA.