Miracle-dz/UniFAR-FTUs huggingface dataset:
The mitochondrial genome of the scarlet-backed flowerpecker (Dicaeum cruentatum Linnaeus) from southwestern China

Abstract Dicaeum (family Dicaeidae) is a genus of passerines distributed in tropical southern Asia and Australasia. The scarlet-backed flowerpecker (Dicaeum cruentatum Linnaeus) is a member of this genus found in China, India, and many Southeast Asian countries. To contribute to phylogenetic studies of Dicaeum, the complete mitochondrial genome of D. cruentatum was sequenced using a next-generation sequencing platform. The mitogenome of D. cruentatum (GenBank accession number MW629122) was 16,812 bp in length with an overall base composition of 30.97% A, 23.67% T, 30.28% C, and 14.07% G. The whole genome contained 13 protein-coding genes (PCGs), two ribosomal RNAs (rRNAs), and 22 transfer RNAs (tRNAs). Almost all PCGs had an ATN start codon except COX1 which started with GTG. Most of the PCGs terminated with a complete stop codon, but ND2, COX3, and ND4 had incomplete stop codons. The 16S rRNA was 1599 bp in length, while 12S rRNA was 986 bp. The Dicaeum species showed monophyletic clustering in the ML phylogeny and shows D. cruentatum as a sister taxa of D. eximium and D. concolor. This new mitogenome will provide genomic resources for future phylogenetic studies in passerines.
Effects of a 12-week multimodal exercise intervention among older patients with advanced cancer - results from a randomized controlled trial.

BACKGROUND Older patients with cancer are at risk of physical decline and impaired quality of life during oncological treatment. Exercise training has the potential to reduce these challenges. The study aim was to investigate the feasibility and effect of a multimodal exercise intervention in older patients with advanced cancer (stages III/IV). PATIENTS AND METHODS 84 older adults (≥65 years) with advanced pancreatic, biliary tract or non-small cell lung cancer who received systemic oncological treatment were randomized 1:1 to an intervention group or a control group. The intervention was a 12-week multimodal exercise-based program including supervised exercise twice weekly followed by a protein supplement, a homed-based walking program, and nurse-led support and counseling. The primary endpoint was change in physical function (30-second chair stand test) at 13 weeks. RESULTS Median age of the participants was 72 years (interquartile range (IQR) 68-75). Median adherence to the exercise sessions was 69% (IQR 21-88) and 75% (IQR 33-100) for the walking program. At 13 weeks, there was a significant difference in change scores of 2.4 repetitions in the chair stand test, favoring the intervention group (P < 0.0001). Furthermore, significant beneficial effects were seen for physical endurance (6-minute walk test), hand grip strength, physical activity, symptom burden, symptoms of depression and anxiety, global health status (quality of life), and lean body mass. No effects were seen for dose intensity, hospitalizations, or survival. CONCLUSION A 12-week multimodal exercise intervention with targeted support proved effective in improving physical function in older patients with advanced cancer during oncological treatment. IMPLICATIONS FOR PRACTICE Exercise is generally recommended during anti-cancer treatment to maintain physical function and to increase psychological wellbeing. While current evidence is mainly based on younger and middle-aged patients, this study showed that a multimodal exercise and support intervention increased physical function in older patients (≥65 years) with advanced cancer during oncological treatment. Hence, these new findings strengthen the basis for existing recommendations and highlight the importance of focusing on and supporting older patients with cancer in a physically active lifestyle and engagement in exercise training.
The Association Between Sagittal Plane Alignment and Disc Space Narrowing of Lumbar Spine in Farmers

Objective To investigate whether lumbar lordosis (LL) and lumbar segmental lordosis (LSL) are related to sex, age, low back pain (LBP), and lumbar disc space narrowing (DSN). Methods A total of 569 farmers were recruited. In lateral spine radiograph, LL (L1–L5) and LSL (L1, L2, L3, L4, and L5) were measured using Cobb’s method. The differences in LSL values (ΔLSL) according to the presence or absence of a DSN were calculated as LSLDSN – LSLnoDSN for each DSN level. Results In male, the lateral spine radiograph showed significantly greater L4-LSL and L5-LSL and smaller L1-LSL and L2-LSL compared to female. LLs in the 50–59 and ≥60 years age groups were significantly smaller compared to those in the <50 years age group. In subjects with LBP, LL and L4-LSL were significantly smaller than in those without. The ΔLSLs at the disc level with DSN showed the greatest decrease: L1-ΔLSL (Δ-3.99°), L2-ΔLSL (Δ-3.31°), L3-ΔLSL (Δ-2.87°), L4-ΔLSL (Δ-3.31°), and L5-ΔLSL (Δ-4.44°) in L1/2, L2/3, L3/4, L4/5, and L5/S1 DSN, respectively. Conversely, distant ΔLSLs were inversely increased: L1-LSL (Δ0.75°) with L4/5 DSN and L2-LSL (Δ0.94°) with L5/S1 DSN. Conclusion Sagittal plane alignment was significantly associated with sex, age, LBP, and DSN. LSLs around the levels of DSN were decreased, and there was compensational increase of LSL distant to the DSN to maintain the overall LL.
Identification of HIF-1α/VEGFA signaling pathway and transcription factors in Kashin-Beck disease by integrated bioinformatics analysis

Kashin-Beck disease (KBD) is a chronic and endemic osteoarthropathy. The pathogenesis of KBD has yet to be fully elucidated, although previous studies have shown that its etiology may be associated with low selenium abundance and high exposure to mycotoxins, such as T-2 toxin. In the present study, the Comparative Toxicogenomics Database was used to identify key genes associated with KBD, T-2 toxin and selenium. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to identify the biological processes and pathways that key genes may be associated with. By searching the Search Tool for the Retrieval of Interacting Genes database and the Molecular Complex Detection plug-in with Cytoscape, it was possible to construct a KBD-associated protein-protein interaction (PPI) network, and screen the core modules and genes. Western blot analysis was subsequently used to verify the expression levels of hypoxia-inducible factor-1α (HIF-1α) and vascular endothelial growth factor A (VEGFA), two components that are associated with the HIF-1 signaling pathway in KBD disease. Via this approach, a total of 301 key genes were identified that were associated with KBD, T-2 toxin and selenium. The results of the GO and KEGG enrichment analyses demonstrated that these key genes were mainly involved in the process of apoptosis. Previous studies have demonstrated that excessive apoptosis of chondrocytes plays a crucial role in the pathophysiology of KBD, and that HIF-1α has an important role in chondrocyte apoptosis; therefore, the present study was focused on the expression level of HIF-1α in KBD. By analyzing the PPI network constructed from the key genes, a total of 10 core genes were obtained that may be associated with KBD. The results of western blotting experiments revealed that, after treating chondrocytes with different concentrations of T-2 toxin, the expression levels of HIF-1α and VEGFA were markedly downregulated. The iRegulon plug-in for Cytoscape was used to predict the transcription factors that may regulate HIF-1α and VEGFA in the HIF-1 signaling pathway. Using this approach, 10 core genes and 15 transcription factors were obtained. These results may help to clarify the pathogenesis of KBD, thereby providing further avenues for the therapeutic treatment of KBD.
Smoothed Contrastive Learning for Unsupervised Sentence Embedding

Unsupervised contrastive sentence embedding models, e.g., unsupervised SimCSE, use the InfoNCE loss function in training. Theoretically, we expect to use larger batches to get more adequate comparisons among samples and avoid overfitting. However, increasing batch size leads to performance degradation when it exceeds a threshold, which is probably due to the introduction of false-negative pairs through statistical observation. To alleviate this problem, we introduce a simple smoothing strategy upon the InfoNCE loss function, termed Gaussian Smoothed InfoNCE (GS-InfoNCE). In other words, we add random Gaussian noise as an extension to the negative pairs without increasing the batch size. Through experiments on the semantic text similarity tasks, though simple, the proposed smoothing strategy brings improvements to unsupervised SimCSE.
Optimizing timetable and network reopen plans for public transportation networks during a COVID19-like pandemic

The recovery of the public transportation system is critical for both social re-engagement and economic rebooting after the shutdown during pandemic like COVID-19. In this study, we focus on the integrated optimization of service line reopening plan and timetable design. We model the transit system as a space-time network. In this network, the number of passengers on each vehicle at the same time can be represented by arc flow. We then apply a simplified spatial compartmental model of epidemic (SCME) to each vehicle and platform to model the spread of pandemic in the system as our objective, and calculate the optimal open plan and timetable. We demonstrate that this optimization problem can be decomposed into a simple integer programming and a linear multi-commodity network flow problem using Lagrangian relaxation techniques. Finally, we test the proposed model using real-world data from the Bay Area Rapid Transit (BART) and give some useful suggestions to system managers.
Learning the Physics of Particle Transport via Transformers

Particle physics simulations are the cornerstone of nuclear engineering applications. Among them radiotherapy (RT) is crucial for society, with 50% of cancer patients receiving radiation treatments. For the most precise targeting of tumors, next generation RT treatments aim for real-time correction during radiation delivery, necessitating particle transport algorithms that yield precise dose distributions in sub-second times even in highly heterogeneous patient geometries. This is infeasible with currently available, purely physics based simulations. In this study, we present a data-driven dose calculation algorithm predicting the dose deposited by mono-energetic proton beams for arbitrary energies and patient geometries. Our approach frames particle transport as sequence modeling, where convolutional layers extract important spatial features into tokens and the transformer self-attention mechanism routes information between such tokens in the sequence and a beam energy token. We train our network and evaluate prediction accuracy using computationally expensive but accurate Monte Carlo (MC) simulations, considered the gold standard in particle physics. Our proposed model is 33 times faster than current clinical analytic pencil beam algorithms, improving upon their accuracy in the most heterogeneous and challenging geometries. With a relative error of 0.34±0.2% and very high gamma pass rate of 99.59±0.7% (1%, 3 mm), it also greatly outperforms the only published similar data-driven proton dose algorithm, even at a finer grid resolution. Offering MC precision 4000 times faster, our model could overcome a major obstacle that has so far prohibited real-time adaptive proton treatments and significantly increase cancer treatment efficacy. Its potential to model physics interactions of other particles could also boost heavy ion treatment planning procedures limited by the speed of traditional methods.
Mini Cheetah, the Falling Cat: A Case Study in Machine Learning and Trajectory Optimization for Robot Acrobatics

Seemingly in defiance of basic physics, cats consistently land on their feet after falling. In this paper, we design a controller that lands the Mini Cheetah quadruped robot on its feet as well. Specifically, we explore how trajectory optimization and machine learning can work together to enable highly dynamic bioinspired behaviors. We find that a reflex approach, in which a neural network learns entire state trajectories, outperforms a policy approach, in which a neural network learns a mapping from states to control inputs. We validate our proposed controller in both simulation and hardware experiments, and are able to land the robot on its feet from falls with initial pitch angles between −90 and 90 degrees.
Structured Winkler Foundation

The Winkler foundation is a simple model widely used for decades to account for the surface response of elastic bodies. It models the response as purely local, linear, and perpendicular to the surface. We extend this model to the case where the foundation is made of a structured material such as a polymer network, which has characteristic scales of length and time. We use the two-fluid model of viscoelastic structured materials to treat a film of finite thickness, supported on a rigid solid and subjected to a concentrated normal force at its free surface. We obtain the Winkler constant (foundation modulus) as a function of the film’s thickness, intrinsic correlation length, and viscoelastic moduli, for three choices of boundary conditions. The results can be used to readily extend earlier applications of the Winkler model to more complex, microstructured substrates. They also provide a way to extract the intrinsic properties of such complex materials from simple surface measurements.
Cartography Active Learning

We propose Cartography Active Learning (CAL), a novel Active Learning (AL) algorithm that exploits the behavior of the model on individual instances during training as a proxy to find the most informative instances for labeling. CAL is inspired by data maps, which were recently proposed to derive insights into dataset quality (Swayamdipta et al., 2020). We compare our method on popular text classification tasks to commonly used AL strategies, which instead rely on post-training behavior. We demonstrate that CAL is competitive to other common AL methods, showing that training dynamics derived from small seed data can be successfully used for AL. We provide insights into our new AL method by analyzing batch-level statistics utilizing the data maps. Our results further show that CAL results in a more data-efficient learning strategy, achieving comparable or better results with considerably less training data.
A Quantitative Scaling Theory for Meridional Heat Transport in Planetary Atmospheres and Oceans

The meridional temperature profile of the upper layers of planetary atmospheres is set through a balance between differential radiative heating by a nearby star, or by intrinsic heat fluxes emanating from the deep interior, and the redistribution of that heat across latitudes by turbulent flows. These flows spontaneously arise through baroclinic instability of the meridional temperature gradients maintained by the forcing. When planetary curvature is neglected, this turbulence takes the form of coherent vortices that mix the meridional temperature profiles. However, the curvature of the planet favors the emergence of Rossby waves and zonal jets that restrict the meridional wandering of the fluid columns, thereby reducing the mixing efficiency across latitudes. A similar situation arises in the ocean, where the baroclinic instability of zonal currents leads to enhanced meridional heat transport by a turbulent flow consisting of vortices and zonal jets. A recent scaling theory for the turbulent heat transport by vortices is extended to include the impact of planetary curvature, in the framework of the two‐layer quasi‐geostrophic beta‐plane model. This leads to a quantitative parameterization providing the meridional temperature profile in terms of the externally imposed heat flux in an idealized model of planetary atmospheres and oceans. In addition, it provides a quantitative prediction for the emergent criticality, that is, the degree of instability in a canonical model of planetary atmosphere or ocean.
Constants of Motion: The Antidote to Chaos in Optimization and Game Dynamics

Several recent works in online optimization and game dynamics have established strong negative complexity results including the formal emergence of instability and chaos even in small such settings, e.g., $2\times 2$ games. These results motivate the following question: Which methodological tools can guarantee the regularity of such dynamics and how can we apply them in standard settings of interest such as discrete-time first-order optimization dynamics? We show how proving the existence of invariant functions, i.e., constant of motions, is a fundamental contribution in this direction and establish a plethora of such positive results (e.g. gradient descent, multiplicative weights update, alternating gradient descent and manifold gradient descent) both in optimization as well as in game settings. At a technical level, for some conservation laws we provide an explicit and concise closed form, whereas for other ones we present non-constructive proofs using tools from dynamical systems.
Multi-Tensor Network Representation for High-Order Tensor Completion

This work studies the problem of high-dimensional data (referred to as tensors) completion from partially observed samplings. We consider that a tensor is a superposition of multiple low-rank components. In particular, each component can be represented as multilinear connections over several latent factors and naturally mapped to a specific tensor network (TN) topology. In this paper, we propose a fundamental tensor decomposition (TD) framework: Multi-Tensor Network Representation (MTNR), which can be regarded as a linear combination of a range of TD models, e.g., CANDECOMP/PARAFAC (CP) decomposition, Tensor Train (TT), and Tensor Ring (TR). Specifically, MTNR represents a high-order tensor as the addition of multiple TN models, and the topology of each TN is automatically generated instead of manually pre-designed. For the optimization phase, an adaptive topology learning (ATL) algorithm is presented to obtain latent factors of each TN based on a rank incremental strategy and a projection error measurement strategy. In addition, we theoretically establish the fundamental multilinear operations for the tensors with TN representation, and reveal the structural transformation of MTNR to a single TN. Finally, MTNR is applied to a typical task, tensor completion, and two effective algorithms are proposed for the exact recovery of incomplete data based on the Alternating Least Squares (ALS) scheme and Alternating Direction Method of Multiplier (ADMM) framework. Extensive numerical experiments on synthetic data and real-world datasets demonstrate the effectiveness of MTNR compared with the start-of-the-art methods.
Three-level Spaser System: a Semi-Classical Analysis

We theoretically study a nanospaser system, which consists of a spherical silver nanoparticle embedded inside a sphere composed of dye molecules. The gain of the system, dye molecules, are described by a three-level model, where the transition frequency between the lowest two energy levels is close to the surface plasmon frequency of the nanosphere. Contrary to a two-level model of spaser, the three-level model takes into account finite relaxation time between the high energy levels of the gain medium. These relaxation processes affect both the spaser threshold and the number of generated plasmons in the continuous wave regime. While for a two-level model of a spaser the number of generated plasmons has a linear dependence on the gain, for the three-level model this dependence becomes quadratic.
Pushing Back Against the Microaggression Pushback in Academic Psychology: Reflections on a Concept-Creep Paradox

Echoing the 1960s, the 2020s opened with racial tensions boiling. The Black Lives Matter movement is energized, issuing pleas to listen to Black voices regarding day-to-day discrimination and expressing frustrations over the slow progress of social justice. However, psychological scientists have published only several opinion pieces on racial microaggressions, primarily objections, and strikingly little empirical data. Here I document three trends in psychology that coincide with the academic pushback against microaggressions: concept-creep concerns, especially those regarding expanded notions of harm; the expansion of right-leaning values in moral judgments (moral foundations theory); and an emphasis on prejudice symmetry, with the political left deemed equivalently biased against right-leaning targets (e.g., the rich, police) as the right is against left-leaning targets (e.g., Black people, women, LGBT+ people). Psychological scientists have ignored power dynamics and have strayed from their mission to understand and combat prejudice against disadvantaged populations, rendering researchers distracted and ill-equipped to tackle the microaggression concept. An apparent creep paradox, with calls to both reduce (e.g., harm) and expand (e.g., liberal prejudices, conservative moral foundations) concepts, poses a serious challenge to research on prejudice. I discuss the need for psychology to better capture Black experiences and to “tell it like it is” or risk becoming an irrelevant discipline of study.
Utility of 4D Flow MRI in Thoracic Aortic Diseases: A Literature Review of Clinical Applications and Current Evidence

Despite the recent technical developments, surgery on the thoracic aorta remains challenging and is associated with significant mortality and morbidity. Decisions about when and if to operate are based on a balance between surgical risk and the hazard of aortic rupture. These decisions are sometimes difficult in elective cases of thoracic aortic diseases, including aneurysms and dissections. Abnormal wall stress derived from flow alterations influences disease progression. Therefore, a better understanding of the complex hemodynamic environment inside the aortic lumen will facilitate patient-specific risk assessments of complications, which enable clinicians to provide timely prophylactic interventions. Time-resolved 3D phase-contrast (4D flow) MRI has many advantages for the in vivo assessment of flow dynamics. Recent developments in 4D flow imaging techniques has led to significant advances in our understanding of physiological flow dynamics in healthy subjects and patients with thoracic aortic diseases. In this clinically focused review of thoracic aortic diseases, we demonstrate the clinical advances acquired with 4D flow MRI from published studies. We provide a systematic overview of key evidences and considerations regarding normal thoracic aortas, thoracic aortic aneurysms, aortic dissections, and thoracic aortas with prosthetic graft replacement.
Computational Polarization: An Information-Theoretic Method for Resilient Computing

We introduce an error resilient distributed computing method based on an extension of the channel polarization phenomenon to distributed algorithms. The method leverages an algorithmic split operation that transforms two identical compute nodes into slow and fast workers, which parallels the channel split operation in Polar Codes. This operation preserves the average runtime, analogous to the conservation of Shannon capacity in channel polarization. By leveraging a recursive construction in a similar spirit to the Fast Fourier Transform, the method synthesizes virtual compute nodes with dispersed return time distributions, which we call computational polarization. We show that the runtime distributions form a functional martingale process, identify their limiting distributions in closed-form expressions together with non-asymptotic convergence rates, and prove strong convergence results in Banach spaces. We provide an information-theoretic lower bound on the overall runtime of any coded computation method and show that the computational polarization approach asymptotically achieves the optimal runtime for computing linear functions. An important advantage is the near linear time decoding procedure, which is significantly faster than Maximum Distance Separable codes.