Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypofractionated radiotherapy with immunochemotherapy for extensive-stage small-cell lung cancer

Introduction The combination of a PD-L1 inhibitor plus carboplatin/cisplatin and etoposide (EC/EP) has become a new standard first-line treatment for extensive-stage small-cell lung cancer (ES-SCLC). Combining concurrent palliative hypofractionated radiotherapy of the thorax (HFRT) and immunochemotherapy may have a synergistic effect. In this study, we explored an optimal model of combination radiotherapy with immunochemotherapy as first-line treatment of ES-SCLC. Patients and methods In this multicenter single-arm phase 2 trial, patients with ES-SCLC received atezolizumab with EC/EP for two cycles (induction phase), then, those who did not progress received concurrent palliative HFRT and two cycles of atezolizumab with EC/EP (combination phase). Afterward they received atezolizumab every 3 weeks for a maximum of 2 years after study enrolment (maintenance phase). Prophylactic cranial irradiation (PCI) was recommended. The primary endpoints were safety and tolerance; the second endpoints were progression-free survival (PFS). Results Forty patients were enrolled, and all had completed palliative HFRT and four cycles of immunochemotherapy. There were seven grade 3 adverse events (3 decreased neutrophil count, 1 anemia, 2 pneumonitis, 1 esoenteritis), two grade 4 adverse events (2 decreased white cell count) and no grade 5 toxicities. The pneumonitis rate was 12.5% (three grade 2 and two grade 3 events). At the median follow-up of 14.2 months (range, 6.8–28.7), the median PFS was 8.6 months (95%CI, 6.1–11.1). Conclusion The addition of concurrent hypofractionated thoracic radiotherapy to first-line immunochemotherapy for ES-SCLC was well tolerated and showed promising clinical efficacy. Additional randomized trials are needed to validate benefits. Clinical trial registration https://clinicaltrials.gov/ (NCT 04636762).
Gut microbiota and intestinal barrier function in subjects with cognitive impairments: a cross-sectional study

Background Gut-brain axis might play an important role in cognitive impairments by various diseases including Alzheimer’s disease (AD). Objective To investigate the differences in gut microbial composition, intestinal barrier function, and systemic inflammation in patients with AD or mild cognitive impairment (MCI), and normal control (NC) cases. Methods A total of 118 subjects (45 AD, 38 MCI, and 35 NC) were recruited. Cognitive function was assessed using Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment Scale (MoCA). Functional ability was assessed using Activity of Daily Living Scale (ADL). The composition of gut microbiome was examined by 16S rRNA high-throughput sequencing. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict functional transfer of gut microbiota. Gut barrier dysfunction was evaluated by measuring the levels of diamine oxidase (DAO), D-lactic acid (DA), and endotoxin (ET). The serum high-sensitivity C-reactive protein (hs-CRP) level was used to indicate systemic inflammation. Results Compared with normal controls, patients with cognitive impairments (AD and MCI) had lower abundance of Dorea and higher levels of DAO, DA, and ET. Kyoto Encyclopedia of Genes and Genomes (KEGG) results showed that the pathways related to glycan biosynthesis and metabolism increased in MCI patients, while the ones related to membrane transport decreased. The abundance of Bacteroides and Faecalibacterium was negatively correlated with the content of ET, and positively correlated with the scores of MMSE and MoCA. The hs-CRP levels were similar among the three groups. A significant negative correlation was observed between the severity of gut barrier dysfunction and cognitive function. Conclusion Cognitive impairments might be associated with gut microbial dysbiosis and intestinal barrier dysfunction.
A SVM-based method for identifying fracture modes of rock using WVD spectrogram features of AE signals

In order to achieve the highly efficient and accurate identification of fracture modes including tension or shear fractures during rock failure, an intelligent identification method based on Wigner-Ville distribution (WVD) spectrogram features of acoustic emission (AE) signals was proposed. This method was mainly constructed by the following steps: Firstly, AE hits corre-sponding to tension and shear fractures were obtained through conducting the Brazilian disc test (tension fracture) and direct shear test (shear fracture) of limestone. Secondly, the WVD spectro-grams of these tensile-type and shear-type AE hits were respectively extracted and then trans-formed into the image features of relatively low-dimension as the sample set based on the gray-level cooccurrence matrix (GLCM) and histogram of oriented gradient (HOG). Finally, on the basis of the processed and classified sample set of the WVD spectrogram features, an identifica-tion model of rock fracture modes was established by a support vector machine (SVM) learning algorithm. To verify this method, the fracture modes of limestone subjected to biaxial compres-sion were identified by the method. The results showed that the method not only can greatly re-veal the fracture modes change from tension-dominated to shear-dominated fractures, but also has advantages over the RA-AF value method, such as applicability, accuracy and practicality.
Achieving a full color palette with thickness, temperature, and humidity in cholesteric hydroxypropyl cellulose

Hydroxypropyl cellulose (HPC) is a sustainable, cost-efficient, and bio-compatible cellulose derivative that forms cholesteric liquid crystalline phases in highlyconcentrated water solutions that reflects colour in the visible range. While there have been studies exploiting HPC’s structural coloration and transferring the cholesteric order of the solutions into solid form via cross-linking, there is still lack of understanding on the thermotropic mechanisms that enable the transfer of the structural ordering of the pure HPC at higher temperatures. In this work, we demonstrate the balance between the temperature, humidity, and film thickness to achieve a full color palette of pure HPC. We reveal that at the early stages of the evaporation, formation of a dense skin over the lyotropic phase facilitates the thermal expansion of the HPC during the heat treatment. Increasing the thickness, applying higher drying temperatures, and exposing the samples to higher humidity during the evaporation all result with increased pitch values that cause a red-shift in coloration in the solid state. Our analysis of the HPC samples dried in controlled temperature and humidity conditions at a fixed thickness provided an understanding of the dominance of the thermal expansion which drives the final structural organization in the solid cholesteric phase. When the thickness of the films was varied against fixed temperature and humidity conditions, the color shift from red to violet follows the thickness gradient of the sample due to the change in the drying time required to reach the solid form.
Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning

Cotton is widely used in textile, decoration, and industry, but it is also threatened by soil salinization. Drip irrigation plays an important role in improving water and fertilization utilization efficiency and ensuring crop production in arid areas. Accurate prediction of soil salinity and crop evapotranspiration under drip irrigation is essential to guide water management practices in arid and saline areas. However, traditional hydrological models such as Hydrus require more variety of input parameters and user expertise, which limits its application in practice, and machine learning (ML) provides a potential alternative. Based on a global dataset collected from 134 pieces of literature, we proposed a method to comprehensively simulate soil salinity, evapotranspiration (ET) and cotton yield. Results showed that it was recommended to predict soil salinity, crop evapotranspiration and cotton yield based on soil data (bulk density), meteorological factors, irrigation data and other data. Among them, meteorological factors include annual average temperature, total precipitation, year. Irrigation data include salinity in irrigation water, soil matric potential and irrigation water volume, while other data include soil depth, distance from dripper, days after sowing (for EC and soil salinity), fertilization rate (for yield and ET). The accuracy of the model has reached a satisfactory level, R2 in 0.78-0.99. The performance of stacking ensemble ML was better than that of a single model, i.e., gradient boosting decision tree (GBDT); random forest (RF); extreme gradient boosting regression (XGBR), with R2 increased by 0.02%-19.31%. In all input combinations, other data have a greater impact on the model accuracy, while the RMSE of the S1 scenario (input without meteorological factors) without meteorological data has little difference, which is -34.22%~19.20% higher than that of full input. Given the wide application of drip irrigation in cotton, we recommend the application of ensemble ML to predict soil salinity and crop evapotranspiration, thus serving as the basis for adjusting the irrigation schedule.
Body condition and corticosterone stress response, as markers to investigate effects of human activities on Adélie penguins (Pygoscelis adeliae)

Introduction In Antarctica, there is growing concern about the potential effect of anthropogenic activities (i.e., tourism, research) on wildlife, especially since human activities are developing at an unprecedented rate. Although guidelines exist to mitigate negative impacts, fundamental data are currently lacking to reliably assess impacts. Physiological tools, such as circulating corticosterone levels, appear promising to assess the potential impact of human disturbance on Antarctic vertebrates. Methods In this study, we compared the body condition, and the physiological sensitivity to stress (i.e., basal and stress-induced corticosterone level) of adult and chick Adélie penguins between a disturbed and an undisturbed area (i.e., 2 colonies located in the middle of a research station exposed to intense human activities and 2 colonies located on protected islands with minimal human disturbance). Results We did not find any significant impact of human activities on body condition and corticosterone levels in adults (incubating adults, brooding adults). In chicks, there were significant inter-colony variations in stress-induced corticosterone levels. Specifically, the chicks from the disturbed colonies tended to have higher stress-induced corticosterone levels than the chicks from the protected areas although this difference between areas was not significant. In addition, and independently of human disturbance we also found significant differences in adult body condition, and chick corticosterone level between colonies. Discussion Overall, our study suggests that this species is not dramatically impacted by human activities, at least when humans and penguins have cohabited for several decades. Our results support therefore the idea that this species is likely to be tolerant to human disturbance and this corroborates with the persistence of Adélie penguin colonies in the middle of the research station. However, our results also suggest that chicks might be more sensitive to human disturbance than adults and might therefore potentially suffer from human disturbance. Our study also suggests that specific individual and environmental variables outweigh the potential minor impact of human disturbance on these variables. Combining corticosterone with complementary stress-related physiological markers, such as heart rate, may strengthen further studies examining whether human disturbance may have subtle detrimental impacts on individuals.
Connectedness of loss landscapes via the lens of Morse theory

Mode connectivity is a recently discovered property of neural networks stating that two weight configurations of small loss can usually be connected by a path of small loss. The mode connectivity property is interesting practically as it has applications to design of optimizers with better generalization properties and various other applied topics as well as theoretically as it suggests that loss landscapes of deep networks have very nice properties even though they are known to be highly non-convex. The goal of this work is to study connectedness of loss landscapes via the lens of Morse theory. A brief introduction to Morse theory is provided.
Linear Convergence of Gradient Descent For Finite Width Over-parametrized Linear Networks With General Initialization

Recent theoretical analyses of the convergence of gradient descent (GD) to a global minimum for over-parametrized neural networks make strong assumptions on the step size (infinitesimal), the hidden-layer width (infinite), or the initialization (spectral, balanced). In this work, we relax these assumptions and derive a linear convergence rate for two-layer linear networks trained using GD on the squared loss in the case of finite step size, finite width and general initialization. Despite the generality of our analysis, our rate estimates are significantly tighter than those of prior work. Moreover, we provide a time-varying step size rule that monotonically improves the convergence rate as the loss function decreases to zero. Numerical experiments validate our findings.
P REDICTIVE LEARNING ENABLES NEURAL NETWORKS TO LEARN COMPLEX WORKING MEMORY TASKS

Brains are thought to engage in predictive learning learning to predict upcoming stimuli to construct an internal model of their environment. This is especially notable for spatial navigation, as first described by Tolman’s latent learning tasks. However, predictive learning has also been observed in sensory cortex, in settings unrelated to spatial navigation. Apart from normative frameworks such as active inference or efficient coding, what could be the utility of learning to predict the patterns of occurrence of correlated stimuli? Here we show that prediction, and thereby the construction of an internal model of sequential stimuli, can bootstrap the learning process of a working memory task in a recurrent neural network. We implemented predictive learning alongside working memory match-tasks, and networks emerged to solve the prediction task first by encoding information across time to predict upcoming stimuli, and then eavesdropped on this solution to solve the matching task. Eavesdropping was most beneficial when neural resources were limited. Hence, predictive learning acts as a general neural mechanism to learn to store sensory information that can later be essential for working memory tasks.
A Novel Differentiable Mixed-Precision Quantization Search Framework for Alleviating the Matthew Effect and Improving Robustness

Network quantization is an effective and widely-used model compression technique. Recently, several works apply differentiable neural architectural search (NAS) methods to mixed-precision quantization (MPQ) and achieve encouraging results. However, the nature of differentiable architecture search can lead to the Matthew Effect in the mixed-precision. The candidates with higher bit-widths would be trained maturely earlier while the candidates with lower bit-widths may never have the chance to express the desired function. To address this issue
Minority Oversampling for Imbalanced Data via Class-Preserving Regularized Auto-Encoders

Class imbalance is a common phenomenon in multiple application domains such as healthcare, where the sample occurrence of one or few class categories is more prevalent in the dataset than the rest. This work addresses the class-imbalance issue by proposing an over-sampling method for the minority classes in the latent space of a Reg-ularized Auto-Encoder (RAE). Specifically, we construct a latent space by maximizing the conditional data likelihood using an Encoder-Decoder structure, such that oversampling through convex combinations of latent samples preserves the class identity. A jointly-trained linear classifier that separates convexly coupled latent vectors from different classes is used to impose this property on the AE’s latent space. Further, the aforesaid linear classifier is used for final classification without retraining. We theoretically show that our method can achieve a low variance risk estimate compared to naive over-sampling methods and is robust to overfitting. We conduct several experiments on benchmark datasets and show that our method outperforms the existing oversampling techniques for handling class imbalance. The code of the proposed method is available at: https://github. com/arnabkmondal/
A new simplified method for anti-symmetric mode in-plane vibrations of frame structures with column cracks

This paper is on the natural frequency and mode shape computation of frame structures with column cracks. First, a model of intact frame structures is built to perform vibration analysis. Beam elements are considered as lumped masses and rotational springs at the storey levels of frames. Equivalent model of columns and lumped mass-stiffness effects of beams have been combined to carry out continuous solution for the anti-symmetric mode in-plane vibrations of frames. In addition, frame systems with multiple column cracks are analyzed in terms of anti-symmetric mode vibration characteristics. Cracks are considered as massless rotational springs in compliance with the local flexibility model. Compatibility and continuity conditions are satisfied at crack and storey locations of the equivalent column, modeled using the Euler–Bernoulli beam theory. The proposed method is tested for single-storey single- and multi-bay, H-type and double-storey single-bay frame systems with intact and cracked columns. Results are validated by those given in the current literature and/or obtained by the finite element analyses.
Capturing Formality in Speech Across Domains and Languages

The linguistic notion of formality is one dimension of stylistic variation in human communication. A universal characteristic of language production, formality has surface-level realizations in written and spoken language. In this work, we explore ways of measuring the formality of such realizations in multilingual speech corpora across a wide range of domains. We compare measures of formality, contrasting textual and acoustic-prosodic metrics. We believe that a combination of these should correlate well with downstream applications. Our findings include: an indication that certain prosodic variables might play a stronger role than others; no correlation between prosodic and textual measures; limited evidence for anticipated inter-domain trends, but some evidence of consistency of measures between languages. We conclude that non-lexical indicators of formality in speech may be more subtle than our initial expectations, motivating further work on reliably encoding spoken formality.
Impact of the Omicron strain on febrile convulsions in children: A single-center observational study (preprint)

<Purpose> The emergence of the Omicron strain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the end of December 2021 has drastically increased the number of infected children in Japan, along with the number of children with febrile convulsions. However, impact of the Omicron strain on the febrile convulsions in children is not clear. <Methods> We compared the frequency of SARS-CoV-2 infection in children hospitalized with febrile convulsions with the frequency of SARS-CoV-2 infection in children with fever and respiratory symptoms without convulsions. <Results> In 2021 and 2022, 49 and 58 children, respectively, required emergency hospitalization for febrile convulsions (FC group), in which 24 and 38 children underwent a Filmarray respiratory panel ® test (FA test) and quantitative antigen test for SARS-CoV-2, respectively. In 2022, only six patients tested positive for SARS-CoV-2 (10.3%, 6/58). As a reference group, 655 children aged < 10 years who underwent the FA test for fever and respiratory symptoms during the same period were investigated, and 4 (1.8%, 4/223) and 42 (9.7%, 42/432) tested positive for SARS-CoV-2 in 2021 and 2022, respectively. Rhinovirus/enterovirus (RV/EV) was the most frequently detected virus, followed by respiratory syncytial virus (RSV) and parainfluenza virus 3 (PI3); no significant difference in the trend of detected viruses was observed between the two groups. <Conclusions> The frequency of febrile convulsions associated with SARS-CoV-2 infection of the Omicron strain in children may be similar to that of other common respiratory viruses.
Principled Approaches for Private Adaptation from a Public Source

A key problem in a variety of applications is that of domain adaptation from a public source domain, for which a relatively large amount of labeled data with no privacy constraints is at one’s disposal, to a private target domain, for which a private sample is available with very few or no labeled data. In regression problems, where there are no privacy constraints on the source or target data, a discrepancy minimization approach was shown to outperform a number of other adaptation algorithm baselines. Building on that approach, we initiate a principled study of differentially private adaptation from a source domain with public labeled data to a target domain with unlabeled private data. We design differentially private discrepancy-based adaptation algorithms for this problem. The design and analysis of our private algorithms critically hinge upon several key properties we prove for a smooth approximation of the weighted discrepancy, such as its smoothness with respect to the (cid:96) 1 -norm and the sensitivity of its gradient. We formally show that our adaptation algorithms beneﬁt from strong generalization and privacy guarantees.
Preparation and Characterization of Alginate Hydrogels with High Water-Retaining Capacity

Hydrogels are very attractive materials due to their multifunctional properties. Many natural polymers, such as polysaccharides, are used for the preparation of hydrogels. The most important and commonly used polysaccharide is alginate because of its biodegradability, biocompatibility, and non-toxicity. Since the properties of alginate hydrogel and its application depend on numerous factors, this study aimed to optimize the gel composition to enable the growth of inoculated cyanobacterial crusts for suppressing the desertification process. The influence of alginate concentration (0.1–2.9%, m/v) and CaCl2 concentration (0.4–4.6%, m/v) on the water-retaining capacity was analyzed using the response surface methodology. According to the design matrix, 13 formulations of different compositions were prepared. The water-retaining capacity was defined as the system response maximized in optimization studies. The optimal composition of hydrogel with a water-retaining capacity of about 76% was obtained using 2.7% (m/v) alginate solution and 0.9% (m/v) CaCl2 solution. Fourier transform infrared spectroscopy was used for the structural characterization of the prepared hydrogels, while the water content and swelling ratio of hydrogels were determined using gravimetric methods. It was concluded that alginate and CaCl2 concentrations play the most important role regarding the gelation time, homogeneity, water content, and swelling ratio of the hydrogel.