Molecular production at a wide Feshbach resonance in Fermi-gas of cooled atoms

The problem of molecular production from degenerate gas of fermions at a wide Feshbach resonance, in a single-mode approximation, is reduced to the linear Landau-Zener problem for operators. The strong interaction leads to significant renormalization of the gap between adiabatic levels. In contrast to static problem the close vicinity of exact resonance does not play substantial role. Two main physical results of our theory is the high sensitivity of molecular production to the initial value of magnetic field and generation of a large BCS condensate distributed over a broad range of momenta in inverse process of the molecule dissociation.Comment: 4 pages, no figure

BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection

PCL detection task is aimed at identifying and categorizing language that is patronizing or condescending towards vulnerable communities in the general media.Compared to other NLP tasks of paragraph classification, the negative language presented in the PCL detection task is usually more implicit and subtle to be recognized, making the performance of common text-classification approaches disappointed. Targeting the PCL detection problem in SemEval-2022 Task 4, in this paper, we give an introduction to our team's solution, which exploits the power of prompt-based learning on paragraph classification. We reformulate the task as an appropriate cloze prompt and use pre-trained Masked Language Models to fill the cloze slot. For the two subtasks, binary classification and multi-label classification, DeBERTa model is adopted and fine-tuned to predict masked label words of task-specific prompts. On the evaluation dataset, for binary classification, our approach achieves an F1-score of 0.6406; for multi-label classification, our approach achieves an macro-F1-score of 0.4689 and ranks first in the leaderboard

Looking and Listening: Audio Guided Text Recognition

Text recognition in the wild is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest vision and language processing are effective for scene text recognition. Yet, solving edit errors such as add, delete, or replace is still the main challenge for existing approaches. In fact, the content of the text and its audio are naturally corresponding to each other, i.e., a single character error may result in a clear different pronunciation. In this paper, we propose the AudioOCR, a simple yet effective probabilistic audio decoder for mel spectrogram sequence prediction to guide the scene text recognition, which only participates in the training phase and brings no extra cost during the inference stage. The underlying principle of AudioOCR can be easily applied to the existing approaches. Experiments using 7 previous scene text recognition methods on 12 existing regular, irregular, and occluded benchmarks demonstrate our proposed method can bring consistent improvement. More importantly, through our experimentation, we show that AudioOCR possesses a generalizability that extends to more challenging scenarios, including recognizing non-English text, out-of-vocabulary words, and text with various accents. Code will be available at https://github.com/wenwenyu/AudioOCR

Chronic Infection Depletes Hematopoietic Stem Cells through Stress-Induced Terminal Differentiation

Chronic infections affect a third of the world’s population and can cause bone marrow suppression, a severe condition that increases mortality from infection. To uncover the basis for infection-associated bone marrow suppression, we conducted repeated infection of WT mice with Mycobacterium avium. After 4–6 months, mice became pancytopenic. Their hematopoietic stem and progenitor cells (HSPCs) were severely depleted and displayed interferon gamma (IFN-γ) signaling-dependent defects in self-renewal. There was no evidence of increased HSPC mobilization or apoptosis. However, consistent with known effects of IFN-γ, transcriptome analysis pointed toward increased myeloid differentiation of HSPCs and revealed the transcription factor Batf2 as a potential mediator of IFN-γ-induced HSPC differentiation. Gain- and loss-of-function studies uncovered a role for Batf2 in myeloid differentiation in both murine and human systems. We thus demonstrate that chronic infection can deplete HSPCs and identify BATF2 as a mediator of infection-induced HSPC terminal differentiation

Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration

We propose a novel end-to-end document understanding model called SeRum (SElective Region Understanding Model) for extracting meaningful information from document images, including document analysis, retrieval, and office automation. Unlike state-of-the-art approaches that rely on multi-stage technical schemes and are computationally expensive, SeRum converts document image understanding and recognition tasks into a local decoding process of the visual tokens of interest, using a content-aware token merge module. This mechanism enables the model to pay more attention to regions of interest generated by the query decoder, improving the model's effectiveness and speeding up the decoding speed of the generative scheme. We also designed several pre-training tasks to enhance the understanding and local awareness of the model. Experimental results demonstrate that SeRum achieves state-of-the-art performance on document understanding tasks and competitive results on text spotting tasks. SeRum represents a substantial advancement towards enabling efficient and effective end-to-end document understanding.Comment: Accepted to ICCV 2023 main conferenc

Copper-doped nano laponite coating on poly(butylene succinate) scaffold with antibacterial properties and cytocompatibility for biomedical application

An ideal artificial bone will likely be multifunctional, combining different technologies to simultaneously promote bone regeneration while inhibiting microbial infection. In this study, copper- (Cu-) doped nano laponite (cnLAP) was prepared by a cation-exchanged method, and the cnLAP coating on poly(butylene succinate) (PBSu) scaffold was fabricated by poly(dopamine) modification. The results showed that incorporation of Cu ions into nano laponite (nLAP) did not have obvious effects on the morphology and surface area of cnLAP (compared with nLAP), which could be coated easily on macroporous PBSu scaffolds. In addition, the cnLAP-coated PBSu scaffolds could inhibit the growth of both Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), indicating good antibacterial activity. Moreover, the cnLAP-coated PBSu scaffolds significantly promoted proliferation and improved alkaline phosphatase (ALP) activity of bone mesenchymal stem cells (BMSCs) compared with PBSu scaffolds. Furthermore, no obvious differences in cell responses to cnLAP- and nLAP-coated PBSu scaffolds were found, indicating that incorporation of Cu into nLAP had no negative effects on its cytocompatibility. The results suggested that the cnLAP-coated PBSu scaffolds exhibited excellent cytocompatibility and antimicrobial activity, which might offer promising opportunities for promoting bone regeneration and prevention of infectious from bacteria and effective treatment of bone defects.National Natural Science Foundation of China [51772194, 81771990]; Key Medical Program of Science and Technology Development of Shanghai [17441900600, 15441902500]; Ministry of Education, Youth and Sports of the Czech Republic Program NPU I [LO1504

Association between high-density lipoprotein cholesterol and type 2 diabetes mellitus among Chinese: The Beijing longitudinal study of aging

Background: Some previous studies on different populations have yielded inconsistent findings with respect to the relationship between levels of high-density lipoprotein cholesterol (HDL-C) and future type 2 diabetes mellitus (T2DM) incidence. This study was designed to gain further insight into this relationship through a cohort study with a 25-year follow-up duration. Methods: In total, 1462 individuals that were 55 years of age or older and were free of T2DM at baseline were enrolled in the present study. T2DM incidence among this study population was detected through self-reported diagnoses or the concentration of fasting plasma glucose. The data were derived from nine surveys conducted from 1992 to 2017. The correlation between HDL-C levels and the T2DM risk was assessed through Cox proportional-hazards model and proportional hazards model for the sub-distribution with time-dependent variables. Results: Over the follow-up period, 120 participants were newly diagnosed with new-onset T2DM. When research participants were separated into four groups on the basis for quartiles of their levels of HDL-C measured at baseline, and incidence of diabetes declined with higher baseline HDL-C levels at 12.60, 9.70, 5.38, and 5.22 per 1000 person-years, respectively. Adjusted hazard ratios (HRs) were 0.98 (95% confidence interval [CI]: 0.62–1.55), 0.48 (95% CI: 0.27–0.85) and 0.44 (95% CI: 0.25–0.80) for individuals with HDL-C levels within the 1.15–1.39, 1.40–1.69, and ≥ 1.70 mmol/L ranges relative to participants with HDL-C levels \u3c 1.15 mmol/L. Multiple sensitivity analyses similarly revealed reduced risk of diabetes incidence with increased HDL-C levels. Incorporating the levels of HDL-C into a multivariate model significantly enhanced the overall power of the predictive model (P values were 0.0296, 0.0011, respectively, for 5- and 10-year risk of diabetes). Conclusions: Levels of HDL-C were independently and negatively associated with the risk of the new-onset T2DM among middle-aged and elderly Chinese

Altered Brain Function in Treatment-Resistant and Non-treatment-resistant Depression Patients: A Resting-State Functional Magnetic Resonance Imaging Study

ObjectiveIn this study, we used amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) to observe differences in local brain functional activity and its characteristics in patients with treatment-resistant depression (TRD) and non-treatment-resistant depression (nTRD), and to explore the correlation between areas of abnormal brain functional activity and clinical symptoms.MethodThirty-seven patients with TRD, 36 patients with nTRD, and 35 healthy controls (HCs) were included in resting-state fMRI scans. ALFF and ReHo were used for image analysis and further correlation between abnormal brain regions and clinical symptoms were analyzed.ResultsANOVA revealed that the significantly different brain regions of ALFF and ReHo among the three groups were mainly concentrated in the frontal and temporal lobes. Compared with the nTRD group, the TRD group had decreased ALFF in the left/right inferior frontal triangular gyrus, left middle temporal gyrus, left cuneus and bilateral posterior lobes of the cerebellum, and increased ALFF in the left middle frontal gyrus and right superior temporal gyrus, and the TRD group had decreased ReHo in the left/right inferior frontal triangular gyrus, left middle temporal gyrus, and increased ReHo in the right superior frontal gyrus. Compared with the HC group, the TRD group had decreased ALFF/ReHo in both the right inferior frontal triangular gyrus and the left middle temporal gyrus. Pearson correlation analysis showed that both ALFF and ReHo values in these abnormal brain regions were positively correlated with HAMD-17 scores (P < 0.05).ConclusionAlthough the clinical symptoms were similar in the TRD and nTRD groups, abnormal neurological functional activity were present in some of the same brain regions. Compared with the nTRD group, ALFF and ReHo showed a wider range of brain area alterations and more complex neuropathological mechanisms in the TRD group, especially in the inferior frontal triangular gyrus of the frontal lobe and the middle temporal gyrus of the temporal lobe