Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Intensity Vibration Improves Muscle Healing in a Mouse Model of Laceration Injury

Recovery from traumatic muscle injuries is typically prolonged and incomplete, leading to impaired muscle and joint function. We sought to determine whether mechanical stimulation via whole-body low-intensity vibration (LIV) could (1) improve muscle regeneration and (2) reduce muscle fibrosis following traumatic injury. C57BL/6J mice were subjected to a laceration of the gastrocnemius muscle and were treated with LIV (0.2 g at 90 Hz or 0.4 g at 45 Hz for 30 min/day) or non-LIV sham treatment (controls) for seven or 14 days. Muscle regeneration and fibrosis were assessed in hematoxylin and eosin or Masson’s trichrome stained muscle cryosections, respectively. Compared to non-LIV control mice, the myofiber cross-sectional area was larger in mice treated with each LIV protocol after 14 days of treatment. Minimum fiber diameter was also larger in mice treated with LIV of 90 Hz/0.2 g after 14 days of treatment. There was also a trend toward a reduction in collagen deposition after 14 days of treatment with 45 Hz/0.4 g (p = 0.059). These findings suggest that LIV may improve muscle healing by enhancing myofiber growth and reducing fibrosis. The LIV-induced improvements in muscle healing suggest that LIV may represent a novel therapeutic approach for improving the healing of traumatic muscle injuries.
Learning regression and verification networks for long-term visual tracking

Compared with short-term tracking, the long-term tracking task requires determining the tracked object is present or absent, and then estimating the accurate bounding box if present or conducting image-wide re-detection if absent. Until now, few attempts have been done although this task is much closer to designing practical tracking systems. In this work, we propose a novel long-term tracking framework based on deep regression and verification networks. The offline-trained regression model is designed using the object-aware feature fusion and region proposal networks to generate a series of candidates and estimate their similarity scores effectively. The verification network evaluates these candidates to output the optimal one as the tracked object with its classification score, which is online updated to adapt to the appearance variations based on newly reliable observations. The similarity and classification scores are combined to obtain a final confidence value, based on which our tracker can determine the absence of the target accurately and conduct image-wide re-detection to capture the target successfully when it reappears. Extensive experiments show that our tracker achieves the best performance on the VOT2018 long-term challenge and state-of-the-art results on the OxUvA long-term dataset.
Selective and sensitive determination of tetracyclines by HPLC with chemiluminescence detection based on a cerium(IV)-methoxylated cypridina luciferin analogue system.

A novel approach for the simultaneous and selective analysis of chlortetracycline, doxycycline, 4-epichlortetracycline, oxytetracycline, metacycline, and tetracycline by high-performance liquid chromatography coupled with sensitive post-column chemiluminescence reaction was set up. Six tetracyclines were separated through a simple and effective isocratic procedure using a mixture of acetonitrile and 3.5 mM methanesulfonic acid. After that, intensive chemiluminescence signals were produced by the reaction between cerium(IV) and methoxylated cypridina luciferin analogues system in the presence of tetracyclines. Under the appropriate experimental conditions, the developed method was evaluated by testing of linearity, limits of detection, precision, and accuracy. The limit of detection was from 2 to 10 nM and relative standard deviation of intraday precision was below 9%. The developed high-performance liquid chromatography with chemiluminescence method has been performed for the with trace analysis of tetracycline antibiotic residues in milk samples with satisfactory results.
FK506 Attenuates the MRP1-Mediated Chemoresistant Phenotype in Glioblastoma Stem-Like Cells

Poor response to current treatments for glioblastoma has been attributed to the presence of glioblastoma stem-like cells (GSCs). GSCs are able to expel antitumor drugs to the extracellular medium using the multidrug resistance-associated protein 1 (MRP1) transporter. Tacrolimus (FK506) has been identified as an MRP1 regulator in differentiated glioblastoma (GBM) cells (non-GSCs); however, the effect of FK506 on GSCs is currently unknown. The objective of the following research is to evaluate the effect of FK506 on the MRP1-related chemo-resistant phenotype of GSCs. For this, U87MG and C6 glioma cell lines were used to generate non-GSCs and GSCs. mRNA and MRP1-positive cells were evaluated by RT-qPCR and flow cytometry, respectively. A Carboxyfluorescein Diacetate (CFDA)-retention assay was performed to evaluate the MRP1 activity. Apoptosis and MTT assays were employed to evaluate the cytotoxic effects of FK506 plus Vincristine (MRP1 substrate). GSC-derived subcutaneous tumors were generated to evaluate the in vivo effect of FK506/Vincristine treatment. No differences in transcript levels and positive cells for MRP1 were observed in FK506-treated cells. Lesser cell viability, increased apoptosis, and CFDA-retention in the FK506/Vincristine-treated cells were observed. In vivo, the FK506/Vincristine treatment decreased the tumor size as well as ki67, Glial Fibrillary Acidic Protein (GFAP), and nestin expression. We conclude that FK506 confers a chemo-sensitive phenotype to MRP1-drug substrate in GSCs.
Impacts of a Regional, Multi-Year, Multi-Species Coral Disease Outbreak in Southeast Florida

Globally coral reefs have been declining at alarming rates as a result of anthropogenic stressors, leading to increased frequency and severity of widespread bleaching and disease events. These events are often associated with increased water temperatures due to climate change as well as regional and local stress from nutrient enrichment through runoff and sedimentation from coastal development. In late 2014, a white syndrome disease outbreak was reported off the coast of southeast Florida and was subsequently documented spreading throughout the region. This study examined the regional impacts of the disease event on the southeast Florida stony coral population utilizing stony coral demographic data from the Southeast Florida Coral Reef Evaluation and Monitoring Project (SECREMP). SECREMP is a long-term monitoring project examining 22 sites distributed from Miami-Dade County north to Martin County, Florida. The results revealed significant region-wide declines in stony coral diversity, density, and live tissue area corresponding with increased disease prevalence, which reached its maximum for the study period in 2016. Regional declines in coral density approached 30% loss and live tissue was upwards of 60% as a result of the disease outbreak. Additionally, multiple species were severely impacted, especially the reef building, complexity-contributing species Montastraea cavernosa, Meandrina meandrites, and Siderastrea siderea. The disease outbreak resulted in acute mortality and altered the ecosystem function to a point such that recovery is uncertain. This multiyear, region-wide disease outbreak has been indiscriminate relative to coral species impacted and was arguably the most devastating disturbance event documented on the Southeast Florida Reef Tract.
Multimodal Medical Image Fusion Based on Fuzzy Discrimination With Structural Patch Decomposition

Multimodal medical image fusion, emerging as a hot topic, aims to fuse images with complementary multi-source information. In this paper, we propose a novel multimodal medical image fusion method based on structural patch decomposition (SPD) and fuzzy logic technology. First, the SPD method is employed to extract two salient features for fusion discrimination. Next, two novel fusion decision maps called an incomplete fusion map and supplemental fusion map are constructed from salient features. In this step, the supplemental map is constructed by our defined two different fuzzy logic systems. The supplemental and incomplete maps are then combined to construct an initial fusion map. The final fusion map is obtained by processing the initial fusion map with a Gaussian filter. Finally, a weighted average approach is adopted to create the final fused image. Additionally, an effective color medical image fusion scheme that can effectively prevent color distortion and obtain superior diagnostic effects is also proposed to enhance fused images. Experimental results clearly demonstrate that the proposed method outperforms state-of-the-art methods in terms of subjective visual and quantitative evaluations.
Maternal reproductive senescence shapes the fitness consequences of the parental age difference in ruffed lemurs

In humans, pronounced age differences between parents have deleterious fitness consequences. In particular, the number of children is lower when mothers are much older than fathers. However, previous analyses failed to disentangle the influence of differential parental age per se from a direct age effect of each parent. In this study, we analyse the fitness consequences of both parental age and parental age differences on litter size and offspring survival in two closely related species of lemurs living in captivity. As captive lemurs do not choose their reproductive partner, we were able to measure litter size and offspring survival across breeding pairs showing a wide range of parental age differences. However, we demonstrated that the effect of the parental age difference on litter size was fully accounted for by female reproductive senescence because females mating with much younger males were old females. On the other hand, both parental age difference and female reproductive senescence influenced offspring survival. Our results emphasize the importance of teasing apart the effect of parental reproductive senescence when investigating the health and fitness consequences of parental age differences and also provide new insights for conservation programmes of endangered species.
The Nordic Maintenance Care program: Effectiveness of chiropractic maintenance care versus symptom-guided treatment for recurrent and persistent low back pain—A pragmatic randomized controlled trial

Background For individuals with recurrent or persistent non-specific low back pain (LBP), exercise and exercise combined with education have been shown to be effective in preventing new episodes or in reducing the impact of the condition. Chiropractors have traditionally used Maintenance Care (MC), as secondary and tertiary prevention strategies. The aim of this trial was to investigate the effectiveness of MC on pain trajectories for patients with recurrent or persistent LBP. Method This pragmatic, investigator-blinded, two arm randomized controlled trial included consecutive patients (18–65 years old) with non-specific LBP, who had an early favorable response to chiropractic care. After an initial course of treatment, eligible subjects were randomized to either MC or control (symptom-guided treatment). The primary outcome was total number of days with bothersome LBP during 52 weeks collected weekly with text-messages (SMS) and estimated by a GEE model. Results Three hundred and twenty-eight subjects were randomly allocated to one of the two treatment groups. MC resulted in a reduction in the total number of days per week with bothersome LBP compared with symptom-guided treatment. During the 12 month study period, the MC group (n = 163, 3 dropouts) reported 12.8 (95% CI = 10.1, 15.5; p = <0.001) fewer days in total with bothersome LBP compared to the control group (n = 158, 4 dropouts) and received 1.7 (95% CI = 1.8, 2.1; p = <0.001) more treatments. Numbers presented are means. No serious adverse events were recorded. Conclusion MC was more effective than symptom-guided treatment in reducing the total number of days over 52 weeks with bothersome non-specific LBP but it resulted in a higher number of treatments. For selected patients with recurrent or persistent non-specific LBP who respond well to an initial course of chiropractic care, MC should be considered an option for tertiary prevention.
Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network

Deep neural networks (DNNs) are widely used for image recognition, speech recognition, pattern analysis, and intrusion detection. Recently, the adversarial example attack, in which the input data are only slightly modified, although not an issue for human interpretation, is a serious threat to a DNN as an attack as it causes the machine to misinterpret the data. The adversarial example attack has been receiving considerable attention owing to its potential threat to machine learning. It is divided into two categories: targeted adversarial example and untargeted adversarial example. The untargeted adversarial example happens when machines misclassify an object into an incorrect class. In contrast, the targeted adversarial example attack causes machines to misinterpret the image as the attacker’s desired class. Thus, the latter is a more elaborate and powerful attack than the former. The existing targeted adversarial example is a single targeted attack that allows only one class to be recognized. However, in some cases, a multi-targeted adversarial example can be useful for an attacker to make multiple models recognize a single original image as different classes. For example, an attacker can use a single road sign generated by a multi-targeted adversarial example scheme to make model A recognize it as a stop sign and model B recognize it as a left turn, whereas a human might recognize it as a right turn. Therefore, in this paper, we propose a multi-targeted adversarial example that attacks multiple models within each target class with a single modified image. To produce such examples, we carried out a transformation to maximize the probability of different target classes by multiple models. We used the MNIST datasets and TensorFlow library for our experiment. The experimental results showed that the proposed scheme for generating a multi-targeted adversarial example achieved a 100% attack success rate.
A cumulative score based on preoperative fibrinogen and the neutrophil–lymphocyte ratio to predict outcomes in resectable gastric cancer

Background Recent studies have revealed that preoperative fibrinogen and the neutrophil–lymphocyte ratio (NLR) are associated with poor outcome in gastric cancer (GC). We aimed to evaluate whether the fibrinogen and the NLR score had a consistent prognostic value for resectable GC. Methods We analyzed 1,293 consecutive patients who underwent curative surgery for GC. The F-NLR score was 2 for patients with hyperfibrinogenemia (>400 mg/dL) and elevated NLR (≥5.0), 1 for those with one abnormal index, and 0 for those with no abnormal indices. Results We found that higher F-NLR scores were associated with larger tumor size, deeper tumor invasion and more lymph node metastasis (all P<0.05). In a multivariate analysis, F-NLR independently predicted postoperative survival (P<0.001). When stratified by tumor–node–metastasis (TNM) stage, the prognostic value of F-NLR was still maintained for stages I–II (P<0.001) and stage III (P=0.003). Of note, F-NLR also effectively stratified overall survival (OS) irrespective of age, adjuvant chemotherapy administration, tumor location and histological grade (all P<0.05). Furthermore, F-NLR and TNM stratified 5-year OS from 61% (F-NLR 0) to 15% (F-NLR 2) and from 92% (stage I) to 37% (stage III), respectively. Utilizing both F-NLR and TNM, 5-year OS ranged from 93% (F-NLR 0, TNM I) to 6% (F-NLR 2, TNM III). Conclusion The F-NLR score independently predicts outcomes in GC patients after curative surgery. Therefore, it should be implemented in routine clinical practice for identifying more high-risk patients.
An Energy-Efficient Online-Learning Stochastic Computational Deep Belief Network

Deep neural networks (DNNs) are effective machine learning models to solve a large class of recognition problems, including the classification of nonlinearly separable patterns. The training of DNNs is, however, particularly difficult due to the large size and high energy consumption of the networks. Recently, stochastic computation (SC) has been considered to implement DNNs to reduce the hardware cost. However, it requires a large number of random number generators (RNGs) and long stochastic sequences that lower the energy efficiency of the network. To overcome these limitations, we propose the design of an energy-efficient deep belief network (DBN) with online learning capacity based on stochastic computation. In the SC-DBN, a reconfigurable structure is utilized to implement the fast greedy learning algorithm and an adaptive moment estimation (ADAM) circuit is designed to improve the speed of the training process. An approximate SC activation unit (A-SCAU) is further designed to implement different types of activation functions in the neurons. The A-SCAU is immune to signal correlations, so the RNGs can be shared among all neurons in the same layer with no accuracy loss. The area and energy of the proposed design are less than 5.5% and 3.7% (or 29.3% and 33.3%) of a pipelined 32-bit floating-point (or an 8-bit fixed-point) implementation. The proposed SC-DBN design achieves a higher classification accuracy compared with the fixed-point implementation. The accuracy is in a range of 0.12% to 0.37% lower than the floating-point design with a significantly lower (or slightly higher) energy consumption than the pipelined (or non-pipelined) circuit for both online learning and inference processes.
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization

Semantic specialization is a process of fine-tuning pre-trained distributional word vectors using external lexical knowledge (e.g., WordNet) to accentuate a particular semantic relation in the specialized vector space. While post-processing specialization methods are applicable to arbitrary distributional vectors, they are limited to updating only the vectors of words occurring in external lexicons (i.e., seen words), leaving the vectors of all other words unchanged. We propose a novel approach to specializing the full distributional vocabulary. Our adversarial post-specialization method propagates the external lexical knowledge to the full distributional space. We exploit words seen in the resources as training examples for learning a global specialization function. This function is learned by combining a standard L2-distance loss with a adversarial loss: the adversarial component produces more realistic output vectors. We show the effectiveness and robustness of the proposed method across three languages and on three tasks: word similarity, dialog state tracking, and lexical simplification. We report consistent improvements over distributional word vectors and vectors specialized by other state-of-the-art specialization frameworks. Finally, we also propose a cross-lingual transfer method for zero-shot specialization which successfully specializes a full target distributional space without any lexical knowledge in the target language and without any bilingual data.
Epigenetic Enhancer Marks and Transcription Factor Binding Influence Vκ Gene Rearrangement in Pre-B Cells and Pro-B Cells

To date there has not been a study directly comparing relative Igκ rearrangement frequencies obtained from genomic DNA (gDNA) and cDNA and since each approach has potential biases, this is an important issue to clarify. Here we used deep sequencing to compare the unbiased gDNA and RNA Igκ repertoire from the same pre-B cell pool. We find that ~20% of Vκ genes have rearrangement frequencies ≥2-fold up or down in RNA vs. DNA libraries, including many members of the Vκ3, Vκ4, and Vκ6 families. Regression analysis indicates Ikaros and E2A binding are associated with strong promoters. Within the pre-B cell repertoire, we observed that individual Vκ genes rearranged at very different frequencies, and also displayed very different Jκ usage. Regression analysis revealed that the greatly unequal Vκ gene rearrangement frequencies are best predicted by epigenetic marks of enhancers. In particular, the levels of newly arising H3K4me1 peaks associated with many Vκ genes in pre-B cells are most predictive of rearrangement levels. Since H3K4me1 is associated with long range chromatin interactions which are created during locus contraction, our data provides mechanistic insight into unequal rearrangement levels. Comparison of Igκ rearrangements occurring in pro-B cells and pre-B cells from the same mice reveal a pro-B cell bias toward usage of Jκ-distal Vκ genes, particularly Vκ10-96 and Vκ1-135. Regression analysis indicates that PU.1 binding is the highest predictor of Vκ gene rearrangement frequency in pro-B cells. Lastly, the repertoires of iEκ−/− pre-B cells reveal that iEκ actively influences Vκ gene usage, particularly Vκ3 family genes, overlapping with a zone of iEκ-regulated germline transcription. These represent new roles for iEκ in addition to its critical function in promoting overall Igκ rearrangement. Together, this study provides insight into many aspects of Igκ repertoire formation.
The Presence of Cu Facilitates Adsorption of Tetracycline (TC) onto Water Hyacinth Roots

Batch experiments were conducted to investigate the adsorption characteristics of tetracycline (TC), and the interactive effects of copper (Cu) on the adsorption of TC onto water hyacinth roots. TC removal efficiency by water hyacinth roots was ranging from 58.9% to 84.6%, for virgin TC, 1:1 TC-Cu and 1:2 TC-Cu. The Freundlich isotherm model and the pseudo-second-order kinetic model fitted the adsorption data well. Thermodynamics parameters ΔG0 for TC were more negative in the TC plus Cu than the TC-only treatments, indicating the spontaneity of TC adsorption increased with increasing of Cu concentrations. An elevated temperature was associated with increasing adsorption of TC by water hyacinth roots. The additions of Cu(II) significantly increased TC adsorption onto water hyacinth roots within the pH range 4 to 6, because copper formed a strong metal bridge between root surface and TC molecule, facilitating the adsorption of TC by roots. However, Cu(II) hindered TC adsorption onto water hyacinth roots on the whole at pH range from 6–10, since the stronger electrostatic repulsion and formation of CuOH+ and Cu(OH)2. Therefore, the interaction between Cu(II) and TC under different environmental conditions should be taken into account to understand the environmental behavior, fate, and ecotoxicity of TC.