Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of yeast culture and feed antibiotics on ruminal fermentation and site and extent of digestion in beef heifers fed high grain rations.

The study objective was to investigate the effects of site of delivering Saccharomyces cerevisiae fermentation product (SCFP) on ruminal pH and fermentation characteristics, and the site and extent of feed digestion in the digestive tract of beef heifers fed high-grain diets. Examining the ruminal and postruminal effects of SCFP is important for understanding the potential use of SCFP as an alternative for current industry-standard antibiotics used in beef cattle rations. Five beef heifers (initial BW = 561 ± 11.7 kg) equipped with ruminal and duodenal cannulas were used in a 5 × 5 Latin square design with 28-d periods, including 21 d for adaption and 7 d for data collection. Five treatments were as follows: 1) control diet that contained 10% barley silage and 90% barley concentrate mix (DM basis); 2) control diet supplemented with antibiotics (ANT; 330-mg monensin/d and 110-mg tylosin/d per head); 3) ruminal (top dress) delivery of SCFP (rSCFP; NaturSafe, Diamond V, 18-g SCFP/d); 4) duodenal delivery of SCFP (dSCFP; 18-g SCFP/d, via duodenal cannula); and 5) a combination of rSCFP and dSCFP (rdSCFP; 18-g rSCFP and 18-g dSCFP). Intake of DM tended (P < 0.10) to be greater by heifers fed rdSCFP than those fed control, ANT and rSCFP diets. Minimum ruminal pH was greater (P < 0.05) with rSCFP than control and rdSCFP treatments. The duration of ruminal pH < 5.6 tended (P < 0.10) to be less with rSCFP than control and ANT. Heifers fed the rSCFP diet had greater (P < 0.03) protozoa counts and proportion of acetate than the other treatments. Nutrient flows to the duodenum did not differ (P > 0.19), whereas the amount of truly fermented OM was greater (P < 0.03) with rdSCFP than the other treatments. Ruminal OM digestibility was highest with rSCFP and rdSCFP, intermediate with dSCFP and ANT, and lowest with control (P < 0.03). Intestinal digestibility was similar among treatments. As a result, total tract digestibility of OM (P < 0.07) and NDF (P < 0.01) was greater with rSCFP and rdSCFP than control and ANT. Fecal IgA concentration was highest with ANT, intermediate with dSCFP and rdSCFP, and lowest with control and rSCFP (P < 0.03). These results demonstrate that feeding SCFP improved stability of ruminal pH and digestibility of OM and NDF. Delivery of SCFP to the duodenum appeared to have little effect on nutrient digestibility but improved intestinal immune response. Feeding SCFP performed better or at least equal to antibiotics currently used in beef cattle rations and could be a natural alternative for beef cattle production.
Strategies for sustainable funding: a metadata review of the independent library district model

ABSTRACT Public libraries have seen a decline in public funding, relying on a mixture of external types of funding to keep services running. With state and local governments continuing to face financial pressures, some libraries have explored alternative ways to maximize funding, by forming library districts. This report aimed at exploring library districts in three states, Colorado, Michigan, and Oregon, which have similar funding structures. The report found more stable and predictable funding patterns in library districts versus their counterparts and provides evidence that is a strong alternative for public libraries seeking a change in the way they obtain funds.
Structured Variationally Auto-encoded Optimization

We tackle the problem of optimizing a black-box objective function deﬁned over a highly-structured input space. This problem is ubiquitous in machine learning. Inferring the structure of a neural network or the Automatic Statistician (AS), where the kernel combination for a Gaussian process is optimized, are two of many possible examples. We use the AS as a case study to describe our approach, that can be easily generalized to other domains. We propose an Structure Generating Variational Auto-encoder (SG-VAE) to embed the original space of kernel combinations into some low-dimensional continuous manifold where Bayesian optimization (BO) ideas are used. This is possible when structural knowledge of the problem is available, which can be given via a simulator or any other form of generating potentially good solutions. The right exploration-exploitation balance is imposed by propagating into the search the uncertainty of the latent space of the SG-VAE, that is computed using variational inference. The key aspect of our approach is that the SG-VAE can be used to bias the search towards relevant regions, making it suitable for transfer learning tasks. Several experiments in various application domains are used to illustrate the utility and generality of the approach described in this work.
Knowledge Diffusion for Neural Dialogue Generation

End-to-end neural dialogue generation has shown promising results recently, but it does not employ knowledge to guide the generation and hence tends to generate short, general, and meaningless responses. In this paper, we propose a neural knowledge diffusion (NKD) model to introduce knowledge into dialogue generation. This method can not only match the relevant facts for the input utterance but diffuse them to similar entities. With the help of facts matching and entity diffusion, the neural dialogue generation is augmented with the ability of convergent and divergent thinking over the knowledge base. Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats. The experiment results also show that our model outperforms competitive baseline models significantly.
Accumulation of deleterious mutations in the domestic yak genome

Deleterious mutations play an important functional role, affecting trait phenotypes in ways that decrease the fitness of organisms. Estimating the frequency of occurrence and abundance has been a topic of much interest, especially in crops and livestock. The processes of domestication and breeding allow deleterious mutations to persist at high frequency, and identifying such deleterious mutations is particularly important for breed improvement. Here, we assessed genome-wide patterns of deleterious variation in 59 domestic and 13 wild yaks using genome resequencing data. Based on the intersection of results given by three methods (provean, polyphen2 and sift4g), we identified 3187 putative deleterious mutation sites affecting 2586 genes in domestic yaks and 2067 affecting 1701 genes in wild yaks. Multiple lines of evidence indicate a significant increase in the load of deleterious mutations in domesticated yaks compared to wild yaks. Private deleterious genes were found to be associated with the perception of smell and detection of chemical stimulus. We also identified 36 genes related to Mendelian genetic diseases involved in sensory perception, skeletal development and the nervous and immune systems. This study not only adds to the understanding of the genetic basis of yak domestication but also provides a rich catalog of variants that will facilitate future breeding-related research on the yak genome and on other bovid species.
Hierarchical Multi-Label Classification Networks

One of the most challenging machine learning problems is a particular case of data classiﬁcation in which classes are hierarchically structured and objects can be assigned to multiple paths of the class hierarchy at the same time. This task is known as hierarchical multi-label classiﬁcation (HMC), with applications in text classiﬁcation, image annotation, and in bioinformatics problems such as protein function prediction. In this paper, we propose novel neural network architectures for HMC called HMCN, capable of simultaneously optimizing local and global loss functions for discovering local hierarchical class-relationships and global information from the entire class hierarchy while penalizing hierarchical violations. We evaluate its performance in 21 datasets from four distinct domains, and we compare it against the current HMC state-of-the-art approaches. Results show that HMCN substantially outperforms all baselines with statistical signiﬁcance, arising as the novel state-of-the-art for HMC.
Binary Cooperative Prodrug Nanoparticles Improve Immunotherapy by Synergistically Modulating Immune Tumor Microenvironment

Checkpoint blockade immunotherapy is promising for clinical treatment of various malignant tumors. However, checkpoint blockade immunotherapy suffers from a low response rate due to insufficient tumor immunogenicity and the immunosuppressive tumor microenvironment (ITM). In this study, a tumor‐microenvironment‐activatable binary cooperative prodrug nanoparticle (BCPN) is rationally designed to improve immunotherapy by synergistically modulating the immune tumor microenvironment. BCPN is purely constructed from a tumor acidity and reduction dual‐responsive oxaliplatin (OXA) prodrug for triggering immunogenic cell death (ICD) and eliciting antitumor immunity, and a reduction‐activatable homodimer of NLG919 for inactivating indoleamine 2,3‐dioxygenase 1, which is a key regulator for ITM. Upon tumor‐acidity‐triggered cleavage of the poly(ethylene glycol) shell, PN shows negative to positive charge switch for enhanced tumor accumulation and deep penetration. OXA and NLG919 are then activated in the tumor cells via glutathione‐mediated reduction. It is demonstrate that activated OXA promotes intratumoral accumulation of cytotoxic T lymphocytes by triggering ICD of cancer cells. Meanwhile, NLG919 downregulates IDO‐1‐mediated immunosuppression and suppresses regulatory T cells. Most importantly, PN shows much higher efficiency than free OXA or the combination of free OXA and NLG919 to regress tumor growth and prevent metastasis of mouse models of both breast and colorectal cancer.
Primary care provider adherence to an alert for intensification of diabetes blood pressure medications before and after the addition of a “chart closure” hard stop

Objective To evaluate provider responses to a narrowly targeted "Best Practice Advisory" (BPA) alert for the intensification of blood pressure medications for persons with diabetes before and after implementation of a "chart closure" hard stop, which is non-interruptive but demands an action or dismissal before the chart can be closed. Materials and Methods We designed a BPA that fired alerts within an electronic health record (EHR) system during outpatient encounters for patients with diabetes when they had elevated blood pressures and were not on angiotensin receptor blocking medications. The BPA alerts were implemented in eight primary care practices within UCLA Health. We compared data on provider responses to the alerts before and after implementing a "chart closure" hard stop, and we conducted chart reviews to adjudicate each alert's appropriateness. Results Providers responded to alerts more often after the "chart closure" hard stop was implemented (P < .001). Among 284 alert firings over 16 months, we judged 107 (37.7%) to be clinically unnecessary or inappropriate based on chart review. Among the remainder, which represent clear opportunities for treatment, providers ordered the indicated medication more often (41% vs 75%) after the "chart closure" hard stop was implemented (P = .001). Discussion The BPA alerts for diabetes and blood pressure control achieved relatively high specificity. The "chart closure" hard stop improved provider attention to the alerts and was effective at getting patients treated when they needed it. Conclusion Targeting specific omitted medication classes can produce relatively specific alerts that may reduce alert fatigue, and using a "chart closure" hard stop may prompt providers to take action without excessively disrupting their workflow.
The Therapeutic and Pathogenic Role of Autophagy in Autoimmune Diseases

Autophagy is a complicated cellular mechanism that maintains cellular and tissue homeostasis and integrity via degradation of senescent, defective subcellular organelles, infectious agents, and misfolded proteins. Accumulating evidence has shown that autophagy is involved in numerous immune processes, such as removal of intracellular bacteria, cytokine production, autoantigen presentation, and survival of lymphocytes, indicating an apparent and important role in innate and adaptive immune responses. Indeed, in genome-wide association studies, autophagy-related gene polymorphisms have been suggested to be associated with the pathogenesis of several autoimmune and inflammatory disorders, such as systemic lupus erythematosus, psoriasis, rheumatoid arthritis, inflammatory bowel disease, and multiple sclerosis. In addition, conditional knockdown of autophagy-related genes in mice displayed therapeutic effects on several autoimmune disease models by reducing levels of inflammatory cytokines and autoreactive immune cells. However, the inhibition of autophagy accelerates the progress of some inflammatory and autoimmune diseases via promotion of inflammatory cytokine production. Therefore, this review will summarize the current knowledge of autophagy in immune regulation and discuss the therapeutic and pathogenic role of autophagy in autoimmune diseases to broaden our understanding of the etiopathogenesis of autoimmune diseases and shed light on autophagy-mediated therapies.
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences

We propose a hierarchical model for sequential data that learns a tree on-the-fly, i.e. while reading the sequence. In the model, a recurrent network adapts its structure and reuses recurrent weights in a recursive manner. This creates adaptive skip-connections that ease the learning of long-term dependencies. The tree structure can either be inferred without supervision through reinforcement learning, or learned in a supervised manner. We provide preliminary experiments in a novel Math Expression Evaluation (MEE) task, which is created to have a hierarchical tree structure that can be used to study the effectiveness of our model. Additionally, we test our model in a well-known propositional logic and language modelling tasks. Experimental results have shown the potential of our approach.
In vitro activities of antifungals alone and in combination with tigecycline against Candida albicans biofilms

Background Candida may form biofilms, which are thought to underlie the most recalcitrant infections. Methods In this study, activities of antifungal agents alone and in combination with tigecycline against planktonic cells and mature and developing biofilms of Candida albicans isolates were evaluated. Results Amphotericin B and echinocandins were found to be the most effective agents against mature biofilms, whereas the least effective agent was fluconazole. Furthermore, the most effective anti-fungal monotherapies against biofilm formation were amphotericin B and anidulafungin, and the least effective monotherapy was itraconazole. The combination of tigecycline and amphotericin B yielded synergistic effects, whereas combinations containing itraconazole yielded antagonist effects against planktonic cells. The combination of tigecycline and caspofungin exhibited maximum efficacy against mature biofilms, whereas combinations containing itraconazole exhibited minimal effects. Combinations of tigecycline with amphotericin B or anidulafungin were highly effective against C. albicans biofilm formation. Discussion In summary, tigecycline was highly active against C. albicans particularly when combined with amphotericin B and echinocandins.
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator

Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model 2) they are an "end-to-end" approach, directly optimizing the performance metric of interest 3) they inherently allow for richly parameterized policies. A notable drawback is that even in the most basic continuous control problem (that of linear quadratic regulators), these methods must solve a non-convex optimization problem, where little is understood about their efficiency from both computational and statistical perspectives. In contrast, system identification and model based planning in optimal control theory have a much more solid theoretical footing, where much is known with regards to their computational and statistical properties. This work bridges this gap showing that (model free) policy gradient methods globally converge to the optimal solution and are efficient (polynomially so in relevant problem dependent quantities) with regards to their sample and computational complexities.
Box cutter

Packed atlases, consisting of 2D parameterized charts, are ubiquitously used to store surface signals such as texture or normals. Tight packing is similarly used to arrange and cut-out 2D panels for fabrication from sheet materials. Packing efficiency, or the ratio between the areas of the packed atlas and its bounding box, significantly impacts downstream applications. We propose Box Cutter, a new method for optimizing packing efficiency suitable for both settings. Our algorithm improves packing efficiency without changing distortion by strategically cutting and repacking the atlas charts or panels. It preserves the local mapping between the 3D surface and the atlas charts and retains global mapping continuity across the newly formed cuts. We balance packing efficiency improvement against increase in chart boundary length and enable users to directly control the acceptable amount of boundary elongation. While the problem we address is NP-hard, we provide an effective practical solution by iteratively detecting large rectangular empty spaces, or void boxes, in the current atlas packing and eliminating them by first refining the atlas using strategically placed axis-aligned cuts and then repacking the refined charts. We repeat this process until no further improvement is possible, or until the desired balance between packing improvement and boundary elongation is achieved. Packed chart atlases are only useful for the applications we address if their charts are overlap-free; yet many popular parameterization methods, used as-is, produce atlases with global overlaps. Our pre-processing step eliminates all input overlaps while explicitly minimizing the boundary length of the resulting overlap-free charts. We demonstrate our combined strategy on a large range of input atlases produced by diverse parameterization methods, as well as on multiple sets of 2D fabrication panels. Our framework dramatically improves the output packing efficiency on all inputs; for instance with boundary length increase capped at 50% we improve packing efficiency by 68% on average.
A Simple and Effective Method to Detect Orthogonal Vanishing Points in Uncalibrated Images of Man-Made Environments

This paper presents an effective and easy-to-implement algorithm to compute orthogonal vanishing points in uncalibrated images of man-made scenes. The main contribution is to estimate the zenith and the horizon line before detecting the vanishing points, using simple properties of the central projection and exploiting accumulations of oriented segments around the horizon. Our method is fast and yields an accuracy comparable, and even better in some cases, to that of state-of-the-art algorithms.
Distinction Between Variability-Based Modulation and Mean-Based Activation Revealed by BOLD-fMRI and Eyes-Open/Eyes-Closed Contrast

Recent BOLD-fMRI studies have revealed spatial distinction between variability- and mean-based between-condition differences, suggesting that BOLD variability could offer complementary and even orthogonal views of brain function with traditional activation. However, these findings were mainly observed in block-designed fMRI studies. As block design may not be appreciate for characterizing the low-frequency dynamics of BOLD signal, the evidences suggesting the distinction between BOLD variability and mean are less convincing. Based on the high reproducibility of signal variability modulation between continuous eyes-open (EO) and eyes-closed (EC) states, here we employed EO/EC paradigm and BOLD-fMRI to compare variability- and mean-based EO/EC differences while the subjects were in light. The comparisons were made both on block-designed and continuous EO/EC data. Our results demonstrated that the spatial patterns of variability- and mean-based EO/EC differences were largely distinct with each other, both for block-designed and continuous data. For continuous data, increases of BOLD variability were found in secondary visual cortex and decreases were mainly in primary auditory cortex, primary sensorimotor cortex and medial nuclei of thalamus, whereas no significant mean-based differences were observed. For the block-designed data, the pattern of increased variability resembled that of continuous data and the negative regions were restricted to medial thalamus and a few clusters in auditory and sensorimotor networks, whereas activation regions were mainly located in primary visual cortex and lateral nuclei of thalamus. Furthermore, with the expanding window analyses we found variability results of continuous data exhibited a rather slower dynamical process than typically considered for task activation, suggesting block design is less optimal than continuous design in characterizing BOLD variability. In sum, we provided more solid evidences that variability-based modulation could represent orthogonal views of brain function with traditional mean-based activation.
Cross-sectional study of the association between healthcare professionals’ empathy and burnout and the number of annual primary care visits per patient under their care in Spain

Objective The aim of this study was to evaluate the association between physician and nurse self-reported empathy and burnout and the number of annual primary care visits per patient under their care. Methods Design: A cross-sectional survey study was conducted from January 2013 to July 2014. Site: The 22 primary care centres of the Lleida Health Region in Spain. Main outcome measures: The Jefferson Scale of Physician Empathy and the Maslach Burnout Inventory were used to measure empathy and burnout, respectively. The number of visits and the number of diagnoses coded per visit were obtained through the Region’s electronic health record. Results Two hundred and sixty-seven healthcare professionals (physicians and nurses, 52.6% participation of the total in the region) with 301 657 patients under their care. Healthcare professionals’ degree of burnout and empathy was associated with the number of annual visits per patient under their care. Burned out nurses and physicians received fewer visits (4.5vs3.7 in nurses and 18.1vs18.9 in physicians), whereas more empathic physicians received more visits per patient (19.4vs17.2, p<0.05) and documented more diagnoses per visit (10.2vs9.7, p=0.001). Less burned out and less empathic nurses documented more diagnoses per visit (10.2vs10.0 and 8.2vs9.9, p<0.05). Conclusions The number of annual primary care visits per patient that healthcare professionals receive is closely associated with healthcare professionals’ empathy and burnout. These results should serve to promote empathic skills and establish organisational changes that promote efficiency in the practice and, in turn, reduce the degree of burnout of healthcare professionals.