Miracle-dz/UniFAR-FTUs huggingface dataset:
Injection of an SV40 transcriptional terminator causes embryonic lethality: a possible zebrafish model for screening nonhomologous end-joining inhibitors

Introduction DNA repair by the nonhomologous end joining (NHEJ) pathway promotes tumor recurrence after chemotherapy and radiotherapy. Discovery of rapid and high-throughput techniques to screen for an effective NHEJ inhibitor drug is imperative for the suppression of NHEJ during tumor treatment. However, traditional screening methods are too cumbersome to meet the current need. Zebrafish is an ideal model for drug screening due to the specificity of its early embryonic development and similarity of tumor cell generation. By exploiting the high frequency of NHEJ in early embryonic development, we established a model that uses a transcriptional terminator signal fragment from the Simian virus 40 (SV40) to cause embryonic lethality. SV40 fragment-induced embryonic lethality was alleviated by 5,6-bis ((E)-benzylideneamino)-2-mercaptopyrimidin-4-ol or C18H14N4OS (SCR7), an NHEJ inhibitor. Materials and methods A 122 bp SV40 terminator fragment (10 ng/µL) was microinjected into zebrafish zygotes. SV40 fragment integration into the zebrafish embryonic genome was detected by Southern blot using a DNA probe for the SV40 terminator. Embryonic lethality rates were observed 24 and 48 h after microinjection. A nonhomologous recombinant inhibitor, SCR7 (5 µM), was used to alleviate embryonic lethality. Results Microinjection of zebrafish embryos with the SV40 terminator fragment (10 ng/µL) caused a progressive increase in mortality over time. Using Southern blots, we confirmed that SV40 terminator sequences were integrated into the zebrafish embryonic genome. This phenomenon was effectively alleviated by addition of SCR7. Conclusion Injection of an SV40 terminator into zebrafish embryos may cause embryonic lethality due to NHEJ during early zebrafish development. The high mortality of zebrafish embryos could be alleviated by using the NHEJ inhibitor, SCR7. The zebrafish model presented here is simpler and more convenient than traditional methods of screening for NHEJ inhibitors and can be utilized in large-scale drug screens for NHEJ inhibitors and for the development of novel anticancer drugs.
Working Dogs Drinking a Nutrient-Enriched Water Maintain Cooler Body Temperature and Improved Pulse Rate Recovery After Exercise

Exercise-related physiological changes were evaluated in hydrated, exercise-conditioned working dogs with free access to tap water (TW) with or without a nutrient-enriched water supplement (NW). Physiological samples and measures were collected before and after work-related field tasks in a warm and moderately humid ambient environment. In a cross-over design study, 12 dogs (age range 8–23 months) were evaluated on 3 separate occasions within each period with exercise bouts up to 30 min, on days −4, 3, and 11. Dogs were offered either ad libitum TW or portion-controlled NW daily plus ad libitum TW. Prior to and serially after exercise, pulse rate (PR), core (BTcore) and ear (BTear) temperature were recorded. Urine was collected first thing in the morning, whereas blood samples collected and body weight (BW) recorded pre- and immediately post exercise. Ambient temperature was above 21.7°C (71°F) and relative humidity ranged from 36 to 76%. Activity parameters, AM urine measures, post-exercise percent change of BW, resting PR and resting BTcore did not differ between treatment groups on any exercise day. At the completion of exercise, mean BTcore for all dogs ranged from 104.8 to 105.6°F. Immediate post-exercise BTear was always lower compared to BTcore and means ranged from 103.3 to 104.0°F. The effect of time was highly significant (P < 0.001) for both BT measures with both BTcore and BTear recovering to resting levels by 60 min post exercise. PR and several blood values showed a significant main effect of time. Over the recovery period, dogs in the NW group had lower mean BTear and PR by 0.6°F and 3.4 bpm, respectively. Daily ingestion of a NW in combination with free access to TW can reduce the post-exercise-related BTcore and BTear hyperthermia, and improve pulse rate recovery following exercise in this population of working dogs undergoing 30 min bout of exercise.
Enterococcus faecium HDRsEf1 elevates the intestinal barrier defense against enterotoxigenic Escherichia coli and regulates occludin expression via activation of TLR‐2 and PI3K signalling pathways

Previously, we isolated a novel probiotic strain, designated HDRsEf1. In this study, we investigated the effects of this probiotic strain on intestinal barrier function and how it regulates the tight junction protein occludin in vitro. We used an ETEC‐infected mouse model for the in vivo experiment. Briefly, 40 ICR mice were randomly divided into four groups: control group, assigned to saline gavage; prevention group, given HDRsEf1 before and saline after infection with ETEC; infection group, given saline both before and after infection with ETEC; treatment group, given saline before and HDRsEf1 after infection with ETEC. The weight loss was alleviated both in the prevention and treatment groups. The ETEC‐induced intestinal inflammation was alleviated and the occludin mRNA expression levels in the jejuna of infected mice were increased in the prevention group. We explored the mechanism by which HDRsEf1 regulates occludin expression in vitro and found that HDRsEf1 prevented the downregulation of occludin expression in the prevention group. Simultaneously, we found that toll‐like receptor‐2 (TLR‐2) and phosphoinositide 3‐kinase (PI3K) play an important role in maintaining occludin expression. Therefore, we concluded that HDRsEf1 can prevent ETEC‐induced infection by enhancing the intestinal barrier function and increasing the expression levels of occludin.
The impact of the use of new technologies on farmers’ wheat yield in Ethiopia: evidence from a randomized control trial

Abstract In 2013, Ethiopia's Agricultural Transformation Agency introduced the Wheat Initiative to increase smallholder productivity. In this article, we measure the impacts of the Wheat Initiative package of technologies, and its marketing assistance component alone, on yields among a promotional group of farmers. The package includes improved techniques, improved inputs, and a guaranteed market for the crop. Relying on crop‐cut measures and farmers’ own assessments, we find that full package led to an average 14% higher yields. Implementation of the Wheat Initiative was successful in making certified seed and fertilizer accessible to farmers and increasing their uptake, though only 61% of the intervention group adopted row planting and few farmers received marketing assistance. The measured yield difference may underestimate the true yield difference associated with the technology because of incomplete adoption of the recommended practices by intervention farmers and adoption of some practices by control farmers.
Exploring Biofeedback with a Tangible Interface Designed for Relaxation

Anxiety is a common health issue that can occur throughout one's existence. In this pilot study we explore an alternative technique to regulate it: biofeedback. The long-term objective is to offer an ecological device that could help people cope with anxiety, by exposing their inner state in a comprehensive manner. We propose a first iteration of this device, "Inner Flower" , that uses heart rate to adapt a breathing guide to the user; and we investigate its efficiency and usability. Traditionally, such device requires user's full attention. We propose an ambient modality during which the device operates in the peripheral vision. Beside comparing "Ambient" and "Focus" conditions, we also compare the biofeedback with a sham feedback (fixed breathing guide). We found that the Focus group demonstrated higher relaxation and performance on a cognitive task (N-back). However, there was no noticeable effect of the Ambient feedback, and the biofeedback condition did not yield any significant difference when compared to the sham feedback. These results, while promising, highlight the pitfalls of any research related to biofeedback, where it is difficult to fully comprehend the underlying mechanisms of such technique.
Superpixel Segmentation Based Synthetic Classifications with Clear Boundary Information for a Legged Robot

In view of terrain classification of the autonomous multi-legged walking robots, two synthetic classification methods for terrain classification, Simple Linear Iterative Clustering based Support Vector Machine (SLIC-SVM) and Simple Linear Iterative Clustering based SegNet (SLIC-SegNet), are proposed. SLIC-SVM is proposed to solve the problem that the SVM can only output a single terrain label and fails to identify the mixed terrain. The SLIC-SegNet single-input multi-output terrain classification model is derived to improve the applicability of the terrain classifier. Since terrain classification results of high quality for legged robot use are hard to gain, the SLIC-SegNet obtains the satisfied information without too much effort. A series of experiments on regular terrain, irregular terrain and mixed terrain were conducted to present that both superpixel segmentation based synthetic classification methods can supply reliable mixed terrain classification result with clear boundary information and will put the terrain depending gait selection and path planning of the multi-legged robots into practice.
Alginate/Hydroxyapatite-Based Nanocomposite Scaffolds for Bone Tissue Engineering Improve Dental Pulp Biomineralization and Differentiation

Tissue engineering is widely recognized as a promising approach for bone repair and reconstruction. Several attempts have been made to achieve materials that must be compatible, osteoconductive, and osteointegrative and have mechanical strength to provide a structural support. Composite scaffolds consisting in biodegradable natural polymers are very promising constructs. Hydroxyapatite (HAp) can support alginate as inorganic reinforcement and osteoconductive component of alginate/HAp composite scaffolds. Therefore, HAp-strengthened polymer biocomposites offer a solid system to engineer synthetic bone substitutes. In the present work, HAp was incorporated into an alginate solution and internal gelling was induced by addition of slowly acid-hydrolyzing D-gluconic acid delta-lactone for the direct release of calcium ions from HAp. It has been previously demonstrated that alginate-based composites efficiently support adhesion of cancer bone cell lines. Human dental pulp stem cells (DPSCs) identified in human dental pulp are clonogenic cells capable of differentiating in multiple lineage. Thus, this study is aimed at verifying the mineralization and differentiation potential of human DPSCs seeded onto scaffolds based on alginate and nano-hydroxyapatite. For this purpose, gene expression profile of early and late mineralization-related markers, extracellular matrix components, viability parameters, and oxidative stress occurrence were evaluated and analyzed. In summary, our data show that DPSCs express osteogenic differentiation-related markers and promote calcium deposition and biomineralization when growing onto Alg/HAp scaffolds. These findings confirm the use of Alg/HAp scaffolds as feasible composite materials in tissue engineering, being capable of promoting a specific and successful tissue regeneration as well as mineralized matrix deposition and sustaining natural bone regeneration.
Taxonomy of Big Data: A Survey

The Big Data is the most popular paradigm nowadays and it has almost no untouched area. For instance, science, engineering, economics, business, social science, and government. The Big Data are used to boost up the organization performance using massive amount of dataset. The Data are assets of the organization, and these data gives revenue to the organizations. Therefore, the Big Data is spawning everywhere to enhance the organizations' revenue. Thus, many new technologies emerging based on Big Data. In this paper, we present the taxonomy of Big Data. Besides, we present in-depth insight on the Big Data paradigm.
QuMan

Modern data centers consolidate workloads to increase server utilization and reduce total cost of ownership, and cope with scaling limitations. However, server resource sharing introduces performance interference across applications and, consequently, increases performance volatility, which negatively affects user experience. Thus, a challenging problem is to increase server utilization while maintaining application QoS. In this article, we present QuMan, a server resource manager that uses application isolation and profiling to increase server utilization while controlling degradation of application QoS. Previous solutions, either estimate interference across applications and then restrict colocation to “compatible” applications, or assume that application requirements are known. Instead, QuMan estimates the required resources of applications. It uses an isolation mechanism to create properly-sized resource slices for applications, and arbitrarily colocates applications. QuMan’s mechanisms can be used with a variety of admission control policies, and we explore the potential of two such policies: (1) A policy that allows users to specify a minimum performance threshold and (2) an automated policy, which operates without user input and is based on a new combined QoS-utilization metric. We implement QuMan on top of Linux servers, and we evaluate its effectiveness using containers and real applications. Our single-node results show that QuMan balances highly effectively the tradeoff between server utilization and application performance, as it achieves 80% server utilization while the performance of each application does not drop below 80% the respective standalone performance. We also deploy QuMan on a cluster of 100 AWS instances that are managed by a modified version of the Sparrow scheduler [37] and, we observe a 48% increase in application performance on a highly utilized cluster, compared to the performance of the same cluster under the same load when it is managed by native Sparrow or Apache Mesos.
A Facile Flow-Casting Production of Bioactive Glass Coatings on Porous Titanium for Bone Tissue Engineering

Additive manufacturing enabled the fabrication of porous titanium (PT) with customized porosity and mechanical properties. However, functionalization of PT surfaces with bioactive coatings is being challenged due to sophisticated geometry and highly porous structure. In this study, a facile flow-casting technique was developed to produce homogeneous 45S5 bioactive glass (BG) coatings on the entire surface of PT. The coating weight as a function of BG concentration in a BG-PVA slurry was investigated to achieve controllable coating yield without blocking macropore structure. The annealing-treated BG coating not only exhibited compact adhesion confirmed by qualitative sonication treatment, but also enhanced the mechanical properties of PT scaffolds. Moreover, in-vitro assessments of BG-coated PT cultured with MC3T3-E1 cells was carried out having in mind their potential as bioactive bone implants. The experimental results in this study offer a simple and versatile approach for the bio-functionalization of PT and other porous biomedical devices.
Attentive Sequence to Sequence Translation for Localizing Clips of Interest by Natural Language Descriptions

We propose a novel attentive sequence to sequence translator (ASST) for clip localization in videos by natural language descriptions. We make two contributions. First, we propose a bi-directional Recurrent Neural Network (RNN) with a finely calibrated vision-language attentive mechanism to comprehensively understand the free-formed natural language descriptions. The RNN parses natural language descriptions in two directions, and the attentive model attends every meaningful word or phrase to each frame, thereby resulting in a more detailed understanding of video content and description semantics. Second, we design a hierarchical architecture for the network to jointly model language descriptions and video content. Given a video-description pair, the network generates a matrix representation, i.e., a sequence of vectors. Each vector in the matrix represents a video frame conditioned by the description. The 2D representation not only preserves the temporal dependencies of frames but also provides an effective way to perform frame-level video-language matching. The hierarchical architecture exploits video content with multiple granularities, ranging from subtle details to global context. Integration of the multiple granularities yields a robust representation for multi-level video-language abstraction. We validate the effectiveness of our ASST on two large-scale datasets. Our ASST outperforms the state-of-the-art by $4.28\%$ in Rank$@1$ on the DiDeMo dataset. On the Charades-STA dataset, we significantly improve the state-of-the-art by $13.41\%$ in Rank$@1,IoU=0.5$.
IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings

Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%.
Paraphrases as Foreign Languages in Multilingual Neural Machine Translation

Paraphrases, rewordings of the same semantic meaning, are useful for improving generalization and translation. Unlike previous works that only explore paraphrases at the word or phrase level, we use different translations of the whole training data that are consistent in structure as paraphrases at the corpus level. We treat paraphrases as foreign languages, tag source sentences with paraphrase labels, and train on parallel paraphrases in the style of multilingual Neural Machine Translation (NMT). Our multi-paraphrase NMT that trains only on two languages outperforms the multilingual baselines. Adding paraphrases improves the rare word translation and increases entropy and diversity in lexical choice. Adding the source paraphrases boosts performance better than adding the target ones, while adding both lifts performance further. We achieve a BLEU score of 57.2 for French-to-English translation using 24 corpus-level paraphrases of the Bible, which outperforms the multilingual baselines and is +34.7 above the single-source single-target NMT baseline.
Montclair State University Digital Montclair State University Digital

: Scanning probe microscopy (SPM) techniques have become indispensable tools for studying nano- and microscale materials and processes but su ﬀ er from a trade-o ﬀ between resolution and areal scan rate that limits their utility for a number of applications and sample types. Here, we present a novel approach to SPM imaging based on combining nonlocal scanning line probes with compressed sensing (CS) signal analysis methods. Using scanning electrochemical microscopy (SECM) as an exemplar SPM technique, we demonstrate this approach using continuous microband electrodes, or line probes, which are used to perform chemical imaging of electrocatalytic Pt discs deposited on an inert substrate. These results demonstrate the potential to achieve high areal SPM imaging rates using nonlocal scanning probes and CS image reconstruction.
Phase 1, single‐dose escalating study of marzeptacog alfa (activated), a recombinant factor VIIa variant, in patients with severe hemophilia

Essentials Marzeptacog alfa (activated) [MarzAA] is a novel variant of activated human factor VII. A phase 1 dose escalation trial of MarzAA was conducted in subjects with severe hemophilia. MarzAA was safe and tolerated at intravenous doses up to 30 μg kg−1 Data observed support further trials for hemophilia patients with inhibitors to factors VIII/IX.
Dense 3D Object Reconstruction from a Single Depth View

In this paper, we propose a novel approach, <italic>3D-RecGAN++</italic>, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with <italic>a high resolution of</italic> <inline-formula><tex-math notation="LaTeX">$\boldsymbol{256^3}$</tex-math><alternatives><mml:math><mml:msup><mml:mn mathvariant="bold">256</mml:mn><mml:mn mathvariant="bold">3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="yang-ieq1-2868195.gif"/></alternatives></inline-formula> by recovering the occluded/missing regions. The key idea is to combine the generative capabilities of 3D encoder-decoder and the conditional adversarial networks framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.
Bioaugmentation with Mixed Hydrogen-Producing Acetogen Cultures Enhances Methane Production in Molasses Wastewater Treatment

Hydrogen-producing acetogens (HPA) have a transitional role in anaerobic wastewater treatment. Thus, bioaugmentation with HPA cultures can enhance the chemical oxygen demand (COD) removal efficiency and CH4 yield of anaerobic wastewater treatment. Cultures with high degradation capacities for propionic acid and butyric acid were obtained through continuous subculture in enrichment medium and were designated as Z08 and Z12. Bioaugmentation with Z08 and Z12 increased CH4 production by glucose removal to 1.58. Bioaugmentation with Z08 and Z12 increased the COD removal rate in molasses wastewater from 71.60% to 85.84%. The specific H2 and CH4 yields from COD removal increased by factors of 1.54 and 1.63, respectively. Results show that bioaugmentation with HPA-dominated cultures can improve CH4 production from COD removal. Furthermore, hydrogen-producing acetogenesis was identified as the rate-limiting step in anaerobic wastewater treatment.