Miracle-dz/UniFAR-FTUs huggingface dataset:
Fermentation and purification strategies for the production of betulinic acid and its lupane‐type precursors in Saccharomyces cerevisiae

Microbial production of plant derived, biologically active compounds has the potential to provide economic and ecologic alternatives to existing low productive, plant‐based processes. Current production of the pharmacologically active cyclic triterpenoid betulinic acid is realized by extraction from the bark of plane tree or birch. Here, we reengineered the reported betulinic acid pathway into Saccharomyces cerevisiae and used this novel strain to develop efficient fermentation and product purification methods. Fed‐batch cultivations with ethanol excess, using either an ethanol‐pulse feed or controlling a constant ethanol concentration in the fermentation medium, significantly enhanced production of betulinic acid and its triterpenoid precursors. The beneficial effect of excess ethanol was further exploited in nitrogen‐limited resting cell fermentations, yielding betulinic acid concentrations of 182 mg/L, and total triterpenoid concentrations of 854 mg/L, the highest concentrations reported so far. Purification of lupane‐type triterpenoids with high selectivity and yield was achieved by solid‐liquid extraction without prior cell disruption using polar aprotic solvents such as acetone or ethyl acetate and subsequent precipitation with strong acids. This study highlights the potential of microbial production of plant derived triterpenoids in S. cerevisiae by combining metabolic and process engineering.
Revealing Matrices

This chapTeR uncoveRs soMe noninTuiTive sTRucTuRes in curated databases arising from local activity by the curators as well as from the heterogeneity of the source data. Our example is taken from the fields of art history and archaeology, as these are my trained areas of expertise. However, the findings I present here—namely, that it is possible to visualize the complex structures of databases—can also be demonstrated for many other structured data collections, including biological research databases and massive collaborative efforts such as DBpedia, Freebase, or the Semantic Web. All these data collections share a number of properties, which are not straight-forward but are important if we want to make use of the recorded data or if we have to decide where and how our energies and funds should be spent in improving them. Curated databases in art history and archaeology come in a number of flavors, such as library catalogs and bibliographies, image archives, museum inventories, and more general research databases. All of them can be built on extremely complicated data models, and given enough data, even the most boring examples—however simple they may appear on the surface—can be confusingly complex in any single link relation. The thematic coverage potentially includes all man-made objects: the Library of Congress Classification System, for example, deals with everything from artists and cookbooks to treatises in physics. As our example
Biogenic synthesis of PdNCs embedded α-Fe2O3 microspheres using Myrtus cumini L. leaf extract and a Box-Behnken optimization of its Fenton-like catalytic activity

Microspheres (MSs) of paramagnetic iron oxide (α-Fe2O3) embedded with palladium nanocubes (PdNCs) were biosynthesized using Myrtus cumini L. leaf extract. The synthesized PdNCs embedded α-Fe2O3 microspheres were characterized using FTIR, XRD and SEM/EDX. The FTIR spectrum of the Myrtus cumini L. leaf extract confirmed the presence of phenol, ester and amine groups that were involved in the bio-reduction of PdCl2 to PdNCs. SEM/EDX revealed that the PdNCs with size range of 15–250 nm were present on the surface of the synthesized microspheres. Box-Behnken Design (BBD) was employed for optimization of Methyl Orange (MO) decolorization using α-Fe2O3MSs and PdNCs embedded α-Fe2O3MSs at pH (1, 2 and 3), catalyst dosage (1, 3 and 5 g L-1) and reaction irradiation time (30, 60 and 90 min). It was found that PdNCs embedded α-Fe2O3MSs had maximum decolorization of 96.56 % at pH 1.72, catalyst dosage 2.69 g L-1 and irradiation time 68.34 min.
Interactive Grasping for High-genus Objects using Conﬁguration Space Learning

—We present an interactive grasping algorithm for manipulating high-genus complex rigid objects, without assuming the geometric shape of the object. We use machine learning and particle swarm optimization to automatically compute the grasp poses for a given object and a multi-ﬁngered hand. Our algorithm can autonomously search for feasible grasp poses and determine their grasp stability. We express non-penetration and physically-based conditions as constraints in a grasp stability function. We test our algorithm in simulation using a few high-genus complex objects with different sizes and shapes. Our algorithm is applicable to a wide range of complex objects and multi-ﬁngered robotic hands.
Using Model Checking in the Design of a Sensor Network Protocol

: We investigate how the PRISM and Alloy model checkers can be used in the design of a sensor network communication protocol. We introduce the two model checkers and illustrate how Alloy can be used to specify and analyse an existing communication protocol WirelessHART. We then propose how PRISM and Alloy will be used in the design of a new protocol, Ctrl-MAC, which is currently being developed as part of an EPSRC funded program grant, S4: Science of Sensor System Software. The aim is to exploit the strengths of each approach to allow us to select parameters and conﬁgurations to optimise the protocol.
Shotgun Wedding: Fiscal and Monetary Policy

This review describes interactions between monetary and fiscal policies that affect equilibrium price levels and interest rates by critically surveying theories about ( a) optimal anticipated inflation, ( b) optimal unanticipated inflation, and ( c) conditions that secure a nominal anchor in the sense of a unique price level path. We contrast incomplete theories whose inputs are budget-feasible sequences of government-issued bonds and money with complete theories whose inputs are bond/money strategies described as sequences of functions that map time t histories into time t government actions. We cite historical episodes that confirm the theoretical insight that lines of authority between a Treasury and a central bank can be ambiguous, obscure, and fragile.
Research on Hybrid Feature Selection Method Based on Iterative Approximation Markov Blanket

The basic experimental data of traditional Chinese medicine are generally obtained by high-performance liquid chromatography and mass spectrometry. The data often show the characteristics of high dimensionality and few samples, and there are many irrelevant features and redundant features in the data, which bring challenges to the in-depth exploration of Chinese medicine material information. A hybrid feature selection method based on iterative approximate Markov blanket (CI_AMB) is proposed in the paper. The method uses the maximum information coefficient to measure the correlation between features and target variables and achieves the purpose of filtering irrelevant features according to the evaluation criteria, firstly. The iterative approximation Markov blanket strategy analyzes the redundancy between features and implements the elimination of redundant features and then selects an effective feature subset finally. Comparative experiments using traditional Chinese medicine material basic experimental data and UCI's multiple public datasets show that the new method has a better advantage to select a small number of highly explanatory features, compared with Lasso, XGBoost, and the classic approximate Markov blanket method.
Stabilizing Role of Calcium Store-Dependent Plasma Membrane Calcium Channels in Action-Potential Firing and Intracellular Calcium Oscillations

In many biological systems, cells display spontaneous calcium oscillations (CaOs) and repetitive action-potential firing. These phenomena have been described separately by models for intracellular inositol trisphosphate (IP3)-mediated CaOs and for plasma membrane excitability. In this study, we present an integrated model that combines an excitable membrane with an IP3-mediated intracellular calcium oscillator. The IP3 receptor is described as an endoplasmic reticulum (ER) calcium channel with open and close probabilities that depend on the cytoplasmic concentration of IP3 and Ca 21 . We show that simply combining this ER model for intracellular CaOs with a model for membrane excitability of normal rat kidney (NRK) fibroblasts leads to instability of intracellular calcium dynamics. To ensure stable long-term periodic firing of action potentials and CaOs, it is essential to incorporate calcium transporters controlled by feedback of the ER store filling, for example, store-operated calcium channels in the plasma membrane. For low IP3 concentrations, our integrated NRK cell model is at rest at � 70 mV. For higher IP3 concentrations, the CaOs become activated and trigger repetitive firing of action potentials. At high IP3 con- centrations, the basal intracellular calcium concentration becomes elevated and the cell is depolarized near � 20 mV. These predictions are in agreement with the different proliferative states of cultures of NRK fibroblasts. We postulate that the stabilizing role of calcium channels and/or other calcium transporters controlled by feedback from the ER store is essential for any cell in which calcium signaling by intracellular CaOs involves both ER and plasma membrane calcium fluxes.
Integrating Clustering Method in Compactly Supported Radial Basis Function for Surface Approximation

—Radial basis function, one of the mesh-free meth-ods, makes it convenient to interpolate and approximate high dimensional data points. Interpolation fits the data points exactly, whereas approximation fits the data points approximately. The approximation method is more appropriate for large and noisy data points in comparison to the interpolation method. Com-pactly supported radial basis function is extensively discussed in the literature of approximation theory. It also becomes popular as a result of its computational advantages. Hence, in this study, Wendland’s compactly supported radial basis function is chosen. The main contribution of this paper is integrating the clustering procedures to determine the recommended number of reference points, which is expected to provide as few reference points as possible for surface approximation. Three bivariate test functions are selected to generate a moderately large amount of data points followed by the process of adding different levels of noise in order to observe the effectiveness of the proposed method. The results obtained are further confirmed and analysed through error analysis. Finally, the experimental results show that the surfaces are well approximated from the recommended number of reference points gained from the proposed method.
Metformin delivery using chitosan-capped gold nanoparticles in glioblastoma cell lines

Abstract Introduction: Metformin (MET), an old anti-diabetic drug, has proven unexpected anti-glioblastoma effects, by impacting cell proliferation, migration and invasion. However, its remarkable anti-cancer efficacy is mainly limited to the use of high millimolar concentrations in in vitro studies, which are hard to be attained in the clinical setting. Aim: The aim of this paper was to synthetize gold nanoparticles loaded with MET and to test if an enhanced drug delivery via nanotechnology could overcome the limitations of small drug concentrations. Materials and Methods: Gold nanoparticles were functionalized with chitosan (GNPc) and loaded with 80 μM of MET. Their size, zeta potential and stability were characterized and their internalization within tumor cells was assayed through dark field microscopy. Three primary glioblastoma stem cell lines were treated with 5, 10 and 20 μg/mL concentrations of nanoparticles and irradiated. The anti-tumoral effect was evaluated through the MTT cell viability assay. Results: MET-GNPc are easily synthetized and have a positive zeta potential, spherical shape and a median size of 26 nm. MET-GNPc have an increased cell internalization and affect the viability of all three glioblastoma cell lines used compared to control and free MET. However, their anti-cancer effect is not statistically different when compared to GNPc, although a slight tendency to a better response may be observed. Conclusion:Despite an increased cell internalization, the small micromolar concentrations of metformin does not bring an additional benefit to chitosan-based GNPs. Novel delivery methods being able to carry a higher drug concentration of metformin should be tested.
Meaning-Making and Early Literacy: A Future Challenge for Swedish Teachers and Preschool Teachers?

This article explores tentative links between findings from a study of how Early Literacy was nurtured in 40 Swedish preschool groups and another study focusing Student Teachers’ ability to perform text analyses of basals used for literacy learning. Our inquiries focused how meaning-making practices were implemented in preschools and how critical awareness was fostered within the preschools’ literacy practices. In addition, we wanted to build an understanding of how Student Teachers perceived the contents in basals, in order to highlight Student Teachers’ own capacity to read critically, so that teachers’ use of textbooks fosters children’s critical awareness and challenges taken-for-granted perceptions of contents. 40 preschool teachers/preschool teacher training students made observations of read-alouds with the children during one week. The informants also wrote narratives where they described their practices, which enabled us to use both statistical descriptions and the informants perceptions as expressed in their own words as a basis for our analyses. The preschool-teachers indicated that both lack of knowledge of the importance of ‘talking text’ and lack of time made both read-alouds and follow-up activities very scarce. When 20 Student Teachers examined the contents of eight reading materials they arrived at very shallow analyses of the contents and discourses visible between the lines were seldom detected. Taken together these findings imply that critical awareness and meaningmaking appears to be crucial development areas in both in-service training and teacher training, in particular, as the needs of children with diverse backgrounds may be neglected when meaning-making is not given first priority.
Electron Microscopic Findings in Patients with Glomerulonephritis in Sudan

1 Department of Medical Laboratory Sciences, Faculty of Applied Medical sciences, Northern Border University, Arar, KSA 2 Department of histopathology, Faculty of Medical laboratory sciences, Al-Zaiem Al-Azhari, University, Khartoum north, Sudan 3 Department of Pathology, faculty of medicine, Al-Zaiem Al-Azhari, University, Khartoum north, Sudan 4 Department of Clinical Laboratory Sciences, College of Applied Medical sciences, Jouf University, Qurayyat, KSA 5 College of Applied Medical Science, Jouf University, Sakaka, Jouf, Saudi Arabia 6 Department of Pathology, College of Medicine, University of Hail, Kingdom of Saudi Arabia
Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection

Our works experimented DeepLabV3+ with different backbones on a large volume of steel images aiming to automatically detect different types of steel defects. Our methods applied random weighted augmentation to balance different defects types in the training set. And then applied DeeplabV3+ model three different backbones, ResNet, DenseNet and EfficientNet, on segmenting defection regions on the steel images. Based on experiments, we found that applying ResNet101 or EfficientNet as backbones could reach the best IoU scores on the test set, which is around 0.57, comparing with 0.325 for using DenseNet. Also, DeepLabV3+ model with ResNet101 as backbone has the fewest training time.
Automated Spelling Correction for Clinical Text Mining in Russian

The main goal of this paper is to develop a spell checker module for clinical text in Russian. The described approach combines string distance measure algorithms with technics of machine learning embedding methods. Our overall precision is 0.86, lexical precision - 0.975 and error precision is 0.74. We develop spell checker as a part of medical text mining tool regarding the problems of misspelling, negation, experiencer and temporality detection.
Learning to Visually Navigate in Photorealistic Environments Without any Supervision

Learning to navigate in a realistic setting where an agent must rely solely on visual inputs is a challenging task, in part because the lack of position information makes it difficult to provide supervision during training. In this paper, we introduce a novel approach for learning to navigate from image inputs without external supervision or reward. Our approach consists of three stages: learning a good representation of first-person views, then learning to explore using memory, and finally learning to navigate by setting its own goals. The model is trained with intrinsic rewards only so that it can be applied to any environment with image observations. We show the benefits of our approach by training an agent to navigate challenging photo-realistic environments from the Gibson dataset with RGB inputs only.
Designing Precise and Robust Dialogue Response Evaluators

Automatic dialogue response evaluator has been proposed as an alternative to automated metrics and human evaluation. However, existing automatic evaluators achieve only moderate correlation with human judgement and they are not robust. In this work, we propose to build a reference-free evaluator and exploit the power of semi-supervised training and pretrained (masked) language models. Experimental results demonstrate that the proposed evaluator achieves a strong correlation (> 0.6) with human judgement and generalizes robustly to diverse responses and corpora. We open-source the code and data in https://github.com/ZHAOTING/dialog-processing.
Spatial Priming for Detecting Human-Object Interactions

The relative spatial layout of a human and an object is an important cue for determining how they interact. However, until now, spatial layout has been used just as side-information for detecting human-object interactions (HOIs). In this paper, we present a method for exploiting this spatial layout information for detecting HOIs in images. The proposed method consists of a layout module which primes a visual module to predict the type of interaction between a human and an object. The visual and layout modules share information through lateral connections at several stages. The model uses predictions from the layout module as a prior to the visual module and the prediction from the visual module is given as the final output. It also incorporates semantic information about the object using word2vec vectors. The proposed model reaches an mAP of 24.79% for HICO-Det dataset which is about 2.8% absolute points higher than the current state-of-the-art.
Beyond Disentangled Representations: An Attentive Angular Distillation Approach to Large-scale Lightweight Age-Invariant Face Recognition

Disentangled representations have been commonly adopted to Age-invariant Face Recognition (AiFR) tasks. However, these methods have reached some limitations with (1) the requirement of large-scale face recognition (FR) training data with age labels, which is limited in practice; (2) heavy deep network architecture for high performance; and (3) their evaluations are usually taken place on age-related face databases while neglecting the standard large-scale FR databases to guarantee its robustness. This work presents a novel Attentive Angular Distillation (AAD) approach to Large-scale Lightweight AiFR that overcomes these limitations. Given two high-performance heavy networks as teachers with different specialized knowledge, AAD introduces a learning paradigm to efficiently distill the age-invariant attentive and angular knowledge from those teachers to a lightweight student network making it more powerful with higher FR accuracy and robust against age factor. Consequently, AAD approach is able to take the advantages of both FR datasets with and without age labels to train an AiFR model. Far apart from prior distillation methods mainly focusing on accuracy and compression ratios in closed-set problems, our AAD aims to solve the open-set problem, i.e. large-scale face recognition. Evaluations on LFW, IJB-B and IJB-C Janus, AgeDB and MegaFace-FGNet with one million distractors have demonstrated the efficiency of the proposed approach. This work also presents a new longitudinal face aging (LogiFace) database for further studies in age-related facial problems in future.
$μ$Tiles: Efficient Intra-Process Privilege Enforcement of Memory Regions

With the alarming rate of security advisories and privacy concerns on connected devices, there is an urgent need for strong isolation guarantees in resource-constrained devices that demand very lightweight solutions. However, the status quo is that Unix-like operating systems do not offer privilege separation inside a process. Lack of practical fine-grained compartmentalization inside a shared address space leads to private data leakage through applications' untrusted dependencies and compromised threads. To this end, we propose $\mu$Tiles, a lightweight kernel abstraction and set of security primitives based on mutual distrust for intra-process privilege separation, memory protection, and secure multithreading. $\mu$Tiles takes advantage of hardware support for virtual memory tagging (e.g., ARM memory domains) to achieve significant performance gain while eliminating various hardware limitations. Our results (based on OpenSSL, the Apache HTTP server, and LevelDB) show that $\mu$Tiles is extremely lightweight (adds $\approx 10KB$ to kernel image) for IoT use cases. It adds negligible runtime overhead ($\approx 0.5\%-3.5\%$) and is easy to integrate with existing applications for providing strong privilege separation.
Parsing-Based View-Aware Embedding Network for Vehicle Re-Identification

Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario. The main challenges of this task are the large intra-instance distance caused by different views and the subtle inter-instance discrepancy caused by similar vehicles. In this paper, we propose a parsing-based view-aware embedding network (PVEN) to achieve the view-aware feature alignment and enhancement for vehicle ReID. First, we introduce a parsing network to parse a vehicle into four different views and then align the features by mask average pooling. Such alignment provides a fine-grained representation of the vehicle. Second, in order to enhance the view-aware features, we design a common-visible attention to focus on the common visible views, which not only shortens the distance among intra-instances, but also enlarges the discrepancy of inter-instances. The PVEN helps capture the stable discriminative information of vehicle under different views. The experiments conducted on three datasets show that our model outperforms state-of-the-art methods by a large margin.