Miracle-dz/UniFAR-FTUs huggingface dataset:
TITLE : 1 Large-scale quantification of human osteocyte lacunar morphological 2 biomarkers as assessed by ultra-high-resolution desktop micro-computed 3 tomography 4

AUTHORS: Elliott Goff1, Federica Buccino1, Chiara Bregoli1, Jonathan P. McKinley1,2, Basil 5 Aeppli1, Robert R. Recker3, Elizabeth Shane4, Adi Cohen4, Gisela Kuhn1, Ralph Müller1* 6 AFFILIATIONS: 7 1 Institute for Biomechanics, ETH Zurich, Zurich, Switzerland 8 2 Department of Mechanical Engineering, University of California Berkeley, Berkeley, CA, USA 9 3 Department of Medicine, Creighton University Medical Center, Omaha, NE, USA 10 4 Department of Medicine, Columbia University College of Physicians & Surgeons, New York, 11 NY, USA 12 13
Sex and origin-specific inbreeding effects on flower attractiveness to specialised pollinators

We investigate whether inbreeding has particularly fatal consequences for dioecious plants by diminishing their floral attractiveness and the associated pollinator visitation rates disproportionally in females. We also test whether the magnitude of such effects depends on the evolutionary histories of plant populations. We recorded spatial, olfactory, colour and rewarding flower attractiveness traits as well as pollinator visitation rates in experimentally inbred and outbred, male and female Silene latifolia plants from European and North American populations differing in their evolutionary histories. We found that inbreeding specifically impairs spatial and olfactory attractiveness. Our results support that sex-specific selection and gene expression partially magnified these inbreeding costs for females, and that divergent evolutionary histories altered the genetic architecture underlying inbreeding effects across population origins. Moreover, they highlight that inbreeding effects on olfactory attractiveness have a huge potential to disrupt interactions among plants and specialist moth pollinators, which are mediated by elaborate chemical communication.
Metabolic engineering of Saccharomyces cerevisiae for efficient conversions of glycerol to ethanol

Glycerol is an eco-friendly solvent enhancing plant-biomass decomposition through the glycell process to bio-based chemicals. Nonetheless, the lack of efficient conversion of glycerol by natural Saccharomyces cerevisiae restrains many biorefineries-scenarios. Here, we outline a comprehensive strategy for generating efficient glycerol fermenting S. cerevisiae via rewriting the oxidation of cytosolic nicotinamide adenine dinucleotide by O2-dependent dynamic shuttle while abolishing glycerol phosphorylation and biosynthesis pathways. By following a vigorous glycerol oxidative pathway, our engineered strain demonstrated a breakthrough in conversion efficiency (CE), reaching up to 0.49g-ethanol/g-glycerol—98% of theoretical conversion—with production rate >1 gL−1h−1 on rich-medium. Interestingly, the glycerol consumption and its fermentation unrepressed during the mixing by glucose until the strain produced >86 g/L of bioethanol with 92.8% of CE. Moreover, fine-tuning of O2 boosted the production rate to >2 gL−1h−1with 82% of CE. Impressively, the strategy flipped the ancestral yeast even from non-growing on glycerol, on the minimal medium, to a fermenting strain with productivities 0.25-0.5 gL−1h−1 and 84-78% of CE, respectively. Our findings promote utlising glycerol efficiently in several eco-friendly biorefinery approaches. Summary Efficient fermentation of glycerol in S. cerevisiae was established by comprehensive engineering of glycerol pathways and rewriting NADH pathway.
DeepFrag: a deep convolutional neural network for fragment-based lead optimization

Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is less often applied to lead optimization, the process of identifying chemical fragments that might be added to a known ligand to improve its binding affinity. We here describe a deep convolutional neural network that predicts appropriate fragments given the structure of a receptor/ligand complex. In an independent benchmark of known ligands with missing (deleted) fragments, our DeepFrag model selected the known (correct) fragment from a set over 6,500 about 58% of the time. Even when the known/correct fragment was not selected, the top fragment was often chemically similar and may well represent a valid substitution. We release our trained DeepFrag model and associated software under the terms of the Apache License, Version 2.0. A copy can be obtained free of charge from http://durrantlab.com/deepfragmodel.
Characterization of a New Biofunctional, Exolytic Alginate Lyase from Tamlana sp. s12 with High Catalytic Activity and Cold-Adapted Features

Alginate, a major acidic polysaccharide in brown algae, has attracted great attention as a promising carbon source for biorefinery systems. Alginate lyases, especially exo-type alginate lyase, play a critical role in the biorefinery process. Although a large number of alginate lyases have been characterized, few can efficiently degrade alginate comprised of mannuronate (M) and guluronate (G) at low temperatures by means of an exolytic mode. In this study, the gene of a new exo-alginate lyase—Alys1—with high activity (1350 U/mg) was cloned from a marine strain, Tamlana sp. s12. When sodium alginate was used as a substrate, the recombinant enzyme showed optimal activity at 35 °C and pH 7.0–8.0. Noticeably, recombinant Alys1 was unstable at temperatures above 30 °C and had a low melting temperature of 56.0 °C. SDS and EDTA significantly inhibit its activity. These data indicate that Alys1 is a cold-adapted enzyme. Moreover, the enzyme can depolymerize alginates polyM and polyG, and produce a monosaccharide as the minimal alginate oligosaccharide. Primary substrate preference tests and identification of the final oligosaccharide products demonstrated that Alys1 is a bifunctional alginate lyase and prefers M to G. These properties make Alys1 a valuable candidate in both basic research and industrial applications.
Electrochemical Detection of Protein Using a Double Aptamer Sandwich

Abstract We have developed an electrochemical sensor system for protein detection using two different antithrombin aptamers that recognize different parts of the target protein. In this case, thrombin was detected using a sandwich format comprised of a glucose dehydrogenase (GDH)‐labeled antithrombin aptamer and another aptamer that was immobilized onto the gold electrode for capturing the thrombin onto it. The electric current generated by the GDH when glucose was added was measured, and 1 µM of thrombin was detected selectively.
A membrane filtration procedure for the enrichment, separation, and flame atomic absorption spectrometric determinations of some metals in water, hair, urine, and fish samples

AbstractA cellulose acetate membrane filter was applied for solid-phase extraction of Cd(II), Co(II), Cu(II), Fe(III), Ni(II), and Pb(II) from urine, hair, fish, and various water samples. The analyte ions were adsorbed on membrane filter as Cochenille red complexes and then eluted using 10 mL 3 M CH3COOH. The matrix effects from concomitants and analytical parameters, including pH, sample volume, membrane type, and flow rates, were investigated. The analyte ion detection limit for the proposed method was in the range 1.0–5.2 μg/L. The preconcentration factors were calculated as 30 for Cu(II), 40 for Co(II), Ni(II), Cd(II) as well as Pb(II), and 50 for Fe(III). The proposed method was validated by analyzing certified reference materials. This method was successfully applied to water, hair, fish, and urine samples with good results.
Intrusion Detection System Based on Gradient Corrected Online Sequential Extreme Learning Machine

Nowadays, Intrusion Detection System (IDS) is an active research topic with machine learning nature. A single-hidden layer feedforward neural network (SLFN) trained on the approach of extreme learning machine (ELM) is used for (IDS). The encouraging factors for its usage are its fast learning and supportability of sequential learning in its online sequential extreme learning machine (OSELM) variant. An issue with OSELM that has been addressed by researchers is its random weights nature of the input-hidden layer. Most approaches use the concept of metaheuristic optimisation for determining the optimal weights of OSELM and resolve the random weight. However, metaheuristic approaches require many trials to determine the optimal one. Hence, there is concern about the convergence aspect and speed. This article proposes a novel approach for finding the optimal weights of the input-hidden layer. This article presents an approach for an integration between OSELM and back-propagation designated as (OSELM-BP). After integration, BP changes the random weights iteratively and uses an iterated evaluation of the generated error for feedback correction of the weights. The approach is evaluated based on various scenarios of activation functions for OSELM on the one hand and the number of iterations for BP on the other. An extensive evaluation of the approach and comparison with the original OSELM reveal a superiority of OSELM-BP in reaching optimal accuracy with a small number of iterations.
Detailed 3D Human Body Reconstruction From a Single Image Based on Mesh Deformation

In the research fields of 3D animation, virtual reality, etc., it is a very important task to reconstruct a 3D human body model with the surface textures. However, due to factors such as diversities of human body poses, uncertainty of depth, and a wide range of viewpoints, the reconstruction of the 3D human body from a single image is an enormous challenge. To solve the aforementioned challenge, this paper proposes a novel architecture to reconstruct the 3D human mesh with great surface details. The key point of the proposed architecture is to combine the topology of the SMPL (Skinned Multi-Person Linear) template mesh and the flexible mesh deformation. Firstly, the typical 2D CNN architecture, ResNet-50, extracts the perceptual features from the input image. The image features are fused into the 3D space via perceptual feature transformation. Our approach does not rely on the parameter space, although the mesh topology of the SMPL is preserved. The approach fully exploits the ability of the SMPL to represent various human body shapes, while avoiding the problem of the spatial loss caused by the parametric method. Secondly, to recover more details of the human body, a graph-based convolutional network is adopted to deform the 3D human body mesh. The network allows starting with fewer vertices and distributing vertex features to the representative positions in a coarse-to-fine manner, which fully utilizes the mesh topology. Experiments demonstrate that our approach can generate 3D human body mesh with great details qualitatively. Compared with the state-of-the-art methods, our approach also achieves better accuracy quantitatively.
Impacts of Emerging Information Technologies on Supply Chains: A Systematic Literature Review

With the development of Industry 4.0 strategy, emerging information technologies exert tremendous influence on supply chains. Whereas more and more scholars pay attention to this phenomenon, it remains a question that which emerging information technologies impact supply chains the most. Using the bibliometrics method, this study identifies the influencing information technologies and analyzes the opportunities and challenges of applying them in supply chains.
Computer Simulation of a Series-Connected Induction Generator and Determination of Minimum Required Capacitance for Self-Excitation for Wind Energy Applications

A series-connected induction machine is obtained when the stator and rotor windings of a conventional three-phase wound-rotor induction machine are connected in series but with opposite phase sequence. For this machine to become self-excited, the machine terminals must be connected in parallel with a three-phase capacitor bank. In this paper, Direct-Quadrature (D-Q) theory and space vectors are applied to investigate the performance of a series-connected induction machine operating as a standalone generator as in a wind-turbine application, for example. Accurate models of the generator are derived at no-load and with resistive and resistive-inductive loads. The minimum capacitance for self-excitation at a constant speed is also obtained at no-load and at a loaded condition. Numerical simulations predict the generator output voltage as a function of the amount of capacitance at the machine terminals during steady-state operation.
Flicker-Free Resonant LED Driver With High Power Factor and Passive Current Balancing

A flicker-free resonant LED driver with high power factor (PF) and passive current balancing is proposed and analysed in this paper. The proposed LED driver are composed a buck power factor correction (PFC) unit and a resonant dc-dc unit by sharing an active switch. Based on this topology, the low output current ripple can be achieved via small voltage ripple on bulk capacitor and fast voltage-mode control loop. By the charge balance for resonant capacitor, the output current of each LED string can be balanced. In a half line cycle, owing to the small voltage ripple on bulk capacitor of pre-stage output, the duty cycle of active switch is almost constant. Therefore, when the inductor current of buck PFC unit operates in discontinuous conduction mode, high PF can be realized. Moreover, as the resonance is existed between resonant capacitor and leakage inductor of transformer, the diodes in resonant dc-dc unit can achieve zero-current turn-off. Thus, the efficiency can be improved. Finally, an 82W LED driver prototype with three-output is built to verify the studied results.
Paraphrase Identification Based on ResNeXt

The misuse of paraphrase generation technology has flooded the Internet with low-quality, unclear manuscripts, deteriorating the user’s reading experience. This paper mainly investigates into the paraphrase identification from the deep learning perspective, and proposes a paraphrase identification model based on deep ResNeXt. Our model captures the semantic and syntactic features of the given text with the help of deep neural network. Specifically, we select multiple convolution kernels with different sizes to extract the features of word sequences of different lengths. At the same time, the residual block which helps build a paraphrase identification model suitable for various current platforms. The data preprocessing includes data cleaning, word segmentation, and word vectorization. Experiments on benchmark dataset MSRPC show that our model achieved a strong result of 85.4% on accuracy rate.
Komplexone XXIX. Ein grosser Chelateffekt besonderer Art

Complexing agents of the type: (HOOCCH2)(2=N(CH2))n N = (CH2COOH)2 with n-values from 2–8 have been investigated. The stability constants of the 1:1 complexes of Ca2+, Cd2+ and Hg2+ have been determined for nearly the whole series of these complexons and a few values have been obtained also for the complexes of Mg2+, Sr2+, Ba2+ and Go2+. There is generally a strong decrease in complex stability when n is increased from 2 t o 4, where an almost constant value is reached which does not alter anymore to an appreciable extent hy a further increase of the chain length 11. The mercury complexes behave exceptionally. Replacement of one or two CH2-groups in the chain connecting the two nitrogens by O, S, and N(CH2) increases the complex stability to a great extent if new 5-membered chelate rings can be formed by coordination of this heteroatom.
Consensus+Innovations Distributed Estimation With Random Network Graphs, Observation Matrices and Noises

We analyze the convergence of distributed consensus+innovations parameter estimation algorithms over uncertain networks with communication noises. The linear observation of the unknown parameter by each agent, the underlying noisy communication network, and the noises therein are respectively characterized by a sequence of randomly time-varying observation matrices, random digraphs, and random variables. At each time step, every agent updates its estimation upon its measurement and interaction with its neighbors iteratively. By martingale convergence, algebraic graph and stochastic time-varying system theories, we prove that the algorithm gains can be designed properly such that all agents' estimates converge to the real parameter in mean square if the observation matrices and communication graphs satisfy the stochastic spatio-temporal persistence of excitation condition.
Actuator Saturation Compensation for Fast Tool Servo Systems With Time Delays

Fast tool servo systems with high precision and high speed pose significant challenges to servo control at the nano-scale. The saturation of the control actions, time delays and system uncertainties further complicate the control challenges. To address these problems, we propose a Smith predictor based robust anti-windup scheme in this article, such that high performance servo with nano-accuracy can be achieved in the case of saturations, time delays and model uncertainties. According to the input/output based equivalent representation, the saturation nonlinearity is transformed to the dead zone nonlinearity fulfilling a sector condition. Based on the Popov criterion, the stability conditions for the anti-windup compensator are derived, which are further formulated as an $H_\infty $ optimization problem with robustness against model uncertainties. The effectiveness of the proposed control architecture is verified through real-time experiments, where excellent servo performance, saturation compensation and robustness are demonstrated, outperforming existing results.
On Attack Mitigation in Supervisory Control Systems: A Tolerant Control Approach

This paper investigates attack mitigation problem in supervisory control of discrete event systems. We consider the scenario where the system is subject to actuator enablement attack. We explicitly distinguish between controllable events and defendable events; the former are events that can be disabled by the normal supervisor but may be subject to attack, while the latter are events that can be defensed (disabled definitely) by the mitigation module but possibly with higher costs. The objective is to design an attack mitigation strategy to prevent serious damage from attack. We formulate the attack mitigation problem as a tolerant control problem under partial observation. Particularly, in addition to guarantee safety, we aim to maximize the desirable behavior (normal specification) while minimize the tolerable behavior (safe but not desirable). We provide an effective online algorithm for solving this problem, which yields a novel attack mitigation strategy that generalizes the existing one in the literature. Specifically, we show that the proposed strategy may still prevent damage even when the safe-controllability condition, which is required by the existing strategy, does not hold.
Short-Circuit Fault Detection and Remedial in Full-Bridge Rectifier of Series Resonant DC-DC Converter Based on Inductor Voltage Signature

This paper proposes a method to detect the short-circuit fault (SCF) in the full-bridge diode rectifier at the output side of the series resonant dc-dc converter (SRC). The full-bridge inverter is employed at the input side. The utilized diagnostic/signature signal to detect the SCF is the voltage of the resonant inductor, which increases after fault occurrence. Also, sensing the inductor voltage is an easy way through inserting a few turns into the core of the resonant inductor without affecting its operation. The sensed inductor voltage is compared with a threshold voltage, which is calculated based on the converter parameters. After the identification of the SCF fault, the input-side bridge is reconfigured into a half-bridge structure to recover the normal operation of the converter because the output side is operating as a voltage doubler in this case. Since in the proposed method, localization of the exact faulty diode is not considered important in the further operation, fast recovery of the fault condition can be achieved. The simulation results provided show the feasibility of the detection algorithm and the remedial action at different operating conditions.
A Novel Algorithm of Expansion Term Selection and Weight Assignment for Query Expansion of Chinese EMR Retrieval

The techniques of information retrieval eliminate the difficulty of finding specific information in electronic medical records (EMR), and the methods of query expansion (QE) improve the recall of EMR retrieval. However, most existing QE methods of EMR retrieval can’t get high-quality expansion terms and corresponding weights for Chinese EMR retrieval because of low-quality sources and improper calculation methods. In this paper, we propose a novel algorithm of expansion term selection and weight assignment for QE of Chinese EMR retrieval based on the clinical needs and unique characteristics of Chinese medical terms. The algorithm first selects expansion terms from a high quality Chinese medical knowledge graph and standard medical term sets, which can ensure the quality of expansion terms. Then it assigns weights to the selected expansion terms based on semantic similarities and manually designed expansion categories that reflect the opinions of medical experts. Experiment results show that our algorithm gets higher-quality expansion terms and more rational weights compared with four benchmark algorithms, using Precision at 10, Recall, Mean Average Precision and Binary Preference-based Measure as evaluation metrics, which implies that our algorithm can significantly improve the effectiveness of QE.
Board Level Solder Joint Reliability Design and Analysis of FOWLP

This paper presents a comprehensive board level solder joint reliability study under thermal cycling (TC) loading by both numerical simulation and experimental test. TC profile is from −40°C to 125°C. In numerical simulation, both epoxy molding compound (EMC) and dielectric are modeled as viscoelastic materials. SAC is modeled as a creep material while the remaining materials are assumed to be elastic. The critical solder joint location predicted by modeling agrees well with the experimental result. Results show: (1) lower CTE PCB improves the board level solder joint fatigue life as creep strain energy density range of solder joint is reduced when PCB CTE is reduced from 17 ppm/°C to 10ppm/°C; (2) thinned PCB thickness improves the board level solder joint fatigue life as creep strain energy density range of solder joint is reduced when PCB thickness is reduced from 1.5 mm to 1 mm. Finally, life prediction model for solder joints of FOWLP has been successfully established as well.
Performance and Variability Analysis on 7nm FINFET Circuits at Near Threshold

This paper studies the performance and variability characteristics of 7nm FINFET circuits. Studies are carried out on XOR gate implemented with CMOS and Pass Transistor Logic (PTL) in CADENCE tool adopting 7nm FINFET High Performance Predictive Technology Models. Different supply voltages and temperature conditions are applied to measure the Power Delay Product (PDP) at various operating conditions. The result obtained shows 99% reduction in PDP value by using PTL compared to CMOS logic in near threshold at room temperatures. Also, we observe 12% and 5% average variation in PDP values at 27°C and 125°C respectively for PTL logic compared to 18% and 8% at 27°C and 125°C respectively for CMOS logic at near threshold operation.