alpha -Lactalbumin (LA) Stimulates Milk beta-1,4-Galactosyltransferase I (beta 4Gal-T1) to Transfer Glucose from UDP-glucose to N-Acetylglucosamine: CRYSTAL STRUCTURE OF beta 4Gal-T1·LA COMPLEX WITH UDP-Glc*

beta-1,4-Galactosyltransferase 1 (Gal-T1) transfers galactose (Gal) from UDP-Gal to N-acetylglucosamine (GlcNAc), which constitutes its normal galactosyltransferase (Gal-T) activity. In the presence of alpha -lactalbumin (LA), it transfers Gal to Glc, which is its lactose synthase (LS) activity. It also transfers glucose (Glc) from UDP-Glc to GlcNAc, constituting the glucosyltransferase (Glc-T) activity, albeit at an efficiency of only 0.3-0.4% of Gal-T activity. In the present study, we show that LA increases this activity almost 30-fold. It also enhances the Glc-T activity toward various N-acyl substituted glucosamine acceptors. Steady state kinetic studies of Glc-T reaction show that the Km for the donor and acceptor substrates are high in the absence of LA. In the presence of LA, the Km for the acceptor substrate is reduced 30-fold, whereas for UDP-Glc it is reduced only 5-fold. In order to understand this property, we have determined the crystal structures of the Gal-T1·LA complex with UDP-Glc·Mn2+ and with N-butanoyl-glucosamine (N-butanoyl-GlcN), a preferred sugar acceptor in the Glc-T activity. The crystal structures reveal that although the binding of UDP-Glc is quite similar to UDP-Gal, there are few significant differences observed in the hydrogen bonding interactions between UDP-Glc and Gal-T1. Based on the present kinetic and crystal structural studies, a possible explanation for the role of LA in the Glc-T activity has been proposed

Near-threshold fatigue crack growth in bulk metallic glass composites

A major drawback in using bulk metallic glasses (BMGs) as structural materials is their extremely poor fatigue performance. One way to alleviate this problem is through the composite route, in which second phases are introduced into the glass to arrest crack growth. In this paper, the fatigue crack growth behavior of in situ reinforced BMGs with crystalline dendrites, which are tailored to impart significant ductility and toughness to the BMG, was investigated. Three composites, all with equal volume fraction of dendrite phases, were examined to assess the influence of chemical composition on the near-threshold fatigue crack growth characteristics. While the ductility is enhanced at the cost of yield strength vis-à-vis that of the fully amorphous BMG, the threshold stress intensity factor range for fatigue crack initiation in composites was found to be enhanced by more than 100%. Crack blunting and trapping by the dendritic phases and constraining of the shear bands within the interdendritic regions are the micromechanisms responsible for this enhanced fatigue crack growth resistance

DELAMINATION PREDICTION IN DRILLING OF CFRP COMPOSITES USING ARTIFICIAL NEURAL NETWORK

Carbon fibre reinforced plastic (CFRP) materials play a major role in the applications of aeronautic, aerospace, sporting and transportation industries. Machining is indispensible and hence drilling of CFRP materials is considered in this present study with respect to spindle speed in rpm, drill size in mm and feed in mm/min. Delamination is one of the major defects to be dealt with. The experiments are carried out using computer numerical control machine and the results are applied to an artificial neural network (ANN) for the prediction of delamination factor at the exit plane of the CFRP material. It is found that ANN model predicts the delamination for any given set of machining parameters with a maximum error of 0.81% and a minimum error of 0.03%. Thus an ANN model is highly suitable for the prediction of delamination in CFRP materials

CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks

Verifying robustness of neural network classifiers has attracted great interests and attention due to the success of deep neural networks and their unexpected vulnerability to adversarial perturbations. Although finding minimum adversarial distortion of neural networks (with ReLU activations) has been shown to be an NP-complete problem, obtaining a non-trivial lower bound of minimum distortion as a provable robustness guarantee is possible. However, most previous works only focused on simple fully-connected layers (multilayer perceptrons) and were limited to ReLU activations. This motivates us to propose a general and efficient framework, CNN-Cert, that is capable of certifying robustness on general convolutional neural networks. Our framework is general -- we can handle various architectures including convolutional layers, max-pooling layers, batch normalization layer, residual blocks, as well as general activation functions; our approach is efficient -- by exploiting the special structure of convolutional layers, we achieve up to 17 and 11 times of speed-up compared to the state-of-the-art certification algorithms (e.g. Fast-Lin, CROWN) and 366 times of speed-up compared to the dual-LP approach while our algorithm obtains similar or even better verification bounds. In addition, CNN-Cert generalizes state-of-the-art algorithms e.g. Fast-Lin and CROWN. We demonstrate by extensive experiments that our method outperforms state-of-the-art lower-bound-based certification algorithms in terms of both bound quality and speed.Comment: Accepted by AAAI 201

Investigation on the influence of augmented reality adoption and cybersecurity in the Architecture, Engineering and Construction industry

Augmented Reality (AR) applications and the digitization of Architecture, Engineering, and Construction (AEC) industry processes, such as the online building permit process (BPP), have introduced new developments that simplifies tasks, and enhanced data accessibility for stakeholders. This created a digital landscape where sensitive information and critical processes are intertwined, making them vulnerable to cyber-attacks. Existing studies and frameworks on a) AR adoption doesn’t provide essential information (e.g., material availability and costs from different suppliers) and AR features to cater to the diverse needs of different stakeholders. b) threat modeling requires a domain and context specific need investigation, process exploration, critical asset mapping, and subsequent adoption. This thesis addresses these gaps to comprehensively understand the influence of a specific technology (AR applications on the home remodeling market in the current context) and investigate the critical need for robust cybersecurity measures in the context of online BPP. Adopting a multi-faceted approach, the study aims to a) conduct user market research and identify unique value propositions of AR-based applications b) assess potential threats and vulnerabilities of the online building permit process (BPP) followed by the building inspection division in the city of Jacksonville’s (COJ). To achieve these, a) questionnaire surveys were distributed to diverse stakeholders such as homeowners, contractors, realtors, and suppliers. b) focused interviews were conducted to determine the need for cybersecurity investigation and an existing threat modeling framework was applied to further analyze the online BPP process. Two of the major outcomes are a) almost all of the 15 surveyed participants raised concerns of final outcome visualization before completion and apprehension about their ability to execute the task correctly b) preliminary threat models that emphasize the who (are the potential malicious actors), why (would they intend to perform such actions), where (are the avenues of attacks), what (are the implications), and how (can these be avoided or countered) of the existing COJ’s BPP. The findings therefore directly contribute to the importance of innovative solutions that prioritize both technological advancements and data protection within the evolving AEC landscape

Short Review of Salt Recovery from Reverse Osmosis Rejects

The membrane treatment is a physical separation which also generates considerable amount of waste, called as reject/concentrate. The reject/concentrate is more than three times concentrated than the feed water in terms of feed water salts. Recovery of valuables from reverse osmosis (RO) reject for its reuse of inorganic salts would be most obvious solution to eliminate environmental damage. In this report what are the available methods for the recovery of valuables from waste saline stream by selective crystallization method, chemical precipitation and physico-thermal route discussed in details. Also, methods to treat organic contamination in the residual solution through advanced oxidation treatment methods

A Chemoenzymatic Approach toward the Rapid and Sensitive Detection of O-GlcNAc Posttranslational Modifications

We report a new chemoenzymatic strategy for the rapid and sensitive detection of O-GlcNAc posttranslational modifications. The approach exploits the ability of an engineered mutant of β-1,4-galactosyltransferase to selectively transfer an unnatural ketone functionality onto O-GlcNAc glycosylated proteins. Once transferred, the ketone moiety serves as a versatile handle for the attachment of biotin, thereby enabling chemiluminescent detection of the modified protein. Importantly, this approach permits the rapid visualization of proteins that are at the limits of detection using traditional methods. Moreover, it bypasses the need for radioactive precursors and captures the glycosylated species without perturbing metabolic pathways. We anticipate that this general chemoenzymatic strategy will have broad application to the study of posttranslational modifications

Comparative Analytical study on management of Pauwel’s Type II and III Neck of Femur Fracture treated by Cancellous Screw Fixation Alone Versus Additional Valgus Osteotomy with Internal Fixation

INTRODUCTION: Fracture neck of femur is aptly called as “the unsolved fracture”. This is because even with so much of advances in orthopaedic field, there is no simple method of treatment which can give consistently successful results for this fracture. Management of fracture neck of femur (Pauwel’s type II and III) especially in younger patients is a really demanding and challenging task for any orthopaedic surgeon. AIM AND OBJECTIVES: To compare the outcome on the management of neck of femur fracture (Pauwel’s Type II and III) treated with cancellous screws fixation alone versus additionalvalgus osteotomy with internal Fixation. MATERIALS AND METHODS: Study conducted at the Institute of Orthopaedics and Traumatology, Madras Medical College, Chennai. Duration of study is from April 2016 - September 2016. It is both retrospective & prospective study. The total number of cases studied were 20. Cases selected based on our inclusion and exclusion criteria. 10 patients undergone fixation with cancellous screws alone and 10 patients undergone additional valgus osteotomy with internal fixation.Both groups undergo same postoperative protocol and are followed up with serial X rays. Radiological assessment done with the post-operative follow up X-rays and functional assessment withHarris hip score. RESULTS: We achieved union in 9 cases treated with additional valgus osteotomy and internal fixation. Union rate is 90% when compared to other group fixed with cancellous screw alone (60%). There is a statistically significant decrease in complication rate and increase in fracture union rate and functional outcome when fracture neck of femur treated with additional valgus osteotomy with internal fixation. CONCLUSION: For the patients under 60 years of age with fracture of the femoral neck of Pauwel’s type II and II, additional valgus osteotomy produces good results in terms of fracture union and very low possibility of avascular necrosis of femoral head, whereas internal fixation alone in type II and III fractures has more failure rate for fracture union

Gradient-trained Weights in Wide Neural Networks Align Layerwise to Error-scaled Input Correlations

Recent works have examined how deep neural networks, which can solve a variety of difficult problems, incorporate the statistics of training data to achieve their success. However, existing results have been established only in limited settings. In this work, we derive the layerwise weight dynamics of infinite-width neural networks with nonlinear activations trained by gradient descent. We show theoretically that weight updates are aligned with input correlations from intermediate layers weighted by error, and demonstrate empirically that the result also holds in finite-width wide networks. The alignment result allows us to formulate backpropagation-free learning rules, named Align-zero and Align-ada, that theoretically achieve the same alignment as backpropagation. Finally, we test these learning rules on benchmark problems in feedforward and recurrent neural networks and demonstrate, in wide networks, comparable performance to backpropagation.Comment: 22 pages, 11 figure