Structural basis for activation of trimeric Gi proteins by multiple growth factor receptors via GIV/Girdin.

A long-standing issue in the field of signal transduction is to understand the cross-talk between receptor tyrosine kinases (RTKs) and heterotrimeric G proteins, two major and distinct signaling hubs that control eukaryotic cell behavior. Although stimulation of many RTKs leads to activation of trimeric G proteins, the molecular mechanisms behind this phenomenon remain elusive. We discovered a unifying mechanism that allows GIV/Girdin, a bona fide metastasis-related protein and a guanine-nucleotide exchange factor (GEF) for Gαi, to serve as a direct platform for multiple RTKs to activate Gαi proteins. Using a combination of homology modeling, protein-protein interaction, and kinase assays, we demonstrate that a stretch of ∼110 amino acids within GIV C-terminus displays structural plasticity that allows folding into a SH2-like domain in the presence of phosphotyrosine ligands. Using protein-protein interaction assays, we demonstrated that both SH2 and GEF domains of GIV are required for the formation of a ligand-activated ternary complex between GIV, Gαi, and growth factor receptors and for activation of Gαi after growth factor stimulation. Expression of a SH2-deficient GIV mutant (Arg 1745→Leu) that cannot bind RTKs impaired all previously demonstrated functions of GIV-Akt enhancement, actin remodeling, and cell migration. The mechanistic and structural insights gained here shed light on the long-standing questions surrounding RTK/G protein cross-talk, set a novel paradigm, and characterize a unique pharmacological target for uncoupling GIV-dependent signaling downstream of multiple oncogenic RTKs

The impact of alternative routeing and packaging scenarios on carbon and sulphate emissions in international wine distribution.

There is a large body of research related to carbon footprint reduction in supply chains and logistics from a wide range of sectors; however the decarbonisation of freight transport is mostly explored from a single mode perspective and at a domestic/regional level. This paper takes into account a range of alternative transport modes, routes and methods with particular reference to UK wine imports from two regions: northern Italy and Southeast Australia. The research examines supply chain structures, costs and the environmental impact of international wine distribution to the UK. A number of options are evaluated to calculate the carbon footprint and sulphate emissions of alternative route, mode, method of carriage, and packaging combinations. The estimation of CO2e emissions incor- porates three main elements - cargo mass, distance and method of carriage; sulphate emis- sions are derived from actual ship routes, engine power and operational speeds. The bottling of wine either at source or close to destination is also taken into consideration. The key findings are: there are major differences between the environmental footprint of different routeing and packaging scenarios; the international shipping leg almost always has a much larger footprint than inland transport within the UK except in the hypothetical case of the rail shipments from Italy using flexitanks. With reference to sulphate, the low- est cost scenario among the sea maximizing options is also the sulphate minimising solution

Horizontal logistics collaboration for enhanced supply chain performance: an international retail perspective

Purpose – The paper aims to develop a supply chain-driven model horizontal logistics collaboration (HLC). HLC initiatives can fail. To improve the chance of success, a thorough consideration of the potential issues involved, such as seeking supply chain partners’ support, ensuring access to information/data security and assessing whether an HLC model could bring improvements to a wide range of supply chain metrics rather than reductions in distribution costs only, needs to be understood before deciding to proceed with such an initiative. Design/methodology/approach – A two-stage methodology is deployed. As part of Stage 1, a series of 20 semi-structured interviews with senior managers from retailers, retailers’ suppliers and logistics service providers were undertaken. Subsequently, in Stage 2, a focus group with practitioners from retailers and logistics service providers was run to verify the findings gathered during Stage 1. Four elements of a new HLC project being considered are investigated by supply chain champions across the UK Fast-Moving Costumer Goods industry, namely, consideration factors, required synergies, enablers and anticipated output metrics. Findings – When considering whether to embark on an HLC project, the supply chain requirements need to be taken into account and potential supply chain performance benefits projected. The paper identified several consideration factors; synergies and enablers that support the development of HLC projects are identified, such as legislation, trust among partners, common suppliers and delivery bases, capable third party logistics (3PL) and an effective commercial model, including a fair sharing of benefits. Research limitations/implications – The research provides new understanding in accounting for the needs of the supply chain when considering an HLC initiative involving leading players from the retail sector. Practical implications – The importance of taking a supply chain approach when evaluating the feasibility of HLC is demonstrated. HLC arrangements among competing supply chains need to be designed and run by taking account of all supply chain partners, namely, suppliers, 3PLs and customers (in this case, retailers). Originality/value – The contribution is threefold: identification of outset consideration factors, ideal required synergies, actioning enablers and wider supply chain metrics of HLC; development of a supply chain-driven model for HLC, which includes in the decision-making whether or not to adopt a horizontal logistics collaboration model, wide supply chain metrics such as stock levels of finished products and shelf availability, inventory, working and fixed capital, and product waste in addition to distribution costs; and, the proposal of a new definition for HLC which challenges published definitions

Deletion of the glycosyltransferase bgsB of Enterococcus faecalis leads to a complete loss of glycolipids from the cell membrane and to impaired biofilm formation

<p>Abstract</p> <p>Background</p> <p>Deletion of the glycosyltransferase <it>bgsA </it>in <it>Enterococcus faecalis </it>leads to loss of diglucosyldiacylglycerol from the cell membrane and accumulation of its precursor monoglucosyldiacylglycerol, associated with impaired biofilm formation and reduced virulence in vivo. Here we analyzed the function of a putative glucosyltransferase EF2890 designated <it>biofilm-associated glycolipid synthesis B (bgsB) </it>immediately downstream of <it>bgsA</it>.</p> <p>Results</p> <p>A deletion mutant was constructed by targeted mutagenesis in <it>E. faecalis </it>strain 12030. Analysis of cell membrane extracts revealed a complete loss of glycolipids from the cell membrane. Cell walls of 12030Δ<it>bgsB </it>contained approximately fourfold more LTA, and ¹H-nuclear magnetic resonance (NMR) spectroscopy suggested that the higher content of cellular LTA was due to increased length of the glycerol-phosphate polymer of LTA. 12030Δ<it>bgsB </it>was not altered in growth, cell morphology, or autolysis. However, attachment to Caco-2 cells was reduced to 50% of wild-type levels, and biofilm formation on polystyrene was highly impaired. Despite normal resistance to cationic antimicrobial peptides, complement and antibody-mediated opsonophagocytic killing in vitro, 12030Δ<it>bgsB </it>was cleared more rapidly from the bloodstream of mice than wild-type bacteria. Overall, the phenotype resembles the respective deletion mutant in the <it>bgsA </it>gene. Our findings suggest that loss of diglucosyldiacylglycerol or the altered structure of LTA in both mutants account for phenotypic changes observed.</p> <p>Conclusions</p> <p>In summary, BgsB is a glucosyltransferase that synthesizes monoglucosyldiacylglycerol. Its inactivation profoundly affects cell membrane composition and has secondary effects on LTA biosynthesis. Both cell-membrane amphiphiles are critical for biofilm formation and virulence of <it>E. faecalis</it>.</p

Detection and measurement of impacts in composite structures using a self-powered triboelectric sensor

Composite structures as e.g. aircrafts, wind turbines or racing cars are frequently subjected to numerous impacts. For example, aircrafts may collide with birds during take-off and landing or get damaged due to the impact of hailstones. These impacts harm the integrity of the composite laminates used in their structures which results in delamination and other failures which are usually very difficult to detect by visual inspections. Hence, the detection and quantification of impacts is of vital importance for monitoring the health state of composite structures. Recently, triboelectric sensors have been demonstrated to detect touches, pressures, vibrations and other mechanical motions with the advantages of being self-powered, maintenance-free and easy to fabricate. However, there is no research focusing on the potential of triboelectric sensors to detect impacts in a wide energy range. In this paper, a self-powered triboelectric sensor is developed to measure impacts at high energy in structures made of composite materials. This could be particularly beneficial for the detection of bird strikes, hailstones and other high energy impacts in aircraft composite structures. For that purpose, composite plates are subjected to various energy impacts using a drop weight impact machine and the electric responses provided by the developed triboelectric sensor are measured in terms of voltage and current. The idea is to evaluate the sensitivity of the electrical signals provided by the sensor to changes in the impact energy. The results prove that the generated electric responses are affected by the energy of the impact and their amplitude increases linearly with the impact energy. The voltage and current sensor responses demonstrate a very good impact sensitivity of 160 mV/J and a strong linear relationship to the impact energy (R = 0.999) in a wide energy range from 2 to 30 J. This work suggests a novel approach to measure the magnitude of the impacts in composite structures using the newly developed triboelectric sensor. The findings of this work demonstrate that the developed triboelectric sensor meets the urgent needs for monitoring high energy impacts for aeronautic and civil composite structures

Planeamiento tributario y el impuesto a la renta de las MYPES comerciales de productos electrónicos, en Lima Cercado 2021

En nuestro país persiste un fenómeno que trasgrede las normas por la informalidad en la que se desarrollan al no pagar los impuestos que corresponden, es aquí que presentamos los objetivos de estudio, planeamiento tributario y el impuesto a la renta, fuentes de origen, las rentas y la Administración Tributaria de las MYPES comerciales de productos electrónicos que se desarrollan irregularmente por la falta de capacitación en normas tributarias para el pago de los impuestos. La investigación es de tipo aplicada, diseño no experimental, transversal descriptivo, correlacional, conto con una población de 30 comerciantes del Cercado de Lima, se usó la recolección de datos a la muestra censal, el cuestionario fue validado por el juicio de siete expertos, se aplicó el indicador estadístico de Alfa de Cronbach para la confiabilidad, con los resultados del coeficiente de Rho Spearman se aceptaron las hipótesis de estudio. Se concluyo que existe relación entre el planeamiento tributario y el impuesto a la renta, estos resultados arrojaron que los comerciantes de productos electrónicos conocen sobre el tema, pero no lo aplican en sus negocios, desconociendo los beneficios que es trabajar respetando la ley

Las finanzas públicas del Municipio de Ciénaga (Magdalena)

Ciénaga en los últimos años ha manifestado crisis en sus finanzas públicas presentando una estructura poco funcional, con una gestión tributaria ineficiente. Con el desarrollo de esta investigación se propuso analizar la situación de las finanzas públicas del municipio, en el periodo comprendido entre 2002 – 2007, examinando la dinámica de los rubros que componen las ejecuciones presupuestales de este periodo, a través de la construcción de algunos indicadores, según la metodología propuesta por el Departamento Nacional de Planeación – DNP en el año 2008, que fueron calculados una vez se deflactó la información financiera disponible, para traerla a valores constantes. En este sentido, los cálculos permiten concluir que Ciénaga depende en gran medida de las transferencias de la nación, específicamente de los recursos del SGP, además existe poco esfuerzo fiscal en la generación de recursos propios y el recaudo de los impuestos

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

International Brain Tumor Segmentation (BraTS) challengeGliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.This work was supported in part by the 1) National Institute of Neurological Disorders and Stroke (NINDS) of the NIH R01 grant with award number R01-NS042645, 2) Informatics Technology for Cancer Research (ITCR) program of the NCI/NIH U24 grant with award number U24-CA189523, 3) Swiss Cancer League, under award number KFS-3979-08-2016, 4) Swiss National Science Foundation, under award number 169607.Article signat per 427 autors/es: Spyridon Bakas1,2,3,†,‡,∗ , Mauricio Reyes4,† , Andras Jakab5,†,‡ , Stefan Bauer4,6,169,† , Markus Rempfler9,65,127,† , Alessandro Crimi7,† , Russell Takeshi Shinohara1,8,† , Christoph Berger9,† , Sung Min Ha1,2,† , Martin Rozycki1,2,† , Marcel Prastawa10,† , Esther Alberts9,65,127,† , Jana Lipkova9,65,127,† , John Freymann11,12,‡ , Justin Kirby11,12,‡ , Michel Bilello1,2,‡ , Hassan M. Fathallah-Shaykh13,‡ , Roland Wiest4,6,‡ , Jan Kirschke126,‡ , Benedikt Wiestler126,‡ , Rivka Colen14,‡ , Aikaterini Kotrotsou14,‡ , Pamela Lamontagne15,‡ , Daniel Marcus16,17,‡ , Mikhail Milchenko16,17,‡ , Arash Nazeri17,‡ , Marc-Andr Weber18,‡ , Abhishek Mahajan19,‡ , Ujjwal Baid20,‡ , Elizabeth Gerstner123,124,‡ , Dongjin Kwon1,2,† , Gagan Acharya107, Manu Agarwal109, Mahbubul Alam33 , Alberto Albiol34, Antonio Albiol34, Francisco J. Albiol35, Varghese Alex107, Nigel Allinson143, Pedro H. A. Amorim159, Abhijit Amrutkar107, Ganesh Anand107, Simon Andermatt152, Tal Arbel92, Pablo Arbelaez134, Aaron Avery60, Muneeza Azmat62, Pranjal B.107, Wenjia Bai128, Subhashis Banerjee36,37, Bill Barth2 , Thomas Batchelder33, Kayhan Batmanghelich88, Enzo Battistella42,43 , Andrew Beers123,124, Mikhail Belyaev137, Martin Bendszus23, Eze Benson38, Jose Bernal40 , Halandur Nagaraja Bharath141, George Biros62, Sotirios Bisdas76, James Brown123,124, Mariano Cabezas40, Shilei Cao67, Jorge M. Cardoso76, Eric N Carver41, Adri Casamitjana138, Laura Silvana Castillo134, Marcel Cat138, Philippe Cattin152, Albert Cerigues ´ 40, Vinicius S. Chagas159 , Siddhartha Chandra42, Yi-Ju Chang45, Shiyu Chang156, Ken Chang123,124, Joseph Chazalon29 , Shengcong Chen25, Wei Chen46, Jefferson W Chen80, Zhaolin Chen130, Kun Cheng120, Ahana Roy Choudhury47, Roger Chylla60, Albert Clrigues40, Steven Colleman141, Ramiro German Rodriguez Colmeiro149,150,151, Marc Combalia138, Anthony Costa122, Xiaomeng Cui115, Zhenzhen Dai41, Lutao Dai50, Laura Alexandra Daza134, Eric Deutsch43, Changxing Ding25, Chao Dong65 , Shidu Dong155, Wojciech Dudzik71,72, Zach Eaton-Rosen76, Gary Egan130, Guilherme Escudero159, Tho Estienne42,43, Richard Everson87, Jonathan Fabrizio29, Yong Fan1,2 , Longwei Fang54,55, Xue Feng27, Enzo Ferrante128, Lucas Fidon42, Martin Fischer95, Andrew P. French38,39 , Naomi Fridman57, Huan Fu90, David Fuentes58, Yaozong Gao68, Evan Gates58, David Gering60 , Amir Gholami61, Willi Gierke95, Ben Glocker128, Mingming Gong88,89, Sandra Gonzlez-Vill40, T. Grosges151, Yuanfang Guan108, Sheng Guo64, Sudeep Gupta19, Woo-Sup Han63, Il Song Han63 , Konstantin Harmuth95, Huiguang He54,55,56, Aura Hernndez-Sabat100, Evelyn Herrmann102 , Naveen Himthani62, Winston Hsu111, Cheyu Hsu111, Xiaojun Hu64, Xiaobin Hu65, Yan Hu66, Yifan Hu117, Rui Hua68,69, Teng-Yi Huang45, Weilin Huang64, Sabine Van Huffel141, Quan Huo68, Vivek HV70, Khan M. Iftekharuddin33, Fabian Isensee22, Mobarakol Islam81,82, Aaron S. Jackson38 , Sachin R. Jambawalikar48, Andrew Jesson92, Weijian Jian119, Peter Jin61, V Jeya Maria Jose82,83 , Alain Jungo4 , Bernhard Kainz128, Konstantinos Kamnitsas128, Po-Yu Kao79, Ayush Karnawat129 , Thomas Kellermeier95, Adel Kermi74, Kurt Keutzer61, Mohamed Tarek Khadir75, Mahendra Khened107, Philipp Kickingereder23, Geena Kim135, Nik King60, Haley Knapp60, Urspeter Knecht4 , Lisa Kohli60, Deren Kong64, Xiangmao Kong115, Simon Koppers32, Avinash Kori107, Ganapathy Krishnamurthi107, Egor Krivov137, Piyush Kumar47, Kaisar Kushibar40, Dmitrii Lachinov84,85 , Tryphon Lambrou143, Joon Lee41, Chengen Lee111, Yuehchou Lee111, Matthew Chung Hai Lee128 , Szidonia Lefkovits96, Laszlo Lefkovits97, James Levitt62, Tengfei Li51, Hongwei Li65, Wenqi Li76,77 , Hongyang Li108, Xiaochuan Li110, Yuexiang Li133, Heng Li51, Zhenye Li146, Xiaoyu Li67, Zeju Li158 , XiaoGang Li162, Wenqi Li76,77, Zheng-Shen Lin45, Fengming Lin115, Pietro Lio153, Chang Liu41 , Boqiang Liu46, Xiang Liu67, Mingyuan Liu114, Ju Liu115,116, Luyan Liu112, Xavier Llado´ 40, Marc Moreno Lopez132, Pablo Ribalta Lorenzo72, Zhentai Lu53, Lin Luo31, Zhigang Luo162, Jun Ma73 , Kai Ma117, Thomas Mackie60, Anant Madabhushi129, Issam Mahmoudi74, Klaus H. Maier-Hein22 , Pradipta Maji36, CP Mammen161, Andreas Mang165, B. S. Manjunath79, Michal Marcinkiewicz71 , Steven McDonagh128, Stephen McKenna157, Richard McKinley6 , Miriam Mehl166, Sachin Mehta91 , Raghav Mehta92, Raphael Meier4,6 , Christoph Meinel95, Dorit Merhof32, Craig Meyer27,28, Robert Miller131, Sushmita Mitra36, Aliasgar Moiyadi19, David Molina-Garcia142, Miguel A.B. Monteiro105 , Grzegorz Mrukwa71,72, Andriy Myronenko21, Jakub Nalepa71,72, Thuyen Ngo79, Dong Nie113, Holly Ning131, Chen Niu67, Nicholas K Nuechterlein91, Eric Oermann122, Arlindo Oliveira105,106, Diego D. C. Oliveira159, Arnau Oliver40, Alexander F. I. Osman140, Yu-Nian Ou45, Sebastien Ourselin76 , Nikos Paragios42,44, Moo Sung Park121, Brad Paschke60, J. Gregory Pauloski58, Kamlesh Pawar130, Nick Pawlowski128, Linmin Pei33, Suting Peng46, Silvio M. Pereira159, Julian Perez-Beteta142, Victor M. Perez-Garcia142, Simon Pezold152, Bao Pham104, Ashish Phophalia136 , Gemma Piella101, G.N. Pillai109, Marie Piraud65, Maxim Pisov137, Anmol Popli109, Michael P. Pound38, Reza Pourreza131, Prateek Prasanna129, Vesna Pr?kovska99, Tony P. Pridmore38, Santi Puch99, lodie Puybareau29, Buyue Qian67, Xu Qiao46, Martin Rajchl128, Swapnil Rane19, Michael Rebsamen4 , Hongliang Ren82, Xuhua Ren112, Karthik Revanuru139, Mina Rezaei95, Oliver Rippel32, Luis Carlos Rivera134, Charlotte Robert43, Bruce Rosen123,124, Daniel Rueckert128 , Mohammed Safwan107, Mostafa Salem40, Joaquim Salvi40, Irina Sanchez138, Irina Snchez99 , Heitor M. Santos159, Emmett Sartor160, Dawid Schellingerhout59, Klaudius Scheufele166, Matthew R. Scott64, Artur A. Scussel159, Sara Sedlar139, Juan Pablo Serrano-Rubio86, N. Jon Shah130 , Nameetha Shah139, Mazhar Shaikh107, B. Uma Shankar36, Zeina Shboul33, Haipeng Shen50 , Dinggang Shen113, Linlin Shen133, Haocheng Shen157, Varun Shenoy61, Feng Shi68, Hyung Eun Shin121, Hai Shu52, Diana Sima141, Matthew Sinclair128, Orjan Smedby167, James M. Snyder41 , Mohammadreza Soltaninejad143, Guidong Song145, Mehul Soni107, Jean Stawiaski78, Shashank Subramanian62, Li Sun30, Roger Sun42,43, Jiawei Sun46, Kay Sun60, Yu Sun69, Guoxia Sun115 , Shuang Sun115, Yannick R Suter4 , Laszlo Szilagyi97, Sanjay Talbar20, Dacheng Tao26, Dacheng Tao90, Zhongzhao Teng154, Siddhesh Thakur20, Meenakshi H Thakur19, Sameer Tharakan62 , Pallavi Tiwari129, Guillaume Tochon29, Tuan Tran103, Yuhsiang M. Tsai111, Kuan-Lun Tseng111 , Tran Anh Tuan103, Vadim Turlapov85, Nicholas Tustison28, Maria Vakalopoulou42,43, Sergi Valverde40, Rami Vanguri48,49, Evgeny Vasiliev85, Jonathan Ventura132, Luis Vera142, Tom Vercauteren76,77, C. A. Verrastro149,150, Lasitha Vidyaratne33, Veronica Vilaplana138, Ajeet Vivekanandan60, Guotai Wang76,77, Qian Wang112, Chiatse J. Wang111, Weichung Wang111, Duo Wang153, Ruixuan Wang157, Yuanyuan Wang158, Chunliang Wang167, Guotai Wang76,77, Ning Wen41, Xin Wen67, Leon Weninger32, Wolfgang Wick24, Shaocheng Wu108, Qiang Wu115,116 , Yihong Wu144, Yong Xia66, Yanwu Xu88, Xiaowen Xu115, Peiyuan Xu117, Tsai-Ling Yang45 , Xiaoping Yang73, Hao-Yu Yang93,94, Junlin Yang93, Haojin Yang95, Guang Yang170, Hongdou Yao98, Xujiong Ye143, Changchang Yin67, Brett Young-Moxon60, Jinhua Yu158, Xiangyu Yue61 , Songtao Zhang30, Angela Zhang79, Kun Zhang89, Xuejie Zhang98, Lichi Zhang112, Xiaoyue Zhang118, Yazhuo Zhang145,146,147, Lei Zhang143, Jianguo Zhang157, Xiang Zhang162, Tianhao Zhang168, Sicheng Zhao61, Yu Zhao65, Xiaomei Zhao144,55, Liang Zhao163,164, Yefeng Zheng117 , Liming Zhong53, Chenhong Zhou25, Xiaobing Zhou98, Fan Zhou51, Hongtu Zhu51, Jin Zhu153, Ying Zhuge131, Weiwei Zong41, Jayashree Kalpathy-Cramer123,124,† , Keyvan Farahani12,†,‡ , Christos Davatzikos1,2,†,‡ , Koen van Leemput123,124,125,† , and Bjoern Menze9,65,127,†,∗Preprin