MaxDIA: Taking proteomics to the next level

ENG: A new software improves data-independent acquisition proteomics by providing a computational workflow that permits highly sensitive and accurate data analysis. Proteins are essential for our cells to function, yet many questions about their synthesis, abundance, functions, and defects still remain unanswered. High-throughput techniques can help improve our understanding of these molecules. For analysis by liquid chromatography followed by mass spectrometry (MS), proteins are broken down into smaller peptides, in a process referred to as “shotgun proteomics.” The mass-to-charge ratio of these peptides is subsequently determined with a mass spectrometer, resulting in MS spectra. From these spectra, information about the identity of the analyzed proteins can be reconstructed. However, the enormous amount and complexity of data make data analysis and interpretation challenging.

Protein ratio distributions in the four species. Credit: Max-Planck-Gesellschaft

Two main methods are used in shotgun proteomics: Data-dependent acquisition (DDA) and data-independent acquisition (DIA). In DDA, the most abundant peptides of a sample are preselected for fragmentation and measurement. This allows to reconstruct the sequences of these few preselected peptides, making analysis simpler and faster. However, this method induces a bias towards highly abundant peptides. DIA, in contrast, is more robust and sensitive. All peptides from a certain mass range are fragmented and measured at once, without preselection by abundance.

Jürgen Cox and his team have now developed a software that provides a complete computational workflow for DIA data. It allows, for the first time, to apply algorithms to DDA and DIA data in the same way. Consequently, studies based on either DDA or DIA will now become more easily comparable. MaxDIA analyzes proteomics data with and without spectral libraries. Using machine learning, the software predicts peptide fragmentation and spectral intensities. Hence, it creates precise MS spectral libraries in silico. In this way, MaxDIA includes a library-free discovery mode with reliable control of false positive protein identifications.

RO: Un nou software îmbunătățește proteomica de achiziție independentă de date prin furnizarea unui flux de lucru computațional care permite o analiză a datelor foarte precisă. Proteinele sunt esențiale pentru funcționarea celulelor noastre, însă multe întrebări despre sinteza, abundența, funcțiile și defectele acestora rămân încă fără răspuns. Tehnicile de mare randament pot contribui la îmbunătățirea înțelegerii noastre despre aceste molecule. Pentru analiza prin cromatografie lichidă urmată de spectrometrie de masă (SM), proteinele sunt descompuse în peptide mai mici, într-un proces denumit “shotgun proteomics”. Raportul masă-încărcare al acestor peptide este determinat ulterior cu un spectrometru de masă, rezultând spectrele SM. Din aceste spectre, pot fi reconstruite informații despre identitatea proteinelor analizate. Cu toate acestea, cantitatea enormă și complexitatea datelor fac ca analiza și interpretarea datelor să fie o provocare.

Două metode principale sunt utilizate în proteomica shotgun: Achiziția dependentă de date (ADD) și achiziția independentă de date (AID). În ADD, cele mai abundente peptide ale unei probe sunt preselectate pentru fragmentare și măsurare. Acest lucru permite reconstruirea secvențelor acestor câteva peptide preselectate, ceea ce face ca analiza să fie mai simplă și mai rapidă. Cu toate acestea, această metodă induce o prejudecată în favoarea peptidelor foarte abundente. AID, în schimb, este mai robustă și mai sensibilă. Toate peptidele dintr-un anumit interval de masă sunt fragmentate și măsurate deodată, fără preselecție în funcție de abundență.

Jürgen Cox și echipa sa au dezvoltat acum un software care oferă un flux de lucru complet de calcul pentru datele AID. Acesta permite, pentru prima dată, aplicarea algoritmilor la datele ADD și AID în același mod. Prin urmare, studiile bazate pe cele două tipuri de achiziții vor deveni acum mai ușor de comparat. Metoda analizează datele proteomice cu și fără biblioteci spectrale. Folosind învățarea automată, software-ul prezice fragmentarea peptidelor și intensitățile spectrale. Prin urmare, acesta creează biblioteci spectrale SM precise. În acest fel, MaxDIA include un mod de descoperire fără bibliotecă, cu un control fiabil al identificărilor de proteine fals pozitive.

Source (Max-Planck-Gesellschaft. “MaxDIA: Taking proteomics to the next level.” ScienceDaily. ScienceDaily, 12 July 2021.)

Paper: Sinitcyn, P., Hamzeiy, H., Salinas Soto, F., Itzhak, D., McCarthy, F., Wichmann, C., Steger, M., Ohmayer, U., Distler, U., Kaspar-Schoenefeld, S. and Prianichnikov, N., 2021. MaxDIA enables library-based and library-free data-independent acquisition proteomics. Nature Biotechnology, pp.1-11.

A personalized exosuit for real-world walking

ENG: Researchers have developed a new approach in which robotic exosuit assistance can be calibrated to an individual and adapt to a variety of real-world walking tasks in a matter of seconds. The bioinspired system uses ultrasound measurements of muscle dynamics to develop a personalized and activity-specific assistance profile for users of the exosuit.

Credit: Harvard John A. Paulson School of Engineering and Applied Sciences

People rarely walk at a constant speed and a single incline. We change speed when rushing to the next appointment, catching a crosswalk signal, or going for a casual stroll in the park. Slopes change all the time too, whether we’re going for a hike or up a ramp into a building. In addition to environmental variably, how we walk is influenced by sex, height, age, and muscle strength, and sometimes by neural or muscular disorders such as stroke or Parkinson’s Disease.

Researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a new approach in which robotic exosuit assistance can be calibrated to an individual and adapt to a variety of real-world walking tasks in a matter of seconds. The bioinspired system uses ultrasound measurements of muscle dynamics to develop a personalized and activity-specific assistance profile for users of the exosuit.

The new system only needs a few seconds of walking, even one stride may be sufficient, to capture the muscle’s profile. For each of the ultrasound-generated profiles, the researchers measure how much metabolic energy the person uses during walking with and without the exosuit. The researchers found that the muscle-based assistance provided by the exosuit significantly reduced the metabolic energy of walking across a range of walking speeds and inclines. The exosuit also applied lower assistance force to achieve the same or improved metabolic energy benefit than previous published studies.

RO: Cercetătorii au dezvoltat o nouă abordare prin care asistența exoscheletului robotic poate fi calibrată în funcție de individ și se poate adapta la o varietate de sarcini de mers în lumea reală în doar câteva secunde. Sistemul bioinspirat utilizează măsurători cu ultrasunete ale dinamicii musculare pentru a dezvolta un profil de asistență personalizat și specific activității pentru utilizatorii exocostumului.

Oamenii merg rareori la o viteză constantă și cu o singură înclinație. Schimbăm viteza atunci când ne grăbim să ajungem la următoarea întâlnire, când prindem semnalul unei treceri de pietoni sau când mergem la o plimbare ocazională în parc. De asemenea, pantele se schimbă tot timpul, fie că mergem într-o drumeție, fie că urcăm o rampă de acces într-o clădire. Pe lângă variabilitatea mediului, modul în care mergem este influențat de sex, înălțime, vârstă și forță musculară și, uneori, de tulburări neuronale sau musculare, cum ar fi accidentul vascular cerebral sau boala Parkinson.

Cercetătorii de la Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) de la Harvard au dezvoltat o nouă abordare prin care asistența exocostumului robotic poate fi calibrată în funcție de o persoană și se poate adapta la o varietate de sarcini de mers în lumea reală în câteva secunde. Sistemul bioinspirat utilizează măsurători cu ultrasunete ale dinamicii musculare pentru a dezvolta un profil de asistență personalizat și specific activității pentru utilizatorii exosuitului.

Noul sistem are nevoie doar de câteva secunde de mers, chiar și un singur pas poate fi suficient, pentru a capta profilul mușchilor. Pentru fiecare dintre profilurile generate de ultrasunete, cercetătorii măsoară câtă energie metabolică folosește persoana în timpul mersului cu și fără exocostum. Cercetătorii au constatat că asistența bazată pe mușchi oferită de exocostum a redus semnificativ energia metabolică a mersului pe o gamă largă de viteze și înclinații de mers. De asemenea, exocostumul a aplicat o forță de asistență mai mică pentru a obține un beneficiu energetic metabolic identic sau mai bun decât în studiile publicate anterior.

Source (Harvard John A. Paulson School of Engineering and Applied Sciences. “A personalized exosuit for real-world walking: Ultrasound measurements of muscle dynamics provide customized, activity-specific assistance.” ScienceDaily. ScienceDaily, 10 November 2021.)

Original paper: R. W. Nuckols, S. Lee, K. Swaminathan, D. Orzel, R. D. Howe, C. J. Walsh. Individualization of exosuit assistance based on measured muscle dynamics during versatile walkingScience Robotics, 2021; 6 (60) DOI: 10.1126/scirobotics.abj1362

Camera Calibration in Python with OpenCV 

Previously, we touched on the topics of intrinsic and extrinsic camera parameters, as well as camera calibration to remove lens distortion. In the following video, a more comprehensive explanation is presented to link these together, with the added touch of being able to implement in Python these concepts.

Video description: In this Computer Vision and OpenCV Tutorial, We’ll talk about Camera Calibration and Geometry. We will first talk about the basics of camera geometry and how it can be used for calibrating cameras. We will see different types of distortion on cameras and images. At the end of the video, I’ll show you in a Python script how to apply what we have learned and calibrate a camera from a practical computer vision setup.

Camera Calibration with MATLAB

Continuing the study of mathematics in the field of optical vision, we take a look at how camera calibration can be obtained in Matlab.

Video description: Camera calibration is the process of estimating the intrinsic, extrinsic, and lens-distortion parameters of a camera. It is an essential process to correct for any optical distortion artifacts, estimate the distance of an object from a camera, measure the size of objects in an image, and construct 3D views for augmented reality systems. Computer Vision Toolbox™ provides apps and functions to perform all essential tasks in the camera calibration workflow, including:

– Fully automatic detection and location of checkerboard calibration pattern, including corner detection with subpixel accuracy

– Estimation of all intrinsic and extrinsic parameters, including axis skew

– Calculation of radial and tangential lens distortion coefficients Correction of optical distortion

– Support for calibrating standard, fisheye lens, and stereo vision cameras

Camera Calibrator App and Stereo Camera Calibrator App both allow interactively selecting the calibration images, setting up the distortion coefficients, and then estimating the camera parameters you can export to MATLAB.

Computer Vision Toolbox: https://bit.ly/2XEJCL4

MATLAB for Image Processing and Computer Vision: https://bit.ly/2WUlzEi

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Intrinsic and Extrinsic Matrices of a Camera

Today we take a look at the fundamental theory behind camera parameters to better understand how matrix multiplication is employed.

Video description: First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners and enthusiasts who have no prior knowledge of computer vision.

Faster path planning for rubble-roving robots

ENG: Robots that need to use their arms to make their way across treacherous terrain just got a speed upgrade with a new path planning approach. The improved algorithm path planning algorithm found successful paths three times as often as standard algorithms, while needing much less processing time.

A new algorithm speeds up path planning for robots that use arm-like appendages to maintain balance on treacherous terrain such as disaster areas or construction sites, U-M researchers have shown. The improved path planning algorithm found successful paths three times as often as standard algorithms, while needing much less processing time. The research enables robots to determine how difficult the terrain is before calculating a successful path forward, which might include bracing on the wall with one or two hands while taking the next step forward.

The method uses machine learning to train the robot how to place its hands and feet to maintain balance and make progress, then a divide-and-conquer approach is employed to split the path according to the level of traverse difficulty. To do this, they need a geometric model of the entire environment. This could be achieved in practice with a flying drone that scouts ahead of the robot. In a virtual experiment with a humanoid robot in a corridor of rubble, the team’s method outperformed previous methods in both success and total time to plan — important when quick action is needed in disaster scenarios. Specifically, over 50 trials, their method reached the goal 84% of the time compared to 26% for the basic path planner, and took just over two minutes to plan compared to over three minutes for the basic path planner.

RO: Roboții care trebuie să își folosească brațele pentru a se deplasa pe terenuri periculoase tocmai au primit o îmbunătățire a vitezei cu o nouă abordare de planificare a traiectoriei. Algoritmul îmbunătățit de planificare a traiectoriei a găsit trasee de succes de trei ori mai des decât algoritmii standard, necesitând în același timp mult mai puțin timp de procesare.

Un nou algoritm accelerează planificarea traiectoriei pentru roboții care folosesc apendice asemănătoare brațelor pentru a-și menține echilibrul pe terenuri dificile, cum ar fi zonele calamitate sau șantierele de construcții, au arătat cercetătorii de la U-M. Algoritmul îmbunătățit de planificare a traiectoriei a găsit trasee de succes de trei ori mai des decât algoritmii standard, necesitând în același timp mult mai puțin timp de procesare. Cercetarea permite roboților să determine cât de dificil este terenul înainte de a calcula o cale de succes înainte, care ar putea include susținerea pe perete cu una sau două brațe în timp ce fac următorul pas înainte.

Metoda utilizează învățarea automată pentru a antrena robotul cum să își plaseze mâinile și picioarele pentru a-și menține echilibrul și a progresa, apoi se folosește o abordare de tip divide et impera pentru a împărți calea în funcție de nivelul de dificultate a traversării. Pentru a face acest lucru, au nevoie de un model geometric al întregului mediu. Acest lucru ar putea fi realizat în practică cu o dronă zburătoare care cercetează înaintea robotului. În cadrul unui experiment virtual cu un robot umanoid într-un coridor de moloz, metoda echipei a depășit metodele anterioare atât în ceea ce privește succesul, cât și timpul total de planificare – important atunci când este nevoie de o acțiune rapidă în scenarii de dezastru. Mai exact, pe parcursul a 50 de încercări, metoda lor a atins obiectivul în 84% din cazuri, față de 26% în cazul planificatorului de traseu de bază, și a avut nevoie de puțin peste două minute pentru a planifica, față de peste trei minute pentru planificatorul de traseu de bază.

Source (University of Michigan. “Faster path planning for rubble-roving robots.” ScienceDaily. ScienceDaily, 13 August 2021.)

Original paper: Lin, Y.C. and Berenson, D., 2021. Long-horizon humanoid navigation planning using traversability estimates and previous experience. Autonomous Robots45(6), pp.937-956.

Melodies of an Endless Journey

Video description: The free and peaceful Mondstadt, as well as the bustling port of Liyue, mark every step of your journey. The melodies of wind and rock intertwine on a whole new stage to compose a unique chapter of your adventure.

For the first year anniversary of Genshin Impact, its developer Mihoyo prepared an exquisite concert that takes viewers along a ride inside the game’s world, showcasing landscapes, characters and stories.

Virgin Hyperloop

In an ambitious promotional video, Virgin Hyperloop explains how it envisions its high-speed transportation system working in the future. Pods that will hold up to 28 passengers will travel at speeds surpassing 670 miles per hour — three times faster than high-speed rail and 10 times faster than traditional rail — using proprietary magnetic levitation and propulsion that guides the vehicles on the track. The pods will travel in convoys down the tube so they can head to different destinations. The company has made some bold claims, betting its system will be more sustainable, cost-effective and convenient than presently available modes of transportation, leaving many skeptical about whether the plans will ever come to fruition as promised. 

Here are some insights from the interview with Virgin Hyperloop CEO and co-founder Josh Giegel, for Changing America:

Tell me about the technology. What makes it different from a maglev — magnetic levitation — train in terms of infrastructure and potential? 

What we started working on over the last few years is this idea of “smart pod, dumb tube.” Instead of having switches that move like a train, we just have things that act like an off-ramp so if the pod [the vehicle passengers ride in] wants to get off and take people to a certain city it just pulls off by turning on an electromagnet. So you can now have a really high-capacity system without all these safety risks associated with track-switching.  You put the pod inside of a tube, you take most of the air out, you have a very low energy consumption, you actually make it inherently safer, you make it weatherproof, and you can move as many people as a 30-lane highway in the space of a tube going each direction. It’s all from this foundational premise that we created: new propulsion, new levitation, new batteries, new ways of working all these things together to make a “smart pod, dumb tube,” so a pod one hundred years from now could ride in the same tube we build today. 

What’s the timeline like? When can I buy a ticket to ride? 

What we set out to do last year was show the technology could be made safe. That culminated in myself and one of my colleagues Sara riding on a prototype in November. I think that allayed a lot of concerns about whether we can make it safe.  The next level is getting approved by an independent body and commercializing the technology. We’re in the process of building our commercial technology now, which are these 25 to 30 passenger pods. 

We’ll begin to look at pilot projects that will move cargo first, so think of shorter projects starting around the 2024 through 2026 timeframe. At the same time we’ll be getting independent safety approval needed to get a product certified for passengers. And then ultimately from there go into building the projects out from 2026 through the rest of the decade. So you’ll be looking at the decade of hyperloop, starting with Sara and I riding on it and ending with, I’m hoping, billions of passengers riding, but I will settle for tens, if not hundreds, of millions of passengers in the U.S. and around the world. 

Source (The Hill, “Will the 2020s be the decade of hyperloop?”, 01.09.2021)

Unveiling a century-old mystery: Where the Milky Way’s cosmic rays come from

Astronomers have succeeded in quantifying the proton and electron components of cosmic rays in a supernova remnant. At least 70% of the very-high-energy gamma rays emitted from cosmic rays are due to relativistic protons, according to the novel imaging analysis of radio, X-ray, and gamma-ray radiation. The acceleration site of protons, the main components of cosmic rays, has been a 100-year mystery in modern astrophysics.

Credit: Nagoya University

Astronomers have succeeded for the first time in quantifying the proton and electron components of cosmic rays in a supernova remnant. At least 70% of the very-high-energy gamma rays emitted from cosmic rays are due to relativistic protons, according to the novel imaging analysis of radio, X-ray, and gamma-ray radiation. The acceleration site of protons, the main components of cosmic rays, has been a 100-year mystery in modern astrophysics, this is the first time that the amount of cosmic rays being produced in a supernova remnant has been quantitatively shown and is an epoch-making step in the elucidation of the origin of cosmic rays.

The originality of this research is that gamma-ray radiation is represented by a linear combination of proton and electron components. Astronomers knew a relation that the intensity of gamma-ray from protons is proportional to the interstellar gas density obtained by radio-line imaging observations. On the other hand, gamma-rays from electrons are also expected to be proportional to X-ray intensity from electrons. Therefore, they expressed the total gamma-ray intensity as the sum of two gamma-ray components, one from the proton origin and the other from the electron origin. This led to a unified understanding of three independent observables. This method was first proposed in this study. As a result, it was shown that gamma rays from protons and electrons account for 70% and 30% of the total gamma-rays, respectively. This is the first time that the two origins have been quantified. The results also demonstrate that gamma rays from protons are dominated in interstellar gas-rich regions, whereas gamma rays from electrons are enhanced in the gas-poor region. This confirms that the two mechanisms work together and supporting the predictions of previous theoretical studies.

Source (Nagoya University. “Unveiling a century-old mystery: Where the Milky Way’s cosmic rays come from.” ScienceDaily. ScienceDaily, 23 August 2021.)

Original paper: Fukui, Y., Sano, H., Yamane, Y., Hayakawa, T., Inoue, T., Tachihara, K., Rowell, G. and Einecke, S., 2021. Pursuing the origin of the gamma rays in RX J1713. 7$-$3946 quantifying the hadronic and leptonic components. arXiv preprint arXiv:2105.02734.

A universal equation for the shape of an egg

Researchers have discovered a universal mathematical formula that can describe any bird’s egg existing in nature — a significant step in understanding not only the egg shape itself, but also how and why it evolved, thus making widespread biological and technological applications possible.

Credit: University of Kent

The egg, as one of the most traditional food products, has long attracted the attention of mathematicians, engineers, and biologists from an analytical point of view. As a main parameter in oomorphology, the shape of a bird’s egg has, to date, escaped a universally applicable mathematical formulation. Analysis of all egg shapes can be done using four geometric figures: sphere, ellipsoid, ovoid, and pyriform (conical or pear-shaped). The first three have a clear mathematical definition, each derived from the expression of the previous, but a formula for the pyriform profile has yet to be derived. To rectify this, the researchers have introduced an additional function into the ovoid formula.

The subsequent mathematical model fits a completely novel geometric shape that can be characterized as the last stage in the evolution of the sphere—ellipsoid—Hügelschäffer’s ovoid transformation, and it is applicable to any egg geometry. The required measurements are the egg length, maximum breadth, and diameter at the terminus from the pointed end. This mathematical analysis and description represents the sought-for universal formula and is a significant step in understanding not only the egg shape itself, but also how and why it evolved, thus making widespread biological and technological applications theoretically possible.

Source (University of Kent. “A universal equation for the shape of an egg.” ScienceDaily. ScienceDaily, 31 August 2021.)

Original paper: Narushin, V.G., Romanov, M.N. and Griffin, D.K., 2021. Egg and math: introducing a universal formula for egg shape. Annals of the New York Academy of Sciences.