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This article/ excerpt that I have written here is inspired by the YouTube video, posted on the channel Chris Williamson. In the video, Dr. Andrew Huberman narrates his experience of being with the high-tolerance performer, the American runner, and Guinness record holder David Goggins. You can read the excerpt below. David Goggins, America's toughest runner, once held the Guinness record for the highest number of pull-ups at 4030 in 17 hours. According to Andrew Huberman, what one sees on social media is actually what one gets from David Goggins in real-life interactions with him. Everything you read and hear about David Goggins is exactly how he shows up in real life. Andrew Huberman mentioned that he has a virtual version of sharks set up in his lab. Even though it's not exactly the same as a real experience, people who are afraid of sharks can have a scary experience, allowing Dr. Huberman to study fear. David Goggins showed interest in experiencing the virtual reality of sharks, despite being afraid of them due to his past as a Navy SEAL for the US. He found the experience very scary but constantly wanted to experience it more, despite the fear. According to Andrew Huberman, David was constantly pushing the friction lever. David Goggins was observed to have the power to push himself forward when circumstances are unfavorable, and both spiritual and mechanical energy are low. Andrew Huberman has coined a term for this, which he calls limbic friction. This is the friction that one has to overcome to get up and take action when lying on the bed totally exhausted; to lean into action in a calm and controlled way when the situation is stressful. For example, facing a public audience and delivering a talk in a calm and composed way when you know you are stressed out. According to Andrew Huberman, limbic friction is the ability to tolerate high adrenaline. Adrenaline is an epinephrine that prepares us for a fight-or-flight response. Physiologically, adrenaline is produced from the adrenal gland above the kidneys. This adrenaline is in the blood and can't cross the so-called blood-brain barrier, which is a high restriction fence. Small molecules of adrenaline are produced within the brain from a cluster of neurons called the Locus ceruleus. In stressful situations, adrenaline is produced in the body and the brain. This hijacks the body and certain organ systems - for example, you breathe faster, your heartbeats go faster, your vision narrows, etc. Looking at stress inoculation protocols like the ice bath challenge, cold shower, and cyclic hyperventilation process, lots of adrenaline is produced in the body. What if it is you who evoked the adrenaline in your body and brain; let's say by challenging yourself with an ice-cold bath, cold shower, or purposefully being in a stressful situation, and then you try to solve a math problem or try to relax? Stressful situations can arise when you are in a relationship, in situations while delivering a public lecture, while performing a crucial part of your high-risk project, or in a near-accident situation, etc. Let's say while driving, you almost hit another vehicle, and this is a high-adrenaline situation. Chances are higher for you to freak out or engage in road rage. But if you are someone who is aware of this adrenaline rush or someone who is conditioned to behave calm and composed in a high-adrenaline situation, you will behave calm and composed in a real-life situation, as just mentioned above, and will have better consequences too. Adrenaline is a generic hormone, and there is no specific adrenaline for the "car crash," "the heights," or the "relationship situations," etc. If you are someone who has trained and conditioned the body and the brain to think and act thoughtfully and calmly in a high-adrenaline situation, by evoking high adrenaline during stress-inoculation protocols like cold showers, and hyper cyclic-ventilation, then it raises the stress threshold. A person like David Goggins is trained to tolerate high-stress situations. So by being in situations that stimulate or trigger adrenaline, for example, in a hot or cold environment, or in a low-oxygen environment, one slowly raises the tolerance threshold or stress threshold. A hot environment can cause a burn, and a low-oxygen environment can cause suffocation. So, people trained in Navy SEALs are often presented with cold showers and ice baths as part of training.

What is this program about? This is called the DBT Skill Vigyan program, sanctioned by the Department of Biotechnology, Govt. of India. Where is this program going to be conducted? The program is going to be conducted in Kerala, with the participation of 15 state academic and research institutions. Which is the kerala's undertaking body of this skill development program? The DBT Skill Vigyan Program will be run and managed by the Kerala Biotechnology Commission (KBC) and Kerala State Council for Science Technology and Environment (KCSTE). What are the training programs offered? The training programs included are Student Training program and Technician Training program. Duration of the Training Program? Student Training program Technician Training Program Duration: 3 Months Targeted Participants Student Training Program Technician Training Program Any Stipend Provided to the Participants? Yes, the programs are stipendiary. Also, this is a Hands-on program. What is the major area of focus of the Program? Diverse biotechnology fields. What are the Collaborative Partners? The program is developed in collaboration with four prominent sector skill councils under the Government of India. Life Science Sector Skill Development Council (LSSSDC) Agriculture Skill Council of India (ASCI) Food Industry Capacity and Skill Initiative (FICSI) Healthcare Sector Skill Council (HSSC) Eligibility Criteria The program is open to Plus 2 students, graduates, and Post-graduates in Biotechnology and allied subjects. Is the Application Online? Yes, the application is online. Is there a registration fee involved? Yes, there is a registration fee of Rs. 750/- for applicants from the General category, and Rs. 375/- for applicants from the SC and SC category. Is there are application deadline? Yes, 31 December, 2023. How is the selection process? There will be a thorough review of your application. Also, there will be a mandatory examination for the selection. Any Accommodation Arrangements made for successful candidates? Yes, accommodation arrangements will be made, and it will be at the discretion of the partner institutions. Contact for Further Details For more details, please contact the State Nodal Officer, DBT Skill Vigyan programme: Name: DR. SARIKA A R Email: kbc.kscste@kerala.gov.in Phone: 0471-2548406, 0471-2548254 Student Training Program Details Technician Training Program

This blog is about the __hash__() method in Python, which is a dunder method that returns the hashvalue of an object.

1. INTRODUCTION TO PYTHON FOR BIOLOGY Link: Introduction to Python for Biology - Transmitting Science Date: February 12th-March 11th, 2024 Tutors: Nic Bennett The University of Texas at Austin United States of America 2. INTRODUCTION TO GENOME-WIDE ASSOCIATION STUDIES (GWAS) Link: Assembly and Annotation of genomes - physalia-courses Date: 18-22 March 2024 Tutors: Dr. Giulio Formenti(The Rockefeller University, USA), Dr. Guido Gallo (University of Milan, Italy) 3. INTRODUCTION TO GENOME-WIDE ASSOCIATION STUDIES (GWAS) Link: Introduction to genome-wide association studies (GWAS) - physalia-courses Date: 27 November - 1 December 2023 Tutors: Dr. Filippo Biscarini, Dr. Oscar Gonzalez-Recio, Dr. Christian Werner 4. COMPUTATIONAL TUMOUR EVOLUTION FROM BULK DNA SEQUENCING Link: Computational tumour evolution from bulk DNA sequencing - physalia-courses Date: 26 February - 1 March 2024 Tutors: Prof. Giulio Caravagna (University of Trieste), Elena Buscaroli 5. GENOME ASSEMBLY USING OXFORD NANOPORE SEQUENCING Link: GENOME ASSEMBLY USING OXFORD NANOPORE SEQUENCING - physalia-courses Date: 4– 8 March 2024 Tutors: Dr. Amanda Warr (Roslin Institute, UK), Dr. Natalie Ring (Roslin Institute, UK) 6. Introduction to Bayesian Data Analysis Link: Bayesian data analysis: Theory & practice - physalia-courses Date: 12-16 February 2024 Tutors: Prof. Michael Franke(University of Tübingen, Germany) 7. RNA-SEQ ANALYSES IN NON-MODEL ORGANISMS Link: RNA-seq Analyses in non-model organisms - physalia-courses Date: 11th-15th December 2023 Tutors: Dr. Nicolas Delhomme, Dr. Bastian Schiffthaler 8. INTRODUCTION TO R SHINY Link: Introduction to R Shiny - physalia-courses Date: 29-30 January 2024 Tutors: Dr Mohamed EL Fodil Ihaddaden

A Talk by Experts on Luminescence Dating Today I felt delighted to have attended a talk on the topic: Luminescence dating for dating terracotta. Speakers of the Talk The talk was delivered by Dr. Morthkai, Scientist-D, Birbal Sahni Institute of Palaeoscience, Lucknow, India. I hereby gist the valuable information delivered by Dr. P Morthkai, the speaker. Dr. P Morthkai is a scientist at the distinguished Birbal Sahni Institute of Palaeoscience. The talk provided insights into the scientific underpinning Luminescence Dating - a geochronological method, like carbon dating that can be used for studying the age of paleontological and archaeological materials. How does Carbon-14 dating differ from Luminescence Dating Two important graphs we need to understand that distinguish the Luminescence dating from the carbon-14 dating. Carbon Dating In carbon dating, as you see, we measure or quantify the carbon-14, the radioactive isotope of carbon as time passes. It takes 5730 years for a given amount of carbon to become half of its initial quantity. Carbon dating is based on the carbon-14 that is absorbed into living beings while they are alive. To speak of the formation of carbon-14 and its entry into the living body, first of all, the carbon-14 is produced in the lower stratosphere and upper troposphere by the activity of the cosmic rays. Cosmic rays generate neutrons, and those when travel through the atmosphere strike with the nitrogen-14 and turn them into carbon-14. These carbon-14, when they combine with oxygen, form carbon dioxide. Plants use these carbon dioxide for photosynthesis and, as consumers, when animals including humans ingest plant foods they take in carbon-14. These carbon-14 are exchanged with the atmosphere and therefore maintaining a constant ratio of C-14 to C-12. When an organism dies, there is no more addition of C-14 into the system and the existing C-14 starts to decay to N-14. Studies show that the ratio of C-14 to C-12 is 1.25 parts of C-14 to 10^12 parts of C-12, and this ratio remains almost constant along the lifespan of animals. If the body mass of an animal is known, it is possible to estimate the initial number of carbon atoms and Carbon-14 atoms. Since the half-life of Carbon-14 is 5730, after 10 half-lives, almost 99% of the original Carbon-14 would have been decayed, and therefore fossils that are at most 50,000 years old can be dated accurately. Luminescence Dating Whereas C-14 dating involves the calculation of age of an archaeological object by measuring the number of C-14 atoms after decay, luminescence dating involves determining the age of archaeological and paleontological objects by measuring optically stimulated luminescence (OSL). The OSL is due to the accumulation of the trapped electrons between the valence and the conduction bands. The brightness or the intensity of the OSL is based on the number of electrons that are trapped between valence and the conduction band. When the material is optically stimulated, the electrons are ejected from the hole-traps (between the valence and the conduction band) and fall to the valence band, thus emitting light. The intensity of luminescence depends on the number of electrons that were thrown out from the valence band upon bombardment by high eV (electron volt) radiation energy (alpha, beta particles, gamma rays, etc.) . In the image, there is a reference to a point in time at which the signal is reset to 0. At this point, all the trapped electrons have been evacuated from the hole-traps and they are now in the valence band. This happens when the material is subjected to intense heat - for example in the case of preparation of terracotta materials - or exposure to sunlight. Here, we need to understand that sediments and archaeological artifacts contain dosimeters - quarts and feldspars - and they can trap electrons when irradiated. These electron traps are nothing but holes or impurities that can accept electrons. If there are radiation energy in the surrounding environment of a dosimeter, the electrons get expelled from the electron-dense valence band to the electron-free conduction band overcoming a binding energy. If the binding energy needed for electrons in the valence band of Silicon atom (of SiO2) is 6 eV, the radiation energy of alpha, beta particles, and gamma rays is in the order of millions. This would result in the mass ejection of electrons from the valence band of the Silicon and other atoms to the respective conductions bands. The electrons stay only temporarily in the conduction band and fall back only to get tapped in the holes between the valence and the conduction band. When the dosimeters are exposed to sunlight or any thermal source, the electrons that are trapped come back to the valence band, emitting light. Calculation of Age of a paleontological/ archaeological object If we know the total electrons that were trapped and the rate at which the electrons trapped, we can calculate the time it took for the electrons to become trapped; and that is the time we refer to as the age of the material concerned. Time = Dose (or quantity of the trapped electrons in Gy)/ Dose Rate (Gy/Ka) We will get the time in kilo year or 1000 years. Here, the rate at which electrons trapped is calculated indirectly by the calculating the rate at which radiation energy is emitted/ dose rate (expressed per 1000 years) Singhvi's beaker model Singhvi’s beaker model can be used to understand the applicability of Luminescence dating in calculating the age of paleontological and archaeological objects. Here, how do we calculate the time taken to fill the water? Obviously by dividing the volume by drop rate. Here, the value is affected by the size of the beaker, and variability in the drop rate. So, assumptions are made that beakers are of comparable size and drop rate is uniform. In the same sense, when we measure time/ age of an archaeological object, the dosimeter is like a beaker, the electrons that are trapped in the hole-trap is the water that is collected in a beaker, and the dose rate is like drop rate. Here, we make the assumption that the dosimeter is irradiated at a constant rate.

1. What is this post about: Evaluation of Genetic Resistance against Parasitic Infection (Haemonchus contortus) in Indigenous Goat Breeds of Tamil Nadu through Candidate Gene Approach 2. Institute associated with the post: Tamil Nadu Veterinary and Animal Sciences University, Department of Animal Genetics and Breeding, Veterinary College and Research Institute, Tirunelveli - 627 358 3. Name of the post: Young Professional II 4. Number of Posts: 1 5. Qualification: B.Sc./B.Tech in Agricultural Science/B.V.Sc. OR M.Sc./M.Tech/M.V.Sc., etc., in any branch of science 6. Duration of the Post: Young Professional-II will be engaged initially for 01 year and extendable for further two years (1+1+1 i.e. one year at a time) based on performance to be assessed by the Unit In Charge and continuation of the schemes 7. Payment: Consolidated pay – Rs 35,000/- per month 8. How to Apply: Application in the prescribed format along with all enclosures (as proof) may be submitted by email to agbvcritni@tanuvas.org.in/by post to the address “The Professor and Head, Department of Animal Genetics and Breeding, Veterinary College and Research Institute, Tirunelveli - 627 358” on or before 10th December 2023 5.00 PM. Application without supporting documents for proof will be summarily rejected. Applications thus received will be shortlisted for an interview. Original documents of the candidates selected for an interview will be verified on the day of the interview. 9. Deadline: 10th December 2023 5.00 PM 10. Address for Correspondence: The Professor and Head, Department of Animal Genetics and Breeding, Veterinary College and Research Institute, Tirunelveli - 627 358 11. Documents to be Submitted

Let’s plot a bar graph. Not a simple static bar graph, but the kind that is animated, as you see below. Let’s see how we can plot this animated graph using the ‘FuncAnimation’ class, of the matplotlib library. First of all, we need to import the necessary modules. We import pyplot class from the matplotlib module; animation class from the matplotlib module. We will be using the FuncAnimation class of the animation base class, to create the animation. Next, we need to create an instance of a figure and Axes to be placed on the figure. Once the figure and Axes have been created, next we need to draw artists on the figure for bar plots. The artist for bar plot is drawn using the matplotlib.pyplot.bar() method. Basically, and essentially, we need to provide two arrayLike data to matplotlib.pyplot.bar(), that correspond to the x-coordinates and the heights of the bars. Here, we like to plot 5 bar plots, and the x-coordinates can be generated as follows. This generates an arrayLike data. Next, we need to generate values that feed an arrayLike data for bar heights. Let’s go ahead and define a function that takes an integer argument, and generates a list of float values, according to the numerical value of the integer, as given below. According to the above code snippet, the list of values generated is a multiple of 2. Once the x-coordinates and the heights are ready, now it is time to plot the bars, as follows. Next, we need to set y-axis limits. Up to this point, we have created artists to plot a simple static bar plot. We will keep this as a starting point and update the artists on each frame, to create animation. In order to update the artists, we will use a func parameter of the FuncAnimation class. Now, let’s call the func update function by creating a FuncAnimation object. And, we also need to set parameters for the FuncAnimation object. Most importantly, we need to set the number of frames; the number of frames will dictate the number of containers used to plot the bar graph. For each frame, we will have distinct arrayLike data for bar heights. Here, frames is set to the variable ‘n’ that holds a numerical value. Every time we call the func parameter, the function iterates through ‘n’. Now, let’s look at the func function parameter, and see how artists are updated. As I have mentioned above, every time we call the func function, it iterates through the frames. First of all we will call the function that generates arrayLike data for bar heights. Here, ‘i’ iteratively takes value of frames: 0, 1, 2,…….,n Next, we need to iterate through the bar container -barcollection here - , and grab artists for individual bars. Each bar within the bar container is an object of the class 'matplotlib.patches.Rectangle’. We will use enumerate method to iteratively grab the artist and the index. Artist for each bar will be set with a particular height. The height value will be chosen from the arrayLike data, and will be of the same index as that of the bar in the bar container. Next, we need to update the axes. In order to maintain a continuous motion of the bars and the y-axis, we use the following configuration of the y-axis. That’s it! we have coded our update function. Now, we need to save the plot in ‘gif’ format. We use imagemagick writer for writing the plot to any one of the formats i.e. .mp4, .mkv, .gif The complete code

Let’s look at an instance of plotting an animated graph. Let’s plot the trajectory of motion of an object that is moving at constant acceleration / deceleration. The distance - time graph of an object moving at a constant acceleration has a parabolic shape. For example, I have given a graph, here. Now let us create and animate a distance- time graph for a constant acceleration, using a sequence of time values. Before plotting this graph, we need to set a couple variable ready, for the calculation of distance (Using Newton’s equation). Importing the modules The important modules to perform this programing task are given below. pyplot class from the matplotlib library FuncAnimation class from the matplotlib.animation numpy library Creating instances of figure and axes Now we need to create a figure and axes, to draw artists and set data, respectively. Generating the time values We can generate a set of time values in the form of an array. There are two ways by which we can generate a sequence of time values: 1. using the np.arange() function, and 2. np.linspace() function. If we use np.linspace(), we will have a definite number of time values between a specified time range. On the contrary, if we use np.arange(), there will be a definite number of values at a constant interval. For the time being, I have used the np.arange() , and the array of time values is set to a variable. Defining the variables and the calculation of distances We need to create variables for acceleration/ deceleration, and the Initial velocity. We will be drawing a line plot for a trajectory, for a specific initial velocity, and a scatter plot for the trajectory, for a different initial velocity. We will be calculating the distance travelled, under a constant acceleration/ deceleration, using the following equation. s = ut + 1/2*at**2 Drawing the initial figure for line and scatter plot Now let’s draw initial figures for line plot and scatter plot. The line plot is drawn using the Axes.plot() method and scatter plot is drawn using the Axes.scatter method. Axes.plot() returns a matplotlib.lines.Line2Dobject and Axes.scatter returns a matplotlib.collections.PathCollectionsobject. Here we have set an initial value for the x-data and y-data. Instead of this, we can also set an empty list. For the scatter plot, the other parameters determine the property of the plot: c is for color of the marker, s is for the size of the marker, label will be used for the legends. Setting the x-limit and y-limit We can set the x-limit and y-limit by providing a list of lower-bound and upper-bound. Optionally we can also set the x-label and the y-label. Creating a FuncAnimation object Now, let’s create a FuncAnimation object, and pass in the following parameters. fig: The argument should be the figure instance on which the artists (line2D or pathCollection) are drawn. func: A function to modify the data for each each frame. The function returns the artist. frames: This determines the length of the animation. For each frame, a sequential subset of data will be used for drawing. interval : This is the duration in milliseconds between the drawing of two consecutive frames. Defining the func(function) parameter The func parameter allows us to modify the data and return corresponding artist to be drawn on the figure. The figure is updated with the modified data for each frame. For a Line2D artist, the figure is updated using the set_data method. For a pathCollection artist, that draws scatter plot on the figure, the data are updated using the set_offsets method. The complete code

Let's create an animated graph as you see below. Importing the necessary modules These are the necessary modules to perform this programming task: 1. Numpy to be imported as np, 2. pyplot class of matplotlib as plt, 3. animation class of matplotlib as animation. import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation Generating the datapoints We use linspace function of the Numpy library to generate an evenly-spaced array of a certain number of numerical objects. Here we have generated 100 numerical objects between 0 (including) and 10. These are the x-values. Then for the y-value, we have generated values by passing the x-values to a sine() function of the np library. # Some example data x = np.linspace(0, 10, 100) y_plot1_fourier = np.sin(x) Setting the variables for the lower and the upper x-limit Now, we are going to set variable that are going to be the lower and the upper x-limits. This is for the initial couple of frames, until the nth value of the x-value is greater than the upper x-limit. x_l_plot = 0 x_u_plot = 5 Creating instances of figure and axis and setting the x-limits and y-limits We create an instance of figure and axis by using the subplots() function of pyplot. Then we set the x-axis limit by using the set_xlim function. Then we set the y-axis limit using the set_ylim function. # Create a figure and axis fig, ax = plt.subplots() ax.set_xlim([x_l_plot, x_u_plot]) ax.set_ylim(-1.5, 1.5) Using the plot() to plot a lineplot Using the plot() we plot a lineplot. The x-values and the y-values are dynamically added. Therefore empty lists are added as arguments to the plot() function. # Create a plot (line plot in this case) line, = ax.plot([], [], label='y_plot1_fourier', c='r', linewidth=2) Invoking the FuncAnimation function to draw animated graph. Next we invoke the FuncAnimation function of the animation class of the matplotlib library. The parameters are: 1. figure instance to draw the animated graph. 2. a function to be invoked such that the data are generated iteratively for each frame, and the x-limits are set dynamically. 3. frames: this is the total number of data points on the x-axis. If it is a list of data points, you can use the len(); for a numpy array, you can use the size function. For every iteration through the frames, a certain number of data-points are set to the plot(). 4. interval this is the interval between frames. A smaller value cause frame transition faster and therefore a faster animated graph. 5. Blit: when Blit is set to True, it only redraws the artists returned by the function, and it does not draw the frame. When you run the code version, where Blit is set to True, you can observe that the x-axis remains unchanged. This happens because only the artist (Line2D here) is drawn, and the entire frame is not re-drawn. ani = animation.FuncAnimation(fig, func, frames=len(x), interval=200, blit=False) The function parameter Here the x-data and y-data are iteratively updated. Then axis limit is set. When the x-value becomes greater than the upper x-axis limit, the whole x-axis limit is reset: the lower bound is set to x[n] - x_u_plot, and the upper bound is x[n]. But, in the example code, as you can see, the conditional is such that when x[n] > x_u_plot - 3, the whole axis is reset; and corresponding changes have been made to the axis limit. # Function to update the plot for each frame def func(n): line.set_data(x[0:n], y_plot1_fourier[0:n]) if x[n] > x_u_plot - 3: ax.set_xlim(x[n] - (x_u_plot - 3), x[n] + 3) else: ax.set_xlim(x_l_plot, x_u_plot) return line The complete code import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation # Some example data x = np.linspace(0, 10, 100) y_plot1_fourier = np.sin(x) x_l_plot = 0 x_u_plot = 5 # Create a figure and axis fig, ax = plt.subplots() ax.set_xlim([x_l_plot, x_u_plot]) ax.set_ylim(-1.5, 1.5) # Create a plot (line plot in this case) line, = ax.plot([], [], label='y_plot1_fourier', c='r', linewidth=2) # Function to update the plot for each frame def func(n): line.set_data(x[0:n], y_plot1_fourier[0:n]) if x[n] > x_u_plot - 3: ax.set_xlim(x[n] - (x_u_plot - 3), x[n] + 3) else: ax.set_xlim(x_l_plot, x_u_plot) return line # Create the animation ani = animation.FuncAnimation(fig, func, frames=len(x), interval=200, blit=False) plt.show() The code version that has blit parameter set to True import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation # Some example data x = np.linspace(0, 10, 100) y_plot1_fourier = np.sin(x) x_l_plot = 0 x_u_plot = 5 # Create a figure and axis fig, ax = plt.subplots() ax.set_xlim([x_l_plot, x_u_plot]) ax.set_ylim(-1.5, 1.5) # Create a plot (line plot in this case) line, = ax.plot([], [], label='y_plot1_fourier', c='r', linewidth=2) # Function to update the plot for each frame def func(n): line.set_data(x[0:n], y_plot1_fourier[0:n]) if x[n] > x_u_plot - 3: ax.set_xlim(x[n] - (x_u_plot - 3), x[n] + 3) else: ax.set_xlim(x_l_plot, x_u_plot) return line, # Create the animation ani = animation.FuncAnimation(fig, func, frames=len(x), interval=200, blit=True) plt.show()

Explore exciting opportunities as a Junior Research Fellow in the European Union DST project 'Saraswati 2.0' at IIM Mumbai.

International Conference on One Health: Biotechnology as a Catalyst for Sustainable Development (HEAL-BioTec) What is this post about? This post highlights an upcoming International Conference on "One Health: Biotechnology as a Catalyst for Sustainable Development (HEAL-BioTec)." The conference aims to bring together experts, researchers, scholars, and industry professionals to discuss and exchange ideas on various aspects of biotechnology and its role in fostering sustainable development. Date and Venue Date: 7th to 9th December 2023 Venue: School of Life Sciences, JSS Academy of Higher Education and Research, Mysuru. Conference Theme The central theme of the conference is "One Health: Biotechnology as a Catalyst for Sustainable Development (HEAL-BioTec)." This theme reflects the interdisciplinary approach to health, encompassing human, animal, and environmental health, and the pivotal role biotechnology plays in achieving sustainable development. Organizers and Contact Information Organizing Institute: JSS Academy of Higher Education and Research Organizers: Dr. Gopenath TS, Dr. Kirankumar MN Contact Information: Email: onehealthbcsdconference@gmail.com Mobile: 9600499823/8892778778 Registration Details Early Bird Registration (1st October – 24th November, 2023): Amount (Rs. Including GST): 885/- (Students), 1180/- (Scholars), 1770/- (Faculty), 2950/- (Industry) Late Registration (25th November – 02nd December, 2023): Amount (Rs. Including GST): 1180/- (Students), 1475/- (Scholars), 2065/- (Faculty), 3540/- (Industry) Spot Registration: Amount (Rs. Including GST): 1475/- (Students), 1770/- (Scholars), 2360/- (Faculty), 4130/- (Industry) Abstract Submission Last Date for Abstract Submission: 15th November 2023 Abstract Format: Title of the presentation Order of authors and their affiliations Corresponding author's email address Presenting author's name in bold For Poster Presentation, the size should not exceed 4x4 feet Abstract should be 250 words excluding title, authors, and author affiliations Keywords and sections: Background, Materials and Methods, Results and Discussion, Conclusions Poster Dimension Dimension: 4x4 feet Thematic Areas Diagnostics & Biotherapeutics / Phytotherapeutics in Healthcare Crop production, Plant Health & Management Food Fermentation, Process, Technology & Security Targeting Molecular and Cellular Pathways Environmental Pollution & Emerging diseases Artificial Intelligence in Healthcare/Agri-Food/Environment Bioinformatics & Computational Biology Innovations, Entrepreneurship & IPR Organizing Committee Chief Patron: His Holiness Jagadguru Sri Shivarathri Deshikendra Mahaswamiji Patrons: Dr. C. G. Betsurmath, Dr. B Suresh, Dr. Surinder Singh, Dr. B. Manjunatha Convener: Prof. Raveesha KA Co-Convener: Dr. Balasubramanya S Resource Persons A distinguished lineup of resource persons including Dr. Claus Heiner Bang-Berthelsen, Dr. Balasubramanya S, Dr. Pannaga Krishnamurthy, and many more will share their expertise.

What is this position about? This opportunity revolves around the Project Assistant Position at the Regional Center for Biotechnology, focusing on elucidating metabolic signatures in symbiotic and pathogenic legume-microbe interactions. Title of the Project: Elucidating the Metabolic Signatures Delineating Symbiotic and Pathogenic Legumes-Microbe Interaction. Duration of the Project: The initial engagement spans 6 months, with the potential for extension up to 1 year, providing a substantial period for meaningful contributions. Number of Positions Available: One Seize this exclusive opportunity as there is one position available for a qualified and motivated candidate. Principal Investigator: Guided by the Principal Investigator, Dr. Ankita Alexander, MK Bhan Fellow, under the mentorship of Dr. Divya Chandran, Associate Professor. The successful candidate will have the chance to contribute to groundbreaking research. Qualifications of the Successful Candidate: A graduate degree in Life Science, with a stellar academic record of at least 65% in the Bachelor's degree, sets the stage for success. Skills and Experience: Candidates with wet lab experience in Microbiology, Plant Molecular Biology, and Plant-Microbe Interaction are strongly encouraged to apply. Financial Assistance: 20000 + 24% HRA Receive competitive financial support for your dedication and contributions to the project. Interview Date: 30.01.2023 Mark your calendar for the interview date, your opportunity to showcase your passion and expertise. How to Apply: Bring your original documents, an updated CV, and details of at least 2 referees with contact numbers to the interview. Address for Correspondence: Regional Centre for Biotechnology, NCR Biotech Science Cluster, Faridabad-Gurgaon Expressway, 3rd Milestone, Faridabad. For any queries: Feel free to reach out to: ankita.alexander@rcb.res.in or divya.chandran@rcb. res.in for any clarifications or additional information.