Search Results

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.

What is this post about? This post introduces a remarkable opportunity for students looking to dive into the world of research – the International Max Planck Research School for Cell, Developmental, and Systems Biology (IMPRS -CellDevSys) Student Research Internship. What is the name of this internship program? The program is called the International Max Planck Research School for Cell, Developmental, and Systems Biology (IMPRS -CellDevSys) Student Research Internship. Who can apply for this internship program? This internship is open to both Bachelor and Master students who have a passion for gaining research experience in the fields of cell, developmental, or system biology during their university studies. What are the peculiarities of this internship program? Purely Research-Based: This internship stands out for being entirely research-focused, providing students with a deep dive into their chosen field of study. International Work Environment: IMPRS interns can expect to work in a highly cooperative and international work environment, fostering cross-cultural learning and collaboration. Research Question Focus: The internship period revolves around working on a specific research question. Interns receive support from experienced research groups to navigate and contribute meaningfully to ongoing projects. Eligibility To be eligible for the IMPRS -CellDevSys Student Research Internship, applicants must meet the following criteria: University Students: Open to students pursuing a Bachelor's or Master's degree. Field of Study: While the primary focus is on Life Sciences, the program extends its arms to students from Chemistry, Physics, Computer Science, Applied Mathematics, and Engineering. Academic Excellence: Applicants are expected to showcase excellent academic performance. Language Proficiency: A high proficiency in the English language is a prerequisite. Eligibility for Bachelor's Students Bachelor's students must have completed at least four semesters of their studies to be eligible for this internship. Duration of the Internship The internship spans over 2 to 3 months, allowing students a substantial period to immerse themselves in their research projects. Financial Assistance For the duration of the internship, students will receive financial support, including: Monthly Stipend: A generous monthly stipend of 934 euros. Additional Support: Financial assistance for travel, housing, and VISA approval. Admission Process The path to becoming an IMPRS intern involves a well-defined admission process: Formal Offer: Formal offers to participate in the IMPRS Student Research Internship program are extended by the IMPRS program coordinator. Online Interview: Candidates will undergo an online interview to assess their suitability for the program. Referee Contact: Referees may be contacted as part of the evaluation process before the final decision is made. Deadline If this opportunity piques your interest, mark your calendar – the deadline for applications is on 09.02.2024. Don't miss the chance to embark on a journey of discovery and innovation through the IMPRS -CellDevSys Student Research Internship. Apply now and take the first step towards shaping your future in the world of research!

Introduction: Discover a golden opportunity for post-doctoral researchers in the realm of life sciences and biotechnology – the MK Bhan - Young Researchers Fellowship Program (MKB-YRFP). Launched by the Department of Biotechnology (DBT), Ministry of Science and Technology, Government of India, this program is a beacon for emerging talents keen on advancing cutting-edge research. Program Overview: The MKB-YRFP aims to empower young researchers, specifically those who have completed their Ph.D., encouraging them to pursue impactful research within the country. It offers independent research grants to Post-Doctoral fellows, focusing on addressing national challenges. Key Highlights: 1. Institute Limitation: The fellowship is exclusive to researchers in DBT-autonomous institutes. 2. Eligibility Criteria: - Must be an Indian citizen. - Ph.D. holder in life science/biotechnology. - No permanent position in any college or university. - Demonstrated excellence through publications, technology development, patents, etc. - Identification of a host institute and mentor. The application must be endorsed by the mentor and the Head of the Department (HoD) of the host institute. 3. Fellowship Details: - Co-terminus with the project duration. - Upper age limit: 35 years. - Monthly remuneration: ₹75,000/- - Contingency grant: ₹20 lakhs per year for various purposes, including manpower hiring, consumables, minor equipment, domestic travels, and other contingent expenditures. 4. Duration and Number of Fellowships: - Up to 50 fellowships annually. - Maximum duration: Three years, extendable by two years under specific conditions. 5. Work Location: - Fellows can work at any DBT-autonomous institute. 6. Host Institute Changes: - Fellows can change the host institute once during the fellowship, provided there is a prior no-objection certificate from the host institute. 7. Nomination and Application: - Candidates cannot submit nominations through multiple host institutes. - Host institutes can nominate more than one candidate. 8. Financial Support: - Up to ₹10 lakhs for non-recurring entities like capital equipment. - A fellowship amount of ₹75,000/- per month for three years. - Contingency grant: ₹10 lakhs for the first year and ₹20 lakhs for subsequent years. - Fellows can engage one JRF/SRF/project assistant. - Travel expenses limited to ₹50,000/- per year, for domestic travel. 9. Application Process: - Applications must be forwarded by the mentor and the HoD of the host institute. 10. Necessary Attachments: - Proof of Date of Birth - Proof of Master's degree and Ph.D. certificate - One copy of each relevant publication - Recognition/award details - Consent form from the mentor 11. Previous Year Selections: - The list of selected candidates for the previous year can be found in the Result of the MK Bhan-Young Research Fellowship Program - Result of the MK Bhan-Young Research Fellowship Program - 2022-23.pdf (dbtindia.gov.in) 12. Deadline for Submission: - Applications must be submitted by 30.11.2023.

The assembler hit a limbo when it was no longer able to generate temp files. I was running the CLC genomics workbench for my de novo assembly work. CLC Genomics workbench is a licensed software that has all the packages for de novo assembly of DNA sequence reads and downstream analysis of contigs. Morning, when I checked the progress, I had a warning message on the screen that said: “There is not enough space in the root directory”. As of now, a stage of the assembly called “Contig generation is progressing”, and the process seems stuck at 14%. It took about 24 hours to have progress by 1%, from 13%. The root directory(' / ') in my system is mounted onto the /dev/sda2. When I checked, the free-space was 0%. I found out that the /var/log/error_log file had consumed the majority of the disk space, and ~600 GB was freed upon deleting the file (but I observed that the same file was being generated and filled at a faster pace). When I read about the CLC server, it says that it takes a lot of disk space for temporary files. These temporary files are needed for analysis while performing the local assembly, and these temporary files are written to the system’s default temporary directory. By default temporary directory, I believe they meant '/tmp '. When I checked the '/tmp', I found a FASTA file, sized ~ 185 GB. When I checked the size of the /tmp folder, I got a result that says /tmp is of 174GB. The Disk Usage Analysis shows this output: Here, almost 77% of the total used-space in the root directory is consumed by the /temp directory; of this, 99% is claimed by the fasta file, apparently generated by the CLCServer. This totally clears my doubt as to if there was a specific size-allocation for the /temp. When I ran df -h, the output shows that the collective size of all tmpfs amounts to ~174GB. but it looks like they haven’t been used to the full capacity (not more than 1%). Having said that, yet I am not clear what exactly those 'tmpfs' do and how they were distinguished from the /tmp directory. Anyway, later I figured out that 'tmpfs' are something that I should ignore in this situation, as they are just 'ramdisks', and the total size is related to the RAM available in the computer. Here, in my case, there is no separate munt for the /tmp directory, but instead it is a folder within the root directory. Therefore the space available to the root directory is all that matters. As of writing this, the /dev/sda2 (where root directory is mounted on) has a free space of 500GB. I was confused if /tmp directory was full; if there was a specific size-allocation for the /temp directory. But, the fact is there is no size-allocation for the /temp directory, and the size of the device on which /temp is mounted is wholly available. At the same time the error_log file located at /var/log/cups/error_log also raises a challenge, because it consumes disk space much faster than the assembler writes temporary files to the /temp. I don’t understand what the error is and why the error_log was getting generated. So, what can I do to prevent the root directory from getting filled up so fast as to give room for the temporaray files from CLCServer? Should I allocate a separate partition for the /tmp? Is it possible to stop the error_log file? Or dedicate a separate partition for the /tmp directory? Few suggestions from the Linux/ Ubuntu experts One of the responses I received says that it is - though it not a recommended solution - better to purchase a 2 -4 TB SSD and mount on the /tmp file system. After installing the SSD, format it, create a filesystem, create a mount point on /tmp, and edit the /etc/fstab. It is also possible to remount a partition, in a different hard disk, on /tmp. For example, if you have a hard disk partition /dev/sda2, that can be mounted on /tmp mount point. The advice I received was to boot Linux on a live USB stick. /dev/sda2 partition should be of ext4 filesystem. Once it is formatted and mounted on /tmp, open the nano editor and add the following items on the /etc/fstab. UUID=abc1234-whatever UUID /tmp ext4 defaults 0 0 The major reason why the root directory immediately became full was an error_log located at /var/log/cups/error_log. The error_log was consuming the diskspace at an alarming pace. Here is a screenshot of the contents of the error_log file. The error_log file was deleted using the following command. find . -type f -iname error_log -delete Here we use the findcommand. The (.) after find command triggers searching for the file in the present directory. The Type parameter specifies a file, in other words it prompts to search only for files. And, -iname error_log specifies the files named error_log. The -delete keyword commands to delete the file. But this is going to be a tedious process as the error_log file is getting generated at an alarming pace. One way to address this problem is to set a timer to periodically truncate the error_log file. The following command truncates the error_log file every 60 seconds. while sleep 60; do : >|/var/log/error_log; done But when I ran this command, I hit an error as you see below. I didn’t quite get why it raised the permission issue, because the ownership is with the user. Another method is to disable logging (the process of generating logs) in the application, or enable settings that does less verbose logging. Or, another important work-around is to change the default temporary directory that CLC Genomics Work bench uses to write the temporary files. In the CLC Server installation folder, there is a file called CLCServer.vmoptions. This file can be opened using a text Editor, and one needs to add a new line -Djava.io.tmpdir=/path/to/tmp, where path to the new temporary directory should be provided. Restart the CLCServer, for the changes to take effect. Finally Finally, I ran the following command to periodically delete the error_log, so that it does not consume the root directory and incapacitate the assembler to write the temporary files. watch -n 60 find . -type f -iname error_log -delete

This post is about errors and exceptions in Python that interrupts the stability of a Python code, also about try and except blocks.

The counter () class of Python can be used for counting items in an iterable.