Unlocking the role of biological clock molecules in cancer cells – Research Matters

An artistic representation of the L-I-C network in cancer cells depicted in the Indian art form, Yakshagana and Somana Kunitha style (Credit Kirti Lathoria)

Our body functions tune to the circadian rhythm the 24- hour day-night cycle. A biological clock comprising special cell components and genes keep track of time and operate synchronously in a complex molecular mechanism. The timekeeping molecules control several crucial physiological processes like metabolism, cell division and proliferation, immune response, and gene expression (decoding genetic information to produce proteins) to maintain the delicate balance of the rhythm. However, science has shown that when the circadian rhythm is severely hampered, some cells break away from the clock control and turn cancerous.

Existing literature shows that cancer cells have an erratic circadian rhythm and abnormal metabolism. Besides, they sustain the onslaught of cytokines from the immune system. However, how exactly these processes promote cancer cell growth has remained elusive.

A team of researchers from the National Brain Research Centre, Manesar, has decoded the interplay between cancer cell metabolism, pro-inflammatory cytokines (inflammation-causing immune molecules), and the biological clock molecules. Their study shows that the cancer cells chemically rewire the production of an inflammation-inducing mediatorIL-1 and lactate - a by-product of cancer cell metabolism. Furthermore, these two components chemically manipulate the clock machinery to trigger further production of lactate and IL-1. Thus the three components work in tandem to promote a vicious loop of tumour growth. The team has published their results in the journal Molecular and Cellular Biology.

The results of this research will provide a framework for cancer chronotherapy a novel treatment method by which anticancer drugs are administered at optimal timings to enhance their chemotherapeutic potential.

The team conducted laboratory experiments on cultured brain cancer cells by employing several molecular testing methods to observe the function of the cell processes.

Cancer cells hoodwink the circadian checkpoints and alter their metabolism to feed the rapidly growing cells. The common feature of cancer cell metabolic reprogramming is glucose addiction a heightened glucose uptake as compared to a normal cell, says Dr Ellora Sen, Principal Investigator of the study. Unlike healthy cells, cancer cells metabolise glucose even in the presence of oxygen. This aerobic glycolysis produces large amounts of lactate, resulting in a highly acidic environment around the tumour a hallmark feature of cancer. Also, the high acidic environment triggers some genes which help tumour growth.

When there is a disease or injury, the immune system dispatches pro-inflammatory cytokines to induce a mild inflammation at the injury site. The inflammation destroys the diseased cells and facilitates healing. IL-1 is once such cytokine released in response to cancerous growths. Our findings indicate that high lactate levels trigger expression of IL-1, says Dr Sen.

The rapidly multiplying cancer cells activate the immune system to send a surge of IL-1 cytokines. In our earlier study, we found that the cancer cells use IL-1 to enhance the expression of another protein called Hexokinase (HK2), which increases the glucose uptake, adds Pruthvi Gowda, first author of the study. With HK2 promoting glucose intake, the cancer cells get more fuel to multiply.

The team then found that high lactate and IL-1 also increased two crucial circadian molecules, Clock and Bmal1. Bmal1 regulates cell division and is bound to the Clock molecule in what is chemically called a transcriptional dimer. In healthy cells, Clock and Bmal1 work in a complementary manner to control cell growth. However, the researchers noticed that both lactate and IL-1 chemically modify Bmal1 to increase the Clock -Bmal1 binding stability.

Further molecular experiments revealed that Clock/Bmal1 activated a Lactate producing enzyme called LDH-A and IL-1, suggesting the presence of a feed-forward network, says Dr Sen. The researchers thus posit that the three components work in a mutually supportive loop that facilitates the rapid growth of the tumour cells.

To establish their results, the team knocked down Clock-Bmal1 molecules in cancer cells. They noticed that downregulating the clock proteins resulted in lower lactate and IL-1 levels. The team further correlated their results for stomach and cervical cancer cell lines and conducted computer simulations on clinical samples data. They observed that patients who had lower Clock, Bmal1, LDHA and IL-1 levels in their samples survived longer.

The team now looks forward to developing a mathematical model for Lactate- IL-1- Clock (LIC) feed-forward regulatory structure in collaboration with IIT Mumbai. When fitted to the patient molecular profile of LIC components, the model could provide valuable inputs for cancer chronotherapy.

This article has been run past the researchers, whose work is covered, to ensure accuracy.

Read the original:
Unlocking the role of biological clock molecules in cancer cells - Research Matters

Fate Therapeutics to Host Virtual Event Highlighting Interim Phase 1 Clinical Data from its Off-the-Shelf, iPSC-derived NK Cell Franchise for B-cell…

News and research before you hear about it on CNBC and others. Claim your 1-week free trial to StreetInsider Premium here.

SAN DIEGO, July 16, 2021 (GLOBE NEWSWIRE) -- Fate Therapeutics, Inc. (NASDAQ: FATE), a clinical-stage biopharmaceutical company dedicated to the development of programmed cellular immunotherapies for cancer, today announced that management will host a virtual event to highlight interim Phase 1 clinical data from its FT596 and FT516 programs for the treatment of relapsed / refractory B-cell lymphomas on August 19, 2021 at 4:30 p.m. ET.

The live webcast of the presentation can be accessed under "Events & Presentations" in the Investors section of the Company's website at http://www.fatetherapeutics.com. The archived webcast will be available on the Company's website beginning approximately two hours after the event.

About Fate Therapeutics iPSC Product PlatformThe Companys proprietary induced pluripotent stem cell (iPSC) product platform enables mass production of off-the-shelf, engineered, homogeneous cell products that can be administered with multiple doses to deliver more effective pharmacologic activity, including in combination with other cancer treatments. Human iPSCs possess the unique dual properties of unlimited self-renewal and differentiation potential into all cell types of the body. The Companys first-of-kind approach involves engineering human iPSCs in a one-time genetic modification event and selecting a single engineered iPSC for maintenance as a clonal master iPSC line. Analogous to master cell lines used to manufacture biopharmaceutical drug products such as monoclonal antibodies, clonal master iPSC lines are a renewable source for manufacturing cell therapy products which are well-defined and uniform in composition, can be mass produced at significant scale in a cost-effective manner, and can be delivered off-the-shelf for patient treatment. As a result, the Companys platform is uniquely capable of overcoming numerous limitations associated with the production of cell therapies using patient- or donor-sourced cells, which is logistically complex and expensive and is subject to batch-to-batch and cell-to-cell variability that can affect clinical safety and efficacy. Fate Therapeutics iPSC product platform is supported by an intellectual property portfolio of over 350 issued patents and 150 pending patent applications.

About FT516FT516 is an investigational, universal, off-the-shelf natural killer (NK) cell cancer immunotherapy derived from a clonal master induced pluripotent stem cell (iPSC) line engineered to express a novel high-affinity 158V, non-cleavable CD16 (hnCD16) Fc receptor, which has been modified to prevent its down-regulation and to enhance its binding to tumor-targeting antibodies. CD16 mediates antibody-dependent cellular cytotoxicity (ADCC), a potent anti-tumor mechanism by which NK cells recognize, bind and kill antibody-coated cancer cells. ADCC is dependent on NK cells maintaining stable and effective expression of CD16, which has been shown to undergo considerable down-regulation in cancer patients. In addition, CD16 occurs in two variants, 158V or 158F, that elicit high or low binding affinity, respectively, to the Fc domain of IgG1 antibodies. Numerous clinical studies with FDA-approved tumor-targeting antibodies, including rituximab, trastuzumab and cetuximab, have demonstrated that patients homozygous for the 158V variant, which is present in only about 15% of patients, have improved clinical outcomes. FT516 is being investigated in a multi-dose Phase 1 clinical trial as a monotherapy for the treatment of acute myeloid leukemia and in combination with CD20-targeted monoclonal antibodies for the treatment of advanced B-cell lymphoma (NCT04023071). Additionally, FT516 is being investigated in a multi-dose Phase 1 clinical trial in combination with avelumab for the treatment of advanced solid tumor resistant to anti-PDL1 checkpoint inhibitor therapy (NCT04551885).

About FT596FT596 is an investigational, universal, off-the-shelf natural killer (NK) cell cancer immunotherapy derived from a clonal master induced pluripotent stem cell (iPSC) line engineered with three anti-tumor functional modalities: a proprietary chimeric antigen receptor (CAR) optimized for NK cell biology that targets B-cell antigen CD19; a novel high-affinity 158V, non-cleavable CD16 (hnCD16) Fc receptor, which has been modified to prevent its down-regulation and to enhance its binding to tumor-targeting antibodies; and an IL-15 receptor fusion (IL-15RF) that augments NK cell activity. In preclinical studies of FT596, the Company has demonstrated that dual activation of the CAR19 and hnCD16 targeting receptors enhances cytotoxic activity, indicating that multi-antigen engagement may elicit a deeper and more durable response. Additionally, in a humanized mouse model of lymphoma, FT596 in combination with the anti-CD20 monoclonal antibody rituximab showed enhanced killing of tumor cells in vivo as compared to rituximab alone. FT596 is being investigated in a multi-center Phase 1 clinical trial for the treatment of relapsed / refractory B-cell lymphoma as a monotherapy and in combination with rituximab, and for the treatment of relapsed / refractory chronic lymphocytic leukemia (CLL) as a monotherapy and in combination with obinutuzumab (NCT04245722).

About Fate Therapeutics, Inc.Fate Therapeutics is a clinical-stage biopharmaceutical company dedicated to the development of first-in-class cellular immunotherapies for patients with cancer. The Company has established a leadership position in the clinical development and manufacture of universal, off-the-shelf cell products using its proprietary induced pluripotent stem cell (iPSC) product platform. The Companys immuno-oncology pipeline includes off-the-shelf, iPSC-derived natural killer (NK) cell and T-cell product candidates, which are designed to synergize with well-established cancer therapies, including immune checkpoint inhibitors and monoclonal antibodies, and to target tumor-associated antigens using chimeric antigen receptors (CARs). Fate Therapeutics is headquartered in San Diego, CA. For more information, please visit http://www.fatetherapeutics.com.

Contact:Christina TartagliaStern Investor Relations, Inc.212.362.1200christina@sternir.com

Go here to read the rest:
Fate Therapeutics to Host Virtual Event Highlighting Interim Phase 1 Clinical Data from its Off-the-Shelf, iPSC-derived NK Cell Franchise for B-cell...

New algorithm can be a more effective way to analyze models of biological systems – News-Medical.Net

From biochemical reactions that produce cancers, to the latest memes virally spreading across social media, simple actions can generate complex behaviors. For researchers trying to understand these emergent behaviors, however, the complexity can tax current computational methods.

Now, a team of researchers has developed a new algorithm that can serve as a more effective way to analyze models of biological systems, which in turn allows a new path to understanding the decision-making circuits that make up these systems. The researchers add that the algorithm will help scientists study how relatively simple actions lead to complex behaviors, such as cancer growth and voting patterns.

The modeling framework used consists of Boolean networks, which are a collection of nodes that are either on or off, said Jordan Rozum, doctoral candidate in physics at Penn State. For example, a Boolean network could be a network of interacting genes that are either turned on -- expressed -- or off in a cell.

Boolean networks are a good way to capture the essence of a system. It's interesting that these very rich behaviors can emerge out of just coupling little on and off switches together -- one switch is toggled and then it toggles another switch and that can lead to a big cascade of effects that then feeds back into the original switch. And we can get really interesting complex behaviors out of just the simple couplings."

Jordan Rozum, doctoral candidate in physics at Penn State

"Boolean models describe how information propagates through the network," said Rka Albert, distinguished professor of physics and biology in the Penn State Eberly College of Science and an affiliate of the Institute for Computational and Data Sciences. Eventually, the on/off states of the nodes fall into repeating patterns, called attractors, which correspond to the stable long-term behaviors of the system, according to the researchers, who report their findings in the current issue of Science Advances.

Even though these systems are based on simple actions, the complexity can scale up dramatically as nodes are added to the system, especially in the case when events in the system are not synchronous. A typical Boolean network model of a biological process with a few dozen nodes, for example, has tens of billions of states, according to the researchers. In the case of a genome, these models can have thousands of nodes, resulting in more states than there are atoms in the observable universe.

The researchers use two transformations -- parity and time reversal -- to make the analysis of Boolean networks more efficient. The parity transformation offers a mirror image of the network, switching nodes that are on to off and vice versa, which helps identify which subnetworks have combinations of on and off values that can sustain themselves over time. Time reversal runs the dynamics of the network backward, probing which states can precede an initial input state.

The team tested their methods on a collection of synthetic Boolean networks called random Boolean networks, which have been used for than 50 years as a way to model how gene regulation determines the fate of a cell. The technique allowed the team to find the number of attractors in these networks for more than 16,000 genes, which, according to the researchers, are sizes larger than ever before analyzed in such detail.

According to the team, the technique could help medical researchers.

"For example, you might want a cancer cell to undergo apoptosis (programmed cell death), and so you want to be able to make the system pick the decisions that lead towards that desired outcome," said Rozum. "So, by studying where in the network these decisions are made, you can figure out what you need to do to make the system choose those options."

Other possibilities exist for using the methods to study issues in the social sciences and information technology.

"The propagation of information would also make an interesting application," said Albert. "For example, there are models that describe a society in which people have binary opinions on a matter. In the model people interact with each other, forming a local consensus. Our methods could be used to map the repertoire of consensus groups that are possible, including a global consensus."

She added that uses could extend to any area where researchers are trying to find ways to eliminate pathological behaviors, or drive the system into more normal behaviors.

"To do this, the theory existed, methodologies existed, but the computational expense was a limiting factor," said Albert. "With this algorithm, that has to a large part been eliminated."

The researchers have developed a publicly available software library and the algorithms have already been used in studies carried out by her group, according to Albert.

Computations for the study were performed using Penn State's Roar supercomputer.

Albert and Rozum worked with Jorge Gmez Tejeda Zaudo, postdoctoral associate at Broad Institute and Dana-Farber Cancer Institute; Xiao Gan, postdoctoral researcher at the Center for Complex Network Research; and Dvid Deritei, graduate research fellow at Semmelweis University.

Originally posted here:
New algorithm can be a more effective way to analyze models of biological systems - News-Medical.Net

Bioengineering discovery paves way for improved production of bio-based goods using Bakers yeast – Newswise

Newswise Scientists have uncovered a way to control many genes in engineered yeast cells, opening the door to more efficient and sustainable production of bio-based products.

The study, published in Nucleic Acids Research by researchers from DSMs Rosalind Franklin Biotechnology Center in Delft, the Netherlands, and the University of Bristol, has shown how to unlock CRISPRs potential for regulating many genes simultaneously.

Bakers yeast, or Saccharomyces cerevisiae to give it its full name, is considered as a workhorse for biotechnology. Not only has it been used for producing bread and beer for thousands of years, but today it can also be engineered to produce an array of other useful compounds that form the basis of pharmaceuticals, fuels, and food additives. However, achieving optimal production of these products is difficult, requiring the complex biochemical networks inside the cell to be rewired and extended through the introduction of new enzymes and the tuning of gene expression levels.

Klaudia Ciurkot, first author of the study and an EU-funded industrial PhD student based at DSM stated: To overcome the challenges of optimising S. cerevisiae cells for bio-production, we explored the use of a less widely employed CRISPR technology based on the Cas12a protein. Unlike the Cas9 protein that is more commonly used, Cas12a can be rapidly programmed to interact with sequences that are responsible for controlling gene expression and easily targeted to many different sequences at the same time. This made it an ideal platform for carrying out the complex gene regulation often required for producing industrially relevant compounds.

She went on to add: What was particularly exciting for me was that this study is the first to demonstrate Cas12as ability to control gene expression in S. cerevisiae and through joint research across DSM and the University of Bristol, we were able to figure out the rules for how this system is best designed and used.

Thomas Gorochowski, a co-author on the work and Royal Society University Research Fellow based in the School of Biological Sciences at the University of Bristol further stated: It is hugely exciting that Cas12a has been shown to work so well for gene regulation in the yeast S. cerevisiae, an organism that has huge industrial importance. In addition, the systematic approach we have taken to pull apart and analyse the many difficult aspects of the system, act as a firm foundation for future optimisation.

In addition to analysing how the Cas12a-based system is best engineered, the scientists went on to show its use in robustly controlling the production of -carotene an industrially important compound used in production of food additives and nutraceuticals.

Ren Verwaal, senior author and Senior Scientist at DSM ended by stating: By demonstrating the capabilities of this system to control the biosynthesis of -carotene, we have opened the gates to its broader application for other key bio-based products. I cannot wait to see how our system is used to develop more sustainable production platforms for everyday products we all rely on.

The study was funded by the European Unions Horizon 2020 Research and Innovation Programme (ITN SynCrop) under the Marie Skodowska-Curie grant agreement No 764591, BrisSynBio, a BBSRC/EPSRC Synthetic Biology Research Centre, the Royal Society, and supported by the Bristol BioDesign Institute (BBI).

Paper

Efficient multiplexed gene regulation inSaccharomyces cerevisiaeusing dCas12a inNucleic Acids Research by Klaudia Ciurkot, Thomas E. Gorochowski, Johannes A. Roubos and Ren Verwaal.

View post:
Bioengineering discovery paves way for improved production of bio-based goods using Bakers yeast - Newswise

Cross-Resistance: One Cancer Therapy Can Undermine the Next – The Scientist

Targeted therapy and immunotherapy are often employed as a one-two punch to treat certain cancers, but sometimes this approach falls short. In a study published on July 15 in Nature Cancer, researchers found that dendritic cells, cells crucial for activating the immune system during immunotherapy, were less active and less numerous in mouse models of melanoma that had become resistant to targeted therapy, explaining why these tumors were less sensitive to immunotherapy. Stimulating dendritic cells restored the tumors response to immunotherapy.

This study provides mechanistic insight into a phenomenon that many melanoma experts have observed firsthand in the clinic and that has recently been described in retrospective studies: poor response to immunotherapy following the development of resistance to [targeted] therapy, Brent Hanks, a medical oncologist at Duke University who was not involved in this study, tells The Scientistin an email.Overall, this is an important contribution to melanoma research that may have implications in the management of other . . . cancers as well.

Indeed, it was early clinical data that sparked the interest of Anna Obenauf, a cancer researcher at the Research Institute of Molecular Biology in Vienna, Austria, who led the international team behind the new study. This is a clinical puzzle in a way, because how can these two different types of therapies be connected to each other, and this resistance to one lead to cross-resistance to the other? While targeted therapy blocks specific molecular pathways within cancer cells to stop proliferation, immunotherapy works by stimulating immune cells to eradicate tumor cells.

Their work showing that you can reverse the phenotype by adding in these dendritic cellstimulating agents was a nice proof of principle to show that it really was those cells that were being restricted.

Brian Ruffell, Moffitt Cancer Center

Obenauf and her colleagues started by recapitulating these clinical observations in a mouse model. Using two murine melanoma cell lines, the researchers established tumors in mice, which they treated with dabrafenib, a targeted therapy approved for use in the treatment of melanoma patients who have a mutation in the BRAF gene. Dabrafenib interrupts the MAP kinase pathway by inhibiting the B-Raf enzyme. While the tumors initially responded to the therapy, the cancer eventually relapsed and became resistant. Taking cells from the treatment-sensitive tumors and the treatment-resistant tumors, the researchers established cell lines. These cells were again injected into mice, which were treated with anti-PD-1 or anti-CTL-4 checkpoint inhibitors, immunotherapies aimed at releasing the brake on the immune system. Anti-PD-1 and anti-CTL-4 checkpoint inhibitors are also approved for treating certain patients with melanoma.

Using this approach, the researchers could implant resistant tumors into mice that had not been exposed first to the targeted therapy. This allowed the team to assess whether the targeted therapy has a direct effect on immune cells that could lead to immunotherapy resistance, or if something else is going on within the tumor. It turned out to be the latter. [Treatment-resistant] tumors are indeed cross-resistant to checkpoint inhibitors, says Obenauf.

Immunotherapies usually act by promoting T cell responses, so the group looked more closely at how the mices T cells behaved. While T cells were able to kill treatment-resistant tumor cells in vitro,when the researchers used a mouse model lacking endogenous T cells and added T cells they could track using luciferase, they saw that the T cells couldnt infiltrate the resistant tumor; the tumor kept growing. That has led us to the question [of] whether the tumor microenvironment is mediating resistance, Obenauf recalls.

So the researchers created mix-and-match melanoma mice. When they placed treatment-resistant tumor cells within a large treatment-sensitive tumor, the resistant tumor cells were killed. It seemed that treatment-sensitive tumors had an immune-permissive tumor microenvironment, Obenauf explains. Conversely, when the researchers placed treatment-sensitive cells within a large treatment-resistant tumor, the cells survived, apparently shielded from T cellmediated killing.

A tumor naive to targeted therapy (top) contains many more immune cells (red and green) than one that has acquired resistance (bottom).

IMP/Izabela Krecioch

RNA sequencing and flow cytometry analysis revealed that dendritic cells, a cell type crucial for activating the immune system during immunotherapy, were less abundant in mice with treatment-resistant tumors. When the researchers co-cultured dendritic cells with T cells, they saw that the dendritic cells from resistant tumors didnt activate T cells or spur them to proliferate as dendritic cells from sensitive tumors did. Collaborating with a team at the University of Sydney in Australia, the group acquired biopsies from patients with melanoma who were treated with a targeted therapy. Once the patients had become resistant to the treatment, their tumors contained fewer dendritic cells than before.

Collectively, the results suggest that a drop in dendritic cells generates an immune-evasive tumor microenvironment that is poorly responsive to subsequent checkpoint inhibitor immunotherapy, Hanks explains.

Notably, the effect was reversible. After treating the mouse models with experimental immunostimulants that mature and expand dendritic cell pools, the researchers saw greater numbers of T cells infiltrating the animals tumors, which shrunk as a result. Their work showing that you can reverse the phenotype by adding in these dendritic cellstimulating agents was a nice proof of principle to show that it really was those cells that were being restricted, Brian Ruffell, a cancer immunologist at the Moffitt Cancer Center who was not involved in this study, tells The Scientist.

I think this [study] really breaks down some of the biology of why youd want to treat patients with immunotherapy before you come in and allow resistant clones to develop from targeted therapy, Ruffell adds. From a basic science point of view, it really helps to add to the growing body of literature that we need to study all therapies in the context of immunotherapy or the immune system.

To understand how cells were developing cross-resistance, Obenauf and colleagues analyzed the transcriptomes of cells from both mice and patient samples that had grown resistant to a therapy that targeted the MAP kinase signaling pathway. A hyperactive MAP kinase pathway leads to uncontrolled cell proliferation but is turned down by the inhibitor, and tumors shrink in response. When tumors relapse and become resistant to inhibitors, the MAP kinase pathway is frequently re-activated.

We can very strongly conclude that that pathway reactivation is whats driving the immune therapy resistance.

Kristian Hargadon, Hampden-Sydney College

In their samples, Obenaufs team identified a signature of genes that are differentially expressed in targeted therapyresistant tumors versus sensitive tumors. Using a computational analysis to find the regulators that govern this genetic signature, the scientists found that the MAP kinase signaling pathway was turned on again in resistant tumors and apparently now driving immune evasion. It was surprising that the differences between the [treatment-sensitive] and the [treatment-resistant] tumors were predicted to be driven by the MAP [kinase] pathway, Obenauf says, because the [treatment sensitive] tumors, despite the MAPK pathway being already hyperactive, were so sensitive to immunotherapy, whereas the [treatment resistant] tumors, where the MAPK pathway is being re-activated, were so resistant to immunotherapy.

It turned out that the MAP kinase pathway in resistant tumors more strongly drives gene expression of target genes than it does in sensitive tumors. Components of the pathway also had access to new gene regulatory sites, meaning that they could drive the expression of different genes. The MAP kinase pathway, the same pathway that is very important for tumor initiation, is rewired and enhanced in this process of therapy resistance to establish a very different immune phenotype, says Obenauf.

We can very strongly conclude that that pathway reactivation is whats driving the immune therapy resistance, saysKristian Hargadon, a biologist at Hampden-Sydney College not connected with the study. And that is something that people would not have expected up until this point, yet now that explains a lot of previous observations.

Pulling all these strands together, the team treated mice that had targeted treatmentresistant tumors with a MEK inhibitor, which inhibits the MAP kinase pathway at a different point than does the targeted treatment used initially. In vitro, this inhibition reverted the expression of 80 percent of the genes that formed the signature for resistance back to the treatment-sensitive expression signature. When mice with a treatment-resistant tumor were given the MEK inhibitor, dendritic cells became more numerous and active, inducing T cell proliferation. When the researchers gave the animals immunotherapy, the T cells were able to bring the tumors under control, and the mice survived longer. The effects were quite drastic, indicating that the MAP kinase pathway along with the dendritic cells really are responsible for mediating cross-resistance, says Obenauf.

This is a very elegant, intricate, thorough study, Hargadon concludes. Several different tumor models were studied, several different therapeutic regimens were evaluated, all pointing to the same phenomenon here.

Read the original here:
Cross-Resistance: One Cancer Therapy Can Undermine the Next - The Scientist

UCT student graduates after coming to SA with only R500 – IOL

By Staff Reporter Jul 13, 2021

Share this article:

A UCT student from Namibia will finally graduate this week after she first came to the country with only R500.

Aune Angobe will graduate with an MSc Molecular and Cell Biology degree after achieving over 95% for her course.

She was raised by her grandparents in Ongongo village.

She said she was privileged to have grandparents who had always known the value of education.

I attended primary and secondary school in the northern part of Namibia under their tender care. Throughout my schooling journey Id always enjoyed science subjects, and I have no doubt that I was a scientist from birth.

Despite my poor family background, I studied hard and matriculated with good grades. In 2013, I was granted admission to the University of Namibia for an honours degree programme in science (microbiology), which was funded by a government loan, she said.

After completing her undergraduate studies in 2017 she never had any plans of studying further, but that all changed in 2018.

Angobe said she started growing a strong feeling for furthering her studies and searched for opportunities in numerous universities in Namibia and South Africa.

She he was admitted at UCT for her MSc in Molecular and Cell Biology, however funding was her biggest obstacle.

I remember clearly that when I arrived in Cape Town, I did not have funds for my accommodation and living expenses. I had only R500.

I was accommodated by a friend where I stayed for about two weeks. During this period, my supervisor, my friend and I were constantly worried about how I was going to survive, she said.

Angobe said she then decided to approach student housing where she cried her lungs out to put her plea across, and was eventually given accommodation.

She said she always felt like an outsider coming from a foreign country and struggled with the language barrier and being away from her support system.

Angobe added, My advice to others going through the same experience is that persistence is key. Where theres a will, theres always a way. So dont give up. To current students, self-confidence is key. Always believe in yourself and keep pushing, no matter the circumstances.

Associate Professor Inga Hitzeroth, Angobes supervisor said she is an amazing student who is a go getter.

What stood pout for me was how organised she was, she did not wait for you to organise stuff for her she was very proactive. She is very positive with a lovely personality, said Hitzeroth.

| Weekend Argus

Originally posted here:
UCT student graduates after coming to SA with only R500 - IOL

Computer simulation model identifies key factors for successful transit of sperm in the genital tract – News-Medical.net

A research team at the Humboldt University Berlin and the Leibniz Institute for Zoo and Wildlife Research (Leibniz-IZW) developed an agent-based computer model to simulate the journey of sperm cells through the female genital tract. Key factors for a successful transit could be identified without the use of animal experiments and were published in the scientific journal "PLoS Computational Biology".

During mating in wildlife species, males transfer millions of sperm into the female genital tract. On the way to the egg cell the sperm have to pass through the genital tract. Very few of the sperm cells actually succeed in passing through and reaching the vicinity of the egg cell. Those that do will then be conditioned for fertilisation. Mechanisms underlying sperm selection and, therefore, reproductive success are largely unknown, as their experimental study in the living organism is very difficult for both ethical and practical reasons. A deeper understanding of the factors which favour successful sperm migration and selection in the context of species-specific reproductive systems would be of great fundamental as well as of applied interest, since for threatened wildlife species this will help recognise reproductive problems and optimise assisted reproduction techniques such as artificial insemination.

The scientist team developed a spatio-temporal computer simulation model of the mammalian female genital tract, in which individual sperm cells were treated as independent agents equipped with a set of biophysical characteristics specifying concrete properties and subjected to specific rules for motion and interaction with the female genital tract. The first implementation used data on bovine genital tract geometry and the biophysical properties and principles of sperm motion of bovine sperm as observed in test tubes. Thus, sperm preferentially swam against a fluid stream (positive rheotaxis) and moved along wall structures (thigmotaxis).

In order to ensure that the model was reasonably realistic in depicting salient features of the interaction between sperm and the female genital tract, the simulation results were compared with published data derived from cattle. The simulation results demonstrated a close match with the observed timing and number of sperm actually reaching the entry of the oviducts.

As expected, we found that physical sperm characteristics such as velocity and directional stability are essential for successful sperm. In addition, the ability to swim against the mucus flow of cervical secretions as well as the ability of sperm to align to epithelial walls of the genital tract turned out to have a tremendous impact on the chances of a successful transit of sperm to the oviduct."

Jorin Diemer, Doctoral Student, Humboldt-Universitt zu Berlin

Karin Mller, leader of the andrology lab at Leibniz-IZW, concludes, "that these identified characteristics of sperm should be considered in future attempts to condition sperm in artificial selection procedures since natural selection processes are normally bypassed in reproductive test tube technologies."

This is of particular importance because a species-specific optimal time window for sperm accumulation in the oviduct exists in relation to the timing of ovulation when the oocyte is liberated for fertilisation. "The big advantage of our model is its flexibility, it can be extended and generalised to other systems," highlights Edda Klipp, leader of the Theoretical Biophysics department at Humboldt-Universitt zu Berlin.

Predictions from this computer simulation system have the potential to improve assisted reproduction in endangered species, livestock and perhaps humans without using animal experiments.

Source:

Journal reference:

Diemer, J., et al. (2021) Sperm migration in the genital tractIn silico experiments identify key factors for reproductive success. PLOS Computational Biology. doi.org/10.1371/journal.pcbi.1009109.

Read the rest here:
Computer simulation model identifies key factors for successful transit of sperm in the genital tract - News-Medical.net

Understanding human behavior as part of the IPM process – Pest Management Professional – Pest Management Professional magazine

PHOTO: KAMELEON007/ISTOCK / GETTY IMAGES PLUS/GETTY IMAGES

There are lots of ways to practice integrated pest management (IPM), but many pest management professionals (PMPs) would agree that IPM includes sanitation, exclusion and chemical control elements as part of a multifaceted approach to managing pest populations.

Many PMPs also would agree that one of the biggest hurdles to implementing a successful IPM program for clients are the clients themselves.

A commonly recommended IPM tactic is to keep doors and windows closed to keep flying insects from entering a building. Yet, why do we find this suggestion so often ignored? Here are a few questions to consider that could help get to the root cause:

Certainly, budget limitations can affect how quickly (and effectively) your exclusion recommendations are executed. And operational constraints can make sanitation tricky. But the biggest hurdle almost always is people.

By taking a closer look and understanding the underlying behaviors that are making effective pest management more difficult, the root cause can be addressed. Everybody wins.

The rest is here:
Understanding human behavior as part of the IPM process - Pest Management Professional - Pest Management Professional magazine

Don’t rely too much on social media to understand human behavior – The Next Web

Since the early days of social media, there has been a lot ofexcitement about how data traces left behind by users can be exploited for the study of human behavior. Nowadays, researchers who were once restricted to surveys or experiments in laboratory settings have access to huge amounts of real-world data from social media.

The research opportunities enabled by social media data are undeniable. However, researchers often analyze this data with tools that were not designed to manage the kind of large, noisy observational sets of data you find on social media.

We explored problems that researchers might encounter due to this mismatch between data and methods.

What we found is that the methods and statistics commonly used to provide evidence for seemingly significant scientific findings can also seem to support nonsensical claims.

The motivation for our paper comes from a series of research studies that deliberately present absurd scientific results.

One brain imaging study appeared to show the neural activity of a dead salmon tasked with identifying emotions in photos. An analysis of longitudinal statistics from public health records suggested that acne, height, and headaches are contagious. And an analysis of human decision-making seemingly indicated people can accurately judge the population size of different cities by ranking them in alphabetical order.

Why would a researcher go out of their way to explore such ridiculous ideas? The value of these studies is not in presenting a new substantive finding. No serious researcher would argue, for example, that a dead salmon has a perspective on emotions in photos.

Rather, the nonsensical results highlight problems with the methods used to achieve them. Our research explores whether the same problems can afflict studies that use data from social media. And we discovered that indeed they do.

When a researcher seeks to address a research question, the method they use should be able to do two things:

For example, imagine you have chronic back pain and you take a medical test to find its cause. The test identifies a misaligned disc in your spine. This finding might be important and inform a treatment plan.

However, if you then discover the same test identifies this misaligned disc in a large proportion of the population who do not have chronic back pain, the finding becomes far less informative for you.

The fact the test fails to identify a relevant, distinguishing feature of negative cases (no back pain) from positive cases (back pain) does not mean the misaligned disc in your spine is non-existent. This part of the finding is as real as any finding. Yet the failure means the result is not useful: evidence that is as likely to be found when there is a meaningful effect (in this case, back pain) as when there is none is simply not diagnostic, and, as result, such evidence is uninformative.

Using the same rationale, we evaluated commonly used methods for analyzing social media data called null hypothesis significance testing and correlational statistics by asking an absurd research question.

Past and current studies have tried to identify what factors influence Twitter users decisions to retweet other tweets. This is interesting both as a window into human thought and because resharing posts is a key mechanism by which messages are amplified or spread on social media.

So we decided to analyze Twitter data using the above standard methods to see whether a nonsensical effect we call XYZ contagion influences retweets. Specifically, we asked

Does the number of Xs, Ys, and Zs in a tweet increase the probability of it being spread?

Upon analyzing six datasets containing hundreds of thousands of tweets, the answer we found was yes. For example, in a dataset of 172,697 tweets about COVID-19, the presence of an X, Y, or Z in a tweet appeared to increase the messages reach by a factor of 8%.

Needless to say, we do not believe the presence of Xs, Ys, and Zs is a central factor in whether people choose to retweet a message on Twitter.

However, like the medical test for diagnosing back pain, our finding shows that sometimes, methods for social media data analysis can reveal effects where there should be none. This raises questions about how meaningful and informative results obtained by applying current social science methods to social media data really are.

As researchers continue to analyze social media data and identify factors that shape the evolution of public opinion, hijack our attention, or otherwise explain our behavior, we should think critically about the methods underlying such findings and reconsider what we can learn from them.

The issues raised in our paper are not new, and there are indeed many research practices that have been developed to ensure results are meaningful and robust.

For example, researchers are encouraged to pre-register their hypotheses and analysis plans before starting a study to prevent a kind of data cherry-picking called p-hacking. Another helpful practice is to check whether results are stable after removing outliers and controlling for covariates. Also important are replication studies, which assess whether the results obtained in an experiment can be found again when the experiment is repeated under similar conditions.

These practices are important, but they alone are not sufficient to deal with the problem we identify. While developing standardized research practices is needed, the research community must first think critically about what makes a finding in social media data meaningful.

Article by Jason Burton, PhD researcher, Birkbeck, University of London; Nicole Cruz, Postdoctoral Research Associate, UNSW, and Ulrike Hahn, Professor of Psychology, Birkbeck, University of London

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Go here to see the original:
Don't rely too much on social media to understand human behavior - The Next Web

The Office Teaches Human Behavior to AI. Is That Really a Good Thing? – DesignNews

Humans know how to interact with each other using body language and communication, but robots developed to work alongside people dont have this innate sensibility.

Artificial intelligence (AI) is helping collaborative robots predict how a person likely will behave so it can respond accordingly. That is one reason why researchers have been using movies, popular TVs shows, and sports to train the system on human behavior.

Related: Will Advanced Artificial Skin Raise Hope in Restoring a Sense of Touch?

Researchers from Columbia Engineering developed a computer-vision technique for giving machines a more intuitive sense for patterns of human behavior by using higher-level associations between people, animals, and objects. They also leveraged an ancient form of geometry in their mathematical framework for how the AI functions.

The algorithm, the most accurate method to date for predicting video action events up to several minutes in the future, analyzed thousands of hours of movies, sporting events, and TV shows like The Office. In this way, it learned to predict hundreds of human interactions and activities, such as handshakes or fist bumps.

Related: Can New Materials Discovery get a Boost From Artificial Intelligence?

When the system cant predict the specific action, it finds a higher-level concept that is related to the action, such as, in the case of handshakes or fist bumps, the word greeting, researchers said.

Our algorithm is a step toward machines being able to make better predictions about human behavior, and thus better coordinate their actions with ours, said Carl Vondrick, assistant professor of computer science at Columbia, who directed the study. Our results open several possibilities for human-robot collaboration, autonomous vehicles, and assistive technology.

The team is no stranger to predictive machine learning. However, in the past, its efforts, like those of other scientists, included predicting just one action at a time. In this scenario, the algorithms must decide how to classify an actsuch as a hug, high five, or even someones lack of response, which would be classified as ignore.

However, when algorithms couldnt with high certainty identify an action, most couldnt find common threads between following potential options for action, researchers said.

To find a new way to develop long-range prediction models, Columbia Engineering Ph.D. students Didac Suris and Ruoshi Liu took a different approach based on the idea that not everything in the future is predictable, Suris said in a press statement.

When a person cannot foresee exactly what will happen, they play it safe and predict at a higher level of abstraction, he said in the statement. Our algorithm is the first to learn this capability to reason abstractly about future events.

In other words, the new AI model can recognize when it cant predict a future action with certainty and, like people do all the time, can make a guess as to what it will be by relating it to a concept.

To develop the model based on this idea, Suris and Liu used unusual geometries that arent commonly taught in high-school mathematics; instead, they date back to the time of ancient Greeks. These geometries have counter-intuitive properties in which the straight lines of typical geometry can bend, and triangles dont have three even sides but instead bulge, researchers said.

Using principles of this unusual type of geometry, researchers constructed AI models that could organize high-level concepts to predict future human behavior based on how predictable events are in the future, they said.

For example, humans know that swimming and running are both types of exercise. The system can categorize such related activities on its while also being aware of uncertainty. The latter instance provides more specific actions when there is a certainty and more generic predictions when there is not, researchers said.

Human behavior is often surprising, Vondrick said in a press statement. Our algorithms enable machines to anticipate better what they are going to do next.

Researchers wrote a paper on their work and presented the study at the International Conference on Computer Vision and Pattern Recognition on June 24.

While computers currently take action based on preprogramming, the model developed by the team can help give collaborative robots more spontaneity, just as humans do in their interactions, Liu said. This can help humans who in the future work closely with robots form a type of relationship, he said.

Trust comes from the feeling that the robot understands people, Liu explained in a press statement. If machines can understand and anticipate our behaviors, computers will be able to seamlessly assist people in daily activity.

Researchers plan to continue their work by verifying how the algorithm works outside of the lab in diverse settings rather than merely testing it on benchmark tasks, they said. If its successful, the model can develop and eventually use robots that can work alongside humans to improve our safety, health, and security, researchers said.

The team also plans to continue to improve the algorithms performance with larger datasets and computers, and other forms of geometry.

Stills from The Cider House Rules (top) and Mumford (bottom).

Elizabeth Montalbano is a freelance writer who has written about technology and culture for more than 20 years. She has lived and worked as a professional journalist in Phoenix, San Francisco, and New York City. In her free time, Elizabeth enjoys surfing, traveling, music, yoga, and cooking. She currently resides in a village on the southwest coast of Portugal.

Read this article:
The Office Teaches Human Behavior to AI. Is That Really a Good Thing? - DesignNews