An Integrative and Comparative Analysis of Transcriptome and Targetome Data of Medulloblastoma

Medulloblastoma (MB) arises in the cerebellum and is the most common brain tumor seen in the field of pediatrics. Primary and recurrent MBs are often found to contain deregulated Atonal Homolog 1 (ATOH1) expression among SHH/PTCH signals. Therefore, mice models were generated for research by inducing expression of the Atoh1 transgene in the cerebellum of Ptch1+/- mice. The overexpression of the Atoh1 transgene in the animals transform the non-metastatic brain tumor to a metastatic tumor that disseminates to the spinal cord and other parts of the brain. In order to understand the molecular and cellular events involved in the cascade of metastatic MB, statistical analysis of the transcriptome and targetome were applied. RNA-Sequencing was run first to generate a common list of shared differentially expressed genes and then followed by the addition of chromatin immunoprecipitation sequencing. From the data obtained, pathway analysis was applied. The data from the mice were then subject to comparison to a cohort of human data on MB to further investigate the similarities and differences in the biological causes for the formation of the disease. Das Medulloblastom entstammt im Kleinhirn und ist der häufigste pädiatrische Gehirntumor. Es wird häufig festgestellt, dass primäre und rezidivierende Medulloblastome deregulierte atonale Homolog 1 (ATOH1)-Expression unter SHHPTCH-Signalen enthalten. Darum wurden Mäusemodelle in der Forschung erstellt, indem die Expression des Atoh1-Transgens im Kleinhirn von Ptch1+/ - Mäusen induziert wurde. Die Überexpression dieses Transgens in den Tieren wandelt den gutartigen Gehirntumor in einen metastatischen Tumor um, der sich auf das Rückenmark und andere Teile des Gehirns verbreitet. Um die molekularen und zellulären Ereignisse nachzuvollziehen, die an der Kaskade metastatisches Medulloblastoms beteiligt sind, wurden statistische Analysen des Transkriptoms und des Targetoms durchgeführt. Die RNA-Sequenzierung wurde zuerst durchgeführt, um eine gemeinsame Liste von differentiell exprimierten Genen zu erstellen, gefolgt von dem Zusatz der ChromatinImmunopräzipitationssequenzierung. Von den erhaltenen Daten wurde eine Weganalyse durchgeführt. Die Daten der Mäuse wurden dann einem Vergleich mit einer Kohorte menschlicher Daten zum MB unterzogen, um die Ähnlichkeiten und Unterschiede in den biologischen Ursachen für die Entstehung der Krankheit weiter zu untersuchen

Evasion Attacks against Machine Learning at Test Time

In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack scenario, an adversary may attempt to evade a deployed system at test time by carefully manipulating attack samples. In this work, we present a simple but effective gradient-based approach that can be exploited to systematically assess the security of several, widely-used classification algorithms against evasion attacks. Following a recently proposed framework for security evaluation, we simulate attack scenarios that exhibit different risk levels for the classifier by increasing the attacker's knowledge of the system and her ability to manipulate attack samples. This gives the classifier designer a better picture of the classifier performance under evasion attacks, and allows him to perform a more informed model selection (or parameter setting). We evaluate our approach on the relevant security task of malware detection in PDF files, and show that such systems can be easily evaded. We also sketch some countermeasures suggested by our analysis.Comment: In this paper, in 2013, we were the first to introduce the notion of evasion attacks (adversarial examples) created with high confidence (instead of minimum-distance misclassifications), and the notion of surrogate learners (substitute models). These two concepts are now widely re-used in developing attacks against deep networks (even if not always referring to the ideas reported in this work). arXiv admin note: text overlap with arXiv:1401.772

Impacts of crop insurance on cash rents

This study examines the degree to which net payments from federal crop insurance products impact cash rents paid for farmland. A spatial panel model is employed to control for spatial dependence and heterogeneity in cash rental rates. Results show that producers factor a statistically significant proportion of the value received from crop insurance into cash rents. However, the directly measurable rate is lower than found in previous studies. This result likely reflects the complexity in the relationship between losses and crop insurance rates, and the aggregation across producers in both measured rent and estimates of the net value of crop insurance to a producer. Further, the indirect effects of crop insurance and the ancillary impacts of a producer’s risk profile are difficult to identify independently due to the highly variable nature of crop insurance payments, and the smoothed nature of cash rental values. Nonetheless, even as the model removes much of the variation in the data, this analysis shows crop insurance is an important factor in a producer’s expected revenue, as cash rents are positively affected in counties that receive consistent and positive net value

Exploiting Machine Learning to Subvert Your Spam Filter

Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—even if the adversary’s access is limited to only 1 % of the training messages. We further demonstrate a new class of focused attacks that successfully prevent victims from receiving specific email messages. Finally, we introduce two new types of defenses against these attacks.

Near-Optimal Evasion of Convex-Inducing Classifiers

Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade detection at near-minimal cost. We generalize results of Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms that construct undetected instances of near-minimal cost using only polynomially many queries in the dimension of the space and without reverse engineering the decision boundary.Comment: 8 pages; to appear at AISTATS'201

An Artificial Habitat Increases the Reproductive Fitness of a Range-shifting Species within a Newly Colonized Ecosystem

When a range-shifting species colonizes an ecosystem it has not previously inhabited, it may experience suboptimal conditions that challenge its continued persistence and expansion. Some impacts may be partially mitigated by artificial habitat analogues: artificial habitats that more closely resemble a species\u27 historic ecosystem than the surrounding habitat. If conditions provided by such habitats increase reproductive success, they could be vital to the expansion and persistence of range-shifting species. We investigated the reproduction of the mangrove tree crab Aratus pisonii in its historic mangrove habitat, the suboptimal colonized salt marsh ecosystem, and on docks within the marsh, an artificial mangrove analogue. Crabs were assessed for offspring production and quality, as well as measures of maternal investment and egg quality. Aratus pisonii found on docks produced more eggs, more eggs per unit energy investment, and higher quality larvae than conspecifics in the surrounding salt marsh. Yet, crabs in the mangrove produced the highest quality larvae. Egg lipids suggest these different reproductive outcomes result from disparities in the quality of diet-driven maternal investments, particularly key fatty acids. This study suggests habitat analogues may increase the reproductive fitness of range-shifting species allowing more rapid expansion into, and better persistence in, colonized ecosystems

Tree of Attacks: Jailbreaking Black-Box LLMs Automatically

While Large Language Models (LLMs) display versatile functionality, they continue to generate harmful, biased, and toxic content, as demonstrated by the prevalence of human-designed jailbreaks. In this work, we present Tree of Attacks with Pruning (TAP), an automated method for generating jailbreaks that only requires black-box access to the target LLM. TAP utilizes an LLM to iteratively refine candidate (attack) prompts using tree-of-thought reasoning until one of the generated prompts jailbreaks the target. Crucially, before sending prompts to the target, TAP assesses them and prunes the ones unlikely to result in jailbreaks. Using tree-of-thought reasoning allows TAP to navigate a large search space of prompts and pruning reduces the total number of queries sent to the target. In empirical evaluations, we observe that TAP generates prompts that jailbreak state-of-the-art LLMs (including GPT4 and GPT4-Turbo) for more than 80% of the prompts using only a small number of queries. Interestingly, TAP is also capable of jailbreaking LLMs protected by state-of-the-art guardrails, e.g., LlamaGuard. This significantly improves upon the previous state-of-the-art black-box method for generating jailbreaks.Comment: An implementation of the presented method is available at https://github.com/RICommunity/TA