HPTDP

We present a unique, extensive, and open synaptic physiology analysis platform and dataset. Through its application, we reveal principles that relate cell type to synaptic properties and intralaminar circuit organization in the mouse and human cortex. The dynamics of excitatory synapses align with the postsynaptic cell subclass, whereas inhibitory synapse dynamics partly align with presynaptic cell subclass but with considerable overlap. Synaptic properties are heterogeneous in most subclass-to-subclass connections. The two main axes of heterogeneity are strength and variability. Cell subclasses divide along the variability axis, whereas the strength axis accounts for substantial heterogeneity within the subclass. In the human cortex, excitatory-to-excitatory synaptic dynamics are distinct from those in the mouse cortex and vary with depth across layers 2 and 3.

Malicious software utilizes HTTP protocol for communication purposes, creating network traffic that is hard to identify as it blends into the traffic generated by benign applications. To this aim, fingerprinting tools have been developed to help track and identify such traffic by providing a short representation of malicious HTTP requests. However, currently existing tools do not analyze all information included in the HTTP message or analyze it insufficiently. To address these issues, we propose Hfinger, a novel malware HTTP request fingerprinting tool. It extracts information from the parts of the request such as URI, protocol information, headers, and payload, providing a concise request representation that preserves the extracted information in a form interpretable by a human analyst. For the developed solution, we have performed an extensive experimental evaluation using real-world data sets and we also compared Hfinger with the most related and popular existing tools such as FATT, Mercury, and p0f. The conducted effectiveness analysis reveals that on average only 1.85% of requests fingerprinted by Hfinger collide between malware families, what is 8-34 times lower than existing tools. Moreover, unlike these tools, in default mode, Hfinger does not introduce collisions between malware and benign applications and achieves it by increasing the number of fingerprints by at most 3 times. As a result, Hfinger can effectively track and hunt malware by providing more unique fingerprints than other standard tools.

Activation of the stimulator of interferon genes (STING) pathway promotes antitumor immunity but STING agonists have yet to achieve clinical success. Increased understanding of the mechanism of action of STING agonists in human tumors is key to developing therapeutic combinations that activate effective innate antitumor immunity. Here, we report that malignant pleural mesothelioma cells robustly express STING and are responsive to STING agonist treatment ex vivo. Using dynamic single-cell RNA sequencing of explants treated with a STING agonist, we observed CXCR3 chemokine activation primarily in tumor cells and cancer-associated fibroblasts, as well as T-cell cytotoxicity. In contrast, primary natural killer (NK) cells resisted STING agonist-induced cytotoxicity. STING agonists enhanced migration and killing of NK cells and mesothelin-targeted chimeric antigen receptor (CAR)-NK cells, improving therapeutic activity in patient-derived organotypic tumor spheroids. These studies reveal the fundamental importance of using human tumor samples to assess innate and cellular immune therapies. By functionally profiling mesothelioma tumor explants with elevated STING expression in tumor cells, we uncovered distinct consequences of STING agonist treatment in humans that support testing combining STING agonists with NK and CAR-NK cell therapies.