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Immune cells have intensely physical lifestyles characterized by structural plasticity and force exertion. To investigate whether specific immune functions require stereotyped mechanical outputs, we used super-resolution traction force microscopy to compare the immune synapses formed by cytotoxic T cells with contacts formed by other T cell subsets and by macrophages. T cell synapses were globally compressive, which was fundamentally different from the pulling and pinching associated with macrophage phagocytosis. Spectral decomposition of force exertion patterns from each cell type linked cytotoxicity to compressive strength, local protrusiveness, and the induction of complex, asymmetric topography. These features were validated as cytotoxic drivers by genetic disruption of cytoskeletal regulators, live imaging of synaptic secretion, and in silico analysis of interfacial distortion. Synapse architecture and force exertion were sensitive to target stiffness and size, suggesting that the mechanical potentiation of killing is biophysically adaptive. We conclude that cellular cytotoxicity and, by implication, other effector responses are supported by specialized patterns of efferent force. 

Abstract Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The ‘one-size-fits-all’ approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow. 

With computing impacting most every professional field, it has become essential to provide pathways for students other than those majoring in computer science to acquire computing knowledge and skills. Virtually all employers and graduate and professional schools seek these skills in their employees or students, regardless of discipline. Academia currently leans towards approaches such as double majors or combined majors between computer science and other non-CS disciplines, commonly referred to as “CS+X” programs. These programs tend to require rigorous courses gleaned from the institutions’ courses for computer science majors. Thus, they may not meet the needs of majors in disciplines such as the social and biological sciences, humanities, and others. The University of Maryland, Baltimore County (UMBC) is taking an approach more suitably termed “X+CS” to fulfill the computing needs of non-CS majors. As part of a National Science Foundation (NSF) grant, we are developing a “computing” minor specifically to meet their needs. To date, we have piloted the first two of the minor’s approximately six courses. The first is a variation on the existing Computer Science I course required for majors but restricted to nonmajors. Both versions of the course use the Python language and cover the same programming content, but with the non-majors assigned projects with relevance to non-CS disciplines. We use the same student assessment measures of homework, projects, and examinations for both courses. After four semesters, results show that non-CS majors perform comparably to majors. Students also express increased interest in computing and satisfaction with being part of a non- CS major cohort. The second course was piloted in fall 2019. It is a new course intended to enhance and hone programming skills and introduce topics such as web scraping, HTML and CSS, web application development, data formats, and database use. Students again express increased interest in computing and were already beginning to apply the computing skills that they were learning to their non-CS courses. As a welcome side effect, we experienced a significant increase in the number of women and under-represented minorities (URMs) in these two courses when compared with CS-major specific courses. Overall, women comprised 52% of the population, with URMs following a similar upward trend. We are currently developing the third course in the computing minor and exploring options for the remaining three. Possibilities include electives from our Information Systems major. We will also be working with our science, social science, and humanities departments to utilize existing courses in those disciplines that apply computing. The student response that we have received thus far provides us with evidence that our computing minor will be popular among UMBC’s non-CS population, providing them with a more suitable and positive computing education than existing CS+X efforts. 

A search for hidden-charm pentaquark states decaying to a range of ${\mathrm{\Sigma }}_c\overline{D}$and ${\mathrm{\Lambda }}_c^{+}\overline{D}$final states, as well as doubly charmed pentaquark states to ${\mathrm{\Sigma }}_{c}D$and ${\mathrm{\Lambda }}_c^{+}D$, is made using samples of proton-proton collision data corresponding to an integrated luminosity of $5.7{\mathrm{fb}}^{-1}$recorded by the LHCb detector at $\sqrt{s}=13\mathrm{TeV}$. Since no significant signals are found, upper limits are set on the pentaquark yields relative to that of the ${\mathrm{\Lambda }}_c^{+}$baryon in the ${\mathrm{\Lambda }}_c^{+}\to p{K}^{-}{\pi }^{+}$decay mode. The known pentaquark states are also investigated, and their signal yields are found to be consistent with zero in all cases. © 2024 CERN, for the LHCb Collaboration2024CERN 

The production of 𝜂 and 𝜂′ mesons is studied in proton-proton and proton-lead collisions collected with the LHCb detector. Proton-proton collisions are studied at center-of-mass energies of 5.02 and 13TeV and proton-lead collisions are studied at a center-of-mass energy per nucleon of 8.16TeV. The studies are performed in center-of-mass (c.m.) rapidity regions 2.5<𝑦c.m.<3.5 (forward rapidity) and −4.0<𝑦c.m.<−3.0 (backward rapidity) defined relative to the proton beam direction. The 𝜂 and 𝜂′ production cross sections are measured differentially as a function of transverse momentum for 1.5<𝑝T<10GeV and 3<𝑝T<10GeV, respectively. The differential cross sections are used to calculate nuclear modification factors. The nuclear modification factors for 𝜂 and 𝜂′ mesons agree at both forward and backward rapidity, showing no significant evidence of mass dependence. The differential cross sections of 𝜂 mesons are also used to calculate 𝜂/𝜋0 cross-section ratios, which show evidence of a deviation from the world average. These studies offer new constraints on mass-dependent nuclear effects in heavy-ion collisions, as well as 𝜂 and 𝜂′ meson fragmentation. 

The ratios of branching fractions R(D*)= B(B0 --> D*+tau- nu(bar))/ B(B0--> D*+mu- nu(bar)) and R(D)= B(B0 --> D0tau- nu(bar))/ B(B0 --> D0mu- nu(bar)) are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0 fb−1 of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode τ− → μ−ντν¯μ. The measured values are R*D*)= 0.281+/- 0.018+/- 0.024 and R(D0)=0.441+/- 0.060+/- 0.066, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is ρ= −0.43. The results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the standard model