Nonequilibrium hysteresis and spin relaxation in the mixed-anisotropy dipolar-coupled spin-glass LiHo0.5Er0.5F4

We present a study of the model spin-glass LiHo0.5Er0.5F4 using simultaneous ac susceptibility, magnetization, and magnetocaloric effect measurements along with small angle neutron scattering (SANS) at sub-Kelvin temperatures. All measured bulk quantities reveal hysteretic behavior when the field is applied along the crystallographic c axis. Furthermore, avalanchelike relaxation is observed in a static field after ramping from the zero-field-cooled state up to 200–300 Oe. SANS measurements are employed to track the microscopic spin reconfiguration throughout both the hysteresis loop and the related relaxation. Comparing the SANS data to inhomogeneous mean-field calculations performed on a box of one million unit cells provides a real-space picture of the spin configuration. We discover that the avalanche is being driven by released Zeeman energy, which heats the sample and creates positive feedback, continuing the avalanche. The combination of SANS and mean-field simulations reveal that the conventional distribution of cluster sizes is replaced by one with a depletion of intermediate cluster sizes for much of the hysteresis loop.

Minimal residual disease in cancer therapy - Small things make all the difference

Minimal residual disease (MRD) is a major hurdle in the eradication of malignant tumors. Despite the high sensitivity of various cancers to treatment, some residual cancer cells persist and lead to tumor recurrence and treatment failure. Obvious reasons for residual disease include mechanisms of secondary therapy resistance, such as the presence of mutant cells that are insensitive to the drugs, or the presence of cells that become drug resistant due to activation of survival pathways. In addition to such unambiguous resistance modalities, several patients with relapsing tumors do not show refractory disease and respond again when the initial therapy is repeated. These cases cannot be explained by the selection of mutant tumor cells, and the precise mechanisms underlying this clinical drug resistance are ill-defined. In the current review, we put special emphasis on cell-intrinsic and -extrinsic mechanisms that may explain mechanisms of MRD that are independent of secondary therapy resistance. In particular, we show that studying genetically engineered mouse models (GEMMs), which highly resemble the disease in humans, provides a complementary approach to understand MRD. In these animal models, specific mechanisms of secondary resistance can be excluded by targeted genetic modifications. This allows a clear distinction between the selection of cells with stable secondary resistance and mechanisms that result in the survival of residual cells but do not provoke secondary drug resistance. Mechanisms that may explain the latter feature include special biochemical defense properties of cancer stem cells, metabolic peculiarities such as the dependence on autophagy, drug-tolerant persisting cells, intratumoral heterogeneity, secreted factors from the microenvironment, tumor vascularization patterns and immunosurveillance-related factors. We propose in the current review that a common feature of these various mechanisms is cancer cell dormancy. Therefore, dormant cancer cells appear to be an important target in the attempt to eradicate residual cancer cells, and eventually cure patients who repeatedly respond to anticancer therapy but lack complete tumor eradication.

LOGITCPRPLOT: Stata module to graph component-plus-residual plot for logistic regression

logitcprplot can be used after logistic regression for graphing a component-plus-residual plot (a.k.a. partial residual plot) for a given predictor, including a lowess, local polynomial, restricted cubic spline, fractional polynomial, penalized spline, regression spline, running line, or adaptive variable span running line smooth

CPRPLOT2: Stata module to graph component-plus-residual plots for transformed regressors

cprplot2 is a variation of official Stata's cprplot and is used for graphing component-plus-residual plots (a.k.a. partial residual plots). Additional features (compared to cprplot): (1) cprplot2 can handle variables that enter the model repeatedly via different transformations (for example, polynomials). (2) cprplot2 can display component-plus-residual plots using the original units for transformed variables in the model. (3) A wrapper is provided to quickly display several component-plus-residual plots in a single image.