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Introduction Precision medicine in psychiatry is still in its infancy. To establish patient-tailored treatment, adequate indicators predicting treatment response are required. Electroconvulsive therapy (ECT) is considered one of the most effective options for pharmacoresistant major depressive disorder (MDD), yet remission rates were reported to be below 50%. Methods Since epigenetics of the stress response system seem to play a role in MDD, we analyzed the DNA methylation (DNAm) of genes encoding the glucocorticoid receptor (NR3C1) and proopiomelanocortin (POMC) through Sanger Sequencing. For analysis, blood was taken before and after the first and last ECT from MDD patients (n=31), unmedicated depressed controls (UDC; n=19, baseline), and healthy controls (HC; n=20, baseline). Results Baseline DNAm in NR3C1 was significantly lower in UDCs compared to both other groups (UDC: 0.014(±0.002), ECT: 0.031(±0.001), HC: 0.024(±0.002); p Discussion Our findings indicate that both genes might play a role in the chronification of depression and NR3C1 may be relevant for ECT response prediction.

We investigate a semi-online variant of load balancing with restricted assignment. In this problem, we are given n jobs, which need to be processed by m machines with the goal to minimize the maximum machine load. Since strong lower bounds rule out any competitive ratio of o(log⁡n), we may reassign jobs at a certain job-individual cost. We generalize a result by Gupta, Kumar, and Stein (SODA 2014) by giving a O(log⁡log⁡mn)-competitive algorithm with constant amortized reassignment cost.

First investigations regarding dosimetric properties of the hybrid, pixelated, photon-counting Dosepix detector in the direct beam of a pulsed photon field (RQR8) for the personal dose equivalent $H\mathrm{_p(10)}$ are presented. The influence quantities such as pulse duration and dose rate were varied, and their responses were compared to the legal limits provided in PTB-A 23.2. The variation of pulse duration at a nearly constant dose rate of about 3.7$\,$Sv/h shows a flat response around 1.0 from 3.6$\,$s down to 2$\,$ms. A response close to 1.0 is achieved for dose rates from 0.07$\,$Sv/h to 35$\,$Sv/h for both pixel sizes. Above this dose rate, the large pixels (220$\,\mu$m edge length) are below the lower limit. The small pixels (55$\,\mu$m edge length) stay within limits up to 704$\,$Sv/h. The count rate linearity is compared to previous results, confirming the saturating count rate for high dose rates., Comment: 6 pages, 4 figures. This work was accepted for publication in IEEE Transactions on Nuclear Science

First measurements characterizing dosimetric properties of a dosimetry system designed for the purpose of active personal dosimetry for photons with mean energies from 12.4 to 1250 keV according to Physikalisch-Technische Bundesanstalt (PTB) requirements are presented. The system consists of three Dosepix detectors, which is a hybrid, pixelated, photon-counting X-ray detector. The energy and angular dependence of the normalized response and the coefficients of variation of the personal dose equivalents $H_{\mathrm{p}}{(10)}$ and $H_{\mathrm{p}}{(0.07)}$ are determined on an International Organization for Standardization (ISO) water slab phantom in continuous reference photon radiation fields according to ISO 4037-1 and ISO 4037-3. The energy response is presented for the narrow spectra N-15 to N-300 and the radiation qualities S-Cs and S-Co for angles of incidence of 0°, 30°, and ±60°. The highest deviation of the response from the reference response is found at N-60, −60° with 1.179 ± 0.007 (standard deviation) for $H_{\mathrm{p}}{(10)}$ . The energy range of use for $H_{\mathrm{p}}{(10)}$ is expected to extend from 12.4 to 1250 keV. For the personal dose equivalent $H_{\mathrm{p}}{(0.07)}$ , the normalized response at ±60° is below the lower limit for N-15, N-20, and N-25. It results in an energy range of use from 24.6 to 1250 keV. The coefficient of variation increases with increasing photon energy and stays below 1% for all measurements.

Background Major depressive disorder (MDD) represents a serious global health concern. The urge for efficient MDD treatment strategies is presently hindered by the incomplete knowledge of its underlying pathomechanism. Despite recent progress (highlighting both genetics and the environment, and thus DNA methylation, to be relevant for its development), 30–50% of MDD patients still fail to reach remission with standard treatment approaches. Electroconvulsive therapy (ECT) is one of the most powerful options for the treatment of pharmacoresistant depression; nevertheless, ECT remission rates barely reach 50% in large-scale naturalistic population-based studies. To optimize MDD treatment strategies and enable personalized medicine in the long- term, prospective indicators of ECT response are thus in great need. Because recent target-driven analyses revealed DNA methylation baseline differences between ECT responder groups, we analyzed the DNA methylome of depressed ECT patients using next-generation sequencing. In this pilot study, we did not only aim to find novel targets for ECT response prediction but also to get a deeper insight into its possible mechanism of action. Results Longitudinal DNA methylation analysis of peripheral blood mononuclear cells isolated from a cohort of treatment-resistant MDD patients (n = 12; time points: before and after 1st and last ECT, respectively) using a TruSeq-Methyl Capture EPIC Kit for library preparation, led to the following results: (1) The global DNA methylation differed neither between the four measured time points nor between ECT responders (n = 8) and non-responders (n = 4). (2) Analyzing the DNA methylation variance for every probe (=1476812 single CpG sites) revealed eight novel candidate genes to be implicated in ECT response (protein-coding genes: RNF175, RNF213, TBC1D14, TMC5, WSCD1; genes encoding for putative long non-coding RNA transcripts: AC018685.2, AC098617.1, CLCN3P1). (3) In addition, DNA methylation of two CpG sites (located within AQP10 and TRERF1) was found to change during the treatment course. Conclusions We suggest ten novel candidate genes to be implicated in either ECT response or its possible mechanism. Because of the small sample size of our pilot study, our findings must be regarded as preliminary.

The speed-robust scheduling problem is a two-stage problem where, given m machines, jobs must be grouped into at most m bags while the processing speeds of the machines are unknown. After the speeds are revealed, the grouped jobs must be assigned to the machines without being separated. To evaluate the performance of algorithms, we determine upper bounds on the worst-case ratio of the algorithm’s makespan and the optimal makespan given full information. We refer to this ratio as the robustness factor. We give an algorithm with a robustness factor 2-1/m for the most general setting and improve this to 1.8 for equal-size jobs. For the special case of infinitesimal jobs, we give an algorithm with an optimal robustness factor equal to e/(e-1)≈1.58. The particular machine environment in which all machines have either speed 0 or 1 was studied before by Stein and Zhong (ACM Trans Algorithms 16(1):1-20, 2020. https://doi.org/10.1145/3340320). For this setting, we provide an algorithm for scheduling infinitesimal jobs with an optimal robustness factor of (1+√2)/2≈1.207. It lays the foundation for an algorithm matching the lower bound of 4/3 for equal-size jobs.

We study a fundamental online job admission problem where jobs with deadlines arrive online over time at their release dates, and the task is to determine a preemptive single-server schedule which maximizes the number of jobs that complete on time. To circumvent known impossibility results, we make a standard slackness assumption by which the feasible time window for scheduling a job is at least $$1+\varepsilon $$ 1 + ε times its processing time, for some $$\varepsilon >0$$ ε > 0 . We quantify the impact that different provider commitment requirements have on the performance of online algorithms. Our main contribution is one universal algorithmic framework for online job admission both with and without commitments. Without commitment, our algorithm with a competitive ratio of $$\mathcal {O}(1/\varepsilon )$$ O ( 1 / ε ) is the best possible (deterministic) for this problem. For commitment models, we give the first non-trivial performance bounds. If the commitment decisions must be made before a job’s slack becomes less than a $$\delta $$ δ -fraction of its size, we prove a competitive ratio of $$\mathcal {O}(\varepsilon /((\varepsilon -\delta )\delta ^2))$$ O ( ε / ( ( ε - δ ) δ 2 ) ) , for $$0 0 < δ < ε . When a provider must commit upon starting a job, our bound is $$\mathcal {O}(1/\varepsilon ^2)$$ O ( 1 / ε 2 ) . Finally, we observe that for scheduling with commitment the restriction to the “unweighted” throughput model is essential; if jobs have individual weights, we rule out competitive deterministic algorithms.

We consider a fundamental online scheduling problem in which jobs with processing times and deadlines arrive online over time at their release dates. The task is to determine a feasible preemptive schedule on a single or multiple possibly unrelated machines that maximizes the number of jobs that complete before their deadline. Due to strong impossibility results for competitive analysis on a single machine, we require that jobs contain some slack ɛ > 0, which means that the feasible time window for scheduling a job is at least 1+ɛ times its processing time on each eligible machine. Our contribution is two-fold: (i) We give the first non-trivial online algorithms for throughput maximization on unrelated machines, and (ii), this is the main focus of our paper, we answer the question on how to handle commitment requirements which enforce that a scheduler has to guarantee at a certain point in time the completion of admitted jobs. This is very relevant, e.g., in providing cloud-computing services, and disallows last-minute rejections of critical tasks. We present an algorithm for unrelated machines that is \(\Theta (\frac{1}{\varepsilon })\) -competitive when the scheduler must commit upon starting a job. Somewhat surprisingly, this is the same optimal performance bound (up to constants) as for scheduling without commitment on a single machine. If commitment decisions must be made before a job’s slack becomes less than a δ-fraction of its size, we prove a competitive ratio of \(\mathcal {O}(\frac{1}{\varepsilon - \delta })\) for 0 < δ < ɛ. This result nicely interpolates between commitment upon starting a job and commitment upon arrival. For the latter commitment model, it is known that no (randomized) online algorithm admits any bounded competitive ratio. While we mainly focus on scheduling without migration, our results also hold when comparing against a migratory optimal solution in case of identical machines.